Importance of data selection and filtering in species distribution models: A case study on the Cantabrian brown bear

Abstract

Species distribution models (SDMs) are powerful tools in ecology and conservation. Choosing the right environmental drivers and filtering species' occurrences taking their biases into account are key factors to consider before modeling. In this case study, we address five common problems arising during the selection of input data for presence-only SDMs on an example of a general-ist species: the endangered Cantabrian brown bear. First, we focus on the selection of environmental variables that may drive its distribution, testing if climatic variables should be considered at a 1-km analysis grain. Second, we investigate how filtering the species' data in view of (1) their collection procedures , (2) different time frames, (3) dispersal areas, and (4) subpopulations affects the performance and outputs of the models at three different spatial analysis grains (500 m, 1 km, and 5 km). Our results show that models with different input data yielded only minor differences in performance and behaved properly in terms of model validation, although coarsening the analysis grain deteriorated model performance. Still, the contribution of individual variables and the habitat suitability predictions differed among models. We show that a combination of limited data availability and poor selection of environmental variables can lead to inaccurate predictions. Specifically for the brown bear, we conclude that climatic variables should not be considered for exploring habitat suitability and that the best input data for modeling habitat suitability in the study area originate from (1) observations and traces from the (2) most recent period (2006-2019) in which the population is expanding, (3) not considering cells of dispersing bear occurrences and (4) modeling sub-populations independently (as they show distinct habitat preferences). In conclusion , SDMs can serve as a useful tool for generalist species including all available data; still, expert evaluation from the perspective of data suitability for the purpose of modeling and possible biases is recommended. This is especially important when the results are intended for management and conservation purposes at the local level, and for species that respond to the
environment at coarse analysis grains.

Full text

ARTICLE
Importance of data selection and filtering in species
distribution models: A case study on the Cantabrian
brown bear
Alejandra Zarzo-Arias
1,2,3
| Vincenzo Penteriani
4,5
| Luk
aˇ
sG

abor
3,6,7
|
Petra
ˇ
Símov
a
3
| Florencia Grattarola
3
| Vítˇ
ezslav Moudrý
3
1
Department of Biogeography and Global
Change, Museo Nacional de Ciencias
Naturales (MNCN-CSIC), Madrid, Spain
2
Universidad de Oviedo, Oviedo, Spain
3
Department of Spatial Sciences, Faculty
of Environmental Sciences, Czech
University of Life Sciences Prague,
Prague, Czech Republic
4
Department of Evolutionary Ecology,
Museo Nacional de Ciencias Naturales
(MNCN-CSIC), Madrid, Spain
5
Biodiversity Research Institute (IMIB,
CSIC-UO-PA), Mieres, Spain
6
Department of Ecology and Evolutionary
Biology, Yale University, New Haven,
Connecticut, USA
7
Center for Biodiversity and Global
Change, Yale University, New Haven,
Connecticut, USA
Correspondence
Vincenzo Penteriani
Email: v.penteriani@csic.es
Funding information
European Union-NextGenerationEU,
Ministerio de Universidades y Plan de
Recuperaci
on, Transformaci
on y
Resilien cia, Universidad de Ovied o,
Grant/Award Number: Margarita Salas;
Faculty of Environmental Sciences, Czech
University of Life Sciences Prague,
Grant/Award Number: 2022B0012; Spanish
Ministry of Science and Innovation, the
Agencia Estatal de Investigaci
on (AEI), and
the Fondo Europeo de Desarrollo Regional
(FEDER, EU), Grant/Award Number:
PID2020-114181GB-I00
Abstract
Species distribution models (SDMs) are powerful tools in ecology and conser-
vation. Choosing the right environmental drivers and filtering species’occur-
rences taking their biases into account are key factors to consider before
modeling. In this case study, we address five common problems arising during
the selection of input data for presence-only SDMs on an example of a general-
ist species: the endangered Cantabrian brown bear. First, we focus on the
selection of environmental variables that may drive its distribution, testing if
climatic variables should be considered at a 1-km analysis grain. Second, we
investigate how filtering the species’data in view of (1) their collection proce-
dures, (2) different time frames, (3) dispersal areas, and (4) subpopulations
affects the performance and outputs of the models at three different spatial
analysis grains (500 m, 1 km, and 5 km). Our results show that models with
different input data yielded only minor differences in performance and
behaved properly in terms of model validation, although coarsening the analy-
sis grain deteriorated model performance. Still, the contribution of individual
variables and the habitat suitability predictions differed among models. We
show that a combination of limited data availability and poor selection of envi-
ronmental variables can lead to inaccurate predictions. Specifically for the
brown bear, we conclude that climatic variables should not be considered for
exploring habitat suitability and that the best input data for modeling habitat
suitability in the study area originate from (1) observations and traces from
the (2) most recent period (2006–2019) in which the population is expanding,
(3) not considering cells of dispersing bear occurrences and (4) modeling sub-
populations independently (as they show distinct habitat preferences). In con-
clusion, SDMs can serve as a useful tool for generalist species including all
available data; still, expert evaluation from the perspective of data suitability
for the purpose of modeling and possible biases is recommended. This is espe-
cially important when the results are intended for management and
Received: 9 March 2022 Revised: 1 July 2022 Accepted: 7 July 2022
DOI: 10.1002/ecs2.4284
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2022 The Authors. Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America.
Ecosphere. 2022;13:e4284. https://onlinelibrary.wiley.com/r/ecs2 1of23
https://doi.org/10.1002/ecs2.4284

Handling Editor: Bryan Kluever conservation purposes at the local level, and for species that respond to the
environment at coarse analysis grains.
KEYWORDS
brown bear, climatic variables, data filtering, model performance, species data, species
distribution models, Ursus arctos
INTRODUCTION
Exploring how species respond to different environmen-
tal characteristics is key to creating successful manage-
ment and recovery plans (Jetz et al., 2012). Species
distribution models (SDMs) are currently popular tools
for this purpose, especially considering the increase in
the occurrence data availability (Guisan et al., 2013;
Rodríguez et al., 2007). As an example of this trend in
animal and plant modeling, we can name the increas-
ingly used MaxEnt. This method based on the
maximum-entropy approach (Merow et al., 2013;
Phillips & Dudík, 2008) has become popular over the last
decade because it can work with presence-only records of
species’occurrences that are increasingly available from
global databases (Jayathilake & Costello, 2020; Kuralt &
Kos, 2018; Zhang et al., 2021). There are, however, key
factors that need to be considered by researchers before
modeling, such as choosing the right environmental
drivers (Gardner et al., 2019) or filtering available species’
occurrences and coping with their biases, for example,
sampling bias or positional error (Fernandes et al., 2018;
G
abor, Moudrý, Lecours, et al., 2020; Kramer-Schadt
et al., 2013; Moudrý &
ˇ
Símov
a, 2012). If not, SDMs may
provide misleading results and conclusions, which can be
particularly problematic when applied to population
management and conservation.
SDMs relate species’occurrences to the environmen-
tal characteristics of their habitat and assess potential
landscape–species relationships and geographical range
suitability (Elith et al., 2011). Thus, the correct selection
of environmental characteristics that may play a role in
the distribution patterns of target species is one of the
crucial modeling steps. Not much attention has been
given to the selection of variables and there is little con-
sistency among studies regarding the used variables
(Austin & Van Niel, 2011; Fourcade et al., 2018;
G
abor et al., 2022). Some studies even rely only on
climate-derived variables; this is especially true for
studies predicting species shifts due to climate change,
although the climate may not be enough to predict major
shifts in species’distribution (Araújo & Guisan, 2006).
Especially in vertebrates, such as large mammals,
climatic variables might influence some species only
indirectly by affecting their food and shelter as their
habitat selection would be more likely driven by land-
scape features and structure and/or human impact
(Berteaux & Stenseth, 2006; Greenberg et al., 2014). Thus,
many studies fail at choosing important variables shown
to be physiologically relevant to the species (Gardner
et al., 2019).
Furthermore, species occurrences mostly come from
non-systematic observations from museum collections,
administrations, or citizen science, usually accessible
from global public databases, such as Global Biodiversity
Information Facility (GBIF, www.gbif.org) (Moudrý &
Devillers, 2020; Newbold, 2010). Information from these
sources can be affected by several (generally more than
one) errors related to the observer, and the tools and/or
collection methods employed (Marcer et al., 2022;
Moudrý & Devillers, 2020). For example, data might suf-
fer from positional uncertainty/inaccuracy, sampling
effort bias (i.e., sampling toward places where it is easy to
locate individuals or that are highly visited, but not from
areas with reduced visibility or more remote and inacces-
sible), or different ecological meaning (i.e., observations
recorded where a specific behavior takes place, such as
locations of damages to human property or of water
sources) (Anderson, 2012; Fourcade et al., 2014;
Guillera-Arroita, 2017; Kramer-Schadt et al., 2013). Thus,
the proper selection of species’input records is a neces-
sary step toward construction of accurate habitat models
(Beck et al., 2014; Boria et al., 2014). Common
approaches to reduce the impact of bias in occurrence
data include, for example, spatial or environmental filter-
ing (G
abor, Moudrý, Bart
ak, & Lecours, 2020; Inman
et al., 2021; Varela et al., 2014; Veloz, 2009), subsampling
(Beck et al., 2014), or the use of systematic sampling or a
bias file to account for sampling bias and to weight occur-
rences accordingly (El-Gabbas & Dormann, 2018;
Fourcade et al., 2014; Tessarolo et al., 2014). Still,
performing a careful revision of the available data to eval-
uate and understand various errors and biases in view of
the data sources or collection methods before modeling is
uncommon. Ignoring the intrinsic inaccuracies and eco-
logical differences of the data can, however, produce mis-
leading results and conclusions (Guillera-Arroita, 2017;
Isaac & Pocock, 2015; Leit˜
ao et al., 2011).
2of23 ZARZO-ARIAS ET AL.

