Carbon dioxide, water vapour and energy fluxes of a recently burned boreal jack pine stand in north‐western Québec, Canada Kelly Nugent Master of Science Department of Natural Resource Sciences McGill University Montréal, Québec November 2013 A thesis submitted to McGill University, in partial fulfillment of the requirement of the degree of Master of Science © Kelly Nugent 2013
i Abstract The circumpolar boreal forest is an extensive carbon (C) reservoir, storing an estimated 88 petagrams (Pg) of C in vegetation biomass with an additional 471 PgC residing within the soil itself. In the North American boreal, fire disturbance acts as the main stand‐renewing agent along an approximate 100‐year return interval. However, recent studies suggest that fire intensity and severity are increasing, driven by disproportionate climate warming of the northern latitudes. In this study, we examine carbon dioxide (CO2), water vapour and energy exchange in a 7‐year old, post‐burn, jack pine stand located in the eastern James Bay region of the North American boreal; an area currently under‐represented in fire studies. Over 1.5 years, covering two growing seasons and the spring and fall transitions, we measured net CO2 and energy exchange at the ecosystem level using an eddy covariance tower, and supplemented this with chamber measurements of soil respiration. The objectives of this study were to determine the environmental controls on the variability of the mass and energy fluxes. Net ecosystem exchange of CO2 (NEE) over the stand was typically small (‐2.3 to 1 gC m‐2 d‐1), with respect to other young boreal stands, at times flipping between net uptake and release on a day‐to‐day basis. Annual cumulative NEE was determined to between +7 and ‐6 gC m‐2 y‐1, classifying it as approximately carbon‐neutral. Cumulative ecosystem respiration and gross ecosystem productivity were smaller on an annual basis compared to other recently disturbed stands. The low productivity was associated with a lower vegetation abundance and LAI at the site due to very dry soil conditions. The increase in latent heat exchange (and decrease in sensible heat exchange) between growing seasons was determined to be primarily moisture‐driven, with evaporation the dominant pathway. Little change in summertime albedo between years suggested that deciduous plant growth was not significant at the site. ii Résumé La forêt boréale circumpolaire est un important réservoir de carbone (C) contenant 88 pétagrammes (Pg) de C dans la biomasse végétale et 471 PgC dans le sol lui‐même. Dans les secteurs boréaux d’Amérique du Nord, les feux sont les principaux agents de renouvellement des forêts, avec un cycle d’environ 100 ans. De récentes études suggèrent que la sévérité et l’intensité des feux sont en hausse, due aux effets amplifiés des changements climatiques dans les latitudes élevées. La présente étude vise à documenter les échanges de dioxyde de carbone (CO2), de vapeur d’eau et d’énergie dans une forêt dominée par le pin gris (Pinus banksiana), brûlée il y a 7 ans. Le site d’étude est situé à l’est de la Baie James, dans la partie est de la forêt boréale nord‐américaine. Cette région est sous représentée en terme d’études sur les impacts des feux de forêts. Durant une période de un an et demi, incluant deux saisons de croissance et les périodes de transitions d’automne et du printemps, nous avons mesuré les émissions nettes de CO2 et d’énergie à l’échelle de l’écosystème, en utilisant une tour de mesure de covariances des turbulences. Des mesures secondaires ont été prises en utilisant des chambres statiques afin de mesurer la respiration du sol. Les objectifs de cette recherche étaient de déterminer les contrôles environnementaux sur la variabilité des échanges de CO2 et d’énergie. Les valeurs d’échanges écosystémiques nets de CO2 (ÉÉN) étaient faibles (‐2.3 à 1 gC m‐2 d‐1) comparativement aux valeurs observées dans d’autres jeunes forêts boréales, passant d’une source à un puit de CO2 sur une base journalière. L’ÉÉN cumulatif annuel était +7 et ‐6 gC m‐2 y‐
1
, indiquant que le site était presque neutre en terme d’échange de carbone. La respiration à l’échelle de l’écosystème et la productivité brute annuelles étaient plus basses que d’autres sites comparables qui ont été aussi récemment perturbés. Cette productivité basse a été associée avec une faible présence de végétation résultant des conditions de sol sèches. Le contenu en eau du sol a été identifié comme un facteur important de l’augmentation des échanges de chaleur latente (et la réduction des échanges de chaleur sensible) entre les saisons de croissance, l’évaporation constituant la voie principale d’échange. Le faible changement de l’albédo du site entre les deux étés indique que la croissance des feuillus est relativement faible sur le site à l’étude. iii Acknowledgements Completion of this thesis was only achieved through the involvement, guidance and support of many people. First and foremost, I would like to sincerely thank my supervisor Dr. Ian Strachan, as his positive attitude and open office door allowed me to overcome personal insecurities alongside dataset issues. I deeply appreciate all of the opportunities you have given me‐ and I’m pretty sure that I am now spoiled when it comes to helicopter rides! But in all seriousness, I couldn’t have asked for a better supervisor and if I ever make up my mind to become a professor, it will definitely be in large part due to your inspiration. I owe thanks also to the masterminds behind Matlab, Marie‐Claude Bonneville and Dr. Onil Bergeron. The number of times MCB re‐gap‐filled my dataset is more than I can count on one hand and therefore worthy of praise for her continued dedication. Likewise, I’m not sure that this thesis would have ever been completed without Onil’s involvement and so I am deeply grateful. Many thanks also to Dr. Jim Fyles, Dr. Benoit Côté and Dr. Caroline Begg for lending me instruments and providing helpful advice. I would also like to thank my AER labmates‐ Luc Pelletier, for showing me that you can still love research even when it appears bent on ruining your ability to sleep and Stephanie Crombie, my guide in the lab or, as I liked to refer to her, my cheat sheet! Steph you get a special shout out as, somehow, we managed to remain friends even while sitting two paces away from one another for the better part of two years. There’s an enduring friendship. Family and friends are also owed a big thank you, for their encouragement and helpful ears. In particular, I’m so glad to have had my mom by my side in spirit through these past two years. As well, I would like to acknowledge my father, brother, step‐father and my ‘quasi’ in‐
laws. Last, but never least, Rich, for basically everything above all rolled into one. The first year of this thesis was financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through an Alexander Graham Bell Canada Graduate Scholarship. Financial support was also received from McGill University through Graduate Excellence Awards and a GREAT travel award which allowed me to present my findings at a scientific conference. iv Contribution of Authors “As an alternative to the traditional thesis format, the thesis can consist of a collection of papers of which the student is an author or co‐author. These papers must have a cohesive, unitary character making them a report of a single program of research.” This thesis consists of one main manuscript. Carbon dioxide, water vapour and energy fluxes of a recently burned boreal jack pine stand in north‐western Quebéc, Canada Kelly A. Nugent & Ian B. Strachan Department of Natural Resource Sciences, McGill University, Montréal, Québec, Canada This manuscript is the original work of Kelly A. Nugent with the following exceptions: Marie‐
Claude Bonneville performed post‐processing, cleaning and gap‐filling processes on CO2 and energy fluxes. Dr. Onil Bergeron provided occasional technical assistance and guidance in the analysis of high frequency data using Matlab. Dr. Ian Strachan provided expert advice and financial support and contributed to the editing process of the manuscript. v Table of Contents Abstract… …………………………………………………………………………………………………………………………………………………i Résumé… …………………………………………………………………………………………………………………………………………………ii Acknowledgements ..................................................................................................................................... iii Contribution of Authors .............................................................................................................................. iv Table of Contents .......................................................................................................................................... v List of Figures ............................................................................................................................................... ix List of Tables ................................................................................................................................................ xi Chapter 1 Introduction .................................................................................................................................................. 1 1.1 Importance of Boreal Forests in a Carbon‐Climate Context ............................................................. 1 Chapter 2 Literature Review ......................................................................................................................................... 4 2.1 Overview of boreal forests ................................................................................................................. 4 2.1.1 Boreal fire regime characteristics ............................................................................................... 7 2.2 Species of interest: Jack pine ............................................................................................................. 8 2.2.1 Range and stand characteristics ................................................................................................. 8 2.2.2 Carbon allocation and storage .................................................................................................. 10 2.3 Boreal forest microclimatology ........................................................................................................ 11 2.3.1 Radiative balance ...................................................................................................................... 11 2.3.2 Energy exchange ........................................................................................................................ 12 2.3.2.1 Energy exchange in a recently burned conifer stand.……………………………………………………15 2.4 Carbon exchange .............................................................................................................................. 16 2.4.1 Biological processes .................................................................................................................. 16 2.4.2 Exchange in a mature stand ...................................................................................................... 17 2.4.2.1 Daily and seasonal patterns………………………………………………………………………………………….17 2.4.2.2 Environmental controls…………………………………………………………………………………………………18 2.4.3 Exchange at a recently burned stand ........................................................................................ 19 2.4.3.1 Initial biophysical impacts…………………………………………………………………………………………….19 2.4.3.2 Impact on patterns and controls…………………………………………………………………………………..21 2.4.3.3 Source/sink turnover period……..…………………….…………………………………………………………..21 vi 2.5 Water vapour exchange ................................................................................................................... 22 2.5.1 Exchanges in a mature stand .................................................................................................... 22 2.5.2 Exchange in a recently burned stand ........................................................................................ 23 2.5.2.1 Impact on patterns and controls…………………………………………………………………………….…….23 2.6 Measuring carbon and water vapour exchange .............................................................................. 24 2.6.1 Eddy covariance ......................................................................................................................... 24 2.6.2 Chronosequencing ..................................................................................................................... 27 2.6.3 Modelling evapotranspiration .................................................................................................. 28 2.7 Boreal C cycling under a changing fire regime ................................................................................ 29 Preface to Chapter 3 ................................................................................................................................... 31 Chapter 3 Carbon dioxide, water vapour and energy fluxes of a recently burned boreal jack pine stand in north‐
western Québec, Canada ........................................................................................................... 32 3.1 Introduction ...................................................................................................................................... 32 3.1.1 The Eastmain‐1 Net Greenhouse Gas Emissions Project ......................................................... 32 3.2 Methods and materials .................................................................................................................... 33 3.2.1 Area and site description .......................................................................................................... 33 3.2.2 Site characterization .................................................................................................................. 34 3.2.2.1 Leaf area index……………………………………………………………………………………………………………..35 3.2.2.2 Soil sampling…………………………………………………………………………………………………………………36 3.2.2.3 Biomass sampling………………………………………………………………………………………………………...36 3.2.3 Instrumentation and measurements ........................................................................................ 37 3.2.3.1 EC measurements…………………………………………………………………………………………………………37 3.2.3.2 Flux calculations……………………………………………………………………………………………………………37 3.2.3.3 Environmental measurements……………………………………………………………………………………..38 3.2.3.4 Chamber measurements………………………………………………………………………………………………39 3.2.4 Data handling and gap filling procedures ................................................................................. 40 3.2.4.1 Quality control steps…………………………………………………………………………………………………….40 3.2.4.2 Gap‐filling NEE, ER and GEP………………………………………………………………………………………….41 3.2.4.3 Gap‐filling QH and QE………………………………………………………………………………………………..…..42 3.2.5 Temperature sensitivity and base respiration .......................................................................... 42 3.2.6 Light response parameterization .............................................................................................. 43 vii 3.2.7 Ecosystem diagnostics ............................................................................................................... 43 3.3 Results ............................................................................................................................................... 46 3.3.1 Environmental conditions ......................................................................................................... 46 3.3.2 Site characteristics ..................................................................................................................... 47 3.3.3 Growing season length .............................................................................................................. 48 3.3.4 CO2 exchange ............................................................................................................................. 48 3.3.4.1 Chamber measurements………………………………………………………………………………………………50 3.3.5 Energy exchange ........................................................................................................................ 50 3.3.6 GEP and ER response to environmental factors ....................................................................... 52 3.3.6.1 Temperature sensitivity and base respiration………………………………………………………………52 3.3.6.2 Response of NEE to light……………………………………………………………………………………………….52 3.3.7 QE response to environmental factors ...................................................................................... 53 3.4 Discussion ......................................................................................................................................... 55 3.4.1 Placement of FJP05 within the North American boreal stand age‐carbon balance curve ...... 55 3.4.2 What are the factors driving carbon dioxide fluxes at a recently burned boreal jack pine forest in eastern North America? ...................................................................................................... 56 3.4.2.1 Was the inter‐seasonal increase in CO2 exchange driven by vegetative growth?...........58 3.4.3 What are the factors driving energy exchange at a recently burned boreal jack pine forest in eastern Northern America? ............................................................................................................... 63 3.4.3.1 Was the inter‐seasonal increase in water vapour exchange driven by vegetative growth?.........................................................................................................................................65 3.5 Conclusions ....................................................................................................................................... 66 Chapter 4 Conclusion .................................................................................................................................................. 68 Tables and Figures ...................................................................................................................................... 72 References ................................................................................................................................................ 104 Appendix A List of Symbols .......................................................................................................................................... 119 Appendix B List of Abbreviations ................................................................................................................................. 122 Appendix C Supplementary Data Collection ............................................................................................................... 124 I.I Secondary sites ................................................................................................................................. 124 viii I.I.I Site description ........................................................................................................................... 124 I.I.II Site characterization ................................................................................................................. 125 I.I.III LAI‐2000 instrument theory..................................................................................................... 125 I.I.IV LAI field methods ..................................................................................................................... 126 I.I.V Results ....................................................................................................................................... 127 ix List of Figures Figure 2.1 Conceptual model of forest recovery from fire disturbance (Goulden et al., 2011) ................. 82 Figure 2.2 Annual carbon balance at boreal stands disturbed by fire or harvesting. Please refer to Table 3.9 for a description of referenced sites. .................................................................................... 83 Figure 3.1 Study area and location of the recently burned jack pine site, FJP05 and the mature black spruce site FBS24 ........................................................................................................................ 84 Figure 3.2 Standardized (a) air temperature and (b) precipitation anomaly according to the Canadian Climate Normal at Chapais II station, Quebec. Red bars denote significantly positive anomaly while blue bars denote a significantly negative anomaly ........................................................... 85 Figure 3.3 Site fractional surface coverage by type. Error bars represent ± S.E ........................................ 86 Figure 3.4 Diurnal pattern of NEE. Error bars represent ± S.E ................................................................... 87 Figure 3.5 Daily average of NEE for the period DOY 189 (July 8, 2011) to DOY 262 (September 18, 2012). Error bars represent ± S.E .......................................................................................................... 88 Figure 3.6 Daytime and nighttime averages of NEE for the period DOY 189 (July, 8 2011) to DOY 262 (September 18, 2012). Error bars represent ± S.E ..................................................................... 89 Figure 3.7 Monthly cumulative fluxes of ER, GEP and NEE ......................................................................... 90 Figure 3.8 Comparison of cumulative daily average ER, GEP and NEE over July, August and September . 91 Figure 3.9 Comparison of cumulative daily average ER and GEP over two representative weeks in each of July, August and September for each year of the study ............................................................ 92 Figure 3.10 Cumulative daily average ER, GEP and NEE for the period DOY 189 (July 8, 2011) to DOY 189 (July 7, 2012) ............................................................................................................................... 93 x Figure 3.11 Ecosystem respiration measured using static chamber and modeled from eddy covariance. Each chamber point represents sampling along a ten‐collar transect averaged over an intensive campaign period. Each tower point represents an average of 13:00 ± 2 hours over the same period. Error bars represent ± S.E .............................................................................................. 94 Figure 3.12 Comparison of diurnal patterns of energy exchange (a‐d) and environmental controls (e‐h) for the months of July and August. Error bars represent ± S.E .................................................. 95 Figure 3.13 Diurnal patterns of sensible heat in (a) 2011 and (b) 2012 and latent heat in (c) 2011 and (d) 2012. Error bars represent ± S.E ................................................................................................ 96 Figure 3.14 Response of ER to (a) soil temperature at 5 cm (Ts) and (b) soil moisture content (θ) for the 2011 and 2012 growing seasons. Bin‐averaged data are presented by 2 °C and 0.2 m3 m‐3 (n>10). Error bars represent ± S.E .............................................................................................. 97 Figure 3.15 Response of (a) NEE to PPFD and GEP to (b) PPFD, (c) T and (d) D during the 2011 and 2012 optimal growing seasons. Bin‐averaged data are presented (bin size = 100 µmol m‐2 s‐1; 2 °C; and 0.2 kPa, n> 10 for each bin). Error bars represent ± S.E ..................................................... 98 Figure 3.16 Monthly response of GEP to (a) Ta and (b) Da during the 2012 growing season. Error bars represent ± S.E ........................................................................................................................... 99 Figure 3.17 Comparison of the diurnal pattern of (a‐b) PT‐α, (c‐d) β and (e‐f) EF for July, August and September. Error bars represent ± S.E ..................................................................................... 100 Figure 3.18 Response of daytime dry‐foliage surface conductance (gs) to atmospheric evaporative demand (Da) based on all available half‐hourly data for 2011 and 2012. Data have been bin averaged by Da (bin size 0.2 kPa) and stratified by Q*. Circles represent Q* < 100 W m‐2, squares 100 < Q* < 400 W m‐2 and triangles Q* > 400 W m‐2 .................................................. 101 Figure 3.19 (a) comparison of daytime average Bowen ratio (β) in 2011 and 2012 and (b) half hourly Bowen ratio (β) (daytime only), soil moisture content (θ) and precipitation (PPT) from DOY 222 to 242 (August 9 – 29, 2012) .......................................................................................... 102 Figure 3.20 Daily precipitation (PPT) total and soil moisture content (θ) average, for May through September 2012. 2011 data was not included as only two weeks were available for comparison ........................................................................................................................... 103 xi List of Tables Table 3.1 Site characteristics. Error bars represent ± S.E. ........................................................................... 72 Table 3.2 Soil measurements. Error bars represent ± S.E .......................................................................... 73 Table 3.3 Aboveground tree biomass measurements. Error bars represent ± S.E .................................... 74 Table 3.4 Cumulative monthly ER, GEP, NEE, QH and QE exchange. Units for ER, GEP and NEE are gC m‐2 period‐1 while units for QH and QE are GJ m‐2 period‐1 ................................................................ 75 Table 3.5 Q10 and base respiration parameters. Error bars represent ± S.E .............................................. 76 Table 3.6 Comparison of light response curves during July, August and September. The warm season dataset was defined by T > 0 °C and T > ‐5 °C, while the optimal growing season dataset included only data measured between July 1st and August 31st with 15 °C < T < 25 °C and T > 5 °C. Error bars represent ± S.E .................................................................................................. 77 Table 3.7 Comparison of a wet (DOY 213‐219) and dry (DOY 220‐226) week in August 2012. Error bars represent ± S.E ........................................................................................................................... 78 Table 3.8 Comparison of monthly‐averaged daytime dry‐foliage ecosystem diagnostics during July, August and September. Error bars represent ± S.E .................................................................... 79 Table 3.9 List of North American flux sites commonly referred to in this study. ........................................ 80 Table 3.10 Comparison of growing season onset, end and length determined using two distinct methods.
.................................................................................................................................................... 81 1 Chapter 1 Introduction 1.1 Importance of Boreal Forests in a Carbon‐Climate Context The boreal biome is the largest terrestrial biome on Earth, encompassing 25% of global terrestrial biomass in forest cover as well as extensive wetland area (Conard et al., 2002). Due to its circumpolar extent and large, temperature‐regulated soil carbon (C) reservoirs, sequestration in boreal forests plays an important role in the overall global C balance (Amiro, 2001; Dixon et al., 1994; Larsen, 1980). Approximately 16% of atmospheric CO2 is understood to be cycled through the global land surface annually (Prentice et al., 2001), with terrestrial ecosystems as the main driver of intra‐annual variation in atmospheric concentrations (Peylin et al., 2005; Friend et al., 2007). Traditional views of forests acting as static environments, consistently storing C, has been replaced in the last few decades by the notion of a dynamic system losing and gaining C through the interplay of disturbances and succession (Amiro, 2001). Fire is the most pervasive natural disturbance in the North American boreal forest and the main stand‐
renewing agent, playing a central role in shaping stand structure and functioning (Rowe & Scotter, 1973; Johnson, 1992; Turner & Romme, 1994). Wildfire is responsible for significant annual inputs of CO2 to the atmosphere; 1959 to 1999 mean direct emissions have been estimated at 27 TgC yr‐1, whereas a more recent 1992‐2003 estimate is 106‐209 TgC yr‐1 (Amiro, 2001; Kasischke et al., 2005). While estimates of direct C emissions from combustion are becoming more accurate, indirect loss due to post‐fire effects on decomposition and regeneration has only recently become a focus of research. Paradoxically, it has been estimated that post‐fire C releases are in the order of three times the amount directly released through combustion in the boreal biome (Auclair & Carter, 1993). The fire regime return interval is approximated at 100 years, a shorter period than that required for conifer stands to reach succession endpoint (Heinselman, 1981; Bonan & Shugart, 1989; Van Cleve et al., 1983). As a result, stands may still be undergoing active sequestration when disturbed. Extensive crown fires dominate the boreal biome, due to the large fuel loads 2 that accumulate as stands age (Odum, 1969; Bergeron, 1991; Johnson, 1992; Litvak et al., 2003). From a birds‐eye view, the landscape is a mosaic, with patches varying in age, size and structure, illustrating the unique responses of stands to the characteristics of a particular fire event (Bonan & Shugart, 1989; Johnson, 1992; Turner & Romme, 1994). Fire removes carbon from the aboveground vegetation, litter and surface soil layers. However, C removal from aboveground biomass pools should be reintegrated into the ecosystem as the forest regenerates, resulting in no loss or gain of carbon over a stand life‐cycle (Litvak et al., 2003; Odum, 1969). Conversely, combustion of soil organic carbon (SOC) can result in a net release, as soil pools operate on different timescales, dependent on landscape features and micro‐topography (Harden et al., 2000; Harden et al., 2012; Kasischke et al., 1995). Currently, it is estimated that 471 petagrams (Pg) of C (23%) is stored in global boreal forest soils whereas 88 PgC (19%) resides in the vegetation biomass itself (IPCC, 2001). Consequently, sustained C removal from soil pools could result in boreal forests transitioning from being a significant long‐term terrestrial sink to being a net source of carbon (Harden et al., 2012; Kurz et al., 2008). Boreal forests influence the climate through physical, biological and chemical processes that affect the global energy balance, regional hydrology and atmospheric greenhouse gas content (Bonan, 2008). In turn, boreal forest structure and functioning are strongly linked to environmental conditions and as such respond to the transient effects of climate change (Amiro et al., 2010; Barr et al., 2007; Krezek‐Hanes et al., 2011; Kurz et al., 2008a; Soja et al., 2007). Global average temperatures have risen by 0.6°C relative to preindustrial values (IPCC, 2001; Li, Flannigan, & Corns, 2000). Boreal forests, situated at high latitudes, are experiencing disproportionate warming, currently showing a 3‐5°C increase (Chapman & Walsh, 1993; IPCC, 2001). Changes in disturbance regimes are expected as fire activity is moderated by climate factors and forest structure (Flannigan et al., 2005). Recent studies have reported that fire intensity (amount of energy released) and severity (amount of material combusted) are increasing, with the regime evolving toward a shorter return interval (Flannigan et al., 2009; Gillett et al., 2004; Harden et al., 2012; Turetsky et al., 2010). A regime shift will have an impact on gross combustion, post‐fire decomposition and primary production (Harden et al., 2012; Mack et al., 2004; O'Donnell et al., 2011). The net effect of a more frequent fire regime appears 3 to be an accelerated C cycle with shorter residence times in ecosystem C pools (Kimball et al., 2007). Enhanced net primary production (NPP) is expected with climate warming, due to CO2 fertilization effects (Walther et al., 2002). However, enhanced biomass growth may not be significant enough to offset C losses from accelerated fire disturbance (Flannigan et al., 2005; Kurz et al., 2008a). Model predictions have indicated that the Canadian boreal forest may have already transitioned from sink to source within the last decade due to more fire activity and cyclical insect disturbance (Kurz et al., 2008a). Climate change impacts in this region may be a positive feedback on the global climate system, with boreal forests continuing to be a C source to the atmosphere even if anthropogenic C emissions were mitigated (Kurz et al., 2008a; Kurz et al., 2008b). Due to the large amount of carbon stored in boreal forests and the disproportionate climate warming already occurring, understanding carbon dynamics over a forest life‐cycle is essential. The specific objectives of my thesis research are: 1. to determine the daily and seasonal patterns of carbon, water vapour and energy exchange; 2. to determine the environmental controls on carbon, water vapour and energy exchange; and 3. to determine the relationships between biophysical properties and tower flux measurements. I used the eddy covariance technique to measure net ecosystem exchange of carbon, water vapour and energy at a recently burned jack pine stand east of James Bay, Quebec. This thesis contributes to our understanding of boreal forest dynamics by studying a stand located in the eastern boreal, an ecozone currently under‐represented in studies compared to the western and central boreal, and through the analysis of direct and continuous measurements over 1.5 years at a jack pine stand recovering from a severe fire disturbance. Following a literature review (Chapter 2) the results of my field measurements and analysis are presented (Chapter 3) and a final chapter (Chapter 4) summarizes the thesis findings. 4 Chapter 2 Literature Review 2.1 Overview of boreal forests The boreal biome is an extensive complex of forested and partially‐forested ecosystems, making up 25% of the world’s forest cover (Apps et al., 1993; Conard et al., 2002). Circumpolar in extent across the northern latitudes of North America and Eurasia, the biome encompasses 14.7 million km2 of peatlands and coniferous‐dominated forests (Bonan & Shugart, 1989). North America’s portion comprises one third of the total, ranging across Canada and up into Alaska (Larsen, 1980). At the northern limit, the boreal forest transitions into treeless tundra while the more irregular southern boundary encounters a spectrum of ecosystems from deciduous forest to semi‐desert, dependent on regional climate influences (Apps et al., 1993). The general physical structure of the boreal forest is a single‐layered canopy of a particular tree species. Lichens and mosses are also present, significantly in the same order of biomass as trees and shrubs combined (Scotter, 1964; Kershaw & Rouse, 1971a,b; Kershaw, 1978; Ahti & Oksanen, 1990). Lichens play a crucial role in water or temperature stressed areas whereas mosses are substantial in the southern reaches and in locations with plentiful moisture, such as wetland areas (Kershaw, 1978; Bonan & Shugart, 1989). Although the North American boreal forest is remarkably uniform in overall appearance, important distinctions are visible regionally. Broadly speaking, mean annual temperature decreases northward while annual precipitation increases from west to east (Bergeron et al., 2007). The western and central portion of the boreal experiences a dry continental climate compared to the moist continental and maritime climates of the eastern region, defined as east of the Manitoba‐Ontario border (Rizzo & Wiken, 1992). The maritime influence manifests itself through prevailing environmental conditions such as cloudy growing seasons and winter snow accumulation (Bergeron et al., 2007). This is markedly different from the hot, dry summers and harsh winters of the central and west boreal (Plamondon‐Bouchard, 1975). 5 A characteristic soil found across the boreal is the podzol. Podzols typically develop on moderately well‐drained sites of coarse‐grained materials and are easily identified by an ashen grey A horizon overlaying a red‐brown B horizon (Larsen, 1980). In North America, these soils began forming following the retreat of the Wisconsin glaciation (Davis, 1969; Pare et al., 2010). The retreat removed previous soils and exposed large tracts of Canadian Shield bedrock, leaving behind areas of glacial till and outwash deposits (Soil Classification Working Group, 1998). However, the distinct soil character is owed in large part to the climate; podzols are encountered in areas with relatively high rainfall, cold temperatures and in the presence of needle leaf trees (Larsen, 1980). The acidic soil solution produced from conifer needle litter results in leaching of minerals from the A to B soil horizon, further aided by a low evaporation to precipitation ratio (Krausse, 1965; Larsen, 1980). Similarly, clay and other nutrients migrate to lower soil layers, leaving behind coarse‐textured sand close to the surface. In the case of dry upland areas, immature podzols are often found instead as humus accumulation is minor and leaching not as intense, delaying the development of soil horizons (Larsen, 1980). Other soil types found across the North American boreal forest include brunisols and luvisols. These are found in areas where podsolization processes are less intense, where parent materials include greater clay content or where the parent material is calcareous (Larsen, 1980; Soil Classification Working Group, 1998). North American boreal species composition at the local level is strongly influenced by soil texture and moisture. For example, trembling aspen (Populus tremuloides) are most often found on finely textured upland soils, whereas jack pine (Pinus banksiana Lamb.) occur on extremely well drained, sandy upland soils (Gower et al., 1997). In contrast, black spruce (Picea mariana (Mill.)) commonly grow in poorly drained, organic lowland soils but can also be found on upland mineral soils (Gower et al., 1997). Deciduous tree species such as birch and poplar have a widespread distribution however, the four genera most common are pine, spruce, larch and fir, all coniferous (Hare & Ritchie, 1972; Sellers et al., 1995). Of the four, spruce stands dominate the North American landscape and, notably, hold the greatest total ecosystem carbon content (Viereck & Johnston, 1990; Gower et al., 1997; Bergeron et al., 2007). 