Biodivers Conserv (2013) 22:1731–1754
DOI 10.1007/s10531-013-0509-1
ORIGINAL PAPER
Using unclassified continuous remote sensing data
to improve distribution models of red-listed plant species
Miia Parviainen • Niklaus E. Zimmermann • Risto K. Heikkinen
Miska Luoto
•
Received: 30 November 2012 / Accepted: 6 June 2013 / Published online: 16 June 2013
Ó Springer Science+Business Media Dordrecht 2013
Abstract Remote sensing (RS) data may play an important role in the development of
cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers
to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution
of 25 ha are improved when advanced RS-variables are included as unclassified continuous
predictor variables, in addition to more commonly used climate and topography variables.
Using generalized additive models (GAMs), we studied whether the spatial predictions of
the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil
index and Tasseled Cap transformations) information into species-environment models.
Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote
sensing variables, and evaluated by four-fold cross-validation with the area under the curve
(AUC) statistics. The inclusion of RS variables improved both the explanatory power (on
average 8.1 % improvement) and cross-validation performance (2.5 %) of the models.
Hybrid models produced ecologically more reliable distribution maps than models using
only climate-topography variables, especially for mire and shore species. In conclusion,
M. Parviainen (&)
Finnish Forest Research Institute, University of Oulu, P.O. Box 413, 90014 Oulu, Finland
e-mail: [email protected]
N. E. Zimmermann
Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
R. K. Heikkinen
Finnish Environment Institute, Natural Environment Centre, P.O. Box 140, 00251 Helsinki, Finland
M. Luoto
Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki,
Finland
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Landsat ETM? data integrated with climate and topographical information has the
potential to improve biodiversity and rarity assessments in northern landscapes, especially
in predictive studies covering extensive and remote areas.
Keywords Endangered plant species GAM High-latitude landscape Landsat ETM? Predictive modelling Productivity Remote sensing
Introduction
Growing concern over the loss of biodiversity has increased the need for developing
conservation and management strategies to reduce and prevent further losses (Sala et al.
2000; Young et al. 2005, Redpath et al. 2013). For example, ca. 80 % of all red-listed
(threatened or near-threatened) species recorded in Finland (2,247 species; vertebrates,
invertebrates, plants, fungi) are primarily threatened by habitat changes (Rassi et al. 2010).
Especially in insufficiently known areas, robust and rapidly generated predictions of redlisted species distributions may play a significant role in present-day conservation (Carroll
and Johnson 2008; Wilson et al. 2010), management planning (Fernandez et al. 2006), and
estimating the biological effects of global change (Thuiller et al. 2008; Elith and Leathwick
2009b).
Species responses to the environmental factors are increasingly assessed using predictive species distribution models (SDMs) (e.g. Franklin 1995; Wu and Smeins 2000; Seoane
et al. 2003; Rushton et al. 2004; Guisan and Thuiller 2005; Araújo and Guisan 2006;
Thuiller et al. 2008; Elith and Leathwick 2009b; Newbold 2010; Zimmermann et al. 2010;
Austin and Van Niel 2011b). SDMs have proven valuable for generating biodiversity
information that can be applied across a broad range of fields, including conservation
biology, ecology, land use planning (Guisan and Thuiller 2005; Pearson 2007; Elith and
Leathwick 2009b), and species responses to climate change (e.g. Thuiller et al. 2005; Elith
and Leathwick 2009a; Elith and Leathwick 2009b; Austin and Van Niel 2011a). As a
special case, SDMs may provide useful predictions for inadequately surveyed areas and
thereby provide guidelines for seeking new populations of rare species (e.g. Guisan et al.
2006; Newbold 2010 and the references therein). However, developing accurate predictions for the occurrences of species at the local or landscape scale is difficult if solely
climatic variables are used (Pearson et al. 2004; de Siqueira et al. 2009).
The interest in applying SDMs has grown alongside with the increasing interest in
developing means for ‘cost-effective’ forecasting of species diversity. Such modelling
approaches, which are based on a few readily measured environmental variables, may be
particularly useful in assessing the impacts of anthropogenic and natural disturbances on
biodiversity under limited resources. Remote sensing (RS hereafter) offers an inexpensive
means to derive spatially complete surrogates and forecasts of biodiversity patterns for
large areas in a consistent and regular manner (Muldavin et al. 2001; Foody and Cutler
2003), and holds the promise to improve the accuracy of local and regional scale SDMs
(Zimmermann et al. 2007). A number of studies have provided support for the usefulness
of RS-information in predicting species distributions (e.g. Levin et al. 2007; Zimmermann
et al. 2007; Buermann et al. 2008; John et al. 2008; Saatchi et al. 2008; Cord and Rödder
2011; Schmidtlein et al. 2012). In particular, recent improvements in spectral and spatial
resolution have enhanced the capacity to more accurately link RS data to ecological studies
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(Kerr and Ostrovsky 2003; Gillespie et al. 2008; Wang et al. 2010). However, to optimally
utilise these recent RS products in biodiversity modelling and conservation planning, we
need to critically evaluate best practices for using advanced RS information for describing
and modelling species patterns (Nagendra 2001; Kerr and Ostrovsky 2003; Turner et al.
2003; Seto et al. 2004; Gillespie et al. 2008; Rocchini et al. 2010; Wang et al. 2010). This
challenge needs to be addressed if SDMs aim at improving the assessment of global change
(Zimmermann et al. 2007).
