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\begin{document}

\title{Black-tailed deer resource selection reveals some mechanisms behind the
`luxury effect' in urban wildlife}



\author[1]{Jason Fisher}%
\author[1]{Hugh Fuller}%
\author[1]{Adam Hering}%
\author[1]{Sandra Frey}%
\author[1]{Alina Fisher}%
\affil[1]{University of Victoria}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
The global urban population is expected to increase by 2.5 billion
people over the next 30 years. Yet the doubling of urban landscapes in
the last decades have already led to habitat loss and concomitant
impacts to biodiversity. Nonetheless urban landscapes remain important
for wildlife, and global syntheses have revealed that wealthy urban
areas house more biodiversity, a `luxury effect'. We researched some of
the mechanisms for the luxury effect for urban black-tailed deer, a
species of increasing concern in urban landscapes across the
northwestern Nearctic. We satellite collared twenty deer in an urban
landscape in British Columbia, Canada, with high-resolution fix rates.
We used generalized models in an information-theoretic framework to
weigh evidence for competing hypotheses about the role of tree cover,
productivity, public green spaces, and wealth in explaining deer
selection. Wealth, manifesting as housing lot size, emerged as the
dominant predictor of deer space-use, which is highly concentrated into
very small home-ranges. Other landscape elements stemming from
affluence, including golf courses and parklands, were also strongly
selected by deer. We show post-colonization landscape conversion from
dry semi-arid savannah to well-watered high-productivity landscapes is
supporting deer, with ramifications for the rest of the biotic
community. With urban landscapes becoming an increasingly important for
biodiversity conservation, understanding these mechanisms can help to
promote wildlife-human coexistence.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The global urban population is expected to increase by 2.5 billion
people over the next 30 years (Seto, G\selectlanguage{ngerman}üneralp and Hutyra 2012),
following decades of continued urban growth (Seto, Fragkias, Güneralp
and Reilly 2011). Consequently, urban landscapes have doubled in the
last few decades leading to direct and indirect forest loss (van Vliet
2019). By 2030, global urban land cover is projected to increase between
430,000 km\textsuperscript{2}~and 12,568,000 km\textsuperscript{2}(Seto,
Güneralp and Hutyra 2012). With urbanization comes a loss of natural
habitats -- especially tree cover -- and increase in impervious
surfaces, of low value to biodiversity (Nowak and Greenfield 2020). Thus
urbanization generally correlates to losses in biodiversity (McDonald,
Güneralp, Huang, Seto and You 2018, van Vliet 2019). However, many
anthrophilic species continue to coexist with humans in urban
environments (Magle, Hunt, Vernon and Crooks 2012, Møller 2012) and
there is sustained advocacy, research, and planning for urban areas that
promote wildlife-human coexistence (Apfelbeck, Snep, Hauck, Ferguson,
Holy, Jakoby, Scott MacIvor, Schär, Taylor and Weisser 2020, Larson,
Lerman, Nelson, Narango, Wheeler, Groffman, Hall and Grove 2022).

Although urban wildlife ecology as a discipline was advocated by Aldo
Leopold in the 1930s it remains markedly understudied in academia (Adams
2005). The biodiversity outcomes of urban development have been varied,
from local extirpation of undesirable species such as predators
(McCance, Decker, Colturi, Baydack, Siemer, Curtis and Eason 2017) to
multi-taxic rapid phenotypic changes in urban centers, implicating
urbanization as a mode of evolutionary change (Alberti, Correa,
Marzluff, Hendry, Palkovacs, Gotanda, Hunt, Apgar and Zhou 2017). In
summary species' responses vary within and among cities and
contextualizing the mechanisms behind responses remains a key endeavor
(Magle, Hunt, Vernon and Crooks 2012, McDonald, Mansur, Ascensão,
Crossman, Elmqvist, Gonzalez, Güneralp, Haase, Hamann and Hillel 2020,
Seto, Güneralp and Hutyra 2012).