6 One of the defining characteristics of the boreal is its slow decomposition rates (Tamm, 1953; Van Cleve & Veireck, 1983; Rapalee et al., 1998). Low soil temperatures inhibit biological activity during the cooler seasons allowing nutrients and biomass to accumulate as litter and soil organic matter (Tamm, 1953; Moore, 1980; Moore, 1981; Van Cleve & Veireck, 1983; Van Cleve & Yarie, 1986; Bonan & Shugart, 1989). Discontinuous permafrost along the northern reaches also combines to further restrict decomposition rates by limiting nutrient availability and impeding water infiltration (Van Cleve & Veireck, 1983; Van Cleve & Yarie, 1986; Ford & Bedford, 1987). Permanent shading from evergreen trees results in lower evaporation rates annually, causing high soil moisture retention in areas with thick moss or lichen coverage (Kershaw & Rouse, 1971a,b; Larsen, 1980; Longton, 1980). The presence of a thick moss layer drives wetland formation as colder soil temperatures and soil moisture retention are promoted, decreasing decomposition rates and nutrient availability (Van Cleve & Veireck, 1983; Antonovsky et al., 1987; Bonan & Shugart, 1989). These conditions encourage moss growth while inhibiting tree growth and regeneration (Bonan & Shugart, 1989). As such, a natural transformation over time is the replacement of jack pine‐lichen forests by black spruce‐moss forests, followed by black spruce bogs and concluding as treeless peatland (Bonan & Shugart, 1989). In high‐latitude forests, growing seasons are restricted by temperature, light intensity (irradiance) and day length (Kramer & Kozlowski, 1979; Suni et al., 2003a). Latitudinal and climate‐induced insufficiencies in nutrient availability result in boreal forests have low leaf area (McGuire et al 1992). Consequently, these forests fix lower amounts of CO2 relative to temperature or tropical forests, as trace gas exchange and light absorption is scaled to leaf area index (Baldocchi & Vogel, 1996). Despite this, long‐day photoperiods and coniferous adaptations to low light angles enable net primary productivity (NPP) to be relatively high over the short growing season (Bonan & Shugart, 1989). Relatively high rates of productivity in the short but warm growing season combined with low decomposition rates during the long, cold winter have enabled the build‐up of large carbon stores in the aboveground biomass and soils. Several natural and anthropogenic disturbances are prevalent in the boreal, affecting forest structure and functioning. Major disturbances in North America include: fire, insect 7 outbreaks, disease, harvesting, wind throw, flooding and ice storms (McCullough et al., 1998; Engelmark, 1999; Dale et al., 2001). Of these, wildfire is the most pervasive, accounting for on average 18,471 km2 of Canadian forested land burned annually between 1959 and 2007 (Krezek‐Hanes, 2011). Fire is regulated by climate and weather conditions as well as landscape, soil and vegetative characteristics (Johnson, 1992; Turner & Romme, 1994; Kasischke et al., 1995; Flannigan et al., 2005). The frequency and severity of fire events are a controlling process on C storage and release in boreal ecosystems (Kasischke et al., 1995). The nature of nutrient cycling and productivity within the boreal is integral to its status as an important terrestrial carbon sink. In all, it is estimated that the boreal biome contains 48% of the world’s carbon stored in aboveground biomass and up to 42% of the global soil carbon pool (Dixon et al., 1994; Bergner et al., 2004). The relationship between soil temperature and decomposition rates is the key factor that has shaped the boreal forest as a significant carbon reservoir since the last glacial period (Pare et al., 2010). Natural wildfire commonly disturbs these carbon stores while also affecting the structure and function of the recovering stand (Amiro et al., 2010; Goulden et al., 2011). Currently, gaps exist in our knowledge of the short and long‐term effects of fire on ecosystem processes (Amiro et al., 2003; Litvak et al., 2003; Bond‐Lamberty et al., 2004; Goulden et al., 2006). Changes in the fire regime would likely have significant consequences for carbon feedbacks to the climate system (Flannigan et al., 2000; Dale et al., 2001; Gillet et al., 2004; McGuire et al., 2004). For these reasons and in light of climate change, research has focussed more and more on understanding the role of fire disturbance in altering forest processes over a forest life‐cycle (Harden et al., 1997; Amiro et al., 2001; Law et al., 2002; Litvak et al., 2003; Bond‐Lamberty et al., 2004; Clark et al., 2004; Amiro et al., 2006; Coursolle et al., 2006; Goulden et al., 2006; Humphreys et al., 2006; Randerson et al., 2006; Sass, 2007; Mkhabela et al., 2009; Zha et al., 2009; Goulden et al., 2011). 2.1.1 Boreal fire regime characteristics The traditional view that forests are largely static environments able to act as stable C sinks has been replaced by the concept of a dynamic system undergoing renewal through disturbance (Apps et al., 1993; Kurz & Apps, 1999; Amiro, 2001). Fire is the most pervasive natural disturbance averaging about three million ha of burned area annually (Murphy et al., 8 2000; Stocks et al., 2003). As the main stand‐renewing agent, fire disturbance creates a mosaic of even‐aged stands with differing biophysical characteristics. Wildfire affects the broader North American boreal forest however, according to the Canadian Large Fire Database, more burns occur in the west and central regions than in the east (Stocks et al., 2003). A fire regime can be decomposed into six elements: frequency, size, seasonality, type, intensity and severity (Flannigan et al., 2000). Fire return intervals vary across the North American boreal forest, ranging between 70 and 500 years, with the average interval simplified to 100 years (Johnson, 1992; Payette, 1993; Stocks & Kauffman, 1997). The most damaging natural fires are large crown fires initiated by lightning strikes (Stocks & Kauffman, 1997). These fires are characterized by high intensity and high severity (Harden et al., 2000) and are associated with ground fires, capable of removing litter and organic matter stored in the soil layers (Brown & Davis, 1973). Soil organic material can burn to varying depths, dependent on local soil moisture, thus there can be substantial heterogeneity in soil burn severity at a single site (Jin et al., 2012). Temperature appears to be the most accurate predictor of fire occurrence, with warmer temperatures associated with increased area burned within a given year (Duffy et al., 2005; Flannigan et al., 2005). 2.2 Species of interest: Jack pine 2.2.1 Range and stand characteristics The jack pine species is endemic to North America, ranging from Nova Scotia and central Quebec to northern British Columbia (Dep. of Northern Affairs and National Resources, 1956). Large monoculture stands are found mainly in the Canadian Shield region, in fire‐prone areas across northern Saskatchewan and Manitoba, and south of Hudson Bay in glacial outwash sand plains (Fowells, 1965; Carleton & Maycock, 1977; Larsen, 1980). In the northwest of Alberta the jack pine range overlaps with that of the lodgepole pine (Pinus contorta Dougl.), where the two species are known to hybridize (Rweyongeza et al., 2007). Jack pine can occur with other tree species such as black spruce and balsam fir (Abies balsamea (L.) Mill.) as understory or co‐
dominant species (Carleton & Maycock, 1977). Jack pine are highly shade‐intolerant and have a relatively short lifespan with greatest growth occurring in the first 50 years (Fowells, 1965). As such, the likelihood of a stand attaining old growth stages is low, due to cyclical fire disturbance 9 or alternatively through invasion by other longer‐lived species (Larsen & MacDonald, 1998; Pinard, 1999). Jack pine success is ensured through morphological and ecological features that allow it to take advantage of large stand‐renewing fires (Parisien al., 2004). Seritonous cones provide an aerial seed bank that only becomes available under intense heat (Lamont et al., 1991); vigorous germination allows jack pine seedlings to rapidly colonize over a range of soil conditions (Thomas & Wein, 1990); and rapid juvenile growth rates enable persistence until invasion by another species (Parisien al., 2004). In fact, fire disturbance is integral in the maintenance of jack pine stands as otherwise distribution becomes reduced to isolated rocky or exposed areas (Larsen, 1980). Reduced seritony levels make persistence without fire possible (Abrams 1984; Conkey et al., 1995) however, it is clear that the spatial and temporal distribution of jack pine populations is controlled by fire disturbance (Eyre & LeBarron, 1944; Heinselman, 1973; Cayford & McRae, 1983; Gagnon 1990; Gauthier et al., 1993). Jack pine stands are characteristically an open canopy allowing a diverse understory to develop. Seedlings growing in a lower stratum can include black spruce, white spruce, balsam fir, paper birch (Betula papyrifera) and trembling aspen. Common understory shrub and herb species include green alder (Alnus crispa), wild rose (Rosa spp.), bunchberry (Cornus canadensis), bearberry (Arctostaphylos spp.), blueberry (Vaccinium spp.), sagebush (Artemisia frigida) and Laborador tea (Ledum groenlandicum) (Gower et, 1997; Harden et al., 1997; Amiro et al., 2006). The groundcover usually consists of lichen (e.g. Cladonia rangiferina) or feather mosses (Pleurozium, Hylocomium or Ptilium spp.) (Larsen, 1980; Gower et, 1997; Harden et al., 1997; Amiro et al., 2006). As forests develop, understory vegetation also experiences successional dynamics with changing patterns of abundance, composition and diversity (Hart & Chen, 2006). Commonly, shrub species reduce with canopy closure while shade‐tolerant mosses and lichen proliferate. However, because of a tendency toward an increasingly open canopy, jack pine stands nonetheless support a well‐developed understory of herbs and shrubs throughout their growth stages (Larsen, 1980). Jack pine trees are relatively short, reaching 9‐22 m in height and are characterized by needles of 2 to 4 cm growing in fascicles of two (Dep. of Northern Affairs and National 10 Resources, 1956). Cones from the tree are usually slim and curved and are located primarily at the crown (Dep. of Northern Affairs and National Resources, 1956). The seritonous cones stay tightly closed unless experiencing intense temperatures, namely by fire but occasionally from direct sunlight on an extremely warm day (Beaufait, 1960; Gauthier et al., 1993). Nutrient availability limits leaf area indices (LAI) across the boreal, especially in the sandy soils favored by jack pine (Gower et al., 1997). As an illustration, jack pine sites in the central boreal were found to have LAI values of around 1.8 to 2.8 compared with aspen, 2.2 to 3.3, and black spruce, 4.2 to 5.6 (Gower et al., 1997). However the opposite case is found where jack pine develop in association with alder, a nitrogen fixing shrub, resulting in measured LAI increases of 29‐34% (Vogel & Gower, 1997). Along with low LAI, jack pine trees also have a high degree of needle clumping (Gower et al., 1997). Both of these canopy architecture elements affect light interception, reducing relative rates of photosynthesis, thereby influencing C exchange with the atmosphere (Gower et al., 1997). 2.2.2 Carbon allocation and storage A process‐based understanding of the spatial and temporal dynamics of C budgets is essential when mapping boreal C cycles (Wang et al., 2003). The number of boreal forest C budgets, both measured and modelled, is substantial (e.g. Apps et al., 1993; Schlesinger, 1997; Kurz & Apps, 1999; Schulze et al., 1999; Kasischke & Stocks, 2000; Gower et al., 2001; Chen et al., 2002; Wang et al., 2003). C distribution is strongly influenced by climate, forest type and age, topography, soil moisture, nutrient availability and disturbance regimes (Flanagan & Van Cleve, 1983; Gower et al., 1992; Bisbee et al., 2001; Wang et al., 2001; Wang et al., 2003). Due to ecophysiology and substrate preferences, jack pine forests may not allocate C in the same manner as other boreal forest types. In fact, jack pine stands have been found to have lower amounts of organic material stored in the soil compared to upland black spruce stands (Harden et al., 2000). For one, jack pine trees allocate more NPP toward tree stem growth and less toward finer material production, such as fine roots (Gower et al., 1997; Steele et al., 1997). As well, faster decomposition rates have been measured attributed to warmer surface temperatures from the more open canopy and sandy substrate preference (Harden et al., 2000). Reduced moisture availability, associated with the rapid drainage of sandy soils, has been linked 11 with lower soil fluxes, both post‐fire and in undisturbed stands (Burke et al., 1997). Thus higher soil temperatures and less than optimum moisture content combined with poorer substrate quality likely yields a lower capability to store C both in the short and long term relative to other upland stand types. 2.3 Boreal forest microclimatology Solar energy (heat) and water (mass) cycling fundamentally drive Earth’s atmospheric system (Oke, 1987). Boreal ecosystems interact with the atmosphere through ecosystem processes that both respond to and control heat and water exchanges. The boreal forest plays an essential role due to its large extent and distinct climate (Baldocchi et al., 2000) allowing it to shape seasonal mass and energy patterns in the northern latitudes (Chapin et al., 2000). 2.3.1 Radiative balance All boreal ecosystem processes are driven by solar energy, which enters the Earth‐
atmosphere system through a relatively narrow band of the electromagnetic spectrum, from 0.3 to 3.0 μm in wavelength (Lafleur, 2008). The amount of solar radiation received at a surface varies in time, diurnally and seasonally and in space with respect to latitude and weather conditions (Oke, 1987). Incoming solar radiation (K↓) reaches the surface as direct or diffuse beams of which a portion is reflected from the surface (K↑) (Lafleur, 2008). While incoming solar radiation is important for studying the climate, solar wavelengths between 0.4 and 0.7 μm are central in plant photosynthesizing processes (Lafleur, 2008). Termed photosynthetically active radiation (PAR), this variable is drawn on to better understand plant physiology responses making it applicable for ecology and plant physiology studies (Oke, 1987). Albedo (α), the fraction of incident solar energy reflected by a surface (K↑/K↓), directly controls energy partitioning at the surface (Betts & Ball, 1997; Wang 2005). Albedo is useful for comparing the biophysical nature of different ecosystems as well as seasonal progressions in reflectivity. For example, a mature jack pine stand will typically have a summer albedo of 0.08 and a winter average of 0.15 while grass, in comparison, will exhibit a much greater range seasonally from 0.2 up to 0.9 (Betts & Ball, 1997; Sharrat 1998; Amiro et al., 2006). On a global scale, conifer boreal forests stand out as regions of low maximum winter albedo (Bonan et al., 1992; Thomas & Rowntree, 1992; Viterbo & Betts, 1999) resulting in up to 5°C in regional 12 surface warming relative to non‐forested areas (Otterman et al., 1984; Betts & Ball, 1997). This local warming effect is due to the conical and evergreen nature of conifers which shed snow easily while trapping radiation in the canopy, not allowing solar energy to reflect from the snowpack to the same degree as in deciduous or mixed stands (Betts & Ball, 1997). Longwave radiation, comprising wavelengths of approximately 3.0 to 100 μm, is the other major portion of radiative energy in the Earth‐Atmosphere system (Lafleur, 2008). Longwave radiation is emitted upward by the Earth’s surface (L↑), primarily as a function of surface temperature according to the Stefan‐Boltzmann law: Energy emitted
σ
, (2.1) where ε is the surface emissivity, σ is the Stefan‐Boltzmann proportionality constant = 5.67x10‐8 W m‐2 K‐4 and T0 is the surface temperature of the body in degrees Kelvin (°K) (Oke, 1987; Baldocchi et al., 2000; Lafleur, 2008). Gases and particles in the atmosphere also emit and reflect longwave energy, both upward and downward (L↓). The net radiation flux of an ecosystem is thus the balance of downwelling and upwelling short and long wave fluxes, Q* = K↓ ‐ K↑ + L↓ ‐ L↑ , (2.2) measured in watts per square meter (W m‐2). From an ecosystem perspective, net longwave radiation can be important after disturbance (Liu & Randerson, 2008), particularly at severely burned stands with exposed ash‐covered soils. A review on wetland functioning notes that variation in longwave energy loss among sites is on the same order of magnitude as the effects of albedo differences on Q*, and suggests that the two components are offsetting (Lafleur, 2008). 2.3.2 Energy exchange The typical diurnal progress of Q* involves a daytime radiant energy surplus at the surface, due to net shortwave gain exceeding net longwave loss and a nighttime deficit when net longwave loss is unopposed by a solar energy input (Oke, 1987). This diurnal surface radiative imbalance is corrected through convective exchanges of energy, either as sensible (QH) or latent (QE) heat, and by conduction of soil heat (QG) upward or downward through the soil column (Oke, 1987). As such, the end result of the radiation budget is also the basic input into the surface energy balance 13 Q
QH
QE
QG
ΔQ S , (2.3) where ΔQS is the storage of heat within the biomass and canopy air space, and all are measured in W m‐2. The exact partitioning of Q* is governed by the fundamental properties of the forest; its albedo, leaf area index, surface roughness and vegetation type, as well as the abilities of the soil and atmosphere to transfer heat (Amiro et al., 2006). A positive Q* flux denotes a gain in energy at the surface while a positive energy balance component represents a loss at the surface, either upward toward the atmosphere or downward through the soil. The term ΔQS is present to correct for the input and output of individual energy fluxes not balancing across the canopy space boundaries (Oke, 1987) In jack pine forests, sensible and latent heat fluxes change both diurnally and seasonally, driven by incoming irradiance and co‐variations in available energy and atmospheric evaporative demand (Baldocchi & Vogel, 1996). For forests, due to their large surface, soil heat fluxes and heat storage in the phytomass and canopy air can collectively constitute a significant component of the overall energy balance (Bailey et al., 1997). QG exchange is greatest at sunrise when convective fluxes are constrained by the shallow planetary boundary layer (Bailey et al., 1997). During daylight hours, QG peaks before Q* and becomes negative before sunset (Bailey et al., 1997). In contrast, QH over the course of an ideal summer day will follow the parabolic rise and fall of Q* (Baldocchi et al., 2000). QE is less sensitive to Q* and will tend to plateau between 10 and 18h (McCaughey et al., 1997; Baldocchi et al., 2000). Significantly, jack pine stands have been observed to have the lowest growing season midday QE flux of all boreal stand types, with QH values exceeding QE even during peak hours (Baldocchi & Vogel, 1996; McCaughey et al., 1997). This is contrary to most other boreal systems where, with greater water availability, the importance of QH falls while that of QE rises (Baldocchi & Vogel, 1996). At upland sites, high rates of QH exchange have a positive impact on growth of the planetary boundary layer (McNaughton & Spriggs, 1986; Culf, 1992). Deeper boundary layers are more difficult to humidify, consequently high atmospheric evaporative deficits are maintained near the surface limiting transpiration through stomatal closure and reinforcing QH exchange (Baldocchi & Vogel, 1996). 14 Seasonally, QH should play a larger role during the non‐growing season when soils are still cool or frozen and when air temperature is lower (Baldocchi et al., 2000). QE is highest during the warmest months when plants are most active and atmospheric evaporative demand is strong because of the deeper boundary layers present (Baldocchi & Vogel, 1996). Notably, pure conifer stands do not express large differences between the growing and non‐growing seasons. An expression of their evergreen nature, conifers can photosynthesize and transpire on warm spring days when deciduous plants are still dormant and in late autumn after deciduous plants and leaves have senesced (Black et al., 1996; Jarvis et al., 1997). At night, energy fluxes are the opposite of daytime, with Q* loss being replenished by upward QG conduction through the soil layers and smaller downward convective fluxes of QH and QE (Oke, 1987). Although QG is a significant flux on an hourly basis, the net effect is small as the flux does not differ much in strength when integrated over a full day (Oke, 1987). In contrast, over a year, QH and QE transport energy back to the atmosphere equivalent to an average global surface radiant energy surplus of 29% (Oke, 1987). Regardless of stand type or seasonality, a convective flux will always act as the principle mode of heat transportation (Oke, 1987). The Bowen ratio, expressed as β
QH
QE
, (2.4) (Bowen, 1926) therefore is a useful metric as it examines how much of the available energy (QA = Q* ‐ QG – ΔQS) is partitioned into QH and QE. Ultimately it indicates how much an ecosystem contributes in terms of energy to the regional climate (Lafleur, 2008). β values greater than unity occur when the majority of available energy is being consumed by QH while a β value below unity expresses a system where QE fluxes are dominant at that particular time. Comparing a boreal jack pine stand and a temperate broad‐leaf forest, the average summertime daily Bowen ratios were β ≥ 3 and β ≤ 1, respectively (Baldocchi & Vogel, 1996). Although their differences can be partially attributed to biome distinctions, these results can also occur within boreal regions between different stand types (Amiro et al., 2006). It is important to note differences in energy exchange between boreal stands dominated by conifers and their mixed coniferous‐deciduous counterparts. Conifer forests in particular have a greater ability for mass and energy exchange (Baldocchi et al., 2000). Conifer stands are 15 aerodynamically rougher, enhancing their ability to transfer mass and energy through turbulence (Jarvis et al., 1976; Jarvis & McNaughton, 1986). Their needles are also optically darker allowing the trees to absorb more solar energy, increasing the potential for QH and QE exchanges with the surrounding air and soil (Jarvis et al., 1976; Shuttleworth, 1989; Kelliher et al., 1993; Sellers et al., 1995; Betts & Ball, 1997; Baldocchi et al., 2000). Furthermore, conifer stands tend to have lower density, as a result of lower leaf area indices and highly clumped shoots on narrow tree crowns (Chen, 1996). In an open‐canopy jack pine stand, these ecophysiological elements allow more solar radiation to reach the soil surface enabling more exchanges at the forest floor rather than in the canopy relative to other stand types. For example, 20 to 30% of net ecosystem mass and energy exchange was found to occur at the forest floor of a mature jack pine stand compared with 5% at a temperature deciduous stand (Baldocchi & Vogel, 1996). 2.3.2.1 Energy exchange in a recently burned conifer stand Fire modifies the fundamental properties of a forest in turn altering surface energy exchanges (Chambers & Chapin, 2003). The most obvious changes occur in spring when snow cover exposure at recently burned stands reflect solar energy, reducing available energy for QH and QE fluxes (Liu & Randerson, 2008; Jin et al., 2012). Snow cover exposure also allows more rapid melting resulting in a rapid increase in Q* in a very short period of time, rather than a slow increase matching the pattern of K↓ (Liu et al., 2005). In summer, a lack of overstory results in more K↓ receipt at the forest floor (Liu & Randerson, 2008). Consequently, soil surface temperature increases by several degrees causing L↑ to increase, resulting in a reduced Q* (Chambers & Chapin, 2002; Liu et al., 2005). As deciduous species tend to dominate the early stages of succession, their presence results in higher stomatal and canopy conductance (Dang et al., 1997; Hogg et al., 1997), causing higher peak summer QE fluxes and lower allocation of available energy to QH (Eugster et al., 2000; Chambers & Chapin, 2003; Liu et al., 2005; Amiro et al., 2006). The lighter‐coloured deciduous component also results in a higher albedo, further reducing the magnitude of Q* at a recently burned stand (Chambers & Chapin, 2003). During the fall and winter, all components of the surface energy budget are small because of the small amount of incoming solar energy (Liu & Randerson, 2008). The extended 16 conifer growing season at mature sites, however, leads to continuing surface energy exchanges, particularly of QE (Liu et al., 2005). During winter, albedo differences persist contributing to greater absorption of solar energy at mature conifer stands (Liu et al., 2005). An important ramification of higher surface albedo is that C losses linked with increased burn severity may be partially offset by a negative radiative forcing on the regional climate (Jin et al., 2012). 2.4 Carbon exchange 2.4.1 Biological processes In addition to driving water and energy cycling, solar energy also drives the mass exchange of carbon, through the biological processes of photosynthesis and respiration (Oke, 1987). During photosynthesis, plants take up atmospheric CO2 through small pores called stomata on the leaf surface. Stomata remain open during the day to capture CO2 and expel O2 as a by‐product of energy conversion to carbohydrates (Geider et al., 2001). However a large portion of CO2 fixed during the day is consumed by metabolic respiration over the course of the evening and night or is exuded through the root systems (Geider et al., 2001). Transpiration of water vapour also occurs as open stomata expose the interior of the plant to lower atmospheric humidity levels (Geider et al., 2001). Stomata are of climatic significance as they have the ability to open or close through the operation of guard cells within the interior of the stomatal aperture (Monteith, 1965). The degree of opening depends on environmental factors including light intensity, temperature, humidity and CO2 concentrations in the leaves (Farquhar & Sharkey, 1982). Closure will occur with insufficient light levels or excessive transpiration, making plants an active agent in C and water vapour cycling (Monteith, 1965). On a global scale, CO2 emissions from soils through plant and microbial activity account for 20‐38% of the total natural and anthropogenic CO2 input to the atmosphere annually (Kicklighter et al., 1994). Although CO2 is the form most prevalent in exchanges between forest ecosystems and the atmosphere, other forms are also present on smaller scales. Methane (CH4) is both produced and consumed through microbial processes in forest soils. In non‐saturated environments such as upland forests, methane oxidation results in a small net uptake of CH4 (Schiller & Hastie, 1996; Burke et al., 1997). In contrast, the strength and direction of carbon monoxide (CO) fluxes is more strongly linked to solar intensity than to microbial activities (Zepp 17 et al., 1997). Abiotic photoproduction and thermal decomposition at the surface is partially offset by microbial oxidation within the soil (Zepp et al., 1997). Thus CO fluxes are a net sink at the surface in closed canopy conditions and a net source in recently disturbed areas when light levels are high (Zepp et al., 1997). When compared with black spruce stands, mature jack pine stands have been shown to be stronger CH4 sinks, smaller CO sinks and smaller CO2 sources from their soils; all a result of canopy and soil structural differences (Burk et al., 1997; Zepp et al., 1997). Fire‐disturbed stands of either jack pine or black spruce in contrast, may be significant local sources of CO during summer days and slightly stronger CH4 sinks than mature stands (Burk et al., 1997; Zepp et al., 1997). Fire effects on soil CO2 effluxes are complex and vary significantly according to local abiotic and biotic conditions. 2.4.2 Exchange in a mature stand 2.4.2.1 Daily and seasonal patterns Patterns in C exchange between a jack pine stand and the atmosphere are dependent on plant and microbial activities. The net ecosystem exchange (NEE) is the difference between gross photosynthetic uptake (GEP) and ecosystem respiration (ER) – the combined soil CO2 effluxes from autotrophic (AR) and heterotrophic (HR) sources (Litvak et al., 2003). In micrometeorological studies, NEE follows a convention with a positive flux denoting C movement toward the atmosphere and a negative flux representing net uptake at the surface (e.g. Baldocchi, 2003). Over the course of a day, NEE will follow the patterns of GEP and ER, with NEE becoming more negative when GEP fluxes are dominating and more positive when ER fluxes have a larger magnitude; all three fluxes are often described in units of mass flux density (e.g. gC m‐2 d‐1). On an ideal sunny summer day, GEP will follow a parabolic pattern driven by irradiance while ER will typically follow changes in soil temperature (Mkhabela et al., 2009). At night, with photosynthesis absent, NEE is equal to ER representing a continuous efflux to the atmosphere. Mature jack pine ecosystems should be moderate sinks of carbon during the growing season and small sources of carbon during the winter (Suni et al., 2003a). In spring, the transition to the growing season can occur rapidly as conifer seasonality is not determined by leaf‐out (Krammer & Kozlowski, 1979; Suni et al., 2003a). Jack pine stands tend to show a behavior 18 toward being larger net daily sinks at the beginning of the growing season before gradually shifting toward carbon neutrality or even a net daily source toward the mid‐end (Jarvis, 1994). This is in part a result of jack pine stands expending a disproportionate amount of fixed C through plant metabolic respiration, for example 68% at a central boreal site (Baldocchi et al., 1997a). It is also possible for an ecosystem to flip from being a sink to a source on a daily basis during peak growing season due to high respiration fluxes or environmental stifling of GEP (Baldocchi et al., 1997a). During autumn, peak rates of GEP and nighttime ER are expected to diminish with weakening environmental drivers. As conifer forests have evergreen needles, photosynthesis will only cease with freezing in late autumn (Goulden et al., 1997). Finally, in winter, NEE will exhibit a small but steady efflux attributed to ongoing soil respiration occurring at deeper, warmer depths (Winston et al., 1997). 2.4.2.2 Environmental controls Irradiance, air temperature, soil temperature, soil moisture, atmospheric evaporative demand, needle photosynthetic capacity and leaf area all control the exchange of C to various degrees. The onset of the growing season is dependent on day length and air and soil temperatures above freezing (Suni et al., 2003a). As conifers respond opportunistically to favorable environmental conditions, earlier photosynthesis can occur with intermittent high temperatures (Ensminger et al., 2004). During the growing season, optimum photosynthesis occurs between 15°C and 25°C (Baldocchi et al., 1997a), however, irradiance is the dominant controller of instantaneous photosynthesis (Goulden et al., 1997). At dry, upland sites, high irradiance combined with a low evaporative demand on stomata results in ideal CO2 capture rates (Jarvis & McNaughton, 1986). Limited soil moisture and diurnal increases in humidity deficits with the development of deep boundary layers can severely limit C exchanges over the course of the day (Baldocchi et al., 2000). ER is highly correlated with soil temperatures and will respond to soil warming over the course of the day and season (O’Neill et al., 2006). Thus at dry jack pine sites, the diurnal NEE pattern is characterised by maximum uptake in the morning, a gradual decrease through the afternoon and a sharp decrease in the evening (Baldocchi et al., 1997a; McCaughey et al., 1997). Smaller daytime uptake coupled with strong nighttime release 19 during peak growing season can result in low net C uptake over the growing season and a net source annually (Jarvis, 1994). The timing of the start of spring photosynthesis is an important determinant of growing season length (GSL) and total annual productivity in northern forest ecosystems (Suni et al., 2003a; McMillan et al., 2008). Similarly, the timing of autumn freeze can also create year to year variability in the annual C budget of an ecosystem (Goulden et al., 1997). The onset of the growing season has been found to be constrained by ambient temperature while the end is correlated with day length yet temperature can have a large impact as well (Suni et al., 2003a; Piao et al., 2008). Consistent favorable temperatures during early spring or late fall will have more positive impact on total annual GEP than a similar temperature increase during summer (Goulden et al., 1997; Ensminger et al., 2004). However, warmer autumn soil temperatures can prolong high ER counteracting the positive benefits of a higher GEP on annual NEE (Goulden et al., 1997; Piao et al., 2008). 2.4.3 Exchange at a recently burned stand 2.4.3.1 Initial biophysical impacts Fire disturbance can cause a high degree of change to the community structure and function of a stand. Vegetation succession is affected by the severity of the fire, the legacy of the pre‐fire community and current environmental conditions (Amiro, 2001; Johnstone & Kasischke, 2005). Meanwhile, post‐fire changes in soil temperature, moisture and nutrients influence plant regeneration and microbial decomposition (Litvak et al., 2003). Climate and weather conditions control ecosystem processes, resulting in highly variable NEE over this period. Even after succession reaches the point of source/sink turnover, over a given year environmental variation can still cause stands to be net C sources annually (Goulden et al., 2011). The passing of a severe crown fire characteristically results in little remaining aboveground apart from charred stems (Amiro et al., 2003). Jack pine trees do not reproduce via sucker roots thus death of the aboveground portion will result in root death as well (Amiro et al., 2006). Asexual regeneration of shrubs and mosses is limited by a severe burn, at times necessitating nearby‐community recruitment in order to re‐establish vegetation cover 20 (Johnstone & Kasischke, 2005). Figure 2.1 (Goulden et al., 2011) highlights predicted trajectories of GEP, HR, AR and NEP over a burn cycle as well as expected changes in individual forest C stocks. Both GEP and AR (Figure 2.1c,d) are expected to be zero immediately following a stand‐
