species distribution modelling for the people

BIODIVERSITYRESEARCH

Species distribution modelling for thepeople: unclassified landsat TM imagerypredicts bird occurrence at fineresolutionsS. M. Shirley1*, Z. Yang1, R. A. Hutchinson2, J. D.Alexander3,

K. McGarigal4 and M. G. Betts1

1Department of Forest Ecosystems and

Society, Oregon State University, 321

Richardson Hall, Corvallis, OR 97331, USA,2School of EECS, Oregon State University,

Corvallis, OR 97331, USA, 3Klamath Bird

Observatory, P.O. Box 758, Ashland, OR

97520, USA, 4Department of Environmental

Conservation, University of Massachusetts,

160 Holdsworth Way, Amherst, MA 01003-

9285, USA

*Correspondence: S. M. Shirley, Department

of Forest Ecosystems and Society, Oregon

State University, 321 Richardson Hall,

Corvallis, OR 97331, USA.

E-mail: [email protected]

ABSTRACT

Aim Assessing the influence of land cover in species distribution modelling is

limited by the availability of fine-resolution land-cover data appropriate for

most species responses. Remote-sensing technology offers great potential for

predicting species distributions at large scales, but the cost and required exper-

tise are prohibitive for many applications. We test the usefulness of freely avail-

able raw remote-sensing reflectance data in predicting species distributions of

40 commonly occurring bird species in western Oregon.

Location Central Coast Range, Cascade and Klamath Mountains Oregon, USA.

Methods Information on bird observations was collected from 4598 fixed-

radius point counts. Reflectance data were obtained using 30-m resolution

Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used

boosted regression tree (BRT) models to analyse relationships between distribu-

tions of birds and reflectance values and evaluated prediction performance of

the models using area under the receiver operating characteristic curve (AUC)

values.

Results Prediction success of models using all reflectance values was high

(mean AUC = 0.79 � 0.10 SD). Further, model performance using individual

reflectance bands exceeded those that used only Normalized Difference Vegeta-

tion Index (NDVI). The relative influence of band 4 predictors was highest,

indicating the importance of variables associated with vegetation biomass and

photosynthetic activity. Across spatial scales, the average influence of predictors

at the 2000 m scale was greatest.

Main Conclusions We demonstrate that unclassified remote-sensing imagery

can be used to produce species distribution models with high prediction success.

Our study is the first to identify general patterns in the usefulness of spectral

reflectances for species distribution modelling of multiple species. We conclude

that raw Landsat Thematic Mapper data will be particularly useful in species

distribution models when high-resolution predictions are required, including

habitat change detection studies, identification of fine-scale biodiversity hotspots

and reserve design.

Keywords

Boosted regression trees, land-cover, model validation, species distribution

modelling, unclassified remote-sensing imagery, western Oregon.

DOI: 10.1111/ddi.12093ª 2013 John Wiley & Sons Ltd. http://wileyonlinelibrary.com/journal/ddi 1

Diversity and Distributions, (Diversity Distrib.) (2013) 1–12A

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INTRODUCTION

Land cover is considered to be one of the most important

drivers of biodiversity, affecting patterns of species diversity

(Jetz et al., 2007; Coops et al., 2009), species distributions

(Wilcove et al., 1998; Opdam & Wascher, 2004; Thuiller

et al., 2008) and ecological processes (Dickinson, 1991; Dale,

1997; Allan, 2004). However, we have a rather limited under-

standing of how recent land-cover changes have resulted in

changes to species distributions over larger scales (regional,

continental and global). Clearly, land cover exerts an influ-

ence on species distributions; the negative effects of habitat

loss on species extinctions are also well known (Balmford

et al., 2003). Land-cover changes over broad scales have the

potential to affect biodiversity through a number of mecha-

nisms such as habitat loss and fragmentation, enabling bio-

logical invasions and impairment of ecological processes

critical to ecosystem function. Whereas climate is hypo-

thesized to exert the major influence on distributions at

broad spatial scales, land cover may be the dominant influ-

ence over short terms and at finer spatial scales (Lemoine

et al., 2007; Soberon, 2007). However, this hypothesis has

been difficult to test because our ability to predict the effects

of land cover and land-cover change on species distributions

has been limited by the availability of estimates of land cover

at appropriate spatial and temporal scales. Habitat data at a

sufficiently fine resolution for most organisms under study

(Mladenoff et al., 1999; Bowman et al., 2001; Mitchell et al.,

2001; Betts et al., 2006) are rarely available at broad scales.

