Oikos 122: 727–738, 2013
doi: 10.1111/j.1600-0706.2012.20655.x
© 2012 The Authors. Oikos © 2012 Nordic Society Oikos
Subject Editor: Martin F. Quigley. Accepted 29 June 2012
Seasonal, not annual precipitation drives community productivity
across ecosystems
Todd M. P. Robinson, Kimberly J. La Pierre, Matthew A. Vadeboncoeur, Kerry M. Byrne,
Michell L. Thomey and Samantha E. Colby
T. M. P. Robinson ([email protected]), W. K. Kellogg Biological Station, Michigan State Univ., 3700 E. Gull Lake Dr. Hickory Corners,
MI 49060, USA. – K. J. La Pierre, Dept of Ecology and Evolutionary Biology, Yale Univ., New Haven, CT 06511, USA. – M. A. Vadeboncoeur,
Earth Systems Research Center, Univ. of New Hampshire, Durham, NH 03824, USA. – K. M. Byrne, Graduate Degree Program in Ecology,
Colorado State Univ., Fort Collins, CO 80523, USA. – M. L. Thomey, Dept of Biology, Univ. of New Mexico, Albuquerque, NM 87131,
USA. – S. E. Colby, Dept Botany and Plant Pathology, Oregon State Univ., Corvallis, OR 97331, USA.
Understanding drivers of aboveground net primary production (ANPP) has long been a goal of ecology. Decades of
investigation have shown total annual precipitation to be an important determinant of ANPP within and across ecosystems. Recently a few studies at individual sites have shown precipitation during specific seasons of the year can more
effectively predict ANPP. Here we determined whether seasonal or total precipitation better predicted ANPP across a
range of terrestrial ecosystems, from deserts to forests, using long-term data from 36 plant communities. We also determined whether ANPP responses were dependent on ecosystem type or plant functional group. We found that seasonal
precipitation generally explained ANPP better than total precipitation. Precipitation in multiple parts of the growing
season often correlated with ANPP, but rarely interacted with each other. Surprisingly, the amount of variation explained
by seasonal precipitation was not correlated with ecosystem type or plant functional group. Overall, examining seasonal
precipitation can significantly improve ANPP predictions across a broad range of ecosystems and plant types, with implications for understanding current and future ANPP variation. Further work examining precipitation timing relative to
species phenology may further improve our ability to predict ANPP, especially in response to climate change.
Understanding the variable nature of aboveground net
primary production (ANPP) is a fundamental goal in
ecology (Sala and Austin 2000). Historically, studies of
ANPP have focused on regional and global patterns, linking temporal changes in ANPP to abiotic factors, especially
annual precipitation and temperature (Rosenzweig 1968,
Leith and Whittaker 1975, Sala et al. 1988). These studies
have increased our knowledge of carbon cycling, trophic
interactions, and predictions of agricultural yields
(McNaughton et al. 1989, Baker et al. 1993, Motha and
Baier 2005). Recent predictions of altered precipitation patterns (Easterling et al. 2000, Weltzin et al. 2003,
Diffenbaugh et al. 2005) have initiated new studies on
how increased precipitation variability affects plant community dynamics (Heisler-White et al. 2009, Robinson
and Gross 2010, Yang et al. 2011). While most studies
have accounted for the effect of total annual precipitation
on ANPP patterns (Lauenroth and Sala 1992, Ma et al.
2008), several site-specific studies have recently found that
precipitation within specific segments of the year (hereafter
‘seasonal precipitation’) can explain the variation in yearly
ANPP better than total precipitation for some ecosystems
(Milchunas et al. 1994, Briggs and Knapp 1995, Jobbágy and
Sala 2000, Knapp et al. 2006, Chou et al. 2008, Muldavin
et al. 2008, Xia et al. 2010, La Pierre et al. 2011). Climate
models consistently project increased climate variability to
include altered seasonal precipitation regimes (IPCC 2007,
Schoof et al. 2010), therefore, it is essential to understand
how seasonal precipitation affects ANPP across ecosystems.
Changes in precipitation during a period when production
is most sensitive to water availability may have a disproportionate impact on total annual production and potentially
feedback to the global carbon cycle (Schimel et al. 2001).
While climate change is expected to alter precipitation
regimes at a global scale, analysis of the effects of changes
in seasonal precipitation on ANPP to date has generally
been limited to single-system studies (Suttle et al. 2007, but
see Vermeire et al. 2009) with no generalization across
ecosystem types. Despite its importance, understanding
changes in ANPP due to variability in precipitation across
ecosystems is problematic because plant communities can
differ in sensitivity to precipitation variability (i.e. rainuse efficiency, Huxman et al. 2004). For example, ANPP
in desert and grassland ecosystems is highly sensitive to
inter-annual rainfall variability but the magnitude of the
change is likely limited by plant density. In contrast, solar
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radiation, nutrient supply, or growing season length may
be more important in explaining ANPP in forested ecosystems that are characterized by high soil water content
and low precipitation variability (Webb et al. 1978, 1983,
Knapp and Smith 2001, Newman et al. 2006).
In addition to ecosystem type, differences in co-occurring
plant functional groups within an ecosystem are also likely
to interact with changes in the distribution of precipitation
to control ANPP (Fay et al. 2002). For example, grasses
generally have relatively shallow, fibrous root systems
that take advantage of soil moisture at the surface, while
shrubs and trees often have deeper or dimorphic root systems that can access both shallow and deep soil moisture
(Walter 1979, Ogle and Reynolds 2004). In addition to
plant functional groups at the same site using water from
different depths along the soil profile (Schenk and Jackson
2002, Nippert and Knapp 2007), previous studies have
also shown that they may be using precipitation from different parts of the growing season (Jobbágy and Sala 2000,
Huenneke et al. 2002, Robertson et al. 2010) as well. It could
be conjectured that specific plant functional groups may
drive the patterns observed in the seasonal precipitation–
ANPP relationship within a particular ecosystem, but play
a minimal role in another ecosystem. On the other hand,
particular functional group combinations may stabilize
overall ANPP responses to precipitation fluctuations by
varying in opposite directions. This community level stability could then mask the impacts of precipitation variability
on the relative abundance of functional groups. If such a
mechanism exists, then it would be important to understand how plant functional group productivity changes with
seasonal rainfall distributions across a broad range of
ecosystems.
Here we aim to examine:
1. whether the variability of ANPP is best explained by
precipitation amounts in specific seasonal periods as
opposed to total annual or total growing season precipitation for multiple ecosystem types across North America;
2. whether this sensitivity to precipitation at either the
seasonal or annual level can be generalized across all
ecosystems or if it varies by ecosystem type; and
3. whether the effects of seasonal precipitation vary across
plant functional groups.
Across ecosystems, we predict that seasonal precipitation
will show a stronger correlation with ANPP than total precipitation in arid and semiarid ecosystems (desert and xeric
grassland) since water is the most limiting resource in these
systems and because production is known to be sensitive and
respond rapidly to changes in water availability (Huxman
et al. 2004, Yang et al. 2011). We further predict that
ANPP will be better predicted by precipitation during the
growing season in mesic grassland ecosystems because precipitation inputs in these systems are similar to potential
evapotranspiration. This leads to more intermittent periods
of water limitation and a greater possibility of water storage from previous parts of the growing season. We predict
that ANPP in forested ecosystems will correlate best with
pre-season precipitation, which recharges deeper soil layers,
or not respond to annual or seasonal precipitation at all.
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We also predict that sensitivity to seasonal precipitation will
vary between plant functional groups. Production of
shallow-rooted species (annuals and grasses) may be more
sensitive to precipitation during the growing season while
deeper rooted species (perennial herbaceous and woody
species) will be more sensitive to pre-season precipitation
(Notaro et al. 2010).
