Integrating Taxonomic and Trait-Based Approaches to Evaluate Beta Diversity of Freshwater Invertebrates as Bioindicators of Environmental Change in Western Canada
Bibliographic record
Abstract
Lakes and streams are among the ecosystems most impacted by recent global change. Aquatic invertebrate communities have long been used as indicators (i.e., bioindicators) of anthropogenic and natural environmental changes. Morphologically defined species have traditionally been used to assess aquatic invertebrates as bioindicators. Later (i.e., 1970s), a species trait-based approach has been recommended in which features of species that define their ecological roles are used to translate taxonomic changes into potential impacts on ecosystem function. Major knowledge gaps exist about how complementary versus redundant these two approaches are across different types of aquatic communities, ecosystems, and environmental changes. My thesis research combines biomonitoring approaches, determining taxonomic and functional turnover (i.e., beta diversity) of mountain zooplankton and stream macroinvertebrate communities to gain insights into ecological factors and potential consequences for ecosystem function. Multivariate data analyses ranging from indirect to direct gradient analyses quantify and illustrate temporal and spatial beta diversities related to environmental change. My analyses of zooplankton communities in naturally fishless alpine lakes stocked with sportfish show that a shift in trait selection from initial tolerance of predation (e.g., body size) to subsequent potential for recolonization (e.g., asexual reproduction) explains their contrasting responses to fish introductions and later removal over several decades. At a broader landscape scale, my analyses indicate that climatic and sportfish variables mainly explain the spatial beta-diversities of zooplankton communities across 85 mountain lakes. However, these drivers are not closely related to the temporal beta-diversities observed within the lakes over the past 60 years. In contrast, spatial beta-diversity of stream macroinvertebrate communities and their traits across tributaries spanning the North Saskatchewan River watershed within Alberta best indicated shifts in human land uses against the backdrop of a natural biogeographical gradient. In conclusion, the high degree of redundancy observed between the taxonomic and trait-based beta diversities of each of these communities allowed for confident interpretation of the results, providing ecological insights into how they were indicative of environmental changes along both spatial and temporal scales. Future research should use such comparative approaches to focus, where possible, on testing the validity of assumptions and models commonly used in bioindicator investigations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".