Multi‐level functional responses for wildlife conservation: the case of threatened caribou in managed boreal forests
Bibliographic record
Abstract
Summary 1. The selection for particular habitat patches can vary as a function of local and regional levels of anthropogenic disturbance. Although such functional responses can better reveal habitat loss for species of precarious status faced with dwindling resources, they remain rarely used in conservation planning. We show that functional responses can occur at multiple levels, even as nested hierarchies, and that they can explain the plasticity in habitat selection observed in threatened forest‐dwelling caribou Rangifer tarandus caribou , within and among home‐ranges. 2. Twenty‐seven caribou were followed with global positioning system collars between 2005 and 2010. Generalized linear mixed models served as the basis from which we built multi‐level functional responses characterizing how caribou alter their selection for closed‐canopy conifer forests, depending upon the availability of these forests and the amount of cutovers and roads. 3. Caribou increased their selection for closed‐canopy conifer forests in areas of their home‐range that were comprised of a high proportion of recent cutovers during calving and summer and of high closed‐canopy conifer forests during winter. Also, caribou that were established in highly disturbed areas displayed an overall stronger selection for conifer forests. These individuals further adjusted their selection for conifer forests in areas of their home‐ranges that were largely comprised of recent cutovers. This concurrent response to local and global anthropogenic disturbances provides evidence of nested‐hierarchical functional responses. 4. Synthesis and applications . Reliable characterization of disturbance effects on animals is necessary for conservation planning. Multi‐level functional responses can accurately describe animal distribution, and we provided a framework for modelling these responses. Our multi‐level functional responses indicate that fixing habitat requirements based on patterns of habitat selection for the average amount of disturbance can be misleading because it overlooks plasticity in the response of animals to habitat heterogeneity. For example, selection of closed‐canopy conifer forests by caribou generally became stronger with increasing disturbance levels. Anthropogenic disturbance thus could not only lead to the functional loss of residual habitat, but it can also increase the ‘relative value’ of residual patches. Our study provides a tool for more thorough assessments of spatial variation in the attractiveness of resource patches and, presumably, in the fitness benefits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".