Methods
Site selection
We chose sites from the Long-Term Ecological Research
(LTER) network based on the availability of long-term
( 10 years) annual ANPP records from unmanipulated
or minimally manipulated communities. Of the 26 LTER
sites, 10 met our criteria and are included in our analysis
(Table 1). These sites span a wide range of climatic and
vegetation types across North America, from deserts to forests (Table 1). We included four additional sites, Capulin
Volcano National Monument (CVO), Hopland Field
Station (HOP), Teakettle Experimental Forest (TEA), and
the US Sheep Experiment Station (USE), in the analysis
to represent vegetation types not covered by LTER data
meeting our criteria. Because some sites have multiple community types (e.g. grass and shrub dominated communities at the Sevilleta LTER; SEV) or multiple types of ANPP
proxy data (e.g. litterfall and tree cores from different sites
at Hubbard Brook Experimental Forest; HBR), we included
a total of 36 communities in our analysis (Table 1). The
locations of all of the included sites are shown in
Supplementary material Appendix A1 Fig. A1.
ANPP and precipitation data
ANPP data characterized whole-growing-season production at all sites, though a variety of methods were employed
(see Supplementary material Appendix A1 for specific
details on ANPP collection and data processing at each
site). In general, forested sites used dendrometers, increment
cores and/or litterfall collection to determine ANPP, while
herbaceous sites determined ANPP by clipping current-year
aboveground biomass within a given area at ground level.
However some sites, including Jornada Experimental Range
(JRN), SEV and USE, used non-destructive measurements
to determine ANPP (Huenneke et al. 2001, Muldavin
et al. 2008). A subset of herbaceous-dominated sites separated biomass by functional type prior to weighing by
growth form and/or life history; the communities with this
level of data are noted in Table 1. ANPP data were divided
by growth form (grass, forb, woody) in 14 communities and
in seven of these the data were also divided by life-history
(annual, perennial).
Precipitation data were collected daily from weather
stations at or near each site (Supplementary material
Appendix A1). We calculated total annual precipitation as
the sum of all precipitation from the end of one growing
season through the end of the next (Fig. 1). We calculated total annual precipitation based on a plant-centric
Table 1. Study sites analyzed in this study, arranged from lowest to highest mean annual precipitation (MAP). Due to different methodologies
in forested sites, we have not reported average ANPP values. More complete site descriptions are in Supplementary material Appendix A1.
Average
ANPP
22
(g m )
MAP
(mm)
63–99
245
68–104
269
Site
Sevilleta National
Wildlife Refuge (SEV)
Jornada Experimental
Range (JRN)
CV
Vegetation
type
Growing
season
(DOY)
Community types
(where multiple)
Dataset
length
Growth
form* Lifespan*
92
280
0.22 desert and
creosote shrubland,
shrubland
black grama grassland
0.32 desert and
black grama grassland,
shrubland
mesquite shrubland,
creosote shrubland,
tarbush shrubland
0.17 forest
floodplain (early and
late succ.) upland
(early and late succ.)
0.18 shrubland
n/a
13–26
353
0.20 grassland
ridgetop, midland, swale 96–217 1983–2006
yes
yes
442
0.20 shrubland
105–287 1951–1991
no
no
332–567
807
0.24 grassland
85–278 1984–2008
yes
no
Cedar Creek Ecosystem
191–417
Science Reserve (CDR)
Kellogg Biological Station 416–599
(KBS)
Hopland Field Station
230
(HOP)
Teakettle Experimental
Forest (TEA)
Harvard Forest (HFR)
815
0.20 grassland
115–288 1982–2006
yes
yes
855
0.25 grassland
908
Bonanza Creek
Experimental Forest
(BNZ)
US Sheep Experiment
Station (USE)
Shortgrass Steppe LTER
(SGS)
Capulin Volcano
National Monument
(CVO)
Konza Prairie Biological
Station (KNZ)
269
Hubbard Brook
Experimental Forest
(HBR)
Coweeta Experimental
Forest (CWT)
uplands and lowlands in
annual, 4-yr, and
20-yr burn regimes
n/a
84–278 1999–2008
yes
yes
84–281 1990–2008
no
no
115–267 1994–2008
no
no
84–217 1936–1954
no
no
85–248 1990–2008
yes
yes
0.23 grassland
early and mid
successional
n/a
288–156 1952–1966
no
no
1113
0.47 forest
n/a
105–287 1997–2008
no
no
1119
0.16 forest
115–267 1965–2007
no
no
1448
0.13 forest
115–267 1956–2005
no
no
1619
0.19 forest
hemlock, hardwood,
mixed
northern hardwood
(1 area with cores,
1 with litter)
oak-pine, cove
hardwoods, mixed
oak lowland, mixed
oak highland,
northern hardwoods
115–297 1999–2008
no
no
*Functional groups.
precipitation year rather than the calendar year, excluding
months that could not influence measured ANPP (i.e. precipitation received after biomass collection). For example, in
ecosystems with a spring–summer growing season and late
September harvest, this would lead to plant-centric total
‘annual’ precipitation being summed from October of one
year until September of the next year as opposed to January
to December (Fig. 1). We determined the start and end
of the growing season for each site based on a review of
the literature or personal communication with principal
investigators at the site (Table 1). We added ten days to
the beginning and end of the growing season as a buffer against yearly variation in timing of emergence and
senescence.
Growing season precipitation (pg)
Dormant (p0)
Early (p2) Middle (p3) Late (p4)
Pre (p1)
Plant-centric Annual Precipitation (pt)
Biomass
harvest
De
ce
m
be
r
be
r
pt
em
Se
Ju
ne
h
ar
c
M
nu
ar
y
Ja
Oc
to
be
r
Calendar Year Annual Precipitation
Figure 1. Yearly precipitation may be broken up in a variety of ways. We used a plant-centric definition for annual precipitation based
on the growth period for a site and compared that to subdivisions of the year to determine which method better explained variation in
aboveground net primary productivity. This is a generic example; growing season start and end dates were determined individually for each
study site.
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We then sub-divided the plant-centric annual precipitation data into five periods for each site based on the site’s
growing season. First, we took the growing season for each
site and split it into three equal parts (Fig. 1): an early
growing season period (P2), a mid-growing season period
(P3), and a late growing season period (P4). We defined
the period immediately before the growing season as the
‘pre-season’ (P1, Fig. 1) that we hypothesized might affect
emergence and initial plant growth due to recharging soil
moisture immediately preceding the growing season. The
length of this period was 1/3 of the growing season length,
the same length as the individual components (early, mid
and late) of the growing season. The rest of the year was
aggregated into a dormant period (P0, Fig. 1), to account
for water storage since the end of the previous growing
season. In addition to the three subdivisions of the growing season, we also used total growing season precipitation
(Pg, Fig. 1) calculated as the sum of all precipitation during the early, middle, and late part of the growing season
(P2–P4).
Model development and analysis
First we examined whether breaking annual precipitation into five distinct periods improved the fit of linear
regression models relating precipitation to ANPP across
communities in all of our sites. We used Akaike information criterion corrected for small sample sizes, AICc, to
compare a model with all five periods to a model using
only total precipitation, treating community as a random
effect. While this allowed us to generalize across all communities, we also wanted to examine specific combination
of seasonal periods with the idea that communities in different ecosystems might respond differently. We developed 17
biologically relevant models (Table 2) to determine the
extent to which ANPP in different ecosystems correlated with
annual precipitation, total growing season precipitation,
or precipitation during individual or consecutive seasonal
periods and their interactions. Twelve of the models included
either total annual precipitation, growing season precipitation, or combinations of seasonal precipitation variables
only, while five additional models included the interactions
between consecutive seasonal periods. We only included
combinations of sequential seasonal periods in these models,
as we considered these periods to be the ones most likely to
interact to influence ANPP. We did not include interactions
between precipitation during the dormant period and any
other season in the models, as we thought this interaction
would be less likely to influence ANPP. Models did not
include all combinations of seasonal precipitation variables
or interactions in order to limit the identification of spurious
correlations.