The goal of our study was to assessing the capacity of RS-information to enhance the
performance of SDMs for conservation-targeted species. This is particularly important at
an intermediate spatial scale (meso-scale) employing dimensions of ca. 500 9 500 m to
2 9 2 km (Heikkinen et al. 1998; Gould 2000; Luoto et al. 2002; Parviainen et al. 2008,
2010, since many decisions on the conservation and management of species are made at
meso- to landscape-scale. In order to address this question, we used generalized additive
models (GAMs) to study whether the spatial predictions of the distribution of red-listed
plant species in boreal landscapes are improved by incorporating advanced RS information
into species–environment models, and whether such data have the potentiality to provide
useful complementary information for SDM-based conservation planning. The advanced
RS information tested here included normalized difference vegetation index, normalized
difference soil index and three Tasseled Cap transformations (Crist and Cicone 1984). In
our study setting, models were fitted using three different sets of explanatory variables: (1)
climate-topography only; (2) remote sensing only; and (3) combined climate-topography
and remote sensing variables, and evaluated by four-fold cross-validation with the area
under the curve (AUC) statistics. Recent studies have demonstrated that the performance of
species–distribution models may also depend on the characteristics of the species (e.g.
Luoto et al. 2005; Seoane et al. 2005; Guisan et al. 2007; McPherson and Jetz 2007;
Zimmermann et al. 2007; Pöyry et al. 2008). Thus, we will also investigate whether the
importance of remote sensing variables in the models varies between species inhabiting
different habitats.
Materials and methods
Study area
The study area (41,750 km2) is located between 26°–30°450 E and 65.50°–68°N in northeastern Finland (Fig. 1). Phytogeographically, the study area lies within the northern boreal
zone (Ahti et al. 1968) where climate is more continental than in most other parts of
northern Europe, but still contains some maritime (humid) influence (Atlas of Finland
1987). Topography varies conspicuously and elevation ranges from 46 to 624 m (Atlas of
Finland 1990). The calcareous soil and the complex topography of the landscape provide
many different biotopes for the plants (Vasari et al. 1996; Parviainen et al. 2008). The
major part of the flora is of Southern origin, i.e. consists of species that have spread from
the south after the last glacial period (11,000 years before present) (Vasari et al. 1996).
Plant species data
We used the occurrence records from the national database of red-listed vascular plant
species (Rassi et al. 2010) (Table 1, Appendix Table 6). Comprehensive field records
originating from both voluntary amateurs and professional botanists constitute the major
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Fig. 1 The location of the study area in boreal landscape, north-eastern Finland, together with the major
vegetation zones and sectors. The black dots indicate known presence points of the modelled threatened
plant species. The vegetation zones are divided into the following sectors according to the variation in
climate. O1 = slightly oceanic, OC = indifferent, C1 = slightly continental (Ahti et al. 1968; Heikkinen
2005). Land use-classification is based on Corine 2000 land-cover classification
data source in this database, but information on species occurrences was also gathered from
the scientific literature and from herbaria (Ryttäri and Kettunen 1997; Rassi et al. 2010).
Species data included detailed information on the geographical location of the occurrences
(coordinates in the uniform grid system, Grid 27°E). A total of 28 plant species with ten or
more records among the 1,677 grid squares of 25 ha and covering the whole study area was
used in the analyses (Fig. 1; Table 1). Only observations with an accuracy better than
100 m were selected for this study (see Parviainen et al. 2008 for more details).
As the database of red-listed species does not include records of the absence of species,
the assumption was made that the absence of a record from a sampled grid square
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Table 1 The studied 28 nationally red-listed vascular plant species
Species
Abbreviation
Frequency
ntot = 1,677
Prevalence
(%)
Main
habitat
Conservation
status
Botrychium boreale
BOTBOR
65
3.88
Cultural
VU
Botrychium lanceolatum
BOTLAN
46
2.74
Cultural
VU
Asplenium ruta-muraria
ASPRUT
44
2.62
Rocky
EN
Moehringia lateriflora
MOELAT
215
12.82
Rocky
NT
Minuartia biflora
MINBIF
21
1.25
Rocky
NT
Cerastium alpinum
(ssp. alpinum)
CERALP
67
4.00
Rocky
EN
Lychnis alpina var.
serpentinicola
LYCALP
34
2.03
Rocky
NT
Silene tatarica
SILTAT
101
6.02
Shore
VU
Gypsophila fastigiata
GYPFAS
34
2.03
Forest
EN
Primula stricta
PRISTR
46
2.74
Shore
EN
Saxifraga hirculus
SAXHIR
370
22.06
Mire
VU
Epilobium laestaedii
EPILAE
25
1.49
Mire
EN
Gentianella amarella
GENAMA
61
3.64
Cultural
EN
Lonicera caerulea
LONCAE
12
0.72
Shore
EN
Arnica angustifolia
ARNANG
31
1.85
Rocky
EN
Cypripedium calceolus
CYPCAL
282
16.82
Forest
NT
Epipogium aphyllum
EPIAPH
22
1.31
Forest
VU
Dactylorhiza traunsteineri
DACTRA
113
6.74
Mire
VU
Dactylorhiza lapponica
DACLAP
17
1.01
Mire
VU
Dactylorhiza incarnata
ssp. cruenta
DACINC
81
4.83
Mire
VU
Calypso bulbosa
CALBUL
287
17.11
Forest
VU
Schoenus ferrugineus
SCHFER
32
1.91
Mire
EN
Carex appropinquata
CARAPP
60
3.58
Mire
VU
Carex heleonastes
CARHEL
166
9.89
Mire
VU
Carex lepidocarpa ssp.
jemtlandica
CARLEPJEM
27
1.61
Mire
VU
Carex viridula var. bergrothii
CARVIRBER
50
2.98
Mire
VU
Carex microlochin
CARMIC
21
1.25
Shore
EN
Elymus fibrosus
ELYFIB
103
6.14
Shore
VU
86.9 ± 91.3
5.18 ± 5.54
Mean ± std
For the list of different habitats included in the five main habitat categories see Appendix Table 6
The conservation status is: EN = endangered, VU = vulnerable, NT = near threatened (Rassi et al. 2010)
corresponded to true absence of the species, because a quasi-exhaustive sampling could be
assumed for most squares with presence records (Guisan and Zimmermann 2000). Thus,
for a given target species, pixels with presence of any other of the 28 species that did not
have a presence of the target species observed where considered absence plots of the target
species.
