DOI: 10.5061/dryad.xksn02vf2
title: Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes

description: 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 anthropogenic early seral forage subsidies support high
 springtime reproductive success, mitigating or exceeding winter losses,
 maintaining populations. 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.
description: We deployed 62 camera-trap sites (Reconyx PC900
 Hyperfire<sup>TM</sup> 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<i> et
 al.</i> (2020). Following Burton<i> et al.</i> (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 <i>ca.</i> 3500 km<sup>2</sup> minimum convex
 polygon surrounding camera sites. Cameras were placed
 <i>ca.</i> 1m from the ground facing the wildlife trail and
 set to high sensitivity with 3-s delay.
 <i>Spatial reproductive success</i>
 We identified all camera trap images containing white-tailed deer
 and created a monthly detection-nondetection dataset with three states:
 breeding (fawns present in spring; hereafter “fawning”), 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.
 We used
 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.
 Here, the response variable is a number of months of deer with fawns
 detected (1) or not (0), ranging from 0-9 months (3 spring months over 3
 years). We modeled the number of months in which fawns were observed using
 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). <i>Landscape
 data</i> 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. 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. Spatial data remained fixed
 during the three years of study. In all models, we omitted correlated
 variables (r &gt; 0.7) from multiple-variable models to prevent
 multicollinearity. We combined similar variables only sparsely represented
 in the data (&lt; 1-2% of area) into a single, combination variable
 (Table 1), and rescaled each variable (mean=0, s.d.=1) to compare effect
 sizes. We weighed the evidence in support of models
 corresponding to competing hypotheses using model selection in an
 information-theoretic framework. We validated best-supported models using
 k-fold cross validation in R package <i>boot</i>, and
 calculated deviance explained.
description: Annotated R code is included.