Dark diversity in Amazonian stream fish communities: What factors determine species absence along environmental gradients?
Bibliographic record
Abstract
Abstract Species distribution models often fail to predict observed patterns of species diversity, and this is because some species within a regional pool that are tolerant of conditions at a given location may nevertheless be absent from the local community. These missing species have been termed “dark diversity”. In the present study, we investigated which factors explain dark diversity among fish assemblages in Amazonian streams. We sampled 71 streams in areas with different types of land use within two river basins and estimated dark diversity from patterns of species co‐occurrence, using Beals’ index, along environmental gradients. From this procedure, taxa are designated as dark diversity components when they are absent from a given stream, but often co‐occur with the local species at other streams, indicating similar ecological requirements. We used generalised linear models both to determine whether environmental or landscape variables, connectivity, instream environmental heterogeneity or some combination of these factors explained dark diversity of fishes, and to evaluate whether ecomorphology is associated with the extent to which a species contributes to dark diversity and which specific traits contribute the most to explaining variation in dark diversity. Mean local diversity exceeded observed dark diversity. The magnitude of dark diversity was directly associated with the proportion of secondary forest in the immediate catchment and with the index of proximity to anthropogenic impact. Species that have high affinity for environments with higher current velocity, low swimming ability and that capture food mainly on the surface contributed more to dark diversity, which suggests that swimming ability, habitat preference and aspects related to diet are key predictors of the probability that a given species will be present at locations with suitable habitat. Our findings reinforce the idea that dark diversity results from interactions between species traits and environmental factors, including anthropogenic impacts. Understanding the interplay among environmental factors and species traits that contribute to dark diversity provides targets for improved ecosystem restoration and sustainability of native species assemblages.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".