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\begin{document}

\title{Wild canids and ecological traps, facing deforestation and climate
change in the Amazon Rainforest}



\author[1]{Geovana Oliveira}%
\author[1]{Paulo Trindade}%
\author[1]{Iara Santos}%
\author[2]{Paula  Almeida-Maués}%
\author[1]{Fernando Carvalho}%
\author[3]{Daniel Paiva Silva}%
\author[4]{Nelson Pinto}%
\author[5]{Øystein Wiig}%
\author[1]{Leonardo Sena}%
\author[1]{Ana Cristina  Mendes-Oliveira}%
\affil[1]{Federal University of Para}%
\affil[2]{Universidade Federal do Para}%
\affil[3]{Instituto Federal de Educacao Ciencia e Tecnologia Goiano - Campus Urutai}%
\affil[4]{Secretaria de Estado de Educação, Goiania, Goiás}%
\affil[5]{University of Oslo}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Ecological traps occur when species choose to settle in lower quality
habitats, even if this reduces their survival or productivity. This
normally occurs in situations of drastic environmental changes,
resulting, for example, from anthropogenic pressures. In the medium and
long term, this could mean the extinction of the species. We
investigated the dynamics of occurrence and distribution of three canid
species (Atelocynus microtis, Cerdocyon thous, and Spheotos venaticus)
related to human threats imposed on their habitats in the Amazon
Rainforest. We analyzed the possible environmental thresholds for the
occurrence of these species and confronted them with the future
projections of climatic niches for each one. All three species will be
negatively affected by climate change in the future, with losses of up
to 91\% of the suitable area of occurrence in the Brazilian Amazon. The
occurrence of C. thous and S. venaticus has been positively influenced
by anthropogenic open areas to the detriment of native environments,
suggesting that these species are being attracted by ecological traps,
which may put them at risk in the future in the Amazon Region. The A.
microtis species is the most dependent on forest habitats, and thus the
most significant threat would be forest loss. Using the canid species as
a model, we could deeply investigate these ecological effects that can
affect a large part of the Amazonian fauna in the current scenario.
Considering the high degree of environmental degradation and
deforestation in the Amazon Rainforest, the theory of ecological traps
must be discussed at the same level that habitat loss is considered a
decisive criterion of threat to biodiversity.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

The Amazon is home to thousands of species of terrestrial vertebrates,
which interact among them and with the environment in a unique way.
These interactions define the ecological niches of these species and how
they are distributed in time and space (Hirzel and Le Lay, 2008).
Deforestation, climate change, and other anthropogenic pressures have
caused drastic changes in the structure of native habitats in the Amazon
in the last 40 years (PRODES, 2021), which have consequences on the
interactions of species with their environment (Coelho et al., 2014;
Mendes-Oliveira et al., 2017; Sales et al., 2019). Deforestation and
climate change already act as associated factors for the transformation
of natural habitats into degraded areas of the Amazon, where the
drought-deforestation feedback will leave the climate in these areas
warmer and drier (Staal et al., 2020).

After nine years of reduction in deforestation, the Brazilian Amazon has
experienced an increase in degradation. During the last two years
(2019-2021), about 21,000 km\textsuperscript{2} of the Brazilian Amazon
Forest were deforested (PRODES-Terrabrasilis/INPE, 2021). About 60\% of
this deforestation was concentrated in the States of Par\selectlanguage{ngerman}á and Mato
Grosso, which border the \emph{Cerrado} Biome, also is called the
Brazilian savannah. Significant forest losses and climatic variations
are projected for this region in the coming years, increasing the
transition zone area between forests and savannah (Lyra et al., 2016).
These changes can negatively affect specialist forest species, and in
turn favor the use of the degraded landscape by species able to use
savannah habitats (Sales et al., 2020). Nevertheless, degraded
landscapes can lead species into ecological traps, where modified
environments may lead to false clues to favorable habitats, making it
impossible to accurately assess the suitability value for species in
these habitats (Hale and Swearer, 2016).

Ecological traps occur when species settle in lower quality habitats,
even if this reduces their productivity or survival (Hale and Swearer,
2016). This situation has been happening mainly due to the rapid
environmental changes caused by anthropogenic actions, which generate
new conditions in the habitats, different from the original ones, but
which may be somehow related to the evolutionary history of the species
(Robertson et al., 2013). In this way, species capture signals, which
are not indicative of habitat quality, and mistakenly establish
themselves in unfavorable locations for their survival in the medium and
long term (Robertson and Huto, 2006). In this paper, we raise the
possibility that this process occurs with some species in the Amazon
region, driven by the intense dynamics of land use and rapid
environmental changes caused by degradation, deforestation, and climate
change. In this way, understanding the factors that determine the
occupation of high-quality sites by species can help to mitigate the
harmful effects of choosing poorly adapted habitats (Robertson and
Hutto, 2006; Gilroy and William, 2007).

Most neotropical vertebrates live in natural environments already
altered at some level, directly or indirectly, by human activities. The
response of the fauna to significant changes in their native habitats
can vary according to their ecological demands and niche differences
(Sih et al., 2011; Hagen et al., 2012). Also, a species' fitness is
closely related to its evolutionary history, shaped by environmental
factors that influenced the ecological preferences and demands of the
species over an evolutionary time (Harper et al., 1961). Although the
main question is how recent significant changes in natural environments
are altering patterns shaped during evolution and whether species'
responses can mislead them into ecological traps and thereby reduce
their fitness. In this sense, it is necessary to have a baseline
knowledge about species' original distribution, habitat preferences, and
what determines their occurrences.

Members of the Canidae family have undergone morphological and
behavioral changes due to environmental changes and degradation of their
native habitats (Yom-Tov et al., 2007; Figueirido et al., 2015; De Moura
Bubadué et al., 2016). Canids appeared around 40\_Ma in North America
(Wang et al., 2007). They diversified throughout the Pleistocene and
occupied different niches and geographical locations (Berta, 1987;
Tchaicka et al., 2016). The emergence of a wide variety of morphologies
and diets over a relatively short evolutionary period (Perini et al.,
2010; Nyakatura and Bininda-Emonds, 2012; Figueirido et al., 2015), make
canids a very interesting group for studies of ecological plasticity.
For instance, small variations in masticatory or locomotor systems have
caused large differences in their adaptive capacity (Figueirido et al.,
2015; Penrose, 2019). Currently, 10 species of canids occur in South
America, with high morphological and ecological diversity among them
(Perini et al., 2010).

