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Projecting Mammal Distributions in Response to Future Alternative Landscapes in a Rapidly Transitioning Region

Abstract and Figures

Finding balance between the needs of people and wildlife is an essential component of planning sustainable landscapes. Because mammals make up a diverse and ecologically important taxon with varying responses to human disturbance, we used representative mammal species to examine how alternative land-use policies might affect their habitats and distributions in the near future. We used wildlife detections from camera traps at 1591 locations along a large-scale urban to wild gradient in northern Virginia, to create occupancy models which determined land cover relationships and the drivers of contemporary mammal distributions. From the 15 species detected, we classified five representative species into two groups based on their responses to human development; sensitive species (American black bears and bobcats) and synanthropic species (red foxes, domestic cats, and white-tailed deer). We then used the habitat models for the representative species to predict their distributions under four future planning scenarios based on strategic versus reactive planning and high or low human population growth. The distributions of sensitive species did not shrink drastically under any scenario, whereas the distributions of synanthropic species increased in response to anthropogenic development, but the magnitude of the response varied based on the projected rate of human population growth. This is likely because most sensitive species are dependent on large, protected public lands in the region, and the majority of projected habitat losses should occur in non-protected private lands. These findings illustrate the importance of public protected lands in mitigating range loss due to land use changes, and the potential positive impact of strategic planning in further mitigating mammalian diversity loss in private lands.
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remote sensing
Article
Projecting Mammal Distributions in Response to
Future Alternative Landscapes in a Rapidly
Transitioning Region
Michael V. Cove *, Craig Fergus, Iara Lacher, Thomas Akre and William J. McShea
Smithsonian Conservation Biology Institute, Front Royal, VA 22630, USA; FergusC@si.edu (C.F.);
LacherI@si.edu (I.L.); AkreT@si.edu (T.A.); McSheaW@si.edu (W.J.M.)
*Correspondence: CoveM@si.edu; Tel.: 203-417-8244
Received: 21 September 2019; Accepted: 22 October 2019; Published: 24 October 2019


Abstract:
Finding balance between the needs of people and wildlife is an essential component of
planning sustainable landscapes. Because mammals make up a diverse and ecologically important
taxon with varying responses to human disturbance, we used representative mammal species
to examine how alternative land-use policies might aect their habitats and distributions in the
near future. We used wildlife detections from camera traps at 1591 locations along a large-scale
urban to wild gradient in northern Virginia, to create occupancy models which determined land
cover relationships and the drivers of contemporary mammal distributions. From the 15 species
detected, we classified five representative species into two groups based on their responses to human
development; sensitive species (American black bears and bobcats) and synanthropic species (red
foxes, domestic cats, and white-tailed deer). We then used the habitat models for the representative
species to predict their distributions under four future planning scenarios based on strategic versus
reactive planning and high or low human population growth. The distributions of sensitive species
did not shrink drastically under any scenario, whereas the distributions of synanthropic species
increased in response to anthropogenic development, but the magnitude of the response varied based
on the projected rate of human population growth. This is likely because most sensitive species
are dependent on large, protected public lands in the region, and the majority of projected habitat
losses should occur in non-protected private lands. These findings illustrate the importance of public
protected lands in mitigating range loss due to land use changes, and the potential positive impact of
strategic planning in further mitigating mammalian diversity loss in private lands.
Keywords: land cover; occupancy; protected lands; remote camera traps; scenario planning
1. Introduction
Eective land planning must balance the needs of people and the natural world [
1
], particularly
because land cover change and habitat loss are among the leading causes of biodiversity loss [
2
].
Mammals are no exception to the global phenomena of range reduction and habitat loss [
3
].
Mammals across a range of body sizes and trophic guilds change their behaviors in direct response to
human-induced changes in land cover and increased human presence [
4
,
5
]. Furthermore, there are
apparent dichotomies in mammal responses to changes in the Anthropocene, with species such as
omnivorous mesopredators, herbivores, and domesticated species exhibiting clear advantages over
obligate carnivores, apex predators, and habitat specialists [
6
9
]. For example, some species within
carnivore guilds avoid urban areas, whereas others increase their abundances in these anthropogenic
environments [1013].
Mammals are often dicult to detect even when present, but technological advances in
non-invasive sampling (e.g., camera traps, genetic tagging, etc.) have helped accumulate global
Remote Sens. 2019,11, 2482; doi:10.3390/rs11212482 www.mdpi.com/journal/remotesensing
Remote Sens. 2019,11, 2482 2 of 16
mammal diversity and distribution data [
14
,
15
]. Despite the increasing availability of wildlife data,
few studies have integrated these data to map the distributions of mammal species in regions
undergoing expansive land changes [
12
,
16
]. These projected species distribution models can then
be used to predict range shifts in response to future landscape scenarios, such as those projected
under climate change [
17
,
18
]. Contemporary biodiversity data provide valuable snapshots of mammal
distributions, but they have not been applied to projections of land cover change to forecast community
distributions in the future. These predictions would be informative to stakeholders, as they coordinate
and select planning policies to preserve biodiversity and their associated ecosystem services.
We projected changes in a suite of habitat-relevant land cover types for a rapidly changing
landscape in northern Virginia under four dierent scenarios that varied in policy to plan development
strategically (e.g., urban centralized) or reactively (e.g., sprawling) over the next 50 years, while
accounting for dierences in the rate of human population growth [
19
]. Scenario planning is a strategic
planning approach whereby alternate storylines or “scenarios” of the future are generated and their
potential impacts are examined. Scenario planning is particularly useful in the conservation realm
because of the high degree of uncertainty associated with future changes and the potential for significant
impacts resulting from divergent policies [
20
,
21
]. This study used scenarios that were developed with
regional stakeholders’ inputs to increase their relevance to the projected future landscapes [19].
We used wildlife detections, obtained through deployments of camera traps along a large-scale
urban-to-wild gradient in northern Virginia, to create occupancy models to estimate land cover eects on
those mammals detected. We predicted that species such as the American black bear (Ursus americanus)
and bobcat (Lynx rufus) would respond negatively to human development and be most sensitive to
habitat loss, because they are large omnivorous and obligate carnivores, respectively, which require
substantial suitable habitats to persist. On the other hand, species such as urban-associated red foxes
(Vulpes vulpes), free-roaming domestic cats (Felis catus), and generalist herbivores like white-tailed deer
(Odocoileus virginianus) were predicted to respond positively to human-derived habitat fragmentation
and land cover change, regardless of planning scenarios. The predictive models can inform planning
commissions as to which scenario would be most eective to sustain mammalian assemblages.
