The Importance of Thermal Preferences in Governing Fish Productivity, Foraging Behaviour, and Interactions in a Changing World
Bibliographic record
Abstract
Fisheries are an important source of food, income, and recreation across the world, yet they are vulnerable to exploitation and environmental change. As ectotherms, a fish’s environment directly affects how it acquires and metabolizes energy. Energy acquisition and metabolism differ among fish species based on their unique diets, behaviours, and habitat preferences, resulting in differential (or asynchronous) responses to environmental change. Therefore, natural resource managers must learn these unique qualities and responses to create adaptive policies under changing conditions and protect fisheries into the future. However, research about the simultaneous effects of multiple interacting environmental factors on multiple interacting species is still at its infancy. My research seeks to understand the effects of environmental factors on the productivity of multiple fish species (referred to as fish productivity for the remainder of this thesis), as well as their behaviours, and interactions. First, I show that species-specific fish productivity (g·ha-1·yr-1) in inland lakes is differentially influenced by various abiotic (e.g. temperature, dissolved organic carbon, habitat area) and biotic (e.g. presence of predators and invasive species) factors. Second, I use stable isotopes to show that behavioural foraging (i.e., trophic position and habitat use) is mediated by local and broad-scale habitat conditions but does not necessarily reflect fish productivity. Third, I used a temperature-dependent food web model to study how thermal guild interactions are altered by changing environmental conditions, which may lead to unexpected outcomes of a species-removal management strategy. Together, my research suggests that fish species respond asynchronously to different environmental conditions. This means that some species may behave differently and be more or less productive than others when exposed to new conditions, which will manifest in unique species interactions and food web dynamics. Such information will be crucial for assessing the effectiveness of adaptive management policies in a changing climate.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".