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\begin{document}

\title{Spatial structure of reproductive success infers mechanisms of ungulate
invasion in Nearctic boreal landscapes.}



\author[1]{Jason Fisher}%
\author[2]{Cole Burton}%
\affil[1]{University of Victoria}%
\affil[2]{University of British Columbia}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
\let\center\flushleft
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\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
1. Landscape change is a key driver of biodiversity declines due to
habitat loss and fragmentation, but spatially shifting resources can
also facilitate range expansion and invasion. Invasive populations are
reproductively successful, and landscape change may buoy this success.
2. We show how modelling the spatial structure of reproductive success
can elucidate the mechanisms of range shifts and sustained invasions for
mammalian species with attendant young. We use an example of
white-tailed deer (deer; Odocoileus virginianus) expansion in the
Nearctic boreal forest, a North American phenomenon implicated in severe
declines of threatened woodland caribou (Rangifer tarandus). 3. We
hypothesized that deer reproductive success is linked to forage
subsidies provided by extensive landscape change via resource
extraction. We measured deer occurrence using data from 62 camera-traps
in northern Alberta, Canada, over three years. We weighed support for
multiple competing hypotheses about deer reproductive success using
multi-state occupancy models and generalized linear models in an
AIC-based model selection framework. 4. Spatial patterns of reproductive
success were best explained by features associated with petroleum
exploration and extraction, which offer early seral vegetation resource
subsidies. Effect sizes of anthropogenic features eclipsed natural
heterogeneity by two orders of magnitude. We conclude that deer
populations are likely buffered from overwinter mortality by landscape
change, wherein early seral forage subsidies support high springtime
reproductive success to offset or exceed winter losses. 5. Synthesis and
Applications. Modelling spatial structuring in reproductive success can
become a key goal of remote camera-based global networks, yielding
ecological insights into mechanisms of invasion and range shifts to
inform effective decision-making for global biodiversity conservation.%
\end{abstract}%



\sloppy


Spatial structure of reproductive success infers mechanisms of ungulate
invasion in Nearctic boreal landscapes.

Jason T Fisher\textsuperscript{1,2, *} and A. Cole
Burton\textsuperscript{3,2}

\textsuperscript{1}University of Victoria, School of Environmental
Studies, Victoria, British Columbia, Canada

\textsuperscript{2}Former Address: InnoTech Alberta, Bag 4000,
Vegreville, Alberta, Canada

\textsuperscript{3}University of British Columbia, Faculty of Forestry,
Vancouver, British Columbia, Canada

*Corresponding author: fisherj@uvic.ca

\textbf{Keywords:} camera trapping; occupancy dynamics; species
distribution modelling; oil sands; landscape fragmentation; boreal
forest

Word count: 6987

\textbf{Summary}

\begin{enumerate}
\tightlist
\item
  Landscape change is a key driver of biodiversity declines due to
  habitat loss and fragmentation, but spatially shifting resources can
  also facilitate range expansion and invasion. Invasive populations are
  reproductively successful, and landscape change may buoy this success.
\item
  We show how modelling the spatial structure of reproductive success
  can elucidate the mechanisms of range shifts and sustained invasions
  for mammalian species with attendant young. We use an example of
  white-tailed deer (deer; \emph{Odocoileus virginianus} ) expansion in
  the Nearctic boreal forest, a North American phenomenon implicated in
  severe declines of threatened woodland caribou (\emph{Rangifer
  tarandus} ).
\item
  We hypothesized that deer reproductive success is linked to forage
  subsidies provided by extensive landscape change \emph{via} resource
  extraction. We measured deer occurrence using data from 62
  camera-traps in northern Alberta, Canada, over three years. We weighed
  support for multiple competing hypotheses about deer reproductive
  success using multi-state occupancy models and generalized linear
  models in an AIC-based model selection framework.
\item
  Spatial patterns of reproductive success were best explained by
  features associated with petroleum exploration and extraction, which
  offer early seral vegetation resource subsidies. Effect sizes of
  anthropogenic features eclipsed natural heterogeneity by two orders of
  magnitude. We conclude that deer populations are likely buffered from
  overwinter mortality by landscape change, wherein early seral forage
  subsidies support high springtime reproductive success to offset or
  exceed winter losses.
\item
  \emph{Synthesis and Applications} . Modelling spatial structuring in
  reproductive success can become a key goal of remote camera-based
  global networks, yielding ecological insights into mechanisms of
  invasion and range shifts to inform effective decision-making for
  global biodiversity conservation.
\end{enumerate}

\textbf{Key-words:} fitness, invasive species, camera trapping,
multistate occupancy models, reproduction, range shifts, landscape
change