2. Materials and Methods
2.1. Study Area
Our study area was a 32,500 km
2
region of northern Virginia centered on the Shenandoah National
Park. This area included the 15 counties plus a 5 km buer that formed the core study area of the
Changing Landscapes Initiative (CLI), a collaborative program initiated in 2015 to guide regional
planning eorts with the goals of long-term preservation of biodiversity and ecosystem services [
19
,
22
].
Roughly 55% of the study area is forested. Nearly a third of that forest is under public stewardship in
the Shenandoah National Park and George Washington National Forest, both of which are protected
from future development but managed for multiple purposes and community benefits. The remaining
forest occurs in patches that are largely privately-owned with variation in protection from future
development across land owners. Other major land cover types include grasslands, composed of both
pasture and herbaceous cover (~33%); urban developments (~7%); and agricultural crops (~5%) [22].
2.2. Mammal Camera Trapping
We used wildlife data from the eMammal database (eMammal.si.edu) to extract information on
a suite of mammals present in the region [
23
]. These species represented five functional guilds that
respond to land cover change in two opposite patterns: large carnivores (>100 kg) and native obligate
carnivores, which should be negatively associated with land cover change; and generalist herbivores,
mesopredators, and exotic carnivores, which should respond positively to anthropogenic land cover.
The eMammal database is a data management platform for camera trap projects with data from
both citizen scientists and professionals [
24
]. We used data from several projects within eMammal in
Remote Sens. 2019,11, 2482 3 of 16
which camera deployments and the data they collected were standardized in camera trap height (~50 cm
above ground) and without bait or lures [
23
,
25
]. Although these projects diered in their original
objectives, all examined wildlife-habitat associations and used passive-infrared motion-activated
camera traps (~90% Reconyx RC55, RC800, orc RC900, RECONYX, Inc., Holmen WI, USA or ~10%
Bushnell TrophyCam HD, Bushnell, Overland Park, KS, USA) [
23
,
25
]. These camera trap data came
from sites within the CLI study area, and the greater Washington DC region as well, to capture a
fuller range of human development (Figure 1). In total, these data were derived from 1591 camera
deployments conducted during the growing seasons from August 2012 through October 2018.
Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 17
The eMammal database is a data management platform for camera trap projects with data from
both citizen scientists and professionals [24]. We used data from several projects within eMammal in
which camera deployments and the data they collected were standardized in camera trap height (~50
cm above ground) and without bait or lures [23,25]. Although these projects differed in their original
objectives, all examined wildlife-habitat associations and used passive-infrared motion-activated
camera traps (~90% Reconyx RC55, RC800, orc RC900, RECONYX, Inc., Holmen WI, USA or ~10%
Bushnell TrophyCam HD, Bushnell, Overland Park, KS, USA) [23,25]. These camera trap data came
from sites within the CLI study area, and the greater Washington DC region as well, to capture a
fuller range of human development (Figure 1). In total, these data were derived from 1591 camera
deployments conducted during the growing seasons from August 2012 through October 2018.
Figure 1. Camera trapping area with 1591 camera trap sites from 2012 to 2018, and the Changing
Landscapes Initiative (CLI) study area from across an urban to rural gradient, northern Virginia.
Following each deployment, we extracted the raw detection data for each species and created
daily detection histories for each site with a minimum of one week of sampling and we truncated the
detection histories at 30 days to realize closure for the sites. Although the data were collected over
several field seasons, we did not resample any sites in a multi-season framework, and we, therefore,
refer to ψ as “site use” [26]; however, we did test for annual and seasonal variation in detection, as
described later in the methods.
2.3. Covariates and Models
To measure habitat variables, we used ArcGIS 10.7 (Environmental Systems Research Institute
[ESRI], Inc., Redlands, CA, USA) to create a land cover map of the camera trapping area based on the
reclassification of the 2011 National Land Cover Database (NLCD—Appendix Table A1) with 30 m
resolution land cover data modified on the Anderson Level II classification system, from previous
CLI research iterations [22,27]. Reclassification was based on generalized landscape characteristics,
Figure 1.
Camera trapping area with 1591 camera trap sites from 2012 to 2018, and the Changing
Landscapes Initiative (CLI) study area from across an urban to rural gradient, northern Virginia.
Following each deployment, we extracted the raw detection data for each species and created
daily detection histories for each site with a minimum of one week of sampling and we truncated the
detection histories at 30 days to realize closure for the sites. Although the data were collected over
several field seasons, we did not resample any sites in a multi-season framework, and we, therefore,
refer to
ψ
as “site use” [
26
]; however, we did test for annual and seasonal variation in detection,
as described later in the methods.
2.3. Covariates and Models
To measure habitat variables, we used ArcGIS 10.7 (Environmental Systems Research Institute
[ESRI], Inc., Redlands, CA, USA) to create a land cover map of the camera trapping area based on
the reclassification of the 2011 National Land Cover Database (NLCD—Appendix A) with 30 m
resolution land cover data modified on the Anderson Level II classification system, from previous
CLI research iterations [
22
,
27
]. Reclassification was based on generalized landscape characteristics,
Remote Sens. 2019,11, 2482 4 of 16
and thus we do not expect these simplifications of land cover to dramatically aect inferences for
mammal responses. We selected habitat covariates based on the known ecology of potential candidate
mammals [7,8,10,11,23,28].
We measured the coverage percentages of the four main land covers: (1) forest, (2) grassland,
(3) crops, and (4) urban development in 500 and 1000 m radii around each 30 x 30 m pixel of land in
the study area. We further measured linear distances (m) to a variety of anthropogenic or natural
edges: (5) distance to development, (6) distance to roads, and (7) distance to crops as indices of human
presence and/or disturbance; (8) distance to grassland, (9) distance to core forest (forest greater than
60 m from other land class), and (10) distance to protected lands, which included private and public
lands protected from future development. We also used a binary covariate (11) to denote sites as
within or outside of those protected areas (i.e., lands managed by the National Park Service, National
Forest Service, other state, local, and private entities as derived from the PADUS [Protected Areas
Database of the United States] layer [
29
]); however, this covariate does exclude smaller or private
protected lands not reported to the PADUS group. Furthermore, protected lands are not necessarily
forested, with private protected lands comprised of more agricultural and utilitarian sites than public
protected lands [
30
]. Finally, we used Fragstats to measure applied landscape covariates, such as (12)
contagion, (13) total edge, and (14) landscape division indices within the same 500 and 1000 m scales
as the land cover percentage measurements [
31
]. We standardized all continuous covariates to zscores
for analyses and excluded any covariates that were highly correlated (r >0.7).
