Combining network theory and reaction–advection–diffusion modelling for predicting animal distribution in dynamic environments
Bibliographic record
Abstract
Abstract Movement is a key process driving animal distributions within heterogeneous landscapes. Graph (network) theory is increasingly used to understand and predict landscape functional connectivity, as network properties can provide crucial information regarding the resilience of a system to landscape disturbances, e.g. removal of habitat patches. The temporal dimension of movement patterns, however, is not generally included in network analysis, which can lead to a discrepancy between observed space use and landscape connectivity. Reaction–advection–diffusion models, when coupled with network analysis, could provide a powerful mechanistic framework based upon spatio‐temporal dimensions of animal movement, but this approach remains poorly developed for ecological studies. We developed a mechanistic space use model that considers both residency time in resource patches and movement amongst those patches within a spatial network. The framework involves two main steps: first, the network topology that best reflects functional connectivity for the study system is identified; second, a spatio‐temporal flow dynamic is implemented within the network using reaction–advection–diffusion modelling. To illustrate the approach, we used observations of radiocollared plains bison Bison bison bison that were travelling in a meadow network within a forest matrix. In the model application, we found that the graph best reflecting the functional connectivity of bison was a complex graph of ultra‐small world scale‐free network type. The reaction–advection–diffusion model involved the effect of meadow area and inter‐meadow distance on bison travels. Simulations showed that a simple graph or distance‐based graphs provided less accurate predictions of bison distribution, while also predicting different management actions to effectively impact bison space use. Our study demonstrates how reaction–advection–diffusion modelling, coupled with network theory, can provide a robust mechanistic framework for predicting animal distribution in dynamic environments. The modelling approach can be applied to a large range of systems that are subjected to rapid environmental changes due to habitat management or natural resource extraction, for example. Furthermore, our study demonstrates that management and conservation planning can strongly depend upon network structure, and that a faulty assessment of network topology can result in poor planning, with potential unexpected impacts on animal distributions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".