\textbf{Introduction}

Reproduction is vital to population persistence and distribution
dynamics. Reproductive success is tightly linked to the quality and
spatial distribution of available suitable habitat (Pulliam \& Danielson
1991; Kurki \emph{et al.} 2000) and so anthropogenic landscape change
can markedly alter a species' spatial distribution. These effects are
typically negative, through fragmentation and habitat loss (Fahrig 1997;
Fahrig 2002; Fahrig 2003) but are positive for some species,
facilitating range expansions or invasions (Ewers \& Didham 2006; Didham
\emph{et al.} 2007). Linking spatial variability in reproductive success
with landscape change (or disturbance) is key to understanding
mechanisms of invasion and range shifts, an increasingly important
endeavor under climate change (Lawler \emph{et al.} 2008; Lawler
\emph{et al.} 2009).

Quantifying spatial variation in reproductive success has been mostly
limited to taxa with stationary offspring such as plants (Mu\selectlanguage{ngerman}ñoz \&
Arroyo 2006) and nesting birds (Rosenberg, Swindle \& Anthony 2003;
León-Ortega \emph{et al.} 2017). Mammals are much harder to quantify due
to their large size, widespread ranges, and vagile young. Camera
trapping (Burton\emph{et al.} 2015; Steenweg \emph{et al.} 2016) can
bridge this data gap, generating data on mammalian distribution and
density. Many mammal species keep young at heel during early maternal
care and this state can can be likewise observed with camera traps.
Applied to camera data for grizzly bears (Fisher, Wheatley \& Mackenzie
2014) and European brown bears (Burton \emph{et al.}2018), we showed how
spatial variation in reproductive success can be modelled to identify
landscape mechanisms affecting success. Though further elaborated since
(MacKenzie\emph{et al.} 2017) the diverse opportunities of this approach
have yet to be widely realized. Here, we illustrate how camera trap data
can help infer mechanisms of species invasion and range expansion, using
an example from the Nearctic boreal forest.

Boreal landscapes have been markedly changed by widespread and
economically important resource extraction (Schindler \& Lee 2010;
Venier \emph{et al.} 2014). The epicenter of change are Canada's oil
sands, the third largest global oil deposit and a driver of global
economies (Bayoumi \& Mhleisen 2006). Petroleum exploration and
extraction create an altered landscape without analogs (Schneider, Dyer
\& Parks 2006; Pickell, Andison \& Coops 2013; Pickell \emph{et al.}
2015). Landscape change affects the entire boreal forest mammal
community (Fisher \& Burton 2018), but most notably manifest in woodland
caribou declines (\emph{Rangifer tarandus} ) (Hervieux \emph{et al.}
2013; Hebblewhite 2017). Wolf predation is a primary cause (Boutin
\emph{et al.} 2012), with wolf populations bolstered by high-density
invading white-tailed deer (deer; \emph{Odocoileus virginianus} )
(Latham \emph{et al.} 2011; Latham \emph{et al.} 2013).

White-tailed deer range expansion is a pan-continental phenomenon
(Laliberte \& Ripple 2004; Heffelfinger 2011) impacting entire
ecosystems (Côté \emph{et al.} 2004). Research on deer expansion south
of the boreal has focused on population biology (DeYoung 2011), movement
(Beier \& McCullough 1990), and predation (Ballard \emph{et al.} 2001).
Boreal deer invasion has been linked to landscape and climate change
(Dawe, Bayne \& Boutin 2014; Fisher \& Burton 2018; Fisher \emph{et al.}
2020) but the mechanisms remain unidentified. We sought to examine
whether anthropogenic landscape change is linked to spatial patterns of
deer reproductive success, as a possible mechanism of boreal forest
invasion.

Deer balance energy intake from early-seral deciduous forage (Ditchkoff
2011) with metabolic demands markedly increased by cold temperatures and
deep snow, historically limiting white-tailed deer range (Parker,
Barboza \& Gillingham 2009; Hewitt 2011). In the boreal, climate change
has produced warmer winters (Karl \& Trenberth 2003); concurrently,
landscape change has generated more abundant early-successional
vegetation (Finnegan, MacNearney \& Pigeon 2018; Finnegan, Pigeon \&
MacNearney 2019; MacDonald \emph{et al.} 2020) that is strongly
spatially linked to deer abundance and persistence (Fisher \emph{et al.}
2020). Deer mortality risk is greatest in the first year of life (Lesage
\emph{et al.} 2001), decreasing markedly for 1-2 year-olds
(Delgiudice\emph{et al.} 2006). Fawn growth and survival is largely
based on maternal body condition, governed by food availability
(Therrien \emph{et al.} 2008), so examining how spatial resource
availability contributes to breeding success within the first year helps
us understand how landscape change contributes to boreal deer expansion.