In recent decades, modelling the effects of land cover on

species distributions at larger spatial scales has been aided

greatly by the development of remote-sensing technology.

Satellite-borne optical remote sensors collect data from a

region at a variety of spatial resolutions. For example, the

Landsat 7 satellite captures six bands of the visible and infra-

red spectrums at resolutions of 30 m every 16 days (Kerr &

Ostrovsky, 2003; Turner et al., 2003). Satellite observations

of the earth’s surface reflectance at moderately high resolu-

tions (e.g. 30 9 30 m) and over large scales have provided

the basis for the development of data map layers that

describe discrete land-cover classes (e.g. Zinner et al., 2002;

Venier et al., 2004). In addition, indices derived from

remote-sensing data such as the Normalized Difference Vege-

tation Index (NDVI) have been used extensively in species

distribution modelling (e.g. Osborne et al., 2001; Roura-

Pascual et al., 2006; Bino et al., 2008). Techniques such as

LiDAR that provides very fine-scale habitat characterization

offer the potential for improving predictions of species distri-

butions (Swatantran et al., 2012); however, these data are

simply not available for large spatial extents.

Despite these advances, there are several limitations to be

considered in current uses of remote-sensing data (Turner

et al., 2003; Bradley & Fleishman, 2008) for species distribu-

tion modelling. First, aggregation of raw remote-sensing data

into discrete land-cover classes results in misclassification

errors (Foody, 2002; Gottschalk et al., 2005; Gillespie et al.,

2008). Perhaps more importantly, substantial information is

lost when continuous data on the earth’s reflectance are clas-

sified into discrete categories (e.g. coniferous forest, urban,

agricultural), limiting the ability of land-cover maps to

improve predictions of species distributions (Bradley & Fle-

ishman, 2008). Further, native species may not respond

directly to such human-defined boundaries (Betts et al.,

2007; Zitske et al., 2011). This problem is particularly critical

in extensive remote areas that are often categorized into a

single classification (e.g. mature boreal forest, uncut Amazo-

nian forest), so little variability in land-cover composition is

present across entire study areas. Finally, classification of raw

remote-sensing data can be expensive. Even when imagery is

freely available, there can be additional costs for processing

software and computer hardware. Importantly, classification

of images is a non-trivial task that requires technical special-

ists trained in the appropriate techniques; this renders the

use of remote-sensing imagery inaccessible to many research-

ers, particularly in developing countries. Although there are

many tools available for simple image transformation includ-

ing R tools, making useful information out of spectral data

cannot be a simple black-box operation.

One potential solution to these difficulties is to use raw

data on reflectance as explanatory variables in species distri-

bution models. Because the original information for each

pixel is retained, such a modelling approach has the potential

to improve both the spatial resolution and accuracy of

predictions and avoids the subjectivity associated with the

choice of classification method and the loss of information

associated with classifying an inherently continuous attribute.

Further, because such data are now freely available at high

temporal resolution for many areas of the earth’s surface

(http://landsat.usgs.gov/Landsat_Search_and_Download.php),

such an approach would enable researchers in regions where

no classified images currently exist to model distributions as

a function of land cover.

Here, we present results of an analysis of relationships

between species occurrences and a set of raw remote-sensing

reflectance variables using data collected as part of a study of

birds in forest landscapes of western Oregon, USA. We

hypothesized that occurrences of several bird species would

be associated with the reflectance values for certain bands.

Spectral values for different bands have been associated with

a variety of landscape characteristics. For example, values for

both red and near-infrared reflectance (bands 3 and 4) are

associated with vegetation features such as green-leaf bio-

mass, leaf area and photosynthetic activity (Tucker, 1979;

Sellers, 1985; Spanner et al., 1990), while middle-infrared

reflectance as recorded by bands 5 and 7 is sensitive to

changes in soil and plant moisture and can provide a mea-

sure of senescent biomass (Rey-Benayas & Pope, 1995; Kerr

& Ostrovsky, 2003). We expected that those bands associated

with canopy structure and biomass would be the most

important predictors of bird distributions. If predictive,

maps based on fine-scale spectral data may be relevant to

fundamental objectives in conservation biology and

2 Diversity and Distributions, 1–12, ª 2013 John Wiley & Sons Ltd.

S. M. Shirley et al.

landscape ecology including quantifying species-specific rates

of habitat loss, forest management planning, protected areas

design (Possingham et al., 2006) and testing the relative

importance of climate change versus habitat loss at broad

spatial scales.

