Understanding How Northern North American Freshwater Fishes are Responding to Rapid Environmental Change
Bibliographic record
Abstract
Many northern fishes are experiencing unprecedented environmental changes across their ranges. However, limited empirical evidence exists for understanding how these changes may combine and potentially interact to influence fish diversity, individual species loss and gains, and overall productivity at broad spatial scales relevant for informing evidence-based conservation and management efforts. \n \nThis dissertation investigated how northern fishes are being impacted by cumulative and potentially interacting environmental changes across broad spatial scales including climate change, water quality, and land use. I examined two main research questions including 1) what are the key environmental variables currently influencing northern freshwater fishes? And 2) how is climate interacting with other variables to produce non-additive effects (i.e., antagonistic or synergistic interactions). To do this, I used fish community data from small streams across relatively intact regions of Alaska, small boreal streams in a more developed region of Alberta, Canada, and subarctic lakes in a rapidly changing permafrost region in the lower Mackenzie River basin. \n \nWarming temperatures were linked to increased community diversity and abundance in small streams. However, this trend was not observed for fish communities in small subarctic lakes, where water quality degradation appeared to have a stronger role in regulating fish community health than direct warming. In all three chapters, there was evidence for localized gains and losses of individual fish species presence or relative abundance in association with mounting environmental changes that included warming, changes in precipitation, and land use. Some species may be temporarily benefiting from warmer conditions across their northern ranges, for example, Pacific salmon species including Coho Oncorhynchus kisutch, Chinook Oncorhynchus tshawytscha, and Sockeye Oncorhynchus nerka appeared to be experiencing a net distribution gain possibly due to warmer and longer growing seasons. In contrast, species declines associated with environmental changes were more likely to occur for Arctic specialists, species intolerant to environmental degradation, and large-bodied benthic species. Interactions between climate warming and land use were synergistic (i.e., the combined effect of both stressors was greater than the sum of their individual effects), highlighting the potential for amplified species declines under future warming and land use scenarios. Together, these results provide new information that can be used to inform improved northern conservation planning amid rapid environmental changes.
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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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".