We hypothesized that anthropogenic landscape change in the northern
boreal forest is providing resource subsidies that bolster reproductive
success for invading white-tailed deer. If true, we predicted that
anthropogenic features representing conversion of mature forest to early
seral vegetation would explain variability in the spatial distribution
of deer reproductive success. We define reproductive success as a deer
occurrence with at least one attendant fawn in the summer months. This
requires that a female achieve oestrus, breed, produce offspring, and
maintain that offspring into the summer months, thus drawing close to
recruitment--and is a measure that can be consistently applied to all
mammal species with attendant young at heel.

\textbf{Methods}

\emph{Study Area}

We surveyed white-tailed deer distribution in the boreal forest of
northeast Alberta, Canada (Fig. 1). The 3500
km\textsuperscript{2}landscape is a mosaic of aspen (\emph{Populus
tremulodies} ), white spruce (\emph{Picea glauca} ), black spruce
(\emph{P. mariana} ), and jack pine (\emph{Pinus banksiana} ) forests,
interspersed with \emph{Ledum groenlandicum} -dominated muskeg.
Widespread petroleum exploration and extraction features, roads (car
accessible), trails (off-road vehicle accessible), forest harvesting,
and other anthropogenic features are dispersed throughout the study area
(Fig. 1).

We deployed 62 camera-trap sites (Reconyx PC900
Hyperfire\textsuperscript{TM} infra-red remote digital; Holmen, WI, USA)
in a constrained stratified random design (Supplementary Information),
sampled continuously between November 2011- November 2014, as in Fisher
and Burton (2018); Fisher \emph{et al.} (2020). Following Burton
\emph{et al.} (2015), we define 'site' as the average area used by a
deer (seasonally, in a 3-month window), centered on the camera detection
zone. We define 'study area' as the \emph{ca.} 3500
km\textsuperscript{2} minimum convex polygon surrounding camera sites.
Cameras were placed \emph{ca.} 1m from the ground facing the wildlife
trail and set to high sensitivity with 3-s delay.

\emph{Spatial reproductive success}

We identified all camera trap images containing white-tailed deer and
created a monthly detection-nondetection dataset with three states:
breeding, non-breeding, or no deer detected. We discretized continuous
camera sampling into monthly survey occasions. If a fawn(s) appeared in
an image within the survey month, we classified that site as
''breeding'' for that survey (Fig. 2). If fawns were not detected, we
classified the site as ''non-breeding'' -- which includes males and/or
females that did not successfully rear a fawn into spring and summer

Multiple approaches are used for modelling serial occurrence data
generated by camera traps (Rota \emph{et al.} 2009; ; Burton \emph{et
al.} 2015). We therefore analysed camera data using two approaches.
First, we sought to account for false absences which is a potential
problem in wildlife surveys (MacKenzie 2005), including camera-trap
surveys (Burton \emph{et al.}2015). Just as species may be detected
imperfectly, age-sex classes may also be detected imperfectly, when
neither age nor sex is known with accuracy. In our case, ''breeding''
sites could be misclassified as ''non-breeding'' if we missed
photographing extant fawns at the cameras. To account for this error, we
used occupancy models (MacKenzie \emph{et al.} 2002) which estimate the
probability of detecting that species if present (\emph{p} ) and based
on \emph{p} , the probability of site occupancy (\selectlanguage{greek}\emph{ψ} \selectlanguage{english}). With
hierarchical multi-state occupancy models (Nichols et al. 2007;
MacKenzie et al\emph{.} 2009) we estimated the probability for each site
that deer were either absent, present without breeding, or present with
breeding. We also estimated the probability that deer were detected in
each of the two occupied states. Occupancy models can be considered as
simultaneous generalized linear models (GLMs) applied to the detection
and occupancy submodels, with binomial errors (logistic link).

We separated continuous camera data into month-long (30.4 day)
''secondary'' survey periods \emph{sensu}MacKenzie \emph{et al.} (2003).
Three such surveys comprised a three-month ''primary'' sampling season
within which occupancy states were assumed to be closed. We considered
only the fawning season (spring, April -- June), and post-fawning
(summer, July -- September). We assumed non-Markovian variation in deer
site-use among months within a 3-month season primary season (MacKenzie
\emph{et al.} 2006). In an occupancy framework this variation represents
``detection error'', attributed mainly to movement in and out of the
camera detection zone (Burton \emph{et al.} 2015). The full data frame
for the study is thus 6 seasons, with 3 repeated monthly surveys within
each season, for a total of 18 surveys at each site with each survey
comprised of deer detection-nondetection within the month.

