Relative effects of landscape‐scale wetland amount and landscape matrix quality on wetland vertebrates: a meta‐analysis
Bibliographic record
Abstract
Conservation management of wetland-dependent species generally focuses on preserving or increasing wetland habitat. However, the quality of the landscape matrix (the intervening non-wetland portion of the landscape) has been shown to be more important than wetland availability for some wetland-dependent species. We used meta-analysis to compare the effects of wetland amount (measured as the area of wetland habitat in a landscape) and matrix quality (measured as the area of forest cover in the same landscape) on the population abundance of wetland-dependent vertebrates. We combined data across 63 studies conducted in forested ecoregions worldwide and extracted 330 population responses for 155 species, at the spatial scale that best predicted the effects of wetland. amount and forest amount for each response. In addition, to ensure that our results were not biased by the scale selected, we assessed whether the relative effects of wetland and forest amount were scale dependent. We found that the amount of wetland in a landscape had a larger effect than the amount of forest on the abundance of mammals and birds whereas, surprisingly, for amphibians the amount of forest in a landscape was more important than the amount of wetland. For reptiles, both wetland amount and forest amount showed only weak,effects on abundance. These results were not scale dependent, i.e., they were consistent across spatial scales. Our results suggest that the population distribution of wetland-dependent amphibians is more strongly related to landscape matrix quality than to wetland availability in a landscape, likely due to their requirement for access to terrestrial resources. We conclude that conservation policies for wetland biodiversity that focus only on wetland habitat will be ineffective in conserving many of these species. In addition, population viability analyses based only on wetland amount may overestimate the capacity of a landscape to support populations of wetland-dependent species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".