Still, the sources of possible bias are broader than just
“how and by whom the data are collected.”Long-term
monitoring information available for some species has
the potential to provide quality data for studying species
distributions (Brotons et al., 2007). However, different
periods might reflect different population statuses associ-
ated with density-dependent use of space and, conse-
quently, different habitat requirements (Seoane &
Carrascal, 2008). Besides, data collected more recently
are likely to be more accurate in their geolocation due to
the wide availability and ongoing improvement of the
GPS technology (Tomaˇ
stík & Varga, 2021). Population
status should also be considered when filtering species
data as, for example, dispersing individuals in expanding
populations can appear in unfavorable areas while
searching for new landscapes or moving between suitable
habitat patches; their habitat selection may considerably
differ with their breeding status as well (Moudrý
et al., 2017; Rayment et al., 2015; Stamps, 2001).
Additionally, different subpopulations or clusters might
differ in the (1) growth/reproductive rate, (2) habitat or
resources availability, (3) behavior, (4) impact of human
infrastructure and density, (5) size/number of individuals
(which might affect density-dependent patterns), or
(6) number of occurrences or sampling effort. All of these
can influence the models’behavior. Models may, there-
fore, be improved by splitting the species into subunits
with different ecological characteristics or with different
sampling efforts (Fourcade et al., 2014; Stockwell &
Townsend Peterson, 2002).
Faced with the many problems of ecological
modeling, a question arises: Is it better to use as much
data as possible, disregarding their error or ecological
meaning, or should we try to screen the data and mini-
mize the bias? This is a key question that should be
acknowledged before modeling species distributions and
considered when building models, interpreting the
results, and drawing conclusions (Bloom et al., 2018;
Guillera-Arroita et al., 2015; Kramer-Schadt et al., 2013).
In this study, we intended to test the importance of
selecting and filtering proper input data (i.e., variables
and occurrence data) to build SDMs in a case study for a
generalist species, as well as to make these decisions
based on expert knowledge of the analyzed species and
populations. For this, we applied MaxEnt modeling to
the distribution data on the endangered Cantabrian
brown bear (Ursus arctos) population located in north-
western Spain from 1985 to 2019. The small number of
bears in this population (Pérez et al., 2014), their com-
plete isolation, and the limited connectivity between sub-
populations make this population an interesting model in
terms of conservation and management. We hypothe-
sized that (1) climatic variables would not correctly
define bear habitat selection as this species is dependent
rather on landscape features and inhabits a wide range of
climatic regions all around the world (Krofel et al., 2021;
Penteriani et al., 2021) and (2) that thorough filtering of
species’occurrences will produce better models relying
on both model validation and expert opinion.
METHODS
Study area and species
In our study, we focus on the brown bear population
inhabiting the Cantabrian Mountains, located in the
northwest of the Iberian Peninsula (Figure 1), a
typical human-modified landscape of southern Europe
(Penteriani et al., 2021). This population is divided
into the western subpopulation, with 203 bears
(95% CI =168–260) in 2014 (Pérez et al., 2014) occupying
an area of more than 7000 km
2
, and the eastern subpopu-
lation, with 19 bears (95% CI =12–40) in 2014 (Pérez
et al., 2014), occupying around 4000 km
2
(Lamamy
et al., 2019; Penteriani et al., 2021). The long-term moni-
toring of the Cantabrian brown bear population, which
started in the 1980s, is essentially based on yearly direct
sightings, indirect signs (traces) of presence (footprints,
hairs, and scats), images from camera traps randomly
located in the bear distribution range (Penteriani
et al., 2019; Zarzo-Arias et al., 2019), and records of dam-
age caused to livestock, beehives, crops, human activities,
and infrastructures (Zarzo-Arias et al., 2020). Data have
been gathered by the personnel of the Principado de
Asturias and Junta de Castilla y Le
on, primarily the
Patrulla Oso, as well as all the other guards of both
regional governments, by Fondo para la Protecci
on de los
Animales Salvajes (FAPAS), Fundaci
on Oso de Asturias
(FOA), Fundaci
on Oso Pardo (FOP), and by the
Cantabrian Brown Bear Research Group (http://www.
cantabrianbrownbear.org/es/).
Data were divided into groups considering first the
type of data: (1) direct and indirect (camera traps) bear
observations; (2) traces (footprints, hairs, and scats); and
(3) damages, as each one has different intrinsic ecological
and sampling considerations. Then, we divided the data
into (1) the whole time frame (1985–2019), (2) the older
(1985–2005), and (3) the recent period (2006–2020). This
temporal division corresponds to the main trend change
observed in this bear population, which shifted from slow
recovery to population increase and colonization of new
areas that occurred around 2005 (Mateo-S
anchez
et al., 2015; Penteriani et al., 2021; Zarzo-Arias
et al., 2019). We also grouped the data according to the
western and eastern subpopulations, reflecting the
ECOSPHERE 3of23