We compared all models using AICc, with the lowest
value indicating the model that had the greatest support
(Burnham and Anderson 2002). We considered models
within two AICc points of each other to have equal empirical support (Burnham and Anderson 2002) and in these
cases we report all tied models. We then examined the best
models for statistical support and biological relevance.
Models were considered to be statistically supported when
they had a p  0.05 and biologically relevant when they had
2
2
an adjusted R  0.30. We used adjusted R to evaluate the
explanatory power of our models because our models varied in the number variables. If the model(s) with the lowest
AICc value for each community was statistically supported
and biologically relevant, we report it as the best model
for that community. We conducted these analyses for all
communities on total ANPP data and in the subset of communities that had functional group data we ran the same
analyses on functional group production. We ran all statistics in R 2.10.1 (R Foundation for Statistical Computing)
using the packages lme4, qPCR, minpack.lm, and rgl.
Results
ANPP response to seasonal and annual precipitation
Breaking annual precipitation into five periods led to
models with greater support from the data across all of
the sites than simply using total annual precipitation
(ΔAICc  8), despite the penalty that AICc imposes for
Table 2. List of statistical models.
No.
Model
Description
1
2
3
4
5
5a
6
6a
7
7a
8
9
9a
10
10a
11
12
ANPP  p1
ANPP  p2
ANPP  p3
ANPP  p4
ANPP  p2  p3
ANPP  p2  p3  p2*p3
pre-season only
early growing season only
mid-growing season only
late growing season only
early and mid-growing season
early and mid-growing season, plus interaction
mid- and late growing season
mid- and late growing season, plus interaction
pre-season and early growing season
pre-season and early growing season, plus interaction
dormant season only
all three segments of the growing season
all three segments of the growing season, plus interactions
all five segments of the year
all five segments of the year, plus interactions
total yearly precipitation
total growing season precipitation
730
ANPP  p3  p4
ANPP  p3  p4  p3*p4
ANPP  p1  p2
ANPP  p1  p2  p1*p2
ANPP  p0
ANPP  p2  p3  p4
ANPP  p2  p3  p4  p2*p3  p3*p4
ANPP  p0  p1  p2  p3  p4
ANPP  p0  p1  p2  p3  p4  p1*p2  p2*p3  p3*p4
ANPP  pt
ANPP  pg
Number of communities
12
8
4
0
Annual
Seasonal
Tied
Best model type
Figure 2. The distribution of best models for the communities
by model type. Models within 2 AICc points of each other were
considered ‘tied’.
extra variables. When we analyzed each community individually using our subset of 17 hypothesized models, we
found that 21 of the 36 communities were statistically
supported by models that explained annual variance in
ANPP (see Table A1 for model results). In agreement
with our general analysis, models that included some
combination of seasonal variables generally outperformed
models that only included annual precipitation (Fig. 2).
Total annual precipitation alone (model 11) was the single best model in only three communities (JRN black
grama grassland; SEV creosote shrubland; USE) although
it was tied for best model in six additional communities
(KNZ annually burned upland; KNZ annually burned
lowland; KNZ upland burned every four years; SGS ridge,
Figure 3. Breakdown of the significant (p  0.1) results across the different periods of precipitation tested in the models for (a) total
ecosystem ANPP, and where data were available, (b) annual, (c) perennial, (d) grass, (e) forb, (f ) woody plant functional groups individually,
at sites where such data were available (Table 1). The value on the y-axis is the number of models where a particular period was significant
in the best model. Positive values indicate that the relationship between precipitation and ANPP was positive; negative values indicate
that the relationship is negative.
731
Figure 4. Comparison of the performance of the best seasonal
model with a model that uses only total annual precipitation
to predict ANPP. The dashed line indicates a 1:1 relationship
(null hypothesis), above which ANPP is better predicted by seasonal precipitation and below which ANPP is better predicted by
total annual precipitation.
midland, and swale; Fig. 2). The annual and seasonal ties
along with ties between multiple seasonal models led to
a total of 30 statistically supported models across the 21
communities.
Across all models, the impact of precipitation on ANPP
was overwhelmingly positive (52 of 55 significant effects;
Fig. 3). The best seasonal models often included more
than one period (70% of seasonal models) and across the
30 best statistically supported models, the most common term included was mid-growing season precipitation
(P3; significant in 47% of the best models), followed by
early and late growing season precipitation (P2 and P4;
significant in 38% and 32% of the best models, respectively, Fig. 3). Interactions were included in only 3 of
the best models, and where significant they were always
negative (Supplementary material Appendix A1 Table A1).
The seasonal periods that significantly affected ANPP were
not consistent across ecosystem type (Fig. 3), though it is
worth noting that total annual precipitation and total growing season precipitation (models 11 and 12) were never the
‘best’ model in any forested system.
To assess the utility of seasonal precipitation data in
predicting annual ANPP across all 36 communities, we
2
compared the adjusted R of the best seasonal model from
2
each community to the R of model 11, which included
only total annual precipitation (Fig. 4). In all but four
communities examined (JRN black grama grassland, SEV
creosote shrubland, SGS midland, USE) the seasonal
model explained more variance than annual precipitation.
2
In 19 of the communities, the improvement in adjusted R
in the seasonal model over model 11 was greater than 0.10,
and in 10 of the communities the improvement was greater
than 0.20 (Fig. 4). Communities showing this greatest
level of improvement when seasonal data were used to
predict ANPP were found at BNZ (all), CVO, HOP, KBS
(annually burned), KNZ (uplands and lowlands burned
every 20 years), and SEV (black grama grassland). Across
2
all sites, improvement in adjusted R did not correlate
significantly with either the mean or the coefficient of
variation (CV) of total annual precipitation (Fig. 5).
Functional group responses to seasonal and annual
precipitation
In the seven communities where annual and perennial
biomass were examined separately, production by annuals tended to be better explained by seasonal precipitation
(four of five communities with significant models; Fig. 3,
6), while production by perennials responded more to
total annual precipitation (three of four communities
with significant models; Fig. 3, 6). In about half of the 14
communities with growth form data, precipitation and
ANPP variation were correlated for grasses and forbs, but
not for woody species. Grass production was correlated
roughly equally with seasonal (three communities) and
total annual precipitation (four communities; Fig. 3, 6)
while forb production tended to be correlated with seasonal
precipitation (four out of six communities where precipitation was significant). Precipitation models were only
Figure 5. Improvement across all communities of the best seasonal model over the model including only total annual precipitation in
predicting ANPP versus (a) mean annual precipitation and (b) the CV of annual precipitation. ANPP in communities with positive
improvement values are better predicted by seasonal precipitation while ANPP in communities with negative improvement values are
better predicted by total annual precipitation.
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Figure 6. Comparison of the performance of the best seasonal model with a model that uses only total annual (plant-centric) precipitation
to predict total-above ground biomass of (a) grasses, (b) forbs, (c) woody, (d) annual, and (e) perennial plants at sites where such data
were available (Table 1). The dashed line indicates a 1:1 relationship, above which biomass is better predicted by seasonal precipitation and
below which biomass is better predicted by total annual precipitation.
related to woody shrub production in two communities, with one seasonal and one total annual precipitation
model (Fig. 3, 6). Interestingly, the same model (model 11)
best explained all three functional groups in only one community (USE).
precipitation. Our results go beyond previous site-specific
work (Suttle et al. 2007) to demonstrate the generality
and strength of the effects of variation in precipitation at
sub-annual time scales on annual ANPP in a wide array of
ecosystems.
