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Record W7052409810

Spatial ecology and variables influencing habitat use and fitness proxies of Eastern hog-nosed snakes (Heterodon Platirhinos) within an anthropogenic landscape

2023· dissertation· en· W7052409810 on OpenAlexfundaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersOntario Ministry of Natural Resources and ForestryMinistry of Natural Resources
KeywordsHabitatHome rangeThreatened speciesRange (aeronautics)WildlifePopulationWildlife conservationWildlife management
DOInot available

Abstract

fetched live from OpenAlex

Changes in environmental conditions and threats from human activities present unique challenges to persistence and management of wildlife populations at range peripheries. For successful recovery and conservation of reptile species at risk (SAR), basic knowledge of their life histories and ecologies must be understood. Such basic information was lacking for a recently reported population of eastern hog-nosed snakes (Heterodon platirhinos) in southwestern Ontario. To successfully meet government-mandated recovery actions for the population of Threatened snakes, I investigated whether hog-nosed snakes’ space use, habitat preferences, and proxies of fitness were impacted by widespread anthropogenic activity in Huron County, Ontario. I estimated movement and home range size (mean ± SD) from locations of hognosed snakes (n = 10) outfitted with radio transmitters from the 2018 to 2020 active seasons, and then calculated using kernel density estimates (KDE) and minimum convex polygons (MCP). I evaluated habitat selection at three scales (i.e., exact, local, and landscape) using resource selection function models and ranked models using Akaike’s Information Criterion corrected for small sample sizes. Proxies of fitness I assessed included quantification of thermal habitat from hourly ground temperatures and snake body condition estimated from residuals of body mass on snout-vent length regressions. I found that when compared to conspecific populations throughout their range, Huron County snakes moved greater distances (49 ± 13 m per day, 127 ± 41 m per move) within larger-sized home ranges (64 ± 30 ha), as well as were heavier and in better body condition compared to conspecific populations near Point Pelee, Ontario. At the coarsest scale, I found that occurrence of snakes in Huron County was positively associated with forest, beach, and old field habitats, while at finer scales, habitat characteristics with structural complexity, cover, and thermal suitability influenced snake occurrence. While proxies of snake fitness did not appear imminently impacted by human activity in Huron County, widespread disturbance did influence how hog-nosed snakes used the landscape: snakes moved greater distances (than those in conspecific populations) to use pockets of natural habitats that were thermally suitable and structurally complex. To ensure effective management of the population stewardship efforts should focus on protection and enhancement of snake habitat and have such actions follow recommendations from this study. My study exemplifies the complex nature of SAR management in Ontario and how understanding space use, habitat selection, and fitness proxies are important to successfully conserving and recovering a snake species at risk.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.525

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes2
Has abstractyes

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