Three species of canids are represented in the Amazon: \emph{Atelocynus
microtis} (Sclater, 1883) (short-eared dog), \emph{Speothos
venaticus}(Lund, 1842)\emph{,} (bush dog), and \emph{Cerdocyon
thous}(Linnaeus,1766) (crab-eating fox) (Appendix, Figure 1). These are
small to medium-sized (from 4 to 11 Kg) canids with a broad and partly
sympatric distribution in the Amazon region. The occurrence and
distribution of canids have been associated with environmental
characteristics and interspecific competition (De Oliveira and Pereira,
2014; De Moura Bubadué et al., 2016; Rocha et al., 2020). Rocha et al.
(2020) showed a strong connection of \emph{A. microtis} to forest
habitats, and how it has been negatively affected by deforestation in
the Amazon.

Competition between canid species that occur in sympatry is commonly
reported in the literature (Cypher, 1993; Johnson et al., 1996; Di
Bitetti et al., 2009). Under environmentally favorable conditions,
sympatry species can coexist by partitioning spatial or temporal niches
(Bueno and Mota-Junior, 2004; Di Bitetti et al., 2009). Usually, the
dominant species with greater body mass consume the larger prey species,
while the sub-dominant species consume foods with lower nutritional
value (Penrose, 2019). There are several reports of sympatry
between\emph{A. microtis} and \emph{S. venaticus} (Michalski, 2010;
Santos and Mendes-Oliveira, 2012), and between \emph{C. thous} and
\emph{S. venaticus} (Da Silva et al., 2013; Vieira and Oliveira, 2020);
however, to our knowledge only one occurrence of sympatry has been
reported between \emph{A. microtis} and \emph{C. thous} (Carvalho et
al., 2014).

In this paper, we investigate the dynamics of occurrence and
distribution of \emph{A. microtis, C. thous, and S. venaticus} related
to anthropogenic threats imposed on their habitats in the Amazon Forest.
We investigate the possible environmental thresholds for the occurrence
of the three species and how their distribution might be influenced by
climate change and deforestation, and whether these factors can lead
these species to fall into ecological traps in the Amazon Rainforest. We
also model the future occurrence and distribution of those species,
considering a pessimistic climate change scenario. Our hypothesis is
that all species are influenced by different environmental variables,
and changes in these variables can lead species to ecological traps. We
also believe that these environmental variables explain different
patterns of occurrences and niche distributions now and in the future.

\textbf{METHODS}

\emph{The target species}

\emph{Atelocynus microtis}(Appendix-Figure 1, A and B) is the only
endemic species in the Amazon Biome, but with a wide distribution in the
region, extending from Ecuador to Brazil (Leite-Pitman and Willians,
2011). It is considered rare, based on a low number of records by most
studies during the last 30 years (Peres, 1991; Koester et al., 2008;
Michalski, 2010; Blake et al., 2012; Ayure and González-Maya, 2014).
However, this species seems to have been more frequently recorded
previously (Grimwood, 1968). Possible reasons for its apparent reduced
population size are infection by diseases acquired through contact with
domestic dogs and the loss of habitat (Leite-Pitman and Willians, 2011).
\emph{Atelocynus microtis} is a mesocarnivorous canid with a generalist
carnivore diet but also including fruit (Leite-Pitman and Williams,
2004; Blake et al., 2012; Penrose, 2019). It is solitary (Peres, 1991),
usually with a diurnal activity (Leite-Pitman and Williams, 2004; Blake
et al., 2012). Individuals of this species does not seem to tolerate
disturbed habitats and prefer continuous primary lowland and upland
forests (Michalski, 2010; Leite-Pitman and Willians, 2011; Rocha et al.,
2020). This species is classified as ''Near Threatened'' in the IUCN Red
List of Threatened Species (Leite-Pitman and Willians, 2011).

\emph{Cerdocyon thous} (Appendix-Figure 1, C and D) has the smallest
distribution area in the Amazon Biome of the three species. However, it
has a wide distribution in the rest of South America. It is common in
the \emph{Pantanal} , Atlantic Forest, \emph{Cerrado} , Amazon,
and\emph{Caatinga} biomes, and frequently recorded in forest edges
(Brady, 1979; Courtenay and Maffei, 2004). Individuals of this species
have a twilight and nocturnal habits, are solitary or lives in small
groups (Macdonald and Courtenay, 1996). Their diet is considered
omnivorous, generalist, and opportunistic, feeding on invertebrates,
small vertebrates, and even carrion, but they also frequently feed on
fruits (Bisbal and Ojasti, 1980; Macdonald and Courtenay, 1996). Of the
three species, \emph{C. thous} can be considered the one that least
depends on the forest, and being quite adapted to open environments,
including human-made areas (Faria-Corrêa et al., 2009; Ferraz et al.,
2010). This species is classified as ``Least Concern'' in the IUCN Red
List (Lucherini, 2015).

\emph{Speothos venaticus} (Appendix-Figure 1, E and F) is the canid with
the broadest distribution in the Amazon, although it is not common where
it occurs (DeMatteo and Loiselle, 2008; Guimarães et al., 2015; De
Oliveira et al., 2018). In Brazil, this animal occurs in the
Amazon,\emph{Pantanal} , Atlantic Forest, \emph{Cerrado} and
\emph{Caatinga}biomes, especially in humid forests and preserved
riparian forests. Individuals of this species have a strong relationship
with forest environments (DeMatteo and Loiselle, 2008; Michalski, 2010).
However, it has also been recorded in open areas of the \emph{Cerrado}
(Silveira et al., 1998; Lima et al., 2015), and altered and fragmented
environments in the Amazon (DeMatteo and Loiselle, 2008). Recently, its
suitability for native environments in the Cerrado was confirmed (Lima
et al., 2015). \emph{Speothos venaticus} can be considered a
hypercarnivore, feeding on small vertebrates and larger mammals such as
pacas, agoutis, and even capybaras (Beisiegel and Ades, 2002; Zuercher
et al., 2005). They are social animals, which may have a cooperative
hunting system, but lone individuals are also frequently observed
(Beisiegel and Ades, 2002; DeMatteo et al., 2011). This species is
classified as ``Near Threatened'' in the IUCN Red List (DeMatteo et al.,
2011). Its greatest threat appears to be habitat degradation.

\emph{Species occurrence data}

The study area encompasses the whole Amazon Biome, also known as the
Amazon Rainforest, which constitutes the largest tropical forest in the
world, covering nine countries in South America: Bolivia, Brazil,
Colombia, Ecuador, French Guyana, Guyana, Peru, Suriname and Venezuela
(IBGE, 2004; Junk et al., 2011). Despite that our discussion is focused
on the Amazon Biome, our species distribution database was built
considering the whole South America, so that the potential distribution
model covered the entire range of the species studied (Figure 1).

