Quantitative Spatial Ecology to Promote Human-Wildlife Coexistence: A Tool for Integrated Landscape Management
Bibliographic record
Abstract
Understanding, predicting and controlling animal movement is a fundamental problem of conservation and management ecology. The need to mitigate human-wildlife conflicts, such as crop raiding by large herbivores, is becoming increasingly urgent. Because of the substantial costs or the possibility of unsuitable outcomes on wildlife, managers are often encouraged to deploy interventions that can achieve their objective while minimizing the impact on animal populations. We propose an adaptive management framework that can identify cost-effective solutions to reduce human-wildlife conflicts, while also minimizing constraints on animal movement and distribution. We focus on conflicts involving animals for which conflict zones occupy only a portion of their home-range. The adaptive management approach includes four basic steps: define and spatialize conflict areas, predict animal distribution from functional connectivity and patch residency time, predict the impact of management actions on animal distribution, and test predictions and revise predictive models. Key to the process is development of a mathematical model that can predict how habitat-animal interactions shape animal movement dynamics within patch networks. In our model, networks consist of a set of high-quality patches connected by links (i.e., potential inter-patch movements). Inter-patch movement rules and determinants of patch residency time need to be determined empirically. These data then provide information to parameterize a reaction-advection-diffusion model that can predict animal distribution dynamics given habitat features and movement taxis towards (or against) conflict areas depending on management actions. Illustrative simulations demonstrate how quantitative predictions can be used to make spatial adjustments in management interventions (e.g., length of diversionary fences) with respect of conflict areas. Simulations also show that the impact of multiple interventions cannot be considered as simply having additive effect, and their relative impact on animal equilibrium distribution depends on how they are added and deployed across the network. Following the principles of adaptive, integrated landscape management, the predictive model should be revised as monitoring provides new information about the response of animals to the set of interventions. We contend that the proposed quantitative approach provides a robust framework to find cost-effective strategy toward sustainable human-wildlife conflicts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".