From individual movement behaviour to landscape-scale invasion dynamics and management: a case study of lionfish metapopulations
Bibliographic record
Abstract
Modelling the dynamics of small, interconnected populations, or metapopulations, can help pinpoint habitat patches that are critical for population persistence in patchy habitats. For conservation purposes, these patches are typically earmarked for protection, but for invasive species management, these patches could be targeted to hasten the populations' demise. Here, we show how metapopulation modelling, coupled with an understanding of size-dependent dispersal behaviour, can be used to help optimize the distribution of limited resources for culling specific populations of invasive Indo-Pacific lionfish (Pterois volitans) in the western Atlantic. Through simulation using fitted model parameters, we derive three insights that can inform management. First, culling lionfish from target patches reduces the probability of lionfish occupancy at surrounding patches. Second, this effect depends on patch size and connectivity, but is strongest at the local scale and decays with distance. Finally, size-dependent dispersal in lionfish means that size-selective culling can change both a population's size distribution and dispersal potential, with cascading effects on network connectivity, population dynamics and management outcomes. By explicitly considering seascape structure and movement behaviour when allocating effort to the management of invasive species, managers can optimize resource use to improve management outcomes. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".