Trait‐based and multi‐scale approach provides insight on responses of freshwater mussels to environmental heterogeneity
Bibliographic record
Abstract
Abstract Our understanding of the factors driving the distribution of metacommunities at different scales can be obscured by high variation in species composition between sites and a lack of fine‐scale distribution data. Trait‐based approaches have long been used to better identify and examine ecological patterns. Most recent studies of riverine metacommunities examining trait‐based patterns have focused on shorter lived organisms. Here we focused on a group of longer lived, sedentary riverine organisms, unionid freshwater mussels. The objective of this study was to examine how (1) the distribution of mussels with different life history strategies (trait‐based approach) and (2) the relative importance of environmental and spatial factors (as a proxy for dispersal) would differ with spatial scale and position in the river; and to (3) further compare this with patterns derived from a taxonomic approach. Fine‐scale distribution data of mussels and environmental factors were collected every 100 m in spatially extensive surveys in an upstream and downstream segment (200 sites/20‐km segment) of a semiarid river, making them some of the most spatially intensive surveys documented to date. A combination of redundancy analysis, asymmetric eigenvector mapping, and variation partitioning analyses revealed that more variation was explained by environmental factors where more environmental differences occur between sites. Where environmental heterogeneity was lower, the amount of variation explained by smaller scale spatial factors was higher, likely mostly associated with stochastic rather than dispersal processes. A higher amount of unexplained variation at the taxonomic level suggests that stochasticity may also play an important role in determining species composition. In contrast, different life history groups had a highly predictable distribution pattern driven by environmental heterogeneity, especially between river segments and mesohabitat, which was associated with different flow conditions. The role we predict for environmental heterogeneity and stochasticity in shaping the distribution of mussels in our study river likely also applies to other taxa and ecosystems at a spatial scale at which neither dispersal limitation nor mass effects occur. Thus, understanding the magnitude and extent of dispersal relative to the amount of environmental heterogeneity may be key for predicting metacommunity structure and dynamics for different organisms.
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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) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".