Resilience of an endangered salmon complex: understanding the relationships between habitat heterogeneity, environmental variability, and population diversity
Bibliographic record
Abstract
Habitat heterogeneity, defined by spatial variation in local environmental conditions, fosters variation in organism traits and life history strategies, which in turn can stabilize populations by buffering against climate fluctuations. In Pacific salmon (Oncorhynchus spp.), large watersheds with intact habitats support commercially, ecologically, and culturally important populations by generating a variety of life history strategies that contribute to population stability and resilience. However, habitat loss and modification have homogenized river networks inhabited by salmon, diminishing intraspecific diversity and its stabilizing effects. In systems with highly modified habitats, it is unclear whether the processes that generate intraspecific diversity are still intact. Furthermore, it is yet to be explored what level of population diversity is sufficient to ensure the survival and stability of small, imperiled populations, especially those experiencing increasingly extreme environmental conditions. This dissertation addresses these knowledge gaps by exploring the relationship between habitat diversity and population diversity within modified habitats of an endangered salmon population complex, aiming to understand its resilience amidst increasingly variable environmental conditions. I focus on a population of critically endangered Central California Coast coho salmon (O. kisutch) in the Lagunitas Creek watershed in Marin County, California as a case study of an imperiled population in a small watershed at the southern edge of the species’ range experiencing effects of climate change. In Chapter 1, I examine how diverse habitats across the watershed differentially filter extreme drought conditions and the consequences for juvenile coho persistence, foraging behavior, and growth opportunities. Building on observed variation in juvenile traits during a summer of drought in Chapter 1, I assess diversity in long-term juvenile subpopulation dynamics across the region and the effects of seasonal flow conditions in Chapter 2. In Chapter 3, I explore trait variation at the adult stage, leveraging monitoring data and isotopic analysis of fish ear stones to characterize spawning distributions in space and time in relationship to winter hydrology. Spawning dispersal data also revealed key habitats that support returning adults. Findings from all three chapters highlight the importance of habitat diversity and connectivity for population persistence amidst environmental variability and disturbance. By better understanding the underlying dynamics of an endangered population, we can better assess vulnerability to extirpation and create targeted restoration actions to enhance population resilience.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".