Because camera traps were deployed across years and seasons, we developed and compared 7 a
priori models, including a constant (null) and global (year +season) set, to estimate and account for
the eects of season-specific covariates on detection probabilities of each species. We then used the
covariates that contained high model support and strong eects on species detection as a constant
covariate in the subsequent occurrence models for each species.
For the occurrence models, we developed and compared 30 a priori hypotheses to predict the
distributions of mammals in our study area dependent on land cover and our derived covariates
(Supplemental Table S1). The approach was hierarchical in that we compared 14 preliminary models
with habitat covariates at local and broader scales and then used model selection to determine which
scale (e.g., 500 m or 1000 m) was more appropriate to include in further additive and global models for
each specific species. We developed and compared occupancy models in the “unmarked” package in
R [32,33].
2.4. Model Selection
We evaluated the best approximating models based on their Akaike information criterion (AIC)
and Akaike weights (
ωi
). We considered covariate eects to be substantial if 95% confidence intervals
excluded 0. To makehabitat distribution models for the contemporary time-period, we evaluated model
predictive power using the area under the ROC (Receiver Operating Characteristics) curve (AUC) and
considered models to have strong predictive power if the AUC was greater than 0.7. We further used
the false positive rate (fpr) as an index of our predictive power and applied conservative (fpr <0.1)
and more liberal thresholds (fpr <0.3) to generate predicted core (conservative) habitat and suitable
(less conservative and inclusive of all core) habitats for each species, respectively. We implemented
these analyses within the “ROCR” package [34].
2.5. Predicting under Land-Planning Scenarios
Using planning scenarios developed for the region via a stakeholder-driven scenario planning
process [
19
], we projected dierences in land cover use across the CLI study area. During two regional
meetings, local stakeholders defined four scenarios by crossing the poles of two drivers of change:
human population growth and the degree of strategic planning. The results are as follows: (HS)
high human population growth and strategic planning, (HR) high population growth and reactive
planning, (LR) low population growth and reactive planning, and (LS) low population growth and
Remote Sens. 2019,11, 2482 5 of 16
strategic planning (Figure 2). The stakeholders also provided estimates of the amount of dierence
they expected to see between scenarios that we used in the quantification process described below.
Remote Sens. 2019, 11, x FOR PEER REVIEW 5 of 17
difference they expected to see between scenarios that we used in the quantification process described
below.
Figure 2. Schematic of the four quadrants defining our scenario plans along population growth and
strategic planning gradients for the CLI study area in northern Virginia.
We simulated these scenarios with the spatially-explicit modeling platform Dinamica EGO
(Environment for Geoprocessing Objects) version 3.41 [35]. Dinamica uses a Markov chain/cellular
automata framework to project change at one location on a landscape, based on information about
the surrounding area. We used that framework to project landscape configurations 50 years into the
future, using primary inputs derived from two patterns of land use change observed between our
calibration landscapes; NLCD 2001 and NLCD 2011. We parameterized each model by making
modifications to the patterns observed.
The first input pattern was the amount of area that changed for each class-to-class transition for
each of the four classes (i.e., both forest that becomes grass and grass that becomes forest). This value
controls the number of changes Dinamica makes to the initial landscape. We did not track transitions
from development to other classes because this was a rare event. We increased the rate of change for
development in the HR and HS scenarios to represent the increased need for housing and services
created by higher human population density [36]. The amount of change was quantified by first
matching observed rates of development growth to observed population growth and then projecting
that relationship to decadal population values 24% higher than those projected by the Weldon Cooper
Center [37]. For the LR and LS scenarios, we kept the observed rate of development transitions
constant to represent low population growth. We then applied a stakeholder-driven, percent-based
reduction to both the modified HS rates and unedited LS rates to represent the smaller footprint
caused by centralized high density housing expected under strategic planning. We also made
modifications for transitions between forest, grass, and crops based on stakeholder input regarding
agricultural change [19].
The second input was the “weights of evidence” values calculated for each class-to-class
transition in relation to a suite of environmental and socioeconomic variables. These values control
where Dinamica makes changes to the initial landscape through maps of the accumulated weights of
all variables for each transition type. To distinguish between the centralized growth plans expected
in strategic scenarios and the more sprawled growth expected in reactive scenarios, we modified the
transition to development weight maps such that the accumulated weights near urban centers were
higher for HS and LS scenarios and lower for HR and LR scenarios.
Figure 2.
Schematic of the four quadrants defining our scenario plans along population growth and
strategic planning gradients for the CLI study area in northern Virginia.
We simulated these scenarios with the spatially-explicit modeling platform Dinamica EGO
(Environment for Geoprocessing Objects) version 3.41 [
35
]. Dinamica uses a Markov chain/cellular
automata framework to project change at one location on a landscape, based on information about the
surrounding area. We used that framework to project landscape configurations 50 years into the future,
using primary inputs derived from two patterns of land use change observed between our calibration
landscapes; NLCD 2001 and NLCD 2011. We parameterized each model by making modifications to the
patterns observed.
The first input pattern was the amount of area that changed for each class-to-class transition for
each of the four classes (i.e., both forest that becomes grass and grass that becomes forest). This value
controls the number of changes Dinamica makes to the initial landscape. We did not track transitions from
development to other classes because this was a rare event. We increased the rate of change for development
in the HR and HS scenarios to represent the increased need for housing and services created by higher
human population density [
36
]. The amount of change was quantified by first matching observed rates
of development growth to observed population growth and then projecting that relationship to decadal
population values 24% higher than those projected by the Weldon Cooper Center [
37
]. For the LR and
LS scenarios, we kept the observed rate of development transitions constant to represent low population
growth. We then applied a stakeholder-driven, percent-based reduction to both the modified HS rates and
unedited LS rates to represent the smaller footprint caused by centralized high density housing expected
under strategic planning. We also made modifications for transitions between forest, grass, and crops
based on stakeholder input regarding agricultural change [19].
The second input was the “weights of evidence” values calculated for each class-to-class transition
in relation to a suite of environmental and socioeconomic variables. These values control where
Dinamica makes changes to the initial landscape through maps of the accumulated weights of all
variables for each transition type. To distinguish between the centralized growth plans expected in
strategic scenarios and the more sprawled growth expected in reactive scenarios, we modified the
Remote Sens. 2019,11, 2482 6 of 16
transition to development weight maps such that the accumulated weights near urban centers were
higher for HS and LS scenarios and lower for HR and LR scenarios.
