Using a Nested Sampling Design Across Spatial Scales to Gain Insights Into Distribution Patterns of Fishes, Mussels and Macroinvertebrates in a Riverine System
Bibliographic record
Abstract
ABSTRACT Aim The goal of our study was to use a nested sampling design to compare the distribution of different groups of organisms with different dispersal modes and examine their responses in community composition to environmental heterogeneity at different spatial scales (local pool vs. riffle habitat, sections within a river and between rivers). Location Upper Colorado River basin, Texas, USA. Taxon Fishes, mussels and macroinvertebrates. Methods We sampled 100 sites using a nested sampling design across five rivers (four tributaries and the mainstem), with four river sections and five sampling sites per section, each consisting of a pool and riffle habitat. Collected data on species abundances, local and landscape environmental variables and spatial variables were analysed using a combination of dissimilarity, redundancy and variation partitioning analysis. Results At the river scale, environmental heterogeneity explained 28% and 34% of the variation in mussel and fish communities, respectively, and 15% for macroinvertebrates. Community changes between sections in the tributaries were highest for fishes, but similarly high for fishes and macroinvertebrates in the mainstem. Significant patterns for mussels were only detected in rivers with higher abundances. No significant differences in dissimilarity were found between mesohabitats, but a small significant effect of mesohabitat was detected for mussels with RDA after removing the river effect. Main Conclusions Although the depauperate mussel communities made it more difficult to draw any conclusions, there were some indications that their distribution was driven by environmental heterogeneity across scales. Section and river scales were relevant for fish, whereas macroinvertebrates showed a weaker response to environmental heterogeneity at these scales. The combination of approaches detected more ecologically meaningful patterns than one analysis alone would have. This study highlights the complexity of riverine community dynamics and underscores the need for a multiscale approach to identify their distribution patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".