replacing disturbance (Litvak et al., 2003; Chapin et al., 2011). Conversely, HR (Figure 2.1d) can exhibit a complicated and variable response. Changes in HR are controlled principally by soil temperature, with other significant physical and biological variables being soil moisture content, substrate quality and microbial biomass (Kasischke & Stocks, 2000; Litvak et al., 2003). Figure 2.1 (from Goulden et al., 2011) shows a hypothesized positive pulse in HR following a severe fire (Litvak et al., 2003). The pulse stems from the idea that an influx of fire‐killed materials and higher soil temperatures, both from the direct effect of fire as well as energy absorbed through a more open canopy, would stimulate microbial activity (Odum, 1969; Harden et al., 2000; Schulze et al., 2000; Amiro et al., 2003; Litvak et al., 2003; Wang et al., 2003). In contrast, recent studies have measured little change or even a dampening effect post disturbance (Weber, 1988; Burke etal., 1997; Zepp et al., 1997; Amiro, 2001; Czimczi et al., 2006; Amiro et al., 2010; Goulden et al., 2011; Moore et al., 2013). One possible hypothesis is that severe fires remove microbial populations alongside SOC which, compounded with root die‐off, would result in a reduced ER flux (Amiro, 2001). Moore et al. (2013) suggest that the dampening stems from microbial populations no longer having a constant supply of photosynthetic carbon around tree roots. Conversely, soil measurements in eastern Alaska have found that HR emissions from burned materials are in the order of 1.0 to 2.7 times greater than non‐burned humic soils (O'Neill et al., 2002; O’Neill et al., 2006). Furthermore, the burned soil samples had significantly higher concentrations of accessible P, K and Ca (O’Neill et al., 2006). Thus, heightened sensitivity to soil moisture levels, reduced microbial biomass and reduced supply of C may all act to constrain HR, dampening the pulse expected in warmer post‐burn soils (Burke et al., 1997; O’Neill et al., 2006). Isolating HR fluxes is difficult in field experiments, as such environmental controls on HR processes are poorly understood relative to vegetation processes (Harden et al., 2000; O’Neill et al., 2006; Trumbore, 2006). 21 2.4.3.2 Impact on patterns and controls Within a year of the burn, seedlings and understory species are already renewing, resulting in the slow but steady re‐establishment of photosynthesis and respiration patterns. Thus, with vegetation establishment, young stands can be strong daytime C sinks (Amiro, MacPherson, & Desjardins, 1999; Amiro et al., 2003), or even strong daily sinks (Amiro, 2001; Litvak et al., 2003) during the growing season while still being C sources annually owing to ongoing ER (Amiro et al., 2006; Mkhabela et al., 2009). During the first decade of recovery, deciduous and herbaceous vegetation generally dominate primary production (Amiro et al., 2006; Litvak et al., 2003). Seedlings are present but low needle leaf biomass results in a small contribution to annual GEP (Amiro et al., 2010). Owing to their physiological differences, deciduous species have a shorter growing season than conifers, reflected in dissimilar seasonal NEE patterns (Goulden et al., 2006). A young deciduous‐dominated stand’s growing season length is comparatively short, for instance 65 days compared to 130 days at a mature conifer stand (Goulden et al., 2006). The transition from deciduous to conifer during succession will affect both the maximum uptake and seasonal pattern of GEP (Goulden et al., 2006; McMillan et al., 2008; Mkhabela et al., 2009). Shifts in stand coverage have been found to occur between 15 and 23 years at which point maximum uptake falls and annual GEP increases significantly, for instance from 450 gC m‐2 y‐1 to 710 gC m‐2 y‐1 (Goulden et al., 2011). 2.4.3.3 Source/sink turnover period Figure 2.2 combines measurements at jack pine sites along the three North American chronosequences. Even allowing for climate and weather variability among and within sites, a clear decreasing trend from strong source towards C neutrality can be seen. Amiro et al. (2010) found the net C source/sink transition to occur at 10.5 years while Goulden et al. (2011) determined the turnover at 11.5 years. These findings are important and unexpected; initially, the scientific community had predicted a transition around 20 to 30 years, dependent on species type (Amiro et al., 2006). Extrapolated to the biome level, these findings demonstrate that renewing boreal stands are removing carbon from the atmosphere at least a full decade earlier than first predicted. 22 2.5 Water vapour exchange In boreal forest studies, evapotranspiration (ET), the mass equivalent of QE, is often of interest. Using energy provided by the energy balance, ET is the combined loss of water to the air through evaporation from surfaces and transpiration from the vegetation. Forming the atmospheric input for the hydrological cycle, water vapour is carried up into the atmosphere where it eventually cools, condenses and falls as precipitation. Near surface water can also be deposited by condensation, sublimation or impact, a more common example being dew (Oke, 1987). Over terrestrial surfaces, precipitation exceeds ET, thus excess surface water on land is transported to the oceans via runoff and surface and subsurface waterways. As runoff lost from one system will be gained by another, the net water balance for the entire Earth‐atmosphere system can be simplified to precipitation = ET (Oke, 1987). The mass flux (ET) and energy flux (QE) are linked by QE
LV · ET , (2.5) where ET is expressed in units of kg m‐2 s‐1 and Lv is the latent heat of vaporization in J kg‐1 (~2.45 MJ kg‐1 at 20°C) (Oke, 1987). 2.5.1 Exchanges in a mature stand CO2 and water vapour share the same pathway in plants with water vapour diffusing from stomata as CO2 simultaneously enters (Lafleur, 2008). When jack pine close their stomata in response to temperature and humidity stresses, CO2 and transpiration exchanges are effectively stopped. However, evaporation from canopy and forest floor surfaces will continue as long as there is positive net radiation. 2.5.1.1 Diurnal and seasonal patterns Diurnally, QE is less sensitive to the pattern of Q*, and plateau between approximately 10h and 18h before dropping below zero during nighttime hours (McCaughey et al., 1997; Baldocchi et al., 2000). This short‐term insensitivity results from ET being closely coupled to the atmospheric evaporative demand (Da) (Bailey et al., 1997). Seasonally, QE has been shown to lag behind the typical seasonal progression of Q* due to the surface and vegetative factors controlling ET (Brummer et al., 2012). QE is highest during the warmest months when plants are 23 most active and warm temperatures cause high ET from the canopy and forest floor surfaces (Baldocchi & Vogel, 1996). 2.5.1.2 Environmental controls The boreal environment, through short‐term and long‐term forcings, drives ET rates (Baldocchi et al., 2000). Short‐term forcings created by temperature, precipitation, soil water content and stomatal physiology affect the demand and supply of water vapour to the atmosphere (Baldocchi et al., 2000). QE at sandy upland sites is sensitive to variation in Da (Baldocchi & Vogel, 1996) with QE being shown to be instantaneously inhibited by Da in contrast with well‐watered sites which respond favorably (Baldocchi & Vogel, 1996; Kelliher et al., 1998; Baldocchi et al., 2000). Long‐term forcings related to temperature and precipitation regimes influence growth, photosynthetic capacity and stomatal conductance (Baldocchi & Vogel, 1996). These factors interact in a moisture‐stressed jack pine stand to form a more open canopy with a lower leaf area index, reinforcing lower ET rates (Baldocchi et al., 2000). Upland conifer forests typically evaporate at rates between 33% and 50% of available surface energy (Baldocchi et al., 2000). This contrasts with broad‐leaf stands with rates of 60% and wetlands where up to 75% of available energy may be used for evaporation purposes (Baldocchi et al., 2000). 2.5.2 Exchange in a recently burned stand Changes in albedo, snow cover, surface roughness and vegetation type after fire disturbance all have implications for the partitioning of Q* into its respective surface energy fluxes (Liu & Randerson, 2008). Fire combustion of vegetation removes the biological pathway for water vapour exchange, altering the total QE flux for the stand. 2.5.2.1 Impact on patterns and controls With little to no vegetation present after fire, transpiration will be severely reduced while a more open canopy will allow more solar energy to be received at the soil surface. The latter could result in greater partitioning of Q* to QH and QG (Liu et al., 2005) yet more sunlight received at the surface could in turn drive more soil evaporation (Baldocchi et al., 2000; Chapin et al., 2000). Sass (2007) showed that QE exchange at a four‐year old site was almost half that of 24 thirteen‐year old site on average over half hourly periods. Similarly, Liu et al. (2005) found that annual E was reduced by 33% at a 3‐year burn site compared with an 80‐year old black spruce stand in interior Alaska. Mkhabela et al. (2009) compared newly fire disturbed and harvested sites and determined that fire sites have higher total annual ET hypothesized as a result of greater deciduous plant growth at fire sites. The same study also concluded that water use efficiency, the ratio of the sum of GEP to the sum of ET, was lowest at newly disturbed sites compared with mature stands. Water use inefficiency at the fire disturbed sites was attributed to relative greater evaporation off of surfaces with less transpiration, and as such less C uptake (Mkhabela et al., 2009). Over all, QH fluxes from boreal forests are approximately two to three‐fold greater than in deciduous stands, leading to a positive feedback on boundary layer growth (Chapin et al., 2000). In a fire‐disturbed stand this positive feedback loop could result in higher levels of ET, reducing soil moisture levels as the growing season progresses (Chapin et al., 2000; Sass, 2007). Drought in general causes lower soil moisture content and alters surface energy exchanges resulting in increased QH and QG and decreased QE fluxes (Sass, 2007). Drought in a recovering stand could have a much larger impact with the lack of vegetation buffer resulting in chronically low water reserves, inhibiting plant productivity. 2.6 Measuring carbon and water vapour exchange Measurement versatility over appropriate spatial and temporal scales is essential in order to study the biophysical responses of ecosystems to environmental variability and disturbance. Net ecosystem exchange (NEE) is an effective measurement evaluation of landscape‐level carbon exchanges. 2.6.1 Eddy covariance The eddy covariance (EC) technique is a powerful tool widely used by today’s micrometeorological community to measure ecosystem‐scale trace gas exchange. The main advantage of this technique is that it spatially integrates fluxes over 100’s m2 to several km2 and does so over time scales from hourly to comparisons of over a decade of continuous measurements (Schmid, 1994; Baldocchi, 2003; Zha et al., 2009). As the only direct method of 25 measurement at the ecosystem scale, EC is indispensable for understanding and monitoring the global carbon cycle (Friend et al., 2007). Because of the relative ease of trace gas transport in the lower atmosphere, understanding near‐surface exchange processes is extremely important (Dalbert, 1993). In the atmosphere, mean horizontal airflows contain irregular or random turbulent motions, moving upward and downward carrying trace gases (Dalbert, 1993; Baldocchi, 2003). These motions, called eddies, are set into motion by free or forced convection. Free convection, also known as buoyancy, is initiated via density differences between the particular eddy and surrounding air parcels. Forced convection, on the other hand, is a product of surface and atmospheric conditions. Windier conditions tend to drive greater atmospheric mixing, resulting in larger fluxes while ecosystem surfaces play a smaller role through their physical extent and height, defined as roughness (Oke, 1987; Lafleur, 2009). A concentration gradient results when a trace gas is unequally distributed across an interface. When this occurs, the particular gas will diffuse from higher to lower concentration in an attempt to equalize the gradient. CO2, CH4 and nitrogen oxide (N2O) are three important GHGs studied across the ecosystem spectrum due to their global warming potential (IPCC, 2007). At upland boreal sites, plant and soil processes do not exchange CH4 and N2O as readily as CO2 hence research has prioritized CO2 exchange studies (e.g. the BOREAS project, Sellers et al., 1997). Water vapour is also a research focus due to its shared physiological pathway with CO2 in plants and important role in altering local physical climate systems. The EC method directly samples turbulence to determine the net movement of trace gases across the surface‐atmosphere interface (Baldocchi, 2003). The flux (Fc) is the time averaged product of the instantaneous departures from the mean vertical velocity (w’) and the constituent mixing ratio (c’), multiplied by average air density (ρa). F
ρ wc
, (2.6) Fast response (≥10 Hz) instrumentation is used to measure vertical wind speed and the gas mixing ratio as eddies rotate past instrument sensors (Finnigan, 2008). 30 minutes is the preferred averaging period as it allows adequate sampling of a broad range of eddy sizes while removing the effect of synoptic climate conditions such as weather patterns. 26 Several limitations and systematic errors must be considered when applying the EC technique. Ideally, tower placement is on flat terrain with a large upwind presence of homogenous vegetation (Webb et al., 1980; Baldocchi 2003). Meticulous placement will ensure that readings do not include non‐representative or horizontally advected fluxes, allowing the exchange term to be assessed based only on the measured turbulent flux and a calculated storage flux (Fs) (Aubinet, 2008; Aubinet et al., 2012) NEE
F
F . (2.7) Additionally, the EC technique is most applicable during fully turbulent atmospheric conditions (Baldocchi, 2003). If conditions are less than ideal, systematic errors become apparent. For example, at night and during sunrise, thermal stratification of the atmosphere inhibits CO2 movements, preventing fluxes from reaching tower instruments set above the canopy (Baldocchi, 2003). This systematic nighttime bias produces an underestimation of measured ecosystem respiration, in turn causing overestimates of daily, seasonal and annual NEE (Baldocchi, 2003; Aubinet, 2008). During the break‐up of the stable nocturnal boundary layer, venting of the accumulated CO2 can cause an overestimation of fluxes at that particular time, affecting the pattern of diurnal NEE (Baldocchi, 2003). A systematic error that has been attributed to EC instrumentation is the general lack of energy balance closure at tower sites (Wilson et al, 2002). A formulation of the first law of thermodynamics, closure occurs when the sum of QH and QE is equal to QA, calculated as of net radiation minus the soil heat flux and any changes in various energy storage terms (Wilson et al, 2002). Most sites experience a 20% imbalance suggesting either a systematic under‐evaluation of turbulent flux measurements or an overestimation of available energy through an underestimation of one or more energy sinks (Aubinet et al., 2000; Wilson et al., 2002; Oliphant et al., 2004). While the closure problem can be significant, no consensus has been reached in the literature at this point due to the difficulty in critically evaluating all possible sources of the imbalance (Wilson et al., 2002). Even allowing for limitations and potential bias errors, EC is a powerful technique as it produces a direct and continuous measure of net ecosystem exchange (Baldocchi 2003). EC has been essential in the creation of long‐term continuous data records at mature and disturbed 27 stands and has been integral in furthering global terrestrial carbon budget estimations (Baldocchi 2003; Gu et al., 2012). 2.6.2 Chronosequencing The boreal forest biome is a mosaic of stand ages and types that are persistently being renewed through periodic disturbances of fire, insect, disease and harvesting among others (Kurz & Apps, 1999; Chen et al., 2000; Kurz et al., 2008a,b; Mkhabela et al., 2009). Natural wildfire has been highlighted as the dominant stand‐renewing agent; fire accounts for over 2 million ha of newly disturbed land area in Canada alone annually (Stocks et al., 2003). Investigating stand dynamics over a fire cycle would require roughly 100 years of continuous measurements if at a single site; neither time nor cost‐effective. In lieu, the chronosequence approach has EC towers at multiple sites covering an age spectrum from newly disturbed through maturity (Amiro et al., 2010). Eddy covariance chronosequences predate to the establishment of the Boreal Ecosystem‐Atmosphere Study project (BOREAS). The BOREAS project began by focussing on old‐
growth forest functioning, however knowledge gaps were quickly recognized. Tower sites were added in recently burned and young stands and in the early 2000s, after a decade of monitoring, the sites became the first of an international flux tower network presence, FLUXNET (Amiro et al., 2006). FLUXNET is now a reference database for terrestrial ecosystems worldwide, providing a valuable tool for validating remote sensing and model development efforts (Baldocchi et al., 2001; Margolis et al., 2006). Since the inception of BOREAS, chronosequencing has been used extensively in North American forests (e.g. Harden et al., 1997; Amiro et al., 2001; Law et al., 2002; Litvak et al., 2003; Bond‐Lamberty, Wang, & Gower, 2004; Clark et al., 2004; Amiro et al., 2006; Coursolle et al., 2006; Goulden et al., 2006; Humphreys et al., 2006; Randerson et al., 2006; Sass, 2007; Mkhabela et al., 2009; Zha et al., 2009; Goulden et al., 2011). Fire chronosequence studies have been established in Manitoba (e.g. Litvak et al., 2003; Goulden et al., 2006; Goulden et al., 2011), Alaska (e.g. Randerson et al., 2006; Mack et al., 2008) and Saskatchewan (e.g. Amiro et al., 2001; Sass, 2007; Mkhabela et al., 2009) in order to study the effect of post‐fire stand age on forest mass and energy balances. In contrast, the eastern boreal has received notably less 28 attention and no fire chronosequences have been established thus far. Still, significantly, the comparable effect of harvest disturbance on ecosystem‐level CO2 fluxes was studied between a recently harvested and mature site in central Quebec (Bergeron et al., 2008). 2.6.3 Modelling evapotranspiration Using eddy covariance and meteorological measurements, forest evaporation and related canopy characteristics can be quantified through a theoretical framework. ET rates depend on four factors: the strength of the surface‐to‐air vapour concentration gradient, the availability of water and energy, the intensity of turbulence and the physiological role of plant stomatal activity (Oke, 1987). The Penman‐Monteith (PM) equation is an empirical formula that arranges these four factors in a combination model (Penman, 1948, Monteith, 1965; Monteith & Unsworth, 2008). The PM model is one‐dimensional, integrating from the soil surface to the measurement height, and assumes that all water vapour moves through the plant canopy system rather than evaporating from the soil or phytomass (Bailey et al., 1997). The PM model as modified by Monteith and Unsworth (2008) is: ET
∆ Q* QG
/
, ∆
(2.8) where ρa is the density of air, cp is the specific heat capacity of air (Jkg‐1°K‐1), es and ea are the saturation and actual vapor pressures respectively (kPa), ra and rs are the surface and aerodynamic resistances, respectively (sm‐1), Δ is the slope of the curve for saturation vapour density versus temperature (kPa°K‐1) and γ is the psychrometric constant (0.067 kPa°K‐1 at 20°C). The PM model was redefined by McNaughton and Jarvis (1986) to include the additive combination of equilibrium and imposed evapotranspiration, ET and ET
where ET
ET
1
Ω · ET
Ω ET
, respectively: (2.9) is a function of atmospheric demand and physiological supply and Ω is used to express the degree of coupling between the vegetation and planetary boundary layer via: Ω
(McNaughton & Jarvis, 1983). g (r
∆
γ
∆
γ
1
1
) and g (r
g
g
,
(2.10)
) are aerodynamic and surface conductance respectively, in m s‐1, with g computed by rearranging the PM model while g is estimated 29 from an empirical model with wind speed (McNaughton & Jarvis, 1983; Monteith & Unsworth, 2008). Ω values range between 0 to 1; an Ω value approaching 0 signifies surface water vapour fluxes driven by an atmospheric deficit along a humidity gradient, while values closer to 1 point to a system controlled solely by solar radiation input (McNaughton & Jarvis, 1983). Mature upland boreal forests typically exhibit Ω ranging between 0.08 to 0.35 due to their large aerodynamic roughness and strong control on water vapour loss through stomatal openings (Jarvis & McNaughton, 1986; Baldocchi & Ryu, 2011; Brummer et al., 2012). Where water is freely available, the equilibrium rate of evaporation may be reached where water transfer is a function of energy availability alone. After studying ET over moist surfaces in humid climates, Priestley and Taylor (1972) came up with a simple empirical relation (PT‐α) to explain regional potential ET: ET = PT‐α ∆
∆ γ
Q ‐ QG
,
(2.11)
where, PT‐α, the Priestley Taylor coefficient, was found to be on average 1.26 (Priestley & Taylor, 1972). ET rates higher than equilibrium indicate that the average planetary boundary layer is maintaining a consistent surface‐to‐air vapour pressure deficit due to the downward mixing of dry air from the free atmosphere above (Bailey, Oke & Rouse, 1997). In boreal forests, average PT‐α values range from 0.53 for conifer‐deciduous to 1.09 for broadleaved‐deciduous (Baldocchi & Ryu, 2011). According to Baldocchi and Ryu’s (2011) literature synthesis, boreal evergreen conifer forests have a mean PT‐α of 0.55. Empirical evidence has found that PT‐α responds to leaf area index, photosynthetic capacity and soil moisture (Baldocchi & Ryu, 2011). Since jack pine stands have lower values in all three parameters, this may result in lower PT‐α than the 0.55 mean. This variable therefore can be used as a comparative diagnostic between sites or ecosystem types. 2.7 Boreal C cycling under a changing fire regime To understand why the boreal biome has warranted such scientific scrutiny, we must examine the larger picture. In essence, the boreal is extensive, holds large C reserves and is sensitive to climate change. As such this biome has the potential to affect the global climate system by: (i) altering the global carbon budget through changes in C sequestration and release; 30 (ii) modifying the radiation budget through direct fire emissions and albedo change; and (iii) modifying the moisture balance (Walter 1979; Van Cleve & Veireck, 1983; Kurz et al., 1995; Harden et al 2000; Kasischke, 2000; Dale et al., 2001; French, 2002; Soja et al., 2004; Margolis et al., 2006; Soja et al., 2007). One of the goals of understanding the contemporary boreal carbon balance is to predict climate forcings caused by future changes in disturbance (Goulden et al., 2011). Currently the Carbon Budget Model used by the Canadian Forest Sector estimates that the boreal forest is a small net source of carbon to the atmosphere annually (Kasischke et al., 1995; Harden et al., 2000; Kurz et al., 2008). Analysis of burn data shows that the area of boreal forest burned has doubled in the last 20 years from 0.28% to 0.57%, following the recent warming trend of 0.31 ± 0.03°C per decade in Canada (Kasischke et al., 1999; Keyser et al., 2000). Furthermore, future projections indicate that total annual burn area will continue to increase as regional temperatures rise and precipitation patterns change (Stocks et al., 1998; Stocks et al., 2000; Flannigan et al., 2005; Soja et al., 2007; Balshi et al., 2009). Fire regime changes have implications for the climate system through a variety of feedbacks. First, increased trace gas emissions could create a positive feedback on climate warming (Kasischke et al., 1995; Gillet et al., 2004). Second, altered surface energy exchange patterns could modify regional climate feedbacks (Chapin et al., 2000; Chambers & Chapin, 2003; Randerson et al., 2006; Brown & Johnstone, 2011). Third, increased C losses could overcome the positive feedbacks of CO2 fertilization, negating any potential reduction in atmospheric CO2 levels due to greater productivity in northern latitudes (Gillett et al., 2004; Kurz et al., 2008a; Flannigan et al., 2009). Of fundamental importance, changes to the fire regime could have a greater influence on boreal atmospheric feedbacks than any direct ecological responses to temperature and precipitation changes (Chapin et al., 2000). 31 Preface to Chapter 3 A more frequent and intense fire disturbance regime in the boreal biome could result in a net positive radiative forcing (Chapin et al., 2000). A shift toward younger forests necessitates studies of net ecosystem greenhouse gas exchange at recently disturbed stands in order to develop more accurate predictions of the present and future boreal net climate effect. In the following chapter, the patterns of CO2, water vapour and energy exchange are presented at a disturbed boreal jack pine stand. Linkages between environmental controls and the patterns and variability in exchanges are investigated. 32 Chapter 3 Carbon dioxide, water vapour and energy fluxes of a recently burned boreal jack pine stand in north‐western Québec, Canada 3.1 Introduction 3.1.1 The Eastmain‐1 Net Greenhouse Gas Emissions Project Hydro‐Québec is the largest producer of electric power in Canada, with over 57 powerhouses currently in operation, generating approximately 189 TWh annually (Tremblay et al., 2009). Due to growing concern over the long‐term contribution of freshwater reservoirs toward rising atmospheric GHG concentrations, in the early 2000s Hydro‐Québec commissioned a long‐term comprehensive study to determine the total emission count associated with a hydroelectric reservoir construction (Tremblay et al., 2010). The Eastmain‐1 reservoir, constructed in 2005, provided an ideal landscape for such a project; the Eastmain River is one of the major rivers in northern Quebec, draining a total area of 46,400 km2 and discharging into James Bay (Teodoru et al., 2012). Reservoir impoundment began on the 5th of November, 2005 and reached a maximum capacity of 602.9 km2 by June 2006. Since, the addition of a second powerstation, Eastmain‐1A, has considerably increased annual output to 5 TWh. The reservoir impoundment covers a vast swath of terrain that previously held a heterogeneous combination of aquatic, forested and wetland ecosystems. Remote sensing estimates classify 182 km2 of the pre‐flooded landscape as mature forest, 114 km2 as burned forest, 111 km2 as wetlands, as well as an additional 46 km2 of non‐forested soil (Teodoru et al., 2012). A further 150 km2 was previously lakes, stream segments and the actual riverbed (Teodoru et al., 2012). The Eastmain‐1 Net GHG Emissions Project (EM‐1 Project) is the first of its kind in looking beyond gross annual GHG emissions toward the net impact of flooding natural systems. The approach taken was to quantify all major terrestrial and aquatic carbon sources and sinks, pre and post impoundment. From there, the net impact of land cover transformation is simply the difference between the sink/source balance of the pre‐ and post‐flood environments. Significantly, this study will allow a comprehensive comparison with other energy sources, such 33 as fossil fuels, through determining the reservoir carbon footprint over a 100‐year period (www.Eastmain1.org). This project groups experts from a wide range of disciplines, through collaboration between scientists and researchers from Hydro‐Québec, the Université du Québec à Montréal and McGill University. The McGill team is contributing toward the EM‐1 Project through: (1) the use of micrometeorological techniques to measure prior and post‐flood carbon fluxes; (2) the application of remote sensing techniques to provide a comprehensive map of local landscape types; and, ultimately, (3) the creation of a process‐based carbon model, able to estimate the net GHG effect of a hydroelectric reservoir over the projected reservoir lifespan of 100 years. In the summer of 2006, micrometeorological towers were set up over two terrestrial ecosystems, a mature black spruce forest and a bog wetland. As well, a tower was set up on an island, to measure post‐flood carbon fluxes over the reservoir. In 2011, a tower was placed in a recently burned jack pine stand. The focus of the current thesis research is on understanding the drivers of carbon dioxide, water vapour and energy fluxes at the burned jack pine stand. This research will be integrated into the process model under development, allowing simulation of burned ecosystems alongside the other natural ecosystems. 3.2 Methods and materials 3.2.1 Area and site description The EM‐1 hydroelectric reservoir complex is located east of James Bay, Quebec between 51 to 52°N latitude and 72 to 76°W longitude. The region is punctuated by undulating hills and numerous lakes, a legacy of the last glacial period that uncovered Precambrian bedrock and removed much of the organic soil layer (Banville, 2009). Dominant features include a cold humid climate and nutrient‐poor acidic soil conditions (pH<5). In areas not influenced by periodic fire disturbance, the conditions have facilitated the development of a thick organic layer. In contrast, fire‐prone areas have developed open canopy forests due to poor post‐fire regeneration, characteristically underlain by a humo‐ferric podzol (Payette, 2000; Banville, 2009). Annual average temperature and precipitation are 0.0 °C and 961 mm yr‐1, respectively (Environment Canada, 2013). 34 The recently burned jack pine stand, henceforth referred to as FJP05, is situated 40 km west of the Eastmain‐1 complex (52.270°N, 76.748°W) (Figure 3.1). According to the USDA Forest Service Fire Database, the fire occurred around the 5th of June, 2005. Classified as a severe crown fire, it resulted in complete death of the canopy and understory; charred tree stems remain standing at the site. A soil profile dug July 23, 2011 determined a small to non‐
existent humus layer overlying coarse‐grained sand. This qualitative evidence suggests the fire disturbance also had a ground fire component, burning at a high enough intensity to combust organic matter several centimetres deep. Re‐growth is currently dominated by jack pine seedlings (Pinus banksiana) standing at an average height of 0.35 ± 0.03 m. Other presently growing species include Labrador Tea (Rhododendron groenlandicum), Sheep Laurel (Kalmia augustfolia), Canadian blueberry (Vaccinium myrtilloides) and clubmoss (Lycopodium digitatum). Jack pine seedlings cover 12 ± 2 % of the surface area, while shrub and moss species, combined, cover an additional 11 ± 1 %. The remaining qualifies as bare soil and charcoal (63 ± 2 %) or as leaning and fallen coarse woody debris (14 ± 2 %). There are several distinct landforms bordering FJP05. The north side of the site is flanked by large hydroelectric power lines with a main road providing access to the site. The flux tower itself is located approximately 500 m from the de‐vegetated power line area. A topographical change occurs in the southeast portion of the site, resulting in an abrupt transformation into treed bog dominated by black spruce with a tamarack presence. The basic site characteristics found at this eastern boreal site are representative of the Boreal Shield ecozone (Hare, 1950). It is typical to find jack pine trees growing on upland well‐drained mineral soils while having black spruce trees thrive in the more poorly drained areas, frequently in association with a thick peatmoss underlayer. Eddy covariance and meteorological measurements began on June 23, 2011 (DOY 174) and were collected until tower removal on September 19, 2012 (DOY 263). 3.2.2 Site characterization At FJP05, 100 m transects were set up in each cardinal direction, originating from the instrument tower and marked every 10 m. Four 100 m2 (10m x 10m) sampling plots were then marked off, positioned directly left of each transect with the leading edge embedded between the 50 m and 60 m markers. 35 Percent surface coverage was assessed by placing a 0.25 m2 quadrat 20 times per sampling plot. Quadrat placement was pre‐determined using a randomized collection pattern. Tree age was assessed for each live seedling that fell within the quadrat boundaries. Seedling age was evaluated by counting the number of lateral branch series from the soil base to the tree top. As conifers normally grow a new lateral ring of branches once per growing season, this is an effective age determinant when working with smaller trees. Height, current‐year growth and basal diameter were also measured for the selected seedlings. Height was measured using a measuring tape from the soil to the crown tip; current‐year growth, which is distinguishable as a lighter coloration toward the tip of the branches and stem, was measured as well; basal diameter was evaluated as close to the soil surface as possible using vernier callipers. Density of live seedlings, standing dead and fallen dead trees was determined by visual count within the 100 m2 sampling plots. Lastly, a cross‐sectional tree ring count of twelve randomly selected burned trees provided the approximate age of the stand present before the fire disturbance in 2005. 3.2.2.1 Leaf area index Leaf Area Index (LAI), defined as one half the total green leaf area per unit ground area surface (Chen & Black, 1992), is an indispensable parameter for better understanding mass and energy fluxes at tower sites (Chen et al., 2006). At FJP05, LAI was determined through a laboratory‐based sample analysis using the WinSEEDLE system; an image analysis software (WinSEEDLE 2012a, Régent Instruments Inc.). Twelve seedlings were harvested on September 27th, 2012; destructive sampling was done within the four sampling plots, using a pre‐
determined randomized collection pattern. The samples were subsequently stored in a cooler kept at 4°C. Underestimation of LAI due to needle shrinkage was not a large concern, nonetheless, individual samples were analyzed immediately after removal from refrigeration; LAI analysis took place over a period of two weeks. Projected needle area was determined by analyzing digitized trays of needles using a high resolution desktop optical scanner (STD4800, Régent Instruments Inc.). Care was taken to methodically place the needles without overlap onto the clear tray provided. Error was further reduced by using an overhead scanner lighting 36 system. Once analyzed, needle LAI was calculated as the area of projected needles per area of ground (m2 m‐2). 3.2.2.2 Soil sampling Soil cores were taken using a soil probe that had a diameter of 8 cm and a length of 40 cm. Two samples were taken per sampling plot; the first sample was taken in an area relatively free of vegetation while the second was required to be midway beneath a tree or seedling crown. This sampling technique attempts to address the micro‐scale C concentration heterogeneity expected based on distance from vegetation. Once removed from the soil, cores were measured in length and separated into organic and mineral layers. 5 cm (or the maximum available length if less than 5 cm) of each soil layer was retained and stored in a cooler, kept at a temperature of 4°C. In the laboratory, the soil samples were first air‐dried and homogenized, after which they were measured into crucibles and oven‐dried over a 24‐hour period at 105°C. The samples were weighed and subsequently placed in a muffle furnace, set to 360°C, for 4 hours. The samples were then re‐weighed and SOC was calculated by difference as grams of carbon per square meter of soil (gCm‐2). Alongside carbon content, soil moisture content (θ) was measured directly three times per sample during the sampling process using a Time Domain Reflectometry (TDR) instrument. Soil moisture was read by inserting the TDR probe down to the transition depth between the organic and mineral layers. Three measurements were taken in succession, at equal distance from the sample location. 3.2.2.3 Biomass sampling The aboveground seedling carbon stock was determined using destructive sampling techniques on July 31st, 2012. Eight locations per sampling plot were randomly chosen with the placement of the 0.25 m2 quadrat, whereupon any seedling falling under the quadrat was removed. At the laboratory, each seedling was categorized into stem, branch, needle and inflorescence (cones) and placed in a labeled paper bag. Individual seedling height and basal diameter was also measured and the seedling component weights were recorded as the combined weight of all seedlings per quadrat placement. The sample bags were then placed in an oven, set at 55°C, and dried until negligible weight loss was recorded for a selected sample. Finally, the sample weights were recorded as dry weight per unit area (gDW m‐2) and calculated 37 as kgC m‐2 with a conversion factor of 0.25. In conjunction, the height and diameter measurements were used in carbon stock calculations based on allometric relationships derived by Lambert et al., 2005. The allometric‐based calculations were then compared to the measured aboveground stock values in order to validate measurement scale‐up to the ecosystem level. 3.2.3 Instrumentation and measurements 3.2.3.1 EC measurements The eddy covariance technique was used to measure fluxes of sensible heat, latent heat and carbon continuously at FJP05. Instrument height was 6.2 m above the forest floor, extending over the dead overstory within a burn patch of approximately 4 km2. The FJP05 site was accessible by truck along a major gravel road leading from the EM‐1 base camp to the James Bay highway. The EC setup consisted of a three‐dimensional sonic anemometer (CSAT‐3, Campbell Scientific, Edmonton, Canada) to measure the vertical wind speed, an open‐path infrared gas analyzer (IRGA: LI‐7500, LI‐COR, Lincoln, NE, USA) to measure the concentrations of CO2 and H2O and a fine‐wire thermocouple to measure air temperature (T ). The system was connected to a fast response datalogger (CR3000, Campbell Scientific, Edmonton, Canada) and powered by a bank of 10 deep‐cycle 12v batteries, trickle charged by seven 60W solar panels. During the warm season, fluctuations in trace gas concentrations and wind speed were measured at 10 Hz and averaged over 30 minute intervals. Data was stored on 2 GB removable flashcards which were changed approximately every 6 weeks. During the winter months, measurement frequency was reduced to 5 Hz, and data retrieval was stretched to every 8 weeks. Once the data was transferred back to the laboratory, fluxes were calculated using an in‐house Matlab script (v.R2012a, Mathworks, Natick, MA, USA). 3.2.3.2 Flux calculations Several steps were required to calculate NEE. To begin with, the vertical flux (FC) was calculated as a half hourly average of the instantaneous product of vertical velocity (w) and the CO2 mixing ratio (c), multiplied by average air density (ρ ) through the equation: 38 F
ρ wc ,
(3.1) where the overbar represents a time average (Baldocchi, 2003). Subsequently, the WPL correction was applied to account for density variations (Webb et al., 1980) followed by the application of a three‐axis coordinate system rotation to bring mean vertical velocity to zero (Tanner & Thurtell, 1969). Storage change in the air column below the EC measurement level (FS) was calculated following Morgenstern et al. (2004) using F
h ρ
∆c
,
∆t
(3.2) where h is the measurement height, Δt and Δc are the change in time and mixing ratio between previous and subsequent half hours, respectively and ρ is the air density. Half hourly NEE was then computed as the sum of the vertical flux and the storage flux, NEE
F
F .