Discussion
ANPP is more sensitive to seasonal precipitation
than annual precipitation
Across the majority of our ecosystems, which ranged from
deserts to forests, we found that seasonal precipitation
described variation in ANPP as well or better than total
The majority of the best seasonal models showed that ANPP
correlated significantly with only one or two periods of the
733
growing season. Interestingly, though a number of the best
seasonal models included multiple periods, including
interactions between consecutive periods rarely improved
their performance (Supplementary material Appendix A1
Table A1). We expected that in models including sequential time periods, if precipitation in the earlier period was
low, the amount of precipitation in the later period would
become more important to annual production. Conversely,
if the earlier period had high precipitation, precipitation in
the following period of the growing season would be less
important. However, models that included consecutive
periods independently, such as model 5 which included
early and mid-season precipitation, had more statistical
support than models that also included the interaction
between those two periods (e.g. model 5a). Overall, our
results showed few significant interactions between consecutive periods of the growing season.
While different seasonal periods rarely interacted,
including multiple periods did improve model fit in most
cases. Including multiple periods of the growing season
without interactions could add explanatory power if plant
growth is affected by precipitation in particular subsets of
the growing season, as observed by Cable (1975) in desert
grasslands and by Xiao et al. (1996) in semi-arid grasslands as well as in more mesic grasslands such as KNZ
(La Pierre et al. 2011, Craine et al. 2012). This may be
especially true if the growth is highly dependent on precipitation at one time, but still responds to precipitation
in other periods at a lesser rate. If production is more
sensitive to precipitation during one time period than
another, a model that sums these two would underestimate
the effect of precipitation in the important period and overestimate the effect in the less important period. For example, Milchunas et al. (1994) found that while production
responded to both warm- and cool-season precipitation,
production could respond more strongly to cool-season
precipitation at SGS. Moreover, similar to results from our
study, a study at JRN by Huenneke et al. (2002) found that
grass production was greatest late in the monsoon season
while creosote and mesquite shrub production peaked in
the spring following winter precipitation in a Chihuahuan
Desert grassland that is characterized by a bi-modal distribution of precipitation across a year.
The response of ANPP to seasonal precipitation
does not vary by ecosystem type
While we found that models using seasonal precipitation
variables improved our ability to account for variation in
annual ANPP, ecosystem types did not consistently differ in
which periods of the year mattered (Supplementary material
Appendix A1 Table A1). Ecosystems ranging from semiarid
grasslands to forests were all most sensitive to precipitation
during one or more of the three periods of the growing
season, but the improvement from using seasonal precipitation was similar across all ecosystem types and there was
no specific period that was more closely associated with
one ecosystem type over another. Our expectation was that
forests would respond differently than other ecosystem
types, with higher average precipitation preventing water
limitation that could affect ANPP. It is important to note
734
that our study included forested sites with both very high
and low mean annual precipitation, and that the forested
sites with higher mean annual precipitation (e.g. HBR)
showed a smaller improvement than the forest site with the
least precipitation (BNZ; Fig. 5a) when we used seasonal
precipitation variables to predict ANPP. However, additional replication would be required to attribute this pattern
among forests to differences in mean annual precipitation.
While previous work suggested that grassland production should change the most with variation in precipitation
across years (Webb et al. 1978, 1983, Knapp and Smith
2001), our finding that seasonal models generally outperformed models using variation in annual precipitation
shows that a wide array of ecosystems are sensitive to the
distribution of precipitation within the growing season
(Fig. 2, 4). The first two thirds of the growing season were
most commonly important (Fig. 3), which may reflect ties
to plant phenology and is consistent with previously published results from LTER grassland sites (La Pierre et al.
2011, Craine et al. 2012). Early precipitation may affect
germination dynamics for annuals or the growth of plants
like cool-season grasses, while precipitation a little later
in the growing season may affect warm season grasses and
forbs more (Xia et al. 2010). Regardless of the exact mechanism, our results indicate that future changes in the distribution of precipitation during the growing season will
likely have important effects on ANPP across a wide range
of ecosystems.
Response to seasonal precipitation does not vary
by functional groups
We expected that plant functional groups would respond
differentially to precipitation based on differences in
physiology and life history, but our simple functional group
divisions did not consistently increase the explanatory
power of our models in the subset of sites analyzed. However, some interesting trends did emerge in communities
where precipitation did correlate with functional group
ANPP. For example annual species’ biomass generally varied
with seasonal precipitation while the biomass of perennials
varied more with total annual precipitation. The ability of
perennials to make use of water inputs across time makes
sense given their deeper, more extensive, and more persistent rooting systems. In addition, they may mobilize stored
resources to initiate rapid growth as soon as conditions
are favorable, unlike an annual that must germinate and
has a minimal initial biomass.
Our lack of consistent improvement in explaining
ANPP using several broad functional groups is not unprecedented. While some researchers have found that using functional groups increased the amount of variation explained
by precipitation (Jobbágy and Sala 2000), other work has
found little support for this pattern (Briggs and Knapp
1995). Given the divergent results of previous studies and
the lack of a common response in our analyses, it may
be that very basic functional groupings such as annual/
perennial or grass/forb/shrub are too broad to be useful
across multiple ecosystems. It also may be that our conservative criteria for model selection (AICc) and models that
2
we considered biologically relevant (adjusted R  0.30)
also limited our results. Other studies focused on specific
ecosystems could use less conservative metrics when analyzing one system, as compared to our multi-site analysis, or
use more appropriate functional groups. For example, an
analysis using cool (C3) and warm (C4) season grasses, which
have been shown to use different soil moisture pools at KNZ
(Nippert and Knapp 2007), may have more success explaining the sensitivity of grassland ANPP to multiple parts
of the growing season. It is also possible that functionalgroup data will need to be combined with information
on community characteristics such as dominant species
when functional groups do not respond consistently.
System-specific knowledge about species interactions like
nurse plants and other forms of facilitation may become
more important as these interactions may allow for production by species that might not otherwise survive (i.e. Armas
et al. 2011), thereby mediating responses to precipitation
variability.
Conclusions
Here we used a simple method to partition precipitation
throughout the year into five periods across a range of
climate and ecosystem types. With this coarse metric and
a broad definition of growing season length, our analyses
show that including seasonal variation in future work examining the effects of precipitation variability may improve
ANPP predictions. The summing of precipitation over
both ecologically important and unimportant parts of the
growing season in analyses using annual precipitation may
partially explain why previous studies found a weaker relationship between inter-annual variation in precipitation
and ANPP (Smoliak 1986, Briggs and Knapp 1995) than
across spatial gradients in precipitation (Sala et al. 1988,
Lauenroth and Sala 1992). Analyses that take advantage of
specific system knowledge may allow investigators to determine more subtle patterns within a site, for example by
tying the definition of the growing season more tightly to
environmental factors. Examples could include temperature
and moisture in arid areas, growing degree-days in colder
ecosystems, or the timing of specific phenological stages
(Richardson et al. 2010, La Pierre et al. 2011), which may
respond differently to precipitation (Robinson and Gross
2010). Lagged effects of the previous year’s weather conditions may also be important in some systems (Lane et al.
1993), though we did not have the statistical power to
test such effects. Such site-specific analyses could also test
specific combinations of periods not considered here that
might be relevant for a particular system, for example
using only two periods in a system with bi-modal precipitation patterns. Overall, using site-specific biological know­
ledge is likely to provide more distinctive and meaningful
seasonal periods, but even a coarse definition that allows
comparisons across a range of very different ecosystems
can improve our understanding of variation in ANPP.
While we have shown that ANPP responds to precipitation on a seasonal scale more commonly than previously
thought, our results raise several questions. What separates
ecosystems that show a weak correlation between ANPP
and seasonal precipitation from those that show a strong
correlation with seasonal precipitation? Additionally, what
causes certain ecosystems to be sensitive to one part of the
growing season compared to another? Potential explanations include interactions with both abiotic (i.e. nutrients
and temperature) and biotic (i.e. herbivory and competition)
factors that may limit production.
Our results were often congruent with previous sitespecific studies, yet there were also some important discrepancies that show the importance of site-specific research.
