Climate change and vessel traffic create networks of invasion in marine protected areas
Bibliographic record
Abstract
Abstract Establishment of protected areas to maintain biodiversity requires identification, prioritization and management of stressors that may undermine conservation goals. Nonindigenous species and climate change are critical ecosystem stressors that need greater attention in the context of spatial planning and management of protected areas. Risk of invasion into protected areas needs to be quantified under current and projected climate conditions in conjunction with prioritization of key vectors and vulnerable areas to enable development of effective management strategies. We assessed the likelihood of invasion across networks of marine protected areas (MPAs) to determine how invaded MPAs may compromise MPA networks by sharing nonindigenous species. We evaluated invasion risk in 83 MPAs along Canada's Pacific coast for eight nonindigenous species based on environmental suitability under current and future (average conditions from 2041 to 2070) climate conditions and association with shipping and boating pathways. We applied species distribution models and network analysis of vessel tracking data for 805 vessels in 2016 that connected MPAs. The probability of occurrence within MPAs and the proportion of MPA area that is suitable to the modelled species significantly increased under future climate conditions, with six species reaching over 90% predicted occurrence across MPAs and over 70% of suitable area within MPAs. Vessel traffic created four network clusters of 61 highly connected MPAs that spanned the coastline. Occupancy of over 90% of the MPAs within the clusters was predicted for most species. Synthesis and applications . Our results indicate a high likelihood of marine protected area (MPA) network invasion based on current and future environmental conditions and vectors of spread, and the potential for extensive nonindigenous species distributions within MPAs. Our approach highlights how interacting stressors can exacerbate MPA susceptibility to nonindigenous species, adding further challenges for protected area management. Management planning that invests in understanding connectivity and vector processes (human behaviours) is more likely to derive effective policies to stem the flow of nonindigenous species under both current and future conditions. In particular, biosecurity measures including vessel biofouling regulations and MPA‐ and MPA network‐specific plans for prevention, monitoring and mitigation of nonindigenous species are needed.
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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) | 0.002 | 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 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".