Odonata responses to dispersal and niche processes differ across Amazonian endemism regions
Bibliographic record
Abstract
Abstract Niche and dispersal‐based processes govern freshwater communities, such as aquatic insects, shaping their distribution and establishment in the environment. So, we aimed to address the relative influence of niche and dispersal‐based processes on Odonata diversity in Amazonian freshwater systems, and the influence of species dispersal functional traits on their longitudinal and latitudinal ranges. We used the Dispersal‐Niche Continuum Index (DNCI) to test (i) regions more distant would present a prevalence of dispersal‐based processes, and (ii) different patterns would come from the two Odonata suborders; and by applying generalised linear models, we tested (iii) dispersal‐related functional traits from the suborders would influence latitudinal and longitudinal midpoints of the species. We found that more distant regions had lower values of pairwise Dispersal–Niche Continuum, mainly for Zygoptera, corroborating our first hypothesis. Moreover, Zygoptera also presented the lowest absolute values of Dispersal–Niche Continuum and Anisoptera presented a joint influence of niche and dispersal‐based processes, agreeing with our second hypothesis. Only Zygoptera presented a significant association between dispersal functional traits and longitudinal midpoints, corroborating our third hypothesis. Our results indicated a prevalence of dispersal‐related processes for Zygoptera, which can be explained by massive geographical barriers, such as the rivers, and their narrow physiological and ecological tolerance. Moreover, dispersal‐related traits demonstrated significant influence on longitudinal midpoints of Zygoptera, a scenario that the presence of the rivers may explain. A better understanding of the prevalent predictors of the species and how their dispersal ability governs their distribution has conservational implications.
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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 | 0.000 |
| 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) | 0.001 | 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".