The database of geographic records of the three species was built from
literature data and online databases, such as Global Biodiversity
Information Facility (GBIF; http://www.gbif.org/; GBIF Occurrence
Download https://doi.org/10.15468/dl.dyy7qk) and Species Link
(http://splink.cria.org.br/) (Figure 1, Supplemental Material-Table S1).
Especially for \emph{A. microtis} , we used most of the records from
Rocha et al. (2020) publication. In addition, we used data from
unprecedented inventories of camera traps and Linear Transection census,
carried out by the team of the Laboratory of Ecology and Zoology of
Vertebrates of the Federal University of Pará (Brazil), between 2000 and
2019 (Appendix-Figure 1 and Supplemental Material-Table S1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Distribution of occurrence records of the three
species of Amazonian wild canids in South America. Data obtained through
literature, databases, and field inventories. A - \emph{Atelocynus
microtis} (short-eared dog); B - \emph{Cerdocyon thous} (crab-eating
fox); and C - \emph{Spheotos venaticus} (bush dog).

We searched for geographic information on the rare localities and
municipalities whose coordinates were unavailable using Google Earth
v7.1.2 (https://www.google.com/earth/) and online gazetteers (e.g.,
directory of cities and towns in the world; www.fallingrain.com/world).
Using these same resources, we checked all records with coordinates to
ensure the best reliability of the occurrences used in this study.
Specifically, we evaluated whether each point was minimally consistent
with what is already known about each species' distribution. Some doubts
were solved by the researchers who collected the data. In addition,
points that did not have precise information on geographic coordinates,
or corresponded to questionable places, such as rivers or lakes, were
discarded. Also, duplicated coordinates recorded in different databases
were discharged. The remaining records (Supplemental Material-Table S1)
were used for niche modeling, producing maps of the potential
distribution for each species, considering the climatic variables, and
analyzing generalized linear models (GLM) based on land use data.

\emph{Climatic Variables and Niche Modeling}

To generate the distribution predictions for the three canid species, we
gathered 19 climatic variables (Hijmans et al., 2005), available for the
current, and future projections of the SSP585 climate scenario from
global climate models (GCM's: BCC-CSM2-MR; CanESM5; IPSL-CM6A-LR;
MIROC-ES2L; and MIROC6), selected by a cluster analysis (Varela et al.,
2015) in the WorldClim v2.1 database. We cut all variables to the extent
of the South American continent, with a grid resolution of 0.041\selectlanguage{ngerman}º at the
Equator (\textasciitilde{}4 Km or 2.5 arc-min).

We standardized all variables, so they had their means equal to 0 and
their variance equal to 1. Later, we applied a spatialized principal
component analysis (PCA) based on a correlation matrix of the variables
to produce 19 orthogonal/independent principal components (PCs) to be
used as our new environmental variables (De Marco and Nóbrega, 2018). We
selected the first six PCs from those, responsible for
\textasciitilde{}97\% of the original raw climatic variance of the
predictor variables (Appendix, Table 1). These independent variables
allow us to produce more reliable predictions for the species
distributions and avoid model overfitting (Jiménez-Valverde et al.,
2011).

We partitioned the three species occurrences using a checkerboard
pattern (Muscarella et al., 2014; Roberts et al., 2017), with an
aggregation factor of two. According to this partitioning pattern, the
species occurrences are divided into two subsets geographically
structured as a checkerboard table. One of these subsets is used to
train the models on a first run, while the other is used to evaluate the
models. In a second run, these subsets are inverted, and the subset used
to evaluate the models will be used to train the models, and that used
to train the modes will be used to evaluate the models. We consider the
proportion of subsets always 30\% of data for testing and 70\% for
training.

We considered three different modeling methods to produce our models,
ranging from statistical methods {[}i.e., Random Forest (RDF){]},
machine learning methods {[}i.e. MaxEnt (MAX) and Support Vector
Machines (SVM){]}. We trained the models for the three species
considering the ecoregions shapefile provided by the World Wildlife Fund
website (https://www.worldwildlife.org/biomes), restricting the climatic
variables to ecoregions with known occurrences for the species. This
procedure is very important to delimit the M section of the
Biotic-Abiotic-Migration diagram (Soberón and Peterson 2005; Soberón
2007; Barve et al. 2011; Saupe et al. 2012) and improve the model
predictions for the focus species.

We made use of pseudo-absences to train the models. Therefore, we
restricted their allocation in the geographic space after they were
environmentally restricted, considering an environmental space based on
a climatic environment (VanDerWal et al. 2009; Lobo and Tognelli 2011).

To evaluate our models, we considered the Jaccard´s similarity index
proposed by Leroy et al. (2018), which varies from 0 to 1, where around
zero values indicate no-better-than-random predictions, while values
higher than 0.5 are considered as acceptable predictions. Differently
from other metrics commonly used in distribution modelling studies
{[}e.g., area under the curve (Fielding and Bell, 1997) or true skill
statistic (Allouche et al., 2006){]}, the Jaccard index is both
prevalence and pseudo-absence independent, yielding less uncertain
metrics for the models.

To cut the suitability matrices into presence/absence matrices, we
considered the threshold that maximized the Jaccard index. Finally, we
used a mean ensemble weighted by the best Jaccard values in each
modeling method in order to produce the species final distribution range
(Figure 2). We perform the modeling by the package ENMTML (De Andrade et
al., 2020) in \emph{software} R (v4.0).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Methodological procedure for obtaining Niche
Modeling, using climate variables for the current and future period
(2060), configuration and evaluation of ENM available in the ENMTML
package.

\emph{Environmental factors determining the occurrence of the canids}

We only used the records of the three canid species occurring within
Brazil to analyze which environmental variables and landscape structures
influence each species' occurrence. We limited it to Brazilian points
using the Landsat classified image database from MapBiomas. v5.0
collection available just for this country (https//:mapbiomas.org). In
the GeoTiff scenario, this collection informs the annual mapping of land
cover and land use in Brazil between the historical period of 1985 to
2019 (MapBiomas, 2021). We listed the coordinates of each occurrence of
\emph{A. microtis} , with information on scenarios in the Amazon Biome,
from 2002 to 2019, a period that includes all records of occurrences of
this species. We did the same thing for \emph{C. thous} , but using data
from 1988 to 2016, including the Amazon, \emph{Caatinga} ,
\emph{Cerrado} , Atlantic Forest,\emph{Pantanal} , and \emph{Pampas}
biomes. For \emph{S. venaticus} , we used data from 1900 to 2017,
including biomes from the Amazon,\emph{Cerrado} , \emph{Caatinga} ,
Atlantic Forest, and \emph{Pantanal}(Figure 3).

For each coordinate of occurrence of each species, we delimited a buffer
with a radius of 1 km, from which we extracted information on land use.
We obtained the pixel values and land-use class codes in each buffer
(available at: https://mapbiomas.org/downloads\_codigos) and transformed
these values into categories of percentages. If the buffer areas
overlap by more than 20\%, the rightmost occurrence point was excluded
for this analysis. After eliminating buffers with overlapping criteria,
we selected 277 independent records with a minimum distance of 2 km
between them. We set 64 records of \emph{A. microtis} , 63 of \emph{C.
thous} and 100 of \emph{S. venaticus} (Supplemental Material-Table S2).