We know that globally, large carnivores are one of the first groups
extirpated, as we seek to ``make safe'' urban places for humans. One of
the outcomes of extirpating large carnivores from urban environments is
providing prey species with refugia from predation (Gallo, Fidino,
Lehrer and Magle 2019, Møller 2012), often combined with substantial
foraging subsidies for browsing and grazing herbivores (DeStefano and
DeGraaf 2003). These anthropogenic changes to landscapes and wildlife
communities have led to the perception of an ``urban deer
(\emph{Odocoileus spp} .) problem'' (Bowman 2011, Conover 1995, Rondeau
and Conrad 2003) in wildlife management. Fifty-four percent of the
world's population lives in urban areas and is expected to increase to
66\% by 2050 (Soulsbury \& White 2019). Within cities, low-medium
density housing areas the highest likelihood of urban wildlife
interactions due to high species richness and low species extinction
rates (Magle et al. 2016) and the greatest areas of greenspace and
diversity of landcover (Loram et al. 2007). Yet people living within
these low-medium density housing tend to react most negatively to
human-wildlife conflict (Wine et al. 2015).

More than a problem however, urban wildlife is an unplanned scientific
experiment that allows us to examine the roles of different forms of
landscape heterogeneity on species' space-use and resource selection.
Urban areas are complex mosaics of impervious surfaces (buildings,
roads, parking lots), natural or semi-natural greenspaces (parks), and
heavily modified greenspaces (yards, gardens, golf courses), each
offering different resources and risks for different species. Those
resources are the outcome of social and economic drivers within the
human population (Belaire, Westphal and Minor 2016).

One interesting outcome observed in urban ecology is the ``luxury
effect'' wherein differences in affluence among neighborhoods generates
differences in biodiversity. Evidence for a luxury effect dates back
thousands of years, arising from Egyptian archaeological records, and
continue through the Anthropocene (Leong, Dunn and Trautwein 2018). The
luxury effect spans spatial scales, occurring both within and among
cities (Magle, Lehrer and Fidino 2016), albeit inconsistently. Among 20
North American cities studied (Magle, Fidino, Lehrer, Gallo, Mulligan,
Ríos, Ahlers, Angstmann, Belaire and Dugelby 2019) per capita income
played a role in explaining vertebrate diversity in half; instead
species richness was highly (negatively) correlated with urban intensity
(Magle, Fidino, Sander, Rohnke, Larson, Gallo, Kay, Lehrer, Murray and
Adalsteinsson 2021). Affluence is thus a proxy measure for biological
properties associated with rich neighborhoods (Magle, Fidino, Sander,
Rohnke, Larson, Gallo, Kay, Lehrer, Murray and Adalsteinsson 2021): low
human density, energy subsidy, and especially greenness. Indeed the
luxury effect is generally amplified in arid environments (Leong, Dunn
and Trautwein 2018).

Most research on luxury effect uses species richness of plant or animal
assemblages as the metric. For large mammals, individual behavior is a
key mechanism explaining response to urban development (Honda, Iijima,
Tsuboi and Uchida 2018), so we examine luxury effect from this different
angle. We examine resource selection by highly abundant urban
black-tailed deer (\emph{Odocoileus hemionus columbianus} ; deer), a
native to the western Nearctic including the Canadian province of
British Columbia (BC). They are important prey for BC's diverse
carnivore population (Ballard, Lutz, Keegan, Carpenter and deVos Jr
2001) but the changing landscape has led to abundant urban deer
populations. Predator persecution is an obvious culprit, but we suspect
landscape change is an important driver. Deer select high-energy and
high-nutrient plants as forage (Weckerly 1994) and are very sensitive to
factors affecting the recruitment of young deer into the breeding
population (Forrester and Wittmer 2013, Gilbert and Raedeke 2004). The
abundant backyard gardens of urban and suburban areas in affluent
neighborhoods (Larson, Lerman, Nelson, Narango, Wheeler, Groffman, Hall
and Grove 2022) provide ample deer food, potentially allowing deer to
breed more often and more successfully than in `natural' (non-urban)
landscapes.

However, even in natural systems the trade-off between security from
predation and food resources is not well understood (Bowyer, Kie and Van
Ballenberghe 1998), so how deer perceive risk in urban areas -- and how
they capitalize upon potential resource subsidies -- remains unknown.
Urban environments have been shown to impact wildlife behaviour,
resulting in unique adaptations that differ from their non-urban
counterparts (Schell, Stanton, Young, Angeloni, Lambert, Breck and
Murray 2021, Wright, Adams, Stent and Ford 2020). Similarly animal
behaviour and personalities influence the efficacy of behavioural tools
for urban wildlife management such as hazing deterrents and culls
(Honda, Iijima, Tsuboi and Uchida 2018). A better understanding of urban
deer resource selection, avoidance, and spatial landscape use would help
determine if the luxury effect is impacting individual deer behavioural,
and if so, what are some of the proximal mechanisms for this effect.
This information is also an important tool for suburban deer management,
both in terms of minimizing the impacts on wildlife population processes
as well as negative human-wildlife interactions (Klees van Bommel et al.
2020).

