Functional diversity and redundancy of freshwater fish communities across biogeographic and environmental gradients
Bibliographic record
Abstract
Abstract Aim Functional redundancy occurs when species share overlapping ecological functions and is considered an important component of ecosystem resilience. However, much of what we know about functional redundancy comes from relatively species‐rich terrestrial and marine environments. Here, we examined patterns of functional redundancy among Ontario freshwater fish communities with species richness levels ranging from 4 to 30 species across lakes of differing size, depth, productivities and thermal characteristics. Location Six thousand nine hundred and seventy‐seven lakes in Ontario, Canada. Methods We examined functional redundancy by quantifying the relationship between functional diversity and species richness in lakes across Ontario and within smaller biogeographic regions. We used null models to test whether fish communities had greater redundancy than expected from random assemblages. We then used generalized additive models ( GAM s) to predict how patterns of redundancy vary across environmental variables. At last, we compared species‐level functional rarity metrics across fish thermal preference groups, body sizes and species occurrence rates. Results The functional diversity and species richness relationship were saturating among fish communities at the provincial scale but varied between smaller regions with differing biogeographic histories. Most communities fell within expectations from weighted null models of the functional diversity and species richness relationship. The GAM s indicated that fish communities in the largest, deepest and warmest lakes showed the greatest overall functional redundancy. No differences were observed in functional rarity measures between thermal preference groups, across body sizes or across species occurrence rates. Main conclusions Although lakes in this study were relatively depauperate of fish species, Ontario fish communities exhibited functional redundancy at the provincial scale, with variation regionally. North‐eastern communities showed the least saturating relationship overall as predicted by historical biogeographic patterns of freshwater fish colonization. Overall, this study provides a broad perspective of freshwater fish diversity patterns and highlights the importance of investigating redundancy from different perspectives and multiple spatial scales.
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 | 0.007 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.007 |
| 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".