Spatial patterns and factors influencing small vertebrate fauna road-kill aggregations

Clevenger, Anthony P.; Chruszcz, Bryan; Gunson, Kari E.

doi:10.1016/s0006-3207(02)00127-1

Cited by 408 publications

(396 citation statements)

References 35 publications

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“…These crossings should not lead to ecological "dead-ends", but must link to a larger functional landscape and habitat complex that allows wildlife to disperse, move freely, and meet their daily and life needs now and in the future, including projected land-use changes (Beckmann et al, 2010). Maps, road-kill data and GIS tools are useful to identify where to locate wildlife crossings and other types of mitigation (Beckmann et al, 2010;Clevenger et al, 2003). Road-kill data alone is not enough to estimate wildlife movement areas and should be combined with habitat linkage mapping to improve this evaluation in order to choose the places where mitigation measures should be built (Beckmann et al, 2010;Clevenger et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Maps, road-kill data and GIS tools are useful to identify where to locate wildlife crossings and other types of mitigation (Beckmann et al, 2010;Clevenger et al, 2003). Road-kill data alone is not enough to estimate wildlife movement areas and should be combined with habitat linkage mapping to improve this evaluation in order to choose the places where mitigation measures should be built (Beckmann et al, 2010;Clevenger et al, 2003). Usually, considerations of wildlife crossing placement begin by determining the focal species or group (Beckmann et al, 2010;Gunson et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Habitat or matrix: which is more relevant to predict road-kill of vertebrates?

2015

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We believe that in tropics we need a community approach to evaluate road impacts on wildlife, and thus, suggest mitigation measures for groups of species instead a focal-species approach. Understanding which landscape characteristics indicate road-kill events may also provide models that can be applied in other regions. We intend to evaluate if habitat or matrix is more relevant to predict road-kill events for a group of species. Our hypothesis is: more permeable matrix is the most relevant factor to explain road-kill events. To test this hypothesis, we chose vertebrates as the studied assemblage and a highway crossing in an Atlantic Forest region in southeastern Brazil as the study site. Logistic regression models were designed using presence/absence of road-kill events as dependent variables and landscape characteristics as independent variables, which were selected by Akaike's Information Criterion. We considered a set of candidate models containing four types of simple regression models: Habitat effect model; Matrix types effect models; Highway effect model; and, Reference models (intercept and buffer distance). Almost three hundred road-kills and 70 species were recorded. River proximity and herbaceous vegetation cover, both matrix effect models, were associated to most road-killed vertebrate groups. Matrix was more relevant than habitat to predict road-kill of vertebrates. The association between river proximity and road-kill indicates that rivers may be a preferential route for most species. We discuss multi-species mitigation measures and implications to movement ecology and conservation strategies.Keywords: connectivity, conservation, landscape ecology, rivers, road ecology.Habitat ou matriz: qual é mais relevante para prever atropelamentos de vertebrados? ResumoNós acreditamos que nos trópicos, precisamos de uma abordagem de comunidade para avaliar os impactos das estradas sobre a vida silvestre, e então, sugerir medidas de mitigação para grupos de espécies ao invés da abordagem de espécie-foco. Compreender quais características da paisagem indicam eventos de atropelamento podem também fornecer modelos que podem ser aplicados em outras regiões. Nós pretendemos avaliar se habitat ou matriz é mais relevante para prever eventos de atropelamento para grupos de espécies. Nossa hipótese é: matriz mais permeável é o fator mais relevante para explicar os eventos de atropelamentos. Para testar essa hipótese, escolhemos vertebrados como a assembléia estudada e uma rodovia cruzando uma região de Mata Atlântica no sudeste do Brasil como área de estudo. Modelos de regressão logística foram criados usando presença/ausência de eventos de atropelamentos como variáveis dependentes e características da paisagem como variáveis independentes, os quais foram selecionados pelo Critério de Informação de Akaike. Nós consideramos um conjunto de modelos candidatos contendo quatro tipos de modelos de regressão simples: modelo de efeito de habitat; modelos de efeito de tipos de matriz; modelo de efeito da rodovia; e, mode...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Habitat or matrix: which is more relevant to predict road-kill of vertebrates?