highway dividing the area and acting as the main dis-
persal barrier (Figure 1).
Environmental data
We considered ecologically relevant environmental vari-
ables related to the terrain (5), climate (19), habitat (7),
and human activity (5). Terrain, habitat, and human
activity data are available for download from the
Spanish National Center of Geographic Information
(https://centrodedescargas.cnig.es/). The habitat vari-
ables were derived from the national Corine Land Cover
database for the year 2018. For each analysis grain, we
calculated the area of the following land uses to get
the percentage per cell: urban areas, forests, crops,
shrublands, pastures, rocks, and conifers. In addition, we
calculated the distance to highways, roads, trails, and riv-
ers. The terrain variables (Lecours et al., 2017) were
derived from a high-accuracy digital terrain model at a
25-m resolution (Moudrý et al., 2018). The terrain attri-
butes were derived using Horn’s algorithm at that resolu-
tion and subsequently aggregated to the analysis grain
using the mean value from each cell (Moudrý
et al., 2019). The human population density obtained from
Gallego (2010) was also included in the analysis. Nineteen
standard bioclimatic variables were downloaded from the
CHELSA climatic portal (https://chelsa-climate.org/). We
extracted the information from all variables in grid cells
of 5 km, 1 km, and 500 m (i.e., analysis grain) in our study
area using QGIS version 3.8 (Quantum GIS Development
Team, 2015). We examined pairwise correlations of vari-
ables as was done in many other studies (Brun
et al., 2020; Lecours et al., 2020; Parra et al., 2004); from
those showing high mutual correlations (Pearson’s coeffi-
cient >0.7), we always kept the one most relevant to the
species ecology. The correlated variables included mainly
climatic variables (detailed description of variables can be
found in Appendix S1); the four climatic variables most
relevant to the species ecology were kept (isothermality,
temperature seasonality, annual precipitation, and precip-
itation seasonality). Subsequent variance inflation factor
(VIF) analysis did not find any potential multicollinearity
at any analysis grain (VIF < 5; Zuur et al., 2010).
Finally, we removed variables with ≤0.1% mean contribu-
tion to a preliminary set of models at a 1-km grain.
A total of 16 variables were retained for modeling
(see Appendix S1).
Evaluated input data characteristics
It has been hypothesized that organisms respond to their
environment more strongly at some grains than at others.
FIGURE 1 Area of study (the Cantabrian Mountains) and brown bear observations, traces, and damages (brown points) used in this
study. The dotted red line indicates the highway dividing the two bear subpopulations.
4of23 ZARZO-ARIAS ET AL.

In general, it is assumed that climate defines the distribu-
tion of species at broad spatial grains. At successively
finer grains and at regional extents, the importance of
topography variables in controlling species occurrence
increases; at even finer grains, the presence of individual
land cover categories can play a role (Field et al., 2009;
Pearson & Dawson, 2003). As a consequence, the choice
of the analysis grain can strongly affect the ability to
detect and measure species’response to the environment
(Huston, 2002). As it has been shown that different vari-
ables may affect bears’distribution at different spatial
grains (Mateo-S
anchez et al., 2014), we tested three
different spatial analysis grains (square grids of 5 km,
1 km, and 500 m) to account for this.
To choose the optimal way to build the Cantabrian
bear habitat suitability models, we first built a set of
1-km models with and without climatic variables as
climate variables, together with land cover ones, which
were reported to be the most important predictors of this
population at finer analysis grains (see Zarzo-Arias
et al., 2019), while topography does at coarser resolutions.
We used the model performance and expert knowledge
of the species and its distribution within the study area to
evaluate whether climatic variables were driving the
modeled species distribution. Then, we compared models
with input data (1) of different types (observations, traces,
damages, and their combinations); (2) from different time
frames (1985–2005 and 2006–2019); (3) considering grids
with just a single occurrence (i.e., cells used only for dis-
persion that do not show habitat preferences but the
casual movement around the landscape looking for new
suitable places) as non-presences and as presences; and
(4) from the whole population and from each subpopula-
tion independently. Subsequently, we selected the best
combination of input data. Finally, to evaluate the impact
of data filtering on the results, we compared this model
to the one built using non-filtered data (i.e., the model
containing all types of data for the entire time frame
[1985–2019] using all grids with at least one occurrence
for the whole population).
Model fitting and selection
For building the models, we used the software MaxEnt
(Phillips et al., 2004,2006) version 3.3.3k called from the
R environment version 3.5.1 (R Core Team, 2020)using
the packages dismo version 1.3.3 (Hijmans et al., 2017)
and ENMeval version 0.3.1 (Muscarella et al., 2014) fol-
lowing the methodology applied by Zarzo-Arias et al.
(2019) at three different spatial analysis grains (square
grids of 5 km, 1 km, and 500 m). Input data consisted of
the centroid coordinates of the cells where at least one
bear occurrence falls in. It is recommended to evaluate
the best combination of MaxEnt parameter settings to
select the most appropriate models (Morales et al., 2017).
Therefore, to identify an optimal model structure, we
evaluated candidate models with all types of feature
combinations representing different types of parameteri-
zations (linear, product, quadratic, hinge, threshold, and
categorical), each run over a set of regularization multi-
pliers ranging from 0 to 19 to control for overfitting by
penalizing variables that add little to the model (see
Elith et al., 2011, for more details). We used 500 iterations
(maximum number of steps to reach convergence), and a
convergence threshold (probability of predicting no
occurrence where there is one) of 10
5
, using the
method checkerboard1 for data partitioning. This
method aggregates the original environmental input
grids to partition occurrence localities into two bins for
training and testing (Muscarella et al., 2014). The
MaxEnt default number of background points (10,000)
has been criticized as insufficient to capture the total
range of environmental conditions of the targeted land-
scape (Renner & Warton, 2013). For this reason, we used
values from all grids in the study area (specifically 1019
background cells for the 5-km
2
resolution, 25,475 for
1km
2
, and 101,900 for 500 m
2
) to build all the models
(as in, e.g., Ericksson & Dalerum, 2018;Swanepoel
et al., 2013).
We identified the most robust and parsimonious combi-
nation of feature types and regularization multipliers using
Akaike information criterion corrected for small sample
sizes (AIC
c
) following Warren and Seifert (2011). We con-
sidered models within two AIC
c
units to have equivalent
empirical support (Burnham & Anderson, 2002)andchose
the simplest model (lowest number of parameters, and if
equivalent, lowest number of feature types) as the best
model.Themodelstructureandthetotalnumberofgrids
considered for each group of bear data in each analysis
grain are available in Appendix S2.
Model evaluation
It is recommended to assess model performance using
multiple metrics. Therefore, we used three validation
metrics in this study (Grimmett et al., 2020). We assessed
the model discrimination ability (i.e., how well the model
differentiates between the presence and non-presence
sites) using the area under the receiver-operator curve
(AUC; Fielding & Bell, 1997). AUC ranges from 0.5 for
models with no discrimination ability to 1 for models
with perfect discrimination. In addition, we adopted also
the mean absolute error (MAE) approach recently
recommended by Konowalik and Nosol (2021)tobe
ECOSPHERE 5of23

