Infraestruturas lineares, como as estradas, estão por todos os lugares no mundo e os impactos causados por elas são inúmeros e intensos. Focando no impacto de mortalidade de fauna por colisão com veículos, esta tese teve o objetivo de propor diferentes abordagens para identificar locais para a implementação de medidas de mitigação desse impacto. Além da introdução geral, a tese tem três capítulos que correspondem a três artigos científicos. O primeiro capítulo explorou dados de répteis atropelados em 33 meses de monitoramento mensais em 277 km da BR-101 e avaliou tanto o padrão espacial quanto o padrão temporal de fatalidades além de estimar a magnitude de atropelamentos de répteis na estrada. O segundo e o terceiro capítulo exploram abordagens preditivas de atropelamento de fauna para dois diferentes contextos: uma única estrada e uma rede de estradas. O segundo capítulo teve o objetivo de testar se usando características da paisagem, da rodovia e dos animais, nós podemos predizer onde estão os locais com maior chance de um animal ser atropelado. Para isso, também para a BR-101, calculei a probabilidade de travessia através de mapas de conectividade e a probabilidade de colisão através de uma equação que considera o tráfego de veículos, o tamanho dos animais e dos veículos e a velocidade dos animais para duas espécies de mamíferos nativos do Brasil: o furão (Galictis cuja) e o zorrilho (Conepatus chinga). Para o terceiro capítulo, foi utilizado a rede de estradas do estado de Victoria na Austrália, na qual calculei a probabilidade de travessia e de colisão para o canguru cinza oriental (Macropus giganteus), espécie nativa da Austrália. No primeiro capítulo, demonstrei que: 15.377 cágados, lagartos e serpentes são atropelados a cada ano na BR-101 no sul do Brasil; hot moments de atropelamentos de répteis ocorreram no verão, especialmente em dezembro para lagartos e serpentes; hotspots de atropelamentos foram coincidentes para tartarugas, lagartos e serpentes; existiu um efeito positivo do tráfego e da rizicultura nos atropelamentos e negativo da silvicultura; medidas de mitigação nos hotspots prioritários poderiam evitar 45% das fatalidades de répteis. No segundo capítulo, concluí que a probabilidade de fatalidade através da multiplicação das probabilidades de travessia e colisão não teve um bom poder de predição dos atropelamentos e que a probabilidade de colisão sozinha foi melhor em predizer os atropelamentos do que a probabilidade de travessia, entretanto as espécies apresentaram padrões diferentes. No terceiro capítulo, concluí que um modelo aditivo das duas probabilidades foi melhor em predizer os atropelamentos de cangurus do que os modelos individuais de probabilidades de travessia e colisão, entretanto o modelo integrado não apresentou a predição esperada. A probabilidade de travessia foi um preditor melhor dos atropelamentos de cangurus que a probabilidade de colisão para a rede de estradas. Portanto, concluo que: 1) os atropelamentos de fauna podem ser bastante acentuados em determinados contextos e que é possível identificar locais de maior agregação que seriam efetivos para mitigação; 2) é possível usar dados de tráfego de veículos e tamanho e velocidade dos animais para predizer locais de mais atropelamentos, entretanto deve se ter cuidado pois isso é específico para cada espécie; 3) para o contexto de rede de estradas, é possível predizer o atropelamento utilizando a probabilidade de travessia e a probabilidade de colisão em um mesmo modelo. Ainda é necessário explorar outras maneiras de calcular e integrar as probabilidades aqui propostas, mas nesta tese eu demonstrei uma forma possível de predizer atropelamentos para um contexto em que não há dados dessa natureza disponíveis, seja para estradas novas ou para uma rede de estradas. / Linear infrastructures, such as roads, are worldwide and impacts caused by them are innumerable and intense. We focused on impact of road-kills due to wildlife-vehicle collisions and aimed to propose different approaches to identify locations to implement mitigation measures for this impact. Besides the general introduction, this thesis has three chapters which correspond to three scientific papers. The first chapter examined reptile road-kill data from monthly road survey during 33 months in a 277 km of BR-101 road. We evaluated spatial and temporal patterns of road-kills and estimated the magnitude of reptile road-kills on that road. The second and third chapters examined predictive approaches of wildlife road-kills for two different contexts: a single road and a road network. The second chapter aimed to test if it is possible to use of landscape, road, animals features to predict locations where there are more road-kills. For the same road (BR-101), I calculated crossing probability using connectivity maps and collision probability using an equation which considers traffic volume, animal and vehicle size, and animal speed for two native mammal species from Brazil: the Lesser Grison (Galictis cuja) and the Molina’s Hog-nosed Skunk (Conepatus chinga). To the third chapter, I used the road network of Victoria state in Australia, which I calculated crossing and collision probabilities for eastern grey kangaroo (Macropus giganteus), a native species from Australia. In the first chapter, I demonstrated that: 15,377 freshwater turtles, lizards and snakes are road-kills each year in Br-101 in Southern Brazil; road-kill hot moments occur in the summer, specially in December for