We quantified deer locations via satellite telemetry collars and
estimated home-range sizes to better understand urban deer habitat
selection. To evaluate our hypotheses about the luxury effect, we
used resource selection function (RSF) analyses to make inferences about
how deer use different landcover features (Boyce and McDonald 1999,
Seidel, Dougherty, Carlson and Getz 2018). RSFs have been used
extensively to assess animal movement patterns, response to novel
anthropogenic features, and identify movement pathways (Abrahms, Sawyer,
Jordan, McNutt, Wilson and Brashares 2017, Anderson, Turner, Forester,
Zhu, Boyce, Beyer and Stowell 2005, Chetkiewicz and Boyce 2009,
Darlington, Ladle, Burton, Volpe and Fisher 2022, Laforge, Brook, van
Beest, Bayne and McLoughlin 2016, Stewart, Darlington, Volpe, McAdie and
Fisher 2019). We examined the role of (1) vegetation productivity and
tree cover, (2) residential lot size, (3) road density, (4) golf courses
and public green spaces, and combinations thereof. We included variables
measuring these features in a generalized linear model and examined
effect sizes (\selectlanguage{greek}β \selectlanguage{english}coefficients). We hypothesized that if the luxury effect
was apparent, then residential lot size would show the significantly
positive effect size. We also predicted that road density was a risk
deer avoided, and that native (parks) and non-native (golf courses)
forage sources would be selected, but with smaller effect sizes.

\textbf{METHODS}

\emph{Study Area}

Our sampling frame is the western distribution of the Columbia
black-tailed deer (\emph{Odocoileus hemionus columbianus} ) on the
Nearctic Pacific coastline, within the dry Garry Oak (\emph{Quercus
garryana} ) savannah (Barlow 2017, GOERT 2021, McCune, Pellatt and
Vellend 2013, Pellatt and Gedalof 2014). This savannah's Canadian
distribution includes the District of Oak Bay on Vancouver Island,
British Columbia, characterized by small- and large-lot residential
areas, urban development, golf courses, as well as managed and natural
parks throughout. The district is bordered by the Salish Sea to the east
and south (Fig. 1).

\emph{Deer Locations}

We deployed 20 LOTEK Lifecycle GPS collars (Newmarket, ON, CAN) collars
on female black-tailed deer in Oak Bay. We opted for female deer as the
reproductive component of the population is expected to be most
susceptible both to risk and to available forage. Through February -
March 2018 we conducted road-based surveys every morning from sunrise to
11.00 hrs (when deer were most active). We systematically searched the
entire OB period multiple times over this span, ensuring that animals
throughout the study areas had the opportunity for capture. However,
captures are necessarily constrained to where deer occur (Fig. 2).
Female deer were captured using chemical immobilization
\emph{via}darting with a Pneu-Dart - Model 389 Rifle with cartridge
fired projector, 1cc Type C darts with a 1'' needle, a wire barb, and a
tri-port. Deer were immobilized by the wildlife veterinarian (AH) using
1ml of Wildpharm's BAM II Premix (27.3mg of Butorphanol, 9.1mg of
Azaperone, and 10.9mg of Medetomidine) and reversed with 2ml of
Atipamazole (50mg) administered intramuscularly (IM), and 0.5ml of
Naltrexone (25mg) IM. Animals were moved to safety and blindfolded.
Vital parameters including heart rate, respiratory rate, mucus membrane
colour, and body temperature were monitored. Supplemental oxygen was
administered via nasal cannula when needed. Collars were applied with
unique coloured tags to allow field identification.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1.