About 16 land-use classes were recorded in the occurrence scenarios of
the buffers. Some classes with similar landscape effects were grouped
into new more significant categories, and others with lower registration
frequency than three occurrence points were discarded (Supplemental
Material-Table S2). In the end, we considered five significant land use
categories to assess the environmental factors that determine the
occurrence of species. They are: (Forest) Percentage of forest cover,
which includes all native forest formations of the Amazon; (Water)
Percentage of water bodies, including streams, rivers, lakes and
non-forest natural wetlands; (Nat\_open\_areas) Percentage of natural
open areas, including savannas, grasslands and rocky outcrops;
(Urban\_areas) Percentage of urban infrastructure; and
(Anthr\_open\_areas) Percentage of anthropogenic open areas, including
pasture, perennial annual crop, agriculture and pasture mosaic
(Appendix-Figure 2, Supplemental Material-Table S2).

In addition to the land-use variables, we also extracted the altitude
(Alt) and the Enhanced Vegetation Index (EVI). The altitude was
extracted from each point of occurrence of the species from the
satellite images of Google Earth Pro (version 7.3.1). The Enhanced
Vegetation Index (EVI) is an index related to plant complexity and
heterogeneity, since it is based on plant cover and the fraction of
absorbed photosynthetically active radiation (Gurung et al., 2009). The
EVI was extracted from the Earth Explorer platform (available at:
https://earthexplorer.usgs.gov/). For each year of occurrence of the
species, we extract images in the rasters format and calculate the
statistical average of the index using the Quantum Gis
\emph{software}(v2.8.4).

To avoid data multicollinearity, we used Principal Component Analysis
(PCA) to select correlated variables from the first two axes for further
explanation of the results (Kassambara, 2017) (Appendix-Figure 2). We
used the Kaiser criterion to select the variables by eigenvalues, not
including those that were correlated on the same axis (Jackson, 1993).
We selected the following predictor variables to be used in Generalized
Linear Models (GLM's) analyses: Forest, Water, Nat\_open\_areas, Urban\_
areas, and Anthr\_open\_areas (Figure 3, Supplemental Material-Table
S2). We constructed GLM's with binomial distribution and logit linkage
function to verify the influence of environmental predictors on the
occurrence of the three canids. We initially ran all models with
binomial distribution and evaluated the dispersion assumption. In the
case of \emph{A. microtis} , which had a dispersion greater than 1, we
used quasibinomial correction. To analyze the possible effects of
predictor variables on the occurrence of the three species, we built six
models considering different combinations of the predictor variables
(Appendix-Table 2). We use the \emph{glm} function (Hardin et al. 2007)
from the \emph{stats} package (Marschner and Donoghoe, 2018). We
evaluated the dispersion of residues to confirm the absence of
overdispersion. We then ran the analysis and selected the best model
using the table ANOVA criterion, including the null model, and we used
the data generated in the GLM's analysis to interpret the size measure
of the effects of the predictor variables on the occurrence of species
(Figure 3). We considered values for p \emph{\textless{}} 0.05 as level
of significance.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Methodological procedure to obtain the environmental
factors that determine the occurrence of \emph{Atelocynus microtis} ,
\emph{Cerdocyon thous} and\emph{Speothos venaticus} , through the
analysis of Generalized Linear Models (GLM's).

\textbf{RESULTS}

\emph{Niche Modeling}

Overall, we recorded 319 presences of \emph{A. microtis} , 691 of
\emph{C. thous}and 271 of \emph{S. venaticus} , throughout South America
(Appendix-Figure 1 and Supplemental Material- Table S1). In general, our
species niche distribution models reached adequate values for the
Jaccard index (0.612 \emph{+} 0.371; average \emph{+} standard
deviation), reaching higher predictive values than those that would be
expected by chance. All models generated from the three species and the
ranges of suitability of their respective algorithms are found in the
Appendix (Figure 3).

Considering the final weighted ensemble for the three species (Figure
4), \emph{A. microtis} in both current and future scenarios was more
restricted to the western part of the Amazon Forest, including Colombia,
Peru, Bolivia, but also in the northern part, including Venezuela,
Suriname, Guyana, and French Guyana. However, we found that in the
extreme northeast of the Brazilian Amazon, which includes the east of
the state of Amap\selectlanguage{ngerman}á and further north, in the south of Guyana near the
northeast of the state of Roraima (Brazil), there are two spots of
non-suitability for this species, which coincides with large patches of
Brazilian savannah. When comparing the current and future distribution
models, we observed a loss of 18.43\% of the total \emph{A.
microtis}climate suitability area in South America in the future. Of
this total loss, about 15\% is supposed to occur in the Brazilian
Amazon. In this region, these losses are distributed in areas with high
anthropic pressure, close to the southern limit of the Forest, in a
transition zone with the \emph{Cerrado} , in the states of Acre and
Rondônia. However, we also observed areas of loss concentrated in the
eastern borders of the species distribution, which are areas of high
anthropic pressure in the Forest (Figure 4).

\emph{Cerdocyon thous} has a high climatic suitability in open areas and
anthropogenic landscapes, inside or outside the Amazon Biome, in both
current and future scenarios. Its potential distribution within the
Amazon natural savannahs in eastern Amapá state and part of Roraima
state in northern Brazil. The species also shows suitability in
anthropized areas in the southern limits of the Amazon region, and in
the transition zone with the \emph{Cerrado} Biome, especially in the
states of Mato Grosso and Rondônia (Figure 4). In the current scenario,
the area of \selectlanguage{english}\selectlanguage{english}climatic suitability includes a strip in the northeast of
the Brazilian Amazon, where high anthropogenic pressure is concentrated
in the region. Outside the Amazon, this species also had climatic
suitability for natural areas of \emph{Cerrado} and anthropized open
areas in the central-western, southeastern, and southern areas of Brazil
east-central Bolivia, southeastern Paraguay, Uruguay, and northeast of
Argentina. The future projected ensemble model predicts the loss of
56.54\% of the \emph{C. thous} climate suitability area across South
America (Figure 4). About 88\% of this total loss could occur in the
Brazilian Amazon.

The projected area of climatic suitability for \emph{S. venaticus} is
almost as wide as for \emph{C. thous} (Figure 4). However, the current
distribution of \emph{S. venaticus} includes both areas of\emph{Cerrado}
and dense forests, but also other Brazilian biomes and anthropogenic
open areas (Figure 4). When we analyze the projection for the future,
\emph{S. venaticus} is the species that loses the most area with
climatic suitability, among the three canids, with a reduction of
57.18\% for all of South America. However, about 91\% of this total loss
will occur in the Brazilian Amazon (Figure 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Predictions of climatic niche distributions for the
three species of wild amazon canids (\emph{Atelocynus microtis}
,\emph{Cerdocyon thous} , and \emph{Speothos venaticus} ) in South
America, based on a mean ensemble weighted by the best Jaccard values in
each modeling method. Current -- Areas of occurrence considering the
current climate suitability for the species; Future -- Climate niche
projections for 2060 for each species; Loss of Climate Niche -- Areas
that will lose climate suitability for the species in the future.