With this dataset, we ran several competing models, each with different
assumptions about how detectability, breeding occupancy, and
non-breeding occupancy varied through time and in relation to landscape
features. We tested whether the probability of detection was either (1)
constant over time, (2) varied among seasons, or (3) varied among
surveys. We likewise tested whether site occupancy of breeders and
non-breeders was either (1) constant across the study area, or (2)
varied in relation to landscape features. We used hierarchical models in
the program \emph{Presence} (ver. 6.2) to estimate deer occupancy (\selectlanguage{greek}ψ\selectlanguage{english}),
detectability (\emph{p} ), and breeding state (R), where:

\selectlanguage{greek}ψ\selectlanguage{english}\textsubscript{i} = probability that site \emph{i} is occupied,
regardless of reproductive state

R\textsubscript{i} = conditional probability that young occurred, given
that site \emph{i} is occupied

\selectlanguage{greek}ψ\selectlanguage{english}\textsubscript{i(breeding)} = unconditional probability that
site\emph{i} is occupied with breeding = \selectlanguage{greek}ψ\selectlanguage{english}\textsubscript{i
*}R\textsubscript{i}

p(1)\textsubscript{it} = probability that occupancy is detected for
site\emph{i} , period \emph{t} , given that true state = 1
(non-breeding),

p(2)\textsubscript{it} = probability that occupancy is detected for
site\emph{i} , period \emph{t} , given that true state = 2 (breeding),

\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{it} = probability that evidence of successful
reproduction is found, given detection of occupancy at site \emph{i} ,
period \emph{t} , with successful reproduction (Nichols et al. 2007).

Occupancy models provide a per-survey estimate of \emph{p} , and from
this we calculated the probability of false absence (PFA) across the
three surveys in each sampling season as {[}1-\emph{p}
{]}\textsuperscript{3} (Long\emph{et al.} 2008).

It has been argued that the variation among secondary surveys (months)
due to deer movement is an important part of the ecological signal, and
not error as assumed in occupancy models (Neilson \emph{et al.} 2018;
Stewart \emph{et al.} 2018; Broadley \emph{et al.} 2019). We therefore
also treated zeros as signal, not error, and used an alternative
modelling approach--generalised linear models (GLMs)--to determine
whether fawn occurrence varied with landscape features. In this analysis
each month can be considered an independent Bernoulli trial in which
adult female deer with fawns were detected (1) or not (0). We summed the
number of spring months (April, May, June) with and without fawns across
all three survey years creating a 0-9 response variable (3 spring months
over 3 years). We modelled number of breeding-months as a binomial count
model (GLM; binomial errors, log link) in R ver. 3.1.1 (R Foundation for
Statistical Computing 2014) against explanatory variables from three
spatial digital resource inventories (Supplementary Information Table
S1).

Alberta Vegetation Inventory (AVI), a digital forest inventory dataset,
provided percent cover of land cover types within a 1-km radius around
each camera site (Fisher, Anholt \& Volpe 2011; Fisher \emph{et al.}
2020). Alberta Biodiversity Monitoring Institute (ABMI) 2010 Human
Footprint Map Ver 1.1 provided percent of area of polygonal
anthropogenic features. ABMI's Caribou Monitoring Unit (CMU) provided a
GIS layer derived from 2012 SPOT satellite imagery to calculate area of
linear features (buffered to create polygons from polylines) around each
camera. In all models, we omitted correlated variables (r \textgreater{}
0.7) from multiple-variable models (Zuur, Ieno \& Elphick 2010) to
prevent multicollinearity. We combined variables only sparsely
represented in the data (\textless{} 1-2\% of area) into a single,
combination variable (Table 1), and rescaled each variable (mean=0,
s.d.=1) to compare effect sizes.

In occupancy models, we placed covariates on \selectlanguage{greek}\emph{ψ} \selectlanguage{english}and \emph{R} in
hypothesis models or kept them constant in null models (Supplementary
Information Table S2). In GLMs we created multiple \emph{a
priori}models, each corresponding to a hypothesis about the landscape
features explaining variation in deer reproduction (Table 1). As \emph{a
priori}models may still contain uninformative parameters that should be
discarded (Anderson 2007), we created a fully reduced model using
AIC-based stepwise regression (R;\emph{stepAIC} package) to determine
the most parsimonious model explaining variation in deer reproduction.

For both the occupancy models and generalized linear models, we weighed
the evidence in support of models corresponding to competing hypotheses
using model selection in an information-theoretic framework (Burnham \&
Anderson 2002). Each model produces an Akaike Information Criterion
(AIC) score that balances deviance explained by the model with model
complexity -- the number of parameters; low AIC scores suggest a
best-supported model. We normalized AIC scores into 0-1 AIC weights,
analogous to the probability that a given model is the best supported of
the candidate set (Burnham \& Anderson 2002). We further validated
best-supported models using k-fold cross validation in R package
\emph{boot} , and calculated deviance explained (Zuur et al. 2009).

