Accounting for regional niche variation in habitat suitability models
Bibliographic record
Abstract
Ecological niche modeling has become an increasingly important tool to address issues in many fields of basic and applied ecology. The ecological niche space occupied across the geographic range, particularly for wide‐ranging species, may vary for a variety of evolutionary and non‐evolutionary reasons. However, ecological niche models are often applied over large geographic areas without regard for the potential effects of regional variation in adaptation, environmental conditions and their interactions, and species responses, thus significantly reducing their accuracy and utility. We develop regionally partitioned ecological niche models, using GARP, for the wide‐ranging North American tree Gleditsia triacanthos (Fabaceae) . Models were constructed based on known tree occurrences at peripheral and range‐centre locations, as well as across the geographic range as a whole. Our results suggest that the niche space occupied by G. triacanthos varies regionally and that between some regions in particular there may be a complete absence of niche overlap. In particular, while there is some overlap between the niche space occupied by trees in the western and central regions of the range, there appears to be virtually no overlap in the niche space occupied by trees in the south of the range and that occupied by western and central trees. This lack of overlap appears to be driven primarily by regional differences in abiotic conditions, rather than regional adaptation per se. The results of our study have several important implications for the future development of habitat suitability models over large geographic areas. Spatial partitioning of data is clearly necessary to improve predictions of models where regional niche variation occurs. For wide‐ranging species in particular, regional differences in ecological characteristics may cause apparent niche variation.
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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) | 0.005 | 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".