\emph{Generalized Linear Models (GLM's)}

For \emph{A. microtis} , the global model including all predictor
variables was the most adequate model to explain the variation in the
occurrence of this species (Appendix-Table 3). When evaluating the
isolated effect of each variable, we observed that only Nat\_open\_areas
did not affect the distribution of \emph{A. microtis} (Table 1). The
variables, Forest, Water, and Anthr\_open\_areas had a positive effect
on the occurrence of the species (Table 1) (Figure 5A). We observed an
increase in the probability of \emph{A. microtis} by 74.4\% for areas
with higher percentages of these variables, especially Forest and Water.
The Urban\_areas variable negatively affected the occurrence of \emph{A.
microtis} (Figure 5A). We estimated that this variable reduces the
occurrence of the species by about 50\%.

\textbf{Table 1.} Predictive variables selected for \emph{Atelocynus
microtis} by the ANOVA, using the most ajusted model: A.
microtis\textasciitilde{}Forest + Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas + Water\emph{.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Variable under test & ?\selectlanguage{ngerman}² & df & F & p\tabularnewline
\midrule
\endhead
Null & & & &\tabularnewline
Forest & 50,896 & 1 & 79,42 & p\textless{}0.001\tabularnewline
Anthr\_open\_areas & 3,032 & 1 & 4,732 & 0,03\tabularnewline
Water & 24,873 & 1 & 38,813 & p\textless{}0.001\tabularnewline
Urban\_areas & 2,396 & 1 & 3,738 & 0,05\tabularnewline
Nat\_open\_areas & 0,682 & 1 & 1,064 & 0,3\tabularnewline
\bottomrule
\end{longtable}

For \emph{C. thous} , the global model was also the most adequate to
explain the variation in occurrence of this canid in the landscape
(Supplementary Material, Table S4). Analyzing the variables separately,
only the percentage of Water did not affect the occurrence of this
species (Table 2). The variables Anthr\_open\_areas and Urban\_areas
positively affected the occurrence of \emph{C. thous} (Figure 5B),
providing a 73\% increase in the probability of occurrence of this
species. On the other hand, the percentage of Forest and
Nat\_open\_areas negatively affected the occurrence (Figure 5B),
reducing the probability of occurrence by 72\%.

\textbf{Table 2.} Predictive variables selected for \emph{Cerdocyon
thous} by the ANOVA, using the most ajusted model: C.
thous\textasciitilde{}Forest + Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas + Water.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Variable under test & ?\selectlanguage{ngerman}² & df & p\tabularnewline
\midrule
\endhead
Null & & &\tabularnewline
Forest & 20,597 & 1 & p\textless{}0.001\tabularnewline
Anthr\_open\_areas & 24,365 & 1 & p\textless{}0.001\tabularnewline
Water & 0,02 & 1 & 0,88\tabularnewline
Urban\_areas & 4,451 & 1 & 0,03\tabularnewline
Nat\_open\_areas & 7,472 & 1 & p\textless{}0.01\tabularnewline
\bottomrule
\end{longtable}

Also, for \emph{S. venaticus} , the global model was the best to explain
the species occurrence in the landscape (Table S5, Supplemental
Material). The single variables that had an effect on the occurrence
of\emph{S. venaticus} was the percentage of Forest, Anthr\_open\_areas,
and Urban\_areas, all of which positively influenced the occurrence of
the species (Table 3, Figure 5C). On average, areas with higher
percentages of forest and urban areas have a 74\% increase in the
species' probability of occurrence, while in open anthropic areas, the
probability increased by 73\%.

\textbf{Table 3.} Predictive variables selected for \emph{Speothos
venaticus} by the ANOVA, using the most ajusted model: S.
venaticus\textasciitilde{}Forest + Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas + Water.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Variable under test & ?\selectlanguage{ngerman}² & df & p\tabularnewline
\midrule
\endhead
Null & & &\tabularnewline
Forest & 3,585 & 1 & 0,05\tabularnewline
Anthr\_open\_areas & 12,952 & 1 & p\textless{}0.001\tabularnewline
Water & 0,697 & 1 & 0,4\tabularnewline
Urban\_areas & 9,106 & 1 & p\textless{}0.01\tabularnewline
Nat\_open\_areas & 3,175 & 1 & 0,07\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Effect direction (positive or negative) of the
variables selected by the GLM's as main predictors of the occurrence of
canid species in the Amazon. (A) \emph{Atelocynus microtis} ;
(B)\emph{Cerdocyon. thous} , and (C) \emph{Speothos venaticus} .

\textbf{DISCUSSION}

Our results show that the occurrence of the three Amazonian canid
species \emph{C. thous,S. venaticus} and \emph{A. microtis} may be
negatively affected by climate change in the future, with losses of
their entire suitability area in South America from 15\% to 91\% and in
the Brazilian Amazon losses ranging from 18\% to 56\%. However, the
effects can be differentiated between the species according to their
ecological demands and the environmental variables that can modulate
their climatic niches. For \emph{C. thous} and \emph{S. venaticus} , the
climate variables and those associated with anthropogenic disturbances
that modulate their niches today, may not act in the same way in the
future. Thus, the rapid environmental changes might function as
ecological traps for these species (Robertson and Hutto, 2006), and in
the future they may no longer survive in most of the areas considered
suitable for them today.

Among the three species \emph{A. microtis} has the most restricted
distribution confined to the Amazon Forest and the one with the least
projected loss of suitable climatic areas in the future. Apparently,
this species is a specialist in forest habitats, in particular in
continuous and preserved forest (Michalski, 2010; Leite-Pitman and
Willians, 2011). Probably, the loss of suitability for this species is
directly related to the loss of deforestation and forest degradation
(Rocha et al., 2020). In addition to the distribution of the climatic
niche of this species mainly coinciding with the most conserved regions
of the Amazon Forest, our results showed a strong influence of Forests
and Water on the occurrence of \emph{A. microtis} . The association of
this species with watercourses is also known from the literature and is
suggested by interdigital membranes which may be an adaption for moving
on soft soil (Leite-Pitman and Williams 2004; Castell\selectlanguage{ngerman}ó, 2018). Although
the proximity to urban areas was not one of the main variables
determining the occurrence of \emph{A. microtis} , we found that this
variable was negatively related to its occurrence, corroborating its low
tolerance to disturbed habitats (Michalski, 2010; Leite-Pitman and
Williams, 2011; Rocha et al., 2020). Thus, the loss of suitability areas
in the future for \emph{A. microtis} cannot be related to the ecological
trap theory. This species will probably no longer occur in unfavorable
habitats due to its fitness for higher-quality forest habitats.