Fig. 1. We researched black-tailed deer resource selection in the
District of Oak Bay, Vancouver Island, British Columbia, Canada. Oak Bay
is bounded by the ocean (dark blue) to the south and east and by Greater
Victoria to the north and west. Housing is depicted in grey, golf
courses and private and public green spaces in green. \emph{}

\emph{Deer home-range size}

Space use by individual deer is an important parameter and we used
kernel density estimation (KDE) to estimate home-range size. We applied
a smoothed ``kernel'' of equal-sized grid cells in a systematic pattern
across a study region weighted by the density of observations and their
locations (Hooten, Johnson, McClintock and Morales 2017). We calculated
the \emph{97.5\% isopleth} -- the largest area used by the animal
(within the chosen timeframe), excluding the extreme 2.5\% of movements.
Home range analyses were executed using ArcGIS v10.6 (ESRI, Redlands,
CA, USA), using the Geospatial Modelling Environment package and the KDE
tool.

\emph{Landscape Quantification} :

We quantified the landscape at deer locations in ArcGIS 10.6 and R (R
Core Team, 2019) from publicly available digital datasets from BC
provincial and municipal governments, Capital Regional District (CRD)
Land Trust, Habitat Acquisition Trust (HAT) (Blyth 2013); and the US
Geological Survey (USGS). Natural landcover features included vegetation
greenness and tree cover. We derived Normalized Difference Vegetation
Index (NDVI) from cloud-free USGS Landsat-8 2018 imagery for the summer
growing season (April to September 2018) at 30m resolution. Landsat-8
imagery was downloaded from the USGS Earth Explorer
(https://earthexplorer.usgs.gov/).We averaged pixel values across images
from each of the 6 months to determine one greenness value for each
pixel on the finished dataset. NDVI measures vegetation greenness and
can be used to assess vegetation location and density, infer water and
nutrient levels, and infer vegetation structure (for example, irrigated
grass is typically greener than coniferous forest). To represent
vegetation structure in our models, we used a 100-m resolution tree
canopy cover raster dataset derived from high-resolution 2011 CRD aerial
imagery by Habitat Acquisition Trust (Taylor 2013). We used this percent
tree-cover dataset in our RSF analysis.

We extracted residential zoned lots from zoning maps for the
municipalities of Oak Bay, Saanich, and Victoria. We used a BC
provincial dataset
(https://catalogue.data.gov.bc.ca/dataset/bc-parks-ecological-reserves-and-protected-areas)
to quantify parkland and manually digitized golf courses. We discretized
residential lots into three area classes -- small, medium, and large
--using the \emph{Natural Breaks (Jenks) classification} algorithm in
ArcMap. We used a publicly available BC roads dataset
(https://catalogue.data.gov.bc.ca/dataset/digital-road-atlas-dra-master-partially-attributed-roads)
to quantify roads, and a 100-m resolution impervious surfaces
percent-cover dataset from Habitat Acquisition Trust (Blyth 2013) to
quantify non-road urban features.

Following Zuur, Ieno and Elphick (2010) we assessed all variables for
collinearity (r \textless{} 0.7) and variance inflation (VIF \textless{}
3.0). Due to high correlation between impervious surfaces and other
variables (e.g., small residential lots and roads), we excluded
impervious surfaces from our analysis. We scaled all variables (mean=0,
s.d. = 1) to allow comparison of effect sizes.

\emph{Deer resource selection analysis}

We defined ``used'' locations as those where we received a successful
location fix from any of our collared deer across the study period, for
a total of 3,924 used locations. Available locations were randomly
generated across the District of Oak Bay, bounded by the area for which
we searched and captured deer. We generated available points in a 3:1
ratio, the number needed to achieve an asymptotic distribution of
variable means (Gerber and Northrup 2020, Northrup, Hooten, Anderson Jr
and Wittemyer 2013). For each used and available location, we calculated
mean vegetation greenness (NDVI) and the percent area of tree cover
within a 50-m radius buffer, a size selected to minimize error
associated with GIS data resolution while also representing small-scale
resource choice. We also calculated the percent area of three small,
medium, and large residential lots, as well as road density
(km/km\textsuperscript{2}). Parks and golf courses were poorly
represented inside buffer areas, so we measured proximity to these
features.

