Habitat quality does not predict animal population abundance on frequently disturbed landscapes
Bibliographic record
Abstract
Increases in frequency of anthropogenic and natural disturbances exacerbated by climate change are disrupting animal movement patterns and, in turn, species distribution and abundance. Habitat models are a valuable approach for predicting how a species or population is distributed across habitats. However, in these models, the environmental characteristics describing habitat are typically constant over time which is inaccurate for a frequently disturbed landscape. The goal of this study is to assess the degree to which frequent disturbances can alter abundance and distribution, to the point of which inferences from static habitat maps are no longer relevant. We simulated individual animal movement in a dynamically disturbed landscape and compared the observed spatial relative abundance to the one predicted from a static habitat model fit to the simulated trajectories. We found that the prediction error is higher when the landscape is disturbed than where there are no disturbances. This result holds even with the addition of life-history and demographic processes to the model which would facilitate the discovery of novel habitat by increasing density-dependent movement, thereby bringing the spatial distribution of individuals closer to a proportion of habitat quality arising from the population at equilibrium assumption underlying most habitat models. In the context of species at risk recovery planning, using habitat models with constant environmental covariates to forecast animal locations in areas with more frequent disturbances as a result of climate change will likely produce inaccurate predictions.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".