METHODS

Study area

We compiled data from previous studies in two regions at

elevations ranging from sea level to 968 m in north-western

(McGarigal & McComb, 1995) and south-western Oregon,

USA (Alexander et al., 2004). The north-western Oregon

study area is situated in the Drift Creek, Lobster Creek and

Nestucca River basins covering approximately 6500 km2 of

the central Oregon Coast Range and is characterized by wet

winters and dry summers. The south-western Oregon study

area covers a wide range of ecological types ranging from

Cascade slopes to much warmer and drier Klamath Moun-

tains in areas of the Cascade-Siskiyou National Monument

and Applegate Valley. Land cover is dominated by forest pri-

marily composed of Douglas-fir (Pseudotsuga menziesii) and

mountain hemlock (Tsuga mertensiana) with deciduous

species such as red alder (Alnus rubra) and bigleaf maple

(Acer macrophyllum) in the north and oaks (mostly Quercus

garryana and Q. kelloggii), ponderosa pine (Pinus ponderosa)

and Pacific madrone (Arbutus menziesii) at low elevations in

the southern region. Disturbances are primarily from clearcut

timber harvesting in the north-western and clearcut harvest-

ing and wildfires in the south-western study areas.

Bird sampling

In the north-western region, sample points were located in a

uniform grid at 200-m intervals along transects spaced

400 m apart in 30 sub-basins (approximately 300 ha each).

Between 32 and 38 sample points were placed within each

sub-basin, for a total of 505 points. In the south-western

region, 4093 sample points separated by distances of

150–250 m were established along transects to capture eleva-

tion, forest habitat type and disturbance gradients. In both

study areas, avian data were collected using fixed-radius

point counts (Ralph, 1995) conducted between 05:30 and

10:00 Pacific Standard Time at each sample point. In north-

western Oregon, four 8-minute counts were conducted on

separate occasions between May and June in the years from

1990 to 1992. Surveys were conducted 15–20 min before

sunrise to 4 h after sunrise. South-western region point-

count data were collected from 2002 to 2005 using the same

protocols, but points were only visited one to two times dur-

ing the season. Differences in the number of visits between

studies and count duration should result in higher overall

probability of detection for the same species in north-western

than south-western Oregon (Drapeau et al., 1999). However,

all other things being equal, the shape of the response of

individual species to landscape structure should remain the

same (Betts & Villard, 2009). In our analysis, we utilized data

for all birds seen or heard within a 50 m radius. Because

mean bird counts per station tended to be low (< 2) for

most species and we were interested in estimating probability

of occurrence in a way that is consistent with other species

distribution modelling efforts (e.g. Guisan & Thuiller, 2005;

Buermann et al., 2008), we reduced relative abundance data

to presence–absence data for use in binomial models. These

combined datasets resulted in a database of 4598 spatial

sample points, with 127,164 bird detections representing 152

species. For analysis, we selected the 40 most commonly

occurring species (see Table S1 in Supporting Information).

Remote-sensing data

Landsat images corresponding to the survey year were used

for spectral analysis. All the Landsat images were acquired

from USGS (http://landsat7.usgs.gov/index.php). Images

from USGS had sub-pixel geolocation accuracies, and no fur-

ther geometric processing was applied. Although all the

images were chosen to represent the growing season (July

and August in the year of data origin), factors such as sea-

sonal phenology and atmospheric conditions etc. can make

the spectral values recorded by satellite highly variable across

images. For studies such as ours that cover a large geographi-

cal area that may have been collected over several years,

radiometric correction is essential to minimize the noise not

related to ground conditions (Song & Woodcock, 2003;

Schroeder et al., 2006). The cosine–Theta (COST) correction

of Chavez (1996) was used to remove most atmospheric

effects for a single reference image in a given Landsat time

series (LTS). Spectral values for dark objects used in the

COST calculation were based on visual assessment following

the rules outlined in protocols found in Kennedy et al.

(2007a). For each Landsat path/row used in this analysis, one

cloud-free clear image was selected from the Landsat archive

as the reference image. All the other images used were then

normalized to the COST image using the MADCAL (multi-

variate alteration detection and calibration) algorithms of

Canty et al. (2004), which identify stable pixels within a

small subset of the larger image. Clouds and cloud shadow

within each image were identified using Vegetation Change

Tracker of Huang et al. (2010). These pixels were excluded

from further analysis. For each survey point, spectral values

from Landsat bands excluding the thermal infrared band

were summarized using a 150, 500, 1000 and 2000 m radius.