\textbf{Results}

\emph{Evidence of breeding}

Of 112,648 deer images captured during the survey, 12,460 images
(11.1\%) had evidence of young-of-the-year. This included single fawns
(92.9\%), twins (7.6\%) and triplets (0.6\%), though these were not
distinguished in models. There was a marked drop in the distribution of
successful breeding across years. Of 62 sites, successful breeding was
detected at 36 sites (58.1\%) in 2012, 22 (35.5\%) in 2013, and 12
(19.4\%) in 2014. Among all years pooled together, 45 of 62 sites
(72.6\%) had evidence of breeding in at least one year.

\emph{Multi-state deer occupancy}

Reproductively successful deer -- does with fawns -- were estimated to
be widespread across the study area in spring 2012 (\selectlanguage{greek}ψ\selectlanguage{english}\textsubscript{b} =
0.89, SE = 0.14), 2013 (\selectlanguage{greek}ψ\selectlanguage{english}\textsubscript{b} = 0.98; SE = 0.02) and 2014
(\selectlanguage{greek}ψ\selectlanguage{english}\textsubscript{b} = 0.95; SE = 0.03) when modelled without landscape
covariates. The estimated probability of false absences for deer with
fawns was [?] 0.002 in all years, suggesting that we reliably detected
fawns when they occurred.

Anthropogenic landscape features best explained conditional probability
of fawns given occupancy by deer (\emph{R} ) in 2012 (well sites and
seismic lines, cumulative AIC\textsubscript{w} = 0.83), 2013 (seismic
lines, AIC\textsubscript{w} = 0.84) and 2014 (industrial features,
forest cutblocks and total footprint, cumulative AIC\textsubscript{w} =
0.81) (Supplementary Information Table S2). Models in which breeding
varied only with natural vegetation, or was invariant, were not
supported. Hence, occupancy of breeding deer differed from that of
non-breeding deer, and varied with the area of anthropogenic features
across the oil sands landscape. However, multistate occupancy models
contained unresolvable ``border estimates'' for \emph{R} (those close to
0 or 1; (MacKenzie \emph{et al.} 2017)) and exhibited problems with
model convergence, necessitating a companion approach.

\emph{Spatial patterns of reproductive success: GLMs}

Months of occurrence of reproductively successful deer was positively
related to anthropogenic landscape features, as well as natural
landscape features. Models with petroleum features best explained deer
with fawn occurrence, thus corroborating the multi-state occupancy
models. Occurrence of deer with fawns increased with increasing seismic
line density, 3D seismic line density, pipeline density, and deciduous
forest cover; model 31 (AIC\textsubscript{w} = 0.88) and 29
(AIC\textsubscript{w} = 0.11) together carried 99\% of the weight of
evidence (Table 2; Table S3). The effect size (model \selectlanguage{greek}β \selectlanguage{english}coefficients) of
seismic lines on the occurrence of fawns was 100 times greater than the
effect size of the best natural landcover feature: upland deciduous
forests (Fig. 3). Projected across the northeast boreal forest
surrounding the study area, areas of higher probability of deer
reproduction correspond to intensive development (Fig. 4).

\textbf{Discussion}

As mammal distributions shift with climate change, decrease with habitat
loss or capitalize upon change to invade, understanding the features
facilitating reproduction in once barrens landscapes allow us to
elucidate, and address, those mechanisms of change. Global camera-trap
networks coupled with spatial distribution models can yield these
insights.

In our example widespread landscape change from energy extraction is
strongly linked to white-tailed deer reproduction where they have
invaded the western Nearctic boreal forest. Deer invasion of the cold
northern latitudes is consequent to expansion from southern agricultural
areas, a continental phenomenon borne from widespread conversion of
mature forest into early seral vegetation (C\selectlanguage{ngerman}ôté \emph{et al.} 2004;
Heffelfinger 2011). In the last few decades, new advances in forest
harvesting and the dramatic growth of energy exploration and extraction
have radically altered the Alberta boreal landscape (Pickell, Andison \&
Coops 2013; Pickell \emph{et al.} 2015). The density of petroleum
exploration ``seismic'' lines in the landscape had a 100 times greater
effect on deer breeding success than did natural deciduous forest, an
important predictor of adult white-tailed deer individual habitat
selection and distribution (Darlington 2018; Fisher \& Burton 2018;
Fisher \emph{et al.} 2020). Although we hypothesized that forest
harvesting might play a substantial role (Fisher \& Wilkinson 2005), we
found no evidence to support this; and petroleum extraction features are
much more widespread than forest harvest blocks in this region (Pickell,
Andison \& Coops 2013; Pickell \emph{et al.} 2015). We conclude that the
1000s of kilometres of seismic lines, as well as pipelines and 3D
seismic lines, spread across the western Nearctic boreal forest play a
significant role in facilitating the northward expansion of white-tailed
deer.

