Quantifying spatiotemporal occupancy dynamics and multi‐year core‐use areas at a species range boundary
Bibliographic record
Abstract
Abstract Aim Many species face large‐scale range contractions and predicted distributional shifts in response to climate change, shifting forest characteristics and anthropogenic disturbances. Canada lynx (Lynx canadensis) are listed as threatened under the U.S. Endangered Species Act and were recently recommended for delisting. Predicted climate‐driven losses in habitat quality and quantity may negatively affect the north‐eastern Minnesota lynx population, one of the six remaining resident populations in the contiguous United States. We develop a large‐scale monitoring protocol and dynamic occupancy modelling framework to identify multi‐year core‐use areas and track spatiotemporal occurrence at the southern periphery of the species range. Location North‐eastern Minnesota lynx geographic unit, Superior National Forest and designated critical habitat, Minnesota, USA. Methods Spatially and temporally replicated snow track surveys were used to collect lynx detection/non‐detection data across five winters (2014–15 to 2018–19) covering >17,000 km within the 22,100 km2 study area. We used a dynamic occupancy model to evaluate lynx occupancy, persistence, colonization and habitat covariates affecting these processes. Results Lynx occupancy probabilities displayed high spatial and temporal variability, with grid cell‐specific probabilities ranging from 0.0 in periphery regions to consistently near 1.0 in multi‐year core‐use areas, indicating low turnover rates in those areas. Lynx colonization and persistence increased in areas with more evergreen forest and greater average snowfall, while forest characteristics (3–5 and 10–30 m vegetation density) had mixed relationships with occupancy dynamics. We identified 55 grid cells classified as multi‐year core‐use areas across relatively contiguous regions of high average snowfall and per cent conifer forest. Main conclusions Our study demonstrates a landscape‐scale multi‐year monitoring programme assessing the effects of habitat characteristics and anthropogenic factors on species distributional changes and landscape‐level occupancy dynamics. Our framework incorporating landscape‐scale resource selection, core‐use area concepts and dynamic occupancy models provides a flexible approach to identify population‐level mechanisms driving species persistence and key areas for conservation protection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".