Using unclassified continuous remote sensing data to improve

ORI GIN AL PA PER

Using unclassified continuous remote sensing datato 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� 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. Spe-

cifically, at the landscape scale, spatial models for the occurrences of species of conser-

vation 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 pre-

dicted 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 incorpo-

rating 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-topog-

raphy 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, Finlande-mail: [email protected]

N. E. ZimmermannSwiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland

R. K. HeikkinenFinnish Environment Institute, Natural Environment Centre, P.O. Box 140, 00251 Helsinki, Finland

M. LuotoDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014 Helsinki,Finland

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Biodivers Conserv (2013) 22:1731–1754DOI 10.1007/s10531-013-0509-1

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 red-

listed 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 predic-

tive species distribution models (SDMs) (e.g. Franklin 1995; Wu and Smeins 2000; Seoane

et al. 2003; Rushton et al. 2004; Guisan and Thuiller 2005; Araujo 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 predic-

tions 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 Rodder

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

1732 Biodivers Conserv (2013) 22:1731–1754

123

(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; Poyry 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 north-

eastern 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

Biodivers Conserv (2013) 22:1731–1754 1733

123

data source in this database, but information on species occurrences was also gathered from

the scientific literature and from herbaria (Ryttari 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

Fig. 1 The location of the study area in boreal landscape, north-eastern Finland, together with the majorvegetation zones and sectors. The black dots indicate known presence points of the modelled threatenedplant species. The vegetation zones are divided into the following sectors according to the variation inclimate. O1 = slightly oceanic, OC = indifferent, C1 = slightly continental (Ahti et al. 1968; Heikkinen2005). Land use-classification is based on Corine 2000 land-cover classification


123

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.

Table 1 The studied 28 nationally red-listed vascular plant species

Species Abbreviation Frequencyntot = 1,677

Prevalence(%)

Mainhabitat

Conservationstatus

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 incarnatassp. 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

Mean ± std 86.9 ± 91.3 5.18 ± 5.54

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)


123

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 envi-

ronmental 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 tem-

perature of the coldest month (TEMPC) and water balance (WAB) were used as climatic

predictor variables, because they reflect the principal limitations to many species’ occur-

rences 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 Sven-

ning 2004). The climate data with a 10 km resolution (mean values) from the period

1961–1990 (Venalainen and Heikinheimo 2002) were downscaled to 0.5 km (25 ha) grids

by using a linear regression model following the methodology of Vajda and Venalainen

(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 repre-

sent 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 cor-

rection 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 (Harma et al. 2004).


123

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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 ETM4� ETM3ð Þ= ETM4þ ETM3ð ÞNDVI 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 ETM5� ETM4ð Þ= ETM5þ ETM4ð ÞThe 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 red-

listed 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 com-

bination 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).


123

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 (‘re-

substitution 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. Araujo 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 auto-

correlation (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-para-

metric 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 RS-

information 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,


123

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 RS-

based 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

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

Explaineddeviance (%)

Cross-validatedAUC

Explaineddeviance (%)

Cross-validatedAUC

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

Dactylorhiza traunsteineri 8.90 0.66 17.20 0.74

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


123

the species habitat preferences, the increase in the modeling performance was most pro-

nounced 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.

Table 4 Modelling accuracy (mean ± standard deviation) for the 28 red-listed plant species in topo-climatic, RS and hybrid models measured by the amount of explained deviance, calibrated and cross-validated AUC-values and the model stability (i.e. ratio of calibration AUC and four-fold cross-validationAUC)

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

Hybrid model 48.36 ± 19.182 0.897 ± 0.069 0.875 ± 0.076 0.975 ± 0.019

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) andRS versus hybrid (P2) models. Ranks: positive/negative/tied. Positive rank refers to the number of times ahybrid 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 andb in model accuracy (cross-validated AUC) when incorporating RS variables into AIC-based topo-climaticmodels for the 28 red-listed plant species. The 28 models are assigned into different categories according tothe habitat preferences of the species