Within the surrounding neighbourhood of each radius, the

mean and standard deviation for the 6 spectral bands were

summarized to produce the spectral variables for the model.

Data analysis

We developed models for 40 of the most commonly occur-

ring bird species in the dataset (based on � 10% occur-

rence). Of these species, 34 species occurred in the northern

Diversity and Distributions, 1–12, ª 2013 John Wiley & Sons Ltd. 3

Predicting bird distributions with remote-sensing data

region and all 40 species occurred in the southern region.

Our explanatory variables were the means and standard devi-

ations calculated for Landsat remote-sensing reflectance

bands 1, 2, 3, 4, 5 and 7 (hereafter ‘Reflectance Models’). In

addition, we chose to analyse these predictor variables at 4

scales that have been found to be relevant to passerine bird

species: 150, 500, 1000 and 2000 m (Betts et al., 2006) for a

total of 48 predictor variables. Other species distribution

studies commonly use an index derived from bands 3 and 4

known as the Normalized Difference Vegetation Index

(NDVI) (Parra et al., 2004; Gottschalk et al., 2005; Gillespie

et al., 2008; Morán-Ordónez et al., 2012). As a further com-

parison, we developed models for all species using a separate

set of covariates that included the NDVI values calculated at

each scale (hereafter ‘NDVI models’).

We analysed relationships between species distributions of

birds and remote-sensing reflectance using boosted regression

tree (BRT) models (Friedman et al., 2000; Friedman, 2001).

BRT models belong to a class of ensemble or model-averag-

ing models and their use is relatively recent for ecologists

(Leathwick et al., 2006). Unlike traditional regression-based

models that fit a single model using a response variable and

a set of predictors, BRT models start with a simple classifica-

tion or regression tree and use a boosting algorithm to itera-

tively fit new trees to the model in a forward stage-wise

fashion (Elith et al., 2008). Subsequent trees give additional

emphasis to observations poorly predicted by previous trees.

The final BRT model contains many individual model terms

each represented by a tree. BRTs offer a number of advanta-

ges for model fitting. They can be fitted to a variety of

response types including normal, count and binomial data,

and they have the ability to model nonlinear relationships

and interactions between predictor variables. Finally, the

process of boosting has a stochastic component that

improves prediction performance using a random subset of

data to fit each new tree, thereby resulting in slightly differ-

ent final models every time they are run.

Datasets including several remote-sensing predictors from

the same area tend to be highly correlated (Zimmermann

et al., 2007), and our analyses revealed substantial multicol-

linearity in the predictors. Despite this fact, we conducted

analyses using all predictor variables to retain as much infor-

mation as possible to develop relationships and avoid omit-

ting important variables. Correlated input variables are not

problematic for prediction tasks as long as the correlation

structure remains constant over the training and testing

datasets (Dormann et al., 2012). Boosted regression trees do

not encounter the numerical issues faced when fitting linear

models with collinear variables because trees are fit with

recursive partitioning algorithms instead of matrix inversions

(Breiman et al., 1984).

When fitting models with many variables, it is also impor-

tant to avoid overfitting. Overfitting occurs when a model

fits not only the signal, but also the noise in the training

data, resulting in optimistically biased estimates of model

performance on the training data. More complex models

(with more variables) will often perform better on the train-

ing set because they can fit the data more closely. To

compare models of differing complexity fairly, we report per-

formance for all models on an independent test set com-

prised of roughly half of the data points. If overfitting

occurred, we would expect more complex models to perform

more poorly than simpler models on these independent test

data.

We calculated the Moran’s I statistic as a gauge for the

degree to which spatial autocorrelation in residuals of all

BRT models influenced our results. All values were calculated

using the correlog function in the R package ncf (Bjørnstad,

2009). The average Moran’s I value for all species was 0.146,

and although this was a significant correlation (at

alpha = 0.05) in our permutation tests, the effect size was

not substantial (Moran’s I ranges from 0 to 1, with val-

ues > 0.3 considered relatively large (Lichstein et al., 2002)

(see Table S2 in Supporting Information). To minimize the

effects of spatial correlation between the training and test

sets, we used a checkerboard approach similar to those

described in Hochachka et al. (2010), Munson et al. (2010)

and Hochachka et al. (2012). We placed a 15 9 24 checker-

board over the data, resulting in grid cells of roughly

33181.5 NORTHING by 10871.39 EASTING units. The 2395

points in the white squares became the training set, and the

2203 points in the black squares became the test set.

