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Record W7047980317

Impacts of road mortality on the Western Rattlesnake (Crotalus oreganus) in British Columbia

2018· article· en· W7047980317 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2018
Typearticle
Languageen
FieldEngineering
TopicPulsed Power Technology Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationThreatened speciesRange (aeronautics)Mortality ratePopulation growthPopulation sizePopulation decline
DOInot available

Abstract

fetched live from OpenAlex

Direct mortality due to wildlife-vehicle collisions has emerged as a major and worldwide conservation threat to wildlife. This source of mortality may be particularly adverse for populations persisting at the periphery of their range, where existing natural constraints already limit population growth and vigour. As a result, conservation assessment and planning for many peripheral species-at-risk will benefit from a fundamental understanding of the impacts of road mortality, yet these can be difficult to isolate due to the interaction of a number of factors. Using population viability analysis (PVA) I evaluated the persistence of a Western Rattlesnake (Crotalus oreganus) population threatened by road mortality in the dry interior of British Columbia, Canada. From 2015-2016 I quantified road mortality through methodical road surveys and coincidental assessments of scavenging rates and observer detection probability using planted snake carcasses. Additionally, I conducted intensive mark-recapture and radio-telemetry to estimate population density, size, and the range of the study population. After accounting for sources of error, my modelling showed that the estimated number of rattlesnake deaths was 2.7× the number of carcasses detected through unadjusted surveys and incidental observations. Overall, an estimated 6.6% of the population was killed on the road annually under traffic conditions that amounted to a maximum of only 350 vehicles per day. The PVA indicated that the population still was likely to persist for the next 100 years, but with a continual decline under the current, observed road mortality rate. With the loss of 6.6% of the population/year, the projected probability of extinction was <0.01 in 100 years and 0.0 in 50 years. At simulated road mortality rates of ≤6% there was zero probability of extinction for this population of rattlesnakes within the next 100 years. However, at the extinction threshold of road mortality of 6%, the stochastic growth rate was -0.032, and the mean population size was estimated to decrease by 96% in 100 years. Simulations with road mortality rates >6% consistently put the population at risk of extinction over 100 years. In comparison, the growth rate in the absence of road mortality was 0.0047 and the population was projected to increase (60% increase over 100 years). My results also suggest that in theory, improving adult female survival as well as overall longevity of rattlesnakes would significantly increase the population growth rate. My method of estimating population size for the area impacted by road deaths likely presents an overestimate, suggesting that the actual risk to this population is greater than what the models have implied. The detailed PVA using refined road mortality estimates provides strong evidence that road mortality is and will be a significant contributor to population decline, and adds to the growing body of evidence that large populations of long-lived species will face extirpation under low levels of road mortality, even in the absence of other sources of disturbance. Conservation priorities should focus on reducing road mortality and improving habitat availability away from roads.

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)0.001
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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.194
Teacher spread0.188 · 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
Published2018
Admission routes1
Has abstractyes

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