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Environmental predictors
We selected a set of quantitative predictors that cover the main biophysical gradients with
a recognized, physiological influence on plants. The selection of the final predictors was
made so that correlations among variables were \|0.7| in order to minimize the effect of
multicollinearity in the statistical analyses (Zimmermann et al. 2007). In total, 13 environmental predictor variables were calculated for all 1,677 of the grid squares at the
resolution of 25 ha and used to explain red-listed plant species distribution: three climate,
four topography and six remote sensing variables (Table 2). The climate and topography
data used here are described in Parviainen et al. (2008) and thus only briefly discussed
here.
The annual temperature sum above 5 °C (i.e. ‘growing degree days’), the mean temperature of the coldest month (TEMPC) and water balance (WAB) were used as climatic
predictor variables, because they reflect the principal limitations to many species’ occurrences in high-latitude environments: heat, cold-tolerance and humidity requirements
(Kivinen et al. 2008; Parviainen et al. 2008). Water balance was computed as the monthly
difference between precipitation and potential evapotranspiration (PET) (Skov and Svenning 2004). The climate data with a 10 km resolution (mean values) from the period
1961–1990 (Venäläinen and Heikinheimo 2002) were downscaled to 0.5 km (25 ha) grids
by using a linear regression model following the methodology of Vajda and Venäläinen
(2003). In the model, the temperature variables and PET were explained by latitude,
longitude and altitude, whereas precipitation was explained by latitude and longitude
(Astorga et al. 2011). Climatic variables thus obtained are fine-tuned to better describe
local-scale variation in climatic conditions.
Topography is a fundamental geophysical observable that contains valuable information
about the climate, hydrology, nutrient levels, and geomorphology of a region (Pausas et al.
2003; Peterson 2003). In total, four topographical parameters were extracted from the
digital elevation model (DEM) at 25 m resolution and aggregated to the 25 ha resolution
using ArcGIS and ArcView software (ESRI 1991): mean elevation (ELE), mean solar
radiation (RAD), mean topographical wetness index (TWI) and the proportion (%) of steep
topography ([15°) (STEEP). Solar radiation is a direct ecological factor affecting the
habitat conditions (Austin and Meyers 1996). Topographic wetness index describes the
local relative differences in moisture conditions (Gessler et al. 2000). High values represent lower catenary (wet) and small values upper catenary positions (dry).
In total five Landsat 7 ETM images covering the study area were acquired from the
growing seasons of 2000–2002 (Appendix Table 7). All the Landsat images were rectified
according to topographic maps (scale 1:20,000). The geometric correction was successful:
the planimetric root-mean-square error (RMSE) of test ground control points of the images
varied between 12.9 and 18.9 m. The spatial resolution of the rectified Landsat ETM
images was selected to be 25 m, and new values for the pixels were resampled using a
cubic convolution interpolation method (Hjort and Luoto 2006). Topographic variations
may cause variation in reflected radiation, because imaging geometry changes locally.
Thus, the images were topographically corrected using the ‘Ekstrand correction method’
(Ekstrand 1996). Additionally, in order to decrease the effect of atmospheric variation of
the atmosphere between acquisition dates of the five images, the Landsat scenes were
atmospherically corrected based on the SMAC-algorithm, which is a semi-empirical correction method developed at the Technical Research Centre of Finland (Hjort and Luoto
2006). Satellite scenes were provided by the Finnish Environment Institute (SYKE) and
ortho-rectified by METRIA, Sweden (Härmä et al. 2004).
123
WAB
Water balance
TWI
RAD
STEEP
Mean topographical wettness index
Mean radiation
Steep slope ([15°)
NDVIstd
NDSImean
GREENNESmean
GREENNESstd
Normalized difference soil index (mean)
Greennes (mean)
Greennes (std)
–
–
–
–
–
0.036 [0.003–0.136]
0.135 [0.000–0.265]
-0.313 [-0.525–0.006]
0.165 [0.046–0.544]
0.416 [-0.376–0.725]
2.67 [0.00–74.00]
0.43 [0.12–0.87]
j/cm2/a
%
8.24 [0.00–16.16]
213.75 [72.00–582.00]
194.06 [99.41–238.76]
–
m
mm/a
742.62 [458.88–883.80]
-13.39 [-15.06–13.47]
°C
Mean [min–max]
Gdd
Unit
Data sources: FMI = Finnish Meteorological Institute, DEM = Digital Elevation Model, Landsat ETM = Landsat ETM satellite image
NDVImean
Normalized difference vegetation index (mean)
Normalized difference vegetation index (std)
Remote sensing
ELE
Mean elevation
Topography
GDD5
TEMPC
Growing degree days ([5 °C)
Abbreviation
Mean temperature of coldest month
Climate
Environmental variables
Table 2 List of selected environmental variables used as explanatory variables in the modelling
LANDSAT ETM
LANDSAT ETM
LANDSAT ETM
LANDSAT ETM
LANDSAT ETM
DEM
DEM
DEM
DEM
FMI
FMI
FMI
Source
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In this study, the most commonly used vegetation index, the normalized difference
vegetation index (NDVI) (Rouse et al. 1973), was calculated for each 25 ha grid square
using the formula:
NDVI ¼ ðETM4 ETM3Þ=ðETM4 þ ETM3Þ
NDVI is a sensitive indicator of green biomass; the index increases as the vegetation
becomes more dense or greener (Tucker 1978, 1979). In addition, we used the normalized
difference soil index (NDSI), which to our knowledge has not been used in earlier studies
similar to ours. NDSI was calculated as
NDSI ¼ ðETM5 ETM4Þ=ðETM5 þ ETM4Þ
The reflectance of band 5 was used, because only bare soil is more reflective in band 5
than in band 4 (Rogers and Kearney 2004). This index can be expected to inform about
local variations in cover density and soil properties.