All BRT models were fitted in R version 2.13.1 (www.R-

project.org, R Development Core Team, 2012). We supple-

mented the ‘gbm’ package (Ridgeway, 2006) with additional

functions relevant to ecological data (Elith et al., 2008

Appendix). Values for several model parameters were

selected based on information in Elith et al. (2008). The

degree of model stochasticity is controlled by the ‘bag frac-

tion’ that determines the proportion of the data to be

selected as a random subset from the full training set for

each new tree. Following Elith et al. (2008), we used a bag

fraction of 0.5 which has yielded good results for presence–

absence data. The learning rate, which controls the rate at

which additional trees are added to the model, we set to

0.01. Lower values of the learning rate increase the number

of trees required such that each tree has lower individual

influence giving an overall better fit to the model (Friedman,

2001). Tree complexity refers to the number of nodes or

decision rules and controls the level of model interactions

between predictor variables; this was set to 5 in our models.

We assessed the optimal number of trees for each species

using a tenfold cross-validation procedure (Hastie et al.,

2001) where 9/10 of the training data are used for model fit-

ting, 1/10 is withheld for validation, and the process is

repeated ten times. We evaluated prediction performance of

the models using the area under the receiver operating char-

acteristic curve (AUC) on the independent test set, for which

we computed 95% DeLong confidence intervals using the

‘pROC’ R package (Robin et al., 2011).

Using the gbm package, we also calculated the relative

influence of each predictor; this provides a measure of the



strength of each variable’s influence on the total response

and is reflected as a proportion. We summarized the relative

influence of each predictor variable on the model for the

reflectance models and compared the relative influence of

predictor variables across the six bands and four scales using

a Kruskal–Wallis analysis of variance for non-normally

distributed data.

RESULTS

Species distribution models

Prediction success for the reflectance models was high, with

an average AUC value across all species of 0.79 (SD = 0.10)

and ranging from 0.56 to 0.98 (Fig. 1). Prediction accuracies

for 80% of species were above AUC = 0.70, the value consid-

ered necessary for models to have acceptable discriminatory

power (Hosmer & Lemeshow, 2000) with almost 40% of

models having excellent prediction success (AUC � 0.80).

Several of the species with the highest prediction values were

associated with old-growth forest (e.g. Pacific-Slope Fly-

catcher; Fig. 1) or have highly disjunct distributions between

northern and southern areas. This raised the possibility that

high model performance was partly due to differences in

reflectance between study areas. To some extent, these regio-

nal differences in reflectance represent an ecological signal

on which the model can, and should, make distinctions.

However, for species that only occurred in the southern

region, including the absences from the northern region with

different characteristics might make the prediction task

easier. Our interest was in testing whether predictions were

accurate at fine resolutions rather than simply distinguishing

regional differences. To investigate this issue, we ran trained

and tested models on just the data from the southern region

for the six species that never occurred in the northern

region. The average reduction in AUC over these six species

from the original models to the southern region models was

0.03 (standard deviation 0.02; Shirley and Betts unpublished

data). This small difference, which may be partially attribut-

able to the reduced size of the dataset, indicates that regional

differences in reflectance are not a key driver of prediction

accuracy in our study. There was also no correlation between

prevalence and model performance; relatively less common

species performed as well as more common species

(t = �0.668, P = 0.508).

Compared with the reflectance models, the NDVI models

had a significantly lower prediction success (Mann–Whitney

test = 2157.00, P � 0.001) with an average AUC value

across all species of 0.70 (SD = 0.13) and ranging from 0.48

to 0.95. Prediction accuracies for 37% of species were above

AUC = 0.70, the value considered necessary for models to

have acceptable discriminatory power (Hosmer & Leme-

show, 2000) with 20% of models having excellent prediction

success (AUC � 0.80). The superior performance of the

reflectance models over the NDVI models could be due to

information in the bands not used to produce NDVI. Alter-

natively, higher performance in band-only models could

result from additional information in bands 3 and 4 that

are lost when the data are classified to NDVI. To test these

hypotheses, we developed models using only a subset of

Figure 1 The results of species distribution models showing the area under the curve (AUC � CI) for boosted regression tree models

for the 40 most commonly occurring species based on covariates including all spectral reflectances (filled circles) and NDVI index values