combined with AUC for selecting the best performing
models, as the latter might be affected by large numbers
of background cells (Lobo et al., 2008). The MAE of a
model is the mean of the absolute values of the individual
prediction errors (i.e., the difference between the true
value and the predicted value) over all instances in the
evaluation set. Finally, we calculated the Boyce index,
which is recommended for presence-only models and
shows the accuracy of the predicted probability of pres-
ence (Hirzel et al., 2006). To calculate the index, the
predicted suitability is partitioned into suitability classes
and, subsequently, the ratio between the predicted and
expected frequency of evaluation points is plotted against
the corresponding suitability class. If the model correctly
distinguishes between species-suitable and unsuitable
areas, the ratio between predicted and expected (random)
frequencies of presences increases with the suitability
(i.e., the low-suitability classes should contain fewer eval-
uation presences, while high-suitability classes should
contain more evaluation presences than expected by
chance). This increase is measured using the Spearman
rank correlation coefficient, and hence the Boyce index
ranges from 1 to 1. Negative values indicate an incor-
rect model that predicts low suitability in areas with
more frequent presences, while positive values indicate a
model whose suitability predictions match the distribu-
tion of presences in the evaluation dataset. We adopted
the continuous version of Boyce index, which overcomes
the sensitivity of the index to the number of suitability
classes (Hirzel et al., 2006; Jiménez & Sober
on, 2020).
After evaluating the models’predictive performance,
we compared them according to the relative contribution
of each environmental variable, which was calculated by
a jackknife procedure and a heuristic method provided
by MaxEnt. Additionally, with the raster output of the
predictions, for which we used the complementary
log–log format, we performed a niche comparison analy-
sis based on the Schoener’sIstatistic (Warren
et al., 2008), which ranges from 0 (no overlap) to 1
(perfect overlap). Finally, we compared the predicted
suitability at presence locations and map outputs
graphically.
RESULTS
Climatic variables
Models including climatic variables showed higher AUC
but worse performance according to MAE (Figure 2a),
and slightly higher Boyce index values than models with-
out climatic variables (Appendix S2: Table S1). Climatic
variables also presented a high mean percentage
contribution (54.5%), especially isothermality (24%, Bio 3)
and temperature seasonality (29%, Bio 4; Figure 2b).
These variables, together with the distance to highways
(29%), represent the main drivers of bear habitat suitabil-
ity in these models. Once climatic variables were
removed, the distance to highways increased in impor-
tance (+19/48.2% contribution), as well as slope
(+20/22.8%) and altitude (+7/9.3%), which, together with
forests (9.9%), became the most important explanatory
variables. Numerical evaluation of the map predictions of
both sets of models showed high similarity (mean
Schoener’sI=0.933; Appendix S3: Table S1).
On the other hand, models including climatic vari-
ables show more restricted habitat suitability and high
importance of climatic factors for bear distribution.
Taking a closer look at the suitability prediction, the
results of climate variables containing models are mis-
leading; this is especially true for the eastern area, where
models indicate some of the areas of Cantabria
(Figure 2c) to be unsuitable for the bear, while in reality,
bears have inhabited these areas for decades (Clevenger
et al., 1987). Furthermore, the global brown bear distribu-
tion implies good adaptability to a wide variety of
climatic conditions (Kaczensky et al., 2013). Therefore,
we decided to remove climatic variables from further
analysis as they were not appropriate for modeling this
bear population’s distribution.
Analysis grain and type of occurrence
Models performed better at finer analysis grains, espe-
cially in terms of AUC and MAE (0.11 AUC and
0.014 MAE mean difference between the coarsest and
finest analysis grain) (Figure 3a), although only the 5-km
analysis grain slightly underperformed the other analysis
grains according to the Boyce index (Appendix S2:
Table S2). At this analysis grain, models constructed
using different sets of input variables were mutually more
similar than at the other grains (mean Schoener’s
I=0.994; Appendix S3: Table S2). On the other hand,
the importance of individual variables varied more
among models at finer grains (Figure 3b). Distance to
highways was the most important variable at all analysis
grains, while crops and altitude stand out at the 5-km
grain, and slope and forest at 1-km and 500-m grains.
According to validation values, we can conclude that
all models performed properly at all analysis grains for
the different types of data and their combinations
(AUC > 0.7, Figure 3a; Boyce index ≈1, Appendix S2:
Table S2). Considering both the highest AUC and lowest
MAE mean values of all combinations of data
(Appendix S2: Table S1), models including only
6of23 ZARZO-ARIAS ET AL.

observations appear to perform best (Figure 3a), although
the difference in the performance values is very low
(0.06 AUC and 0.015 MAE maximum difference). All
types of data also showed very similar results in terms of
map predictions (mean Schoener’sI=0.985). Models
present slight changes in contributions of the most
important variables, particularly between analysis grains
(Figure 3b). Models based on observations only and
FIGURE 2 Comparison of the preliminary 1-km models with versus without climatic (clim.) variables according to (a) area under the
receiver-operator curve (AUC) and mean absolute error (MAE) values, (b) variable percentage contribution, and (c) suitability map
predictions difference; higher predicted habitat suitability is shown in brown for the model not including climatic variables, and in blue for
the model containing them, while gray areas show very similar suitability predicted by both for the mean of all types of brown bear data
(observations, traces, damages, and their combinations). Alt., altitude; Bio 3, isothermality; Bio 4, temperature seasonality; Bio 12, annual
precipitation; Bio 15, precipitation seasonality; D. hway, distance to highway; D. river, distance to river; D. road, distance to road; D. trail,
distance to trail; H. Dens., human population density; Shrub., shrubland; Sun rad., sun radiation.
ECOSPHERE 7of23

Scale
Type of data
Alt.
Aspect
Crops
D.
hway
D.
trail
D.
river
D.
road
Forest
H.
dens.
Shrub.
Slope
Sun
rad.
5 km
Observations
25
0
21
26
2
0
0
7
0
6
12
0
Traces
6
0
33
48
0
0
0
2
0
1
8
0
Damages
5
0
37
48
3
0
0
3
0
2
1
0
Observations +
Damages
19
0
28
35
2
0
0
5
0
6
4
0
Observations +
Traces
19
5
26
22
0
0
0
2
0
4
10
12
Traces + Damages
13
3
33
32
1
0
0
3
0
2
5
8
All
12
9
29
26
1
0
0
3
0
2
3
16
1 km
Observations
14
1
0
42
1
0
2
11
0
1
27
1
Traces
10
1
3
46
0
0
0
12
0
1
26
1
Damages
7
1
5
46
7
4
3
7
1
2
16
0
Observations +
Damages
5
1
2
53
1
0
2
9
0
1
23
1
Observations +
Traces
11
0
2
46
0
0
0
11
0
3
26
1
Traces + Damages
8
0
5
51
1
0
0
10
0
3
20
0
All
9
0
3
53
0
0
1
9
0
2
22
0
500
m
Observations
9
0
1
53
1
0
2
8
0
0
25
1
Traces
10
0
2
48
2
0
1
14
0
0
24
0
Damages
9
1
5
44
10
5
4
5
1
2
14
1
Observations +
Damages
6
1
1
50
5
2
4
7
1
2
21
2
Observations +
Traces
8
1
1
50
2
0
1
12
0
0
25
1
Traces + Damages
7
1
2
51
3
1
2
11
1
1
20
0
All
8
0
1
54
2
1
2
10
0
0
21
1
(a)
(b)
FIGURE 3 Comparison of models for all types of data (observations, traces, damages, and their combinations) according to (a) area
under the receiver-operator curve (AUC) and mean absolute error (MAE) values, (b) variable percentage contribution (decreasing from
green to blue), and (c) suitability map predictions for the best performing type of data (observations) compared to the best model in terms of
ecology (observations and traces) at the finest analysis grain (500 m), where more differences can be observed; higher predicted habitat
suitability is shown in brown colors for the model built with observations only, and in blues for the model with observations and traces,
while gray areas show very similar suitability predicted by both. Alt., altitude; D. hway, distance to highway; D. river, distance to river;
D. road, distance to road; D. trail, distance to trail; H. dens., human population density; Shrub., shrubland; Sun rad., sun radiation.
8of23 ZARZO-ARIAS ET AL.