Additionally, the Tasseled Cap (TC) Transformation (Crist and Cicone 1984), a linear
recombination of Landsat ETM bands 1–5 and 7, was carried out following the procedure
described in Huang et al. (2002). This resulted in three new products, namely the soil
brightness index (‘brightness’), the green vegetation index (‘greenness’) and the moisture
index (‘wetness’). The Tasseled Cap transformation provides a mechanism for data volume
reduction with minimal information loss and its spectral features can be directly associated
with important physical parameters of the land surface (Crist and Cicone 1984).
Statistical analyses
The response variable, i.e. binary presence/absence data of the occurrences of the 28 redlisted vascular plant species, was related to the predictor variables by means of GAMs
(Hastie and Tibshirani 1990) using the GRASP 3.2 package (Lehmann et al. 2002) for
S-Plus 6.1 (Insightful Corp., Seattle, WA, USA). GAMs have been used extensively in
ecological applications (see Yee and Mitchell 1991; Guisan et al. 2002) because they
permit both parametric and non-parametric additive response shapes, as well as a combination of the two within the same model (Wood and Augustin 2002), and as they have
performed well in many recent model comparison studies (Guisan et al. 2007; Heikkinen
et al. 2012).
GAMs were fitted using three sets of explanatory variables for each of the 28 red-listed
plant species. The first distribution model for each species was built with topography and
climate variables only; hereafter the topo-climatic model. The second model was based on
remotely sensed variables only (RS model; six remote sensing variables). The final model
(hybrid model) included both topo-climatic and remotely sensed variables (three climate,
four topography and six remote sensing variables).
The GAMs were built using a stepwise variable selection procedure to select relevant
explanatory variables, starting with a full model in which all predictors are fitted and
subsequently omitting and re-introducing one predictor variable at each step so that only
variables remain that add significantly to the models based on the Akaike information
criterion (AIC; Akaike 1974). The level of smoothing of the response shapes of the species
to each variable was first fitted with three degrees of freedom and was then dropped to one.
The variable dropping or conversion to linear form was also tested using AIC. A binomial
probability distribution was selected for the response, the link function was set to logit, and
a smoothing spline with three degrees of freedom was applied (Venables and Ripley 2002).
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Moreover, following Manel et al. (2001) and Maggini et al. (2006), we weighted the
absences in GAMs to ensure an equal prevalence (0.5) between presences and absences.
The calibration strength of models was assessed based on the percentage of explained
deviance (D2), and on the area under the curve (AUC) of a receiver operating characteristic
(ROC) plot (Fielding and Bell 1997) statistic computer on the calibration data set (‘resubstitution AUC’). Thereafter, a four-fold cross-validation was employed to examine the
predictive power of the models and to derive ‘cross-validation AUC’ values (Lehmann
et al. 2002). We acknowledge that the four-fold cross-validation carried out here does not
represent a totally independent test for assessing the predictive power of the different
models (cf. Araújo et al. 2005; Heikkinen et al. 2006; Randin et al. 2006). However, as the
25 ha grid cells in our model setup were distributed rather sparsely across the whole study
area (grid cells used in modelling covered ca. 1 % of the whole study area), we assumed
that the effect of spatial autocorrelation was small. Moreover, the results of Parviainen
et al. (2008) based on the same data showed that inclusion of the effect of spatial autocorrelation (autocovariate term reflecting the species occurrences in the surroundings of the
focal grid cell) had only a minor effect on the importance of the environmental variables
and the shapes of predictor-response curves.
To evaluate the stability of the model AUC values we compared the resubstitution
accuracies (resubstitution AUC) with the cross-validated accuracies (cross-validated
AUC). The more the cross-validated AUC is similar to the AUC from resubstitution, the
more stable the model. A clear drop in cross-validated AUC compared to the resubstitution
test indicates that the model is probably overfitted and cannot be robustly fitted to new data
sets or study locations (Maggini et al. 2006). The stability values were calculated using the
following equation:
AUC-stability ¼ AUCevaluation =AUCcalibration
The differences between explained deviance, AUCcalibration and AUCevaluation values of
the climate-topography, remote sensing and hybrid models were tested using non-parametric Wilcoxon’s signed rank test. In other words, the related samples from the three
types of models were tested in a pair-wise manner for the statistical significance (e.g. the
AUC values the of climate-topography model for a given species versus the AUC values
the of hybrid model for the same species across all the 28 study species).
The relative importance of single environmental predictor variables was scrutinized by
calculating the contribution of each predictor to the final models (Lehmann et al. 2002) for
each species model, expressed as a the percentage of the sum of model contributions as
defined in GRASP. Finally, model extrapolations were converted into spatial prediction
maps by selecting threshold probabilities above which presence was established, according
to Kappa-maximized thresholds (KMT). To do so, Kappa scores were calculated for 100
threshold values (in 0.01 increments) and the threshold, which provided the highest Kappa
was selected (Guisan et al. 1998; Thuiller 2003; Jimenez-Valverde and Lobo 2007).