The mechanism for the relationship between linear features and deer
reproductive success is centred on available forage. Nutrition affects
ungulates' probability of pregnancy, over-winter survival, parturition,
and neonatal survival (Parker, Barboza \& Gillingham 2009; Hewitt 2011).
Greater nutrition from abundant available forage prevents metabolic
stress, increasing deer survivorship and reproductive success (Hewitt
2011). However forage biomass is in itself not a good predictor of deer
nutrition, as forage distribution relative to inedible vegetation plays
a significant role (Spalinger \& Hobbs 1992). In this landscape,
abundant edible forage is available in linear features (Finnegan,
MacNearney \& Pigeon 2018; Finnegan, Pigeon \& MacNearney 2019;
MacDonald \emph{et al.} 2020), and may be especially important in spring
during green-up, when energetic demands of gestation are great (Pekins,
Smith \& Mautz 1998).

Research on deer pregnancy rates and recruitment suggests that female
age and body condition affect breeding success (Ozoga, Verme \& Bienz
1982; Ozoga \& Verme 1986; Verme 1989; DelGiudice, Lenarz \& Powell
2007); body condition, in turn, is primarily a function of nutrition
afforded by available browse (Hewitt 2011). Winter induces substantial
metabolic costs on white-tailed deer, but pregnancy and lactation
induces markedly greater metabolic costs on females (Pekins, Smith \&
Mautz 1998; Therrien \emph{et al.} 2008; Ditchkoff 2011). If female deer
in this landscape were metabolically stressed after severe winters,
female mortality, small fawns with low survival (Ditchkoff 2011), and
starvation-induced abortions (Worden 1992, in Pekins et al. 1998) might
be expected to reduce reproductive success. If the early seral
vegetation abundant in anthropogenic landscape features provides forage
subsidies, then metabolic costs would be offset and reproductive success
enhanced. We contend our evidence here, as well as corroborating past
research on adult deer showing positive links to anthropogenic features
(Darlington 2018; Fisher \& Burton 2018), strongly infers that landscape
change is enhancing breeding success and hence, facilitating and
maintaining boreal deer invasions.

\emph{Caveats}

Our research focussed on a heavily developed landscape in the western
Nearctic boreal forest of Alberta, Canada. Extrapolating to other
landscapes in this region should not be done without future research to
understand the range of inference. In their province-wide analysis,
(Dawe, Bayne \& Boutin 2014; Dawe \& Boutin 2016) concluded that deer
expansion is likely facilitated in large part by climate change as the
metabolic costs of cold temperatures and especially deep snow are
ameliorated by contemporary mild winters. Evidence at landscape scales
suggests climate is a contributory mechanism but abundant nutritional
forage is pivotal for deer populations (Fisher \emph{et al.} 2020), and
historically the northern boreal forest has been dominated by largely
inedible conifer (Fisher \& Wilkinson 2005; Pickell \emph{et al.} 2015).
We contend forage subsidies induced by landscape change play a large
role not yet disentangled from climate change; indeed it is likely the
two act synergistically.

\textbf{Applications to Ecology}

In the western boreal forest, petroleum exploration features are
increasing breeding success, and hence possibly (given lifetime success)
fitness of individuals spatially associating with them. In the apparent
competition ``fulcrum'' in which more deer boost wolf populations, which
in turn drive declines in woodland caribou (DeCesare \emph{et al.} 2010;
Latham \emph{et al.} 2011; Boutin \emph{et al.} 2012), deer expansion is
a substantial conservation threat. Conservation will require landscape
management to mitigate the widespread resource subsidies afforded to
deer, including active site restoration, which has been shown to be
promising for mitigating white-tailed deer use of seismic lines
(Tattersall \emph{et al.} 2019). Dauntingly, this restoration is
required for 10,000s of kilometres of seismic lines (Dabros, Pyper \&
Castilla 2018), as well as the other anthropogenic features associated
with resource extraction (Fisher \& Burton 2018; Fisher \emph{et al.}
2020) lending urgency to the need for rapid application of ecological
research to management decisions.

Biodiversity declines due to landscape change are a global problem
(Maxwell \emph{et al.} 2016) as are invasive species (Gurevitch \&
Padilla 2004; Clavero \& García-Berthou 2005) and anthropogenic range
shifts (Lawler\emph{et al.} 2009; Chen \emph{et al.}2011). Understanding
the ecological mechanisms facilitating and sustaining invasions is a key
pursuit for and ecology. Global biodiversity networks can quantify
variation in mammalian distribution and density at large scales
(Steenweg\emph{et al.} 2017) but abundance is not always a reliable
metric for inference of mechanisms (Van Horne 1983; Schlaepfer, Runge \&
Sherman 2002; Battin 2004). Breeding success is more directly reflective
of landscape change's effect on mammalian fitness. These data can be
garnered through camera-trap networks and modelled with data on
landscape change to aid inference about the mechanisms of change: an
intersection of fundamental ecology principles and applied ecology
practice that can aid inferences and the decisions derived from them.

