Environmental factors influencing local distributions of European green crab (<i>Carcinus maenas</i>) for modeling and management applications
Bibliographic record
Abstract
Environmental factors determine the habitat selection, use and distribution of species at various spatial scales. Understanding the factors driving these distributions can help predict areas of higher species occurrence, and be used in species conservation, and management strategies. In this study we reviewed 71 publications to evaluate the most relevant factors shaping local, fine-scale distribution of a globally invasive species, the European green crab (Carcinus maenas). We compared these studies to determine how factors differ (i) between adult and juvenile life stages, (ii) with the influence of internal and temporal variables, and (iii) among clades. Factors of depth, biotic interactions, vegetation, presence of shelter and salinity were found to be important, although the supporting evidence varied between juvenile and adult stages. Internal variables of size, carapace color and sex, and temporal variables such as seasonal, tidal and diel cycles played a role in determining how crabs responded to environmental factors. The importance of environmental factors also varied by clade. All of these factors and variables may be expected to play a role in the local, fine-scale distribution of C. maenas. These variations affect the efficacy of using a single model to anticipate local green crab distribution (e.g., spatial distribution model). Application of different models for adult and juvenile subsets of the population, clades, and accounting for temporally shifting distribution may help accommodate some of this variation. The relative presence of factors in a region and the availability of local, fine-scale environmental data may further influence the efficacy of modeling. The combined effects of such considerations will make predictive local modeling at fine scales challenging, if not impossible, with existing knowledge, data and technology. Nonetheless, our results provide insight into the environmental characteristics most relevant to shaping local distributions of C. maenas, which may inform management strategies such as efficient trap-setting within an ecosystem.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".