Characterizing multispecies connectivity across a transfrontier conservation landscape
Bibliographic record
Abstract
Abstract Connectivity conservation is aimed at sustaining animal movements and ecological processes important to ecosystem functioning and the maintenance of biodiversity. However, connectivity conservation plans are typically developed around a single species and rarely empirically evaluated for their relevance to others, thereby limiting our understanding of how connectivity requirements differ across species. We used an omnidirectional application of circuit theory and GPS data from six species to evaluate connectivity at multiple scales for multiple species within the world's largest transfrontier conservation landscape in southern Africa. We evaluated the effects of linear barriers, natural habitat types and anthropogenic land use on movement. We identified multispecies connectivity hotspots as areas where current flow was concentrated or channelled through pinch points. To evaluate surrogate species for connectivity, we evaluated the correspondence among single‐species connectivity across the entire landscape and also examined whether a more localized corridor for African savanna elephant Loxodonta africana captured high multispecies connectivity values. Connectivity models revealed many intact areas across the landscape with diffuse current flow, but also evidence that fences, rivers, roads and areas of anthropogenic use acted as strong barriers to movement—particularly in the case of fences, which completely blocked female elephant movement. Tests of correspondence among single‐species connectivity models revealed spotted hyaena and African wild dog as the strongest surrogate species of connectivity. Female elephants were found to be the weakest surrogate species of connectivity at the landscape scale. However, focusing within a localized elephant corridor revealed the areas of concentrated or channelled connectivity for most species in our study. Synthesis and applications . Our results suggest that the single‐species focus permeating connectivity literature may result in conservation plans that poorly conserve the connectivity needs of co‐occurring species. Our study also highlights the importance of testing the efficacy of surrogate species for connectivity at multiple scales. We recommend evaluating multispecies connectivity to prioritize areas for conservation that safeguard the connectivity needs of multiple species of conservation concern.
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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) | 0.002 | 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".