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Record W7062083085

SPATIAL ECOLOGY OF ROCKY MOUNTAIN ELK (<i>CERVUS CANADENSIS NELSONI</i>) COWS IN SOUTHEASTERN KENTUCKY

2022· article· en· W7062083085 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2022
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationHabitatVegetation (pathology)ExclosureEctotherm
DOInot available

Abstract

fetched live from OpenAlex

The elk (Cervus canadensis) was extirpated from its range in eastern North America by the end of the 1800s, prompting several U.S. states and Canadian provinces to begin translocation programs with the goal of reestablishing elk populations. While eastern elk managers have relied on information from western herds to guide population and habitat management, there is a need for region-specific research on the spatial ecology and habitat associations of translocated elk given the stark differences in landscape, climate, predator communities, and harvest regimes across the continent. While the Kentucky elk reintroduction is one of the best documented programs to date, including a wealth of scientific study, a deeper understanding of how elk relate to their environment will be required for the future considering changes in landscape and population dynamics.\nWe studied the spatial ecology of female elk across the Kentucky Elk Restoration Zone (KERZ), focusing our inquiry on several aspects of reproduction and behavior via GPS telemetry data. First, we analyzed movement, space, and habitat use patterns during the calving season to predict parturition and early neonatal survival. We trained a random forest model to classify focal time windows as parturition or non-parturition, using birth events confirmed by expulsion of vaginal implant transmitters (VITs). We then applied the model to pregnant females for which parturition and one week calf survival status was unknown. Using a probability-based decision rule, our model was highly accurate (89.5%) at correctly classifying elk with known status. When applied to unknown individuals, we found that less than half (48.6%) were predicted to give birth and rear their calf to a week old, compared to 85.1% of known-status females, suggesting that fetal mortality and early calf loss may contribute more strongly to reproductive success in this population than previously thought.\nSecond, we investigated calving habitat selection by comparing landscape characteristics from confirmed parturition sites to random, “available” locations within elk home ranges. Specifically, we sampled landscape covariates (reflecting vegetation, topography, and human footprint) and both used and available locations and modeled the relative intensity of use with resource selection functions (RSFs) implemented with generalized linear mixed-effects models (GLMMs). We sampled covariates at several spatial grains to capture scale-dependent selection patterns. We found that females selected birth sites with intermediate levels of canopy cover on gentle topography at fine grains, with lower vegetation greenness, higher topographic positions, higher edge densities, and intermediate levels of patch interspersion at coarser grains. This suggests that female elk make multi-scale decisions during parturition to minimize predation risk to their neonate near the birth site while maximizing forage availability in the surrounding area.\nFinally, we characterized the behavioral variation present in Kentucky elk by fitting GLMM RSFs including random slopes to female elk data collected during four biological seasons. We used the random slopes, representing group-level selection coefficients, to assess the effects of landscape composition and configuration on variation in habitat selection (i.e., testing for functional responses) with regression splines. We also used k-means clustering to identify general behavioral tactics within seasons, accounting for multidimensional correlation between behaviors (i.e., behavioral syndromes). We found the highest variability in elk responses to open areas, primary/secondary roads, and successional forests, despite strong population-level selection/avoidance of these covariates. While elk exhibited clear functional responses to availability and configuration of these and other covariates, showing the partial context dependence of habitat selection in this population, clustering analysis assigned groups to major behavioral tactics which better predicted intensity of use over global and functional models.\nOur overall results demonstrate both population- and individual-level patterns of space and landscape use in Kentucky elk. We provide a framework for identifying successful reproduction remotely, without the use of VITs, which can assist in informing population models that may be limited by sample size and/or ignoring the potential influence of fetal mortality on calf recruitment. We also illustrate population wide patterns of parturition site selection that can be useful in delineating suitable calving habitat and targeting management actions such as the introduction of disturbance. Lastly, the extensive variability in female habitat selection across the annual cycle can help explain shifts in elk behavior and landscapes in the KERZ change, highlighting how flexible individuals in the population are to changing and novel landscapes.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.177
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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