2015

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“…The direct mortality of larger animals due to vehicle collisions is relatively apparent and wellquantified (Clarke et al 1998;Cain et al 2003;Clevenger et al 2003;Saeki and MacDonald 2004). Roadkill may affect the conservation status of speciesabout 65% of the mortality of the endangered Florida Panther (Puma concolor coryi) is caused by vehicular collisions (Harris and Gallagher 1989).…”

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confidence: 99%

“…Within this context, the great majority of research into road-kill has dealt with larger mammals. This focus is due to several socio-economic factors, including the danger to people associated with collisions with large mammals, their economic importance in hunting, and their high public profile (Cain et al 2003;Clevenger et al 2003). A few studies have also examined factors influencing road-kills of smaller mammals (Garland and Bradley 1984;Clevenger et al 2003;Saeki and Macdonald 2004).…”

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Road-kill of Mammals in Nova Scotia

Fudge

Freedman

Crowell³

et al. 2007

Can Field Nat

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We examined road mortality of wild mammals in Nova Scotia using data from (a) five years of province-wide data on wildlife collisions involving larger mammals, and (b) 20 months of observations of smaller mammals along a 160-km route near Halifax. An average of 2079 White-tailed Deer was reported killed annually on highways during 1999 through 2003, along with 14 Moose and 33 Black Bear. Female White-tailed Deer were more likely to be road-killed than males (by a factor of 1.8), yearlings more so than older animals, and there were peaks of mortality in the late spring and late autumn. The road-kill of smaller mammals was highest in the summer and least in the winter, and the most frequent mortalities were of Raccoon (28% of observations), Porcupine (27%), Skunk (17%), Snowshoe Hare (6%), Coyote (4%), and Groundhog (3%). The standardized kill-rate (number/100 km) was highest on a 100-series highway compared to trunk highways or urban streets, but the rate standardized to vehicle density was highest on trunk highways.

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“…Of these effects, the most studied in recent years is the roadkills of wild animals (Clevenger et al 2003, Taylor & Goldingay 2004, Pinowski 2005, Coelho et al 2008, Gomes et al 2009). Accidents occur because the highway cuts through the habitat of the species, interfering with their natural range of movement and resulting in collisions with vehicles (Coffin 2007).…”

Section: Introductionmentioning

confidence: 99%

Priority ranking of road sites for mitigating wildlife roadkill

Bager

Rosa

2010

Biota Neotrop.

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. Priority ranking of road sites for mitigating wildlife roadkill. Biota Neotrop. 10(4): http://www.biotaneotropica.org.br/v10n4/en/abstract?article+bn03010042010. Abstract:We propose a ranking index to assign priorities to sites for implementation of measures to mitigate wildlife roadkill. We conducted a case study along 34 km of highway BR 392 in Southern Brazil. We compared priority sites established only according to roadkill rates, with those defined by our index. The index used four parameters: the richness of target species, diversity of roadkilled species, roadkill rate of target species, and presence of endangered species. Although it is impossible to protect the entire community of vertebrates affected by roadkill, we defined nine target species, five mammals and four reptiles. For each parameter, we defined coefficients ranging between 0 and 3. There was a significant change in the priorities of sites for implementing mitigation devices, which caused changes in the species that were protected. The definition of priority sites by considering only the roadkill rate protected reptiles especially, to the detriment of all mammal species, including endangered species. Sites with high roadkill rates continued to be among the most important for conservation, but the index diluted the effect of this parameter, changing the ranking. This ranking index can be an effective tool to aid government institutions in decision-making, especially when more elaborate analyses are not feasible for reasons of time, resources, or lack of trained personnel.

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Spatial patterns and factors influencing small vertebrate fauna road-kill aggregations

Cited by 408 publications

References 35 publications

Habitat or matrix: which is more relevant to predict road-kill of vertebrates?

Habitat or matrix: which is more relevant to predict road-kill of vertebrates?

Road-kill of Mammals in Nova Scotia

Priority ranking of road sites for mitigating wildlife roadkill

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