(open circles). The dashed line indicates the predicted accuracy of the null model. See Table S1 for full species names.



predictors (bands 3 and 4 only [16 variables]); the predic-

tion accuracies of the subset models were very similar to

those of the larger reflectance models (AUC = 0.78) even

with substantially fewer predictor variables. These results

indicate that the NDVI models do not contain as much

information as the raw data from bands 3 and 4 and that

while there is some benefit to including the other 4 bands

in the model, most of the predictive performance rests on

bands 3 and 4.

Relative influence of predictors on model

performance

We summarized the relative influence of predictors by bands

and scale. The average contribution of each reflectance band

to model predictions varied substantially (Fig. 2a, H = 82.89,

n = 40, P � 0.001). Across all species, the relative influence

of band 4 predictors was over three times greater than the

next highest band. The mean from band 4 generally had

greater influence than the standard deviation (65% vs. 35%).

In particular, the average band 4 reflectance at the 2000-m

scale was selected as a top five predictor variable for 60% of

species. The spatial scales we considered as predictors also

differed substantially in their contribution to prediction suc-

cess (Fig. 2b, H = 73.70, n = 40, P � 0.001); the average

influence for predictors at the 2000-m scale was 4.95 times

higher than the next scale. All 40 species had at least one top

five predictor at the 2000-m scale, while predictors at the

other scales had influence for roughly half of the species’

models. Considering each species individually, the relative

influence of predictor variables was fairly even in magnitude

across the predictors. However, for 7/40 species, especially

those with strong associations to old-growth conifer forest,

the range of relative influence values was much more diverse

(e.g. relative influence of both high and low magnitude),

with one predictor (band 4 mean at 2000 m) being the

dominant influence.

DISCUSSION

Use of raw reflectance data for prediction accuracy

The use of remote sensing by ecologists is growing as its

potential for addressing the consequences of large-scale envi-

ronmental change on biodiversity increases. In the last few

decades, a number of remote-sensing technologies have been

used in a wide variety of applications including identifying

habitat relationships (Johnson et al., 1998), quantifying bio-

diversity (Kerr et al., 2001; Levin et al., 2007), modelling

species distributions (Achard et al., 2002; Buermann et al.,

2008) and conservation planning (Rey-Benayas & Pope,

1995; Friedlander et al., 2007). One review compiled infor-

mation on over 120 studies using remote-sensing technology

for analysing avian habitat relationships (Gottschalk et al.,

2005). Despite the benefits offered by remote-sensing tech-

nology, the associated costs and requirement for expertise

preclude their use in many ecological studies. Many types of

spectral data are technically difficult to collect and analyse,

have resolutions that are too coarse to use for fine-scale

land-cover patterns (e.g. MODIS, 250–500 m) or have been

classified into maps that are only available for specific peri-

ods; for example, the US National Land Cover Database

covers large spatial scales, but it is only available for the

years 1992, 2001 and 2006.

We demonstrate that variables obtained from raw unclassi-

fied remotely sensed imagery can be used to produce species

distribution models with high prediction ability and for a

wider temporal range than would be possible for classified

imagery products (Fig. 3). The overall prediction accuracy of

similar studies of avian habitat relationships using a classifi-

cation process varies between 60 and 99% with a mean of

85% (Gottschalk et al., 2005); however, actual classification

accuracy is often lower than expected or unknown due to

the high cost associated with assessment (Shao & Wu, 2008).

In contrast, using raw imagery avoids uncertainty associated

Figure 2 Mean (� SE) relative influence (%) of Landsat TM band covariates across all species grouped by (a) reflectance band and (b)

remote-sensing reflectance scale for boosted regression tree models. The values reflect the number of times a variable is selected for

splitting and weighted by improvements to the model (Friedman, 2001). Higher values indicate a stronger influence on the response

variable.



with a loss of classification accuracy due to errors such as

the misclassification of habitat types to pixels (White et al.,

1997; Shao & Wu, 2008) as well as the omission of fine-scale

local features (e.g. Gottschalk et al., 2005) and subtle changes

in vegetation. In addition, the use of all spectral reflectance

bands as independent predictors as opposed to indices that

focus on a narrow range of the spectrum may improve pre-

dictions by allowing models to select from a wider set of

band combinations (Morán-Ordóñez et al., 2012). The fine-

scale spectral resolution of raw remote-sensing data used in

our study may contribute to the high values of prediction

accuracy observed for most species in our analysis.

