Road impacts on the demography and movement of animal populations; optimising study designs and understanding the long-term consequences
Bibliographic record
Abstract
Given the extent of the global road network, roads are considered one of the most pressing contemporary conservation issues. However, explicit understanding about how roads affect population dynamics, and so how to mitigate these effects, is limited. This thesis aimed to identify the optimal study designs for assessing population-level impacts of roads as well as to understand both animal survival and movements near roads. The west European hedgehog (Erinaceus europaeus), a priority species for conservation in the UK, is used as a case study. \n \nBetween May 2020 and November 2021, vertebrate road mortality was recorded using repeat, standardised road surveys in Nottinghamshire, UK, and analysed using novel equations, Generalised Linear Models (GLM), and Generalised Additive Models (GAM). Along the road survey route, four sites were repeatedly studied using spotlight surveys to quantify hedgehog population dynamics. Simultaneously, GPS-tracking of 127 hedgehogs across the Nottinghamshire sites and seven additional UK-wide sites took place. Road avoidance behaviour was analysed using GLMs and the ‘true’ risk of road mortality was analysed using a Resource Selection Function. \n \nAverage carcass persistence rates ranged between 0.69 and 6.00 days (average 3.10 days ±4.61 SD) and were driven by body mass and road type. Road mortality patterns tracked life history events and were predicted by a combination of road design, urbanity, and traffic volume. With greater densities, survival probabilities and reproductive rates, more urbanised local hedgehog populations appeared to compensate for road mortality. Eighty-four percent (n = 107) of hedgehogs exhibited significant road avoidance behaviour and road crossings showed variation by sex, road type, time of night, and season. Traffic volume and vehicle speed, especially on the outskirts of residential areas, were key components of the ‘true’ risk of road mortality, whilst intermediate roads produced the greatest annual road mortality probability. \n \nThis thesis provides researchers with robust methodology for the accurate interpretation of road mortality rates and their population-level impacts. In addition, this is the first study to establish local population differences in road mortality impacts in hedgehogs and that road mitigation should consider the potential presence of meta-population dynamics. The GPS data suggest that hedgehogs may be perceiving the risks and rewards of traversing road networks and adjusting their behaviour accordingly. Combining insight from the population and movement ecology, as adopted in this thesis, can ease the constraints surrounding decision-making for conservation planning and road mitigation.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".