For example, at our annual grassland site (HOP), we
found no relationship between ANPP and precipitation,
yet previous research at the site found strong precipitation
effects (Murphy 1970). However, the relationship revolved
around precipitation in only one month (November) so it
is likely that the length of our growing season periods were
too long to recreate these results. For that site our generalized approach fell short. In other instances, our results
generally matched previous results, for example like
Milchunas et al. (1994) at SGS we found that precipitation mattered, but did not always find the strong support
they did for separating cool and warm season precipitation
(Supplementary material Appendix A1 Table A1). However, in the most supported seasonal models the earlier
cool season regression coefficients were generally larger
than the later warm season coefficients, consistent with
Milchunas et al.’s (1994) finding that cool season precipitation had a larger impact on ANPP than warm season
precipitation. At JRN, our results and previous research
both found a relationship between ANPP and precipitation
(Huenneke et al. 2002) across multiple communities.
However, while we found evidence that shrub communities responded to seasonal precipitation (Fig. 4, 5,
Supplementary material Appendix A1 Table A1), we did
not find the strong early season signal that Huenneke
et al. (2002) did. One reason for this discrepancy may
be that we used annual ANPP measurements while
Huenneke et al. (2002) measured production at three
points during the year to quantify seasonal production.
Overall, while site-specific studies often captured a more
nuanced picture of the patterns at a given site, our simplified analysis was able to capture a reasonably accurate
picture across a broad range of ecosystems.
Although precipitation provides a convenient and
widely available metric, the effect of a precipitation event
on a plant community depends on a number of abiotic
and biotic factors that affect how precipitation translates
into plant-available soil moisture (Weltzin et al. 2003,
Schwinning and Sala 2004). For example, differences
in soil texture and structure (Sala et al. 1988, Loik et al.
2004), plant litter (Deutsch et al. 2010), or antecedent
soil moisture conditions (Rawls et al. 1993) will influence
the changes in soil moisture that result from a precipitation event. While beyond the scope of this study, research
also indicates that landscape position (run-on/off) and soil
properties may be a more important driver of ANPP in
ecosystems like deserts than precipitation alone (Huenneke
et al. 2002, Muldavin et al. 2008, Sponseller et al. 2012).
While not as commonly available as long-term time series,
soil moisture data can relate more directly to plant growth
and help explain patterns in ANPP (Nippert et al. 2006,
Muldavin et al. 2008). Differences in ANPP measurement
methodology among sites and ecosystem types may obscure
735
some patterns, particularly in the forest and woodland sites
where data sets describing woody biomass and foliar
production were not both available (CVO, CWT, TEA)
or could not be combined (HBR, HFR). While our
study focused on aboveground biomass responses, future
work should also attempt to account for belowground
responses to precipitation such as root production, and
soil respiration (Hui and Jackson 2006, Fay et al. 2008,
Thomey et al. 2011) as more long-term data becomes
available. Predicted shifts in storm intensity and frequency (Easterling et al. 2000, Diffenbaugh et al. 2005,
IPCC 2007) which may independently affect communities (Knapp et al. 2008, Robinson and Gross 2010) will
make it more important than ever to factor in how abiotic
and biotic factors affect the transition from precipitation
to plant-available soil moisture.
The results of our study have implications for predictions of future ANPP under a changing climate. One of the
primary shifts in climate predicted by the Intergovernmental Panel on Climate Change is that higher temperatures
will lead to an overall increase in growing season length
across North America (IPCC 2007). Our results show that
many ecosystem types are sensitive to the timing of precipitation with respect to their growing seasons. If changes in
growing season length are not accompanied by a shift in
the timing of precipitation, the result may be large changes
in production. For example, Ma et al. (2008) showed how
temperature and precipitation interacted in a Mongolian
grassland by observing higher mean annual biomass when
both temperature and pre-season precipitation were higher,
but lower mean annual biomass when temperature alone
was higher. Likewise, Xia and Wan (2012) found that while
increased nighttime temperature resulted in longer growing season length, it was actually changes in accumulated
precipitation that explained a large portion (48%) of the
variation in changes in species percent coverage. The overall
warming predicted for North America may make the seasonal interactions seen in our data even more important in
determining production in the future.
Based on our analyses, one ecosystem that might be
affected would be the eastern deciduous forest if summers
in North America become longer and drier (IPCC 2007).
The two temperate forest sites with significant models
(Coweeta and Harvard Forest) showed the greatest sensitivity to precipitation received in the middle of their growing season, when evapotranspirative demand is generally
greatest and precipitation is predicted to decrease. Even
though mean annual precipitation is predicted to increase
for most of North America, greater variability and uncertainty about the timing of these future events makes the
net effect of increased precipitation on production uncertain. In addition, it is unclear how seasonal precipitation
may interact with concurrent anthropogenic changes to the
environment such as nitrogen deposition, increased ambient
CO2 and O3, non-native species invasions, and disturbance
regimes (Barger et al. 2011, Vargas et al. 2012). Further
research to determine how changes in seasonal precipitation
interact with chronic resource alteration and more variable
weather would be worthwhile.
Quantifying and predicting ANPP is crucial for our
understanding of the carbon cycle and the management of
736
ecosystems. Many studies relating ANPP and precipitation
examine how precipitation affects ANPP on an annual time­
scale, but there is a growing body of site-specific studies
that find a stronger relationship with seasonal precipitation
(Lauenroth and Sala 1992, Milchunas et al. 1994, Briggs
and Knapp 1995, Jobbágy and Sala 2000, Knapp et al.
2006, Chou et al. 2008, Muldavin et al. 2008). However,
we lack a cross-ecosystem understanding of the relationship between seasonal precipitation and ANPP. Our work
examining long-term data sets across a variety of ecosystems in North America indicates that seasonal precipitation patterns often play an important role in determining
ANPP. Indeed, most of the ecosystems in our study were
more responsive to precipitation during specific portions
of the growing season than to annual or growing season
totals. While the 2007 IPCC reports project increased
NPP across North America due to predictions of longer
growing seasons, our findings indicate that understanding
this relationship may require further work. If changes in
temperature are not accompanied by precipitation at the
appropriate time, ANPP may not follow the anticipated
trend. Incorporating a more thorough understanding of how
seasonal precipitation patterns affect ANPP is necessary to
advance our understanding of ANPP dynamics and improve
predictions of how climate change will affect ANPP.
Acknowledgements – This work was supported by a LTER network
synthesis grant. The authors thank Scott Collins and William
Lauenroth for comments on previous drafts of this manuscript
and Steven Rostkowski for his input in project design and assistance with data processing. The authors would also acknowledge
the following NSF LTER sites for providing data: BNZ, CDR,
CWT, HBR, HFR, JRN (in particular J. Yao), KBS, KNZ
SEV, SGS. CVO data are archived by NOAA paleoclimatology
program, and the TEA data were provided by Malcolm North
at the US Forest Service PSW Research Station. Converted
data from Murphy, 1970 for HOP came from Joanna Hsu at UC
Berkeley, and Dr. Peter Alder (Utah State Univ.) provided us
with converted data from Blaisdell 1958 for USE. This is KBS
contribution number 1699.
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Appendix A1
Site-specific details on data collection
Bonanza Creek Experimental Forest (BNZ)
The Bonanza Creek Experimental Forest is a boreal forest study site in the Alaskan interior. The
area is dominated by Picea glauca and Populus balsamifera. Two study communities each were
located on floodplain (150 m a.s.l.) and upland (300 m a.s.l) locations. Daily precipitation data
were collected from a meteorological station located at the Fairbanks Airport (20 km from the
study site). We defined the growing season as 25 May – 24 September
(<www.lter.uaf.edu/bnz_climate.cfm>). Each of the four communities was sampled in three
plots. Annual readings from ten dendrometer bands per species per 10-cm diameter class and plot
inventory data were used to determine annual biomass increment from 1994–2008. ANPP data
were from the following data set:
Ruess, R. 2009. Alaska paper birch NPP per tree calculated from dendrometer bands annually
from 1994 to 2008, Bonanza Creek LTER – University of Alaska Fairbanks. BNZ: 420,
<www.lter.uaf.edu/data_detail.cfm?datafile_pkey=420>
Cedar Creek Ecosystem Science Reserve (CDR)
The Cedar Creek Ecosystem Science Reserve is a 2200 ha area located in central Minnesota. We
used data collected from old-field communities dominated by Elymus repens, Poa pratensis and
Schizachyrium scoparium. Daily precipitation data were collected from a meteorological station
located at CDR. We defined the growing season as 25 April – 15 October each year (Dan
Bahauddin, pers. comm.).