One potential issue with the use of raw bands is the

potential error due to topographical ‘wild areas’, where sha-

dow effects occur due to topographical complexities in slope

and aspect. It is interesting to note that despite this potential

error, our models performed well on independent data.

However, it is also possible that because wild areas are con-

sistently placed across landscapes (e.g. north facing slopes),

these shadows contain biological information that is relevant

to modelling species distributions.

Influence of variables on model prediction

We assessed the relative strength of the reflectance variables

in terms of band and scale of data aggregation. While inter-

pretation of models trained on non-independent input vari-

ables must be carried out with caution, relative influence

metrics can still provide some insight into the inner work-

ings of boosted regression tree models. There has been rela-

tively little analysis of multispecies or communities using

remote-sensing reflectance data (Gottschalk et al., 2005;

Zimmermann et al., 2007). Most studies that have used

reflectance data have used derived vegetation indices (Baret

& Guyot, 1991), NDVI (Gottschalk et al., 2005; Gillespie

et al., 2008) or have been focused on a single species. To our

knowledge, our study is the first to identify some general

patterns in the usefulness of spectral reflectances for species

distribution modelling of multiple bird species within a

region. We found that, across all species, reflectance in band

4 and variables at the scale of 2000 m were the predictors

with the highest relative influence. Band 4 represents reflec-

tance in the near-infrared region of the visible spectrum

(Kerr & Ostrovsky, 2003) and is associated with vegetation

biomass and photosynthetic activity (Sellers, 1985; Spanner

et al., 1990). In addition, in central America, the band 4

radiance was the most important predictor for determining

landscape diversity patterns (Rey-Benayas & Pope, 1995).

Variables derived from satellite images (NDVI-based pre-

dictors) measured at coarser scales have also been shown to

be important for species richness of both boreal plants and

butterflies (Parviainen et al., 2009; Levanoni et al., 2011).

The reasons for these patterns are not well understood, but

it is thought that variables estimated over larger scales better

capture landscape-scale processes such as patch colonization

and habitat selection. The largest of the spatial extents we

(a) (b) (c) (d)

Figure 3 Predicted potential spatial distribution in western Oregon of the Olive-sided Flycatcher (Contopus cooperi) [(a) and (b)] and

Pacific-slope Flycatcher (Empidonax difficilis) [(c) and (d)] in 1995 and 2005 estimated from boosted regression tree models using

Landsat TM spectral reflectance bands as predictor variables. Colours refer to probability of occurrence where yellow indicates the

highest probability and black the lowest probability. In this example, mean reflectance for band 4 at 150 and 2000 m was the predictors

with the largest relative influence for the Olive-sided Flycatcher and Pacific-slope Flycatcher, respectively.



selected likely include the scale relevant to bird species in

natal dispersal (Bowman, 2003) and extraterritorial move-

ments during the breeding season (Norris & Stutchbury,

2001). In a previous effort from Oregon, the 2000 m spatial

extent also emerged as the most important spatial scale

(Betts et al., 2010). Importantly, this study used different

independent data (land-cover classes) and modelled distribu-

tions separately for southern and northern study areas.

Response predictor variables summarized as mean values had

the greatest overall relative influence, although measures of

variation were also important in some instances. This sug-

gests that for many species, the average vegetation composi-

tion around the sample point was more important than the

heterogeneity in vegetation cover.

Applications of models using raw remote-sensing

data

Unclassified Landsat data offer several advantages for use in

species distributions models. Landsat 7 imagery provides data

at a relatively fine spatial resolution of 900 m2 (30 9 30 m),

but these data are available at global spatial extents, so the

typical trade-off between resolution and extent of spatial data

(Betts et al., 2006) is not necessary. Further, images are now

free to the public and easy to obtain. These images are avail-

able on an annual basis offering opportunities for ecologists

to easily access multi-year data over global scales. Indeed,

remote-sensing data are a necessary and sometimes the only

source of land-cover data for many regions of the world. For

example, remote mountainous areas of high conservation

interest can have low economic value in terms of resource

availability, but provide important ecological services; they

are reservoirs of water and biological diversity (Harrison

et al., 2010). Recent developments in remote-sensing process-

ing using open source software such as Quantum GIS

(http://www.qgis.org/) and GRASS GIS (http://grass.osgeo.

org) have made the process of radiometric correction more

accessible and reduced subjectivity associated with visual

assessments to identify dark objects. Using raw remote-sens-

ing technology allows us to develop urgently needed esti-

mates of species distributions in a cost-effective manner and

is critical to the management and conservation of species

over large scales.

