Can multiscale models of species’ distribution be generalized from region to region? A case study of the koala
Bibliographic record
Abstract
Summary While various studies have evaluated the habitat requirements for wildlife in fragmented forest landscapes at multiple spatial scales, few have considered whether there is regional variation in the most important factors. This is a conundrum for managers of any species with a broad geographical range: to what extent should studies in one region inform decisions in another? We addressed this question using a case study of the koala,Phascolarctos cinereus, in three biogeographically different fragmented forested landscapes in eastern Australia. Mixed‐effect logistic models were applied to predict koala occurrence from explanatory variables captured at four spatial scales: the individual tree, the stand (<1 ha), the patch (1–100 ha) and the landscape (100–1000 ha). We used model averaging to account for model and parameter uncertainty, and tested the cross‐regional discrimination ability of the models. We discovered that multiscale models of koala distribution cannot be readily generalized from region to region, and that specific conservation actions for each region, rather than the entire geographical range, are more appropriate. We found a strong justification for adopting a hierarchical landscape approach to koala conservation across its range. However, cross‐regional differences in habitat relationships occurred within this hierarchy. Exceptions were landscape context, which showed a consistently strong effect and high rank in all regions, and the presence of individual preferred tree species of the genusEucalyptus, which showed modest consistency in its interaction with large‐diameter trees across the regions. In contrast, the remaining habitat variables, including patch size (a key management factor), showed moderate to strong cross‐regional variation attributed to the interaction of edaphic factors, landscape history and contemporary land‐use patterns. Synthesis and applications. Adopting a uniform conservation programme over a large geographical area is attractive to policy‐makers and conservation planners. However, our study confirms the lack of generality of species distribution models over large areas. Consequently, we argue against adopting a uniform conservation programme for species with a large geographical range.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".