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


Fisher, Jason (2021), Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes, Dryad, Dataset,


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.


We deployed 62 camera-trap sites (Reconyx PC900 HyperfireTM 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 et al. (2020). Following Burton 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 ca. 3500 km2 minimum convex polygon surrounding camera sites. Cameras were placed ca. 1m from the ground facing the wildlife trail and set to high sensitivity with 3-s delay.

Spatial reproductive success

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).

Landscape data

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 > 0.7) from multiple-variable models to prevent multicollinearity. We combined similar variables only sparsely represented in the data (< 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 boot, and calculated deviance explained.

Usage Notes

Annotated R code is included.


Alberta Innovates, Award: Wildlife CAMERA