Species diversity, hotspot congruence, and conservation of North American damselflies (Odonata: Zygoptera)
Bibliographic record
Abstract
The rapid extinction of species is of considerable concern for biodiversity conservation. Identifying the drivers of species diversity and hotspots is beneficial for developing conservation strategies. Studies on insects have mainly focused on terrestrial species and rarely on semiaquatic species. Using 135,208 georeferenced occurrence records of 296 damselflies across North America, their species richness and endemism (represented by weighted endemism) patterns were mapped in a 100 × 100-km grid size, and the effects of environmental variables on species richness and endemism were investigated using generalized linear models and hierarchical partitioning. Subsequently, the top 5% grids with species richness and weighted endemism were separately selected as hotspots and their congruence was evaluated. Finally, species diversity hotspots were identified by integrating two types of hotspot grids, and gap analysis was performed to evaluate their conservation status. Temperature conditions and water availability had the strongest influence on species richness and endemism, respectively. Low congruence among species richness and endemism hotspots was observed. Moreover, four species diversity hotspots were identified, namely, region of the eastern United States and southeastern Canada, southwestern United States, central Mexico, and southernmost North America. Approximately 69.31% of the hotspot grids are not a part of the existing protected areas, presenting a significant conservation gap. The habitats of taxonomic groups should be considered while identifying the most common driving mechanisms of endemism. Strengthening the establishment of protected areas in regions with conservation gaps is urgently needed to promote the conservation of damselflies in North America.
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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".