High possum mortality on urban roads: implications for the population viability of the common brushtail and the common ringtail possum

Russell, Tracey; Herbert, Catherine A.; Kohen, James

doi:10.1071/zo09079

Cited by 18 publications

(5 citation statements)

References 37 publications

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“…In amphibians, females are more likely to be road-killed because, different from males, females move to breeding locations during rainy nights (Sillero, 2008). In mammals, males usually have larger home ranges than females, which makes them more vulnerable to be road-killed (Russell et al, 2010). In snakes, extensive movements on their home range and the search for mates may increase the risk of being killed in males (Sosa & Schalk, 2016).…”

Section: Discussionmentioning

confidence: 99%

Road mortality in the blue‐black grassquit (Volatinia jacarina) is seasonally driven and sex‐biased

et al. 2023

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Roads are responsible for different negative effects on wildlife, including the isolation of populations and the direct reduction of biodiversity. Millions of animals are killed in traffic every year. Despite that, the influence of animal behaviour on the probability of animal‐vehicle collisions has been overlooked by the literature. The blue‐black grassquit (Volatinia jacarina) is a small, migratory, dichromatic species. During the breeding period, males vigorously defend small, clustered territories. Here, we investigated how the number of roadkills varied spatially and temporally in relation to the breeding activity, density, and sex of grassquits. We demonstrated that the variation in the movement dynamics and reproductive behaviour of the grassquits are associated with a great number of casualties. Road mortality is higher during the breeding period but is not associated with juvenile dispersion. As expected, males have a higher probability of being road‐killed than females, suggesting that territory defence and mate searching may increase the risk of mortality. On the other hand, the clustering of individuals may not increase the risk of road fatalities, suggesting that other environmental attributes may influence the probability of being killed on roads.

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Section: Discussionmentioning

confidence: 99%

Road mortality in the blue‐black grassquit (Volatinia jacarina) is seasonally driven and sex‐biased

et al. 2023

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“…Otras marsupiales atropelladas en gran numero son los posum en Australia, como el de cola de cepillo Trichosurus vulpecula o el de cola anillada Pseudocheirus peregrinus (Russell et al 2009) o las zarigüeyas como Didelphis virginiana en América (Kanda et al 2006).…”

Section: I4 Mortalidad Por Atropellounclassified

Contribuciones al análisis de mortalidad de vertebrados en carreteras

Rabanal¹,

Javier²

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Road-kill is one of the most prominent causes of wildlife mortality. Much research effort has focussed on collisions with ungulates because of traffic safety. However, studies about large carnivore road-kills are scarce despite vehicles being a main cause of mortality. The absence of studies can be explained in part because of difficulties in obtaining sufficient sample sizes. We collected data from locations of 82 wolf road-kill sites in the Castilla y León region, northwest Spain. We evaluated different models to characterise collision localities using logistic regressions with corrections for rare events. The best models included traffic and human disturbance parameters. Landscape variables did not improve predictive power. Fencing was a decisive key predictor; road-kill was proportionally higher along fenced highways than on similar major roads that lacked fences. Wolf-vehicle collisions were more common in agricultural areas, although wolf densities were lower in these zones. Both the higher density of important roads and a greater proportion of roaming wolves on the plateau may explain this pattern.

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“…Past road-kill research has faced an obvious trade-off. Studies that survey multiple stretches of roads had robust spatial coverage but limited temporal implications (Mallick et al 1998;Russell et al 2009), whereas those that lacked an explicit spatial contrast usually compensated with a more frequent and consistent approach to surveying (Ramp et al 2005;Roger et al 2011). Our approach sought to reach an acceptable middle ground: repeatedly surveying enough short segments of roads to yield a broad spatial coverage across all seasons, along with marking and geolocating the wildlife corpses to permit explicit estimation of biases and improve the overall systematic rigor of the surveys.…”

Section: Requirements For Further Targeted Researchmentioning

confidence: 99%

Predicting spatial and seasonal patterns of wildlife–vehicle collisions in high-risk areas

et al. 2022

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Context Vehicle collisions with wildlife can injure or kill animals, threaten human safety, and threaten the viability of rare species. This has led to a focus in road-ecology research on identifying the key predictors of ‘road-kill’ risk, with the goal of guiding management to mitigate its impact. However, because of the complex and context-dependent nature of the causes of risk exposure, modelling road-kill data in ways that yield consistent recommendations has proven challenging. Aim Here we used a multi-model machine-learning approach to identify the spatio-temporal predictors, such as traffic volume, road shape, surrounding vegetation and distance to human settlements, associated with road-kill risk. Methods We collected data on the location, identity and wildlife body size of each road mortality across four seasons along eight roads in southern Tasmania, a ‘road-kill hotspot’ of management concern. We focused on three large-bodied and frequently affected crepuscular Australian marsupial herbivore species, the rufous-bellied pademelon (Thylogale billardierii), Bennett’s wallaby (Macropus rufogriseus) and the bare-nosed wombat (Vombatus ursinus). We fit the point-location data using ‘lasso-regularisation’ of a logistic generalised linear model (LL-GLM) and out-of-bag optimisation of a decision-tree-based ‘random forests’ (RF) algorithm for optimised predictions. Results The RF model, with high-level feature interactions, yielded superior out-of-sample prediction results to the linear additive model, with a RF classification accuracy of 84.8% for the 871 road-kill observations and a true skill statistic of 0.708, compared with 61.2% and 0.205 for the LL-GLM. The lasso rejected road visibility and human density, ranking roadside vegetation type and presence of barrier fencing as the most influential predictors of road-kill locality. Conclusions Forested areas with no roadside barrier fence along curved sections of road posed the highest risk to animals. Seasonally, the frequency of wildlife–vehicle collisions increased notably for females during oestrus, when they were more dispersive and so had a higher encounter rate with roads. Implications These findings illustrate the value of using a combination of attributive and predictive modelling using machine learning to rank and interpret a complexity of possible predictors of road-kill risk, as well as offering a guide to practical management interventions that can mitigate road-related hazards.

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High possum mortality on urban roads: implications for the population viability of the common brushtail and the common ringtail possum

Cited by 18 publications

References 37 publications

Road mortality in the blue‐black grassquit (Volatinia jacarina) is seasonally driven and sex‐biased

Road mortality in the blue‐black grassquit (Volatinia jacarina) is seasonally driven and sex‐biased

Contribuciones al análisis de mortalidad de vertebrados en carreteras

Predicting spatial and seasonal patterns of wildlife–vehicle collisions in high-risk areas

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