Landscape connectivity for African elephants in the world's largest transfrontier conservation area: A collaborative, multi‐scalar assessment
Bibliographic record
Abstract
Abstract Landscape connectivity operates at a variety of scales, depending on the geography of the area in question and the focal species or ecological process under consideration. Most connectivity studies, however, are typically focused on a single scale, which in the case of resistance‐based connectivity modelling, is often the entire landscape or protected area (PA) network. This large, single‐scale focus may miss areas that are important for connectivity at smaller scales and that can be documented via observed animal movements without resorting to landscape‐wide statistical modelling and extrapolation approaches. Here, we characterize landscape connectivity at three different scales (local/micro, inter‐PA, and landscape‐wide/macro), using observed animal movements rather than conventional resistance surface models, to produce a connectivity conservation blueprint for African elephants ( Loxodonta africana ) in the Kavango‐Zambezi transfrontier conservation area (KAZA) in southern Africa. This analysis is based on an extensive, high‐resolution GPS tracking database comprising approximately 4 million GPS locations from nearly 300 tagged elephants and their associated herds. Our results show that high‐fidelity elephant use of micro‐corridors is typically—though not exclusively—related to directed movements towards water, often amidst heavy anthropogenic presence. Movement pathways that connected KAZA's core protected areas were longer and variable, with some channelled into narrow areas of use and others more dispersed across larger sub‐landscapes. At the largest scale, a network analysis incorporating all used landscape grid cells revealed several clusters of large‐scale movement corridors that connected distant parts of KAZA. Synthesis and applications : Our three scales of analyses reveal disparate geographical priorities for connectivity conservation that collectively could help ensure the functional connectivity of KAZA for its largest inhabitants. Each scale will require its own set of inter‐related conservation interventions, while further research into areas with sparse data collection, and other species of conservation concern, could reveal additional connectivity priorities at each scale.
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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 | 0.001 | 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) | 0.000 | 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".