ANPP was harvested from 1982–2006 from control plots of the long-term nitrogen
deposition experiment at CDR. The 46 16-m2 control plots were located in four old fields (A, B,
C, and D; n = 12 per field, except field D where n = 10). ANPP was harvested by clipping all
biomass 1–2 cm above ground level within a 0.3-m2 quadrat of each plot at peak biomass. The
quadrat sampled was relocated yearly to prevent a sampling effect. Collected biomass was
separated by species, dried at 60ºC for 48 h, and weighed. Precipitation and ANPP data were
from the following data sets:
Tilman, D. 2008. Experiment 001 - Aboveground biomass data. St. Paul, MN: Cedar Creek
Ecosystem Science Reserve Data Base: E001. [Database].
<www.cedarcreek.umn.edu/research/data/data.php?input=ple001>
Seeley, M. 2010. Experiment 080 – Meterologic measurements at Cedar Creek Natural History
Area. St. Paul, MN: Cedar Creek Ecosystem Science Reserve Data Base: E080.
[Database]. <www.cedarcreek.umn.edu/research/data/data.php?input=clime080>
Capulin Volcano National Monument (CVO)
Capulin Volcano National Monument was selected randomly from piñion Pinus edulis woodland
sites with modern tree-ring data hosted by the NOAA paleoclimatology database
<www.ncdc.noaa.gov/paleo/treering.html>. The site is located in northeastern New Mexico.
Daily precipitation data are from Des Moines, NM (12 km east of the study site). We defined the
growing season as 15 April – 15 October (Baker 1944).
Tree ring measurements of 23 individual P. edulus collected from 1951–1991 were used
to determine mean annual production per tree by 1) linear detrending, 2) normalization of
residuals to each tree’s mean increment width, and 3) calculation of an unweighted mean
increment width for each year across all cores with complete data for the study period. The
resulting dimensionless values serve as an index of production, but cannot be scaled to ANPP in
g m-2 y-1.
Baker F.S. 1944. Mountain climates of the western United States. – Ecol. Monogr. 14: 223–254.
ANPP data were from the following data set:
Woodhouse, C. A. and Brown, P. M. 1998. Capulin Volcano - PIED - ITRDB NM576.
[Database]
<http://hurricane.ncdc.noaa.gov/pls/paleox/f?p=519:1:2874077818569153::::P1_STUDY
_ID:5287>
Coweeta Hydrologic Laboratory (CWT)
Coweeta Hydrologic Laboratory has a mixed deciduous forest in the mountains of southwestern
North Carolina. Five distinct community types are sampled along an elevation gradient: oak pine,
cove hardwoods, mixed oak lowland, mixed oak highland, northern hardwoods. The oak pine
community is characterized by Pinus rigida, Quercus spp. and Carya spp. The cove hardwoods
are dominated by Liriodendron tulipifera, Quercus prinus and Carya spp. The mixed oak
lowlands and uplands are characterized by Quercus spp., Carya spp. and Rhododendron
maximum. The northern hardwoods are dominated by Betula spp., Tilia heterophylla and Quercus
rubra. Daily precipitation data were collected from raingauge 6 at CWT. We defined the growing
season as 15 April – 24 October (Hwang et al. 2011).
Deciduous species’ litterfall collected annually in ten 0.85 m2 baskets was used as an
index of foliar production at each of the five 20 × 40 m plots. Although species composition
varies across the gradient, values should be comparable within each community type.
Hwang, T. C. et al. 2011. Topography-mediated controls on local vegetation phenology estimated
from MODIS vegetation index. – Landscape Ecol. 26: 541–556.
ANPP data were from the following data set:
Knoepp, J., Clark, J., Vose, J., Haines, B. and Crossley, D. A. Terrestrial gradient leaf litter
weights. Athens, GA: Coweeta Hydrologic Laboratory Database: 1121 [Database].
<http://coweeta.uga.edu/summaries/summary1121_b.html>
Harvard Forest (HFR)
Harvard Forest is a 1214 ha hardwood-hemlock-pine forest located in central Massachusetts. The
area is dominated by both deciduous and evergreen species, including Tsuga canadensis, Betula
lenta, Acer rubrum, Quercus rubra and Pinus strobus. Daily precipitation data were collected
from meteorological stations on-site; missing data (from 1994 and 1995) were filled using NCDC
data from Tully Lake in Athol (13 km NNW of the study site). We defined the growing season as
25 April – 24September (Richardson et al. 2006). ANPP data were collected as foliar litterfall
from deciduous species from 1998–2007 from 34 plots located in the footprint of an HFR flux
tower ('EMS litter'). Additionally, increment cores were taken in six hemlock-dominated plots
and two hardwood plots in 2003, totaling 217 trees sampled across 12 species. Hemlock and
hardwood plots were included as separate communities. Raw data were processed as described
above for CVO.
Richardson, A. D. et al. 2006. Phenology of a northern hardwood forest canopy. – Global Change
Biol. 12: 1174–1188.
Precipitation and ANPP data were from the following data sets:
Boose, E., Gould, E. and Hall, B. 2002. Shaler Meteorological Station (1964–2002). Petersham,
MA: Harvard Forest Data Archive: HF000. [Database]. <
http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf000>
Boose, E. and VanScoy, M. 2010. Fisher Meteorological Station (since 2001). Petersham, MA:
Harvard Forest Data Archive: HF001. [Database].
<http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf001>
Bettmann-Kerson, P. and Ellison, A. 2006. Hemlock Removal Experiment - Dendrochronological
Record. Petersham, MA: Harvard Forest Data Archive: HF086. [Database].
<http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf086>
Munger, W. and Wofsy, S. 2010. EMS Biomass Inventories - Litterfall. Petersham, MA: Harvard
Forest Data Archive: HF069-07. [Database].
<http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf069>
Hopland Field Station (HOP)
Hopland Field Station is a 1902 ha area located in Northern California foothill rangeland. The site
is dominated by annual grasses, including Bromus, Festuca, Avena, and Hordeum species. Daily
precipitation data were collected from a NOAA weather station in Hopland, CA (10 km from
study site). We defined the growing season as 15 October – 5 June (Murphy 1970).
ANPP was collected each year from six 4.05 m2 (0.001 acre) grazing exclosures from
1952–1966. Collected biomass was dried at 60ºC for 48 h and weighed. Data from 1958 and 1967
were not included in the analysis due to missing precipitation data.
Murphy, A. H. 1970. Predicted forage yield based on fall precipitation in California annual
grasslands. – J. Range Manage. 23: 363–365.
Hubbard Brook Experimental Forest (HBR)
Hubbard Brook Experimental Forest is a 3160 ha area in the White Mountain National Forest in
New Hampshire. At the elevations sampled, the forest is dominated by deciduous species,
including Fagus grandifolia, Acer saccharum, Betula alleghaniensis and Fraxinus americana.
Daily precipitation records for three collectors in watershed 6 (litterfall) or watershed 1
(increment cores) were used. We defined the growing season as 25 April – 24 September
(Richardson et al. 2006).
Litter fall was collected each November from 1993–2005 on two 100-m long transects at
525 and 585 m elevation west of the reference watershed. Higher-elevation transects were
excluded from analysis because they were affected by an ice storm in 1998. Each transect had 12
baskets 0.097 m2 in area. Additionally, 145 increment cores taken from four species in a different
location in 1985 (Kim 1988) were processed using the same approach as for CVO (above) and
treated as a separate 'community'.