damages only yielded results more different from the
others (regarding variable contribution and Schoener’sI)
(Figure 3b; Appendix S3: Table S2). The combination of
observations and traces represents the best presence data
for SDMs construction, based on expert knowledge, and
gives better predictions (Figure 3c; Appendix S4) than the
models performing best in numerical analyses. The latter,
including only observations, presented more restricted
habitat suitability and underpredicted the eastern sub-
population habitat.
Time frame
Regarding the time frame, all periods produce
well-performing models when including both observa-
tions and traces at all analysis grains (AUC > 0.74,
Figure 4a; Boyce index ≈1, Appendix S2: Table S3).
Models with data from the first period (1985–2006) stand
out, but validation statistics between the entire time
frame and the most recent period presented very similar
values (0.005 AUC and 0.003 MAE maximum difference,
Figure 4a; Appendix S2: Table S3). Both periods also
show very similar results in terms of map predictions
(Schoener’sI> 0.99), especially at the larger analysis
grains (Appendix S3: Table S3). In the first period, the
contribution of individual variables was slightly different
from the second period and from the entire time span,
especially for the coarsest analysis grain (Figure 4b). By
comparing map outputs between periods at the finest
analysis grain (Figure 4c), predictions do not show
evident differences in any specific area, but models from
the first period appear more restrictive in terms of habitat
suitability (Appendix S5).
Dispersal
When testing the inclusion or exclusion of grids with dis-
persing bear occurrences, both approaches produce
well-performing models at all analysis grains (AUC > 0.74,
Figure 5a; Boyce index ≈1, Appendix S2: Table S4).
However, models considering all grids with more than one
occurrence performed better in terms of AUC, although
they had a higher MAE. Still, as above, the difference in
validation statistics is very low (0.05 and 0.08 units of max
AUC and MAE difference between models, respectively;
Figure 5a; Appendix S2: Table S3). Map predictions from
both models were very similar (Schoener’sI> 0.98): the
coarser the grain, the greater the similarity (Appendix S3:
Table S4). In terms of the contribution of individual vari-
ables to the models, both models present very similar
values, except for the largest analysis grain, where the
importance of slope increases at the expense of crops when
excluding dispersal grids (Figure 5b). By comparing map
outputs from both models at the 500-m analysis grain
(which differed most from the other grains), no under- or
overprediction in any specific area can be seen.
Nevertheless, the model excluding dispersal grids provides
a more restricted habitat suitability, avoiding the
overestimation of habitat quality in areas used to travel
between best landscape patches (Figure 5c;AppendixS6).
(c)
FIGURE 3 (Continued)
ECOSPHERE 9of23

(b)
Scale
5km
1km
500 m
Period
1985
–
2019
1985
–
2005
2006
–
2019
1985
–
2019
1985
–
2005
2006
–
2019
1985
–
2019
1985
–
2005
2006
–
2019
Alt.
19
22
10
11
11
9
8
9
6
Aspect
5
0
5
0
1
1
1
0.4
0
Crops
26
20
24
2
1
2
1
0
1
D. hway
2
24
34
46
46
46
50
54
52
D. trail
0
2
0
0
0
2
0
1
D. river
0
0
0
0
0
0
0
0
0
D. road
0
0
0
0
2
0
1
2
1
Forest
2
8
3
11
10
11
12
9
12
H. dens.
0
0
0
0
0
0
0
0
0
Shrub.
4
5.27
4
3
2
1
0
0
0
Slope
10
19
9
26
26
27
25
23
25
Sun rad.
12
0
12
1
0
2
1
0
1
(a)
(c)
FIGURE 4 Comparison of models for the entire time frame (1985–2019) versus both the older (1985–2005) and recent period
(2006–2019) using observations and traces as input data according to (a) area under the receiver-operator curve (AUC) and mean absolute
error (MAE) values, (b) variable percentage contribution (decreasing from green to blue), and (c) suitability map predictions for the
differences between the older and the recent periods at the finest analysis grain (500 m); higher predicted habitat suitability is shown in
brown for the model built on the data from the older period, and in blue for the recent period, while gray areas show very similar suitability
predicted by both. Alt., altitude; D. hway, distance to highway; D. river, distance to river; D. road, distance to road; D. trail, distance to trail;
H. dens., human population density; Shrub., shrubland; Sun rad., sun radiation.
10 of 23 ZARZO-ARIAS ET AL.

Subpopulations
According to model validation, models including data
for the whole population and each subpopulation
independently performed well at all analysis grains
(AUC > 0.78, Figure 6a;Boyceindex≈1, Appendix S2:
Table S5). Difference in MAE was very low between
models (0.015 unit of maximum MAE difference), but in
(a)
(b)
(c)
Scale
5km
1km
500 m
Grids
All
>1 obs
All
>1 obs
All
>1 obs
Alt.
10
9
9
4
6
4
Aspect
5
0
1
1
0
1
Crops
24
5
2
1
1
0
D. hway
34
34
46
42
52
42
D. trail
0
1
0
0
1
1
D. river
0
0
0
0
0
0
D. road
0
0
0
0
1
2
Forest
3
5
11
13
12
13
H. dens.
0
0
0
0
0
0
Shrub.
4
10
1
1
0
1
Slope
9
36
27
37
25
34
Sun rad.
12
0
2
1
1
1
FIGURE 5 Comparison of models built with all grids with at least one occurrence (i.e., including dispersal grids) versus grids with
more than one occurrence using data on observations (obs) and traces from the period 2006–2019 as input data according to (a) area under
the receiver-operator curve (AUC) and mean absolute error (MAE) values, (b) variable percentage contribution (decreasing from green to
blue), and (c) suitability map predictions for both periods at the finest analysis grain (500 m); higher predicted habitat suitability is shown in
brown for the model including dispersal grids, and in blue for the model excluding them, while gray areas show very similar suitability
predicted by both. Alt., altitude; D. hway, distance to highway; D. river, distance to river; D. road, distance to road; D. trail, distance to trail;
H. dens., human population density; Shrub., shrubland; Sun rad., sun radiation.
ECOSPHERE 11 of 23

(c)
Scale
5km
1km
500 m
Subpop.
East
West
All
East
West
All
East
West
All
Alt.
22
2
9
32
3
4
34
3
4
Aspect
0
2
0
0
1
1
3
0
1
Crops
3
13
5
1
0
1
3
1
0
D. hway
62
11
34
48
29
42
40
40
42
D. trail
1
3
1
5
3
0
3
3
1
D. river
0
0
0
0
0
0
2
0
0
D. road
0
0
0
0
1
0
0
2
2
Forest
11
4
5
10
8
13
13
11
13
H. dens.
0
0
0
0
0
0
0
0
0
Shrub.
0
20
10
0
1
1
1
1
1
Slope
1
38
36
3
51
37
1
38
34
Sun rad.
1
7
0
0
1
1
0
1
1
(a)
(b)
FIGURE 6 Comparison of models constructed using observations and traces from the period of 2006–2019 in all grids with more than one
occurrence for the whole population versus the ensemble of models for the western and the eastern subpopulations (Subpop.) at each analysis
grain as input data according to (a) area under the receiver-operator curve (AUC) and mean absolute error (MAE) values, (b) variable
percentage contribution (decreasing from green to blue), and (c) suitability map predictions for both periods at the finest analysis grain (500 m);
higher predicted habitat suitability is shown in brown colors for the model with the entire population, and in blues for the ensemble of the two
subpopulations, while gray areas show very similar suitability predicted by both. Alt., altitude; D. hway, distance to highway; D. river, distance
to river; D. road, distance to road; D. trail, distance to trail; H. dens., human population density; Shrub., shrubland; Sun rad., sun radiation.
12 of 23 ZARZO-ARIAS ET AL.

