Climate warming moderates the impacts of introduced sportfish on multiple dimensions of prey biodiversity
Bibliographic record
Abstract
Human-assisted introductions of exotic species are a leading cause of anthropogenic change in biodiversity; however, context dependencies and interactions with co-occurring stressors impede our ability to predict their ecological impacts. The legacy of historical sportfish stocking in mountainous regions of western North America creates a unique, natural quasiexperiment to investigate factors moderating invasion impacts on native communities across broad geographic and environmental gradients. Here we synthesize fish stocking records and zooplankton relative abundance for 685 mountain lakes and ponds in the Cascade and Canadian Rocky Mountain Ranges, to reveal the effects of predatory sportfish introduction on multiple taxonomic, functional and phylogenetic dimensions of prey biodiversity. We demonstrate an innovative analytical approach, combining exploratory random forest machine learning with confirmatory multigroup analysis using multivariate partial least-squares structural equation models, to generate and test hypotheses concerning environmental moderation of stocking impacts. We discovered distinct effects of stocking across different dimensions of diversity, including negligible (nonsignificant) impacts on local taxonomic richness (i.e. alpha diversity) and trophic structure, in contrast to significant declines in compositional uniqueness (i.e. beta diversity) and body size. Furthermore, we found that stocking impacts were moderated by cross-scale interactions with climate and climate-related land-cover variables (e.g. factors linked to treeline position and glaciers). Interactions with physical morphometric and lithological factors were generally of lesser importance, though catchment slope and habitat size constraints were relevant in certain dimensions. Finally, applying space-for-time substitution, a strong antagonistic (i.e. dampening) interaction between sportfish predation and warmer temperatures suggests redundancy of their size-selective effects, meaning that warming will lessen the consequences of introductions in the future and stocked lakes may be less impacted by subsequent warming. While both stressors drive biotic homogenization, our results have important implications for fisheries managers weighing the costs/benefits of stocking-or removing established non-native populations-under a rapidly changing climate.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".