2.6. Species’ Habitat Projections
We used the future landscapes for each scenario to generate scenario-specific versions of each
of the model covariates. We then ran the top supported occupancy model on each suite of scenario
covariates to generate predicted distributions (suitable and core habitat) for each species. We calculated
the predicted change in area for both core and suitable habitats between the present and 2060 for each
of the planning scenarios. We further divided those habitats into protected and non-protected areas
(e.g., protected from future development), to more broadly evaluate the importance of the protected
areas network in the study area.
3. Results
3.1. Camera Trap Summary Statistics
Between 2012 and 2018, we obtained 24,866 independent detections of 15 mammals from
36,443 camera trap days (Supplemental Table S2). We evaluated occupancy models for all 15 species,
but excluded 10 species from further analyses beyond the occupancy model comparisons. We excluded
three species (woodchucks (Marmota monax), striped skunks (Mephitis mephitis), and gray foxes (Urocyon
cinereoargenteus)) due to sparse data and five species (eastern gray squirrels (Sciurus carolinensis), eastern
fox squirrels (Sciurus niger), Virginia opossums (Didelphis virginiana), northern raccoons (Procyon lotor),
and coyotes (Canis latrans)) due to low predictive power of the model (AUC <0.70). We excluded
two additional species (eastern chipmunks (Tamias semistriatus) and eastern cottontails (Sylvilagus
floridanus)) due to incongruence in distribution predictions from likely, subtle site-specific detection
biases associated with their small size and fine-grained habitat preferences. This process resulted in
five representative species for all further analyses: American black bears, bobcats, red foxes, domestic
cats, and white-tailed deer (Supplemental Table S3).
3.2. Occupancy Model Results and Habitat Associations
The global models were most supported for black bear and bobcat occurrence probabilities, each
receiving 100% of the Akaike weight. The AUC values for each of these predictive models were
also strong with AUC =0.81 and 0.80 for bears and bobcats, respectively. As predicted, bears and
bobcats were positively associated with natural land covers (forest and grasslands) and were positively
associated with increasing distances from anthropogenic land covers of development, roads, and crops.
However, contrary to our a priori predictions, the binary protected area covariate was not a strong
predictor, with a negative, but non-significant, association (Table 1).
Remote Sens. 2019,11, 2482 7 of 16
Table 1.
Estimated beta coecients (
β
) with standard errors (SE), 95% lower and upper confidence
intervals (LCI, UCI), p-values, and a priori predictions for covariate eects from the top-ranking
occupancy model, explaining variations in occupancy and detection eects of land cover and
anthropogenic covariates on the probability of site use by sensitive mammals from 1591 camera
trap locations across an urban to rural gradient, northern Virginia, 2012–2018.
Species
βSE LCI UCI p-Value a priori
Model
Parameter
American Black Bear
Occupancy
Intercept 0.80 0.17 1.13 0.46 0.00
Forest 1000 1.73 0.22 1.30 2.16 0.00 +
Grass 1000 1.19 0.15 0.89 1.49 0.00 +
Dist Dev 0.48 0.10 0.29 0.66 0.00 +
Dist Road 0.28 0.08 0.12 0.44 0.00 +
Dist Core 0.12 0.13 0.13 0.37 0.35
Dist Crop 0.29 0.09 0.11 0.47 0.00 +
Protected 0.33 0.18 0.67 0.02 0.06 +
Detection
Intercept 2.14 0.05 2.25 2.04 0.00
Spring 0.25 0.08 0.41 0.08 0.00 +
Summer 0.23 0.07 0.10 0.37 0.00 +
Bobcat
Occupancy
Intercept 2.47 0.33 3.11 1.84 0.00 +
Forest 1000 2.22 0.47 1.29 3.14 0.00 +
Grass 1000 0.98 0.36 0.29 1.68 0.01 +
Dist Dev 0.12 0.11 0.09 0.33 0.28 +
Dist Road 0.20 0.10 0.00 0.40 0.05 +
Dist Core 0.17 0.33 0.47 0.81 0.61
Dist Crop 0.43 0.12 0.19 0.67 0.00 +
Protected 0.14 0.28 0.68 0.40 0.62 +
Detection
Intercept 3.01 0.10 3.21 2.82 0.00
Spring 0.37 0.14 0.10 0.65 0.01 +
The anthropogenic models were most supported for domestic cats and red foxes with 70% and
88% of the Akaike weights, respectively. The AUC values for each of these predictive models were also
reliable, with AUC =0.76 and 0.74 for cats and red foxes, respectively. Habitat associations of these
two mesopredators followed our a priori predictions, with both species being positively associated
with increasing human development and negatively associated with increasing distances from roads
and development and increasing core forest (Table 2). White-tailed deer’s occurrence was most
supported by the global model (99% of the Akaike weight) with AUC =0.73, suggesting the species
was negatively associated with increasing forest cover, distances from human development, roads,
and crops, and positively associated with grassland habitats and protected area status (Table 2).
Remote Sens. 2019,11, 2482 8 of 16
Table 2.
Estimated beta coecients (
β
) with standard errors (SE), 95% lower and upper confidence
intervals (LCI, UCI), p-values, and a priori predictions for covariate eects from the top-ranking
occupancy model, explaining variation in occupancy and detection eects of land cover and
anthropogenic covariates on the probability of site use by synanthropic mammals from 1591 camera
trap locations across an urban to rural gradient, northern Virginia, 2012–2018.
Species
βSE LCI UCI p-Value a priori
Model
Parameter
Domestic Cat
Occupancy
Intercept 3.567 0.1981 3.96 3.18 0.00
Dev 500 0.302 0.0813 0.14 0.46 0.00 +
Dist Dev 0.343 0.2163 0.77 0.08 0.11
Dist Road 0.718 0.2955 1.30 0.14 0.02
Core 500 0.084 0.1809 0.44 0.27 0.64
Detection
Intercept 1.72 0.0902 1.90 1.54 0.00
Fall 0.454 0.1875 0.82 0.09 0.02
Red Fox
Occupancy
Intercept 1.386 0.0759 1.53 1.24 0.00
Dev 1000 0.257 0.0799 0.10 0.41 0.00 +
Dist Dev 0.204 0.0987 0.40 0.01 0.04
Dist Road 0.174 0.1096 0.39 0.04 0.11
Core 1000 0.572 0.0945 0.76 0.39 0.00
Detection
Intercept 2.104 0.0492 2.20 2.01 0.00
Fall 0.256 0.074 0.11 0.40 0.00
White-tailed Deer
Occupancy
Intercept 2.508 0.227 2.06 2.95 0.00
For 1000 0.27 0.217 0.70 0.16 0.22 +
Grass 1000 0.185 0.166 0.14 0.51 0.26 +
Dist Dev 0.296 0.111 0.51 0.08 0.01
Dist Road 0.188 0.107 0.40 0.02 0.08
Dist Core 0.497 0.112 0.72 0.28 0.00 +
Dist Crop 0.337 0.125 0.58 0.09 0.01
Protected 0.524 0.295 0.05 1.10 0.08
Detection
Intercept 0.618 0.0132 0.64 0.59 0.00
Spring 0.17 0.0267 0.22 0.12 0.00 +
Habitat associations from our occupancy models of the other species detected generally followed
our a priori predictions, with species such as eastern chipmunks, eastern fox squirrels, gray foxes,
striped skunks, and coyotes responding positively to increasing forest cover, while eastern cottontails
and woodchucks responded positively to increasing grassland cover (Supplemental Table S4).