terms of AUC modeling, each subpopulation indepen-
dently produced better-performing models, especially
at the largest analysis grain (Figure 6a). In terms of
map predictions, models using the whole population
and the combination of the individual subpopulations
yielded similar results (mean Schoener’sI=0.986),
especially at larger analysis grains. They were, how-
ever, similar to the models produced when using only
the western subpopulation occurrences (Appendix S3:
Table S5). Distance to the highway was a highly
important variable in all models, especially at the finer
analysis grains. The contribution of individual vari-
ables, however, differed between subpopulations, with
altitude being more important in the eastern subpopu-
lation and slope in the western one, bringing to light
that the habitat availability differs between these
areas. The contribution of individual variables to the
model was similar for the model built on the whole
population and the one built for the western popula-
tion, especially at smaller analysis grains (Figure 6b).
This can also be inferred from the map predictions,
where the model for the whole population
underpredicts the eastern core’s habitat suitability
(Figure 6c; Appendix S7).
Final comparison
Both the unfiltered model (built once the appropriate
variables were selected, i.e., not including climatic
variables) and the best model (using observations and
traces in 2006–2019 in all grids with more than only
one occurrence for the ensemble of independent
models of the western and eastern subpopulations)
performed well at all analysis grains (AUC > 0.7,
Figure 6a;Boyceindex≥0.98, Appendix S2: Tables S1
and S5). Still, the non-filtered model showed lower
AUC values (0.134 units of maximum AUC difference
in the largest analysis grain) and slightly lower MAE
(0.074 units of maximum MAE difference in the
smallest analysis grain) than the final chosen model.
The two models also showed similar results in terms of
map predictions (mean Schoener’sI=0.977), espe-
cially at the smallest analysis grains (Appendix S3:
TableS6).Ontheotherhand,theimportanceofindi-
vidual variables varied more. The non-filtered model
overestimated the effect of crops and sun radiation at
the largest analysis grain, while slope at the largest and
altitude at the smallest grains had a low contribution
(Figure 7b). Regarding map outputs (Figure 7c), the
filtered model gives a more restricted habitat suitability
and better reflects the suitable areas in the core of the
eastern subpopulation.
DISCUSSION
SDMs are a useful tool, but some considerations regard-
ing the selection of input data must be addressed before
modeling. Several studies have highlighted that before
building SDMs, a good ecological and expert background
knowledge of the species, its ecology, and distribution
are highly recommended (Beck et al., 2014; Harris
et al., 2013; Merow et al., 2017). Thus, carefully revising
and filtering available data so that they fit the ecological
question we are trying to address is necessary to obtain
the results in the way most appropriate for their use in
developing conservation and management recommenda-
tions (Boria et al., 2014; Moudrý &
ˇ
Símov
a, 2012). On an
example of a generalist species, our results highlight the
importance of this, especially when the results are
intended for making decisions at the local level.
Climatic variables
Selecting appropriate environmental variables is the key
to building proper models. If such models are not ecologi-
cally relevant to the species and cannot properly repre-
sent conditions that drive species’distribution, results
and conclusions can be severely prejudiced (Austin &
Van Niel, 2011; Booth, 2021; Gardner et al., 2019). Our
results confirm in a real-world case that we cannot trust
the models to choose the most important variables if they
are not previously “groomed”by expert opinion and
knowledge of the species. As demonstrated before
(e.g., Harris et al., 2013; Porfirio et al., 2014), the results
for the same species can vary greatly depending on the
combinations of variables, from predicting almost extinc-
tion to very little range contraction. Even though
methods for preselecting variables have not yet been
explored deeply so far (Williams et al., 2012), some
authors have tried to assess the best variable combina-
tions through several methods without much success in
finding any universal procedure (Dormann et al., 2008;
Maggini et al., 2006; Meynard & Quinn, 2007). It should
also be noted that the adequacy of selected variables may
also depend on the characteristics of the species,
representing a major problem for highly mobile animals
(Porfirio et al., 2014). Thus, expert opinion or information
about the species should be considered when selecting
the environmental drivers.
Furthermore, climatic variables are usually included
in these types of studies, but, as evidenced by our models,
they may not always yield a good fit. Bears are more likely
influenced by climate indirectly, as shown by the fact that
they can be found in areas with very different climatic
conditions, from Alaska or Russia to Mediterranean
ECOSPHERE 13 of 23

(a)
(b)
(c)
Scale
5 km
1 km
500 m
Filtered
No
Yes*
No
Yes*
No
Yes*
Alt.
12
12
9
18
8
19
Aspect
9
1
0
1
0
2
Crops
29
8
3
1
1
2
D. hway
26
36
53
39
54
40
D. trail
1
2
0
4
2
3
D. river
0
0
0
0
1
1
D. road
0
0
1
1
2
1
Forest
3
7
9
9
10
12
H. dens.
0
0
0
0
0
0
Shrub.
2
10
2
1
0
1
Slope
3
19
22
27
21
20
Sun rad.
16
4
0
1
1
0
FIGURE 7 Comparison of the non-filtered model (all types of data, i.e., observations, traces, and damages, for the entire time frame
[1985–2019] including all grids with at least one occurrence for the whole population) versus the final filtered model (observations and
traces in 2006–2019 including all grids with more than only one occurrence for the ensemble of independent models of the western and
eastern subpopulations) at each analysis grain according to (a) area under the receiver-operator curve (AUC) and mean absolute error
(MAE) values, (b) variable percentage contribution (decreasing from green to blue), and (c) suitability map predictions for both periods at
the finest analysis grain (500 m); higher predicted habitat suitability is shown in brown colors for the non-filtered model, and in blues for the
final filtered model, while gray areas show very similar suitability predicted by both. *AUC, MAE, and variable percentage contribution
values for the filtered model are the means calculated from the independent models of the western and eastern subpopulations. Alt.,
altitude; D. hway, distance to highway; D. river, distance to river; D. road, distance to road; D. trail, distance to trail; H. dens., human
population density; Shrub., shrubland; Sun rad., sun radiation.
14 of 23 ZARZO-ARIAS ET AL.

countries (and historically even the north of Africa), same
as wolves (Canis lupus) (Hovardas, 2018; Kaczensky
et al., 2013; Ripple et al., 2014). Failure to consider the
entire bear distribution (actual but also historical) and,
consequently, all climatic conditions this species can
endure can lead to misleading results (Chevalier
et al., 2021; Faurby & Araújo, 2018). This was also shown
in our models that indicated the climatic variables to be
the most important ones, but such models led to
underprediction of the habitat suitability in areas known
to be inhabited by the species. Moreover, as shown by
Albrecht et al. (2017), climate (increasing winter tempera-
tures in particular) contributed substantially to the
Holocene decline of brown bears mainly indirectly by
facilitating human land use. Furthermore, as omnivorous
species, bears can adapt to various conditions and food
resources depending on natural availability, making these
variables even less important (Penteriani et al., 2019;
Penteriani & Melletti, 2021; Zarzo-Arias et al., 2020). This
emphasizes the need for a basic understanding of the spe-
cies’ecology for the appropriate selection of variables
used for modeling, despite the fact that many studies still
overlook this matter. Our findings also imply the urgent
need to revise studies on the impact of climate change on
these types of species, as the climate itself may fail to
predict real shifts in their home range but rather act
indirectly by affecting resources (Araújo & Guisan, 2006;
Guisan & Thuiller, 2005).
Analysis grain and type of occurrence data
Given that the occurrence data are generally unlikely to
be error-free (although the error magnitude can be mini-
mized by, e.g., balanced survey design, even sampling
effort, and maintaining low positional uncertainty), we
need to focus on how to obtain the right answers by ask-
ing the right questions and knowing which data fit our
question better (Guillera-Arroita et al., 2015). In our
dataset, direct bear observations are usually recorded
from the distance in the mountains, either in the
clear-cuts in forested parts or above the tree line, as well
as from camera traps prevalently located in forested areas
where direct observations are more difficult (Penteriani
et al., 2019; Zarzo-Arias et al., 2019). Traces include foot-
prints or scats, usually located in already existing trails or
mountain paths, and hairs are commonly found in rub
trees or fences in the fields (Gonz
alez-Bernardo
et al., 2021). On the other hand, damages are linked to
human activities, and they are mainly driven by the
accessibility of easy resources such as apiaries, fruit trees,
or livestock (Zarzo-Arias et al., 2020). Also, they are com-
monly georeferenced not from the location at which the
damage occurred, but from where the damaged property
is registered (i.e., the home of the owner). None of the
models with different data combinations stood out in
terms of performance, but the importance of individual
variables and especially spatial predictions differed,
mainly at the largest analysis grain (5 5km).Thisis
of particular concern as bears generally react to the
environment at coarser spatial analysis grains
(Mateo-S
anchez et al., 2014). Although observations are
the species input data type that performed best
(Figure 3), map outputs appear misleading as they
underestimate the core area of the eastern subpopula-
tion, most likely due to the differences in sampling
effort, population size, and habitat characteristics and
availability (Lamamy et al., 2019). Thus, ecologically
speaking, if looking for habitat suitability, the sensible
choice would be to combine observations and traces, as
those are the occurrences showing the natural behavior
of the species. Damages, on the other hand, would not
make good input data as animals approach human
activities predominantly due to the presence of easily
accessible resources, thus not reflecting the natural
behavior (Zarzo-Arias et al., 2020).
Time frame
The dynamics of some carnivore populations in Europe
have changed in recent years, going from relict and almost
extinct to expanding or even recolonizing parts of their
historical ranges, from which they were driven (mainly)
due to human activities. This is especially supported by
conservation and management efforts increasingly focus-
ing on mitigating human impact on populations with poor
conservation status (Chapron et al., 2014;Ripple
et al., 2014). Indeed, the brown bears’population in the
Cantabrian Mountains has also improved from slow recov-
ery to population increase and colonization of new areas
(Mateo-S
anchez et al., 2015; Penteriani et al., 2021;
Zarzo-Arias et al., 2019). Occurrences from the first years
of monitoring show only very specific and limited areas to
be suitable. At that time, bears occupied only the most
suitable areas because they did not have to compete with
other individuals for space due to the low population size
and density (Matthiopoulos et al., 2015). However, using
data from the most recent period would be the sensible
choice for making decisions based on the current status of
the population. As it is currently expanding, the bears
might, providing that their conservation remains a priority
and human-induced mortality is minimum, soon colonize
even landscapes that are more human-disturbed or less
abundant in terms of resources (Ciucci & Boitani, 2008;
Morales-Gonz
alez et al., 2020). Furthermore, the
ECOSPHERE 15 of 23

