Habitat type and dispersal ability influence spatial structuring of larval Odonata and Trichoptera assemblages
Bibliographic record
Abstract
Summary Freshwater invertebrate assemblages are believed to be structured by both local and larger scale processes (i.e. dispersal). In rivers, the extent to which dispersal processes influence local assemblage composition may depend on both the taxon and habitat in question. Poor dispersers should display greater spatial structuring than strong dispersers. Likewise, assemblages in poorly connected habitats should experience greater dispersal limitation, and therefore greater spatial structuring. We sought to test these hypotheses using two contrasting orders of aquatic insect, Odonata and Trichoptera. Odonata are believed to have greater dispersal capacity than Trichoptera. In river ecosystems, these orders inhabit both main channel habitats and more poorly connected riverine wetlands. Multi‐habitat surveys of larval Trichoptera and Odonata assemblages were conducted at 34 sites in three 5th‐order New Brunswick rivers. The degree of spatial and environmental structuring in assemblages was assessed using redundancy analysis‐based variance partitioning. We also assessed the performance of different model‐based spatial predictors (asymmetric eigenvector maps, AEM s and principal coordinates of neighbourhood matrices, PCNM s). For main channel areas, variance explained purely by environmental variables was greater for Odonata, while the purely spatial component of variance was greater for Trichoptera, regardless of the class of spatial descriptor. In riverine wetlands, both the purely environmental and purely spatial components of variance explained were similar or were greater for Trichoptera than for Odonata. The component of variance explained by spatial variables was greater in riverine wetlands than main channel areas for both Odonata and Trichoptera for most spatial descriptors, suggesting that taxa inhabiting riverine wetlands may experience greater dispersal limitation. However, the magnitude of this difference was relatively small in most cases. Eigenvector‐based spatial descriptors ( PCNM s, AEM s, net PCNM s) explained more variance than traditional spatial descriptors. For Trichoptera, network‐based predictors ( AEM s, net PCNM s) explained more variance than PCNM s in main channel areas. Our results suggest that dispersal ability and habitat type can influence the degree of spatial structuring in aquatic insect assemblages. However, these patterns must be investigated across a wider range of insect groups and at larger spatial scales. Our results also suggest that biomonitoring programs should consider assemblage spatial structure in building reference condition models and that aquatic conservation planners must consider the type and spatial arrangement of habitats in reserve design. Eigenvector‐based spatial descriptors hold promise for interpreting biodiversity patterns in freshwater invertebrates, but more work is required to relate patterns to actual dispersal behaviour.
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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 | 0.000 |
| 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 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".