The environmental impact of anthropocentrically induced predictability
Bibliographic record
Abstract
Variability is inherently an important characteristic of natural ecosystems. Like any other parameter that may define an organisms' environment, selection has favoured traits and strategies that exploit patterns in the variability of fluctuating physical and biological resources. Here I consider the ways in which animals may be affected by anthropogenic change that reduces variability in natural ecosystems. -- Through many anthropogenic activities, we have inadvertently introduced resource patches into the environment that are fixed in space and that provide animals with regular access to food through time. These predictable food patches have become ubiquitous, and represent a fundamental change for animals that have adapted to a relationship between variability and scale. I provide a theoretical framework through which we can begin to understand the consequences of breaking such a relationship for animals that use information to make foraging decisions. I conclude that predictable resource patches should be favoured by foraging animals because the energetic costs of obtaining information are reduced at these sites. -- I use the ideal free distribution (IFD) theory to test the hypothesis that animals will prefer to forage where resource distributions have become predictable. Given the choice between patches of equal value but that differed in the temporal predictability of their food, juvenile cod gradually developed a preference for the predictable patch over a 5-day experimental period. This preference occurred simultaneously with a reduction in patch sampling behaviour, suggesting that cod were able to reduce the costs of obtaining information at the predictable patch. -- Having observed that the distribution of cod shifted towards the predictable patch in an experimental setting, I examine the effects of introducing a predictable resource patch into a natural environment. Aquaculture sea cages are fixed in space and inadvertently provide stable access to resources to wild animals through time. I consider the effect of sea cages on the distribution of wild fish in coastal marine environments, in which patterns of fluctuating resources are distinguished by a large magnitude of variability. I demonstrate that sea cages can alter the distribution of marine life at large spatial scales, suggesting that there is an energetic advantage to foraging at these sites. Understanding the costs and benefits of this behaviour is needed to predict the outcome of anthropogenic changes that alter patterns of variability in natural ecosystems.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".