Consequences of Spatial Exploitation in Complex Adaptive Social-Ecological Systems: Managing for Sustainable Freshwater Fisheries
Bibliographic record
Abstract
Freshwater fisheries are complex adaptive social-ecological systems structured by coupled feedbacks between fish and people (e.g., anglers). For example, fishing quality influences angler site choices, and anglers reciprocally impact fish populations at chosen sites through size-selective harvest, thus demonstrating how feedbacks between fish and anglers permeate through whole ecosystems. Overexploitation increasingly threatens these fisheries challenging management with finding robust solutions to sustain these important resources. Yet, we often lack generalization on the social and ecological processes that limit system resilience. This thesis attempts to gain some of that generalization by exploring how whole-system outcomes emerge from cross-scale interactions between fish and anglers. To do this, I used a combination of theory-driven models and empirical case studies on the rainbow trout Oncorhynchus mykiss and lake trout Salvelinus namaycush fisheries of British Columbia and Yukon to find the landscape, social, and ecological consequences of spatial exploitation on these complex systems. In general, I found that system outcomes, like sustainable or overexploited fisheries, strongly depended on both landscape contexts and the strength of social-ecological feedbacks. In doing so, I was able to generalize the kinds of freshwater landscapes at risk of overexploitation. Next, I found that spatial exploitation patterns had cascading effects across freshwater landscapes by influencing ecological processes like the demographic tradeoff between fish body size and abundance or variation in fish life histories. Additionally, I found that angler site choices were influenced by multiple characteristics, like trip contexts, travel costs and fishing quality, allowing me to better identify potential angler impacts on fish populations. I then integrated these spatial, social, and ecological processes to evaluate the kinds of policies that may improve management of the lake trout fishery and found that conservative regulations better balanced both social and ecological objectives. The results of these studies can help inform management on the feedbacks and processes that drive fishery dynamics, how a landscape of fish populations may respond to spatial exploitation, the kinds of landscapes (and populations within those landscapes) at risk of overexploitation, and the efficacy of regulations that target key spatial, social, and ecological processes to sustain freshwater fisheries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".