Evaluating multispecies landscape connectivity in a threatened tropical mammal community
Bibliographic record
Abstract
Habitat corridors are important tools for maintaining connectivity in increasingly fragmented landscapes, but generally they have been considered in single-species approaches. Corridors intended to facilitate the movement of multiple species could increase persistence of entire communities, but at the likely cost of being less efficient for any given species than a corridor intended specifically for that species. There have been few tests of the trade-offs between single- and multispecies corridor approaches. We assessed single-species and multispecies habitat corridors for 5 threatened mammal species in tropical forests of Borneo. We generated maps of the cost of movement across the landscape for each species based on the species' local abundance as estimated through hierarchical modeling of camera-trap data with biophysical and anthropogenic covariates. Elevation influenced local abundance of banded civets (Hemigalus derbyanus) and sun bears (Helarctos malayanus). Increased road density was associated with lower local abundance of Sunda clouded leopards (Neofelis diardi) and higher local abundance of sambar deer (Rusa unicolor). Pig-tailed macaque (Macaca nemestrina) local abundance was lower in recently logged areas. An all-species-combined connectivity scenario with least-cost paths and 1 km buffers generated total movement costs that were 27% and 23% higher for banded civets and clouded leopards, respectively, than the connectivity scenarios for those species individually. A carnivore multispecies connectivity scenario, however, increased movement cost by 2% for banded civets and clouded leopards. Likewise, an herbivore multispecies scenario provided more effective connectivity than the all-species-combined scenario for sambar and macaques. We suggest that multispecies habitat connectivity plans be tailored to groups of ecologically similar, disturbance-sensitive species to maximize their effectiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".