Evaluating conceptual models of landscape change
Bibliographic record
Abstract
A variety of landscape models are used to conceptualize and interpret human impacts on ecosystems and their biodiversity. The simplest, a ‘patch‐matrix’ model, is rooted in island biogeography theory and assumes a dichotomy between generic, easily‐defined habitat patches and a surrounding matrix that is completely inhospitable. This dichotomy between patch and matrix habitats has been recently relaxed, with the ‘continuum’ model taking this relaxation to its extreme and logical endpoint – a species‐based model with no a priori definition of habitat or matrix, but rather focusing on ecological gradients. Yet, because few empirical comparisons of these bookending models exist, we lack understanding of their relative utility or the merits of hybrid approaches that combine attributes of patch‐matrix and continuum models. To guide such considerations, we first develop a decision‐making framework for the application of patch‐matrix, continuum, and hybrid models. The framework takes into account study objectives, attributes of the landscape, and species traits. We then evaluate this framework by empirically comparing how continuum, patch‐matrix, and hybrid models explain beetle distributions across two contrasting fragmented landscapes, for species differing in trophic level and habitat specificity. Within the Hope River Forest Fragmentation Project, a system with strong landscape contrast and distinct (‘hard’) structural edges between forest fragments and grassland, we find broad support for hybrid models, particularly those incorporating surrounding landscape structure. Conversely, within the Wog Wog Habitat Fragmentation Experiment, a system with weak landscape contrast and ‘soft’ structural edges between natural and plantation forest, we find co‐support for continuum and hybrid models. We find no support in either system for patch‐matrix, relative to continuum and hybrid models. We conclude by considering key questions and areas of research for advancing the application of models to understand species responses and biodiversity patterns associated with land‐use change.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".