\textbf{Authors' Contributions}

JTF conceived the research and the design and conducted statistical
analyses; JTF and ACB conceived the questions and wrote the paper. Both
authors reviewed and edited the draft and gave final approval for
publication.

\textbf{Acknowledgements}

At InnoTech Alberta, thanks to project team M. Hiltz, L. Nolan, L.D.
Roy, S. Melenka, D. Pan, B.R. Eaton, K. Richardson, K. Tereschyn, S.
Eldridge, J. Dennett, J. Watkins, and T. Zembal. At Alberta Environment,
thanks to B. Maile, G. Chapman, C. Dockrill, P. MacMahon, and K.
Norstrom--RIP. Thanks to funding supporters S. Grindal, L. Mayes, T.
Such, and S. Geoghegan. Applied Conservation MacroEcology (ACME) lab
members N. Boucher, L. Eliuk, A. Fisher, S. Frey, and C. Sun provided
valuable commentary. Thanks to A. Roberts and H. Wileman Fuller for
graphical assistance.

\textbf{Funding}

This project was primarily funded by Alberta Environment \& Parks (AEP),
InnoTech Alberta (ITA), Petroleum Technology Alliance of Canada (PTAC)'s
Alberta Upstream Petroleum Research Fund, and MEG Energy. Alberta
Conservation Association also contributed additional funds. Alberta
Biodiversity Monitoring Institute (ABMI)'s Caribou Monitoring Unit
contributed landscape data. ACB was supported by the Canada Research
Chairs program.

\textbf{Research Ethics}

All research was permitted by the Government of Alberta, Ministry of
Environment and Parks, Fish \& Wildlife Division, Collection License
49143.

\textbf{Animal Ethics}

This research was reviewed and approved by InnoTech Alberta's Animal
Care and Use Committee (ACUC), permit ACUC0524.frm /clj/IO.II.02.

Table 1. Hypotheses about the distribution of breeding white-tailed deer
across the boreal forest study area.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Description & Model \# & Hypothesis: White-tailed deer distribution is
explained by \% cover (within a buffer around camera sites)
of:\tabularnewline
\midrule
\endhead
Global model & 1 & All variables\tabularnewline
Natural landcover & 2 & Upland deciduous cover\tabularnewline
& 3 & All mixedwood cover\tabularnewline
& 4 & All conifer cover\tabularnewline
& 5 & Upland spruce\tabularnewline
& 6 & All deciduous + shrubs\tabularnewline
& 7 & Wetland\tabularnewline
& 8 & Upland forest\tabularnewline
& 9 & Lowland forest\tabularnewline
Non-forest & 10 & Early seral\tabularnewline
Forestry & 11 & Cutblocks\tabularnewline
Petroleum & 12 & Well sites\tabularnewline
& 13 & 3D seismic\tabularnewline
& 14 & Seismic lines\tabularnewline
& 15 & Pipelines\tabularnewline
& 16 & Linear features\tabularnewline
& 17 & Block features\tabularnewline
Petroleum + Forestry & 18 & Block features incl.
cutblocks\tabularnewline
Access & 19 & Roads\tabularnewline
& 20 & Trails\tabularnewline
& 21 & Roads and trails\tabularnewline
All anthropogenic & 22 & All anthropogenic features\tabularnewline
Natural + forestry & 23 & Upland deciduous and cutblocks\tabularnewline
& 24 & Shrubs and cutblocks\tabularnewline
& 25 & Openings and cutblocks\tabularnewline
Natural + petroleum & 26 & Upland deciduous and 3D
seismic\tabularnewline
& 27 & Upland deciduous and Seismic lines\tabularnewline
& 28 & Upland deciduous and all anthropogenic\tabularnewline
& 29 & Upland deciduous and all petroleum\tabularnewline
Natural + access & 30 & Upland deciduous and roads and
trails\tabularnewline
\emph{Post-hoc} step-AIC model & 31 & Variables selected by stepwise
regression\tabularnewline
\bottomrule
\end{longtable}

