Modelling the influence of landscape connectivity on animal distribution: a functional grain approach
Bibliographic record
Abstract
Landscape change may reduce the connectivity of landscapes and impact the movement of animals. If movement processes have been influenced by landscape connectivity, we hypothesize that animals may distribute themselves in larger connected regions of the landscape in order to minimize the movement costs associated with obtaining required resources and avoiding predators. We adopt the term functional grain to describe a set of functionally connected regions. In this spatial pattern, each region describes a contiguous area of the landscape within which an animal may move freely below a threshold amount of movement cost. We used telemetry data from woodland caribou Rangifer tarandus caribou to test hypothetical functional grains where connectivity was determined by the spatial configuration of resource patches (patch only), by the resistance to movement presented by landscape features (resistance only), and by a combination of the two (patch + resistance). To identify these functional grains, we used a grains of connectivity approach, and introduced a novel lattice‐based variant of this method to build the resistance only model. We developed a measure of fit that describes caribou distribution with respect to larger functionally connected regions in the grain, and used this to ask: 1) are seasonal caribou locations consistent with a random functional grain, implying that landscape connectivity has not shaped their distribution? 2) Given a functional grain model, are seasonal caribou locations distributed in larger functionally connected regions than random points, implying a response to the shape, size, and location of the connected regions. We found support for landscape connectivity influencing animal distribution using grains based on a landscape resistance model, and that support varied between behaviourally defined seasons. We also discuss how our novel lattice approach may be valuable for highly mobile mammals and other species where the identification of resource patches is a limitation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".