Kim, E. S. 1988. Radial growth patterns of tree species in relation to environmental factors. –
PhD thesis, Yale Univ. New Haven, CT.
Precipitation and ANPP data were from the following data sets:
Campbell, J. 2008. Daily precipitation by watershed data. Woodstock, NH: Hubbard Brook
Ecosystem Study Database: 14 [Database].
<www.hubbardbrook.org/data/dataset.php?id=14>
Kim, E. S. and Bormann, F. H. 1988. Tree core increment data. Woodstock, NH: Hubbard Brook
Ecosystem Study Database: 55 [Database].
<www.hubbardbrook.org/data/dataset.php?id=55>
Fahey, T. J., Hughes, J. W., Wood, C. and Wapner, S. 2006. Fine litterfall data. Woodstock, NH:
Hubbard Brook Ecosystem Study Database: 49 [Database].
<www.hubbardbrook.org/data/dataset.php?id=49>
Jornada Experimental Range (JRN)
The Jornada Experimental Range is a 100 000 ha area located in southern New Mexico. Four
community types within JRN were used in our analysis: black grama grassland, mesquite
shrubland, creosotebush shrubland and tarbush shrubland. The black grama grassland is
dominated by B. eriopoda. Mesquite shrubland is the most widespread community type at JRN
and is dominated by Prosopis glandulosa. The creosotebush shrubland and tarbush shrubland are
dominated by L. tridentata and Flourensia cernua, respectively. Daily precipitation data were
collected from a meteorological station at the Jornada headquarters. We defined the growing
season as 25 April – 5 October (Canfield 1934).
At each site, permanent 1-m2 quadrats are arranged in a square 7 × 7 pattern, with 10 m
between each quadrat. All quadrats are measured at each site for standing biomass three times per
year. Sampling occurs in February–March, May and late summer – October. At each sampling of
standing biomass, all quadrats within each site are measured. Measurements include species
present, and cover, height, count and phenological state of each species. Volume is then
calculated for all species in all quadrats. Specified volumes of plants are harvested from nearby,
and are sorted, dried, and weighed. For some species, plant material is separated into leaves,
stems, and flowers, and each part is weighed separately. Regression of biomass by volume can
then be constructed, and combined with volume data to calculate biomass in each quadrat.
Canfield, R. G. 1934. Stem structure of grasses on the Jornada Experimental Range. – Bot. Gaz.
95: 636–648.
Precipitation data were from the following data set:
Anderson, J. P. 2010. Climatalogical data (Jornada LTER Weather Station): Daily summary
weather data. Las Cruces, NM: Jornada Data Catalog [Database]. <http://jornadawww.nmsu.edu/datacat.php#DsdClimate>
Kellogg Biological Station (KBS)
Kellogg Biological Station is an agricultural research station located in central Michigan. We
used the annually burned successional field data for this analysis. The annually burned
successional field is dominated by herbaceous annuals, including Solidago canadensis, Poa
pratensis and Rhus typhore. Daily precipitation data for KBS was collected from a
meteorological station at the site headquarters. We defined the growing season as 25 – 5
September (Robinson unpubl.).
ANPP was collected from five 1-m2 quadrats located within each community from 1990–
2008. ANPP was harvested by clipping all biomass at ground level within each 1-m2 quadrat.
Collected biomass was sorted by species, dried at 60ºC for 48 h, and weighed. Data from 2007
were not included in the analysis, due to missing values. Precipitation and ANPP data were from
the following data sets:
Bergsma, T. 2010. LTER daily weather (1988 to present). Hickory Corners, MI: Kellogg
Biological Station Long Term Ecological Research Data Base: KBS002. [Database].
<http://lter.msu.edu/datatables/12>
Gross, K, Baker, C. and Robertson, P. 2009. Non-crop biomass (1990 to present). Hickory
Corners, MI: Kellogg Biological Station Long Term Ecological Research Data Base:
KBS019. [Database]. <http://lter.msu.edu/datatables/40>
Konza Prairie Biological Station (KNZ)
Konza Prairie Biological Station is a 3487 ha area of native tallgrass prairie in the Flint Hills
region of northeastern Kansas. The area is dominated by warm-season C4 grasses, particularly
Andropogon gerardii, Sorghastrum nutans and Schizachyrium scoparium. We used data from
three watersheds (ca 60 ha each) burned in the spring at either one-, four-, or twenty-year
intervals (since 1973 for the four- and twenty-year burns and 1978 for the annual burn). Within
each watershed, we focused on two topographic positions: 1) upland, characterized by shallow
Florence soils and 2) lowland, characterized by deep Tully soils, for a total of six community
types. Daily precipitation data were collected at the KNZ headquarters. The growing season was
determined to span, on average, from 15 March – 1 October each year (Weaver 1954; La Pierre
unpubl.).
ANPP was collected from twenty 0.1-m2 quadrats located within each community from
1984–2008. Quadrats were placed every 10 m in a different location each year (to avoid
reharvesting the same quadrat) along four permanent transects (50 m in length), with five
quadrats per transect. ANPP was harvested by clipping all biomass at ground level within each
0.1-m2 quadrat. Collected biomass was dried at 60ºC for 48 h, sorted by growth form (grass, forb,
woody), and weighed. Plots with woody biomass greater than the 95th percentile were dropped
from the analysis (1 quadrat). ANPP was calculated as the sum of current year’s grass, forb, and
woody biomass per quadrat.
Weaver, J. E. 1954. North American prairie. – Johnsen Publ. Co.
Precipitation and ANPP data were from the following data sets:
Knapp, A. K. 2008. Aboveground primary production on fire frequency treatments. Manhattan,
KS: Konza Prairie Data Base: PAB01. [Database].
<www.konza.ksu.edu/datasets/knzdsdetail.aspx?currMenu=0&datasetcode=PAB01>
Briggs, J. M. 2008. Meteorological data. Manhattan, KS: Konza Prairie Data Base: AWE01.
[Database].
<www.konza.ksu.edu/datasets/knzdsdetail.aspx?currMenu=0&datasetcode=AWE01>
Sevilleta National Wildlife Refuge (SEV)
The Sevilleta National Wildlife Refuge is a 100 000 ha site located in central New Mexico. Two
community types within the SEV were used in our analysis: Chihuahuan Desert scrub dominated
by Larrea tridentata and Chihuahuan Desert grassland, dominated by Bouteloua eriopoda. Daily
precipitation data were collected from meteorological station no. 40 located within 8 km of the
creosote shrubland and black grama grassland core communities. We defined the growing season
as 25 March – 5 October (Doug Moore, pers. comm.).
For each community, ANPP data were collected from 80 1-m2 permanent quadrats from
1999–2003 and 40 starting in 2004. ANPP measurements were recorded in the spring and fall of
each year 1999–2009 when plant species had reached peak biomass. Regressions of weight-tovolume were developed by harvesting various sized individuals of each species from adjacent
areas. A positive change in green biomass (current seasons growth) from spring to fall in each
subplot was used as a measure of mean ANPP for all species (detailed methods in Huenneke et al.
2001, Muldavin et al. 2008).
Huenneke, L. F. et al. 2001. Spatial heterogeneity in Chihuahuan Desert vegetation: implications
for sampling methods in semi-arid ecosystems. – J. Arid Environ. 47: 257–270.
Muldavin, E. H. et al. 2008. Aboveground net primary production dynamics in a northern
Chihuahuan Desert ecosystem. – Oecologia 155: 123–132.