The top-ranking occupancy model to predict raccoon occurrence included the derived landscape
division index as the only covariate. Additionally, most species (i.e., coyotes, striped skunks, Virginia
opossums, eastern gray squirrels, and eastern cottontails) responded positively to increasing distances
from development, whereas only eastern fox squirrel models suggested a negative association.
Coyotes, opossums, and gray squirrels were negatively associated with increasing distances from
crops, while striped skunks, fox squirrels, and cottontails responded positively to increasing distance
from crops (Supplemental Table S4).
Remote Sens. 2019,11, 2482 9 of 16
3.3. Predicted Contemporary Distributions
Contemporary black bear habitat estimates ranged from 4067.7 km
2
for core habitat to 10,547.5 km
2
for suitable habitat in the study area, with 56.9% and 38.9% of those areas in protected areas, respectively
(Figure 3a). The contemporary bobcat habitat was more restricted, with estimates ranging from
2268.9 km
2
for core habitat and 5613.3 km
2
for suitable habitat, with the majority of habitat in protected
areas, to 75.5% core and 58.7% suitable habitat (Figure 3b).
Contemporary domestic cat and red fox distributions were largely limited in the study area,
with >90% of habitats occurring in unprotected private lands. The distribution for cats ranged from
5660.1 km
2
(core) to 14,583.4 km
2
(suitable), and the distribution of red foxes ranged from 1927.8 km
2
(core) to 11,999.6 km
2
(suitable) (Figure 3c,d). White-tailed deer were the most ubiquitous species in
the study area with a 6867.9 km2core and an 23,213.5 km2suitable contemporary habitat (Figure 3e).
Figure 3. Cont.
Remote Sens. 2019,11, 2482 10 of 16
Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 17
Figure 3. Predicted occupancy probabilities, core (green) and suitable (purple) habitat areas, and
predicted changes over time for five representative mammals [(a)black bear, (b) bobcat, (c) domestic
cat, (d)red fox, and (e) white-tailed deer] in protected and non-protected areas during the
contemporary period (2011) and under two planning scenarios into 2060. HR represents high human
population growth and reactive planning, whereas S represents low population growth and strategic
planning. Habitat areas were predicted from the top-ranking occupancy model with the effects of
land cover and anthropogenic covariates from 1591 locations across an urban to rural gradient,
northern Virginia, 2012–2018.
3.4. Projected Land Cover and Suitable Habitat Availability Changes under Different Scenarios
Overall, changes under the four planning scenarios are driven by human population growth,
with high human population density models (HS and HR) exhibiting the most urban development
expansion (+381.8–445.2 km
2
) and highest loss of forest (277.4–325.6 km
2
), grasslands (32.5–36.0
km
2
), and cropland (71.883.6 km
2
) (Table 3). The LS scenario with low population expansion and
strategic planning represents the least expansion of urban development (+251.0 km
2
) and the lowest
reduction in forest cover loss (227.9 km
2
), compared to LR with low population and reactive
planning competing (288.0 km
2
of development gain and 271.4 km
2
of forest loss).
Figure 3.
Predicted occupancy probabilities, core (green) and suitable (purple) habitat areas, and predicted
changes over time for five representative mammals [(
a
) black bear, (
b
) bobcat, (
c
) domestic cat, (
d
) red fox,
and (
e
) white-tailed deer] in protected and non-protected areas during the contemporary period (2011)
and under two planning scenarios into 2060. HR represents high human population growth and reactive
planning, whereas S represents low population growth and strategic planning. Habitat areas were predicted
from the top-ranking occupancy model with the effects of land cover and anthropogenic covariates from
1591 locations across an urban to rural gradient, northern Virginia, 2012–2018.
3.4. Projected Land Cover and Suitable Habitat Availability Changes under Dierent Scenarios
Overall, changes under the four planning scenarios are driven by human population growth,
with high human population density models (HS and HR) exhibiting the most urban development
expansion (+381.8–445.2 km
2
) and highest loss of forest (
277.4–325.6 km
2
), grasslands (
32.5–36.0 km
2
),
and cropland (
71.8
83.6 km
2
) (Table 3). The LS scenario with low population expansion and strategic
planning represents the least expansion of urban development (+251.0 km
2
) and the lowest reduction in
forest cover loss (
227.9 km
2
), compared to LR with low population and reactive planning competing
(288.0 km2of development gain and 271.4 km2of forest loss).
Predicted core habitat losses for bears and bobcats ranged from low under the LS planning
scenario with 2.2% and 3.6% losses, respectively, to relatively high under the HR (4.7% loss) and HS
(8.6% loss) scenarios, respectively. However, when the study area is stratified by protected status,
it becomes apparent that habitat loss occurs mostly in unprotected habitat, with as high as 9.2% and
23.7% losses for bears and bobcats in scenarios HR and HS, respectively.
Predicted core and suitable habitats increased for domestic cats and red foxes under all planning
scenarios. Substantial expansion, such as 39.3% for domestic cats’ core habitat and 88.8% core habitat
growth for red foxes, was predicted under the HR planning scenario, whereas under the LS planning
scenario, habitat expansion was lowest, ranging from 3.0% to 17.4% and 3.2% to 50.6% for suitable
versus core habitat expansion for domestic cats and red foxes, respectively. Habitat for white-tailed
deer does not vary strongly under any planning scenarios, ranging from a loss of 1.6% under HS to
2.2% gain under the LR planning scenario.
Remote Sens. 2019,11, 2482 11 of 16
Table 3.
Estimated core and suitable habitat area and percent changes over time for five representative
mammals in protected and non-protected areas during the contemporary period (2011) and under
four land planning scenarios into 2060. HS represents high human population growth and strategic
planning, HR represents high human population growth and reactive planning, LR represents low
population growth and reactive planning, and LS represents low population growth and strategic
planning. Habitat areas were predicted from the top-ranking occupancy models with eects of land
cover and anthropogenic covariates from 1591 camera trap locations across an urban to rural gradient,
northern Virginia, 2012–2018.