technology used in the early years of monitoring was less
accurate than in the 21st century with novel technologies
(Rodríguez et al., 2007), and sampling effort in the eastern
subpopulation was lower in the first period
(Fern
andez-Gil et al., 2010), making these data even
poorer in quality.
Dispersal
Considering all cells with at least one record as presences
can shift habitat selection at the population level, while
reviewing and screening species data usually produce
better-performing models (Boria et al., 2014). As men-
tioned before, individuals in increasing and saturated
populations might have to compete for the most suitable
habitats or try to find new favorable territories further
from their actual core/home range. Among bears, young
males are the typical dispersal individuals: they are com-
monly relegated to the less suitable habitats when fight-
ing for the landscape in a density-saturated population
(Zedrosser et al., 2007). They can travel throughout
unsuitable landscapes when moving toward new favor-
able habitat patches, which may place them closer to
humans even if bears generally avoid them (Martin
et al., 2010; Mateo-S
anchez et al., 2014; Stamps, 2001).
In fact, at the coarsest grain of our models, the variable
“crops”appears as decisive in explaining the bear’s habi-
tat suitability (24% contribution) when including dis-
persal grids (cells with at least one occurrence). In turn,
excluding dispersal grids produces models with more
restricted habitat suitability, showing avoidance of more
human-modified areas (Figure 5c) and demonstrates
increased importance of the variable “slope”as bears’
home range is usually linked to steeper territories
(Martin et al., 2012).
Subpopulations
When using SDMs, we intend to obtain information
about the species’habitat suitability, but specific condi-
tions of the landscape inhabited by a single subpopula-
tion might show only a part of the habitat characteristics
that may drive our species’habitat selection, mainly due
to the landscape available in their specific areas (Arthur
et al., 1996; Mysterud & Ims, 1998). Carnivores, for
example, have been restricted to less human-disturbed
habitats as they tend to avoid human activities, and the
habitat availability may vary throughout the landscape
(L
opez-Bao et al., 2017). Indeed, models performed better
when focusing on individual subpopulations (Figure 6a).
The importance of individual variables differed between
the subpopulation models, showing favorable habitat
features according to intrinsic habitat availability
(Lamamy et al., 2019). This has been also reported in
other bear populations (e.g., black bear, Benson &
Chamberlain, 2007; or polar bears, Ferguson et al., 2000)
as well as in other species (e.g., bison, Kuemmerle
et al., 2018; or mice, Wright & Frey, 2015), where differ-
ent subpopulations or clusters show different habitat
preferences. Besides, our models show that when model-
ing the whole population, the size of the population or
the sampling effort in each area can shift the results
toward the subpopulation with more available informa-
tion (western subpopulation, Figure 6b,c). Ensembling
these models allows the combining of different subpopu-
lations, species, or even model approaches in order to
obtain merged predictions, which could be a more appro-
priate way to model species populations (Araújo &
New, 2007).
Conservation applications
In terms of applied conservation, the fact that there are
still favorable and unoccupied habitats in the eastern
area of the Cantabrian Mountains (Figure 8) represents
the most important result obtained in this study. This,
again, brings our attention to the fact that the subpopula-
tion inhabiting this area has not experienced the
same expansion and recovery as the western population
(Penteriani et al., 2018). “Eastern”bears have been
even shown to move to the western areas (Greg
orio
et al., 2020) and have slightly (but not significantly) lower
reproductive rate and success (Penteriani et al., 2018),
which cannot be explained by differences in habitat com-
position and structure (Lamamy et al., 2019); this part of
the region has even more protected areas. As stated
before (Lamamy et al., 2019; Zarzo-Arias et al., 2019), the
factors driving this situation in the eastern brown bear
Cantabrian subpopulation require urgent study to main-
tain the quality and conservation status of this population
and to enhance the connectivity in the future to increase
the gene flow.
CONCLUSIONS
On the example of a generalist species, this study offers
a detailed insight into methods for tackling specific types
of bias that can be found in data available for building
SDMs. In particular, we conclude that for a species such
as the brown bear, selection of relevant variables is more
important than filtering species’occurrences. Since there
is practically no background information on how to
16 of 23 ZARZO-ARIAS ET AL.

select appropriate variables for modeling purposes
(Dormann et al., 2008; Maggini et al., 2006; Meynard &
Quinn, 2007), we show that we cannot expect models
themselves to “decide”if the selected variables are
indeed a good fit. Furthermore, we show that we cannot
just rely on model performance and/or validation met-
rics to tell us if our model predicts the species’habitat
requirements correctly. We first need to choose both the
variables (Austin & Van Niel, 2011; Booth, 2021;
Gardner et al., 2019) and the occurrence data that are
relevant to our research question (Guillera-Arroita
et al., 2015) based on the basic knowledge of our species’
ecology, in order to obtain appropriate results on which
to base applied management and conservation decisions.
This is especially important if their application is
required at a local level and for species that react to the
environment at large spatial scales. Additionally, models
can help answer different questions using species input
data with different filtering in order to, for example,
explore habitat selection by individuals of different age
(Milanesi et al., 2016)orsex(Kwonetal.,2019), differ-
ent life cycle stages (Mateo-S
anchez et al., 2015), or
focus only on damages to get conflict hotspots
(Behdarvand et al., 2014). In short, we highlight several
widely overlooked issues preceding the construction of
SDMs that can affect important predictions on species’
future and bring the issues to the attention of ecologists
building SDMs. The application of these ideas can
improve the selection of appropriate data for building
SDMs that address the posed ecological questions
and highlight the need for expert knowledge on the
target species.
ACKNOWLEDGMENTS
During this research, Alejandra Zarzo-Arias was
financially supported by a Margarita Salas contract
financed by the European Union-NextGenerationEU,
Ministerio de Universidades y Plan de Recuperaci
on,
Transformaci
on y Resiliencia, through the call of the
Universidad de Oviedo (Asturias). Vincenzo Penteriani
was financially supported by the I +D+I Project
PID2020-114181GB-I00 financed by the Spanish Ministry
of Science and Innovation, the Agencia Estatal de
Investigaci
on (AEI), and the Fondo Europeo de
Desarrollo Regional (FEDER, EU). Luk
aˇ
sG

