Measuring the response of animals to contemporary drivers of fragmentationThis review is one of a series dealing with some aspects of the impact of habitat fragmentation on animals and plants. This series is one of several virtual symposia focussing on ecological topics that will be published in the Journal from time to time.
Bibliographic record
Abstract
From the perspective of most animals, fragmentation is a landscape-scale process in which habitat is separated into many smaller patches that have less total area. Here, we examine how two contemporary drivers of fragmentation, anthropogenic climate change and exurbanization, affect movement and responses of animal species to new environmental conditions. We address the definition of fragmentation and how the spatial patterns created by fragmentation can be measured at the scales at which different species of animals respond to their environments. We discuss tools, such as satellite remote sensing, that increasingly make it possible to identify and quantify changes in land cover and vegetation structure across extensive areas. We also describe a range of methods that are available to guide decisions about faunal surveys and monitoring programs in fragments or reference areas. Examination of stochastic changes in land cover and species occurrence over time is important because these shifts can confound detection of systematic responses to fragmentation. Careful evaluation of fragmentation and its influence on the distribution and viability of fauna may help to identify underlying mechanisms and to develop effective strategies for conservation and land use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".