Species Scenario Status Core Habitat Suitable Habitat
Area (km2)Percent Change Area (km2)Percent Change
Bear
Not Protected 1753.3 6439.8
2011 Protected 2314.4 4107.7
HS Not Protected 1619.3 7.6 6262.3 2.8
Protected 2285.3 1.3 4109.8 0.0
Not Protected 1591.4 9.2 6070.4 5.7
HR Protected 2284.2 1.3 4091.1 0.4
LR Not Protected 1607.2 8.3 6200.1 3.7
Protected 2290.9 1.0 4110.3 0.1
Not Protected 1637.7 6.6 6331.6 1.7
LS Protected 2338.1 1.0 4147.0 1.0
Bobcat
Not Protected 555.5 2317.6
2011 Protected 1713.3 3295.6
HS Not Protected 423.7 23.7 1930.3 16.7
Protected 1649.0 3.8 3220.7 2.3
Not Protected 429.7 22.6 1927.1 16.8
HR Protected 1682.3 1.8 3230.8 2.0
LR Not Protected 426.6 23.2 1911.1 17.5
Protected 1695.7 1.0 3231.7 1.9
Not Protected 442.6 20.3 1967.3 15.1
LS Protected 1743.9 1.8 3271.6 0.7
Domestic Cat
Not Protected 5404.4 13484.4
2011 Protected 255.7 1099.0
HS Not Protected 6709.7 24.2 14028.9 4.0
Protected 322.7 26.2 1177.1 7.1
Not Protected 7529.6 39.3 14547.1 7.9
HR Protected 353.5 38.2 1223.1 11.3
LR Not Protected 7075.5 30.9 14257.1 5.7
Protected 336.7 31.7 1194.1 8.7
Not Protected 6336.7 17.3 13866.3 2.8
LS Protected 305.4 19.5 1161.2 5.7
Red Fox
Not Protected 1874.2 11140.8
2011 Protected 53.6 858.8
HS Not Protected 3190.5 70.2 11710.7 5.1
Protected 104.5 95.0 957.9 11.5
Not Protected 3532.0 88.5 12531.7 12.5
HR Protected 107.5 100.6 1038.0 20.9
LR Not Protected 3129.5 67.0 12198.3 9.5
Protected 90.9 69.6 992.3 15.5
Not Protected 2810.8 50.0 11454.4 2.8
LS Protected 91.8 71.4 926.8 7.9
White-tailed
Deer
Not Protected 5160.2 18848.9
2011 Protected 1707.6 4364.5
HS Not Protected 5005.2 3.0 18445.1 2.1
Protected 1769.7 3.6 4385.5 0.5
Not Protected 5096.0 1.2 18576.7 1.4
HR Protected 1806.5 5.8 4379.0 0.3
LR Not Protected 5231.9 1.4 18925.4 0.4
Protected 1787.0 4.6 4376.9 0.3
Not Protected 5067.3 1.8 18649.0 1.1
LS Protected 1752.3 2.6 4324.9 0.9
Remote Sens. 2019,11, 2482 12 of 16
4. Discussion
We were able to model the predicted response of five representative mammal species to four
planning scenarios for the study area. Although there was some variation in species-specific responses
among the scenarios, we still presented unified patterns for use by land managers and development
planners. Future landscapes would be beneficial and useful to both land managers and animal
communities if they retained substantial habitat for sensitive species, and minimized expansion by the
synanthropic species. Based on those criteria, it is not surprising that our results suggest that LS would
be the most useful planning policy, with strategic planning for development and agriculture (and low
population growth) retaining the most forest cover and the associated mammal communities.
Although bears and bobcats are sensitive to habitat loss and fragmentation [
12
,
38
], our results suggest
that future habitat loss in this region is relatively limited, regardless of the planning policies adopted.
This result highlights the observation that bears and bobcats are already largely restricted to the two largest
forested areas (Shenandoah NP and George Washington NF) and that these areas will not lose significant
habitat-appropriate areas in future projections due to their protected status. Counter to our predictions,
the
β
coefficients from the occupancy models suggested a negative but weak association between these
species’ distribution and protected areas. However, the relationship reveals that, aside from these two large
forested areas, most other protected areas remain small or isolated, particularly among private protected
lands [
30
], and their protected status alone is not enough to support these carnivores. In the case of bobcats,
habitat losses are projected to be five times greater in unprotected areas under the high human growth
scenarios. Bears exhibit less pronounced habitat losses in unprotected areas, likely due to their adaptability
to living in exurban areas [
39
]. These results highlight the importance of protected areas for biodiversity
preservation in developing landscapes [
40
], and their importance will only increase in the near future,
as many small private forest patches are lost to development under each of the scenario models.
Both free-roaming domestic cats and red foxes are predicted to expand their distributions in the study
area with the expansion of development and anthropogenic land uses under all scenarios. Those results
correspond with other studies that show these mesopredators are highly associated with humans [
7
,
8
,
10
].
The impacts of range expansion by both species could be detrimental to biodiversity (e.g., small mammals,
songbirds, and herpetofauna), particularly if high densities of cats and foxes are sustained due to subsidies
from humans [
41
43
]. However, our predictive mesopredator models do not include interspecific
interactions, which might further influence these predictions [
43
]. Specifically, coyotes were fairly common
in our study area and they are known to influence the site use, abundance and behavior of both domestic
cats and foxes [
10
,
44
,
45
]. Yet, our occupancy models did not have strong predictive power to accurately
assess coyote distributions, and hence we were unable to directly incorporate these trophic interactions
that might continue to limit the future expansion of mesopredators.
While our species are representative of relatively sensitive or synanthropic species, it is important to
note that these five species and the additional 10 species detected are only a subset of historical mammal
diversity from the study area. Apex predators (i.e., wolves (Canis lupus) and mountain lions (Puma
concolor)) were extirpated, and other obligate carnivores, such as mustelids, are rare and were likely
extirpated from much of the study area. Mesopredators have ascended to be the de facto top predators in
many anthropogenic landscapes, yet we are only now learning the roles of many of these medium-sized
predators in the context of trait-mediated cascades and their effectiveness in regulating herbivores and
lower trophic taxa [
28
,
46
]. As such, our results are applicable for a typical eastern US landscape but
might not represent the community dynamics of areas with intact apex predator guilds, such as the upper
Midwest or Western US.
