Data and code: Microgeographic variation in demography and thermal regimes stabilize regional abundance of a widespread freshwater fish
Bibliographic record
Abstract
Predicting the persistence of species under climate change is an increasingly important objective in ecological research and management. However, biotic and abiotic heterogeneity can drive asynchrony in population responses at small spatial scales, complicating species-level assessments. For widely distributed species consisting of many fragmented populations, such as brook trout (Salvelinus fontinalis), understanding drivers of asynchrony in population dynamics can improve predictions of range-wide climate impacts. We analyzed demographic time-series from mark-recapture surveys of eleven natural brook trout populations in eastern Canada over 13 years to examine the extent, drivers, and consequences of fine-scale population variation. The focal populations were genetically differentiated, occupied a small area (~25 km2) with few human impacts, and experienced similar climate conditions. Recruitment was highly asynchronous, weakly related to climate variables, and showed population-specific relationships with other demographic processes, generating diverse population dynamics. In contrast, individual growth was mostly synchronized among populations and driven by a shared positive relationship with stream temperature. Outputs from population-specific models were unrelated to four of five hypothesized drivers (recruitment, growth, reproductive success, phylogenetic distance), but variation in groundwater inputs strongly influenced stream temperature regimes and stock-recruitment relationships. Finally, population asynchrony generated a portfolio effect that stabilized regional species abundance. Our results demonstrate that population demographic and habitat diversity at microgeographic scales can play a significant role in moderating species responses to climate change. Moreover, we suggest that the absence of human activities within study streams preserved natural habitat variation and contributed to asynchrony in brook trout abundance, while the small study area eased monitoring and increased the likelihood of detecting asynchrony. Therefore, anthropogenic habitat degradation, landscape context, and spatial scale must be considered when developing management strategies to monitor and maintain populations that are diverse, stable, and resilient to climate change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.040 |
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".