We evaluated \selectlanguage{greek}β \selectlanguage{english}coefficients from a single global model containing all
selected landscape features. We chose not to do model selection as our
goal was not to find the most parsimonious (reduced) model with a small
subset of component variables, but rather to ascertain the selection
strength of multiple variables (Burnham and Anderson 2002). We used a
logistic regression in a generalized linear model (GLM with binomial
errors and a logit link) with used locations (1's) and randomly selected
available locations (0's) regressed against landscape covariates. We
examined the second order of selection (Johnson 1980) which examines use
within a group of animals: in this case the population of Oak Bay.
Third-order selection -- selection of resources by individuals within
their home-range -- is a useful analysis when considering
individual-specific behaviours or when resources are not available to
all members of a population (Manly, McDonald, Thomas, McDonald and
Erickson 2007), but given our small study area and the movement of
individuals across that area (making all resources accessible), the
second-order analysis was of interest here. We used k-fold
cross-validation (Roberts, Bahn, Ciuti, Boyce, Elith, Guillera-Arroita,
Hauenstein, Lahoz-Monfort, Schr\selectlanguage{ngerman}öder, Thuiller, Warton, Wintle, Hartig
and Dormann 2017) to examine model fit and calculated odds ratios (OR)
from \selectlanguage{greek}β \selectlanguage{english}coefficients as e\selectlanguage{greek}\textsuperscript{β}.



\selectlanguage{english}\textbf{RESULTS}

\emph{Space-use}

Urban black-tailed deer occupied very small home ranges. Mean core home
range size (50\% isopleth) for the 20 collared does was 0.14
km\textsuperscript{2} (14 ha) (SD=0.07), and the mean outer home range
size (95\% isopleth) was 0.64 km\textsuperscript{2} (64 ha) (SD=0.31).
Female deer displayed high site fidelity over the two-year period,
staying centralized during that time (Fig. 2).

\emph{Deer resource selection analysis}

Urban deer strongly selected large residential lots and areas of high
vegetation productivity (or greenness; NDVI). Model validation did not
indicate any model misspecification (Dsq = 10.21; overdispersion = 0.98;
K-fold \selectlanguage{greek}Δ \selectlanguage{english}= 0.17). Deer were more than twice as likely to use an area
with each unit increase in residential lot size (\selectlanguage{greek}β \selectlanguage{english}= 0.89, s.d. = 0.04,
OR = 2.44) and NDVI (\selectlanguage{greek}β \selectlanguage{english}= 0.80, s.d. = 0.06, OR = 2.21) (Fig. 3). Deer
also selected areas closer to public parks (\selectlanguage{greek}β \selectlanguage{english}= 0.68, s.d. = 0.04, OR =
1.97) and golf courses (\selectlanguage{greek}β \selectlanguage{english}= 0.68, s.d. = 0.05, OR = 1.97) (Fig. 3). Deer
showed a weaker selection for small (\selectlanguage{greek}β \selectlanguage{english}= 0.35, s.d. = 0.05, OR = 1.41)
and medium-sized residential lots (\selectlanguage{greek}β \selectlanguage{english}= 0.21, s.d. = 0.04, OR = 1.23).
Deer strongly avoid areas with high road densities (\selectlanguage{greek}β \selectlanguage{english}= -0.03, s.d. =
0.04, OR = 0.74). Deer did not select or avoid area of treed cover (\selectlanguage{greek}β \selectlanguage{english}=
0.01, s.d. = 0.05, OR = 1.01). Effect sizes varied among landscape
features, and error was small (Fig. 4). Extrapolation of observed deer
responses (i.e., \selectlanguage{greek}β \selectlanguage{english}coefficients) to natural and urban landcover
covariates across our study area highlights affluent neighbourhoods in
Oak Bay are most strongly selected by urban black-tailed deer, along
with golf courses, followed by parks (Fig. 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. Home ranges of satellite-collared female black-tailed deer in
the urban landscape of Oak Bay, British Columbia. Polygons are 97.5\%
kernel density estimates. Each individual deer is represented by a
unique colour. Home ranges are based on satellite telemetry location
fixes collected from February 2018 - March 2020. Main roads (black),
residential roads (grey), and ocean (blue) are depicted. Roadless areas
are golf courses and public parks.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 3. The probability of black-tailed deer selecting (or avoiding)
natural and urban landscape features across Oak Bay, BC. Values greater
than 0 represent selection for these features by deer (e.g., vegetation
greenness, large residential lots) while values less than 0 indicate
avoidance (e.g., roads). Values overlapping zero indicate neither
avoidance nor selection (e.g., tree cover).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 4. Deer selection of landscape features in the urban landscape of
Oak Bay, British Columbia, 2018-2022. Model \selectlanguage{greek}β \selectlanguage{english}coefficients (``estimated
selection'', or effect size) are in blue; grey bands are standard error.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Fig. 5. Predicted black-tailed deer resource selection across the urban
landscape of Oak Bay, BC based on extrapolated model \selectlanguage{greek}β \selectlanguage{english}coefficients.
Green areas are high use, fading to low use in red. Golf courses
(purple) and parks (blue) are outlined. Roads are shown as red lines.