The need for fine-scale resolution data for predicting

potential influences of land-cover change on species distribu-

tions is only starting to be fully appreciated. Land-cover pro-

cesses such as forest management, agricultural and urban

development usually happen incrementally at fine resolutions

(e.g. most clearcuts in temperate zones are < 100 ha (McGa-

rigal & McComb, 1995; Betts et al., 2003); changes in spatial

patterns occurring as a result of these processes could not be

detected using resolutions > 1 km (e.g. MODIS) until the

majority of the habitat had been removed (Balmford et al.,

2003). Small-bodied species likely respond to the landscape at

fine resolutions, for example, individual canopy gaps (McGa-

rigal & McComb, 1995; Betts et al., 2006), which could be

missed with data collected at broad spatial scales. Further,

fine-scale spatial data are needed to predict the precise loca-

tion of a species and its association. For example, ‘fine-filter’

strategies for biodiversity conservation (Scott et al., 1993)

often require identification of sites at scales much finer than

those permissible by coarser remotely sensed data such as

MODIS. Recent advances in remote-sensing technology using

very high-resolution LiDAR in combination with other satel-

lite imagery have shown high prediction success from being

able to produce multidimensional habitat structure maps

(Swatantran et al., 2012); however, LiDAR data are rarely

available at large spatial extents. Our approach of using raw

annual imagery also allows for a fine-scale modelling of spe-

cies distributions as illustrated in Fig. 3 without having to be

constrained by the temporal ranges associated with classified

imagery products (e.g. NLCD 1992 and 2001).

The use of raw reflectances in modelling species distribu-

tions may be limited to certain applications. Unlike detailed

quantification of vegetation structure and composition avail-

able from ground-sampled plots or classified imagery prod-

ucts, raw reflectances are not easily interpretable by ecologists

which may limit their ecological relevance in uncovering spe-

cies–vegetation associations (Ollinger, 2011). Because climate,

soil conditions and vegetation structure may affect the rela-

tionships between bird occurrences and Landsat reflectances,

specific models developed for one image are not necessarily

easily applied to other images (Nagendra, 2001). As in most

instances where species distribution models are applied

beyond the spatial and temporal extents of the existing data,

predictions should be carefully evaluated and used with an

appropriate degree of caution. Despite these limitations, we

conclude that raw remote-sensing data show great promise

for strengthening applications that have previously made use

of remote-sensing data such as forest management planning

(Achard et al., 2002) and habitat change (Kerr & Ostrovsky,

2003; Wolter et al., 2008), identification of biological hotspots

at fine resolutions, and reserve selection (Schulman et al.,

2007). Most importantly, these data are freely obtained, rela-

tively easy to use and available for many areas of the globe.

ACKNOWLEDGEMENTS

This study was supported by a US National Science Founda-

tion grant (NSF-ARC-0941748) to MGB and grant

G11AC20255 from the United States Geological Survey

(USGS). Thanks to Tom Dietterich for helpful conversations.

The authors are grateful to Janet Franklin, Benjamin Zucker-

berg and two anonymous reviewers who provided helpful

comments on this manuscript.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the

online version of this article:

Table S1 List of bird species used to assess the usefulness of

raw unclassified remote-sensing data in predicting species

distributions in Oregon, USA.

Table S2 Results of the Moran’s I test for each bird species

used to assess spatial autocorrelation in the residuals for BRT

models for predicting species distributions in Oregon, USA.

BIOSKETCH

Susan Shirley and Matthew Betts, based in the Depart-

ment of Forest Ecosystems and Society at Oregon State

University (http://www.fsl.orst.edu/flel/index.htm), lead a

research team directed at understanding climatic and land-

use factors underlying the spatial and temporal distribution

of bird species in North America.

Author contributions: S.S. and M.G.B. conceived the ideas; S.S.,

Z.Y., K.M., J.A. and M.G.B. collected the data; S.S., M.G.B. and

R.H. analysed the data; and S.S. and M.G.B. led the writing.

Editor: Janet Franklin



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