Results
The amount of deviance explained by the 28 topo-climatic–climate models ranged from
8.1 to 77.6 % with a mean of 44.7 % (Tables 3, 4). On average, the inclusion of RSinformation increased the performance of the models, with more than half of the hybrid
models showing higher explanatory power and predictive accuracy than topo-climatic
models. Although these differences between the different model types were not very large,
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Table 3 Explained deviance and cross-validated AUC values of the topo-climatic (topography ? climate)
and hybrid (topography ? climate ? remote sensing) models for the 28 study species
Species
Topo-climate model
Hybrid model
Explained
deviance (%)
Cross-validated
AUC
Explained
deviance (%)
Cross-validated
AUC
Botrychium boreale
41.54
0.88
44.80
0.88
Botrychium lanceolatum
24.72
0.76
17.93
0.77
Asplenium ruta-muraria
60.01
0.92
60.01
0.93
Moehringia lateriflora
52.51
0.91
57.02
0.93
Minuartia biflora
77.59
0.96
77.59
0.96
Cerastium alpinum (ssp. alpinum)
63.40
0.95
63.40
0.94
Lychnis alpina var. serpentinicola
75.20
0.96
75.20
0.96
Silene tatarica
29.78
0.83
43.89
0.88
Gypsophila fastigiata
65.47
0.94
65.47
0.94
Primula stricta
31.90
0.83
50.13
0.90
Saxifraga hirculus
13.81
0.73
26.59
0.81
Epilobium laestaedii
24.80
0.74
24.80
0.74
Gentianella amarella
49.58
0.88
53.22
0.90
Lonicera caerulea
68.79
0.86
68.79
0.94
Arnica angustifolia
61.48
0.93
61.48
0.93
Cypripedium calceolus
44.86
0.89
45.92
0.89
Epipogium aphyllum
31.13
0.78
31.13
0.79
8.90
0.66
17.20
0.74
Dactylorhiza traunsteineri
Dactylorhiza lapponica
39.37
0.85
39.37
0.86
Dactylorhiza incarnata ssp. cruenta
17.13
0.75
20.87
0.76
Calypso bulbosa
40.98
0.88
45.57
0.89
Schoenus ferrugineus
54.02
0.90
54.60
0.90
Carex appropinquata
17.85
0.72
24.43
0.75
Carex heleonastes
14.54
0.71
20.65
0.76
Carex lepidocarpa ssp. jemtlandica
61.92
0.94
61.92
0.93
Carex viridula var. bergrothii
60.07
0.90
65.74
0.94
Carex microlochin
68.96
0.93
79.56
0.97
Elymus fibrosus
51.94
0.92
56.71
0.93
The models were built using AIC (Akaike’s Information Criterion) model selection algorithm
they were statistically significant; the hybrid models showed significantly higher amount of
explained variation (Wilcoxon signed rank test, p \ 0.001) and predictive power
(AUCevaluation, Wilcoxon signed rank test, p \ 0.001) than topo-climatic and RS models
(Table 4). In general, the cross-validated accuracies (AUCevaluation) of the hybrid models
were rather good, indicating a good discrimination power of the models. In the case of
stability, hybrid models slightly outperformed topo-climatic models (Table 4).
Interestingly, the increase in the model performance resulting from the inclusion of RSbased variables varied notably among different species, from species where the explained
deviance was ca. doubled (e.g. Saxifraga hirculus and Dactylorhiza traunsteineri) to
species with no difference in explanatory power between the hybrid and topo-climatic
models (e.g. Asplenium ruta-muraria and Epipogium aphyllum (Table 3). With respect to
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Table 4 Modelling accuracy (mean ± standard deviation) for the 28 red-listed plant species in topoclimatic, RS and hybrid models measured by the amount of explained deviance, calibrated and crossvalidated AUC-values and the model stability (i.e. ratio of calibration AUC and four-fold cross-validation
AUC)
Explained deviance
AUC calibration
AUC evaluation
Stability
Topo-climatic model
44.72 ± 20.439
0.879 ± 0.082
0.854 ± 0.088
0.971 ± 0.028
RS model
24.02 ± 12.344
0.787 ± 0.083
0.754 ± 0.089
0.956 ± 0.036
0.975 ± 0.019
Hybrid model
48.36 ± 19.182
0.897 ± 0.069
0.875 ± 0.076
P1
\0.001
0.002
\0.001
n.s.
P2
\0.001
\0.001
\0.001
0.010
Ranks1
16/1/11
16/1/11
24/4/0
15/13/0
Ranks2
27/0/1
27/0/1
28/0/0
20/8/0
The Wilcoxon signed-rank test was used to test the difference between topo-climatic versus hybrid (P1) and
RS versus hybrid (P2) models. Ranks: positive/negative/tied. Positive rank refers to the number of times a
hybrid model was superior to a topo-climatic or RS-model
Fig. 2 Box-Whisker plots illustrating the percentage change (%) of a the amount of explained deviance and
b in model accuracy (cross-validated AUC) when incorporating RS variables into AIC-based topo-climatic
models for the 28 red-listed plant species. The 28 models are assigned into different categories according to
the habitat preferences of the species
the species habitat preferences, the increase in the modeling performance was most pronounced in the case of mire and shore species (Fig. 2), where the increase for most species
is in the range of 18.0–29.7 % (explained deviance) and 4.2–4.8 % (AUC). Overall, in the
derived hybrid models, climate variables were generally selected most often in the models
and showed the largest relative contributions (Table 5), followed by remotely sensed
variables. The standard deviation and mean values in NDVI were the most important RS
variables in explaining the distribution of red-listed plant species.
Similarly as in the increase in model performance, the selected variables and their
response shapes varied considerably from species to species (Appendix Table 8). As an
example, the projected spatial distributions from the models that included different sets of
predictors are presented for two red-listed species, Primula stricta and D. traunsteineri
(Fig. 3). For both species, the inclusion of RS variables increased the modelling accuracy
(Table 3) and the level of spatial detail in the predictions despite the rather small increase
in predictive performance when adding the RS variables.