\section*{DISCUSSION}

{\label{discussion}}

The luxury effect -- the relationship between urban biodiversity and
human affluence -- observed globally (Leong, Dunn and Trautwein 2018,
Magle, Fidino, Sander, Rohnke, Larson, Gallo, Kay, Lehrer, Murray and
Adalsteinsson 2021) manifests here in the behavioral resource selection
by urban black-tailed deer. The greatest drivers of deer selection in
this former savannah urban landscape are residential lot size and
vegetation greenness (i.e., NDVI). Both are products of wealth. Oak Bay
is a highly affluent neighborhood with 2022 house prices ranging from
\$1-12 million CAD (mls.ca). Large lots (mean = 3601
m\textsuperscript{2}, s.d. = 1609, range 2530-14766) are highly
manicured, with gardens offering abundant resource subsidies. Although
the smaller ``high density'' lots in Oak Bay are not very small (mean =
679 m\textsuperscript{2}, s.d. = 176, range 202-1052) and still heavily
gardened and watered they are still not as strongly selected as the
large lots.

Deer selection for areas with high-productivity vegetation highlights
the importance of high-quality forage availability in urban deer
resource selection. Higher vegetation greenness is represented in areas
with healthy and dense vegetation, and is inversely linked to dry or
drought conditions (Drisya and Roshni 2018). We observed a tight
correlation with vegetation greenness (measured as NDVI) and the
normalized difference moisture index (Supplementary Information, Fig.
1), with higher soil moisture associated with higher vegetation
greenness. The unirrigated regions of this landscape experience extended
summer drought; Garry Oak meadows provide dry, nutrient-poor vegetation
(Barlow 2017, Fuchs 2001, GOERT 2021, Pellatt and Gedalof 2014, Pellatt,
McCoy and Mathewes 2015). Parsing apart the effects of urban development
is difficult in any system, as pre-development records are typically
scant; but historically Cowan (1945) observed ``from the standpoint of
deer the food potential of a west coast climax forest is so low that
over vast areas deer are almost non-existent''. At that time dense deer
were only observed in regenerating forestry clearcuts. In the dry summer
months, water was noted as particularly in demand, with a strong
selection for plants in hygric areas (Cowan 1945). Thus, following
European colonization the conversion of historically well-drained,
drought-resistant Garry oak savannah ecosystems maintained by Indigenous
peoples (Barlow, Pellatt and Kohfeld 2021, McCune, Pellatt and Vellend
2013, Pellatt and Gedalof 2014), to modern watered lawns and
high-productivity vegetation is a key contributor to urban black-tailed
deer abundance. This supports other research that shows the luxury
effect is most commonly observed in arid and semi-arid landscapes,
linked to irrigation and diverse plant communities (Leong, Dunn and
Trautwein 2018).

Beyond the effects of natural vegetation, large residential lots are
also a highly significant predictor of urban deer habitat-use in our
study area. Large-sized residential lots are embedded in neighborhoods
of similar-sized lots, generating low housing density. Deer are
therefore likely responding to the decreased human disturbance
associated with these neighborhoods, as well as higher densities of
high-productivity vegetation associated with larger residential lots,
and vegetative cover on lots. Neighborhoods with larger lot sizes and
high investment into landscaping -- features generated by financial
affluence -- are therefore more likely to experience higher deer use,
and to perceive these interactions negatively (Wine et al. 2015).

