Beyond the traditional functional frameworks: novel perspectives on functional structure in fish communities
Bibliographic record
Abstract
Functional ecology offers a powerful lens to describe and understand ecological communities and the processes that structure them. Central to this framework is the concept of functional traits, defined as measurable characteristics of individuals or species that approximate their ecological niches. By examining patterns in species traits, functional ecology provides insights into the processes that shape ecological communities and how these processes influence biodiversity and ecosystem functioning. However, common practices in functional ecology often overlook key dimensions of functional structure. In this thesis, I identify several of these blind spots and propose ways forward, using a dataset of more than 700 lake-fish communities from Ontario, Canada, as a case study. First, I revisit the widely used metric of functional dispersion, a measure of trait dissimilarity among co-occurring species, and explore how trait selection influences its patterns. I developed two trait-pooling strategies: one based on prior knowledge of trait function, and another using a novel algorithm that separate traits that maximizes and minimizes variation in functional dispersion. Both approaches strengthen the development of a priori hypotheses about the processes shaping the structure of ecological communities and improve the predictive performance of environmental models for functional dispersion. Second, I introduce the concept of community functional integration, defined as the pattern and strengths of trait correlations within communities, to examine how these relationships vary across communities and influence community structure. Through two empirical analyses, I demonstrate that functional integration captures important, overlooked variation in functional structure and provides novel ecological insights. Finally, I assess temporal and spatial variations across three dimensions of functional structure - functional composition, dispersion, and integration – alongside taxonomic composition. Each dimension of community functional structure had their unique temporal shifts. We also conducted a spatial analysis to understand at which scale these shifts were structured: temporal shifts in functional composition and community functional integration could be explained by broad and fine scale spatial patterns, underscoring the importance of both broadscale and local processes in the temporal changes in the communities’ functional structure. Together, these findings call for an expanded view of functional ecology that integrates trait relationships and temporal-spatial dynamics to more fully understand community structure and its drivers. This work broadens the functional ecological framework by highlighting underexplored dimensions of trait structure. In doing so, it contributes new tools and perspectives for uncovering the mechanisms that shape biodiversity across space and time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".