For \emph{A. microtis} , one of the consequences of deforestation and
human pressure is increased hunting (Constantino, 2016), including
hunting with domestic dogs. Thus, disease transmission by domestic dogs
will pose a threat to \emph{A. microtis} (Schenck and Stail, 2004;
Leite-Pitman and Williams, 2011). Our results corroborate those of Rocha
et al. (2020), who expected a loss of 30\% of the \emph{A.
microtis}distribution area by 2027, due to forest loss. Based on the
last three years of high deforestation rates in the Amazon (PRODES,
2021), and using a pessimistic projection for the future, the loss of
area of occurrence of this species could be much more severe.

Considering habitat selection, an animal cannot always make the best
choice about where to live (Kristan, 2003; Stamps and Swaisgood, 2007),
but its initial choice may later affect the individual's survival and
its reproductive success. However, the ultimate reason that determined
the choice may have not been evident when the choice was made (Hutto
1985; Kristan, 2003). In the case of altered environments in the Amazon,
it is possible that the attractiveness of these habitats by species such
as \emph{C. thous} or \emph{S. venaticus} , are detaching themselves
from the species' aptitude for survival and reproduction. The preference
of these species for anthropogenic environments was evident from our GLM
analyses, where natural open areas negatively influenced the occurrence
of \emph{C. thous} , while anthropogenic open areas had a positive
impact. The same was apparent for \emph{S. venaticus} where
anthropogenic open areas had a positive effect on its occurrence. These
lower-quality habitats may at some point be just as attractive as
preserved native habitats. This can happen when animals whose behaviors
have been shaped by exposure to different environmental conditions in
their evolutionary history, are suddenly confronted by altered or novel
environments (Iwasa and Levin, 1995). In these situations where
impoverished environments become more attractive and ''bait''
individuals, these environments are called ecological traps (Gates and
Gysel, 1978; Robertson and Hutto, 2006).

Of the three species surveyed, \emph{C. thous} appears to be the least
demanding in terms of habitat, with aptitude for open environments and a
high tolerance for anthropogenic habitats (Ferraz et al., 2010). In the
Amazon, this species is common in natural non-forest environments, such
as in areas of Canga Metalófila in Carajás, State of Pará (Carvalho et
al., 2014), or in areas of the Amazon Cerrado in the State of Amapá
(Coelho et al., 2014). In our occurrence database from the Amazon, we
had no record of \emph{C. thous} in areas of continuous forest and
outside naturally open environments. On the other hand, \emph{C.
thous}has been commonly recorded in areas with eucalyptus (Coelho et
al., 2014) and oil palm plantations (Mendes-Oliveira et al., 2017), and
may occur at the edge of adjacent forests, but not inside them. It also
occurs in mining areas, with a high level of degradation and locations
in the initial phase of forest recovery in the region of Paragominas
State of Pará. Interestingly, two individuals of \emph{C. thous} were
recorded in 2020 within the city of Belém, State of Pará, in the Amazon
(Mendes-Oliveira, personal comm.). \emph{Cerdocyon thous} does not seem
to be distributed within forests, and deforestation has favored its
expansion in the Amazon biome. The same occurred with \emph{Chrysocyon
brachyurus} , (maned wolf), which expanded its extent of \selectlanguage{english}\selectlanguage{english}occurrence in
southwestern Amazonia (see Silva-Diogo et al., 2020), precisely due to
the conversion of forests into open areas in southeastern Amazonia
(Silva-Diogo et al., 2020).

Our current model showed more outstanding suitability of \emph{C.
thous}to more open environments, such as \emph{Cerrado} and more
anthropized open areas in the Amazon, with restrictions to more forested
areas, such as in the central and northwestern portions of the Amazon.
The GLM results also corroborate the conclusion that \emph{C. thous} has
been positively influenced by urban areas and anthropogenic open areas
and has been negatively by forest. However, this species was also
negatively influenced by natural open areas, suggesting a greater
fitness for anthropogenic habitats over natural areas. Although canids
generally do not show cursor adaptations like those of ungulates (Smith
and Savage, 1965), \emph{C. thous} has a more cursorial form than the
other two canid species studied in this research (Penrose, 2019). Longer
limbs favor more extended travel, especially in open areas. Faced with a
scenario of degradation and deforestation, this may be a morphological
adaptation that favors the entry of this species into degraded,
previously forested environments. Another characteristic that confers an
advantage to the ecological plasticity of \emph{C. thous} ,
about\emph{A. microtis} and \emph{S. venaticus} in the Amazon, is its
greater tendency to omnivory. The ability of this species to have a poor
or meatless diet for long periods, feeding opportunistically on what the
environment offers (Macdonald and Courtenay, 1996; De Oliveira, 2009),
allows it a more remarkable ability to thrive in a broader range of
backgrounds and conditions. Considering all these characteristics, we
would imagine that the deforestation of the Amazon Forest has been
favoring \emph{C. thous} . However, in the long term, this species may
be be ``baited'' by lower-quality environments, reducing its success and
leading it to extinction in certain areas in the future.

Considering our future projections of climate suitability for \emph{C.
thous} , we observed a significant loss of suitable areas, especially in
regions already open or currently degraded by anthropogenic action,
mainly in the southern limits of the Amazon Forest and the northeast of
this biome. These climate niche loss projections suggest that
impoverished environments currently suitable for \emph{C. thous} may not
perpetuate in the future. Thus, these new anthropogenic environments may
be working as an ecological trap for \emph{C. thous} in the Amazon.

The same may happen with \emph{S. venaticus} . This species is
considered rare (De Moura Bubadue et al., 2016), and occurs in forest
habitats and forest edges (Zuercher et al., 2005), open areas such as
savanah (Zuercher, 2001), and altered habitats (DeMatteo and Loiselle
(2008). Despite not being a habitat specialist, \emph{S. venaticus} is
the most carnivorous canid in South America. And this is due to the
presence, in individuals of this species, of a large zygomatic arch, a
short snout with a reduced number of molars and a pointed lower first
molar (LM1), important adaptations for hypercarnivory, which increases
the efficiency of meat consumption (Ewer, 1973; Van Valkenburgh and
Koepfli, 1993; Beisiegel and Zuercher, 2005; De Moura Bubadue et al.,
2016)

Thresholds for the occurrence of \emph{S. venaticus} appear to be less
restrictive than for \emph{A. microtis} but more stringent than
for\emph{C. thous} . DeMatteo and Loiselle (2008) mention that 20\% of
the potential distribution areas of \emph{S. venaticus} were associated
with degraded areas, using land-use data from 1992 to 1993. However,
Michalski (2010) recorded \emph{S. venaticus} only in continuous regions
of Forest, even applying an effort using camera traps in fragments
adjacent to continuous areas. In our study, the current model showed
broad distribution suitability in South America for this species, but
with a significant loss in the future (the most extensive loss among the
three species), especially in the Amazon region. The future loss of
climatic niches in the Brazilian Amazon is concentrated both in forest
areas and areas already anthropized, indicating that this species may
suffer from the loss of high and low-quality habitats. However, the GLM
indicated that \emph{S. venaticus} is positively influenced by forest,
urban areas, and anthropogenic open areas. This suggests that \emph{S.
venaticus} may also be involved in an ecological trap when it chooses
lower quality habitats that may not be enough to maintain its survival
in the future. However, unlike \emph{C. thou} , \emph{S. venaticus} also
uses forest habitats, which allows it to survive in areas that can be
protected in the future in the Amazon.

