The role of spatial structure in at‐risk metapopulation recoveries
Bibliographic record
Abstract
Metapopulations are often managed as a single contiguous population despite the spatial structure underlying their local and regional dynamics. Disturbances from human activities can also be spatially structured with mortality impacts concentrated to just a few local populations among the aggregate. Scale transitions between local and regional processes can generate emergent properties whereby the whole system can fail to recover as quickly as expected for an equivalent single population. Here, we draw on theory and empirical case studies to ask: what is the consequence of spatially structured ecological and disturbance processes on metapopulation recoveries? We suggest that exploring this question could help address knowledge gaps for managing metapopulations including: Why do some metapopulations recover quickly while others remain collapsed? And, what risks are unaccounted for when metapopulations are managed at aggregate scales? First, we used model simulations to examine how scale transitions among ecological and disturbance conditions interact to generate emergent metapopulation recovery outcomes. In general, we found that the spatial structure of disturbance was a strong determinant of recovery outcomes. Specifically, disturbances that unevenly impacted local populations consistently generated the slowest recoveries and highest conservation risks. Ecological conditions that dampened metapopulation recoveries included low dispersal, variable local demography, sparsely connected habitat networks, and spatially and temporally correlated stochastic processes. Second, we illustrate the unexpected challenges of managing metapopulations by examining the recoveries of three USA federally listed endangered species: Florida Everglade snail kites, California and Alaska sea otters, and Snake River Chinook salmon. Overall, our results show the pivotal role of spatial structure in metapopulation recoveries whereby the interplay between local and regional processes shapes the resilience of the whole system. With this understanding, we provide guidelines for resource managers tasked with conserving and managing metapopulations and identify opportunities for research to support the application of metapopulation theory to real-world challenges.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".