The role of environmental and spatial processes in structuring native and non‐native fish communities across thousands of lakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quantifying the role of spatial patterns is an important goal in ecology to further understand patterns of community composition. We quantified the relative role of environmental conditions and regional spatial patterns that could be produced by environmental filtering and dispersal limitation on fish community composition for thousands of lakes. A database was assembled on fish community composition, lake morphology, water quality, climatic conditions, and hydrological connectivity for 9885 lakes in Ontario, Canada. We utilized a variation partitioning approach in conjunction with Moran's Eigenvector Maps (MEM) and Asymmetric Eigenvector Maps (AEM) to model spatial patterns that could be produced by human‐mediated and natural modes of dispersal. Across 9885 lakes and 100 fish species, environmental factors and spatial structure explained approximately 19% of the variation in fish community composition. Examining the proportional role of spatial structure and environmental conditions revealed that as much as 90% of the explained variation in native species assemblage composition is governed by environmental conditions. Conversely on average, 67% of the explained variation in non‐native assemblage composition can be related to human‐mediated dispersal. This study highlights the importance of including spatial structure and environmental conditions when explaining patterns of community composition to better discriminate between the ecological processes that underlie biogeographical patterns of communities composed of native and non‐native fish species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it