(3.3) 3.2.3.3 Environmental measurements In addition to the EC system, supporting environmental instrumentation was also installed at FJP05. A four‐component net radiometer (CNR4, Kipp & Zonen, Delft, Netherlands) and PAR Quantum sensor (PQS1, Kipp & Zonen, Delft, Netherlands) were installed on the tower to measure incoming and reflected shortwave, incoming and emitted longwave, and photosynthetically active radiation components. Air temperature (T ) and relative humidity (RH) were monitored (HMP45C212, Vaisala, Helinki, Finland) while wind speed and direction were measured with a wind monitor (RM Young 05103, Traverse City, MI, USA). At the ground surface, a tipping bucket rain gauge (TE525, Texas Electronics, Dallas, TX, USA) measured rainfall while a water content reflectometer (CS616, Campbell Scientific, Edmonton, Canada) measured soil volumetric water content (θ). A soil heat flux plate (HFT3, Campbell Scientific, Edmonton, Canada) and averaging soil thermocouples (TCAV, Campbell Scientific, Edmonton, Canada) were used to acquire soil temperature and the ground heat flux. Finally, a vertical thermocouple profile provided additional soil temperature (T ) values at 5, 10, 20 and 40 cm depths. The environmental instrumentation was initially connected to a CR1000 (CR1000, Campbell Scientific, Edmonton, Canada) datalogger. However, in June 2012 a malfunction occurred requiring that the CR1000 be replaced by a CR23X (CR23X, Campbell Scientific, Edmonton, Canada) for the remainder of the study. 39 Although FJP05 was running not quite 1.5 years, from June 2011 through September 2012, we took steps to obtain two full years of temperature records, for a more complete understanding of the environmental conditions present. First, the six month gap before site set‐
up was filled using an average from 2007‐2011 measurements at a nearby EM‐1 Project site, FBS24. Next, the gap following site take‐down in 2012 was filled using meteorological data obtained from le ministère du Développement durable de L’Environnement, de la Faune et des Parcs (MDDEFP, 2012). As MDDEFP data was not available from September 20th through October 24th, an average of conditions at FBS24 from 2007‐2011 was used in place. Missing rainfall values were not estimated because of the spatial variability inherent in precipitation. Seasonal climate variation was assessed by comparing FJP05 T and rainfall values to the 30‐
year climate normals measured at Station Chapais II near Chibougamau, Quebec (49.78N, 74.85W) (Environment Canada, 2013). However, as the distance to this long‐term station is close to 400 km, the six years of data accumulated at FBS24 for the EM‐1 Project was used as a reference to determine comparability. In order to compare the FBS24 with Chapais II, monthly standardized anomalies (SA) were calculated using the difference in temperature divided by the normal standard error; a SA value greater than 1 indicates significant deviation from the monthly climate normal. 3.2.3.4 Chamber measurements Throughout the 2011 and 2012 growing seasons, soil carbon flux measurements (SR) were taken at FJP05 using a closed chamber system. On July 8th 2011 (DOY 189), ten circular collars of 23.5 cm diameter were installed along the north transect, placed 10 m apart in soil devoid of vegetation. The sampling chamber was made of a Plexiglas material with an open bottom capable of sealing to the collars. As the aim of this experiment was to measure dark respiration solely, the chamber was covered with aluminum foil to reflect away sunlight. SR was determined from changes in CO2 concentration inside the chamber over a three minute period. A small, battery‐operated fan circulated the air while a pump and flow regulator continuously drew air from the chamber interior through an infra‐red gas analyzer (EGM‐4, PP Systems, Massachusetts, USA) at a rate of 350 mL min‐1. CO2 concentrations from the IRGA were manually recorded every ten seconds while T and T (5 cm) were recorded at the beginning of 40 each session. A linear regression of the CO2 measurements against time produced the rate of concentration increase which was used to determine the flux, in mgCO2 m‐2 day‐1, using: F
·R·
V
T
S
·
·
,
(3.4) where f is the rate (ppmv min‐1), V the chamber volume (m3), R the ideal gas constant (0.0821 L atm K‐1 mol‐1), T the air temperature (°C), n the molecular mass of CO2 (0.044 kg mol‐1), S the surface area of the collar (m2), and t = 1440, the number of minutes in a day. Prior to each field campaign, the IRGA was calibrated using a CO2 standard of 400 ppmv. 3.2.4 Data handling and gap filling procedures Specific post‐processing and gap‐filling steps were followed to produce a robust EC and meteorological data set. First, automatic steps were run to create half‐hourly averages from the measured data. Next, data quality assurance steps were performed to remove incorrect or outlying data. Finally, NEE, GEP and ER as well as the energy balance components were gap‐
filled following Fluxnet procedures (e.g. Barr et al., 2004) resulting in a data set running from June 8th, 2011 (DOY 189) to September 18th, 2012 (DOY 262). In the case of the energy balance components, a gap is still present between December 10th, 2011 (DOY 344) and March 18th, 2012 (DOY 78). 3.2.4.1 Quality control steps Data were screened for periods where the diagnostic signal of the IRGA indicated that the optical path was obstructed typically by water on the lens and for periods of instrument malfunction or servicing. Half hourly periods where greater than one third of the high frequency data was missing were removed. Following this, a threshold where CO2 fluxes become independent of wind speed was defined. The friction velocity (u*) was plotted against nighttime CO2 fluxes; the value of u* above which no relationship existed was found to be 0.17 ms‐1 and was taken as the acceptable turbulence threshold for the entire dataset. All half‐hourly flux values corresponding to a u* below this value were discarded. A value of PPFD = 20 μmol m‐2 s‐1 was used as the light level that defined the daytime period rather than the more commonly used incoming solar radiation due to large gaps present in the dataset of that variable. Next, any nighttime periods that indicated photosynthetic uptake during the warm season were removed 41 and subsequently gap‐filled according to the procedure outlined in the following section. Similarly, NEE uptake was also removed for periods of T below 0°C. Finally, data greater than three standard deviations of the monthly mean were rejected. The occurrence of false uptake of CO2 has been well documented with the use of an open‐path IRGA (e.g. Amiro, 2010). This type of analyzer is advantageous for remote studies as it does not require as much power to operate compared with a pump‐operated closed‐path IRGA. However, open‐path IRGAs have a tendency to self‐heat as well as to intercept radiation creating an additional heat flux not accounted for in the WPL density corrections (Amiro, 2010). As a solution, Burba et al. (2008) proposed heat transfer equations to correct for heating issues. This correction has not yet gained general acceptance and is currently restricted to vertically mounted instruments; it is more common to mount the IRGA at a 45° angle to facilitate water drainage following rainfall events. For this study we chose to follow an alternative approach outlined by Mkhabela et al. (2009). All flux measurements during cold temperature periods (T < 0), i.e. the cold season, were first discarded then filled using the relationship between nighttime NEE and T for an air temperature dataset between 0 and 10°C (Sass, 2007). 3.2.4.2 Gap‐filling NEE, ER and GEP Instrumentation failure, rain events and tower maintenance all result in loss or potential rejection of data. Data gaps are a common occurrence with the EC technique and as such the Fluxnet community has developed standardized procedures for filling these gaps (e.g. Barr et al., 2004). Linear interpolation was used for gaps fewer than four half‐hourly periods while models were created for longer gaps. ER was modelled using a relationship between nighttime NEE and T for all periods. GEP was calculated as the residual of NEE and ER when NEE values were present. However, in cases of daytime NEE gaps, GEP was modelled using a hyperbolic function GEP
α
α
PPFD
PPFD
GP
GP
,
(3.5) based on warm season data, where α is the initial slope of the curve known as the apparent quantum yield and GP
is the maximum gross productivity (adapted from Lafleur et al., 2001). The relationship was derived at weekly intervals with specific multiplicative factors for each. In 42 the case of more than a weeks’ worth of missing data, a relationship based on the weeks prior and post was created. GEP values were forced to zero at the beginning and end of the non‐cold season. Finally, the remaining daytime NEE gaps were filled as the residual of ER and GEP. 3.2.4.3 Gap‐filling QH and QE Before beginning the energy balance gap‐filling procedure, storage terms were first calculated for the respective heat flux followed by data quality assurance steps. The sum of the energy storage terms (QS) was gap‐filled with an empirical model using warm season data through: a0
Q
a1 · Q
a2 · T / t
, (3.6) using a moving window of 480 measured data points. This relationship was applied to gaps in the warm season as well as for estimation of QH and QE during the cold season. From this point, separate regressions were used to fill the energy balance component gaps. Gaps in QH, first separated into daytime and nighttime periods, were filled using an empirical model fit QH
a0
a1 Q
Q
,
(3.7) and applied using a moving window of 240 measured data points. QE was gap‐filled in a similar fashion using QE
a0
a1 · Q
Q
·
,
(3.8) with all variables as previously defined. No energy balance closure adjustment was applied to the FJP05 site data set. 3.2.5 Temperature sensitivity and base respiration Regression analyses using non‐gap filled warm season data were performed in order to relate ER to T at 5 cm. A relationship was derived between cleaned nighttime half hourly FC and T through a linear least squares regression of log transformed respiration data for ER > ‐1 µmol m‐2 s‐1 ln ER
1
A
B·T ,
(3.9) where 1 µmol m‐2 s‐1 was added to ER to avoid complications arising from the occurrence of negative values of ER while the log transformation was completed in order to meet the condition of homoscedasticity to perform a linear least squares regression (Bergeron et al., 43 2007). The relative change in respiration rate for a 10°C change in T , Q , and the respiration rate at a reference T of 10°C, R , were calculated as: e
Q
and
R
Q
B
,
(3.10) · eA ,
(3.11) respectively, the latter in µmol m‐2 s‐1 (Humphreys et al., 2005). 3.2.6 Light response parameterization Light responses curves were generated in order to ascertain how the ecosystem responded to changes in incident photosynthetically active radiation (PPFD). The response of GEP and NEE to light was characterized using daytime non‐gap‐filled data with the parameters estimated by Equation (3.5) and NEE
αNEE
αNEE
PPFD
PPFD
NEE
NEE
where αNEE is the apparent quantum yield for NEE, NEE
ER
,
(3.12) is the asymptotic value for NEE and ER is the daytime respiration rate determined as the intercept of NEE = f(PPFD) in µmol m‐2 s‐1 (Griffis et al., 2003; Humphreys et al., 2005). GEP and NEE light response curves were fit to a warm season data subset, where T > 0 °C and T > ‐5 °C, and an optimal season dataset, between July 1 and August 31 when T was between 15 and 25 °C and T was greater than 5 °C. The optimal season choices are based on work done by Bergeron et al. (2007), where optimal environmental conditions were specified to minimize climatic effects while emphasizing physiological differences. 3.2.7 Ecosystem diagnostics Changes in biophysical variables considerably influence the surface partitioning of available energy on a daily and seasonal basis (Cho et al., 2012). Given this, empirical analyses are useful for developing a better understanding of methods of energy partitioning (Cho et al., 2012). The Penman‐Monteith equation provides a theoretical framework for quantifying actual evapotranspiration based on measured values of heat exchange (Monteith & Unsworth, 1990). Modifications to the PM equation allow plant or weather characteristics to be isolated, improving analysis of ecosystem functioning. To investigate the key processes controlling ET, we calculated daytime half hourly values of the surface conductance, g , the Priestley‐Taylor 44 coefficient, PT‐α, the evaporative fraction, EF, and the Bowen ratio, β. We used only values when the foliage was dry, thus excluding periods during and 12 hours following a rain event, following the methodology of Brummer et al. (2012). This ensured that QE fluxes included the transpiration component such that the physiological control on ET could be investigated. The Penman‐Monteith model combines two elements, an energetic term and an aerodynamic term (Monteith & Unsworth, 2008). The energetic term sets the lower limit of ET in the case that the soil moisture supply is not limiting and that ET is not influenced by horizontal or vertical advection (Brummer et al., 2012). The aerodynamic term on the other hand is dependent on surface aerodynamic characteristics and the turbulent intensity of the overlying atmosphere (Brummer et al., 2012). The degree of coupling between the surface and the atmosphere depends on the ratio of g and g . Here, g was calculated by a rearrangement of the PM equation (Lu et al., 2003) ρ c D
γQ E
g
1 ∆ QA
g γ QE
1
1
,
(3.13) where D is the atmospheric evaporative demand (kPa) and all other terms are as previously defined. D was computed as the difference between the saturation vapour pressure (es) at T and the actual vapour pressure (ea) using: 0.6108eT
es
.
T
.
,
(3.14) and ea
es
RH
100
,
(3.15) where RH is the measured relative humidity. The aerodynamic conductance, g was calculated following Brummer et al. (2012), as g
u
u
1
2
ku
ψ
ψ
,
(3.16) where u is the wind speed (m s‐1), u is the friction velocity (m s‐1), k is the von karman constant (0.4), and ψm and ψh are the integral diabatic correction factors for momentum and sensible heat transfer, respectively (Paulson, 1970). 45 The Priestley and Taylor equation was first conceptualized to understand the controls on evaporation for large wetland regions. In 1972, Priestley and Taylor effectively argued that, given sufficient upwind fetch, a well‐watered surface should come into equilibrium with the overlying air stream evaporation rate, Q E , determined as QE
Δ
Δ
γ
Q A . (3.17) However, empirically, QE exchange was found on average to exceed the equilibrium rate. In fact, it was found that the influence of aerodynamic properties would tend toward a constant fraction of radiative receipt, leading to the creation of an empirical coefficient (PT‐) defined as PT‐α
QE
QE
,
(3.18) with a value of 1.26 determined for well‐watered systems (Priestley & Taylor, 1972). In subsequent work, McNaughton and Spriggs (1989) hypothesized that entrainment of drier air into the developing convective boundary layer could explain the enhancement of surface evaporation rates at larger scales. Since its introduction, the Priestley‐Taylor equation has achieved wide support and is often applied as a measure of potential evaporation in areas much smaller than regions and for vegetation not necessarily ‘wet’ (McNaughton & Spriggs, 1989). In the current case, it is useful for describing the seasonal importance of QE in relation to the radiatively‐driven equilibrium rate. The evaporative fraction and Bowen ratio are useful tools for describing the partitioning of available energy at an ecosystem surface. The EF describes the portion of available energy used for evaporative processes through EF
QE
.
Q*
(3.19) This ratio is particularly noted for its relative consistency under sunny conditions (Gentine et al., 2007). It is proposed that clear skies allow the diurnal variability associated with available energy to be excluded, thus allowing soil and plant properties to control flux partitioning (Gentine et al., 2007). We calculated ET at half hourly intervals and then integrated over the daytime. In a similar manner, β was calculated as β
QH
.
QE
(3.20) 46 β is useful for describing the partitioning of available energy between the two turbulent fluxes. Correspondingly, when β exceeds unity, QH is the dominant consumer of available energy while QE exchange dominates when β is less than unity (Bowen, 1926). 3.3 Results 3.3.1 Environmental conditions Standardized anomaly calculations of the FBS24 2007‐2012 average resulted in no significant monthly differences, with the exception of November. In general, the earlier part of the year was found to be slightly cooler while the latter half was warmer, resulting in an annual SA of 0.3. Based on this evaluation, it was concluded that comparing T conditions between Eastmain and the Chibougamau normal is acceptable. Next, SA calculations of mean monthly cumulative precipitation at FBS24 were compared to the normal at Chapais II. In contrast with T conditions, precipitation readings were found to deviate through the months with significantly wetter conditions during the winter and drier conditions during the growing season compared to the normal. In particular, the years 2011 and 2012 at FBS24 experienced low cumulative precipitation during the months of July and August. This is of import as precipitation measurements at FJP05 only allow inter‐
annual comparison of these two particular months. Thus, looking solely at FJP05, it could be mistakenly thought that the area experiences lower rainfall on average, whereas the longer FBS24 record points to a different conclusion. Graphing the 30‐year monthly normal versus the FBS24 six‐year monthly mean shows differing patterns during the growing season. Chapais II is expected to experience increased rainfall from June through August whereas FBS24 saw higher values only in August and, to a lesser degree, in September. A yearly average of the monthly cumulatives show that FBS24 received 77.6 mm month‐1 [range: 71.4‐92.5 mm month‐1] similar to the normal of 80.1 mm month‐1. Thus to summarize, the high degree of variability between local sites should make one critical of assessing precipitation variation using a station several hundreds of kilometers away. However, as the FBS24 and Chapais II comparison did yield similar results, it can be reasoned that broad scale interpretations can be acceptable. Thus, comparing FJP05 directly with the Chapais II normal over the study period, the average T was 0.1°C, similar to the normal of 0.0°C. In 2011, monthly averages during the first 47 half of the year were cooler than normal while October and November were significantly warmer (Figure 3.2). 2012, in contrast, exhibited high variability between months, with March significantly warmer, April and May cooler, June significantly warmer, July significantly cooler and September onward warmer than normal. Both years saw a monthly average T range of ‐
19°C to 16°C. In 2011, maximum monthly T occurred during the month of July. However, due to enduring cloudiness in July of 2012, June was the warmest month. Overall, the peak July‐August average T was 1.1°C warmer in 2011 compared to 2012. Monthly cumulative precipitation can only be evaluated from July through October in 2011 and April through August in 2012. In 2011, three out of the four months classified as significantly drier than normal, with September drier (Figure 3.2). Similarly, all five measurable months in 2012 were drier, with April and June significantly so. Of note, monthly precipitation for both August 2011 and 2012 was significantly lower at FJP05 compared with FBS24, likely because of localized convective rainfall. July 2011 had very low rainfall of 49.3 mm, as did FBS24 (59.1 mm); the FBS24 2007‐2011 mean was 88.5 mm while the Chapais normal is 120.7 mm. 3.3.2 Site characteristics Jack pine seedlings began emerging at FJP05 within a year of the severe crown fire in June 2005 (USDA, 2011). Measurements on June 15th, 2012 found the average seedling age to be 5.8 ± 0.1 years growing at an average density of 6.7 ± 1.0 trees m‐2 (Table 3.1). Mean seedling height was found to be 35.0 ± 2.6 cm with a measured current‐season stem growth of 10.1 ± 0.4 cm. Projected LAI was calculated as 0.55 ± 0.02 m2 m‐2 while mean basal diameter was determined as 0.6 ± 0.1 cm. Fatally burned trees were also present at the site, as standing or fallen stems. Through coring, it was determined that the trees were 32 ± 1 years of age when they burned while the average density of the former canopy was approximately 0.5 trees m‐2. Blueberry and Sheep Laurel shrubs had the greatest coverage aside from jack pine seedlings yet, overwhelmingly, bare soil and char was the predominant surface type (Figure 3.3). Through soil core analysis, average soil organic (SOC) and mineral (SMC) content at FJP05 were established as 3.6 ± 0.4 kgC m‐2 and 1.8 ± 0.3 kgC m‐2, respectively, resulting in a ratio of 2:1 (Table 3.2). The mean organic horizon depth was 5.5 ± 0.4 cm while the volumetric water content from the portable unit averaged 0.11 ± 0.09 m3 m‐3 across the site. The latter value 48 corresponds well with measurements from the soil water content reflectometer operating continuously near the tower (0.10 ± 0.01 m3 m‐3). Directional differences were evident for all of the soil properties described. For instance, the west plot had the highest SOC within an average organic horizon depth while reading the lowest volumetric water content and lowest SOC in the mineral layer. The aboveground seedling carbon stock was determined via destructive sampling techniques. Allometric calculations were performed in order to provide an independent estimate of carbon stocks. Aboveground biomass from allometry determined a site average of 0.18 ± 0.12 kgC m‐2, corresponding well with the value of 0.23 ± 0.03 kgC m‐2, determined directly (Table 3.3). Total aboveground biomass was significantly lower in the north plot due to the stunted nature of the seedlings, also evident in lower measured surface coverage, tree height, basal area and diameter. Interestingly, the north plot measured the oldest seedlings at 6.2 ± 0.2 years. Furthermore, volumetric water content was greatest at the north plot, at 14.6%. 3.3.3 Growing season length The growing season was defined as the period when photosynthesis could occur, triggered by optimal T conditions. The beginning (DOYSTART) and end (DOYEND) of the growing season were identified by five‐day running‐averages of T where the daily average remained above 0.0°C for three consecutive days (e.g. Suni et al., 2003a; Bergeron et al., 2007). Average T was used instead of degree day sums; previous research has shown the former to be more reliable when assessing the onset of photosynthesis in boreal conifer forests (Suni et al., 2003a). After applying this method, DOYSTART in 2011 was DOY 110 (April 20th) while DOYEND was determined to be DOY 311 (Nov 9th). 2012, exhibited a more episodic start, with an early period of above freezing temperatures from DOY 74 ‐ 80 (March 16th‐22nd) and again on DOY 99 (April 8th) before the growing season started on DOY 121 (April 30th). Using the meteorological data obtained from MDDEFP, DOYEND was estimated as DOY 308 (November 3rd). Growing season length (GSL) was 201 and 187 days in 2011 and 2012, respectively. 3.3.4 CO2 exchange Carbon dioxide exchange at FJP05 demonstrated clear diurnal and seasonal variation across both years. The diurnal pattern of NEE during the 2011 growing season was characterized 49 by maximum uptake during the morning, a gradual decrease through the early afternoon followed by a sharp decrease in the evening (Figure 3.4). The 2012 growing season exhibited a similar pattern, however, with more prolonged uptake during the afternoon hours. Moderate nighttime release resulted in daily average NEE extending both above and below 0, at times flipping between net uptake and release on a day‐to‐day basis, dependent on environmental conditions (Figure 3.5). Daily average fluxes ranged from ‐0.55 ± 0.18 to 0.87 ± 0.26 gC m‐2 and ‐
2.31 ± 0.48 to 0.96 ± 0.25 gC m‐2 in 2011 and 2012, respectively. Daytime average fluxes ranged from ‐1.84 ± 0.26 to 0.12 ± 0.19 gC m‐2 and ‐3.84 ± 0.56 to 0.40 ± 0.17 gC m‐2 while nighttime ER ranged from nil to 2.29 ± 0.12 gC m‐2 and 2.78 ± 0.09 gC m‐2, in each of the years respectively (Figure 3.6). Monthly cumulative ER and GEP remained closely matched throughout the 1.5 years, with the only outlier being July 2012, where daily average GEP surpassed ER resulting in the only non‐carbon neutral month (Figure 3.7). Over the winter months, ER was a continuous small efflux, with a mean daily average flux of 0.02 ± 0.01 gC m‐2 d‐1 for the period of December 2011 through February 2012. A comparison of monthly cumulative fluxes for July, August and September illustrated similarities in the seasonal progression of NEE across both years. When integrated over the three months, cumulative NEE followed a similar trajectory in both years, as a relative increase in cumulative GEP of 39 gC m‐2 was counterbalanced by a relative increase in ER of 26 gC m‐2 (Figure 3.8). This resulted in a relative increase in CO2 uptake of 12.9 gC m‐2, from 5.9 gC m‐2 in 2011 to ‐7.0 gC m‐2 in 2012. Broken down by month, cumulative NEE in July measured 2.2 gC m‐2 in 2011 and ‐7.7 gC m‐2 in 2012, while in August net releases of 4.8 gC m‐2 month‐1 and 0.4 gC m‐2 month‐1 occurred, respectively (Table 3.4). In September, cumulative NEE was ‐1.1 and 0.3 gC m‐2 month‐1, respectively. Figure 3.9 illustrates cumulative daily average ER and GEP over two representative weeks of July, August and September. Notable was a larger relative increase in GEP during July 2012 while August showed similar productivity and respiration rates between the two growing seasons. In total, annual NEE from July 8th, 2011 to July 7th, 2012 resulted in a net release of 7.3 ± 0.1 gC m‐2 y‐1 (Figure 3.10). Annual NEE determined for September 19th, 2011 to September 18th, 2012 to incorporate a greater portion of the 2012 growing season yielded a net uptake of ‐5.7 gC m‐2 y‐1. Taken together, these results 50 suggest that FJP05 can be described as roughly carbon neutral at this stage of recovery from fire disturbance. 3.3.4.1 Chamber measurements Figure 3.11 presents soil respiration measured using the static chamber method compared with ER derived through the eddy covariance technique. In Figure 3.11, a single chamber point represents sampling at ten collars, averaged over a period of intensive sampling. The collars were positioned to avoid including vegetation and coarse woody debris. A tower point in turn represents an average of ER, thus plant respiration, microbial respiration in the soil as well as decomposing coarse woody debris. A single tower point is the average of ER from 11:00 to 15:00 for the same campaign dates. Soil respiration in the single spring and two fall periods was comparably low, measuring around 1.0 to 1.5 gC m‐2 d‐1 while mid‐summer values reached 1.5 ± 0.4 gC m‐2 d‐1 in 2011 and 3.4 ± 1.0 gC m‐2 d‐1 in 2012. In general, chamber and tower measurements followed the same pattern throughout the two growing seasons; respiration was lowest in the spring, highest mid‐summer and decreased in the fall. Average soil respiration was found to exceed ER during the August 2012 campaign, an expected finding considering soil respiration should follow temperature closely while ER includes respiration sources that would not necessarily increase under warmer conditions. The large error bound associated with the mid‐summer chamber measurements is likely due to the chamber locations encompassing varying amounts of organic materials which would result in a range of respiration magnitudes. 3.3.5 Energy exchange Net radiation normalized to incoming solar radiation was determined in order to compare summertime Q* without the effects of differing weather conditions (e.g. Amiro et al., 2006b). Days where daily Q* was less than one half of the maximum value were excluded in order to compare clear sky conditions only. Based on this method, peak growing season (July – August) Q*/ K↓ was determined to be 0.70 in 2011 and 0.64 in 2012. In turn, albedo was determined through daily totals, calculated as the ratio of total reflected to total incoming solar radiation (e.g. Amiro et al., 2006b). July – August averages were found to be 0.105 ± 0.001 and 51 0.103 ± 0.002 in 2011 and 2012, respectively, while wintertime albedo reached daily values above 0.6. More net radiation was available overall during the 2011 growing season, of which a greater portion was used for QH exchange (Figure 3.12). Consequently, QH fluxes in 2011 were higher than those of 2012, with respective daytime average ranges of 21 ± 4 to 210 ± 27 W m‐2 and 16 ± 6 to 133 ± 14 W m‐2 in July and August combined (Figure 3.13). Meanwhile, QE fluxes in 2012 generally exceeded those of 2011, even with less available Q*. Daytime average QE ranged from 38 ± 4 to 118 ± 12 W m‐2 in 2011 and 53 ± 4 to 182 ± 15 W m‐2 in 2012 with mean peak daytime values of 167 ± 6 and 202 ± 7 W m‐2 reached in August of 2011 and July of 2012, respectively. Daytime average QG ranged between 5 ± 3 W m‐2 and 37 ± 5 W m‐2 during July and August of 2011 and ‐9 ± 2 W m‐2 and 46 ± 8 W m‐2 in 2012; nighttime ranges were ‐26 ± 2 W m‐2 to ‐2 ± 1 W m‐2 and ‐30 ± 2 W m‐2 to ‐2 ± 2 W m‐2, respectively (data not shown). Mean peak daytime effluxes occurred in July 2011 and August 2012, at 53 ± 1 W m‐2 and 42 ± 1 W m‐2 respectively. The diurnal patterns of the turbulent energy terms, QH and QE, followed a similar pattern to that of NEE (Figure 3.4) over the growing season. During daylight hours, both fluxes increased to a peak at midday before decreasing during the afternoon and evening hours (Figure 3.13). Mean peak fluxes achieved during the growing season were 194 ± 7 and 164 ± 5 W m‐2, respectively. QH followed the pattern of Q* closely while QE increased and decreased more gradually, particularly through the afternoon and evening hours (Figure 3.13). At night, QH was a mean downward flux of 22.6 ± 0.5 W m‐2 whereas QE contributed a mean positive flux of 10.6 ± 0.4 W m‐2 toward the atmosphere. Over the growing season, both heat fluxes followed the seasonal cycle of Q* closely, however with a gradual shift toward QE dominance, relating mainly to a decrease in QH in 2011 and to changes in both QH and QE in 2012 (Figure 3.13). The transition from QH to QE dominance occurred mid‐August in 2011 and early‐July in 2012 and remained so until the onset of freezing temperatures. Maximum daily average QH exchange was achieved in June 2012 whereas QE exchange peaked in August in 2011 and July in 2012. The diurnal pattern of the conductive flux QG followed the pattern of Q* relatively closely. On average, positive daytime fluxes (toward the soil) peaked near 13h00 while 52 nighttime fluxes were negative (toward to atmosphere) (data not shown). July 2011 exhibited the highest average peak while August 2011 and both months of 2012 had closely corresponding diurnal patterns. In 2012, QG began to increase rapidly after DOY 101 (April 10th) with the highest monthly‐averaged diurnal peak achieved in June (data not shown). 3.3.6 GEP and ER response to environmental factors Regression analyses were used to understand how the recently burned ecosystem responded to changes in light availability and temperature, two key variables that drive photosynthesis and respiration. 3.3.6.1 Temperature sensitivity and base respiration The linear regression between T and ER showed moderate correlation, with coefficient of determination (R2) values of 0.59 and 0.48 for 2011 and 2012 relationships, respectively (Table 3.5). Derived Q values were 1.74 ± 0.01 and 1.77 ± 0.01 during the respective years while R was determined to be 1.76 ± 0.03 and 1.87 ± 0.05, respectively. Chamber measurement calculations in turn resulted in Q and R values of 1.40 ± 0.02 and 2.57 ± 0.17, respectively. Responses of tower ER to T and soil water content (θ) were assessed during the optimal growing season, between July 1 and August 31 (Figure 3.14). The ER‐T relationship was similar between years, except a significantly higher rise in 2012 ER between 15 and 20 °C. The relationship between θ and ER was a linear increase overall with a slight decrease at the highest θ measurements. Notably, θ of 0.06 to 0.08 m3 m‐3 in 2012 corresponded with significantly higher respiration relative to the two‐season trend. As θ below 0.08 m3 m‐3 only occurred between August 12th and 17th, this point is representative of conditions during that particular time rather than a growing season average. 3.3.6.2 Response of NEE to light Table 3.6 presents the rectangular hyperbolic function parameters for warm season and optimal growing season conditions. Both GP