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Table 5 The relative roles of individual environmental variables in explaining the distribution of 28 redlisted plant species, as derived from the GAMs based on the AIC model selection algorithm based on model
contributions (in GRASP)
Environmental variables
Mean
Std
Count
GDD5
25.0
27.6
19
TEMPC
11.2
16.4
15
WAB
18.4
25.8
15
ELE
13.7
16.9
15
TWI
5.9
14.4
9
RAD
0.2
1.0
1
STEEP
5.4
17.0
4
NDVImean
3.9
8.0
7
NDVIstd
1.6
3.6
5
NDSImean
1.3
4.1
3
GREENNESmean
3.0
6.0
GREENNESstd
10.3
216
7
11
‘‘Mean’’ = percentage of model contribution provided by GAM analyses; ‘‘Std’’ = standard deviation in
the model contribution provided by GAM analyses; ‘‘Count’’ = number of GAM models in which the
variable was selected. For abbreviations of the environmental variables see Table 2
Discussion
There is considerable knowledge about the ecophysiological processes that underlie species responses to the environment, and such knowledge is important when selecting predictor variables to describe species distributions (Huntley 1995; Guisan and Zimmermann
2000; Austin 2002, 2007). Generally, the distribution of plant species is closely correlated
with climatic factors at large spatial scales (Currie 1991; Wright et al. 1993; Huntley et al.
1995; H-Acevedo and Currie 2003; Thuiller et al. 2004), whereas topography, geology, soil
nutrient and wetness status, and spatial configuration of suitable habitats types modify
species occupancy patterns at finer spatial scales (Pearson et al. 2004; Thuiller et al. 2004;
Virkkala et al. 2005).
In a previous meso-scale study, land cover information from RS data was found to be an
important predictor for modelling red-listed plant species in high-latitude landscapes
(Parviainen et al. 2008). However, spatially explicit information on land cover is often
unavailable, and therefore rarely used in meso-scale species distribution modelling.
Moreover, the classification of RS images into coarse land cover classes can lead to a
severe loss of information (Palmer et al. 2002; Schwarz and Zimmermann 2005), especially when dealing with ecological data. In addition, in predictive SDM studies of plant
species carried out especially at higher spatial resolution, the use of vegetation or land
cover classifications is often not meaningful, simply because the inherent danger of
invoking circularity in such modelling settings (Zimmermann et al. 2007). Rather, subtle
differences in the vegetation/soil properties may thus provide more useful information for
discriminating between suitable and unsuitable sites, which have otherwise appropriate
(climatic) conditions for a target species to occur (Guisan et al. 1998; Zimmermann et al.
2007). Such differences may be captured by continuous gradient predictors derived from
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Fig. 3 Projected distribution of Dactylorhiza traunsteineri and Primula stricta derived from the GAM
models based on the AIC model selection algorithm: models based on a topo-climatic variables only,
b remote sensing variables only, and c combined topo-climatic and remote sensing variables. Black dots
represent the known presence points of the species, dark grey shaded areas modelled as suitable and light
grey areas modelled as non-suitable for the species. The probability level that showed the highest Kappa
value (Kappa-Maximized Threshold, KMT) was used to classify the predicted occurrence probability values
for each species in each grid cell. D2 = percentage of explained deviance, AUC = the area under the curve
based on four-fold cross-validation, Pr = the prevalence of the species, Nr = the number of variables
selected in the model, T = threshold based on KMT criterion
remotely sensed spectral information that may help to improve the calibration of the SDMs
compared to topographic and bioclimatic predictors alone.
More generally, two important implications can be drawn from our results. First,
continuous RS-information appear to have potentiality to directly contribute to the models
by providing landscape-level details on potential habitat characteristics beyond climatic
and topographic conditions, and also beyond simple land cover classes. We acknowledge
here that strictly speaking remote sensing data are never truly ‘continuous’, not in the same
manner as many ecophysiological parameters measured directly in empirical field studies.
Thus, the ecological meaning of continuous is not applicable to remote sensing data. This
is because RS data are always categorized depending on their radiometric resolution, i.e.
the number of bits used. Nevertheless, our results suggest that there may be a difference in
the degree of usefulness between the unclassified ‘continuous’ RS data and the RS data
which have been converted into a number of broad land cover classes. Thus, we argue that
the introduction of unclassified RS-information may generate a useful improvement in
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environmental characterization by conveying part of habitat information into the models
(Saatchi et al. 2008; Cord and Rödder 2011).
The GAMs containing both topo-climatic and RS variables showed highest amounts of
explained deviance and modelling accuracies. Although the absolute increases in the
amount of explained deviance and cross-validated AUC values were not large, they
showed a clear trend, were statistically significant, and changed the projected spatial
patterns of the species. Interestingly, we found that the inclusion of RS-information
improved especially the spatial projection of species with the poorest modelling performance in topo-climate models. Thus continuous RS predictors may significantly improve
modelling success of the species with the most challenging species-habitat relationships,
which is an interesting and important finding (see also Zimmermann et al. 2007). Consequently, for some species the finer scale habitat characteristics are apparently more
important drivers of distributions than the macro-scale climate and topography. Moreover,
hybrid models had approximately similar or slightly higher stabilities compared to climatetopography and solely RS-based models suggesting that hybrid models may also be more
robust for spatial extrapolation. The models based on RS predictors alone generally performed poorer than the other two model types. Thus, meso-scale species distribution
modelling studies that rely merely on continuous RS-data should be interpreted with care,
not least because in many areas similar phenological characteristics of different habitat
types may result in overprediction of species distributions (Roura-Pascual et al. 2006; Cord
and Rödder 2011).
Second, although for most species the best strategy to build models was to use both
topo-climatic and RS information, we found that species with different physiological and
ecological abilities and/or requirements (e.g. Luoto et al. 2005; Seoane et al. 2005; Guisan
et al. 2007; McPherson and Jetz 2007; Zimmermann et al. 2007; Pöyry et al. 2008) may
profit differently from the inclusion of RS predictors. Our results suggested that for species
occupying mire and shore habitats, the addition of RS predictors was most successful. The
first illustrated example species, P. stricta, is a boreal species occurring mainly in proximity of rivers characterized by heterogeneous vegetation cover, consisting mainly on
shrubs and bare soil. In comparison, D. traunsteineri prefers nutrient-rich open wetlands
and only rarely occurs on soils other than peat. For these two species, among others, hybrid
models have the potential to predict spatially more refined distribution patterns compared
to topo-climatic models, resulting in ecologically more reliable predictions of endangered
plant species distributions at the meso- and local scale. When topo-climatic and RS predictors were combined, the model specificity increased suggesting that the predictors were
disentangling distinct areas of expected absence, and thus operated as complementary
predictors (Parra et al. 2004). This suggests that although climate-topography variables
inherently capture the species responses associated with them, they may fail to capture
certain ecosystem characteristics (Hjort and Luoto 2006; Saatchi et al. 2008; Cord and
Rödder 2011).
