Synoptic scale mammal density index map based on roadkill records

Tatewaki, Takafumi; Koike, Fumito

doi:10.1016/j.ecolind.2017.10.056

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…A better understanding of the spatial distribution of WVC [4,20] requires consideration of many factors. They include, but are not limited to, understanding of wildlife movements and behaviors, localization of wildlife corridors [51,[56][57][58], knowledge of population density [59], population dynamics and habitat properties. In line with other authors [15,52,53,60] we also confirm that placing mitigation measures is challenging, because of the lack of knowledge on the local spatiotemporal patterns of wildlife dynamics, including population, behavior, pathways and habitat suitability [13,20].…”

Section: Evaluation Of Wildlife Fencesmentioning

confidence: 99%

Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns

Kučas

Balčiauskas

2021

Land

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The number of road traffic accidents decreased in Lithuania from 2002 to 2017, while the ungulate–vehicle collision (UVC) number increased and accounted for approximately 69% of all wildlife–vehicle collisions (WVC) in the country. Understanding the relationship between UVCs, traffic intensity, and implemented mitigation measures is important for the assessment of UVC mitigation measure efficiency. We assessed the effect of annual average daily traffic (AADT) and wildlife fencing on UVCs using regression analysis of changes in annual UVCs and UVC hotspots on different categories of roads. At the highest rates, annual UVC numbers and UVC hotspots increased on lower category (national and regional) roads, forming a denser network. Lower rates of UVC increase occurred on higher category (main) roads, forming sparser road networks and characterized by the highest AADT. Before 2011, both UVC occurrence and fenced road sections were most common on higher-category roads. However, as of 2011, the majority of UVCs occurred on lower-category roads where AADT and fencing had no impact on UVCs. We conclude that wildlife fencing on roads characterized by higher speed and traffic intensity may decrease UVC numbers and at the same time shifting UVC occurrence towards roads characterized by lower speed and traffic intensity. Wildlife fencing re-allocates wildlife movement pathways toward roads with insufficient or no mitigation measures.

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Section: Evaluation Of Wildlife Fencesmentioning

confidence: 99%

Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns

Kučas

Balčiauskas

2021

Land

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“…Two covariates were considered to analyse their effects on the spatial distribution of roadkills: surface of forests and surface of crop fields, given as a percentage based on a buffer area of 0.5 km around the roads. Several authors have considered these two covariates as possible predictors of wildlife-vehicle collisions, see for instance, Ha and Shilling (2018), Hegland and Hamre (2018), Tatewaki and Kioke (2018) who have considered forests as an explanatory covariate to explain the distribution of roadkills, whilst Hubbard et al (2000), Acevedo et al (2014), Colino-Rabanal andPeris (2016) analysed the effect of the surface of crop fields as a predictor for these type of traffic collisions.…”

Section: Case Study: Wildlife-vehicle Collisions On a Road Networkmentioning

confidence: 99%

Stochastic smoothing of point processes for wildlife-vehicle collisions on road networks

Borrajo

Comas

Costafreda-Aumedes

et al. 2021

Stoch Environ Res Risk Assess

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Wildlife-vehicle collisions on road networks represent a natural problem between human populations and the environment, that affects wildlife management and raise a risk to the life and safety of car drivers. We propose a statistically principled method for kernel smoothing of point pattern data on a linear network when the first-order intensity depends on covariates. In particular, we present a consistent kernel estimator for the first-order intensity function that uses a convenient relationship between the intensity and the density of events location over the network, which also exploits the theoretical relationship between the original point process on the network and its transformed process through the covariate. We derive the asymptotic bias and variance of the estimator, and adapt some data-driven bandwidth selectors to estimate the optimal bandwidth. The performance of the estimator is analysed through a simulation study under inhomogeneous scenarios. We present a real data analysis on wildlife-vehicle collisions in a region of North-East of Spain.

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“…A better understanding of the spatial distribution of WVC [4] requires the consideration of many factors. They include, but are not limited to, understanding of wildlife movements and behaviours, localisation of wildlife corridors [38,[47][48][49], knowledge of population density [50], population dynamics and habitat properties. In line with other authors [15,51,52] we also con rm that placing of mitigation measures is challenging, due to the lack of knowledge on the local spatial-temporal patterns of wildlife dynamics, including population, behaviour, pathways and habitat suitability [13].…”

Section: Evaluation Of Wildlife Fencesmentioning

confidence: 99%

Impact of Road Fencing on Ungulate-Vehicle Collisions and Hotspot Patterns

Kučas

Balčiauskas

2020

Preprint

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Background:The number of road traffic accidents was decreasing in Lithuania in 2002–2017, while ungulate-vehicle collision (UVC) number was increasing and accounted for about 69% of all wildlife-vehicle collisions (WVC) in the country. Identification of impact of locomotor wildlife behavior and locations where accidents occurs most frequently is important for the effective application of mitigation measures.Methods:We assessed the effect of annual average daily traffic (AADT) and wildlife fencing on UVC using regression analysis of changes in annual UVC and UVC hotspots (KDE+ method based clusters) for different categories of roads.Results:At the greatest rates annual UVC numbers and UVC hotspots increased on lower category (national and regional) roads forming denser network. Lower rates of UVC increase occurred on higher category (main) roads, forming sparser road network and characterised by highest AADT. Before 2011, both UVC occurrence and fenced road sections were most common on higher category roads. However, as of 2011, the majority of UVC occurred on lower category roads where AADT and fencing had no impact on UVC.Conclusions:We conclude that wildlife fencing on roads characterised by higher speed and traffic intensity may decrease UVC numbers, in the same time shifting UVC occurrence towards roads characterised by lower speed and traffic intensity. Wildlife fencing re-allocates wildlife movement pathways towards roads with insufficient or no mitigation measures.

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Synoptic scale mammal density index map based on roadkill records

Cited by 13 publications

References 33 publications

Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns

Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns

Stochastic smoothing of point processes for wildlife-vehicle collisions on road networks

Impact of Road Fencing on Ungulate-Vehicle Collisions and Hotspot Patterns

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