and NEE
exchange capacity in 2012, with optimal growing season GP
5.52 ± 0.15 µmol m‐2 s‐1, while NEE
displayed a shift toward greater C increasing from 3.26 ± 0.06 to decreased from ‐3.38 ± 0.07 to ‐4.93 ± 0.17 µmol m‐2 s‐1 53 (Figure 3.15). Correspondingly, ER increased slightly from 1.69 ± 0.04 to 1.78 ± 0.08 µmol m‐2 s‐1. Both αGEP and αNEE increased (decreased) under the optimal growing season parameters in 2011 while little difference was visible in 2012. In 2011, GEP decreased with D greater than 0.5 kPa and peaked at an air temperature of 15 °C (Figure 3.15). 2012 likely also had a similar relationship, however, due to low data representation for temperatures greater than 25 °C, a decrease on the right side of the graph is not present. Based on a breakdown by month (Figure 3.16), optimal air temperature was likely around 20 °C while GEP likely decreased with Da greater than 1.0. Two consecutive one‐week periods of contrasting weather conditions were analyzed to determine ecosystem responses to increased moisture availability. The first week, from DOY 213 to 219 (July 31 ‐ August 6, 2012), experienced 17.8 mm of rain, in contrast with the second week which had 1.5 mm fall between DOY 220 and 226 (August 7 ‐ 13, 2012). Average θ was 0.12 ± 0.01 m3 m‐3 and 0.09 ± 0.01 m3 m‐3, respectively, while mean air temperature, soil temperature, atmospheric evaporative demand and PPFD are listed in Table 3.7. GP
NEE
and values were higher for the wet week while ER was roughly half that of the dry week (Table 3.7). There was carbon uptake during the wet week, with a weekly averaged daily and daytime NEE flux of ‐0.1 ± 0.8 and ‐1.5 ± 0.6 gC m‐2 d‐1 compared with carbon release in the dry week of 0.2 ± 0.6 daily and modest uptake of ‐0.4 ± 0.4 gC m‐2 d‐1 during daytime. Lower ER and higher GEP occurred during the wet week while minimum daytime NEE was reached on DOY 224 through 226, coinciding with high temperatures and low θ. 3.3.7 QE response to environmental factors To investigate the key processes controlling the energy terms, the daytime half hourly values of the Priestley‐Taylor coefficient (PT‐α), Bowen ratio (β), evaporative fraction (EF), and surface and aerodynamic conductances, (g and g , respectively), were calculated for DOY 189 to 327 in 2011 and DOY 79 to 264 in 2012. For the purposes of simplification, from this point forward, July, August and September will be referred to as the growing season. Average growing season PT‐α was 0.92 ± 0.05 and 1.22 ± 0.24 in 2011 and 2012, respectively (Table 3.8). An average below unity in 2011 indicates that QE exchange was slightly smaller than the radiatively‐driven Q E rate, with the opposite situation dominating the 54 second year. Monthly diurnal patterns were similar during the two growing seasons, with minimums of PT‐α coinciding with solar noon (Figure 3.17). Mean β was determined to be 1.27 ± 0.07 and 0.71 ± 0.03 for the respective growing seasons (Table 3.8). QH was the dominant form of energy exchange during the growing season of 2011 whereas QE dominated in 2012 (Figure 3.19a). The diurnal pattern of β reflected well the relative changes in QH and QE magnitudes over the course of a day. 2011 had a significantly larger range between months, especially afternoon fluxes (Figure 3.17), with β during July 2011 the highest peak, at about 2.5 while August and September maximums were about 1.5 and 1.2, respectively. 2012, on the other hand, showed similar magnitudes between months with peak β below unity and always occurring at solar noon (Figure 3.17). July 2012 did stand out slightly due to morning β exceeding unity, the result of strong QH fluxes driven by high net radiation. Mean daytime dry‐foliage EF was determined as 0.51 ± 0.02 and 0.72 ± 0.05 over the 2011 and 2012 growing seasons, respectively (Table 3.8). These results suggest that approximately 20% more energy was partitioned toward ET processes in 2012. The diurnal EF pattern was a similar pattern to PT‐α in both years, with lowest EF occurring around solar noon. Monthly means of daytime dry‐foliage gS were higher and more variable in 2011, with a growing season mean of 6.7 ± 1.0 mm s‐1 compared with a mean of 5.3 ± 0.1 mm s‐1 in 2012. Similarly, g was higher in 2011, at 36 ± 1 mm s‐1, compared with 30 ± 1 mm s‐1. A comparative analysis of D and g was performed in order to understand how surface conductance responded to increasing atmospheric evaporative demand. Higher g clearly corresponded to low D (< 1 kPa) and g decreased with increasing D to a minimum of less than 2 mm s‐1 at D > 2.5 kPa (Figure 3.18). g was found to decrease most quickly at low Q* (< 100 W m‐2), particularly in 2011, however still reaching the same minimum for each Q* stratification; the lower differentiation found in the 2012 relationships may be caused by limited data. 55 3.4 Discussion 3.4.1 Placement of FJP05 within the North American boreal stand age‐carbon balance curve Reliable modelling of the regional carbon balance requires incorporating NEE of representative stands according to age since disturbance. Several studies are in agreement that the transition from a net source of CO2 to sink should occur between 10 and 12 years after disturbance (Amiro et al., 2006; Amiro et al., 2010; Goulden et al., 2011). FJP05 had an estimated annual CO2 balance of +7 and ‐6 gC m‐2 y‐1 at 6 and 7 years, respectively, classifying it as approximately carbon‐neutral. These results are similar to that of a burned jack pine site in Saskatchewan, F98, which had an annual CO2 balance of ‐3 and +43 gC m‐2 y‐1 at 6 and 7 years post‐burn, respectively (Mkhabela et al., 2009). In contrast, the only other young eastern boreal site, HBS00, measured an annual CO2 balance of 87 ± 3 gC m‐2 y‐1 at 8 years following harvesting (Payeur‐Poirier et al., 2011), greater also than an 8‐year old harvest site in Saskatchewan (HJP94 in 2002 = 59 gC m‐2 y‐1; Amiro et al., 2006) (site locations and descriptions provided in Table 3.9). Thus, overall it appears that FJP05 fits well within the stand age‐carbon balance curve shown in Figure 2.2. Furthermore, the above comparison seems to suggest at first glance that the geographic location of the site (climate‐driven differences) might be less important to consider than the type of disturbance and its effect on recovery. This statement has been considered by other studies (e.g. Amiro et al., 2006; Mkhabela et al., 2009; Amiro et al., 2010), with the degree of importance determined to be dependent on the spatial scale being considered. For instance, differences in CO2 exchange between the FJP‐SK and HJP‐SK chronosequences were significant as the areas under consideration were in close proximity (Mkhabela et al., 2009). Equally, a study of the impact of disturbance recovery on the North American temperate and boreal forest carbon balance determined stand‐replacing fire and harvesting to have similar NEE recovery, when considered in a broad sense (Amiro et al., 2010). For the current study, given that there are few studies available that directly determine NEE at recently disturbed boreal sites, comparisons with both burned and harvested sites will continue to be made. When calculating the annual CO2 balance, the difference between including the summer of 2011 versus that of 2012 resulted in FJP05 being labeled as a slight source or sink for CO2, 56 respectively. It is probable therefore that a small change in environmental conditions would tip the site toward non‐neutral conditions. However, at this point of recovery, it is unlikely that FJP05 would be a large source or sink as the magnitude of the GEP and ER terms are small relative to that of the other recently disturbed sites. F98 at 7 years post‐burn had annual GEP and ER totals of 456 and 499 gC m‐2y‐1, respectively, while those of HBS00 at 5 years were 359 and 483 gC m‐2y‐1 and those of HJP94 at 10 years were 353 and 360 gC m‐2y‐1 (Bergeron et al., 2008; Mkhabela et al., 2009). In contrast, FJP05 had annual totals of GEP and ER of about 230 gC m‐2y‐1. However, it must be mentioned that comparisons with HBS00 can only be rudimentary as the site is dominated by black spruce regrowth on mesic soils (Bergeron et al., 2008). Likewise, F98 is composed of a mixture of jack pine, black spruce and trembling aspen regenerating on sandy loam soils (Mkhabela et al., 2009). HJP94 appears the only site to have a similar ecosystem profile to that of FJP05. HJP94 is dominated by a naturally regenerating and relatively pure jack pine stand over sandy soils (Mkhabela et al., 2009). Given these constraints, in order to properly understand and to be able to compare the 1.5 years of data measurements at FJP05, CO2 exchange must be analyzed in the context of climate variation, disturbance recovery and parent site conditions. 3.4.2 What are the factors driving carbon dioxide fluxes at a recently burned boreal jack pine forest in eastern North America? Significant vegetation growth as well as different spring, summer and fall climatic conditions would be expected to affect GEP and ER in differing ways. Depending on the weight of influence, to one CO2 flux term versus the other, the growing season and therefore annual NEE total could be affected as a result. FJP05 showed between‐year differences in its growing season CO2 balance of 13 gC m‐2, with the 2011 growing season a slight net source (6 gC m‐2) and 2012 a small net sink (‐7 gC m‐2). Monthly cumulative ER and GEP remained closely matched throughout the 1.5 years, with the only outlier being July 2012, where daily average GEP surpassed ER resulting in the only non‐carbon neutral month (Figure 3.7). August and September, in contrast, showed similar productivity and respiration rates between the two growing seasons. 57 Assessing seasonal CO2 exchange patterns in the context of those expected of a mature upland boreal stand is useful for determining how quickly a site is recovering from disturbance. To begin with, in springtime, GEP and ER at mature stands are expected to de‐phase due to more rapid warming of the air compared with soils (Dunn et al., 2007; Mkhabela et al., 2009). Favourable light intensity is present by early spring, thus more rapidly rising T should result in a period of maximum CO2 accumulation in advance of the higher summer ER (Falge et al., 2002; Dunn et al., 2007; Mkhabela et al., 2009). At FJP05, a lag in ER during the spring did not occur, with analyses of spring T and T measurements showing an almost synchronous rise in air and soil temperatures following soil thaw. As bare soil overwhelmingly was the dominant surface at this site (Figure 3.3), the lack of delay between the rise of GEP and ER may result from sparse insulation enabling prompt transfer of heat to the soil layers. This is corroborated by measurements of a sudden rise in conductive energy transfer through the soil layers beginning in early April 2012. GEP is generally expected to peak mid‐summer due to receding day length offsetting the positive effect of warmer T (Bergeron et al., 2007). Meanwhile, ER is expected to peak later in the growing season when T is highest (Mkhabela et al., 2009). Lower θ is typical during July and August in upland stands because high rates of ET from warm soils and productive plants draw down soil moisture (Mkhabela et al., 2009). Characteristically, these conditions of higher respiration and lower θ result in a mid‐summer depression in NEE (Griffis et al., 2003; Black et al., 2005; Barr et al., 2007; Bergeron et al., 2007; Dunn et al., 2007). This pattern was not evident at FJP05 however (Figure 3.5), likely because the rapid soil drainage caused consistently lower θ regardless of time of season (Figure 3.20). Peak GEP and ER at FJP05 were conditional on environmental controls which, in both years, significantly deviated from the normal. In 2012, low light levels and significantly cooler T occurred over the month of July as a result of persistent cloudiness and rain. This was measured as an approximate 170 µmol m‐2 s‐1 drop in daytime average PPFD compared with the previous year, with mean temperatures cooler by about 1.5°C. While ER experienced a noticeable dip in July, GEP was not reduced by the overcast conditions, with the diffuse radiation levels adequate for achieving normal photosynthetic rates. Under these conditions, GEP peaked on DOY 190 58 (July 8) while ER peaked on DOY 239 (August 26). 2011, on the other hand, had normal mid‐
summer temperatures however with significantly low precipitation in July. During the 2011 growing season, GEP peaked on DOY 215 (August 3) while ER peaked around DOY 235 (August 23). For comparison, Makhabela et al. (2009) measured GEP and ER peaks near DOY 190 and DOY 230, respectively, across the FJP‐SK and HJP‐SK chronosequences. Similarly, ER peaked around DOY 221 at HBS00 in 2008 (Payeur‐Poirier et al., 2012). Therefore, the GEP maximum at FJP05 in 2012 and both ER peaks occurred within a similar timeframe to that of other disturbed sites. Conversely, the GEP maximum in 2011 occurred much later expected, possibly due to low rainfall and consequently low θ limiting photosynthesis. In autumn, GEP and ER fluxes are expected to decline with decreasing temperature and lower light levels (Goulden et al., 1997; Bergeron et al., 2007). GEP should become more limited as T should decrease more quickly relative to T while shortening day length would increasingly constrain GEP (Suni et al., 2003a; Bergeron et al., 2007). In contrast, T at FJP05 continued to respond promptly to T changes, resulting in a period of neutral CO2 exchange. Significantly warmer temperatures in October and November extended the growing season out to November in both years (DOY 311 and 308), much later than reported at other disturbed sites (DOY 286 ‐ 305) (Zha et al., 2009; Payeur‐Poirier et al., 2012) however with little effect on NEE. 3.4.2.1 Was the inter‐seasonal increase in CO2 exchange driven by vegetative growth or climatic variability? Lower growing season GEP and ER cumulative totals combined with little GEP‐ER spring and fall lag point toward the possibility that FJP05 may be recovering more slowly than other recently disturbed stands, potentially due to parent soil characteristics, such as soil moisture and nutrient limitations (Baldocchi et al., 1997a), and disturbance severity. Yet, considerably differing inter‐seasonal GEP maximums could also be an indicator of substantial vegetation recovery. Combining independent vegetation indices with continuous measurements of CO2 fluxes and environmental variables provides the key to answering the question: was the inter‐
seasonal increase in CO2 exchange driven by vegetative growth or climatic variability? To help answer this question, six potential controls were analyzed in more detail: atmospheric 59 evaporative demand, soil moisture, air temperature, soil temperature, leaf area index and growing season length. Differences in the diurnal (Figure 3.4) and seasonal (Figure 3.5; 3.6) patterns of NEE distinguish the 2011 and 2012 growing seasons at FJP05. For instance, reduction in growing season CO2 uptake during afternoon hours resulted in persistently smaller daytime uptake during 2011 relative to 2012 (Figure 3.4). This phenomenon was previously measured at a post‐
burn jack pine site in northern Manitoba as well as a 10‐year old site in central Saskatchewan (McCaughey et al., 1997; Amiro et al., 2003). In their study, Amiro et al. (2003) briefly argued that the ‘afternoon shoulder’ was likely caused by stomatal closure. Typically, stomatal closure will occur with insufficient light levels or excessive transpiration (Monteith, 1965). Yet, jack pine sites present a unique environment as they have been shown to stimulate deeper planetary boundary layers through high rates of QH exchange, generating high D near the surface, thus triggering stomatal closure (McNaughton & Spriggs, 1986; Culf, 1992; Baldocchi & Vogel, 1996). In Figure 3.15d, GEP was seen to decrease with increasing D in 2011 while less of a trend was apparent for 2012 (see Section 3.3.6.2 for explanation of 2012 data). Daytime average D for July‐August was 0.83 ± 0.06 kPa in 2011, occurring alongside more available Q* and higher QH exchange compared to the conditions seen in 2012, in which D averaged 0.60 ± 0.05 kPa (Figure 3.12). Thus, high D driven by greater relative QH exchange could help explain the 2011 ‘afternoon shoulder’. It is equally possible however that closure at FJP05 was brought on as a result of chronically low θ limiting transpiration. θ during the two weeks measured in July 2011 was very low, ranging between 0.04 m3 m‐3 and 0.07 m3 m‐3. Meanwhile, the 2012 growing season average was 0.10 ± 0.01 m3 m‐3, with a range from 0.05 m3 m‐3 to 0.17 m3 m‐3. Low θ is characteristic of sandy jack pine sites as rapid drainage (Figure 3.20) is expected to occur due to the coarseness of the soil texture (Gower et al., 1997). Comparatively, the mesic eastern sites HBS00, HBS75 and EOBS had soil moisture conditions of 0.29, 0.23 and 0.16 m3 m‐3, respectively (Payeur‐Poirier et al., 2012), while FBS24 was much wetter, at 0.69 m3 m‐3 (Table 3.9); soil moisture levels at the western chronosequences were not available for comparison. Extremes in soil moisture have been shown to have a measurable impact on CO2 exchange in boreal environments (e.g. Wang et al., 2003; Barr et al., 2007; Dunn et al., 2007). 60 Increased water stress from drought has been found to reduce GEP (Barr et al., 2002), while higher respiratory fluxes have been linked with the combined effects of high θ and temperature (McMillan et al., 2008). Jassal et al. (2008) determined that the effect of soil water stress on soil respiration was to lower temperature sensitivity. At their 18‐year old Douglas‐fir stand, θ less than 0.11 m3 m‐3 was established as the threshold at which soil respiration no longer correlated with T and the point at which ET, ER and GEP began to decrease (Jassal et al., 2008). Below the θ threshold, Q10 was significantly less than 2 (Jassal et al., 2008). A Q10 factor of 2 is the typical value assumed for soil respiration in the boreal biome (e.g. Chen & Tian, 2005) and signifies that ER should increase by a factor of 2 for every 10°C rise in temperature. Studies have argued that larger Q10 values indicate greater temperature sensitivity, however, this is true of ecosystems not under water stress (Khomik et al., 2006; Mkhabela et al., 2009). The influence of θ on respiration is clearly seen in Table 3.9, as all disturbed sites except those with sandy textured soil (HJP02, HJP94 and FJP05) had Q10 values above 2. Similar to the work of Jassal et al. (2008), peak respiration at FJP05 was determined to occur alongside θ greater than 0.11 m3 m‐3 (Figure 3.14). Yet, the fact that θ only exceeded 0.11 m3 m‐3 in August and September 2012 illustrates how the majority of the time biological activity would have been operating within a stressful environment. A sequence of wet and dry weeks during August 2012 was analyzed in order to better understand the effect of soil moisture on CO2 exchange at FJP05 (Table 3.7). For the week‐long wet and dry periods, NEE was a net negative flux during the former and a net positive flux during the latter. Modelled GP
and NEE
were both higher during the wet period while ER doubled during the dry period. Yet, ER only increased by 0.2 µmol m‐2 s‐1 during the dry week suggesting that moisture stress repressed respiration responses to the measured temperature increase. Average GEP was lower during the dry period even with approximately 150 µmol m‐2 s‐1 more incoming light daily; a likely consequence of stress. Q10 was slightly higher during the wet period while R10 was slightly lower. Of note, Q10 measured over these two periods was significantly lower than the growing season average while R10 was significantly higher. Chamber measurements taken over the wet period determined an increase (non‐
significant) in soil respiration flux of 3.4 ± 1.0 gC m‐2 d‐1 compared to the 2012 growing season 61 average of 2.4 ± 1.0 gC m‐2 d‐1. Thus, based on this two‐week analysis, moisture availability reduced plant stress, having a positive effect on GEP, while moisture stress prevented the expected increase in ER associated with temperature rise. Unfortunately, due to interrupted measurements of soil moisture in 2011, an inter‐seasonal comparison of wetter and drier conditions was not possible. In general, soil respiration measured by the chambers was significantly higher than the derived ER from EC (Figure 3.11). This result indicates that other sources of respiration measured within the tower footprint, i.e. dark respiration and decomposition of coarse woody debris, had lower effluxes than microbial activity. The potentially higher soil temperatures and less than optimum moisture content combined with poorer substrate quality in jack pine stands results in a difficult surface environment for plant growth (Burke et al., 1997; Harden et al., 2000). Therefore, at the site‐level, the lower base respiration and temperature insensitivity seen may be a multiplicative of the disturbance severity and parent site conditions. Sandy jack pine sites are not very productive compared to other boreal sites, such as the black spruce‐
feathermoss system (McCaughey et al., 1997; Mkhabela et al., 2009; Goulden et al., 2011). For example, Baldocchi et al. (1997a) classified their mature jack pine site as nutrient‐limited due to the site having a low amount of standing biomass and the soil having little organic matter relative to other temperate and boreal sites. Total carbon percentage in the top 5 cm at their site was 25.7 % (Baldocchi et al., 1997a) while, by comparison, FJP05 had a much lower carbon percentage of 6 ± 1 % (data not shown). Having a severe fire remove the vegetation buffer as well as soil carbon would result in a harsh environment of rapid soil heating and cooling, little moisture retention and low nutrient status. As a result, vegetation recovery would likely be a slower process compared to that of more moderate‐condition sites. Air temperature is considered a major control of ecosystem processes alongside solar radiation input (Raich & Schlesinger, 1992; Suni et al., 2003). The impact of temperature on springtime onset of photosynthesis is considered a key factor regulating annual cumulative NEE (Suni et al., 2003), while all ecosystem respiration components are sensitive to temperature variation (Barr et al., 2007; Dunn et al., 2007). The seasonal NEE pattern of increase to a summer peak and then decrease until cessation with freezing (Figure 3.5) demonstrates that air 62 temperature and solar energy drive this ecosystem. However, inter‐ and intra‐seasonal differences in these drivers can result in differing annual NEE totals. Significant differences in mean monthly air temperature were measured at FJP05 over the two growing seasons. In particular, July 2011 had normal air temperature while July 2012 was classified as cooler than normal. This difference is important to consider as temperature can strongly affect canopy responses to available light by inhibiting photosynthetic capacity (Teskey et al., 1995; van Dijk et al., 2005; Bergeron et al., 2007). As mean air temperature in July only deviated by about 1.5°C between the two years (15.9 ± 0.1°C and 14.4 ± 0.1°C), temperature constraints on GEP would seem to be an unlikely driver of the differences in NEE. However, a closer look at the diurnal T pattern reveals that while both years had similar nighttime temperatures, afternoon temperatures in July 2011 were significantly higher (Figure 3.12e,f). Thus, the sunny conditions found in July 2011 in addition to higher daytime temperature peaks could have helped to limit GEP via inducing stomatal closure. Vegetation indices provide an independent measure of the biophysical controls on NEE. Jack pine seedling height was determined to have increased by 7 ± 2 cm and 10 ± 2 cm over the 2011 and 2012 growing seasons, respectively. However, at 7 years post‐burn, mean jack pine LAI measured 0.6 m2 m‐2, at the lower end of reports from other young sites (0.2 to 1.5 m2 m‐2) (Table 3.9). Likewise, GP
was lower than that of an 11 year old jack pine stand in central Manitoba and HSB00 at 4 years of age, at 4.1 µmol m‐2 s‐1 and 4.7 ± 0.1 µmol m‐2 s‐1, respectively (Litvak et al., 2003; Bergeron et al., 2008), compared to 3.5 ± 0.1 µmol m‐2 s‐1 (Table 3.6). Nonetheless, GP
at FJP05 did increase by 2.3 µmol m‐2 s‐1 while NEE
was increasingly negative by 1.6 µmol m‐2 s‐1. These changes in light response alongside the measured increase in jack pine height suggest that the vegetative component at FJP05 was continuing to increase by the end of the second study growing season, albeit more slowly than measured at other disturbed sites. Growing season length can be linked with the total annual productivity of an ecosystem as longer growing seasons generally allow for additional CO2 assimilation (Suni et al., 2003a). Previous studies have reported young stands to have shorter growing seasons with later onsets and earlier endings compared with their intermediate and mature counterparts (Bergeron et al., 63 2008; McMillan et al., 2008; Zha et al., 2009). The delay at recently disturbed stands is explained as a consequence of deciduous‐dominated stands being unable to respond as advantageously as coniferous species to spring transitions (Coursolle et al., 2012). In such studies, the growing season was defined as the period when ecosystems were continuously photosynthetically active on a daily basis based on GEP (e.g. Bergeron et al., 2008). Using the same methodology, similar DOYstart and DOYend dates were found at both FJP05 and FBS24 (Table 3.10). The GEP method defines the onset and end through a threshold of three consecutive days where GEP reads above 10% of a maximum daily total (Bergeron et al., 2008). A higher threshold consequently acts to limit the growing season start and end dates. This begs the question however as to how a young, sparsely‐covered stand could have a similar growing season period to that of a mature black spruce forest. Arguably, a substantial increase in deciduous or herbaceous activity during the summer should result in a higher threshold, limiting the season length. In this case, however, low daily GEP totals meant that the 10% threshold at FJP05 was quite low as well. Therefore, the similar growing season limits between FJP05 and FBS24 is probably an indication of low productivity rather than a suggestion that the jack pine seedlings are the vegetation type dominating CO2 exchange at this time. The above analysis demonstrates that the net increase in gross CO2 exchange and transition to NEE uptake in 2012 was primarily a biological response to moisture availability. This suggests that parent site conditions can have a strong impact on NEE recovery after a disturbance, in this case through limiting vegetation growth and photosynthetic processes as well as soil microbial activities. 3.4.3 What are the factors driving energy exchange at a recently burned boreal jack pine forest in eastern Northern America? Conifer forests are generally regarded as having a greater ability to exchange mass and energy with the atmosphere compared to other vegetation types (Baldocchi et al., 1997b). This advantage is attributed to conifers being optically darker, which allows them the potential to absorb more solar energy and evaporate more water from the surrounding air and soil (Shuttleworth, 1989; Sellers et al., 1995; Baldocchi et al., 1997b). As well, conifers are aerodynamically rougher, facilitating mass and energy transfer with the atmosphere (Jarvis et 64 al., 1976; Shuttleworth, 1989). Fire disturbance upsets this, modifying the fundamental properties of the stand and thereby altering the surface energy balance and the relative magnitudes of its components (Chambers & Chapin, 2003). Q* is expected to decrease after disturbance, with removal of the overstory canopy enabling surface warming, which in turn causes an increase in outgoing longwave radiation (Liu & Randerson, 2008). With the advent of vegetation growth, particularly lighter‐coloured deciduous species, higher summertime albedo should also have a dampening effect on Q*, reducing the amount of energy available for surface exchanges over the first two decades (Amiro et al., 2006b). At FJP05, summertime Q*, normalized by K↓, showed a decrease from 0.70 in 2011 to 0.64 in 2012. This magnitude of decrease is consistent with Amiro et al. (2006b) who found higher values of about 0.7 at very young sites and values below 0.6 at stands aged 10 to 25 years. In their study, Amiro et al. (2006b) attributed the normalized Q* decrease at around 10 years post‐disturbance to increased albedo from deciduous vegetation growth. Yet, over the two growing seasons at FJP05, summertime albedo did not deviate significantly, staying at 0.10. Therefore, it is more likely that the inter‐seasonal decrease in Q* was a result of less incoming shortwave energy due to persistent cloudiness in 2012 (Figure 3.12). Differences in the diurnal patterns of QH and QE distinguish the 2011 and 2012 growing seasons at FJP05 (Figure 3.12; 3.13). An inter‐seasonal decrease in QH was accompanied by a significant increase QE exchange, most notably when comparing the month of July (Table 3.4). Calculations of Bowen ratio revealed that QH was the dominant form of energy exchange during the growing season of 2011 whereas QE dominated in 2012 (Figure 3.19a). In jack pine forests, QH and QE fluxes are strongly driven by incoming irradiance and co‐variations in Q* and D (Baldocchi & Vogel, 1996; Baldocchi et al., 2000). The decrease in QH in July 2012 can therefore be easily connected with the decrease in Q* however the increase in QE exchange is contrary to what would be expected of a system with less available energy. Thus, similar to the case of ecosystem CO2 exchange, we must ask the question, was the inter‐seasonal increase in QE exchange driven by vegetation growth or climatic variability? Here, ecosystem diagnostics based on the Penman‐Monteith and Priestley‐Taylor theorems are used to relate forest evapotranspiration with canopy characteristics and environmental controls. 65 3.4.3.1 Was the inter‐seasonal increase in water vapour exchange driven by vegetative growth or climatic variability? QE fluxes depend on four factors: the strength of the surface‐to‐air vapour concentration gradient, the availability of water and energy, the intensity of turbulence and the physiological role of plant stomatal activity (Oke, 1987). As previously mentioned, greater energy availability cannot explain the inter‐seasonal increase in QE exchange seen in Table 3.4. As well, D was lower on average across the 2012 growing season, reflecting more abundant rainfall and lower incoming solar energy. No significant difference in wind speed over the two growing seasons was visible either, presumably removing turbulence as a significant factor (data not shown). Thus, through elimination, two factors remain that could help explain the divergence: greater water availability and/or increased plant stomatal conductivity. The increase in average growing season PT‐α from below to above unity (Table 3.8) essentially describes QE exchange as being slightly smaller than the radiatively‐driven equilibrium rate on average during 2011, and slightly larger on average in 2012. Empirical evidence has determined that the PT‐α coefficient responds to leaf area index, photosynthetic capacity and soil moisture (Baldocchi & Ryu, 2011). In general, a value above unity indicates a freely transpiring vegetated system that is a function of energy availability alone, however this is the case only where water is readily available (Bailey et al., 1997). Thus, the high 2012 value at FJP05 may be more indicative of the surface type which, due to a major portion being non‐