Precipitation and ANPP data were from the following data sets:
Moore, D. I. 1987-ongoing. Meteorology: Long-Term Ecological Research. Albuquerque, NM:
Meteorological Data: SEV 001 [Database]. <http://sev.lternet.edu>
Muldavin, E. H. 1991-ongoing. Primary production: long-term ecological research. Albuquerque,
NM: Seasonal and Annual Biomass and ANPP for Sevilleta LTER Core Research Sites:
SEV 182 [Database]. <http://sev.lternet.edu>
Shortgrass Steppe Long-Term Ecological Research Site (SGS)
The Shortgrass Steppe Long-Term Ecological Research site is a 6280 ha area located in northern
Colorado in the rain shadow of the Rocky Mountains. The area is dominated by warm-season C4
grasses, particularly Bouteloua gracilis and Buchloe dactyloides. We used data collected from
three community types separated by elevation: ridgeland, midland and swale. Daily precipitation
data for SGS was collected from a meteorological station located at the station headquarters. If
precipitation data were missing (1983–1996), values were filled in from a nearby NOAA weather
station in Nunn, CO (24 km south of the study site). We defined the growing season as 5 April –
5 August each year, as plots were generally clipped the first week in August. The true growing
season ends in September (Lauenroth and Burke 2008).
ANPP was collected from fifteen 0.25-m2 quadrats located within each community from
1983–2006. Quadrats were placed along three permanent transects, with five quadrats per transect
in a different location each year (to avoid reharvesting the same quadrat). ANPP was harvested
by clipping all biomass at ground level within each quadrat. For shrub species, only current
year’s growth was collected. Collected biomass was dried at 60ºC for 48 h, sorted by species and
weighed.
Lauenroth, W. and Burke, I. C. (eds) 2008. Ecology of the shortgrass steppe. – Oxford Univ.
Press.
Precipitation and ANPP data were from the following data sets:
Parton, B. 2010. Standard Met Data: 1969–2010 Manually Collected Standard Meteorological
Data. Fort Collins, CO: Shortgrass Steppe Long Term Ecological Research Site Database:
man11_climdb. <http://sgs.cnr.colostate.edu/dataset_view.aspx?id=man11_climdb> (17 April
2010).
Lauenroth, W. K. 2007. Standard production data: 1983–2007 annual aboveground net primary
production. Fort Collins, CO: Shortgrass Steppe Long Term Ecological Research Site
Database: anpp. <http://sgs.cnr.colostate.edu/dataset_view.aspx?id=anpp> (17 April
2010).
Teakettle Experimental Forest (TEA)
The Teakettle Experimental Forest is a 1300 ha site on the western slope of the Sierra Nevada in
central California. The site is dominated by Abies concolor, Calocedrus decurrens, Pinus jeffreyi
and Pinus lambertiana. Daily precipitation data were collected at the Wishon Dam
meteorological station (10 km from study site). Precipitation data from November 1999 were
incomplete and therefore dropped from the analysis. We defined the growing season as 15 April –
15 October (Baker 1944, Concilio et al. 2009). Dendrometer band data from an unburned,
unthinned area was collected for 26 trees in four conifer species from 1997–2008. As with
increment core data, dimensionless detrended mean proportional residuals (see CVO, above)
were used as a production index.
Baker, F. S. 1944. Mountain climates of the western United States. – Ecol. Monogr. 14: 223–254.
Concilio, A., Chen, J. Q., Ma, S. and North, M. 2009. Precipitation drives interannual variation in
summer soil respiration in a Mediterranean-climate mixed-conifer forest. – Climate
Change 92: 109–122.
ANPP data previously reported by:
Hurteau, M. and North, M. 2010. Carbon recovery rates following different wildfire risk
mitigation treatments. – For. Ecol. Manage. 260: 930–937.
US Sheep Experiment Station (USE)
The US Sheep Experiment Station is an 11 303 ha area spanning the border between Montana
and Idaho. The area is characterized by sagebrush shrubland and is dominated by Artemisia
tripartita, Pseudoroegneria spicata and Balsamorhiza sagittata. Daily precipitation data for USE
was collected from a meteorological station at the station headquarters. We defined the growing
season as 25 March – 5 August (Blaisdell 1958).
Aboveground biomass was visually estimated by growth form (woody, graminoids, forbs,
and succulents) and lifespan (annual, perennial) from 1936–1954 in 125 4.65-m2 (50 ft2)
permanent quadrats in three grazing exclosures.
Blaisdell, J. P. 1958. Seasonal development and yield of native plants on the upper Snake River
plains and their relation to certain climatic factors. – In: Technical Bulletin No. 1190.
USDA, Washington, D.C., pp. 1–63.
Figure A1. Location of sites that provided data for the study and mean annual precipitation.
Table A1. Best statistically supported models for all communities (Com) studied where precipitation had a strong (adjusted R2 ≥ 0.3)
and significant (overall model p < 0.05) effect on production. Communities that did not meet these criteria are not included in this
table. Coefficient parameters and their significance are shown for each model, and sites where the total precipitation model had the
most support have the most supported seasonal model included for comparison.
-2
Site
Com Model Model
rank
SEV CRE 11
best
JRN C
9
best (tied)
6
best (tied)
G
11
best
T
4
best (tied)
BNZ FP3 5a
best
FP4 9a
best
UP2 3
best
UP3 7
best (tied)
9
best (tied)
USE Non 11
best
SGS MID 11
best
12
best seasonal
RID 11
best
10
best seasonal
SWA 9
best (tied)
11
best (tied)
CVO † -10
best
KNZ 1f
5
best (tied)
11
best (tied)
1t
5
best (tied)
5a
best (tied)
11
best (tied)
20f 9
best
4f
5
best (tied)
11
best (tied)
KBS T7 1
best
TEA † -8
best
HFR EMS 3
best
CWT 118 3
best
adj R
0.33
0.73
0.68
0.51
0.35
0.43
0.77
0.54
0.49
0.57
0.45
0.76
0.70
0.60
0.65
0.57
0.53
0.55
0.58
0.54
0.38
0.45
0.31
0.45
0.39
0.34
0.32
0.41
0.33
0.40
2
p
-1
Parameter coefficients (g m mm )
DF Intercept p0
p1
p2
0.036
5E-04
4E-04
0.002
0.011
0.027
0.002
0.003
0.014
0.014
0.007
8E-09
1E-07
3E-06
8E-05
1E-04
2E-05
3E-06
3E-05
2E-05
0.002
0.001
0.002
0.002
0.002
0.002
0.009
0.021
0.049
0.029
9
11
12
13
13
11
9
11
10
9
11
23
23
23
19
21
23
35
22
23
22
21
23
20
21
22
16
9
8
8
10.5
22.9
22.2
3.4
29.0
181.2
-168.8
896.6
244.1
236.1
22.1
-40.6
4.8
-24.4
-16.8
-13.2
-71.2
-203.9
193.2
133.0
345.8
189.1
291.1
269.8
197.6
152.9
284.1
-710.5
186.8
45.7
p3
p4
0.11
0.12
0.55 ***
0.61 ***
p1*p2 p2*p3
p3*p4
pt
pg
0.21 *
0.23
0.39 **
0.57 *
5.82 **
6.03 ***
1.15
1.94
5.30 *** 4.63 ***
-3.31 **
-1.22 * 1.77 *
-1.48 * 1.71 *
-0.08 ***
-0.07 ***
-0.05 ***
0.25 **
0.30 ***
0.30 ***
0.22 ***
0.09
0.40 ** 0.24 ***
0.46 **
0.18 **
0.35 *
0.30 ***
0.91 ***
0.37
0.77 * 1.00 ***
0.45 ***
0.49 ** -0.24
0.39 ***
0.49 ***
0.30 ***
0.49 *
1.24 **
0.51 **
1.12 **
0.28 *
0.32 *
0.33 **
0.30 **
-0.003
0.34 **
-0.35 *
0.23 **
4.26 **
1.00 *
0.47 *
0.10 *
* = p < 0.05; ** = p < 0.01; *** = p < 0.001
†
ANPP data for CVO and TEA are not in units of g m-2 y-1(Supplementary material Appendix A1); intercept and coefficients for
these sites are expressed relative to the largest effect size in each model.