Taxonomic and phylogenetic beta diversity of cricetid rodents in Oaxaca, southern Mexico
Bibliographic record
Abstract
Abstract Understanding the ecological and historical causes and processes that shape biodiversity distribution patterns remains a challenging and fundamental task in biogeography, ecology, and evolution. To address this issue, taxonomic and phylogenetic β diversity can help us to assess the importance of ecological and historical factors that structure these biotic patterns. To make inferences about the processes underlying current spatial patterns in communities of Cricetidae across the state of Oaxaca, Mexico, their taxonomic and phylogenetic β diversity were assessed jointly. Our aims were: 1) to examine taxonomic and phylogenetic β diversity and their turnover and nestedness components among physiographic subprovinces; 2) to test for statistical significance of observed phylogenetic β diversity against the expected values of a null model; and 3) to evaluate if these metrics were correlated with geographical distance. We obtained the species composition for 12 subprovinces based on distribution models for 49 cricetid species present in Oaxaca, then carried out a maximum likelihood analysis to estimate their phylogenetic relationships. Our results show that the taxonomic and phylogenetic dissimilarities mainly were explained by the turnover component of species and lineages. In almost all pairwise comparisons, the null model approach revealed random patterns for phylogenetic β diversity values and its components. Mantel correlation models showed that the values of total taxonomic and phylogenetic diversity and their components are correlated with the geographical distances between subprovinces. Our results suggest that both taxonomic and phylogenetic β diversity are explained by the interplay between biogeographical history from southern Mexico, and the recent speciation processes in cricetid rodents. Given that speciation processes are allopatric for most cricetid taxa, the high values of spatial turnover can be explained by the small ranges of species, coupled with current abiotic conditions that act as filters, promoting specialization of species on particular conditions. Our results show the importance of the phylogenetic approach to unravel the multidimensional spatial patterns of biodiversity.
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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 | 0.001 | 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) | 0.001 | 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".