vegetated, would have resulted in rapid evaporation whenever a precipitation event occurred. Daily and seasonal variations in the Bowen ratio of an ecosystem can be useful for trying to determine drivers of a system due to it reflecting the relative changes between QH and QE. In 2011, β exhibited large diurnal variability in July and peaked highest during that month relative to August and September, with 2.5, 1.5 and 1.0 as respective peaks (Figure 3.17c). These same three months in 2012, in contrast, showed consistency, peaking below unity on average throughout (Figure 3.17d). A comparison of half‐hourly β, θ and precipitation in August 2012 (Figure 3.19b) illustrates the pattern of β when soil moisture levels are replenishing versus draining. Immediately, it is apparent that days without precipitation coincided with β above unity, indicating a well‐drained soil where the absence of rainfall resulted in QH dominating. 66 Thus, even though the vegetation component at FJP05 had grown, the inter‐seasonal increase in QE exchange was nonetheless likely driven by relatively frequent precipitation being immediately evaporated off of surfaces. This is similar to the situation of another young jack pine site in Manitoba, studied during the BOREAS campaign, which also had low soil moisture and saw rapid responses in β to wetting and drying cycles (McCaughey et al., 1997). An analysis of ecosystem diagnostics has established that evaporation was the main pathway for water vapour fluxes toward the atmosphere at FJP05. The inter‐seasonal increase was likewise attributed more toward frequent rainfall in 2012 rather than significant vegetation recovery making use of soil moisture more efficiently. Still, given that growth did occur and that 2012 presented ideal conditions for photosynthesis, it is likely that transpiration made up a greater portion of QE exchange in 2012. 3.5 Conclusions Interactions between boreal stands and the climate system have received increased attention in recent years, due to the understanding that climate warming could cause large‐
scale losses of carbon from protected soil carbon pools (Chapin et al., 2000; McGuire et al., 2004; Liu & Randerson, 2008). The exposure of these C pools through severe fire disturbance is cause for concern as burned area has increased over the last several decades in North America (Kasischke et al., 2000; Kasischke & Turetsky, 2006) and is projected to increase even more, particularly in the continental interior of Canada (Flannigan et al., 2005). Fire disturbance has also been shown to impact climate‐vegetation feedbacks through initiating successional vegetative changes in stands (e.g. Goulden et al., 2006). In particular, the increase in deciduous vegetation during the first 25 years has impacts on radiative exchange and the surface energy budget as much as net ecosystem exchange (Amiro et al., 2006b, Randerson et al., 2006; Liu & Randerson, 2008). In this study, both ER and GEP had smaller growing season and annual totals compared to other recently disturbed stands. The lower productivity was associated with a lower vegetation abundance and LAI at the site due to low water availability and possible low nutrient status. In turn, the lower respiration rates were tentatively linked with water stress, a result of the sandy substrate present. At a broader scale, these results suggest that young jack pine 67 stands may be smaller sources of CO2 at this stage of recovery due to their competitiveness in harsher habitats. Increases in temperature above a threshold of approximately 20 °C and decreases in soil water content below 0.10 m3 m‐3 were found to inversely affect NEE, resulting in daily fluctuations between uptake and release. As a result of environmental conditions and plant growth, growing season NEE was found to be a net release in 2011 and a net uptake in 2012. Based on this, at 7 years post‐fire, FJP05 was categorized as approximately carbon neutral on an annual basis. The main results of this study suggest that under climate change, with conditions of increased temperature and drought frequency and intensity, young jack pine stands would likely become less productive as more time would be needed for these stands to recover from disturbance. However, little reduction in NEE would be incurred on an annual basis as variations associated with photosynthesis and plant and microbial respiration would offset one another over the growing season. Specifically, it is hypothesized that very low rainfall and corresponding low soil water content would act to inhibit ecosystem respiration, paralleling any reductions in GEP, resulting in a neutral ecosystem. The difference in latent heat exchange between growing seasons was determined to be moisture‐driven. However, the conclusions drawn do not discount an increase in transpiration at the site as GEP did increase by approximately 40 gC m‐2 in 2012 relative to the 2011 growing season. Rather the significant increase in rainfall between years at a relatively bare site meant that evaporation was the dominant process through which water vapour was transferred to the atmosphere. Meanwhile, little change in summertime albedo between years suggested that deciduous plant growth was not significant at the site. Taken together, these results suggest that the plant growth occurred but that it was more likely primarily coniferous and, as a result, is distinguishable from other recently disturbed sites which cited significant deciduous plant growth. 68 Chapter 4 Conclusion Concerns over the implications of climate change for terrestrial carbon reserves have led to a large number of studies investigating the processes controlling CO2 exchange in boreal forests. More recent efforts have expanded the focus from mature stands toward a spectrum of stand ages, to account for the important role disturbances play within the boreal biome. Increasingly, research has begun to focus on the effects of disturbance and recovery on carbon and energy exchange, with particular attention given to wildfire, the most pervasive natural disturbance in the North American boreal forest. Wildfire is responsible for significant direct inputs of CO2 to the atmosphere while indirect loss due to post‐fire effects on decomposition and regeneration are estimated to be in the order of three times the amount directly released through combustion (Auclair & Carter, 1993; Kasischke et al., 2005). Furthermore, post‐
disturbance changes in albedo and surface energy processes is expected to result in a net positive transient forcing during the first decade of recovery, with the magnitude dependent on the disturbance and site history (Jin et al., 2012). As such, assessing fire disturbance effects on carbon and energy exchange is essential in order to better predict the climate forcing associated with potential future changes in the boreal disturbance regime (Goulden et al., 2011). The eddy covariance method is the most widely used technique for measuring fluxes of CO2, water vapour and energy at the ecosystem level. Measurement versatility, both spatially and temporally, enables long‐term studies which capture whole‐ecosystem responses to climatic variability. To date, most EC studies in disturbed stands have taken place in the central and western boreal forests of North America (e.g. Wang et al., 2003; Amiro et al., 2006; Mkhabela et al., 2009). However, physical site and climatic conditions vary across the boreal, lending to the idea that generalizing the boreal carbon budget based solely on one segment may not be entirely accurate. The installation of an EC tower in 2003 at a mature black spruce forest near Chibougamau, Québec, marked the first time that an eastern boreal forest was studied (Bergeron et al., 2007). Since then, the Québec chronosequence has expanded to include two other sites, both disturbed by harvesting. It must be noted that the relationship 69 between the carbon balance and forest development following harvesting and fire disturbance is not easily compared (Mkhabela et al., 2009). Yet, scaling up studies of one ecosystem type, such as the widely studied black spruce ecosystem (e.g. Bond‐Lamberty et al., 2004; Bergeron et al., 2008), to represent regions which contain other stand types includes inaccuracies as well. As such, this study attempts to enhance the current literature by studying a recently burned boreal jack pine stand located in the eastern portion of the North American boreal forest. This study, using 1.5 years of nearly continuous field measurements, revealed that a recovering jack pine site near James Bay, Quebec was approximately carbon neutral seven years following a severe fire disturbance. NEE over the stand was typically small (‐2.3 to 1 gC m‐2 d‐1), with respect to other young boreal stands, at times flipping between net uptake and release on a day‐to‐day basis. Annual cumulative NEE was determined to be +7 or ‐6 gC m‐2 y‐1, dependent on if a greater portion of the 2011 or 2012 season was included. A difference in growing season NEE of 13 gC m‐2 was attributed with an inter‐seasonal increase in rainfall more so than vegetative growth. The substantial increase in latent heat exchange (and decrease in sensible heat exchange) from one growing season to the next was also correlated with moisture availability. Fairly consistent rainfall occurred during July and August of 2012 which allowed soil moisture levels to replenish in the short‐term, before draining away within a few days. During that window of opportunity, latent heat exchange became the dominant convective form of energy transfer, with fluxes reaching as high as 202 W m‐2. While transpiration levels almost certainly increased as well based on the inter‐seasonal increase in GEP, evaporation was likely the primary pathway for water vapour transfer to the atmosphere. Overall, the low productivity at FJP05 compared to other young disturbed stands was attributed to the very low soil moisture content that was maintained as a result of rapid drainage through the sandy substrate. Vegetation coverage was sparse, with over 60 % of the site surface classified as bare soil or ash. However, jack pine seedling growth was measurable, illustrating the species’ resiliency in harsh environments. This growth was reflected in a greater capacity for CO2 exchange during the second growing season, determined through light response parameterization. Favourable environmental conditions in July 2012 resulted in NEE uptake that allowed the estimated annual C balance of the site to transition from a net source 70 to a net sink. The main environmental controls on the seasonality of the jack pine ecosystem were therefore not only incoming light intensity and temperature but also rainfall. Cumulative ER and GEP remained closely matched over the two growing seasons, likely due to a lack of insulating layer over the soil and moisture stress suppressing both fluxes in conjunction. The timing of the spring and all fall transitions in carbon uptake were found to be significantly longer than other recovering stands, however with little effect on NEE. Evaluation of the inter‐seasonal variability in CO2 exchange suggests that small changes in environmental conditions could tip the site toward non‐neutral conditions. However, at this point of recovery, it is unlikely that FJP05 would be a large source or sink as the magnitude of the GEP and ER terms were small relative to other recently disturbed sites. Comparability between FJP05 and other disturbance studies was difficult as dominant climate, species, site conditions and even disturbance type varied. Nonetheless it can be stated that NEE of a recovering jack pine stand on sandy soils significantly varies from that of a black spruce ecosystem on mesic soils. The time required for a disturbed stand to begin actively assimilating carbon is strongly linked with leaf area index (Bond‐Lamberty et al., 2002). However, jack pine trees are known to have a lower photosynthetic capacity, due to low LAI from nutrient limitations and a high degree of needle clumping (Gower et al., 1997). Compounding this, FJP05 had very well‐drained soils, another characteristic of jack pine stands. Thus, without significant deciduous or herbaceous growth, as was seen in other cases (e.g. Mkhabela et al., 2009), a slower NEE recovery period would be expected. With future climate change, the loss of carbon from the North American boreal forest is expected to increase as the positive growth effects of CO2 fertilization are overcome by losses due to an increased fire regime (Flannigan et al., 2005; Kurz et al., 2008a). However, based on the argument above, the enhanced biomass growth expected may not apply to sandy jack pine sites. In conjunction, low site productivity and soil carbon content suggest that more frequent and severe fire disturbances would not incur drastic losses of CO2 from jack pine sites. As Baldocchi et al. (1997) put it, jack pine forests are not the missing terrestrial carbon sink. That said, a transition to younger jack pine forests in the future could have a positive climate warming effect through the resulting changes in albedo and energy balance partitioning. 71 Our study contributes to a current knowledge gap on the processes controlling CO2 and energy exchange in recently disturbed boreal stands. In particular, the soil moisture conditions of this jack pine stand were determined to be a strong constraint on CO2 exchange, a biophysical control that is mentioned in a limited number of studies (Baldocchi et al., 1997b; McCaughey et al., 1997). Additionally, while measurements of CO2 fluxes over disturbed stands has become a recent research focus (e.g. Mkhabela et al., 2009; Payeur‐Poirier et al., 2012), this study appears to be one of the first to analyze energy exchange in a disturbed stand. The necessary measurements have been taken in many cases (e.g. Amiro et al., 2006b) however the more pressing need to determine carbon balance responses to climate variation has resulted in fewer publications on energy fluxes. Yet, a multi‐year analysis of energy exchange is key to identifying how disturbed stands currently modify the radiation budget and moisture balance. As fire disturbance appears to be already evolving under a changing climate, we will need more studies of forest successional impacts on both carbon and energy exchange in order to determine the net climate impact. 72 Tables and Figures Table 3.1 Site characteristics. Error bars represent ± 1 S.E. Plot Surface Coverage Age (fraction (years) jack pine) north 0.07 ± 6.2 ± 0.01 0.2 west 0.12 ± 5.9 ± 0.04 0.3 south 0.16 ± 5.7 ± 0.07 0.3 east 0.14 ± 5.5 ± 0.03 0.2 mean 0.12 ± 5.8 ± 0.02 0.1 Tree Height (cm) Site Characteristics Tree Density Live Dead Area Basal Area 2
‐2
(no. tree (no. tree (m m ) (cm2 m‐2) m‐2) m‐2) 27 ± 3 6.7 ± 1.0 0.5 40 ± 6 6.7 ± 1.1 0.8 37 ± 6 7.8 ± 1.2 0.4 34 ± 4 6.7 ± 1.3 0.5 35 ± 3 6.7 ± 1.0 0.5 LAI 0.62 ± 0.03 0.44 ± 0.04 0.70 ± 0.03 0.43 ± 0.05 0.55 ± 0.02 Diameter Basal Range (cm) (cm) 1.4 ± 0.2 0.5 ± 0.0 2.5 ± 0.7 0.6 ± 0.1 3.6 ± 1.4 0.7 ± 0.1 2.6 ± 1.1 0.7 ± 0.1 2.6 ± 0.5 0.6 ± 0.1 0.4 to 0.6 0.3 to 1.1 0.3 to 1.4 0.3 to 1.6 0.3 to 1.6 73 Table 3.2 Soil measurements. Error bars represent ± 1 S.E. Soil Measurements Plot Organic Layer SOC Mineral Layer Ratio Efflux Vol. Water Content (%) SOC O:M (kgC m‐2) Horizon Depth (cm) (kgC m‐2) (growing season only) (gC m‐2 d‐1) north 3.9 6.5 14.6 2.2 1.8:1 n/a west 4.4 5.5 9 1.1 4.0:1 n/a south 3.1 4.8 9.9 2.5 1.2:1 n/a east 3.2 5.2 10.5 1.4 2.3:1 n/a mean 3.6 ± 0.4 5.5 ± 0.4 11.0 ± 0.9 1.8 ± 0.3 2.0:1 2.11 ± 0.25 74 Table 3.3 Seedling aboveground carbon stock measurements (kgC m‐2). Error bars represent ± 1 S.E. Seedling Aboveground Carbon Stock Plot Measured Allometric Estimation Needle Branch Stem Total (crown Stem (wood Total (needle, Crown + stem)1 branch, stem, + bark)1 (foliage + inflorescence) branch)1 north 0.09 ± 0.02 0.06 ± 0.01 ± 0.02 ± n/a n/a n/a 0.01 0.00 0.00 west 0.30 ± 0.06 0.17 ± 0.04 ± 0.07 ± n/a n/a n/a 0.03 0.01 0.02 south 0.28 ± 0.07 0.16 ± 0.04 ± 0.08 ± n/a n/a n/a 0.03 0.01 0.03 east 0.23 ± 0.08 0.15 ± 0.04 ± 0.05 ± n/a n/a n/a 0.04 0.02 0.02 mean 0.23 ± 0.03 0.14 ± 0.03 ± 0.05 ± 0.18 ± 0.12 0.03 ± 0.08 0.15 ± 0.09 0.02 0.01 0.01 1
formula from Lambert et al., 2005 75 Table 3.4 Cumulative monthly ER, GEP, NEE, QH and QE exchange. Units for ER, GEP and NEE are gC m‐2 period‐1 while units for QH and QE are GJ m‐2 period‐1. For comparison purposes, ‘July’ begins on July 8th while ‘September’ ends on September 18th. Month Monthly Cumulative Fluxes NEE ER GEP QH July August September Total 2.2 4.8 ‐1.1 5.9 July August September Total ‐7.7 0.4 0.3 ‐7.0 2011 37.1 56.9 18.6 112.6 2012 42.5 64.9 31.2 138.6 QE 34.9 52.1 19.7 106.7 3.5 3.2 0.6 7.3 2.3 4.0 1.1 7.4 50.2 64.5 30.9 145.6 2.0 2.2 0.5 4.7 3.5 4.8 1.2 9.5 76 Table 3.5 Q10 and base respiration parameters. Error bars represent ± 1 S.E. Q10, R10 Relationships Q10 2011
2012
2012
1.74 ± 0.01 1.77 ± 0.01 1.40 ± 0.02 Tower Measurements R10 A B 1.76 ± 0.01 ± 0.06 ± 0.03 0.02 0.00 1.87 0.06 ± 0.06 ± ±0.05 0.03 0.00 Chamber Measurements 2.57 ± 0.60 ± 0.03 ± 0.17 0.17 0.01 R2 n 0.59 1130 0.48 991 0.55 13 77 Table 3.6 Comparison of light response curve parameters during July, August and September. The warm season dataset was defined by T > 0 °C and T > ‐5 °C, while the optimal growing season dataset included only data measured between July 1st and August 31st with 15 °C < T < 25 °C and T > 5 °C. Error bars represent ± 1 S.E. warm season optimal GS warm season optimal GS warm season optimal GS warm season optimal GS Rectangular Hyperbolic Model Parameters αNEE(αGEP) NEEmax(GPmax) (molCO2/molQ) (µmol m‐2 s‐1) 2011 NEE ‐0.013 ± 0.001 ‐2.37 ± 0.05 ‐0.022 ± 0.002 ‐3.38 ± 0.07 2012 NEE ‐0.017 ± 0.003 ‐2.51 ± 0.09 ‐0.017 ± 0.002 ‐4.93 ± 0.17 2011 GEP 0.009 ± 0.001 2.87 ± 0.08 0.025 ± 0.002 3.26 ± 0.06 2012 GEP 0.023 ± 0.002 3.54 ± 0.08 0.021 ± 0.002 5.52 ± 0.15 R2 ERd 0.94 ± 0.03 0.46 1.69 ± 0.04 0.61 1.32 ± 0.06 0.19 1.78 ± 0.08 0.48 ‐ ‐ 0.24 0.25 ‐ ‐ 0.13 0.34 78 Table 3.7 Comparison of a wet (DOY 213‐219) and dry (DOY 220‐226) week in August 2012. Error bars represent ± 1 S.E. Ecosystem Responses to Weather Conditions Wet Dry Rainfall(sum) (mm) 17.8 1.5 3
‐3
θ (m m ) 0.12 ± 0.01 0.09 ± 0.01 Da (kPa) 0.6 ± 0.2 0.8 ± 0.2 ‐2 ‐1
PPFD (µmol m s ) 558 ± 166 715 ± 193 Ta (°C) 14.9 ± 1.4 15.7 ± 1.3 Ts (°C) 16.9 ± 0.9 17.4 ± 1.2 NEE (µmol m‐2 s‐1) ‐0.1 ± 0.8 0.2 ± 0.6 GEP (µmol m‐2 s‐1) 2.1 ± 0.8 2.0 ± 0.7 ‐2 ‐1
ER (µmol m s ) 2.0 ± 0.2 2.2 ± 0.2 GPmax (µmol m‐2 s‐1) NEEmax 4.4 ± 0.1 ‐5.4 ± 0.3 ‐5.1 ± 1.4 ‐2 ‐1
1.5 ± 0.4 3.4 ± 1.5 (µmol m s ) ERd (µmol m s ) Q10 n/a 1.57 ± 0.01 1.52 ± 0.01 R10 (µmol m‐2 s‐1) 2.18 ± 0.09 2.28 ± 0.07 6.0 ± 0.2 ‐2 ‐1
79 Table 3.8 Comparison of monthly‐averaged daytime dry‐foliage ecosystem diagnostics during July, August and September. Error bars represent ± 1 S.E. Ecosystem Diagnostics Jul Aug Sep mean EF 0.44±0.01 0.53±0.01 0.54±0.01 0.51±0.02 Jul Aug Sep mean 0.75±0.02 0.69±0.02 0.72±0.03 0.72±0.05 2011
α 0.75±0.02 0.90±0.02 1.10±0.03 0.92±0.05 2012 1.28±0.04 0.98±0.23 1.40±0.04 1.22±0.24 β 1.88±0.05 1.11±0.03 0.82±0.02 1.27±0.07 g 4.33±0.32 6.99±0.81 8.88±0.52 6.73±1.01 g 35.5±0.5 35.5±0.5 37.6±0.6 36.2±0.9 0.78±0.01 0.67±0.02 0.68±0.02 0.71±0.03 5.41±0.07 4.80±0.07 5.64±0.10 5.28±0.14 33.1±0.4 26.3±0.3 29.0±0.6 29.5±0.8 80 Table 3.9 List of North American flux sites commonly referred to in this study. North American Fire and Harvest Chronosequences Site Age (in study) Age class2 Province Latitude/Longitude Reference 1
HJP‐SK LAI Q10 R10 θ (m m ) (n/a) (µmol ‐2 ‐1
m s ) (m
‐3
m )
2
‐2
3
HJP02 3 YNG SK 53.9°N/104.7°W Zha et al., 2009 0.2 0.9 3.5 ‐ HJP94 11 YNG SK 53.9°N/104.6°W Zha et al., 2009 1.1 1.6 3.1 ‐ HJP75 30 INT SK 53.9°N/104.7°W Zha et al., 2009 2.9 2.1 3.1 ‐ OJP 90 MAT SK 53.9°N/104.6°W Zha et al., 2009 2.0 2.6 3.3 ‐ F98 7 YNG SK 53.9°N/106.1°W 1.1 2.2 3.2 ‐ F89 16 INT SK 54.3°N/105.9°W 3.0 4.7 5.4 ‐ F77 28 INT SK 54.5°N/105.9°W 3.4 5.7 3.5 ‐ 90 MAT SK 53.9°N/104.7°W Mkhabela et al., 2009 Mkhabela et al., 2009 Mkhabela et al., 2009 Zha et al., 2009 2.0 2.6 3.3 ‐ HBS00 8 YNG QC 49.3°N/74.0°W 2.5 0.29 33 INT QC 49.8°N/74.6°W 3.7 4.2 0.23 EOBS 105 MAT QC 49.7°N/74.3°W 0.7‐
1.5 1.9‐
3.5 3.7 2.9 HBS75 Payeur‐Poirier et al., 2012 Payeur‐Poirier et al., 2012 Payeur‐Poirier et al., 2012 3.8 3.3 0.16 UCI‐1998 6 YNG MB 56.6°N/99.9°W Goulden et al., 2011 1.4 ‐ ‐ ‐ UCI‐1989 15 INT MB 55.9°N/99.0°W Goulden et al., 2011 3.0 ‐ ‐ ‐ UCI‐1981 23 INT MB 55.9°N/98.5°W Goulden et al., 2011 5.5 ‐ ‐ ‐ UCI‐1964 40 INT MB 55.9°N/98.4°W Goulden et al., 2011 5.3 ‐ ‐ ‐ UCI‐1930 74 MAT MB 55.9°N/98.5°W Goulden et al., 2011 7.8 ‐ ‐ ‐ UCI‐1850 154 MAT MB 55.9°N/98.5°W Goulden et al., 2011 5.7 ‐ ‐ ‐ 1
FJP‐SK OJP 1
HBS‐QC FBS‐MB1 Non‐Chronosequence SOBS 130 MAT SK 54.0°N/105.1°W Bergeron et al., 2007 3.8 3.0 5.0 ‐ NOBS 160 MAT MB 55.9°N/98.5°W Bergeron et al., 2007 4.8 3.8 6.2 ‐ FJP05 7 YNG QC 52.3°N/76.7°W current study 0.6 1.5 1.9 0.10 52.1°N/76.2°W 3
1.3 ‐ ‐ 0.69 FBS24 88 MAT QC 1 current study HJP‐SK: harvested jack pine in Saskatchewan; FJP‐SK: Burned jack pine in Saskatchewan; HBS‐QC: harvested black spruce in Quebec; FBS‐MB: burned black spruce in Manitoba 2 YNG: young stand, age < 20; INT: intermediate‐aged stand, 20 < age < 70; MAT: mature stand, age > 70 3 refer to Appendix I for description 81 Table 3.10 Comparison of growing season onset, end and length determined using two distinct methods. Growing Season Determination Ta Method FJP05 FBS24 2011 2012 2011 2012 DOYstart 110 121 118 99 DOYend 311 GSL 308 316 307 198 208 DOYstart 201 187 GEP Method 91 103 91 96 DOYend 314 n/a 314 n/a GSL 216 n/a 216 n/a 82 Figure 2.1 Conceptual model of forest recovery from fire disturbance (Goulden et al., 2011). 83 Figure 2.2 Annual carbon balance at boreal jack pine stands disturbed by fire or harvesting. Please refer to Table 3.9 for a description of referenced sites. 84 Figure 3.1 Study area and location of the recently burned jack pine site, FJP05 and of the mature black spruce site, FBS24. 85 Figure 3.2 Standardized (a) air temperature and (b) precipitation anomaly according to the Canadian Climate Normal at Chapais II station, Quebec. Red bars denote a significantly positive anomaly while blue bars denote a significantly negative anomaly. 86 Figure 3.3 Site fractional surface coverage by type. Error bars represent ± 1 S.E. 87 Figure 3.4 Diurnal pattern of NEE. Error bars represent ± 1 S.E. 88 Figure 3.5 Daily average of NEE for the period DOY 189 (July 8, 2011) to DOY 262 (September 18, 2012). Error bars represent ± 1 S.E. 89 Figure 3.6 Daytime NEE and nighttime ER averages for the period DOY 189 (July 8, 2011) to DOY 262 (September 18, 2012). Error bars represent ± 1 S.E. 90 Figure 3.7 Monthly cumulative fluxes of ER, GEP and NEE. 91 Figure 3.8 Comparison of cumulative daily average ER, GEP and NEE over July, August and September. 92 Figure 3.9 Comparison of cumulative daily average ER and GEP over two representative weeks in each of July, August and September for each year of the study. 93 Figure 3.10 Cumulative daily average ER, GEP and NEE for the period DOY 189 (July 8, 2011) to DOY 189 (July 7, 2012). 94 Figure 3.11 Ecosystem respiration measured using static chamber and modeled from eddy covariance. Each chamber point represents sampling along a ten‐collar transect averaged over an intensive campaign period. Each tower point represents an average of 13:00 ± 2 hours over the same period. Error bars represent ±1 S.E. 95 Figure 3.12 Comparison of diurnal patterns of energy exchange (a‐d) and environmental controls (e‐h) for the months of July and August for each year of the study. Error bars represent ±1 S.E. 96 Figure 3.13 Diurnal patterns of sensible heat in (a) 2011 and (b) 2012 and latent heat in (c) 2011 and (d) 2012. Error bars represent ±1 S.E. 97 Figure 3.14 Response of ER to (a) soil temperature at 5 cm (Ts) and (b) soil moisture content (θ) for the 2011 and 2012 growing seasons. Bin‐averaged data are presented by 2 °C and 0.2 m3 m‐3 (n>10). Error bars represent ±1 S.E. 98 Figure 3.15 Response of (a) NEE to PPFD and GEP to (b) PPFD, (c) T and (d) D during the 2011 and 2012 optimal growing seasons. Bin‐averaged data are presented (bin size = 100 µmol m‐2 s‐
1
; 2 °C; and 0.2 kPa, n> 10 for each bin). Error bars represent ±1 S.E. 99 Figure 3.16 Monthly response of GEP to (a) Ta and (b) Da during the 2012 growing season. Error bars represent ±1 S.E. 100 Figure 3.17 Comparison of the diurnal pattern of (a‐b) PT‐α, (c‐d) β and (e‐f) EF for July, August and September. Error bars represent ±1 S.E. 101 Figure 3.18 Response of daytime dry‐foliage surface conductance (gs) to atmospheric evaporative demand (Da) based on all available half‐hourly data for 2011 and 2012. Data have been bin averaged by Da (bin size 0.2 kPa) and stratified by Q*. Circles represent Q* < 100 W m‐
2
, squares 100 < Q* < 400 W m‐2 and triangles Q* > 400 W m‐2. Error bars represent ±1 S.E. 102 Figure 3.19 (a) comparison of daytime average Bowen ratio (β) in 2011 and 2012 and (b) half hourly Bowen ratio (β) (daytime only), soil moisture content (θ) and precipitation (PPT) from DOY 222 to 242 (August 9 – 29, 2012). 103 Figure 3.20 Daily precipitation (PPT) total and soil moisture content (θ) average, for May through September 2012. 2011 data was not included as only two weeks were available for comparison. 104 References Abrams, M.D. (1984). Uneven‐aged jack pine in Michigan. Journal of Forestry, 82, 306‐307. Ahti, T. & Oksanen, J. (1990). Epigeic lichen communities of taiga and tundra regions. Vegetatio, 86, 39‐
