Can habitat suitability estimated from MaxEnt predict colonizations and extinctions?
Bibliographic record
Abstract
Abstract Aim MaxEnt has been widely used to model species’ geographic distributions as functions of environmental variables and to predict changes in distributions in response to environmental change. Here, we test the predictive ability of MaxEnt models through time by modelling colonizations and extinctions. Location North America. Methods Using data for 21 species from the North American Breeding Bird Survey, we first related avian species’ geographic distributions to the spatial variation in environmental conditions. Then, we modelled site‐specific colonizations and extinctions between 1979 and 2009 as functions of MaxEnt‐estimated habitat suitability and neighbourhood occupancy. Results We found that colonization and extinction probabilities were related to spatial variation in habitat suitability, and to neighbourhood occupancy, in the expected directions. However, change in habitat suitability (which is much smaller through time than through space) is a weak predictor of extinction and worse for colonization. This is because a) for most species and most sites, climatic suitability did not change dramatically between 1979 and 2009, and b) the relationship between colonization or extinction probability and change in climatic variables is very weak (r2 = 0.02). Most colonizations and extinctions are apparently unrelated to climate change. Main conclusions MaxEnt models apparently capture a real effect of habitat suitability on North American bird species’ distributions, but over short and medium time scales, occupancy of neighbouring sites by conspecifics predicts changes in occupancy as well as, or better than changes in climatic habitat suitability, as characterized by MaxEnt. One would not expect species’ distributions to track climate change closely. Prediction of species’ responses to climate change should 1) recognize that the process of colonization and extinction are not equally well predicted by species distribution models and 2) account for the spatial structure of species’ distributions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".