Concentrated, high-quality resources mean deer can maintain smaller home
ranges, which we observed here. Female urban deer home ranges were a
quarter of the size of wild females in nearby Washington State (Bender,
Anderson and Lewis 2004). Small home ranges in suburban environments
have been noted by Happe (1982) and Bender, Anderson and Lewis (2004).
Ideal free distribution theory suggests animals occupy the smallest
areas that provide the resources they require (Fretwell 1969, Harestad
and Bunnel 1979), and this phenomenon has been noted for other deer
species (Said and Servanty 2005). In natural landscapes black-tailed
deer strongly select shrubs (as opposed to graminoids or forbs) in early
successional conifer stands (14-20 years) (Hanley 1984), and the
abundance of hygric shrubs throughout large residential lots offers
substantial subsidies that keep home-ranges small. People living in
large residential lots tend to have the highest per capita income yet
react most negatively to human-wildlife conflict (Wine et al. 2015.
Thus, citizen reports of hyperabundant deer stem from peoples'
negativity bias (Jacobs \& Vaske 2019; Buijs \& Jacobs 2021) and
repeated sightings of deer.

Citizen sightings suggested urban deer were strongly associated with
golf courses and green spaces and our analysis corroborates this
observation. Natural green spaces remain semi-arid oak savannahs but
provide abundant escape cover; golf courses offer abundant well-watered
grazing opportunities. Both parks and golf courses are elements of
wealthy landscapes (Chamberlain, Henry, Reynolds, Caprio and Amar 2019,
Schell, Dyson, Fuentes, Des Roches, Harris, Miller, Woelfle-Erskine and
Lambert 2020), and the relationship between affluence and negativity
bias towards urban wildlife (Wine et al. 2015) are bound to make these
citizen sightings noteworthy.

Habitat selection by urban deer occurs on a predator-free backdrop. No
wolves (\emph{Canis lupus} ), cougars (\emph{Puma concolor} ), or black
bears (\emph{Ursus americanus} ) -- primary prey of Columbia
black-tailed deer -- live in these urban landscapes, as none were ever
detected on cameras. Wolves in particular regulate black-tailed deer; in
wild landscapes on northern Vancouver Island wolf control was associated
with increased deer numbers; modelling (and mortality observations,
(McNay and Voller 1995)) suggested that increased recruitment was the
primary mechanism (Hatter and Janz 1994). Therefore, a predator-free
urban environment is expected to markedly increase recruitment (and
hence abundance) over natural landscapes. However, predator regulation
of black-tailed deer populations is mediated by forage availability and
proximity to carrying capacity; dense deer populations are less affected
by predators (Ballard, Lutz, Keegan, Carpenter and deVos Jr 2001). Even
if urban environments did allow predators, the abundant resource
subsidies are likely to sustain abundant deer populations.

\section*{Conclusions}

{\label{conclusions}}

The luxury effect for urban wildlife manifests as strong selection by
black-tailed deer for landscape features associated with wealth which
provide resource subsidies. Large residential lots of the wealthy had
the greatest positive effect on urban deer habitat selection, in
combination with protected green spaces and golf courses and with a
smaller effect of smaller residential lots. Our research illuminates
some of the mechanisms of the luxury effect of urban wildlife observed
across the globe, which are driven by subsidies of water and vegetation
(Chamberlain, Henry, Reynolds, Caprio and Amar 2019, Leong, Dunn and
Trautwein 2018, Magle, Fidino, Sander, Rohnke, Larson, Gallo, Kay,
Lehrer, Murray and Adalsteinsson 2021). Here, the conversion of
historical drought-resistant Garry oak ecosystems into lush and
landscaped urban environments have altered deer selection. The
consequences for biodiversity more generally remains unknown; we can
surmise greater biodiversity with luxury as observed elsewhere
(Chamberlain, Henry, Reynolds, Caprio and Amar 2019, Magle, Fidino,
Sander, Rohnke, Larson, Gallo, Kay, Lehrer, Murray and Adalsteinsson
2021), but given the negative effects on biodiversity of highly abundant
deer (Beckett, Elle, Kremen, Sherwood, McComb and Martin 2022, C\selectlanguage{ngerman}ôté,
Rooney, Tremblay, Dussault and Waller 2004, Martin, Arcese and Scheerder
2011) this is worth close examination. As urbanization continues to
expand around the world, thrusting humans into higher densities and
radically altering the habitats for millions of species, the mechanisms
driving urban biodiversity should be a 21\textsuperscript{st} century
focus for wildlife ecology, so that future planning can effectively
allow for coexistence of urban population and wildlife approximating as
much as possible natural conditions.

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