70. Amiro, B.D., MacPherson, J.I. & Desjardins, R.L. (1999). BOREAS flight measurements of forest‐fire effects on carbon dioxide and energy fluxes. Agricultural andForest Meteorology, 96, 199‐208. Amiro, B. D. (2001). Paired‐tower measurements of carbon and energy fluxes following disturbance in the boreal forest. Global Change Biology, 7(3), 253‐268. Amiro, B.D., MacPherson, J.I., Desjardins, R.L., Chen, J.M. & Liu, J. (2003). Post‐fire carbon dioxide fluxes in the western Canadian boreal forest: evidence from towers, aircraft and remote sensing. Agricultural and Forest Meteorology, 115, 91‐107. Amiro, B.D., Barr, A.G., Black, T.A., Iwashita, H., Kljun, N., McCaughey, J.H., Morgenstern, K., Murayama, S., Nesic, Z., Orchansky, A.L. & Saigusa, N. (2006a). Carbon, energy and water fluxes at mature and disturbed forest sites, Saskatchewan, Canada. Agricultural and Forest Meteorology, 136, 237‐251. Amiro, B.D., Orchansky, A.L., Barr, A.G., Black, T.A., Chambers, S.D., Chapin III, F.S., Goulden, M.L., Litvak, M., Liu, H.P., McCaughey, J.H., McMillan, A. & Randerson, J.T. (2006b). The effect of post‐fire stand age on the boreal forest energy balance. Agricultural and Forest Meteorology, 140, 41‐60. Amiro, B.D. (2010). Estimating annual carbon dioxide eddy fluxes using open‐path analyzers for cold forest sites. Agricultural and Forest Meteorology, 150, 1366‐1372. Amiro, B.D., Barr, A., Barr, J., Black, T., Bracho, R., Brown, M., Chen, J., Clark, K.L., Davis, K.J., Desai, A.R., Dore, S., Engel, V., Fuentes, J.D., Goldstein, A.H., Goulden, M.L., Kolb, T.E., Lavigne, m.B., Law, B.E., Margolis, H. A., Martin, T., McCaughey, J.H., Misson, L., Montes‐Helu, M., Noormets, A., Randerson, J.T., Starr, G. & Xiao, J. (2010). Ecosystem carbon dioxide fluxes after disturbance in forests of North America. J. Geophys. Res, 115. Antonovsky, M., Glebov, F.Z. & Korzuhin, M.D. (1987). A regional model of long‐term wetland‐forest dynamics. Working Paper WP‐87‐63, International Institute of Applied Systems Analysis, Laxenburg, Austria, 51 pp. Apps, M.J., Kurz, W.A., Luxmoore, R.J., Nilsson, L.O., Sedjo, R.A., Schmidt, R., Simpson, L.G. & Vinson, T.S. (1993). Boreal forests and tundra. Water, Air and Soil Pollution, 70, 39‐53. Aubinet, M., Grelle, A., Ibrom, A., Rannik, U., Moncrieff, J., Foken, T., Kowlaski, A.S., Martin, P.H., Berbigier, P., Bernholfer, Ch., Clement, R., Elbers, J., Granier, A., Grunwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R. & Vesala, T. (2000). Estimates of the annual net carbon and water exchange of European forest: the EUROFLUX methodology. Advances in Eocological Research, 30, 113‐175. Aubinet, M. (2008). Eddy covariance CO2 flux measurements in nocturnal conditions: an analysis of the problem. Ecological Applications, 18(6), 1368‐1378. Aubinet, M., Feigenwinter, C., Heinesch, B., Laffineau, Q., Papale, D., Reichstein, M., Rinne, J. & Van Gorsel, E. (eds.)(2012). Eddy Covariance: A Practical Guide to Measurement and Data Analysis‐ Nighttime flux correction. Springer Atmospheric Sciences, 133‐157. Auclair, A. N. D., & Carter, T. B. (1993). Forest wildfires as a recent source of CO2 at northern latitudes. Canadian Journal of Forest Research, 23(8), 1528‐1536. Bailey, W.G., Oke, T.R. & Rouse, W.R. (eds.) (1997). The Surface Climates of Canada. McGill‐Queen’s University Press, Montreal, pp. 369. 105 Baldocchi & Vogel (1996). Energy and CO2 flux densities above and below a temperate broad‐leaved forest and a boreal jack pine forest. Tree Physiology, 16, 5‐16. Baldocchi, D.D., Vogel, C.A. & Hall, B. (1997a). Seasonal variation of carbon dioxide exchange rates above and below a boreal jack pine forest. Agricultural and Forest Meteorology, 83, 147‐170. Baldocchi, D.D., Vogel, C.A. & Hall, B. (1997b). Seasonal variation of energy and water vapour exchange rates above and below a boreal jack pine forest canopy. Journal of Geophysical Research, 102(D24), 28939‐28951. Baldocchi, D. Kelliher, F.M., Black, T.A. & Jarvis, P. (2000). Climate and vegetation controls on boreal zone energy exchange. Global Change Biology, 6(Suppl. 1), 69‐83. Baldocchi, D., Flage, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C.H., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Yadvinder, M., Meyers, T., Munger, W., Oechel, W., Paw, K.T., Pilegaard, K., Schmid, H.P., Valentini, R., Verma, S., Vesala, T., Wilson, K. & Wofsy, S. (2001). FLUXNET: a new tool to study the temporal and spatial variability of ecosystem‐scale carbon dioxide, water vapour and energy flux densities. Bulletin of the American Meteorological Society, 82, 2415‐2434. Baldocchi, D. (2003). Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9, 479‐492. Baldocchi, D.D. & Ryu, Y. (2011). A synthesis of forest evaporation fluxes‐ from days to years‐ as measured with eddy covariance In Forest Hydrology and Biogeochemistry: Synthesis of Past Research and Future Directions. D.F. Levia et al., (eds.),Ecological Studies, 216, pp. 101‐116. Balshi, M.S., McGuire, A.D., Duffy, P., Flannigan, M., Walsh, J. & Melillo, J. (2009). Assessing the response of area burned to changing climate in western boreal North America using a multivariate adaptive regression splines (MARS) approach. Global Change Biology, 15, 578‐600. Barr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K. & Nesic, Z. (2004). Inter‐annual variability in the leaf area index of a boreal aspen‐hazelnut forest in relation to net ecosystem production. Agirucultural and Forest Meteorology, 126, 237‐255. Barr, A. G., Black, T., Hogg, E., Griffis, T., Morgenstern, K., Kljun, N., Theede, A. & Nesic, Z. (2007). Climatic controls on the carbon and water balances of a boreal aspen forest, 1994–2003. Global Change Biology, 13(3), 561‐576. Beaufait, W.R. (1960). Some effects of high temperatures on the cones and seeds of jack pine. Forestry Science, 6, 194‐199. Bergeron, Y. (1991). The influence of island and mainland lakeshore landscapes on boreal forest fire regimes. Ecology, 1980‐1992. Bergeron, O., Margols, H.A., Black, T.A., Coursolle, C., Bunn, A.L., Barr, A. & Wofsy, S.C. (2007). Comparison of carbon dioxide fluxes over three boreal black spruce forests in Canada. Global Change Biology, 13, 89‐107. Bergeron, O., Margolis, H,A., Coursolle, C. & Giasson, M.‐A. (2008). How does forest harvest influence carbon dioxide fluxes of black spruce ecosystems in eastern North America? Agricultural and Forest Meteorology, 148(4), 537‐548. Bergner, B., Jonstone, J. & Treseder, K.K. (2004). Experimental warming and burn severity alters soil CO2 flux and soil functional groups in a recently burned boreal forest. Global Change Biology, 10, 1996‐2004. Betts, A.K. & Ball, J.H. (1997) Albedo over the boreal forest. Journal of Geophysical Research, 102(D24), 28901‐28909. Betts, R.A. (2000). Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature, 408, 187‐190. Bisbee, K.E., Gower, S.T., Norman, J.M. & Nordhein, E.V. (2001) Environmental controls on ground cover species composition and productivity in a boreal black spruce forests. Oecologia, 129, 261‐270. 106 Black, T.A., Hartog, G.D., Neumann, H.H., Blanken, P.D., Yang, P.C., Russell, C., Nesic, Z., Lee, X., Chen, S.G., Staebler, R. & Novak, M.D. (1996). Annual cycles of water vapour and carbon dioxide fluxes in and above a boreal aspen forest. Global Change Biology, 2, 219‐229. Black, T.A., Gaumont‐Guay, D., Jassal, R.S., Amiro, B.D., Jarvis, P.G., Gower, S.T., Kelliher, F.M., Dunn,A. & Wofsy, S.C. (2005). Measurement of CO2 exchange between boreal forest and the atmosphere In The Carbon Balance of Forest Biomes. H. Griffiths & P.J. Jarvis (Eds), Taylor and Francis Group, New York, pp. 151‐186. Bonan, G. B., & Shugart, H. H. (1989). Environmental factors and ecological processes in boreal forests. Annual review of ecology and systematics, 20, 1‐28. Bonan, G.B., Pollard, D. & Thomson, S.L. (1992). Effects of boreal forest vegetation on global climate. Nature, 359, 716‐718. Bonan, G. B. (2008). Forests and climate change: forcings, feedbacks, and the climate benefits of forests. science, 320(5882), 1444. Bond‐Lamberty, B., Wang, C., Gower, S.T. & Norman, J. (2002) Leaf area dynamics of a boreal black spruce chronosequence. Tree Physiology, 22, 993‐1001. Bond‐Lamberty, B., Wang, C. & Gower, S.T. (2004). Net primary production and net ecosystem production of a boreal black spruce wildfire chronosequence. Global Change Biology, 10, 473‐487. Bonneville, M‐C., Strachan, I.B., Humphreys, E.R. & Roulet, N.T. (2008). Net ecosystem CO2 exhcnage in a temperate cattail marsh in relation to biophysical properties. Agricultural and Forest Meteorolgy, 148, 69‐81. Brown, A.A. & Davis, K.P. (1973). Forest fire: control and use. McGraw‐Hill, New York. Brown, C.D. & Johnstone, J.F. (2011). How does increased fire frequency affect carbon loss from fireÉ A case study in the northern boreal forest. International Journal of Wildland Fire, 20, 829‐837. Brummer, C., Black, T.A., Jassal, R.S., Grant, N.J., Spittlehouse, D.L., Chen, B., … Wolfsky, S.C. (2012). How climate and vegetation type indluence evapotranspiration and water use effeciency in Canadian forest, peatland and grassland ecosystems. Agricultural and Forest Meteorology, 153, 14‐30. Burke, R.A., Zepp, R.G., Tarr, M.A. Miller, W.L. & Stocks, B.J. (1997). Effect of fire on soil‐atmosphere exchange of methane and carbon dioxide in Canadian boreal forest sites. Journal of Geophysical Research, 102(D24), 29289‐29300. Calder, I.R. (1990). Evaporation in the uplands. Wiley, New York, 148 pp. Carleton, T.J. & Maycock, P.J. (1978). Dynamics of the boreal forest south of James Bay, Quebec, Canada. Canadian Journal of Botany, 56, 1157‐1173. Cayford, J.H. & McRae, D.J. (1983). Ecological role of fire in jack pine forests In R.W. Wein and D.A. MacLean (eds.). The role of fire in northern circumpolar ecosystems. Wiley, Toronto, Ontario, pp. 183‐199. Chambers, S.D. & Chapin III, F.S. (2003). Fire effects on surface‐atmosphere energy exchange in Alaskan black spruce ecosystems: implications for feedbacks to regional climate. Journal of Geophysical Research, 108(D1), 8145. Chapin III, F.S., McGuire, A.D., Randerson, J., Pielke Sr., P., Baldocchi, D., Hobbie, S.E., Roulet, N., Eugster, W., Kasischke, E, Rastetter, E.B., Zimov, S.A. & Running, S.W. (2000). Arctic and boreal ecosystems of western North America as components of the climate system. Global Change Biology, 6 (Suppl. 1), 211‐223. Chapin III, F.S., Woodwell, G.M., Randerson, J.T., Rastetter, E.B., Lovett, M., Baldocchi, D.D., Clark, D.A., Harmon, M.E., Schimel, D.S., Valentini, R., Wirth, C., Aber, J.D., Cole, J.J., Goulden, M.L., Harden, J.W., Heimann, M., Howarth, R.W., Matson, P.A., McGuire, A.D., Melillo, J.M., Mooney, H.A., Neff, J.C., Houghton, R.A., Pace, M.L., Ryan, M.G., Running, S.W., Sala, O.E., Schlesinger, W.H. & Schulze, E.‐D. (2006). Reconciling carbon‐cycle concepts, terminology, and methods. Ecosystems, 9, 1041‐1050. 107 Chapin III, F.S., Matson, P.A. & Vitousek, P.M. (2011). Principles of Terrestrial Ecosystem Ecology 2nd ed. Springer, New York, 529 pp. Chapman, W. L., & Walsh, J. E. (1993). Recent variations of sea ice and air temperature in high latitudes. Bulletin of the American Meteorological Society, 74(1), 33‐47. Chen, J.M. & Black, T.A. (1992). Defining leaf area index for non‐flat leaves. Plant Cell Environment, 15, 421‐429. Chen, J.M. (1996). Optically‐based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology, 80, 135‐163. Chen, J.M., Rich, P.M., Gowerm S.T., Norman, J.M. & Plummer, S. (1997). Leaf area index of boreal forests: theory, techniques and measurements. Journal of Geophysical Research, 102(D24), 29429‐29443. Chen, J.M., Chen, W.J., Lui, J., Cihlar, J. & Gray, S. (2000). Annual carbon balance of Canada`s forests during 1895‐1996. Global Biogeochemical Cycles, 14, 839‐850. Chen, W.J., Chen, J.M., Price, D.T. & Cihlar, J. (2002) Effects of stand age on net primary productivity of boreal black spruce forests in Ontario, Canada. Canadian Journal of Forest Research, 32, 833‐
842. Chen, H. & Tian, H.Q. (2005). Does a general temperature‐dependent Q10 model of soil respiration exist at biome and global scale? Journal of Intergrative Plant Biology, 47(11), 1288‐1302. Chen, J.M., Govind, A., Sonnentag, O., Zhang, Y., Barr, A. & Amiro, B. (2006). Leaf area index measurements at Fluxnet‐Canada forest sites. Agricultural and Forest Meteorology, 140, 257‐
268. Cho, J., Oki, T., Yeh, P. J.‐F., Kim, W., Kanae, S. & Otsuki, K. (2012). On the relationship between the Bowen ratio and the enar‐surface air temperature. Theoretical Applications in Climatology, 108, 135‐
145. Clark, K.L., Gholz, H.L. & Castro, M.S. (2004). Carbon dynamics along a chronosequence of slash pine plantations in northern Florida. Ecological Applications, 14, 1154‐1171. Conard, S. G., Sukhinin, A. I., Stocks, B. J., Cahoon, D. R., Davidenko, E. P., & Ivanova, G. A. (2002). Determining effects of area burned and fire severity on carbon cycling and emissions in Siberia. Climatic Change, 55(1), 197‐211. Conkey, L.E., Keifer, M.B. & Llyod, A.H. (1995). Disjunct jack pine (Pinus banksiana Lamb.) structure and dynamics, Acadia National Park, Maine. Ecosience, 2, 168‐176. Coursolle, C., Margolis, H.A., Barr, A.G., Black, T.A., Amiro, B.D., McCaughey, J.H., Flanagan, L.B., Lafleur, P.M., Roulet, N.T., Bourque, C. P.‐A., Arain, M.A., Wofsy, S.C., Dunn, A., Morgenstern, K., Orchansky, A.L., Bernier, P.Y., Chen, J.M., Kidston, J., Saigusa, N. & Hedstrom, N. (2006). Late‐
summer carbon fluxes from Canadian forests and peatlands along an east‐west continental transect. Canadian Journal of Forest Research, 36, 783‐800. Culf, A.D. (1992). An application of simple models to Sahelian convective boundary‐layer growth. Boundary‐Layer Meteorology, 58, 1‐18. Czimczik, C.I., Trumbore, S.E., Carbone, M.S. & Winston, G.C. (2006). Changing sources of soil respiration with time since fire in a boreal forest. Global Change Biology, 12, 957‐971. Dalbert, W.F., Lenschow, D.H., Horst, T.W., Zimmerman, P.R., Oncley, S.P. & Delany, A.C. (1993). Atmosphere‐surface exchange measurements. Science, 260(5113), 1472‐1481. Dale, V.H., Joyce, L.A., McNulty, S., Neilson, R.P., Ayres, M.P., Flannigan, M.D., Hanson, P.J., Irland, L.C., Lugo, A.E., Peterson, C.J., Simberloff, D., Swanson, F.J., Stocks, B.J. & Wotton, B.M. (2001). Climate change and forest disturbances. BioScience, 51(9), 723‐734. Dang, Q.L., Margolis, H.A., Coyea, M.R., Sy, M. & Collatz, G.J. (1997). Regulation of branch‐level gas exchange of boreal trees: roles of shoot water potential and vapour pressure difference. Tree Physiology, 17(8‐9), 521‐535. 108 Davidson, E.A. & Janssens, I.A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165‐173. Davis, M.B. (1969). Climatic changes in southern Connecticut recorded by pollen deposition at Rogers Lake. Ecology, 50, 312. Department of Northern Affairs and National Resources Forestry Branch (1965). Jack Pine In Native Trees of Canada. Bulletin No. 61 5th ed. Ottawa, Canada, pp. 16. Dixon, R.K. & Turner, D.P. (1991). The global carbon cycle and climate change: responses and feedbacks from below‐ground systems. Environmental Pollution, 73(3‐4), 245‐262. Dixon, R. K., Solomon, A., Brown, S., Houghton, R., Trexier, M., & Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science, 263(5144), 185. Duffy, P.A., Walsh, J.E., Graham, J.M., Mann, D.H. & Rupp, T.S. (2005). Impacts of large scale atmospheric‐
ocean variability on Alaskan fire season activity. Ecological Applications, 15(4), 1317‐1330. Dunn, A.L., Barford, C.C., Wofsy, S.C., Goulden, M.L. & Daube, B.C. (2007). A long‐term record of carbon exchange in a boreal black spruce forest: means, responses to interannual variability, and decadal trends. Global Change Biology, 13, 577‐590. Engelmark, 0. (1999). Boreal forest disturbances In Ecosystems of Disturbed Ground. L.R. Walker (ed.). Elsevier Science, 161‐186. Ensminger, I., Sveshnikov, D., Campbell, D.A., Funk, C., Jansson, S., Lloyd, J., Shibistova, O. & Oquist, G. (2004). Intermittent low temperatures constrain spring recovery of photosynthesis in boreal Schots pine forests. Global Change Biology, 10, 995‐1008. Environment Canada (2013). National Climate Data and Information Archive: Canadian Climate Normals 1971‐2000: Station Chapais 2. Eugster, W., Rouse, W.R., Pielke, Sr. R.A., McFadden, J.P., Baldocchi, D.D., Kittel, T.G.F., Chapin III, F.S., Liston, G.E., Vidale, P.L., Vaganov, E. & Chambers, S. (2000). Northern ecosystems and land‐
atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate. Global Change Biology, 6(S1), 84‐115. Eyre, F.H. & LeBarron, R.K. (1944). Management of jack pine in the Lake States. USDA For. Serv. Tech. Bulletin No. 863, 66pp. Falge, E., Tenhunen, J., Baldocchi, D., Aubinet, M., Bakwin, P., Berbigier, P., Bernhofer, C., Bonnefond, J.‐
M., Burba, G., Clement, R., Davis, K.J., Elbers, J.A., Flak, M., Goldstein, A.H., Grelle, A., Granier, A., Grunward, T., Guomundsson, J., Hollingerm D., Janssens, I.A., Keronen, P., Kowlaski, A.S., Katul, G., Law, B.E., Malhi, Y., Meyersm T., Monson, R.K., Moors, E., Munger, J.W., Oechel, Walt., Paw, K.T., Pilegaard, K., Rannik, U., Rebmann, C., Suyker, A., Thoreirsson, H., Tirone, G., Turnipseed, A., Wilson, K. & Wofsy, S. (2002). Phase and amplitude of ecosystemcarbon release and uptake potentials derived from FLUXNET measurements. Agricultural and Forest Meteorology, 113(1‐4), 75‐95. Farquhar, G.D. & Sharkey, T.D. (1982). Stomatal conductance and photosynthesis. Annual Review of Plant Physiology, 33, 317‐345. Finnigan, J. (2008). An introduction to flux measurements in difficult conditions. Ecological Applications, 18(6), 1340‐1350. Flanagan, P.W. & Van Cleve, K. (1983). Nutrient cycling in relation to decomposition and organic matter quality in taiga ecosystems. Canadian Journal of Forest Research, 13(5), 795‐817. Flannigan, M., Stocks, B & Wotton, B. (2000). Climate change and forest fires. The Science of the Total Environment, 262, 221‐229. Flannigan, M., Amiro, B., Logan, K., Stocks, B., & Wotton, B. (2005). Forest fires and climate change in the 21 st century. Mitigation and Adaptation Strategies for Global Change, 11(4), 847‐859. Flannigan, M., Stocks, B., Turetsky, M., & Wotton, M. (2009). Impacts of climate change on fire activity and fire management in the circumboreal forest. Global Change Biology, 15(3), 549‐560. 109 Ford, J., & Bedford, B.L. (1987). The hydrology of Alaskan wetlands, U.S.A.: a review. Arctic and Alpine Research, 19, 209‐229. Fowells, H.A. (1965). Silvics of forest trees of the United States. USDA, Agric. Handbook No. 271. French, N.N.F. (2002). The impact of fire disturbance on carbon and energy exchange in the Alaskan boreal region: a geospatial data analysis. Ph.D. dissertation Thesis, University of Michigan, Ann Arbor, pp. 105. Friend, A.D., Arneth, A., Kiang, N,Y., Lomas, M., Ogee, J., Rodenbeck, C., Running, S.W., Santaren, J‐D., Sitch, S., Viovy, N., Woodward, F.I. & Zaehle, S. (2007). FLUXNET and modelling the global carbon cycle. Global Change Biology, 13(3), 610‐633. Gagnon, J. (1990). Structures d’ages et succession dans des peuplements de pins gris (Pinus banksiana Lamb.) soumis a des regimes de feux differents au sud de la foret boreale, en Abitibi. M.Sc. dissertation, Universite du Quebec a Montreal. Garratt, J.R. (1978). Tranfer characteristics for a heterogeneous surface of large aerodynamic roughness. Quarterly Journal of the Royal Meteorological Society, 104, 491‐502. Gauthier, S., Bergeron, Y. & Simon, J‐P. (1993). Cone seritony in jack pine: ontogenetic, positional and environmental effects. Canadian Journal of Forest Research, 23, 294‐401. Geider, R.J., Delucia, E.H., Falkowski, P.G., Finzi, A.C., Grime, J.P., Grace, J., Kana, T.M., La Roche, J., Long, S.P., Osborne, B.A., Platt, T., Prentice, C., Raven, J.A., Schlesinger, W.H., Smetacek, V., Stuart, V., Sathyendranath, S., Thomas, R.B., Vogelmann, T.C., Williams, P. & Woodward, F.I. (2001). Primary productivity of planet earth: biological determinants and physical constraints in terrestrial and aquatic habitats. Global Change Biology, 7, 849‐882. Gentine, P., Entekhabi, D., Chehbouni, A., Bouletm G. and Duchemin, B. (2007). Analysis of evaporative fraction diurnal behavior. Agricultural and Forest Meteorology, 143(1), 13‐29. Gillett, N., Weaver, A., Zwiers, F., & Flannigan, M. (2004). Detecting the effect of climate change on Canadian forest fires. Geophysical Research Letters, 31(18), L18211. Goulden, M.L., Daube, B.C., Fan, S.‐M., Sutton, D.J., Bazzaz, A., Munger, J.M. & Wofsy, S.C. (1997). Physiological responses of a black spruce forest to weather. Journal of Geophysical Research, 102, 28987‐28996. Goulden, M.L., Winston, G.C., McMillan, A.M.S. Litvak, M.E., Read, E.L., Rocha, A.V. & Elliot, J.R. (2006). An eddy covariance mesonet to measure the effect of forest age on land‐atmosphere exchange. Global Change Biology, 12, 2146‐2162. Goulden, M.L., McMillan, A.M.S., Winston, G.C., Rocha, A.V., Manies, K,L., Harden, J.W. & Bond‐Lamberty, B. (2011). Patterns of NPP, GPP, respiration, and NEP during boreal forest succession. Global Change Biology, 17, 855‐871. Gower, S.T., Vogt, K.A. & Grier, C.C. (1992). Carbon dynamics of Rocky‐mountain Douglas‐fir‐ influence of water and nutrient availability. Ecological Monographs, 62, 43‐65. Gower, S.T., Vogel, J.G., Norman, J.M., Kucharik, C.J., Steele, S.J. & Stow, T.K. (1997). Journal of Geophysical Research, 102, 29029‐29041. Gower, S.T., Krankina, O., Olson, R.J., Apps, M., Linder, S. & Wang, C. (2001). Net primary production and carbon allocation patterns of borea forest ecosystems. Ecological Apllications, 11(5), 1395‐1411. Griffis, T.J., Black, T.A., Morgenstern, K., Barr, A.G., Drewitt, G.B., Gaumont‐Guay, D. & McCaughey, J.H. (2003). Ecophysiological controls on the carbon balances of three southern boreal forests. Agricultural and Forest Meteorology, 117, 53‐71. Gu, L., Massman, W.J., Leuning, R., Pallardy, S.G., Meyers, T., Hanson, P.J., Riggs, J.S., Hosman, K.P. & Yang, B. (2012). The fundamental equation of eddy covariance and its application in flux measurements. Agricultural and Forest Meteorology, 152, 135‐148. 110 Harden, J.W., O’Neill, K.P., Trumbore, S.E. & Stocks, B.J. (1997). Moss and soil contributions to the annual net carbon flux of a maturing boreal forest. Journal of Geophysical Research, 102(D24), 28805‐
28816. Harden, J., Trumbore, S., Stocks, B., Hirsch, A., Gower, S., O'neill, K., & Kasischke, E. (2000). The role of fire in the boreal carbon budget. Global Change Biology, 6(S1), 174‐184. Harden, J. W., Manies, K. L., O'Donnell, J., Johnson, K., Frolking, S., & Fan, Z. (2012). Spatiotemporal analysis of black spruce forest soils and implications for the fate of C. Journal of Geophysical Research, 117(G1), G01012. Hart, S.A. & Chen, H.Y.H. (2006). Understory vegetation dynamics of North American boreal forests. Critical Reviews in Plant Science, 25, 381‐397. Heinselman, M.L. (1973). Fire in the virgin forests of the Boundary Waters Canoe Area, Minnesota. Quarternary Research, 3, 329‐382. Hogg, E.H., Saugier, B., Pontallier, J.Y., Black, T.A., Chen, W.J.,Hurdle, P.A. & Wu, A. (2000). Responses of trembling aspen and hazelnet to vapour pressure deficit in a boreal deciduous forest. Tree Physiology, 20(11), 725‐734. Humphreys , E.R., Black, A.T., Morgenstein, K., Li, Z. & Nesic, Z. (2005). Net ecosystem production of a Douglas‐fir stand for three years following clearcut harvesting. Global Change Biology, 11, 450‐
464. Humphreys, E.R., Black, A.T., Morgenstein, K., Cai, T.B., Drewitt, G.B., Nesic, Z. & Trofymoiw, J.A. (2006). Carbon dioxide fluxes in coastal Douglas‐fir stands at different stages of development after clearcut harvesting. Agricultural and Forest Meteorology, 140, 6‐22. IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. J.T. Houghton et al., eds., Cambridge Press, 881 pp. IPCC (2007). Climate Change 2007: The Physical Science Basis. Working Group 1 Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon et al., eds., Cambridge Press, 1009 pp. Iwata, H., Ueyama, M., Harazono, Y., Tsuyuzaki, S., Kondo, M. & Uchida, M. (2011). Quick recovery of carbon dioxide exchanges in a burned black spruce forest in interior Alaska. Scientific Online Letters on the Atmosphere, 7, 105‐108. Jarvis, P.G., James, G.B. & Landsberg, J.J. (1976). Conifeous forest In Vegetation and the Atmosphere. Vol II. (J.L. Montheith (ed.), Academic Press, London, pp. 171‐240. Jarvis, P.G., & McNaughton, K.G. (1986). Stomatal control of transpiration. Advances in Ecological Research, 15, 1‐49. Jarvis, P.G. (1994). Capture of carbon dioxide by coniferous forests In Resource Capture by Crops. J.L. Monteith, R.K. Scott and M.H. Jarvis (eds.), Nottingham University Press, Nottingham, pp. 234‐
279. Jarvis, P.G., Massheder, J.M., Hale, S.E., Montcrieff, J.B., Rayment, M. & Scott, S.L. (1997). Seasonal variation of carbon dioxide, water vapour, and energy exchanges of a boreal black spruce forest. Journal of Geophysical Research, 102(D24), 28953‐28966. Jin, Y., Randerson, J.T., Goetz, S.J., Beck, P.S.A., Loranty, M.M. & Goulden, M.L. (2012). The influence of burn severity on postfire vegetation recovery and albedo change during easly succession in North American boreal forests. Journal of Geophysical Research, 117, G01036. Johnson, E. A. (1992). Fire and vegetation dynamics: studies from the North American boreal forest. Cambridge University Press, Cambridge, 129 pp. Johnstone, J.F. & Kasischke, E.S. (2005). Stand‐level effects of soil burn density on postfire regeneration in a recently burned black spruce forest. Canadian Journal of Forest Research, 35, 2151‐2163. 111 Kasischke, E. S., Christensen Jr, N., & Stocks, B. J. (1995). Fire, global warming, and the carbon balance of boreal forests. Ecological Applications, 437‐451. Kasischke, E.S. Bergen, K., Fennimore, R., Sotelo, F., Stephens, G., Janetos, A. & Shugart, H. (1999). Satellite imagery gives a clear picture of Russia’s boreal forest fires. EOS Transactions AGU, 80, 141‐147. Kasischke, E.S. (2000). Boreal ecosystems in the global carbon cycle In Fire, Climate Change, and Carbon Cycling in the Boreal Forest. Kasischke, E. S. & Stocks, B. J. (eds.), Springer‐Verlag, New York, pp. 19‐30. Kasischke, E. S., Hyer, E. J., Novelli, P. C., Bruhwiler, L. P., French, N. H. F., Sukhinin, A. I., Hewson, J.A. & Stocks, B. J. (2005). Influences of boreal fire emissions on Northern Hemisphere atmospheric carbon and carbon monoxide. Global Biogeochem. Cycles, 19(1). Kasischke, E.S. & Turetsky, M.R. (2006). Recent changes in the fire regime across the North American boreal region‐ spatial and temporal patterns of burning across Canada and Alaska. Geophysical Research Letters, 33, L09703. Kelliher, F.M., Leuning, R. & Schulze, E.‐D. (1993). Evaporation and canopy characteristics of coniferous forests and grasslands. Oecologia, 95, 153‐163. Kershaw, K.A. & Rouse, W.R. (1971a). Studies on lichen‐dominated systems. I. The water relations of Cladonia alpestris in spruce‐lichen woodland in northern Ontario. Canadian Journal of Botany, 49(8), 1389‐1399. Kershaw, K.A. & Rouse, W.R. (1971b). Studies on lichen‐dominated systems. II. The growth patterns of Cladonia alpestris and Cladonia rangiferina. Canadian Journal of Botany, 49(8), 1401‐1410. Kershaw, K.A. (1978). The role of lichens in boreal tundra transition areas. The Bryologist, 81(2), 294‐306. Keyser, A.R., Kimball, J.S., Nemani, R.R. & Running, S.W. (2000). Simulating the effects of climatic change on the carbon balance of North American high‐latitude forests. Global Change Biology, 6(Suppl. 1), 185‐195. Kicklighter, D.W., Melillo, J.M., Peterjohn, W.T., Rastetter, E.B., McGuire, A.D. & Steudler, P.A. (1994). Aspects of spatial and temporal aggregation in estimating regional carbon dioxide fluxes from temperate forest soils. Journal of Geophysical Research, 99, 1303‐1315. Kimball, J., Zhao, M., McGuire, A., Heinsch, F., Clein, J., Calef, M., Jolly, W.M., Kang, S., Euskirchen, S.E., McDonald, K.C. & Running, S.W. (2007). Recent climate‐driven increases in vegetation productivity for the western Arctic: evidence of an acceleration of the northern terrestrial carbon cycle. Earth Interactions, 11(4), 1‐30. Khomik, M., Arain, M.A. & McCaughey, T.J. (2006). Temporal and spatial variability of soil respiration in a boreal mixedwood forest. Agricultural and Forest Meteorology, 140, 244‐256. Kowalski, A.S., Loustau, D., Berbigier, P., Manca, G., Tedeschi, V., Borghetti, M., Valentini, R., Kolan, P., Berninger, F., Rannik, U., Han, P., Rayment, M., Mencuccini, M., Moncrieff, J & Grace, J. (2004). Paired comparisons of carbon exchange between undisturbed and regenerating stands in four managed forests in Europe. Global Change Biology, 10(10), 1707‐1723. Kramer, P.J. & Kozlowski, T.T. (1979). Photosynthesis In Physiology of Woody Plants. Academic, San Diego, California, USA, pp. 163‐222. Krause, H.H. (1965). Effect of pH on leaching lossses of potassium applied to forest nursery soils. Soil Science Society of America Journal, 29(5), 613‐615. Krezek‐Hanes, C., Ahern, F., Cantin, A., & Flannigan, M. (2011). Trends in large fires in Canada. Kurz, W.A., Apps, M.J., Stocks, B.J. & Volnew, W.J.A. (1995). Global climate change: disturbance regimes and biospheric feedbacks to temperate amd boreal forests In Biotic Feedbacks in the Global Climate System: Will the Warming Feed the Warming? G.M. Woodwell, F.T. MacKenzie (eds.), Oxford University Press, New York, 119‐133. 112 Kurz, W.A. & Apps, M.J. (1999). A 70‐year retrospective of carbon fluxes in the Canadian forest sector. Ecological Applications, 9(2), 526‐547. Kurz, W. A., Stinson, G., & Rampley, G. (2008a). Could increased boreal forest ecosystem productivity offset carbon losses from increased disturbances? Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1501), 2259‐2268. Kurz, W. A., Stinson, G., Rampley, G. J., Dymond, C. C., & Neilson, E. T. (2008b). Risk of natural disturbances makes future contribution of Canada's forests to the global carbon cycle highly uncertain. Proceedings of the National Academy of Sciences, 105(5), 1551. Lafleur, P.M., Roulet, N.T. & Admiral, S.W. (2001). Annual cycle of CO2 exchange at a bog peatland. Journal of Geophysical Research, 106, 3071‐3081. Lafleur, P.M. (2008). Connecting atmosphere and wetland: energy and water vapour exchange. Geography Compass, 2(4), 1027‐1057. Lafleur, P.M. (2009). Connecting atmosphere and wetland: trace gas exchange. Geography Compass, 3(2), 560‐585. Lamont, B.B., La Maitre, D.C., Cowling, R.M. & Enright, N.J. (1991). Canopy seed storage in woody plants. Botanical Review, 57(4), 277‐317. Larsen, J. A. (1980). The boreal ecosystem: Academic Press New York. Larsen, C.P.S. & MacDonald, G.M. (1998). Fire and vegetation dynamics in a jack pine and black spruce forest reconstructed using fossil pollen and charcoal. Journal of Ecology, 86, 815‐828. Law, B.E., Falge, E., Gu, L., Baldocchi, D.D., Bakwin, P., Berbigier, P., Davis, K., Dolman, A.J., Falk, M. & Fuentes, J.D. (2002). Environmental controls over carbon dioxide and water vapour exchange of terrestrial vegetation. Agriculture and Forest Meteorology, 113, 97‐120. Li, C., Flannigan, M., & Corns, I. G. W. (2000). Influence of potential climate change on forest landscape dynamics of west‐central Alberta. Canadian Journal of Forest Research, 30(12), 1905‐1912. LI‐COR, Inc. (1992). LAI‐2000 Plant Canopy Analyzer Operating Manual. LI‐COR, Inc., Lincoln, Nebraska, 174 pp. Litvak, M., Miller, S., Wofsy, S. C., & Goulden, M. (2003). Effect of stand age on whole ecosystem CO2 exchange in the Canadian boreal forest. Journal of Geophysical Research, 108(3). Liu, H.P., Randerson, J.T., Lindfors, J. & Chapin III, F.S. (2005). Changes in the surface energy budget after fire in boreal ecosystems of interior Alaska. Journal of Geophysical Research, 110, D13101. Liu, H. & Randerson, J.T. (2008). Interannual variability of surface energy exchange depends on stand age in a boreal forest fire chronosequence. Journal of Geophysical Research, 113, G01006. Longton, R.E. (1980). Physiological ecology of mosses In The Mosses of North America. R.J. Tayke & A.E. Levito (eds.), San Fransisco, 77‐113. Lu, P., Yunusa, I.A.M., Walker, R.R. & Muller, W.J. (2003). Regulation of canopy conductance and transpiration and their modelling in irrigated grapevines. Functional Plant Biology, 30, 689‐698. Mack, M. C., Schuur, E. A. G., Bret‐Harte, M. S., Shaver, G. R., & Chapin, F. S. (2004). Ecosystem carbon storage in arctic tundra reduced by long‐term nutrient fertilization. Nature, 431(7007), 440‐443. Margolis, H.A., Flanagan, L.B. & Amiro, B.D. (2006). The FLUXNET‐Canada Research Network: influence of climate and disturbance on carbon cycling in forests and peatlands. Agriculture and Forest Meteorology, 140, 1‐5. McCaughey, J.H., Lafleur, P.M., Joiner, D.W., Bartlett, P.A., Costello, A.M., Jelinski, D.E. & Ryan, M.G. (1997). Magnitudes and seasonal patterns of energy, water and carbon exchange at a boreal young jack pine forest in the BOREAS northern study area. McCullough, D.G., Werner, R.A. & Neumann, D. (1998). Fire and insects in northern and boreal forest ecosystems of North America 1. Annual Review of Entomology, 43(1), 107‐127. McGuire, A.D.., Melillo, J.M., Joyce, L.A., Kicklighter, D.W., Grace, A.L., Moore, B. & Vorosmarty, C.J. (1992). Interactions between carbon and nitrogen dynamics in estimating net primary 113 productivity for potential vegetation in North America. Global Biogeochemical Cycles, 6, 101‐
