Power Through or Keep Looking? Comparing Species‐Area Relationships of Habitat Fragments and Their Drivers in Different Ecoregions
Bibliographic record
Abstract
ABSTRACT Our study aimed to (1) determine how the shape varies and mechanisms influence species‐area relationships within the same taxon but between different ecoregions and (2) determine how slope ( z ) and intercept ( c ) values of the linearized power model were influenced by ecoregion. Location: Madagascar. Taxon: Arboreal mammals (lemurs). We surveyed arboreal mammals (lemurs) in 42 tropical dry deciduous forest fragments in Ambanjabe Field Site in Ankarafantsika National Park in Western Madagascar and 27 primary mid‐elevation raiforest fragments in the Tsinjoarivo‐Ambalaomby new protected area in Eastern Madagascar using line‐transect methods. We determined which of 20 species‐area models were the most likely using the ‘ sars ’ R package and AICc in each ecoregion. We compared z and c values of the power model in each ecoregion using ANCOVA. We assessed what drove the shape of the SARs using the Measurement of Biodiversity framework. We found that SAR models differed between ecoregions, with the power model (AICc = 89.04) as the most likely in the west and the Monod model (AICc = 86.98) followed by three candidate models (Kobayashi, AICc = 87.02; logarithmic, AICc = 87.6; and negative exponential, AICc = 88.61) in the east. We found no significant difference in z values between ecoregions ( F 1,66 = 2.991, p = 0.088) and a non‐significant trend in c values between ecoregions ( F 1,65 = 3.938, p = 0.051). Spatial aggregation of species drove species richness patterns in the west, and species diversity and evenness drove species richness patterns in the east. Our study demonstrates that while the power and negative exponential model are good starting points, other models are also likely models to describe SARs in arboreal mammals such as primates. These patterns can reflect different mechanisms driving SARs. Ecoregion was not strongly related to differences in either z or c values of the power model.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".