Our study was one of the first to pair contemporary camera trap data with occupancy models to predict
current species distributions and then explore how those distributions might change under alternative
planning scenarios in a rapidly developing area. However, we were unable to accurately predict the
distributions of several mammalian species in contemporary times, and hence unable to predict potential
changes in the future. Many of those species are common and highly associated with humans; e.g., northern
raccoons and eastern gray squirrels. These synanthropes are likely responding to more fine-scale covariates,
Remote Sens. 2019,11, 2482 13 of 16
as exemplified by raccoons, most positively associated with housing densities and the associated human
refuse, outdoor pet food, bird feeders, and other resource subsidies in anthropogenic and heterogeneous
landscapes [
47
49
]. These unmodeled species will likely interact with other taxa, and therefore, warrant
further examination with higher resolution habitat and anthropogenic covariates. Furthermore, our land
cover predictions and planning scenarios operated over a short time span and did not incorporate potential
climate change. Indeed, contemporary studies have shown that small mammals and plant communities
are stratified and shift along elevational gradients, with prey responding variably to these synchronous
plant community shifts in response to climate change [
50
,
51
]. However, since we cannot account for these
changes in medium and large mammals or our land cover predictions, we do not expect these changes to
affect medium and large mammals strongly beyond the scope of our predicted models, although we might
consider our models conservative with respect to climate uncertainty.
5. Conclusions
Our results, based on contemporary mammal distributions and land cover associations, provide
useful generalized patterns of planning scenarios. Those highlight the value of large and contiguous
public lands and their continued protection from development and expansion as a buer to rapidly
changing environments. Strategic planning will also benefit mammal communities and multiple
stakeholders by retaining habitats for sensitive species and minimizing the expansion of synanthropic
species. Moreover, we find that planning and policy decisions will be particularly influential in
private landscapes, where there is ample opportunity for increasing protected lands area and targeting
landscape features that benefit mammal communities.
Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/11/21/2482/s1.
Table S1: A priori models comparing the eects of landcover, anthropogenic, and geomorphological covariates on
the probability of site use by mammals from 1591 camera trap locations across an urban to rural gradient, northern
Virginia, 2010–2018. Models were run with species-specific detection covariates for all models and species-specific
scales (500m or 100m) in the additive models. Table S2: Summary statistics, including independent detections,
detection rate (per 100 trap days), and naïve occupancy for 15 mammal species detected from 1591 camera trap
locations across an urban to rural gradient, northern Virginia, 2010–2018; Table S3: Model selection statistics
for top occupancy models comparing the eects of landcover, anthropogenic, and geomorphological covariates
on the probability of site use by American black bears, bobcats, domestic cats, red foxes, and white-tailed deer
from 1591 camera trap locations across an urban to rural gradient, northern Virginia, 2010–2018. Symbols:
i is
AICc dierence,
ω
i is the Akaike weight, K is the number of model parameters, and AUC is the area under the
ROC curve. Table S4: Model selection statistics for top occupancy models comparing the eects of landcover,
anthropogenic, and geomorphological covariates on the probability of site use by gray foxes, coyotes, raccoons,
striped skunks, Virginia opossums, eastern fox squirrels, eastern gray squirrels, eastern chipmunks, eastern
cottontail rabbits, and woodchucks from 1591 camera trap locations across an urban to rural gradient, northern
Virginia, 2010–2018. Symbols:
i is AICc dierence,
ω
i is the Akaike weight, K is the number of model parameters,
and AUC is the area under the ROC curve. Bolded entries are significant in that confidence intervals exclude zero.
Coecients are in logit space and relate to standardized covariate values.
Author Contributions:
Conceptualization, M.C., C.F., I.L., T.A., and W.M.; methodology, M.C., C.F., I.L., T.A.,
and W.M.; formal analysis, M.C. and C.F.; resources, M.C., C.F., I.L., T.A., and W.M.; data curation, M.C., C.F.,
and I.L.; writing—original draft preparation, M.C.; writing—review and editing, M.C., C.F., I.L., T.A., and W.M.;
project administration, I.L., T.A., and W.M.; funding acquisition, I.L., T.A., and W.M.
Funding:
This research was funded and supported by the Changing Landscapes Initiative and Smithsonian
Conservation Biology Institute.
Acknowledgments:
Thank you to the eMammal citizen scientists, and SCBI interns that ran camera traps in the
region. Special thanks to Jen Zhao for all of her eorts managing the eMammal database. Thanks to CLI interns
Sarah Halparin and Erin Carroll for all their assistance in developing the metrics for our analyses, and to Brent
Pease for his assistance with code, analyses, and comments on a draft.
Conflicts of Interest: The authors declare no conflict of interest.
Remote Sens. 2019,11, 2482 14 of 16
Appendix A
Table A1.
Reclassification scheme for National Land Cover Database (NLCD) land cover classes to the
Changing Landscapes Initiative (CLI) study area classification in norther Virginia.
CLI Land Cover Class NLCD Land Cover Class
Open Water Open Water
Developed Open Space Developed Open Space
Developed
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Barren Land
Forest
Deciduous Forest
Evergreen Forest
Mixed Forest
Shrub/Scrub
Grass Grassland/Herbaceous
Pasture/Hay
Crop Cultivated Crops
Wetland Woody Wetlands
Emergent Herbaceous Wetlands
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... Although black bears have been recolonizing their former range and even dispersing into new environments (i.e., urban landscapes) over the past several decades 10,19,29 , anthropogenic attractants can lead to more bear-human conflicts. Indeed, American black bears are the most abundant large carnivore in the world 4 , utilizing an array of land cover types (i.e., forest, shrubland, wetland), as well as occupying exurban areas that exhibit lower housing densities and slower development 12,25 . In the state of Michigan (USA), the American black bear population is increasing and expanding farther south in the Lower Peninsula 30 , presenting challenges for wildlife managers, and a growing indifferent public opinion of the species [30][31][32] . ...
... In addition, black bear reintroductions in the southeastern U.S. in Arkansas and Louisiana 3 have also shown to be a successful conservation strategy for the species. Variation in land use among these regions (i.e., forest, agriculture, housing density) and differences in wildlife management policies (i.e., hunting season vs. no hunting season) can have a significant effect on the success of recolonizing populations 12,25 . As such, understanding the influence human activity exerts on the spatial and temporal dynamics of black bears is critical to determine successful management practices of growing carnivore populations that persist across human-dominated landscapes. ...
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... For further evaluation of habitat variable importance, we considered variables significant when the 95% confidence interval (CI) did not cross zero (Cove et al., 2019;Eng & Jachowski, 2019;Lesmeister et al., 2015;Long et al., 2011;Macdougall & Sanders, 2022;Wilson & Schmidt, 2015). Next, to evaluate whether habitat type (reserve, shelterwood, and field) impacts mesopredator occupancy estimates, we ranked three models for each species and season. ...