Table 2. Model selection of generalised linear models relating
probability of occurrence of white-tailed deer with fawn(s) against
natural and anthropogenic landscape features. Model numbers refer to
candidate model sets in Table 2. K = number parameters.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Model \# & K & AIC & \selectlanguage{greek}Δ\selectlanguage{english}AIC & AIC\textsubscript{w} & Cumulative
AIC\textsubscript{w} & -2LL\tabularnewline
\midrule
\endhead
Model 31 & 5 & 406.41 & 0 & 0.88 & 0.88 & -197.66\tabularnewline
Model 29 & 7 & 410.64 & 4.23 & 0.11 & 0.99 & -197.26\tabularnewline
Model 28 & 11 & 415.69 & 9.28 & 0.01 & 1 & -194.15\tabularnewline
Model 27 & 4 & 428.09 & 21.68 & 0 & 1 & -209.69\tabularnewline
Model 16 & 7 & 430.79 & 24.38 & 0 & 1 & -207.34\tabularnewline
Model 22 & 10 & 431.07 & 24.67 & 0 & 1 & -203.34\tabularnewline
Model 14 & 3 & 433.95 & 27.54 & 0 & 1 & -213.77\tabularnewline
Model 1 & 11 & 438.14 & 31.73 & 0 & 1 & -205.38\tabularnewline
Model 4 & 6 & 438.53 & 32.12 & 0 & 1 & -212.49\tabularnewline
Model 7 & 4 & 441.22 & 34.81 & 0 & 1 & -216.25\tabularnewline
Model 6 & 6 & 441.28 & 34.87 & 0 & 1 & -213.86\tabularnewline
Model 26 & 4 & 443.1 & 36.7 & 0 & 1 & -217.19\tabularnewline
Model 23 & 4 & 443.16 & 36.76 & 0 & 1 & -217.23\tabularnewline
Model 30 & 5 & 443.69 & 37.28 & 0 & 1 & -216.3\tabularnewline
Model 2 & 3 & 444.64 & 38.23 & 0 & 1 & -219.11\tabularnewline
Model 8 & 5 & 446.73 & 40.32 & 0 & 1 & -217.82\tabularnewline
Model 15 & 3 & 447.25 & 40.84 & 0 & 1 & -220.41\tabularnewline
Model 17 & 4 & 451.05 & 44.65 & 0 & 1 & -221.17\tabularnewline
Model 10 & 7 & 452.6 & 46.19 & 0 & 1 & -218.24\tabularnewline
Model 18 & 5 & 452.79 & 46.38 & 0 & 1 & -220.85\tabularnewline
Model 20 & 3 & 452.86 & 46.45 & 0 & 1 & -223.22\tabularnewline
Model 12 & 3 & 453.11 & 46.7 & 0 & 1 & -223.34\tabularnewline
Model 5 & 3 & 453.16 & 46.75 & 0 & 1 & -223.37\tabularnewline
Model 21 & 4 & 454.68 & 48.27 & 0 & 1 & -222.98\tabularnewline
Model 19 & 3 & 454.7 & 48.3 & 0 & 1 & -224.14\tabularnewline
Model 9 & 5 & 454.79 & 48.39 & 0 & 1 & -221.85\tabularnewline
Model 13 & 3 & 454.8 & 48.39 & 0 & 1 & -224.19\tabularnewline
Model 11 & 3 & 455.07 & 48.67 & 0 & 1 & -224.33\tabularnewline
Model 3 & 4 & 455.97 & 49.56 & 0 & 1 & -223.63\tabularnewline
Model 25 & 5 & 456.41 & 50 & 0 & 1 & -222.66\tabularnewline
Model 24 & 4 & 457.37 & 50.96 & 0 & 1 & -224.33\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1.} Occurrence of white-tailed deer does with fawns at heel
(inset, lower left) was surveyed at 62 camera sites (large block dots,
scaled to the 0-9 month response variable) in the boreal forest of
northeast Alberta, Canada. Anthropogenic landscape features are
widespread across this landscape, including forest harvesting cutblocks
(grey polygons), well sites (square dots), seismic lines (grey), and
roads and trails (dark grey and colored lines). Lakes are in blue.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2} . In the boreal forest of Alberta, Canada, camera-traps
quantified sites with white-tailed deer fawns -- characterized by their
small size, and for younger animals, the presence of spots. Sites with
fawns appearing in a survey month were recorded as ``breeding'' for that
month.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 3.} Spatial variation in white-tailed deer reproductive
success in the oil sands of the western Nearctic boreal forest of
Alberta, Canada was best explained by petroleum extraction features --
conventional seismic lines, 3D seismic lines, and pipelines -- as well
as upland deciduous forest.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 4.} The probability of white-tailed deer reproduction
across Alberta's northeast boreal forest oil sands' region. Beta
coefficients from the best-supported generalized model explaining
spatial variation in fawn occurrence were extrapolated across the region
using the same spatial data from the models were derived.

\textbf{Data Accessibility Statement}

Data used in these analyses are made available in Supplementary
Information.

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