Evolutionarily seen, the climate has been a more decisive factor than
competition in explaining morphological variation and habitat options in
canids (De Moura Bubadue et al., 2016). They showed that the skull
morphology of \emph{A. microtis} and \emph{S. venaticus} is more related
to warm, humid, and less seasonal environments than \emph{C. thous} .
The use of different habitats and different diets reduces competition
between them. In this way, the coexistence of these species can be
compromised not by competition between them but, on the contrary, by
differences in climatic adaptations and habitat options, as our results
show.

Ecological traps occur when species erroneously choose niches where
their suitability is lower than in others after changes in the
environment (Robertson et al., 2013). Considering the evolutionary
history of species, environments altered by human actions could provide
false clues to their adaptation (Sclaepfer et al., 2002). In the medium
and long term, these ecological traps can lead to significant decrease
or even extinction of populations in these habitats before adaptation to
the new modified environment has occurred (Hale and Swearer, 2016).
For\emph{C. thous} and \emph{S. venaticus} , we believe that rapid
changes in landscapes (habitat degradation) will lead to ``wrong
choices'' for environments that will soon have large climatic
variations, leading these species to be trapped in these ecosystems,
without time to adapt to them.

Further, both \emph{S. venaticus} and \emph{A. microtis} appear to be
forest-dependent and must rely on the goodwill of decision-makers to be
maintained in the future. However, even though \emph{C. thous} is least
dependent on the Amazon Forest, this species is probably the one that
will be most affected in the future due to the ecological traps that the
region has offered. \emph{Speothos venaticus} , can also suffer from the
same process, but if the forest is maintained, the impact on this
species can be less intense. Using the canid species as a model, we
could deeply investigate these ecological effects that can affect a
large part of the Amazonian fauna in the current scenario. The future
perspectives for preserving terrestrial vertebrate fauna in this region
are pretty pessimistic. When it comes to the conservation of
impoverished regions in the Amazon, the theory of ecological traps must
be studied and discussed at the same level that habitat loss is
considered a decisive criterion for biodiversity threat.

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\textbf{Appendix}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 1 Appendix} . Photos of camera traps of wild dog recorded
in the Amazon. (A) \emph{Atelocynus microtis} , photo captured at FLONA
Caxiuan\selectlanguage{ngerman}ã, Pará-Brazil (TEAM-Caxiuanã/Fernanda Santos);
(B)\emph{Atelocynus microtis} , photo captured at ESEC Terra do Meio,
Pará-Brazil (Monitora ICMBIO/Cintia Lopes); (C and D) \emph{Cerdocyon
thous} , photo captured at FLONA Carajás, Pará-Brazil (Ana Cristina
Mendes-Oliveira); (E) \emph{Spheotos venaticus} , photo captured at
CKBV´s Company area, Paragominas, Pará-Brazil (Ana Cristina
Mendes-Oliveira); (F) \emph{Spheotos venaticus} , photo captured at
HYDRO Company area, Paragominas, Pará-Brazil (Ana Cristina
Mendes-Oliveira and Oystein Wiig).

\textbf{Table 1 Appendix} . Principal Component Analysis (PCA) results,
considering the variables to generate distribution models for Amazonian
canid species.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Variable & Name & PC1 & PC2 & PC3 & PC4 & PC5 & PC6\tabularnewline
\midrule
\endhead
Bio1 & Annual Mean Temperature & -2,7E+14 & -2,3E+14 & 1,29E+14 &
-4,2E+14 & -6,5E+14 & -9,4E+14\tabularnewline
Bio2 & Mean Diurnal Range (Mean of monthly (max temp - min temp)) &
1,84E+14 & -2,3E+13 & 1,06E+14 & 4,81E+14 & 1,48E+14 &
-5,3E+14\tabularnewline
Bio3 & Isothermality (Bio2/Bio7) (* 100) & -2,4E+13 & 3,51E+14 &
-3,3E+14 & 9,88E+14 & -2,3E+14 & -5E+14\tabularnewline
Bio4 & Temperature Seasonality (standard deviation *100) & 2,47E+14 &
1,02E+14 & 3,86E+14 & -6,7E+14 & 2,21E+14 & 8,89E+14\tabularnewline
Bio5 & Max Temperature of Warmest Month & -1,9E+14 & -3,2E+14 & 3,45E+14
& -4,2E+14 & 1,33E+14 & -7,6E+14\tabularnewline
Bio6 & Min Temperature of Coldest Month & -2,9E+14 & -1,2E+13 & -3,5E+14
& -1,5E+14 & -9,5E+14 & 1,34E+13\tabularnewline
Bio7 & Temperature Annual Range (Bio5-Bio6) & 2,5E+14 & -1,2E+14 &
3,2E+14 & 1,83E+14 & 2,59E+14 & -8,9E+14\tabularnewline
Bio8 & Mean Temperature of Wettest Quarter & -2,3E+14 & -2,6E+14 &
2,32E+14 & 7,28E+14 & -1,1E+14 & -1,9E+14\tabularnewline
Bio9 & Mean Temperature of Driest Quarter & -2,8E+14 & -1,5E+14 &
-1,8E+14 & -1,7E+14 & -1,3E+14 & -1,8E+14\tabularnewline
Bio10 & Mean Temperature of Warmest Quarter & -2,3E+14 & -2,6E+13 &
3,08E+14 & -9,8E+14 & 2,57E+13 & 1,75E+14\tabularnewline
Bio11 & Mean Temperature of Coldest Quarter & -2,8E+13 & -1,8E+14 &
9,08E+14 & -2,6E+14 & -1E+14 & -3,3E+14\tabularnewline
Bio12 & Annual Precipitation & -2,6E+14 & 2,14E+14 & 3,34E+14 & 1,79E+14
& 2,09E+14 & 6,49E+13\tabularnewline
Bio13 & Precipitation of Wettest Month & -2,7E+14 & 7,04E+14 & -1,2E+14
& 2,23E+14 & 3,42E+14 & 2,12E+14\tabularnewline
Bio14 & Precipitation of Driest Month & -1,5E+14 & 4E+14 & 2,39E+14 &
4,06E+14 & -1,1E+14 & -3,1E+13\tabularnewline
Bio15 & Precipitation Seasonality (Coefficient of Variation) & 3,78E+14
& -3,5E+14 & -4,1E+14 & 3,13E+13 & 1,48E+14 & -4,1E+14\tabularnewline
Bio16 & Precipitation of Wettest Quarter & -2,7E+14 & 7,82E+14 &
-1,1E+14 & 2,29E+14 & 3,41E+13 & 2,13E+14\tabularnewline
Bio17 & Precipitation of Driest Quarter & -1,6E+14 & 3,99E+14 & 2,3E+14
& 3,94E+13 & -8E+13 & -2,8E+14\tabularnewline
Bio18 & Precipitation of Warmest Quarter & -1,6E+14 & 1,79E+14 &
1,92E+14 & 5,98E+14 & -3E+14 & 3,34E+12\tabularnewline
Bio19 & Precipitation of Coldest Quarter & -2E+14 & 2,07E+14 & -7,5E+13
& -2,4E+14 & 5,91E+14 & -2,3E+14\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2 Appendix} . Predictor variables used in the construction
of Generalized Linear Models (GLM's)\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Models} & \textbf{Variable under test}\tabularnewline
\midrule
\endhead
Model 1 & Forest + Anthr\_open\_areas + Urban\_areas + Nat\_open\_areas
+ Water\tabularnewline
Model 2 & Forest + Anthr\_open\_areas + Urban\_areas\tabularnewline
Model 3 & Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas\tabularnewline
Model 4 & Forest + Anthr\_open\_areas\tabularnewline
Model 5 & Anthr\_open\_areas\tabularnewline
Model 6 & Null\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 2 Appendix.} Principal Component Analysis (PCA) to select
correlated variables for explanation of the results\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 3 Appendix.} Models generated from the three species of
canids and the ranges of suitability of their respective algorithms

