Habitat network topology influences the importance of ecological traps in metapopulations
Bibliographic record
Abstract
Abstract The majority of species exist in metapopulations, where populations are linked to one another through dispersal. Disturbances (natural or anthropogenic) are known to affect population vital rates which can reverberate to the metapopulations through dispersal, which is determined by the topology of the habitat network (i.e., the number of patches and their spatial layout). Within a habitat network, disturbed patches become sinks or ecological traps depending on whether individuals avoid or are attracted to the disturbed patch. Ecological traps occur when individuals preferentially disperse to patches that result in low fitness which can have severe consequences for metapopulations. However, the effects of ecological traps should be considered relative to a number of factors, such as the number of disturbed patches, the fitness cost of the disturbance, and dispersal. Further, the relative importance of each factor can vary among different habitat network topologies according to the position and role of the disturbed patches for maintaining the functional connectivity of the entire metapopulation. Using a spatially explicit stochastic stage‐based metapopulation model based on amphibian life history, we investigate how the number of disturbed patches, fitness cost of the disturbance, dispersal rate, and attractiveness of disturbed habitat patches affect the persistence of metapopulations in different habitat network topologies (random, tree, and full). Overall, dispersal, the number of disturbed patches, and the fitness penalty of the disturbance were the most important factors affecting mean metapopulation growth rate, the probability of extinction, and time to extinction. Ecological traps had the largest effect in the tree network topology where the number of links between patches was limited. For all network topologies studied, larger negative effects were observed when disturbed patches were centrally located in the network. Reducing the number and spatial correlation of disturbances is known to be a reliable conservation strategy, we add the importance of considering the location of disturbances within habitat networks and network topology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".