Other shore species where the inclusion of RS predictors into the models caused a clear
increase in model accuracy were Silene tatarica, Carex microlochin and Elymus fibrosus,
and mire species S. hirculus, Carex heleonastes and D. traunsteineri. Interestingly, in some
other mire species, e.g. Dactylorhiza lapponica and Schoenus ferrugineus, we did not
observe corresponding increases in model performance. Other species where no notable
increase in model performance was observed following the inclusion of RS data contained
a number of species of rocky outcrops or other rocky terrain habitats, such as Minuartia
biflora and Cerastium alpinum, and some species of (cultural) grasslands, e.g. Botrychium
lanceolatum, but exceptions occurred also in these habitat categories (Table 3). Other
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shore species where the inclusion of RS predictors into the models caused a clear increase
in model accuracy were S. tatarica, C. microlochin and E. fibrosus, and mire species S.
hirculus, C. heleonastes and D. traunsteineri. Interestingly, in some other mire species, e.g.
D. lapponica and S. ferrugineus, corresponding increase in model performance was not
observed. Other species where no notable increase in model performance was observed
following the inclusion of RS data included a number of species of rocky outcrops or other
rocky terrain habitats, such as M. biflora and C. alpinum, and some species of (cultural)
grasslands, e.g. B. lanceolatum, but exceptions occurred also in these habitat categories
(Table 3). Thus the only broad conclusion to be derived from our results is that species of
sparsely wooded semi-open or wetland habitats with ‘exceptional’ ecological characteristics and physiognomy compared to the landscape matrix may benefit of the incorporation
of RS data into SDMs.
Relevance of RS variables
NDVI is one of the most extensively used vegetation index in RS and known to be
sensitive to both photosynthetic activity and biomass (Tucker 1979; Myneni et al. 1995;
Raynolds et al. 2006), net primary productivity (Box et al. 1989; Reed et al. 1994; Cramer
et al. 1999) and heterogeneity (Rocchini et al. 2004). Furthermore, NDVI has been shown
to produce more accurate estimates of productivity in higher latitudes, in seasonal environments compared to tropics in low-latitudes (Box et al. 1989; Levin et al. 2007; Parviainen et al. 2009, 2010). Interestingly, although Tasseled Cap transformations have been
available as standard tools for almost 30 years, they are less frequently used than NDVI
applications. The Tasseled Cap transformations has been used mainly for vegetation
mapping and monitoring of land cover change (Crist and Cicone 1984; Cohen et al. 1995;
Dymond et al. 2002; Skakun et al. 2003), but to our knowledge only rarely in species
distribution modelling (but see Zimmermann et al. 2007; Bartel and Sexton 2009). The
greenness derived from the Tasseled Cap transformation optimizes the contrast between
near infrared and visible bands, correlating highly with the amount of healthy, green
vegetation (Weiers et al. 2004). It may therefore incorporate highly different kinds of
information of habitat characteristics than band ratios or indices such as NDVI, which
account only for the red and near infrared bands (Crist 1985).
Where mean values of NDVI and greenness can be seen as proxies for productivity, the
standard deviations of these variables may be used as proxies for the variation of productivity or variation in habitat diversity, in other words an index reflecting the finer scale
environmental heterogeneity. Overall, productivity and its variability reflect different
important habitat characteristics, and thus both variables may jointly play an important role
in explaining spatial trends in red-listed species distribution patterns. For example, areas of
sharp environmental transition may be especially rich in rare species because they represent transition zones of different communities and these are often characterised by
unique environmental conditions found in ecotonal environments (see Karka and van
Rensburgb 2006). However, in our study, species distributions responded mainly positively
to the average productivity values and negatively to habitat diversity suggesting that many
of the boreal red-listed plant species particularly prefer sites with rather high resource
abundance. Considering the global variation in productivity, the study area lies in a highlatitude boreal environment, which poses severe limitations to the distribution and persistence of many vascular plant species (Bonan and Shugart 1989). The sites with high
productivity, e.g. the herb-rich forests, are generally associated with increasing calcium
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levels of the soil and bedrock in the study area (Söyrinki and Saari 1980; Parviainen et al.
2008; 2010).
Caveats and strengths
The use of continuous RS-information as a proxy of species distribution has advantages but
also limitations (Zimmermann et al. 2007; Rocchini et al. 2010). The high amount of
unexplained variation was probably due to missing spatial structures and biased species
distribution data. Other important environmental factors—that were not taken into account
in this study and which may operate at different spatial scales—can also modify the
distribution patterns of red-listed plant species. However, at the spatial scale employed in
this study the ecological gradients analysed were not wide, and there were only limited
spatial structures available because of the rather uniform climate, elevational extent and
land cover.
Other potential caveats in our study, as well as other corresponding RS-data based
modelling studies, are that no information is available on the structural variables of the
landscape, e.g. fractal shapes or more general habitat-shape based information. Moreover,
while structural landscape metrics can be evaluated by texture-based methods, continuous
data do not contain such information a priori. It should be also noted that whereas continuous RS data provides a measure of habitat diversity as such, technically it is a landscape summary measure that does not take into account the uniqueness or potential
ecological importance of different habitats (Rocchini et al. 2010).
