The roles of spatial configuration and scale in explaining animal distributions in disturbed landscapes: a case study using pond-breeding anurans
Bibliographic record
Abstract
Scale is fundamental to ecological studies as patterns exist at multiple levels of organisation. Scale is critically important when assessing a species' distribution, and it can influence the results of complex landscape analyses. If ignored, conservation and management decisions may be inappropriate. In this study, we assessed an often overlooked element in landscape analyses, spatial configuration, to uncover patterns of species distribution. Specifically, we evaluated cover by native vegetation within an urban landscape using a non-nested approach based on discrete consecutive rings (e.g., 0-50 m, 50-100 m, etc.) and a traditional nested approach based on concentric circles (e.g., 0-50 m, 0-100 m, etc.) to determine whether these approaches differ in their effectiveness in uncovering relationships between land cover and animal occurrence in a disturbed, urban landscape. We performed spatial configuration analyses using two anuran amphibian species (wood frog, Lithobates sylvaticus; and boreal chorus frog, Pseudacris maculate) sampled via call surveys at 75 wetlands (of constructed stormwater sites [n=58] and natural wetlands [n=17]) located within the city of Edmonton, Alberta, Canada. Furthermore, we evaluated the relationship between proportion of native vegetation and species occurrence at three grain sizes (10 m, 50 m, and 100 m) based on analyses of non-nested concentric zones. The nested and non-nested approaches differed in their predictions regarding the occurrence of the frog species. The nested approach explained a higher percentage of deviance when predicting wood frog occurrence than did the non-nested analyses whereas the non-nested approach explained a higher percentage of deviance when predicting boreal chorus frog occurrence. We identified locations surrounding wetlands that predicted occurrence using the non-nested approach that were not detected by the nested approach, suggesting that non-nested analyses may represent an overlooked tool for habitat assessment. Top models for predicting occurrence varied among the three grain sizes, suggesting that our ability to detect environmental heterogeneity is scale-dependent. Incorporating spatial configuration (non-nested and nested designs) and multiple grain sizes in analyses may provide better resolution of landscape patterns and help uncover causes behind species' distributions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".