abor was
supported by the Faculty of Environmental Sciences,
Czech University of Life Sciences Prague (Internal Grant
Agency project no. 2022B0012). The local administrations
of the Junta de Castilla y Le
on and the Principado de
Asturias provided us with the long-term database on
brown bear observations. In particular, we would like to
thank David Cubero Bausela, Mercedes García
Dominguez, and María Angeles Osorio Polo of the Junta
de Castilla y Léon, and Teresa S
anchez Corominas,
FIGURE 8 Final map prediction without climatic variables, using bear observations and traces data for the most recent period
(2006–2019), including all grids with more than only one occurrence and combining models for each subpopulation independently at the
finest analysis grain (500 m). Bear occurrences are shown as black points, and the dotted red line indicates the highway dividing the two
bear subpopulations.
ECOSPHERE 17 of 23

Víctor V
azquez, and Pedro García-Rovés of the
Principado de Asturias.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
Brown bear occurrences were provided by the
Administrations in the study area (Principado de
Asturias and Junta de Castilla y Le
on) and are not
publicly available; they are, however, accessible upon
reasonable formal request from these Administrations for
research purposes by email to Teresa S
anchéz Corominas
(teresa.sanchezcorominas@asturias.org) and David Cubero
(CubBauDa@jcyl.es).
ORCID
Alejandra Zarzo-Arias https://orcid.org/0000-0001-
5496-0144
Vincenzo Penteriani https://orcid.org/0000-0002-9333-
7846
REFERENCES
Albrecht, J., K. A. Barto
n, N. Selva, R. S. Sommer, J. E. Swenson,
and R. Bischof. 2017. “Humans and Climate Change Drove the
Holocene Decline of the Brown Bear.”Scientific Reports 7(1):
1–11. https://doi.org/10.1038/s41598-017-10772-6.
Anderson, R. P. 2012. “Harnessing the World’s Biodiversity Data:
Promise and Peril in Ecological Niche Modeling of Species
Distributions.”Annals of the New York Academy of Sciences
1260(1): 66–80. https://doi.org/10.1111/j.1749-6632.2011.06440.x.
Araújo, M. B., and A. Guisan. 2006. “Five (or So) Challenges for
Species Distribution Modelling.”Journal of Biogeography
33(10): 1677–88. https://doi.org/10.1111/j.1365-2699.2006.
01584.x.
Araújo, M. B., and M. New. 2007. “Ensemble Forecasting of Species
Distributions.”Trends in Ecology and Evolution 22(1): 42–7.
https://doi.org/10.1016/j.tree.2006.09.010.
Arthur, S. M., B. F. Manly, L. L. McDonald, and G. W. Garner.
1996. “Assessing Habitat Selection When Availability
Changes.”Ecology 77(1): 215–27. https://doi.org/10.2307/
2265671.
Austin, M. P., and K. P. Van Niel. 2011. “Improving Species
Distribution Models for Climate Change Studies: Variable
Selection and Scale.”Journal of Biogeography 38(1): 1–8.
https://doi.org/10.1111/j.1365-2699.2010.02416.x.
Beck, J., M. Böller, A. Erhardt, and W. Schwanghart. 2014.
“Spatial Bias in the GBIF Database and Its Effect on
Modeling Species’Geographic Distributions.”Ecological
Informatics 19: 10–5. https://doi.org/10.1016/j.ecoinf.2013.
11.002.
Behdarvand, N., M. Kaboli, M. Ahmadi, E. Nourani, A. S. Mahini,
and M. A. Aghbolaghi. 2014. “Spatial Risk Model and
Mitigation Implications for Wolf-Human Conflict in a Highly
Modified Agroecosystem in Western Iran.”Biological
Conservation 177: 156–64. https://doi.org/10.1016/j.biocon.
2014.06.024.
Benson, J. F., and M. J. Chamberlain. 2007. “Space Use and Habitat
Selection by Female Louisiana Black Bears in the Tensas River
Basin of Louisiana.”Journal of Wildlife Management 71(1):
117–26. https://doi.org/10.2193/2005-580.
Berteaux, D., and N. C. Stenseth. 2006. “Measuring, Understanding
and Projecting the Effects of Large-Scale Climatic Variability
on Mammals.”Climate Research 32(2): 95–7. https://doi.org/
10.3354/cr032095.
Bloom, T. D. S., A. Flower, and E. G. DeChaine. 2018. “Why
Georeferencing Matters: Introducing a Practical Protocol to
Prepare Species Occurrence Records for Spatial Analysis.”
Ecology and Evolution 8(1): 765–77. https://doi.org/10.1002/
ece3.3516.
Booth, T. H. 2021. “A Problem with Variable Selection in a
Comparison of Correlative and Process-Based Species
Distribution Models: Comments on Higgins et al., 2020.”
Ecology and Evolution 11(19): 13609–12. https://doi.org/10.
1002/ece3.7496.
Boria, R. A., L. E. Olson, S. M. Goodman, and R. P. Anderson. 2014.
“Spatial Filtering to Reduce Sampling Bias Can Improve the
Performance of Ecological Niche Models.”Ecological
Modelling 275: 73–7. https://doi.org/10.1016/j.ecolmodel.2013.
12.012.
Brotons, L., S. Herrando, and M. Pla. 2007. “Updating Bird Species
Distribution at Large Spatial Scales: Applications of Habitat
Modelling to Data from Long-Term Monitoring Programs.”
Diversity and Distributions 13(3): 276–88. https://doi.org/10.
1111/j.1472-4642.2007.00339.x.
Brun, P., W. Thuiller, Y. Chauvier, L. Pellissier, R. O. Wüest,
Z. Wang, and N. E. Zimmermann. 2020. “Model Complexity
Affects Species Distribution Projections under Climate
Change.”Journal of Biogeography 47(1): 130–42. https://doi.
org/10.1111/jbi.13734.
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and
Multimodel Inference: A Practical Information-Theoretic
Approach. Berlin: Springer-Verlag.
Chapron, G., P. Kaczensky, J. D. C. Linnell, M. von Arx, D. Huber,
H. Andrén, J. V. L
opez-Bao, et al. 2014. “Recovery of Large
Carnivores in Europe’s Modern Human-Dominated
Landscapes.”Science 346(6216): 1517–9. https://doi.org/10.
1126/science.1257553.
Chevalier, M., O. Broennimann, J. Cornuault, and A. Guisan. 2021.
“Data Integration Methods to Account for Spatial Niche
Truncation Effects in Regional Projections of Species
Distribution.”Ecological Applications 31(7): e02427. https://
doi.org/10.1002/eap.2427.
Ciucci,P.,andL.Boitani.2008.“The Apennine Brown Bear:
A Critical Review of Its Status and Conservation
Problems.”Ursus 19(2): 130–45. https://doi.org/10.2192/
07PER012.1.
Clevenger, A. P., F. J. Purroy, and M. S. de Buruaga. 1987. “Status
of the Brown Bear in the Cantabrian Mountains, Spain.”
Bears: Their Biology and Management 7: 1–8.
Dormann, C. F., O. Purschke, J. R. García, S. L. M
arquez, and
B. Schröder. 2008. “Components of Uncertainty in
Species Distribution Analysis: A Case Study of the Great Grey
18 of 23 ZARZO-ARIAS ET AL.