124. McGuire, A.D., Apps, F.S., Chapin III, F.S., Dargaviller, R., Flannigan, M.D., Kasischke, E.S., Kicklighter, D., Kimball, J., Kurx, W., McRae, D.J., McDonald, K., Melillo, J., Myneni, R., Stocks, D.L., Verbyla, D.L. & Zhuang, Q. (2004). Landcover disturbances and feedbacks to the climate system in Canada and Alaska In Land Change Science: Observing, Monitoring, and Understanding Trajectories of Change on the Earth’s Surface. G. Gutman (ed.), Kluwer Academy of Dordrecht, Netherlands, pp. 139‐161. McMillan, A.S., Winston, G.C. & Goulden, M. (2008). Age‐dependent response of boreal forest to temperate and rainfall variability. Global Change Biology, 14, 1904‐1916. McNaughton, K.G. & Spriggs, T.W. (1986). A mixed layer model for regional evaporation. Boundary‐Layer Meteorology, 34, 243‐262. McNaughton, K.G. & Spriggs, T.W. (1989). An evaluation of the Priestley and Taylor equation and the complementary relationship using results from a mixed‐layer model of the convective boundary layer. International Association of Hydrological Sciences, 177, 89‐104. MDDEFP, 2012. Ministere du Developpement durable, de L’Environnement et des Parcs, CLIMATOLOGIE, Direction du suivi de l’etat de l’environnement, Quebec. Mkhabela, M.S., Amiro, B.D., Barr, T.A., Black, T.A., Hawthorne, I., Kidston, J., McCaughey, J.H., Orchansky, A.L., Nesic, Z., Sass, A., Shashkov, A. & Zha, T. (2009). Comparison of carbon dynamics and water use efficiency following fire and harvesting in Canadian boreal forests. Agicultural and Forest Meteorology, 149, 783‐794. Monteith, J.L. (1965). Evaporation and environment In Water in the Plant. 205‐234. Monteith, J.L. (1995). A reinterpretation of stomatal responses to humidity. Plant Cell Environment, 18, 357‐364. Monteith, J.L. and Unsworth, M.H. (2008). Principles of environmental physics. Academic Press. 414 pp. Moore, T.R. (1980). The nutrient status of subarctic woodland soils. Arctic and Alpine Research, 12(2), 147‐160 Moore, T.R. (1981). Controls on the decomposition of organic matter in subarctic spruce‐lichen woodlands soils. Soil Science, 131, 107‐113. Moore, D.J.P., Trahan, N.A., Wilkes, P., Quaife, T., Stephens, B.B., Elder, K., Desai, A.R., Negron, J. & Monson, R.K. (2013). Persisten reduced ecosystem respiration after insect disturbance in high elevation forests. Ecology Letters, 16, 731‐737. Morgenstern, K., Black, T.A., Humphreys, E.R., Griffis, T.J., Drewitt, G.B., Cai, T., Nesic, Z., Spittlehouse, D.L. & Livingston, N.J. (2004). Sensitivity and uncertainty of the carbon balance of a Pacific Northwest Douglas‐fir forest during an El Nino/La Nina cycle. Agricultural and Forest Meteorology, 123, 201‐219. Murphy, P.J., Mudd, J.P., Stocks, B.J., Kasischke, E.S., Barry, D., Alexander, M.E. & French, N.F.H. (2000). Historical fore records in the North American boreal forest In Fire, Climate Change and Carbon Cycling in the Boreal Forest. Ecological Studies, 138, Kasischke, E.S. & Stocks, B.J. (eds.) Springer‐
Verlag, New York, pp. 274‐288. O'Donnell, J. A., Harden, J. W., McGUIRE, A. D., Kanevskiy, M. Z., Jorgenson, M. T., & Xu, X. (2011). The effect of fire and permafrost interactions on soil carbon accumulation in an upland black spruce ecosystem of interior Alaska: implications for post‐thaw carbon loss. Global Change Biology, 17(3), 1461‐1474. Odum, E. P. (1969). The strategy of ecosystem development. Science, 164, 262‐270. 1971. Oke, T.R. (1987). Boundary Layer Climates‐ 2nd ed. Routledge, London and New York. 114 Oliphant, A.J., Grimmond, C.S.B., Zutter, H.N., Schmid, H.P., Su, H.‐B., Scott, S.L., Offerle, B., Randolph, J.C. & Ehman, J. (2004). Heat storage and energy balance fluxes for a temperate deciduous forest. Agricultural and Forest Meteorology, 126, 185‐201. O’Neill, K.P., Kasischke, E.S. & Richter, D.D. (2002). Environmental controls on soil CO2 flux following fire in black spruce, white spruce and aspen stands of interior Alaska. Canadian Journal of Forest Research, 32, 1525‐1541. O’Neill, K.P., Richter, D.D. & Kasischke, E.S. (2006). Succession‐driven changes in soil respiration following fire in black spruce stands of interior Alaska. Biogeochemistry, 80, 1‐20. Otterman, J., Chou, M‐D. & Arking, A. (1984). Effects of nontropical forest cover on climate. Journal of Applied Meteorology, 23(5), 762‐767. Parisien M‐A. & Sirois L. (2003). Distribution and dynamics of tree species across a fire frequency gadient in the James Bay region of Quebec. Canadian Journal of Forest Research. 33, 243‐256. Parisien, M.A., Sirois, L. & Babeau, M. (2004). Distribution and dynamics of jack pine at its longitudinal range limits in Quebec In R.T. Engstrom, K.E.M. Galley, and W.J. de Groot (eds.). Proceedings of the 22nd Tall Timbers Fire Ecology Conference: Fire in Temperate, Boreal, and Montane Ecosystems. Tall Timbers Research Station, Tallahassee, FL., pp. 247‐257. Paulson, C.A. (1970). The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology, 9, 857‐861. Payette, S. (1993). Fire as a controlling process in the North American boreal forest In A Systems Analysis of the Global Boreal Forest. H.H. Shugart, R. Leemans & G. Bonan (eds.), Cambridge University Press, Cambridge, pp. 144‐169. Payeur‐Poirier, J.‐L., Coursolle, C., Margolis, H.A. & Giasson, M.‐A. (2012). CO2 fluxes of a boreal black spruce stand chronosequence in eastern North America. Agricultural and Forest Meteorology, 153, 94‐105. Peichl, M. & Arain, M.A. (2006). Above‐ and belowground ecosystem biomass and carbon pools in an age‐sequence of temperate pine plantation forests. Agricultural and Forest Meteorology, 140, 51‐63. Penman, H.L. (1948). Natural evporation from open water, bare soil and grass. Proceedings of the Royal Society of London, Series A, Mathmatical and Physical Sciences, 193(1032), 120‐145. Pereira, A.R. (2004). The Priestley‐Taylor parameter and the decoupling factor for estimating reference evapotranspiration. Agricultural and Forest Meteorology, 125, 305‐313. Piao, S., Ciais, P., Friedlingstein, P., Peylin, P., Reichstein, M., Luyssaert, S., Margolis, H., Fang, J., Barr, A., Chen,A., Grelle, A., Hollinger, D.Y., Laurila, T., Lindroth, A., Richardson, A.D. & Vesala, T. (2008). Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451(3), 49‐53. Pinard, C. (1999). Influence de l’intervalle de feu sur la regeneration du pin gris (Pinus banksiana Lamb.) et de l’epinette noire (Picea mariana (Mill.)B.) dans le Nord de la forest boreale. M.Sc. Thesis, Universite du Quebec a Rimouski, Rimouski, Quebec. Plamondon‐Bouchard, M. (1975). Caractristiques et frequence des nuages bas Poste‐de‐la‐Baleine Nouveau‐Quebec. Cah. Geogr. Que. 19, 311‐330. Priestley, C. and Taylor, R. (1972). On the assessment of surface heat flux and evaporation using large‐
scale parameters. Monthly Weather Review, 100(2), 81‐92. Raich, J.W. & Schlesinger, W.H. (1992). The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B, 44(2), 81‐99. Randerson, J.T., Liu, H., Flanner, M.G., Chamber, S.D., Jin, Y., Hess, P.G., Pfister, G., Mack, M.C., Treseder, K.K., Welp, L.R., Chapin III, F.S., Harden, J.W., Goulden, M.L., Lyons, E., Neff, J.C., Schluur, E.A.G. & Zender, C.S. (2006). The impact of boreal forest fire on climate warming. Science, 314, 1130‐
1132. 115 Rapalee, G., Trumbore, S.E., Davidson, E.A., Harden, J.W. & Veldhuis, H. (1998). Soil carbon stocks and their rates of accumulation and loss in a boreal forest landscape. Global Biogeochemical Cycles, 12(4), 687‐701. Rizzo, B. & Wiken, E.D. (1992). Asssessing the sensitivity of Canada`s ecosystems to climatic change. Climatic Change, 21, 37‐55. Rowe, J., & Scotter, G. W. (1973). Fire in the boreal forest. Quaternary Research, 3(3), 444‐464. Rweyongeza, D.M., Dhir, N.K., Barnhardt, L.K., Hansen, C. & Yang, R‐C. (2007). Population differentiation of the lodgepole pine (Pinus contorta) and jack pine (Pinus banksiana) complex in Alberta: growth survival, and responses to climate. Canadian Journal of Botany, 85, 545‐556. Sass, A.P. (2007). Energy, water, and carbon budgets of young post‐fire boreal forests in central Saskatchewan. M.Sc. dissertation Thesis, University of Manitoba, pp. 101. Schafer, K.V.R., Oren, R. & Tenhunen, J.D. (2000). The effect of tree height on crown level stomatal conductance. Plant Cell Environment, 23, 365‐375. Schiller, C.L. & Hastie, D.R. (1996). Nitrous oxide and methane fluxes from perturbed and unperturbed boreal forest sites in northern Ontario. Journal of Geophysical Research, 101(D17), 22767‐22774. Schlesinger, W.H. (1997). Biogeochemistry: An analysis of global change. Academic Press, San Diego, CA. Schmid, H.P. (1994). Source areas for scalars and scalar fluxes. Boundary Layer Meteorology, 67, 293‐318. Schulze, E.D., Lloyd, J., Kelliher, F.M., Wirth, C., Rebmann, C., Luhker, B., Mund, M., Knohl, A., Milyukova, I.M., Schulze, W., Ziegler, W., Varlagin, A.B., Sogachev, A.F., Valentini, R., Dore, S., Grigoriev, S.F., Kolle, O., Panfyorov, M.I., Tchebakova, N., Vygodskaya, N.N. (1999). Productivity of forest in the Eurosiberean boreal region and their potential to act as a carbon sink‐ a synthesis. Global Change Biology, 5, 703‐722. Schulze, E. D., Wirth, C., & Heimann, M. (2000). Managing forests after Kyoto. science, 289(5487), 2058‐
2059. Scotter, G.W. (1964). Effects of forest fires on the winter range of Barren‐Ground Caribou in Northern Saskatchewan. Ottawa, 111 pp. Sellers, P., Hall, F., Margolis, H, Kelly, B., Baldocci, D., den Hartog, G., Cihlar, J., Ryan, M.G., Goodison, B., Crill, P., Randon, J., Lettenmaier, D. & Wickland, D.E. (1995) The Boreal Ecosystem‐Atmosphere Study (BOREAS): An overview and early results from the 1994 field year. Bulletin of the American Meteorological Society, 76, 1549‐1577. Sellers, P.J., Hall, F.G., Kelly, R.D., Black, A., Bladocchi, D., Berry, J., Ryan, M., Ranson, K.J., Crill, P.M., Lettenmaier, D.P., Margolis, H., Cihlar, J., Newcomer, J., Fitzgarrald, D., Jarvis, P.G., Gower, S.T., Halliwell, D., Williams, D., Goodison, B., Wickland, D.E. & Guertin, F.E. (1997). BOREAS in 1997: Experiment overview, scientific results, and future directions. Journal of Geophyical Research, 102(D24), 28731‐28769. Sharratt, B.S. (1998). Radiative exchange, near‐surface temperature and soil water of forest and cropland in interior Alaska. Agricultural and Forest Meteorology, 89(3‐4), 269‐280. Shuttleworth, W. J. (1989). Micrometeorology of temperate and tropical forests. Philosophical Transactions of the Royal Society of London B, 324, 299‐334. Shuttleworth, W.J. (1993). Evaporation In Handbook of Hydrology. D.R. Maidment (ed.) New York, 4.1‐
4.53. Soil Classification Working Group (1998). The Canadian system of soil classification, 3rd edition. Agriculture and Agri‐Food Canada. Publication 1646, 187 pp. Soja, A.J., Cofer III, W.R., Shugartm H.H., Sukhinin, A.I., Stackhouse Jr., P.W., McRae, D.J. & Conard, S.G. (2004). Estimating fire emissions and disparities in boreal Siberia (1998 through 2002). Journal of Geophysical Research, 109, D14S06. 116 Soja, A. J., Tchebakova, N. M., French, N. H. F., Flannigan, M. D., Shugart, H. H., Stocks, B. J., Sukhinin, A.I., Parfenova, E.I., Chappin III, S. & Stackhouse, P. W. (2007). Climate‐induced boreal forest change: predictions versus current observations. Global and Planetary Change, 56(3‐4), 274‐296. Spittlehouse, D.L. (2003). Water availability, climate change and the growth of the Douglas‐fir in the Georgia Basin. Canadian Water Resources Journal, 28, 673‐688. Steele, S.J., Gower, S.T., Vogel, J.G. & Norman, J.M. (1997) Root mass, net primary production and turnover in aspen, jack pine and black spruce forest in Saskatchewan and Manitoba, Canada. Tree Physiology, 17(8‐9), 577‐587. Stocks, B.J. & Kauffman, J.B. (1997). Biomass comsumption and behavior of wildland fires in boreal, temperate and tropical ecosystems: parameters necessary to interpret historic fire regimes and future fire scenarios. Canadian Forest Service‐ Ontario Region, Sault Ste. Marie, Ontario, Canada, pp. 168‐188. Stocks, B.J., Fosberg, M.A., Lynham, T.J., Mearns, L., Wotton, B.M., Yang, Q., Zin, J.Z., Lawrence, K., Hatley, G.R., Mason, J.A. & McKenney, D.W. (1998). Climate change and forest fire potential in Russian and Canadian boreal forests. Climatic Change, 38(1), 1‐13. Stocks, B.J., Fosberg, M.A., Wotton, M.B., Lynham, T.J. & Ryan, K.C. (2000). Climate change and forest fire activity in North American boreal forests In Fire, Climate Change, and Carbon Cycling in the Boreal Forest. E.S. Kasischke & B.J. Stocks (eds.), Springer‐Verlag, New York, 368‐376. Stocks, B.J., Mason, J.A., Todd, J.B., Bosch, E.M., Wotton, B.M., Amiro, B.D., Flannigan, M.D., Hirsh, K.G., Logan, K.A., Martell, D.L. & Skinner, W.R. (2003). Large forest fires in Canada, 1959‐1997. Journal of Geophysical Research, 108(D1), FFR 5.1‐5.12. Suni, T., Berninger, F., Markkanen, T., Keronen, P., Rannik, U. & Vesala, T. (2003a). Interannual variability and timing of growing‐season CO2 exchange in a boreal forest. Journal of Geophysical Research, 108(D9), 4265. Suni, T., Berninger, F., Vesala, T., Markkanen, T., Hari, P., Makela, A., Ilvesniemi, H., Hanninen, H., Nikinmaa, E., Huttula, T., Laurila, T., Aurela, M., Grelle, A., Lindroth, A., Arneth, A., Shibistova, O. & Lloyd, J. (2003b). Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Global Change Biology, 9, 1410‐1426. Strachan, I.B. & McCaughey, J.H. (1996). Spatial and vertical leaf area index of a deciduous forest resolved using the LAI‐2000 Plant Canopy Analyzer. Forest Science, 42, 176‐181. Tamm, C.O. (1953). Growth, yield and nutrition in carpets of a forest moss (Hylocomium splendens). Medd. Statens Skogsfors, 43, 1‐140. Tanner, C.B. & Thurtell, G.W. (1969). Anemoclinometer measurements of Reynolds stress and heat transport in the atmospheric surface layer. DTIC Document. Thomas, P.A. & Wein, R.W. (1990). Jack pine establishment on ash from wood and organic soil. Canadian Journal on Forest Research, 20, 1926‐1932. Thomas, G. & Rowntree, P.R. (1992). The boreal forests and climate. Quarterly Journal of the Royal Meteorological Society, 118(505), 469‐497. Trumbore, S.E. & Harden, J.W. (1997). Accumulation and turnover of carbon in organic and mineral soils of the BOREAS northern study area. Journal of Geophysical Research, 102(D24), 28817‐28830. Trumbore, S. (2006). Carbon respired by terrestrial ecosystems‐ recent progress and challenges. Global Change Biology, 12, 141‐153. Turetsky, M. R., Kane, E. S., Harden, J. W., Ottmar, R. D., Manies, K. L., Hoy, E., & Kasischke, E. S. (2010). Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nature Geoscience, 4(1), 27‐31. Turner, M. G., & Romme, W. H. (1994). Landscape dynamics in crown fire ecosystems. Landscape ecology, 9(1), 59‐77. 117 Ullah, S., Frasier, R., Pelletier, L. & Moore, T.R. (2009). Greenhouse gas fluxes from boreal forest soils during the snow‐free period in Quebec, Canada. Canadian Journal of Forest Research, 39, 666‐
680. USDA (2011). USDA Forest Service Fire Data Web Services. http://activefiremaps.fs.fed.us/wms.php. Accessed on September 7, 2011. Van Cleve, K., Dyrness, C., Viereck, L., Fox, J., Chapin III, F., & Oechel, W. (1983). Taiga ecosystems in interior Alaska. BioScience, 39‐44. Van Cleve, K. & Viereck, L. (1983). A comparison of successional sequences following fire on permafrost‐
dominated and permafrost‐free sites in interior Alaska. Permafrost: Proceeding of the Fourth International Conference. National Academy Press, Fairbanks, Alaska, 1286‐1291. Van Cleve, K. & Yarie, J. (1986). Interaction of temperature, moisture and soil chemistry in controlling nutrient cycling and ecosystem development in the taiga of Alaska. Canadian Journal of Forest Research, 11, 160‐189. Van Dijk, A., Dolman, A.J. & Schulze, E.D (2005). Radiation, temperature and leaf area explain ecosystem carbon fluxes in boreal and temperate European forests. Global Biogeochemical Cycles, 19(2), GB2029. Viereck, L.A. & Jonston, W.F. (1990). Picea mariana (Mill.) B.S.P., Black Spruce. In Silvics of North America, Vol. 1., Conifers. Edited by R.M. Burns & B.H. Honkala. USDA For Serv., Agric. Hdbk. 654, 227‐237. Viterbo, P. & Betts, A.K. (1999). Impact on ECMWF forecasts of changes to the albedo of the boreal forests on the presence of snow. Journal of Geophysical Research, 104(D22), 27803‐27810. Vogel, J.G. & Gower, S.T. (1998). Carbon and nitrogen dynamics of boreal jack pine stands with and without a green alder understory. Ecosystems, 1(4), 386‐400. Walter, H. (1979). Vegetation of the Earth and ecological systems of the geo‐biosphere. Springer‐Verlag, New York, pp. 274. Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J. C., Fromentin, J.‐M., Hoegh‐
Gulderg, O. & Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416(6879), 389‐395. Wang, C.K., Gower, S.T., Wang, Y.H. Zhao, H., Yan, P. & Bond‐Lamberty, B.P. (2001) The influence of fire on carbon distribution and net primary production of boreal Larix gmelinii forests in north‐eastern China. Global Change Biology, 7(6), 719‐730. Wang, C.K., Bond‐Lamberty, B. & Gower, S.T. (2003). Carbon distribution of a well‐ and poorly‐drained black spruce fire chronosequence. Global Change Biology, 9, 1066‐1079. Wang, S. (2005). Dynamics of surface albedo of a boreal forest and its simulation. Ecological Modelling, 183, 477‐494. Webb, E.K., Pearman, G.I. & Leuning, R. (1980). Correction of flux measurements for density effects due to heat and water vapour transport. Quarterly Journal of the Royal Meteorological Society, 106, 85‐100. Weber, M.G. (1988). Fire and ecosystem dynamics in eastern Canadian Pinus banksiana forests In Vegetation Structure in Relation to Carbon Neutrient Economy. J.T.A. Verhoeven, G.W. Heil, M.J.A. Werger (eds.), SPB Academic Publishing, The Hague, The Netherlands, pp. 93‐105. Welp, L.R., Randerson, J.T. & Liu, H.P. (2006). Seasonal exchange of CO2 and delta18O‐CO2 varies with postfire succession in boreal forest ecosystems. Journal of Geophysical Research, 111, G03007. Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B.E., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Verma, S. & Valentini, R. (2002). Energy balace closure at FLUXNET sites. Agicultural and Forest Meteorology, 113(1‐4), 223‐243. Winston, G.C., Sundquist, E.T., Stephens, B.B. & Trumbore, S.E. (1997). Winter CO2 fluxes in a boreal forest. Journal of Geophysical Research, 102(D24), 28795‐28804. 118 Zepp, R.G., Miller, W.L., Tarr, M.A. & Burke, R.A. (1997). Soil‐atmosphere fluxes of carbon monoxide during early stages of postfire succession in upland Canadian boreal forests. Journal of Geophysical Research, 102(D24), 29301‐29311. Zha, T., Barr, A.G., Black, A., McCaughey, J.H., Bhattis, J., Hawthorme, I., Krishnan, P., Kidston, J., Saigusa, N., Shashkov, A. & Nesic, Z. (2009). Carbon sequestration in boreal jack pine stands following harvesting. Global Change Biology, 15, 1475‐1487. 119 Appendix A List of Symbols c’ Instantaneous CO2 mixing ratio µmol CO2 mol dry air‐1 cp Specific heat capacity of air J kg‐1 °K‐1 Da Atmospheric demand (vapour pressure deficit) kPa ea Actual vapour pressure kPa es Saturated vapour pressure kPa EF Evaporative fraction dimensionless ERd Daytime respiration rate µmol m‐2 s‐1 ET Evapotranspiration kg m‐2 s‐1 ETeq Equilibrium evapotranspiration W m‐2 ETimp Imposed evapotranspiration W m‐2 FC Mean vertical mass flux density µmol m‐2 s‐1 FS Net flux density storage in the canopy air between the ground µmol m‐2 s‐1 and reference height where FC is measured fx Rate of change in CO2 concentration ppmv min‐1 ga Aerodynamic conductance (ra‐1) m s‐1 GPmax Maximum gross productivity µmol m‐2 s‐1 gs Surface conductance (rs‐1) m s‐1 hm Measurement height m k Von Karman constant: 0.4 dimensionless K↓ Global solar radiation W m‐2 K↑ Reflected solar radiation W m‐2 K* Net solar radiation W m‐2 L↓ Downward atmospheric radiation W m‐2 L↑ Upward terrestrial radiation W m‐2 LV Latent heat of vaporization J kg‐1 n Molecular mass of CO2: 0.044 kg mol‐1 120 PT‐α Priestley‐Taylor alpha coefficient dimensionless QA Available energy W m‐2 QE Latent heat flux W m‐2 QG Soil heat flux W m‐2 QH Sensible heat flux W m‐2 QS Sum of the energy storage terms W m‐2 Q10 relative change in respiration rate for a 10°C change in T dimensionless R Ideal gas constant: 0.0821 L atm K‐1 mol‐1
ra Aerodynamic resistance s m‐1 RH Relative humidity % rs Surface resistance s m‐1 R10 Base respiration µmol m‐2 s‐1 S Collar surface area m2 Ta Air temperature °C Ts Soil temperature °C u Wind speed m s‐1 u* Frictional velocity m s‐1 Vc Closed chamber volume m3 w Vertical component of wind speed m s‐1 α Apparent quantum yield for GEP or NEE µmol m‐2 s‐1 Surface albedo dimensionless β Bowen ratio dimensionless γ Psychrometric ‘constant’ dimensionless Δ Slope of the curve for saturation vapour pressure density kPa °K‐1 response to temperature ΔQS Net sensible heat storage in the canopy air between the ground W m‐2 and reference height where Q* is measured ε Surface emissivity dimensionless θ Soil moisture content m‐3 m‐3 ρa Air density kg m‐3 σ Stephan‐Boltzmann constant: 5.6703 x 10‐8
W m‐2 K‐4 Ψh Integral diabatic correction factor for sensible heat transfer dimensionless 121 Ψm Integral diabatic correction factor for momentum dimensionless
Ω Decoupling coefficient dimensionless 122 Appendix B List of Abbreviations AR autotrophic respiration BOREAS Boreal Ecosystem‐Atmosphere Study C carbon CO carbon monoxide CO2 carbon dioxide CH4 methane DOY day of year EC eddy covariance EM‐1 Eastmain‐1 EF evaporative fraction ER ecosystem respiration ET evapotranspiration GEP gross ecosystem productivity GHG greenhouse gas GSL growing season length HR heterotrophic respiration IRGA infrared gas analyzer LAI leaf area index MDDEFP le ministère du Développement durable de L’Environnement, de la Faune et des Parcs NEE net ecosystem exchange N2O nitrogen oxide O2 oxygen PM Penman‐Monteith PPFD Photosynthetic photon flux density ppmv parts per million volume SA standardized anomaly SOC soil organic carbon 123 TDR time domain reflectometry USDA United States Department of Agriculture WPL Web‐Pearman‐Leuning 124 Appendix C Supplementary Data Collection I.I Secondary sites The approach of studying multiple sites of varying age allows one to approximate forest functioning over the lifecycle of a representative stand. Chronosequencing is a valuable tool as it allows researchers to scale ecosystem studies up to the landscape or biome level, necessary for constraining and validating models. Taking this approach to heart, at the beginning of this study two supplementary sites were located in conjunction with FJP05. This enabled experiments performed at FJP05 to also be undertaken at these sites, as well as at one of the original EM‐1 Project sites. As towers were not present at the additional sites, we felt it limiting to discuss the results within the body of this thesis. However, results from these sites have been useful in constraining the EM‐1 Project process model under development. I.I.I Site description Three sites were studied in addition to FJP05. The first was the old growth stand, FBS24, one of the core sites of the EM‐1 Project. This site was located at 52.104 N, 076.196 W, where a 23‐
m tall flux tower had been in operation since summer 2006 until its removal on September 23, 2012 with the termination of the EM‐1 Project measurement phase. FBS24 was the focus of an MSc thesis completed in 2010 by Marie‐Eve Lemieux. Site characterization performed by Marie‐
Eve determined FBS24 to be an 88 ± 12 year old mainly black spruce stand with an average tree height of 6.8 ± 2.8 m (Lemieux, 2010). Two other sites of intermediate age were sought out in 2011. The first is an upland jack pine stand estimated to have burned 15 years ago, codenamed: FJP97, while the second is a 73 ± 10 year old poorly drained black spruce stand, FBS40 (numbers are assigned as year of last burn). For convenience, the sites were located two km apart at 52.188N, 076.189W along the road used to travel to FJP05. 125 I.I.II Site characterization Site age, tree height, tree density, tree diameter, LAI, soil moisture content, soil carbon content and soil C efflux were assessed at both FJP97 and FBS40. In the case of FBS24, any variable not covered by Lemieux (2010) was completed during this study. The majority of characterization methods follow those performed at FJP05 however some exceptions require mentioning. First, when determining the age of FBS40, tree coring was the preferred method. Thus, for each 10 x 10 m plot, cores were removed from eight trees chosen at random and analyzed at the lab under a magnifying glass. Second, soil sampling was not feasible at FBS40 due to peatmoss accumulation and saturated soil conditions. Instead, soil samples were taken at FBS24 which had drier soil conditions and less moss depth. Third, leaf area index at FJP97 and FBS40 was determined through an indirect method rather than the direct destructive sampling technique employed at FJP05. Marie‐Eve Lemieux also utilized an indirect method at FBS24, for further details please refer to Lemieux (2010). I.I.III LAI‐2000 instrument theory The LAI‐2000 plant canopy analyzer utilized at FJP97 and FBS40 (LI‐COR, Lincoln, Nebraska) used a fish‐eye lens with a hemispheric view to measure the fraction of diffuse incident radiation passing through a vegetative canopy, or the gap fraction (LI‐COR, Inc., 1992). The LAI‐2000 detector was composed of five concentric rings sensitive to blue light below 490 nm, each responding over a different range of zenith angles (0‐12°, 15‐28°, 31‐43°, 45‐58°, 61‐
74°) (LI‐COR, Inc., 1992; Lemieux, 2010). Constraints to the LAI‐2000 model design include the assumption that the foliage is black, that foliage elements are smaller than the area of view of each ring detector and that the foliage is azimuthally randomly oriented (LI‐COR, Inc., 1992). Although no canopy conforms perfectly to these assumptions, conifer forests in particular undergo chronic underestimation of effective LAI (L ), due to the non‐random nature of their needle orientation. Conifer needles express a high degree of clumping (e.g. Chen et al., 2006). As such, the following equation is useful for estimating LAI based on L determined through LAI‐2000 measurements: LAI
1
α L γE
, ΩE
126 where ΩE is the element clumping index, γE the needle‐to‐shoot area ratio and α the woody‐to‐
total area ratio (Chen et al., 2006). The values of α and γE can be obtained through labour‐
intensive destructive sampling efforts while ΩE is determined through measurements of a canopy’s gap size distribution and gap fraction, usually by use of a TRAC (Tracing Radiation and Architecture of Canopies) instrument (Chen et al., 2006). While LAI‐2000 and TRAC measurements are fairly straightforward to perform, determining site‐specific α and γE values requires a serious commitment of time and effort. Fortunately, these last two structural parameters vary conservatively among sites of the same species allowing a crude conversion factor to be applied (Chen et al., 1997). For a mature jack pine site the conversion factor is 1.3 (assuming (1 – α) = 0.68 and γE = 1.45) while a mature black spruce stand has a factor of 1.8 (assuming (1 – α) = 0.84 and γE = 1.4) (Chen et al., 1997). I.I.IV LAI field methods For this study, LAI determination at the two secondary sites was performed using only the LAI‐2000 instrument. TRAC measurements were not executed as it was determined that they would be unable to improve the error value on LAI, due to the extremely open nature of the canopy. Instead, ΩE values were taken from an old black spruce stand (NOBS = 0.71) and younger jack pine stand (NYJP = 0.95) from the northern BOREAS study area (Chen et al., 1996). In advance of field measurements, two 100 m transects per site were marked out, with readings to take place each 10 m. In both cases the transect pairs were required to be roughly parallel due to the shape of the site. Measurements took place under a completely overcast sky on the 1st of August, 2012. Following protocol adapted from Strachan and McCaughey (1996), the first measurement was taken in a clearing, corresponding to the above‐canopy brightness. Next, the operator walked one transect, taking a measurement beneath the canopy each 10 m. A transect was completed with a second above‐canopy reading in the same clearing, in order to account for any changes in brightness during the intervening period. Data processing was completed using the FV2000 software (LAI‐2000 File Viewer, LI‐COR, 2007). 127 I.I.V Results Error bars represent ±1 S.E. site FJP05 FJP97 Location latitude longitude 52.270N 52.188N 076.748W 076.189W FBS40 52.197N FBS24 52.104N 076.193W 076.196W elevation
(m) 247 269 267 n/a Descriptive Characteristics description soil texture site age current pre‐burn (years) (years) jp seedlings sandy 5.8 ± 0.3 31 ± 1 jp trees; bs sandy 12 ± 0.4 n/a seedlings bs trees sandy loam 73 ± 1.8 n/a bs, jp, aspen trees sandy loam 88 ± 12 n/a site FJP05 FJP97 FBS40 FBS24 height (m) 0.35 ± 0.03 1.3 ± 0.07 4.5 ± 0.4 6.8 Tree Characteristics density area diameter Live Dead LAI Basal Basal Breast Area Height (no. tree m‐2) (no. tree m‐2) (m2 m‐2) (cm2 m‐2) (cm) (cm) 6.7 ± 0.5 0.5 ± 0.09 0.55 ± 2.6 ± 0.5 0.6 ±0.1 n/a 0.34 0.9 ± 0.03 0.3 ± 0.05 0.53 ± 3.0 ± 0.4 1.8 ± 0.1 n/a 0.09 0.7 ± 0.06 0.0 1.46 ± 28.0 ± n/a 6.4 ± 0.5 0.09 4.1 0.5 n/a 1.34 n/a n/a 6.8 Range (cm) 0.3 to 1.6 0.7 to 3.4 2.0 to 13.0 8.0 to 33.0 site FJP05 FJP97 FBS40 FBS24 1 total (needle, branch, stem, inflorescence) (kgC m‐2) 0.23 ± 0.03 n/a n/a n/a Aboveground Tree Biomass Measurements measured allometric estimation needle branch stem total stem (wood crown (crown + + bark)1 (foliage + stem)1 branch)1 (kgC m‐2) (kgC m‐2) (kgC m‐2) (kgC m‐2) (kgC m‐2) (kgC m‐2) 0.14 0.03 0.05 0.18 ± 0.12 0.03 ± 0.08 0.15 ± 0.09 n/a n/a n/a 0.87 ± 0.12 0.14 ± 0.04 0.73 ± 0.11 n/a n/a n/a 6.57 ± 0.12 3.07 ± 0.04 3.50 ± 0.11 n/a n/a n/a 5.61 ± 0.12 3.85 ± 0.04 1.75 ± 0.11 equations from Lambert et al., 2005 128 site FJP05 FJP97 FBS40 FBS24 1
SOC (kgC m‐2) 3.6 ± 0.4 4.2 ± 0.7 n/a 10.7 ± 0.11 data from Ullah et al., 2009 Soil Measurements organic horizon vol. water depth content (cm) (%) 5.5 ± 0.4 11.0 ± 0.9 4.7 ± 0.6 19.5 ± 2.9 n/a n/a 5.1 ± 0.6 69.0 ± 4.5 mineral SOC (kgC m‐2) 1.8 ± 0.3 2.7 ± 0.4 n/a 64 ± 91
efflux (growing season only) (gC m‐2 d‐1) 2.11 ± 0.25 1.63 ± 0.18 1.95 ± 0.17 0.84 ± 0.191