123

Discussion

There is considerable knowledge about the ecophysiological processes that underlie spe-

cies responses to the environment, and such knowledge is important when selecting pre-

dictor 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), espe-

cially 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

Table 5 The relative roles of individual environmental variables in explaining the distribution of 28 red-listed plant species, as derived from the GAMs based on the AIC model selection algorithm based on modelcontributions (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 7

GREENNESstd 10.3 216 11

‘‘Mean’’ = percentage of model contribution provided by GAM analyses; ‘‘Std’’ = standard deviation inthe model contribution provided by GAM analyses; ‘‘Count’’ = number of GAM models in which thevariable was selected. For abbreviations of the environmental variables see Table 2


123

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

Fig. 3 Projected distribution of Dactylorhiza traunsteineri and Primula stricta derived from the GAMmodels 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 dotsrepresent the known presence points of the species, dark grey shaded areas modelled as suitable and lightgrey areas modelled as non-suitable for the species. The probability level that showed the highest Kappavalue (Kappa-Maximized Threshold, KMT) was used to classify the predicted occurrence probability valuesfor each species in each grid cell. D2 = percentage of explained deviance, AUC = the area under the curvebased on four-fold cross-validation, Pr = the prevalence of the species, Nr = the number of variablesselected in the model, T = threshold based on KMT criterion


123

environmental characterization by conveying part of habitat information into the models

(Saatchi et al. 2008; Cord and Rodder 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 perfor-

mance 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). Con-

sequently, 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 climate-

topography 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 per-

formed 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 Rodder 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; Poyry 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 prox-

imity 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 pre-

dictors 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

Rodder 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


123

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 character-

istics 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 envi-

ronments compared to tropics in low-latitudes (Box et al. 1989; Levin et al. 2007; Par-

viainen 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 pro-

ductivity 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 rep-

resent 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 high-

latitude boreal environment, which poses severe limitations to the distribution and per-

sistence 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


123

levels of the soil and bedrock in the study area (Soyrinki 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 con-

tinuous RS data provides a measure of habitat diversity as such, technically it is a land-

scape 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 cali-

bration 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 hab-

itats’ 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 charac-

teristics 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 volun-teers who contributed their data to the red-listed plant species database. M. J. Bailey helped with correctionof the English text. Terhi Ryttari helped in aggregating the species data for this study. Different parts of thisresearch were funded by the Academy of Finland (project grant 116544) and the EC FP6 Integrated Projects


123

ALARM (GOCE-CT-2003-506675) (Settele et al. 2005), ECOCHANGE (GOCE-2006-036866), and EUFP7 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 studyspecies

Cultural Forest Mire Rocky Shore

Rural biotopesand culturalhabitats

Forests Mires Rock outcrops(incl. erraticboulders)

Aquatic habitats

Seminatural drygrasslands

Seminatural mesicgrasslands

Wooded pasturesand pollardmeadows

Seminatural moistgrasslands

Ditches, etc..Arable landParks, yards and

gardensRoadsides,

railwayembankments,etc.

Buildings (andconstructions)

Heath forestsSub-xeric, xeric

and barren heathforests

Mesic and herb-rich heath forests

Herb-rich forestsDry and mesic

herb-rich forestsMoist herb-rich

forestsMountain birch

forests

Rich fensOpen rich fens (incl.

herb-rich sedgefens)

Rich pine fensRich spruce-birch

fensFensOmbro- and

oligotrophic fensMesotrophic fensPine miresOmbro- and

oligotrophic pinemires

Mesotrophic pinemires

Spruce miresOligotrophic spruce

miresEutrophic and

mesotrophicspruce mires

Calcareous rockoutcrops andquarries

Serpentine rockoutcrops

Canyons andgorges

Caves andcrevices

Other rockoutcrops

Lakes and pondsOligotrophic lakes

and pondsEutrophic and

mesotrophic lakesand ponds

Small ponds (also inmires, etc..)

RiversBrooks and streamsRapidsSpring 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

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