Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes
Bibliographic record
Abstract
Abstract 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. We show how modeling 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 ). 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 multistate occupancy models and generalized linear models in an AIC‐based model selection framework. 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. Synthesis and Applications . Modeling 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".