In the wrong place at the wrong time: Moose and deer movement patterns influence wildlife-vehicle collision risk

Laliberté, Jérôme; St‐Laurent, Martin‐Hugues

doi:10.1016/j.aap.2019.105365

Cited by 32 publications

(27 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We then intersected survey segments with the GPS locations for each moose group detected during a given survey. Segment‐level spatial covariates included a bivariate smooth of geographic coordinates (Universal Transverse Mercator [UTM]) to account for spatially autocorrelated moose detections along with environmental covariates known to influence moose density such as distance to agriculture cover (m; LaForge et al 2016), distance to developed cover (m; Welch et al 2015, Laliberté and St‐Laurent 2020), distance to forest cover (m; van Beest et al 2012, Street et al 2015), distance to shrub cover (m; Welch et al 2015), distance to timber cuts (m; Peterson et al 2020, Blouin et al 2021), distance to wetland cover (m; Ditmer et al 2018 a , Teitelbaum et al 2021), distance to water (m; McLaren et al 2017), elevation (m; Gillingham and Parker 2008, McLaren et al 2017), and days with snow cover (Dussault et al 2005). We identified land cover types such as agriculture, developed, forest, shrub, wetland, and water using United States Geological Survey National Land Cover Data (United States Geological Survey 2016).…”

Section: Methodsmentioning

confidence: 99%

Challenges and opportunities for estimating abundance of a low‐density moose population

et al. 2022

View full text Add to dashboard Cite

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low‐density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance‐sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km2) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280‐km2 Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.

show abstract

Section: Methodsmentioning

confidence: 99%

Challenges and opportunities for estimating abundance of a low‐density moose population

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This also is characteristic to Lithuania, where the growth of moose populations has resulted in an increase in moose-related wildlife-vehicle collisions [90,91]. While mitigation strategies should be species-tailored [92], they are all costly. Moose management measures, such as moose hunting near roads may be another solution [89].…”

Section: Moose-related Problems: Why Is Management Necessary?mentioning

confidence: 98%

Moose Management Strategies under Changing Legal and Institutional Frameworks

2020

View full text Add to dashboard Cite

Moose (Alces alces) management strategies in Lithuania, East Europe, were analyzed. The study was intended to show the (un)sustainability of the current management approach in relation to changes in hunting rules, hunting organization and development of the responsible administrative bodies. Moose population and bag dynamics were analyzed using I index in connected scatterplots and compound annual growth rates (CAGR). In 1962–2020, the CAGR of the moose population was 3.84%, resulting in a population size increase of nearly 10 times. The seesaw principle in moose management was confirmed, showing three periods of population decrease (1973–1977, 1989–1995, 2000–2005), and two periods of hunting bag decrease (1976–1978 and 1990–1993). All decline phases were related to legal and administrative issues in the country. Since 2006, population growth has not been controlled. Lithuania has no long-term strategy of the moose population management at any administrative level. The current management approach is not sustainable, as it has not ensured long-term stability of the moose population. The current continuous growth of population, followed by only a moderate increase in the hunting bag, is related to the possibility for owners to adopt long-term planning of the hunting plot units.

show abstract

“…The main factors involved are the behavior of the species, the conditions of the habitat, the characteristics of the infrastructure, the traffic and the attitude of drivers. The most complete models that address the study of WVCs are those that integrate, in one way or another, most of these aspects, e.g., [6][7][8][9][10]. Significant changes in any of these variables, such as traffic reduction due to COVID-19 lockdown, allow us to investigate the particular impact in detail.…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 Lockdown on Wildlife-Vehicle Collisions in NW of Spain

García-Martínez-de-Albéniz

Ruiz-de-Villa

Rodríguez-Hernández

2022

Sustainability

View full text Add to dashboard Cite

Wildlife–vehicle collisions (WVCs) in many places have a significant impact on wildlife management and road safety. The COVID-19 lockdown enabled the study of the specific impact that traffic has on these events. WVC variation in the Asturias and Cantabria regions (NW of Spain) because of the COVID-19 lockdown reached a maximum reduction of −64.77% during strict confinement but it was minimal or nonexistent during “soft” confinement. The global average value was −30.22% compared with the WVCs registered in the same period in 2019, but only −4.69% considering the average throughout the period 2010–2019. There are huge differences between conventional roads, where the traffic reduction was greater, and highways, where the traffic reduction was lesser during the COVID-19 lockdown. The results depend on the season, the day of the week and the time of day, but mainly on the traffic reduction occurring. The results obtained highlight the need to include the traffic factor in WVC reduction strategies.

show abstract

In the wrong place at the wrong time: Moose and deer movement patterns influence wildlife-vehicle collision risk

Cited by 32 publications

References 75 publications

Challenges and opportunities for estimating abundance of a low‐density moose population

Challenges and opportunities for estimating abundance of a low‐density moose population

Moose Management Strategies under Changing Legal and Institutional Frameworks

Impact of COVID-19 Lockdown on Wildlife-Vehicle Collisions in NW of Spain

Contact Info

Product

Resources

About