Impact assessment research: use and misuse of habituation, sensitisation and tolerance in describing wildlife responses to anthropogenic stimuli
Bibliographic record
Abstract
Studies on the effects of anthropogenic activity on wildlife aim to provide a sound scientific basis for management. However, misinterpretation of the theoretical basis for these studies can jeopardise this objective and lead to management outcomes that are detrimental to the wildlife they are intended to protect. Misapplication of the terms 'habituation', 'sensitisation' and 'tolerance' in impact studies, for example, can lead to fundamental misinterpretations of research findings. Habituation is often used incorrectly to refer to any form of moderation in wildlife response to human disturbance, rather than to describe a progressive reduction in response to stimuli that are perceived as neither aversive nor beneficial. This misinterpretation, when coupled with the widely held assumption that habituation has a positive or neutral outcome for animals, can lead to inappropriate decisions about the threats human interactions pose to wildlife. We review the conceptual framework for the use of habituation, sensitisation and tolerance, and provide a set of principles for their appropriate application in studies of behavioural responses to anthropogenic stimuli. We describe how cases of presumed habituation or sensitisation may actually represent differences in the tolerance levels of wildlife to anthropogenic activity. This distinction is vital because impact studies must address (1) the various mechanisms by which differing tolerance levels can occur; and (2) the range of explanations for habituationand sensitisation-type responses. We show that only one mechanism leads to true behavioural habituation (or sensitisation), while a range of mechanisms can lead to changes in tolerance.
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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 | 0.077 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".