\textbf{Table 3 Appendix} . Results for the models selected by the GLM's
for species through the Anova table.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Model - \emph{Atelocynus microtis}} & \textbf{Model -
\emph{Atelocynus microtis}} & \textbf{?²}\selectlanguage{ngerman} & \textbf{?²}\selectlanguage{ngerman} & \textbf{df} &
\textbf{df} & \textbf{p} & \textbf{p}\tabularnewline
\midrule
\endhead
\emph{A. microtis}\textbf{\textasciitilde{}Forest + Anthr\_open\_areas +
Urban\_areas + Nat\_open\_areas + Water} & \emph{A.
microtis}\textbf{\textasciitilde{}Forest + Anthr\_open\_areas +
Urban\_areas + Nat\_open\_areas + Water} & & & & & &\tabularnewline
\emph{A. microtis}\textasciitilde{}Forest + Anthr\_open\_areas +
Urban\_areas & \emph{A. microtis}\textasciitilde{}Forest +
Anthr\_open\_areas + Urban\_areas & -21,581 & -21,581 & -2 & -2 &
p\textless{}0.001 & p\textless{}0.001\tabularnewline
\emph{A. microtis}\textasciitilde{}Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas & \emph{A. microtis}\textasciitilde{}Anthr\_open\_areas
+ Urban\_areas + Nat\_open\_areas & 18,822 & 18,822 & 0 & 0 &
&\tabularnewline
\emph{A. microtis}\textasciitilde{}Forest + Anthr\_open\_areas &
\emph{A. microtis}\textasciitilde{}Forest + Anthr\_open\_areas & -25,192
& -25,192 & -1 & -1 & p\textless{}0.001 &
p\textless{}0.001\tabularnewline
\emph{A. microtis}\textasciitilde{}Anthr\_open\_areas & \emph{A.
microtis}\textasciitilde{}Anthr\_open\_areas & -44,909 & -44,909 & -1 &
-1 & p\textless{}0.001 & p\textless{}0.001\tabularnewline
\emph{A. microtis}\textasciitilde{}1 & \emph{A.
microtis}\textasciitilde{}1 & -9,019 & -9,019 & -1 & -1 &
p\textless{}0.001 & p\textless{}0.001\tabularnewline
\textbf{Model - \emph{Cerdocyon thous}} & \textbf{?²}\selectlanguage{ngerman} & \textbf{?²}\selectlanguage{ngerman} &
\textbf{df} & \textbf{df} & \textbf{p} & \textbf{p} &\tabularnewline
\emph{C. thous}\textbf{\textasciitilde{}Forest + Anthr\_open\_areas +
Urban\_areas + Nat\_open\_areas + Water} & & & & & & &\tabularnewline
\emph{C. thous} \textasciitilde{}Forest + Anthr\_open\_areas +
Urban\_areas & -76,161 & -76,161 & -2 & -2 & 0,022 & 0,022
&\tabularnewline
\emph{C. thous} \textasciitilde{}Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas & -60,132 & -60,132 & 0 & 0 & & &\tabularnewline
\emph{C. thous} \textasciitilde{}Forest + Anthr\_open\_areas & 16,852 &
16,852 & -1 & -1 & & &\tabularnewline
\emph{C. thous} \textasciitilde{}Anthr\_open\_areas & -19,938 & -19,938
& -1 & -1 & p\textless{}0.001 & p\textless{}0.001 &\tabularnewline
\emph{C. thous} \textasciitilde{}1 & -25,026 & -25,026 & -1 & -1 &
p\textless{}0.001 & p\textless{}0.001 &\tabularnewline
\textbf{Model - \emph{Spheotus venaticus}} & \textbf{?²}\selectlanguage{ngerman} & \textbf{?²}\selectlanguage{ngerman} &
\textbf{df} & \textbf{df} & \textbf{p} & \textbf{p} &\tabularnewline
S. venaticus \textasciitilde{}Forest + Anthr\_open\_areas + Urban\_areas
+ Nat\_open\_areas + Water & & & & & & &\tabularnewline
\emph{S. venaticus \textasciitilde{}Forest + Anthr\_open\_areas +
Urban\_areas} & -57,324 & -57,324 & -2 & -2 & 0,056 & 0,056
&\tabularnewline
\emph{S. venaticus \textasciitilde{}Anthr\_open\_areas + Urban\_areas +
Nat\_open\_areas} & 45,197 & 45,197 & 0 & 0 & & &\tabularnewline
\emph{S. venaticus \textasciitilde{}Forest + Anthr\_open\_areas} &
-11,766 & -11,766 & -1 & -1 & p\textless{}0.001 & p\textless{}0.001
&\tabularnewline
\emph{S. venaticus \textasciitilde{}Anthr\_open\_areas} & -11,776 &
-11,776 & -1 & -1 & p\textless{}0.001 & p\textless{}0.001
&\tabularnewline
\emph{S. venaticus \textasciitilde{}1} & -47,626 & -47,626 & -1 & -1 &
0,029 & 0,029 &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

