Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity
Bibliographic record
Abstract
Abstract Aim Unpartitioned dissimilarity indices such as the Sørensen index (βsor) tend to categorize areas according to species number. The use of turnover indices, such as the Simpson index (βsimp), may lead to the loss of important information represented by the nestedness component (βnest). Recent studies have suggested the importance of integrating nestedness and turnover information. We evaluated this proposition by comparing biogeographical patterns obtained by unpartitioned (βsor) and partitioned indices (βsimp and βnest) on presence data of western Mediterranean butterflies. Location Western Mediterranean. Methods We assessed the regionalization of 81 mainland and island faunas according to partitioned and unpartitioned dissimilarity by using cluster analyses with the unweighted pair‐group method using arithmetic averages (UPGMA) combined with non‐metric multidimensional scaling (NMDS). We also carried out dissimilarity interpolation for βsor, βsimp, βnest and the βnest/βsor ratio, to identify geographical patterns of variation in faunal dissimilarity. Results When the unpartitioned βsor index was used, the clustering of sites allowed a clear distinction between insular and mainland species assemblages. Most islands were grouped together, irrespective of their mainland source, because of the dominant effect of their shared low richness. βsimp was the most effective index for clustering islands with their respective mainland source. βsimp clustered mainland sites into broader regions than clusters obtained using βsor. A comparison of regionalization and interpolation provided complementary information and revealed that, in different regions, the patterns highlighted by βsor could largely be determined either by nestedness or turnover. Main conclusions Partitioned and unpartitioned indices convey complementary information, and are able to reveal the influence of historical and ecological processes in structuring species assemblages. When the effect of nestedness is strong, the exclusive use of turnover indices can generate geographically coherent groupings, but can also result in the loss of important information. Indeed, various factors, such as colonization–extinction events, climatic parameters and the peninsular effect, may determine dissimilarity patterns expressed by the nestedness component.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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 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".