Spatial Complexity, Resilience, and Policy Diversity: Fishing on Lake-rich Landscapes
Bibliographic record
Abstract
The dynamics of and policies governing spatially coupled social-ecological mosaics are considered for the case of fisheries in a lake district. A microeconomic model of households addresses agent decisions at three hierarchic levels: (1) selection of the lake district from among a larger set of alternative places to live or visit, (2) selection of a base location within the lake district, and (3) selection of a portfolio of ecosystem services to use. Ecosystem services are represented by dynamics of fish production subject to multiple stable domains and trophic cascades. Policy calculations show that optimal policies will be highly heterogeneous in space and fluid in time. The diversity of possible outcomes is illustrated by simulations for a hypothetical lake district based loosely on the Northern Highlands of the State of Wisconsin. Lake districts are frequently managed as if lakes were independent, similar, endogenously regulating systems. Our findings contradict that view. One-size-fits-all (OSFA) policies erode ecological and social resilience. If regulations are too stringent, social resilience declines because of the potential rewards of overharvesting. If regulations are too lax, ecological resilience is diminished by overharvesting in some lakes. In either case, local collapses of fish populations evoke spatial shifts of angling effort that can lead to serial collapses in neighboring fisheries and degraded fisheries in most or all of the lakes. Under OSFA management, the natural resources of the entire landscape become more vulnerable to transformation because of changes in, e.g., human population, the demand for resources, or fish harvesting technology. Multiplicity of management regimes can increase the ecological resilience, social resilience, and inclusive value of a spatially heterogeneous social-ecological system. Because of the complex interactions of mobile people and multistable ecosystems, management regimes must also be flexible over time. A rights-based scheme may facilitate policy regimes with appropriate spatial patterns and intertemporal fluidity. In lake fisheries, habitat protection adds an important dimension to policy design. Habitat is a slowly changing variable that creates ecological resilience and thereby provides managers with a broader range of options.
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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 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".