Although the predictive performance of the models in this study was rather high, care
should be taken when interpreting these results, because such evaluation measures are
based on presence-only data and not on observed absences (Zaniewski et al. 2002; Elith
et al. 2006). In other words, models based on presence-only data do not accurately predict
the probability of species presence because the proportions of presences within the calibration sets do not represent the true prevalence of the species in the landscape (Pearce and
Boyce 2006). However, these models are nevertheless useful in their ability to rank habitats’ suitability on a relative scale (Elith et al. 2006; Newbold 2010). In addition, in rare
species with small geographic ranges and/or narrow habitat specificity, spatially well
segregated occurrence patterns that are strongly correlated with specific habitat characteristics may emerge from combined topo-climatic and RS predictors. Such patterns may
be detected and modeled even from comparably few occurrence records. In such cases,
continuous RS-information may increase the efficiency of mapping schemes under limited
logistical and financial resources, and the modelling may be based on limited amount
records from museum collections and databases (see also Ponder et al. 2001; Loiselle et al.
2003). Moreover, one of the major strengths of using continuous RS data is the fact that
uncertainty information is not lost due to the data processing. This is a particularly
important difference to the classified RS data where some of the broad land cover types can
include sites with larger variation in the ground conditions and the related reflectance
values than other types, but the degree of this within-type variation are generally unknown
to the investigator and may thus give rise to unknown bias in the species distribution
modelling.
Acknowledgments A study of this nature would not have been possible without the hundreds of volunteers who contributed their data to the red-listed plant species database. M. J. Bailey helped with correction
of the English text. Terhi Ryttäri helped in aggregating the species data for this study. Different parts of this
research were funded by the Academy of Finland (project grant 116544) and the EC FP6 Integrated Projects
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ALARM (GOCE-CT-2003-506675) (Settele et al. 2005), ECOCHANGE (GOCE-2006-036866), and EU
FP7 project SCALES (project #226852).
Appendix
See Tables 6, 7, and 8.
Table 6 List of different habitats included in the five main habitat categories delimited for the study
species
Cultural
Forest
Mire
Rocky
Shore
Rural biotopes
and cultural
habitats
Forests
Mires
Rock outcrops
(incl. erratic
boulders)
Aquatic habitats
Seminatural dry
grasslands
Seminatural mesic
grasslands
Wooded pastures
and pollard
meadows
Seminatural moist
grasslands
Ditches, etc..
Arable land
Parks, yards and
gardens
Roadsides,
railway
embankments,
etc.
Buildings (and
constructions)
Heath forests
Sub-xeric, xeric
and barren heath
forests
Mesic and herbrich heath forests
Herb-rich forests
Dry and mesic
herb-rich forests
Moist herb-rich
forests
Mountain birch
forests
Rich fens
Open rich fens (incl.
herb-rich sedge
fens)
Rich pine fens
Rich spruce-birch
fens
Fens
Ombro- and
oligotrophic fens
Mesotrophic fens
Pine mires
Ombro- and
oligotrophic pine
mires
Mesotrophic pine
mires
Spruce mires
Oligotrophic spruce
mires
Eutrophic and
mesotrophic
spruce mires
Calcareous rock
outcrops and
quarries
Serpentine rock
outcrops
Canyons and
gorges
Caves and
crevices
Other rock
outcrops
Lakes and ponds
Oligotrophic lakes
and ponds
Eutrophic and
mesotrophic lakes
and ponds
Small ponds (also in
mires, etc..)
Rivers
Brooks and streams
Rapids
Spring complexes
Table 7 List of five Landsat 7 ETM? images used in the study
Landsat ETM
Path
Row
Date
RMSE
Image 1
188
14
26.7.2000
7.9
Image 2
189
12
21.8.2001
18.9
Image 3
189
13
22.8.2001
18.6
Image 4
190
13
30.7.2002
11.6
Image 5
192
12
26.8.2001
17.2
RMSE = root-mean-square error of test ground control points of the image in the georeferencing project
123
123
-
BOTBOR
-
-
-
?
\
MINBIF
CERALP
LYCALP
SILTAT
GYPFAS
\
?
?
\
SCHFER
CARAPP
CARHEL
CARLEPJEM
U
\
?
?
CALBUL
-
?
DACINC
-
-
?
D AC LAP
DACTRA
U
\
?
-
?
?
EPIAPH
?
\
-
?
?
CYPCAL
-
ARNANG
?
?
\
-
-
-
\
-
ELE
\
-
?
GENAMA
?
-
\
?
WAB
LONCAE
-
-
U
?
-
?
TEMPC
EPILAE
SAXHIR
?
-
MOELAT
PRISTR
\
ASPRUT
BOTLAN
GDD5
Species
?
-
-
-
?
\
-
\
TWI
U
RAD
-
-
STEEP
\
?
\
-
?
?
NDVImean
?
?
-
?
\
NDVIstd
?
-
-
NDSImean
?
?
?
-
GREENNESmean
Table 8 Summary of the response shapes between the 28 red-listed vascular plant species and each environmental variable in the hybrid GAM models
-
-
-
-
-
-
-
?
?
?
GREENNESSstd
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Biodivers Conserv (2013) 22:1731–1754
-
ELE
?
TWI
RAD
-
-
STEEP
-
NDVImean
NDVIstd
NDSImean
-
-
-
GREENNESmean
GREENNESSstd
The direction of the effect is indicated with symbols (? = positive linear correlate; - = negative linear correlate; \ = non-linear correlate with a hump-shaped response
curve; U = nonlinear correlate with a downward hump-shaped response curve)
The models were built using climate, topographic and RS variables, and the AIC model selection algorithm. For abbreviations of the species and environmental variables see
Tables 1 and 2
-
\
ELYFIB
?
-
-
WAB
-
?
CARV1RBER
TEMPC
CARMIC
GDD5
Species
Table 8 continued
Biodivers Conserv (2013) 22:1731–1754
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