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Mammalian mesopredators-mid-sized carnivores-are ecologically, economically, and socially important. With their adaptability to a variety of habitats and diets, loss of apex predators, and forest regrowth, many of these species are increasing in number throughout the northeastern United States. However, currently the region is seeing extensive landscape alterations, with an increase in residential and industrial development, especially at the expense of existing forest and small-scale farmland. We sought to understand how important an existing mosaic of working lands (timberland and farmland) in a forested landscape is to mesopredator species. We did this by studying mesopredator occupancy across three land uses (or habitat types): forest reserve (protected), timber harvest (shelterwood cuts), and field (both crop yielding and fallow) in and around a 3200-ha forest in northeastern Connecticut. We examined coyote (Canis latrans), bobcat (Lynx rufus), fisher (Pekania pennanti), and raccoon (Procyon lotor) occupancy using paired camera traps across juxtaposed reserve, shelterwood, and field units from April 2018 to March 2019. We created a priori habitat variable models for each species and season, as well as analyzed the impact of habitat types on each species. Throughout the year bobcats were positively associated with foliage height diversity and had the highest use in shelterwoods and lowest use in fields. Land use utilization varied seasonally for coyotes and raccoons, with higher use of fields than reserves and shelterwoods for half the year and no difference between land uses and the other half. Both species were not strongly associated with any particular habitat variables. Reserve forest was moderate to highly used by all species for at least half the year, and highly use year-round by fishers. Our findings reveal that a mosaic of intact forest and working lands, timber harvest, and agriculture can support mesopredator diversity.
... To combat this problem, modern conservation efforts have focused on setting aside protected lands (Brandon & Wells, 1992;Rodrigues et al., 2004;Scott et al., 2001). Although biodiversity in protected areas is presumed safe from future development (Cove, Fergus, Lacher, Akre, & McShea, 2019), urbanization threatens to significantly degrade biodiversity of protected areas globally in the next few decades (McDonald, Kareiva, and Forman 2008). Those projections were primarily based on patterns of urban spread and resulting loss of protected habitat. ...
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Natural habitats have been converted to urban areas across the globe such that many landscapes now represent matrices of developed and protected lands. As urbanization continues to expand, associated pressures on wildlife will increase, including effects on animals in adjacent protected habitats. For prey species (e.g., ungulates), an understanding of the ecological impacts of urbanization is typically confounded by coincident effects from co-occurring predators. Yet, understanding how urbanization affects prey behaviors in the absence of predators is becoming increasingly relevant as many top predators face extirpation. We placed camera traps at varying distances from urban areas within protected areas in the Florida Keys, USA, to evaluate the influence of urbanization on the behavior of the key deer (Odocoileus virginianus clavium), an endangered species that has been without non-human mammalian predators for ~ 4000 years. We predicted that as distance to urban areas decreased, key deer would use sites at the same rate, exhibit bigger group sizes, and shift activity patterns to be more nocturnal. Our results indicate that intensity of site use decreased with proximity to urban areas, potentially reflecting human avoidance. Group size increased closer to urban areas, consistent with other studies relating this behavior to anthropogenic subsidies and vigilance for humans. Activity patterns changed but did not become more nocturnal near urban areas as predicted by global analyses relating human disturbance to wildlife nocturnality. Our results have important implications for ungulate behavioral ecology and, taken together, suggest that influences on protected species from adjacent land uses are an important consideration when planning land use and designing protected areas.
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Interspecific interactions can provoke temporal and spatial avoidance, ultimately affecting population densities and spatial distribution patterns. The ability (or inability) of species to coexist has consequences for diversity and ultimately ecosystem stability. Urbanization is predicted to change species interactions but its relative impact is not well known. Urbanization gradients offer the opportunity to evaluate the effect of humans on species interactions by comparing community dynamics across levels of disturbance. We used camera traps deployed by citizen scientists to survey mammals along urbanization gradients of two cities (Washington, DC and Raleigh, NC, USA). We used a multispecies occupancy model with four competing predator species to test whether forest fragmentation, interspecific interactions, humans or prey had the greatest influence on carnivore distribution. Our study produced 6,413 carnivore detections from 1,260 sites in two cities, sampling both private and public lands. All species used all levels of the urbanization gradient to a similar extent, but co‐occurrence of urban‐adapted foxes with less urban‐adapted bobcats and coyotes was dependent on the availability of green space, especially as urbanization increased. This suggests green space allows less urban‐adapted species to occupy suburban areas, but focuses their movements through remaining forest patches, leading to more species interactions. Synthesis and applications. Species interactions, forest fragmentation and human‐related covariates were important determinants of carnivore occupancy across a gradient of urbanization with the relative importance of forest fragmentation being highest. We found evidence of both positive and negative interactions across the gradient with some dependent on available green space, suggesting that fragmentation leads to higher levels of spatial interaction. Where green space is adequate, there appears to be sufficient opportunity for coexistence between carnivore species in an urban landscape. Interspecific interactions can provoke temporal and spatial avoidance, ultimately affecting population densities and spatial distribution patterns. The ability (or inability) of species to coexist has consequences for diversity and ultimately ecosystem stability. Urbanization is predicted to change species interactions but its relative impact is not well known. Urbanization gradients offer the opportunity to evaluate the effect of humans on species interactions by comparing community dynamics across levels of disturbance. We used camera traps deployed by citizen scientists to survey mammals along urbanization gradients of two cities (Washington, DC and Raleigh, NC, USA). We used a multispecies occupancy model with four competing predator species to test whether forest fragmentation, interspecific interactions, humans or prey had the greatest influence on carnivore distribution. Our study produced 6,413 carnivore detections from 1,260 sites in two cities, sampling both private and public lands. All species used all levels of the urbanization gradient to a similar extent, but co‐occurrence of urban‐adapted foxes with less urban‐adapted bobcats and coyotes was dependent on the availability of green space, especially as urbanization increased. This suggests green space allows less urban‐adapted species to occupy suburban areas, but focuses their movements through remaining forest patches, leading to more species interactions. Synthesis and applications. Species interactions, forest fragmentation and human‐related covariates were important determinants of carnivore occupancy across a gradient of urbanization with the relative importance of forest fragmentation being highest. We found evidence of both positive and negative interactions across the gradient with some dependent on available green space, suggesting that fragmentation leads to higher levels of spatial interaction. Where green space is adequate, there appears to be sufficient opportunity for coexistence between carnivore species in an urban landscape.
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