lizards and snakes; road-kill hotspots are coincident among freshwater turtles, lizards and snakes; there is a positive effect of traffic and rice plantation on road-kills and a negative effect of silviculture; mitigation measures of priority hotspots could avoid 45% of reptile fatalities. In the second chapter, I concluded that fatality probability though multiplication of crossing and collision probabilities did not have a good predictive power of road-kills and collision probability alone was better to predict road-kills than crossing probability, however species showed different patterns. In the third chapter, I concluded that an additive model with the two probabilities was better to predict kangaroo road-kills than individual models of crossing and collision probabilities, however the integrated model did not present an expected prediction. Crossing probability was a better predictor of kangaroos road-kills than collision probability for the road network. Therefore, I concluded that: 1) wildlife road-kills can be really high in some contexts and it is possible to identify locations with more road-kill aggregations which would be effective for mitigation; 2) it is possible to use traffic volume, animals size and speed to predict location of road-kills, however it is specific for each species; 3) for road network context, it is possible to predict kangaroo road-kills using crossing and collision probability in the same model. Exploring another ways to calculate and integrate the probabilities used here is necessary, however in this thesis I demonstrated one possible manner to predict road-kills in a context which road-kill are not available, such as new roads or road networks.

Roads are essential in modern societies, but as populations grow and traffic volumes rise, roads will continue to be built and expanded. As a result, the effects that roads have on wildlife will likely intensify, making it imperative that managers understand those effects so mitigation can be directed accordingly. In Utah, considerable areas of mule deer (Odocoileus hemionus) habitat have been bisected by roads. Mule deer are commonly involved in vehicle collisions and there is concern that roads and vehicle traffic are impacting populations. This project was conducted to determine the number and demographic effects of deer-vehicle collisions, to examine how movements and survival of deer were impacted by roads, and to develop a smartphone-based reporting system for wildlife-vehicle collisions. Accurate estimates of DVCs are needed to effectively mitigate the effects of roads, but great uncertainty exists with most deer-vehicle collision estimates. I estimated the number of deer-vehicle collisions using carcass surveys, while accounting for several sources of bias to improve accuracy. I estimated that 2-5 % of the statewide deer population was killed in vehicle collisions annually. The effect that vehicle collisions have on deer abundance depended not only on the number of deer killed but also on the demographic groups removed. I found that 65 % of deer killed in vehicle collisions were female and 40 % were adult females. As female deer are the primary drivers of population growth, my data suggest vehicle collisions could significantly affect population abundance. However I was unable to detect a decreasing trend in deer abundance. Deer have distinct movement patterns that affect their distribution in relationship to roads. I analyzed deer movements during two consecutive winters (2010-11 & 2011-12) to determine what effect climate had on deer movements and vehicle collision rates. I observed that as snow depth decreased, the distance that deer occurred from roads increased. As a result road crossing rates declined, as did the number of vehicle collisions. This suggests a causal mechanism by which winter conditions influence vehicle collision rates. Currently there is a need for an efficient wildlife-vehicle collision data collection. I envisioned and, working with colleagues, helped develop a smartphone-based system for reporting wildlife-vehicle collision data. The WVC Reporter system consisted of a mobile web application for data collection, a database for centralized storage of data, and a desktop application for viewing data. The system greatly improved accuracy and increased efficiency of data collection efforts, which will likely result in improved mitigation and ultimately increased safety for motorists and deer.

Road expansion and increased traffic likely exacerbates barriers to amphibian migration and dispersal. Within British Columbia’s south Okanagan valley there is particular concern that the COSEWIC-listed blotched tiger salamander (Ambystoma mavortium melanostictum) and Great Basin spadefoot (Spea intermontana) are vulnerable to road effects in their annual movements from upland overwintering habitat to lowland breeding areas. My study utilizes a before after control impact approach to assess amphibian movement and population threats across this highway-bisected landscape. Throughout the spring and summer of 2010-2012, fifty two kilometers of roadways (31 km of highway, 21 km of paved backroad) were repeatedly surveyed from the Canada-USA border to north of Oliver, BC; surveys were carried out utilising vehicles and on foot. Along Highway 97, a three kilometer four-lane highway expansion project was constructed through 2010 and open to traffic use in 2011. Adjacent to a floodplain, survey effort was focused throughout this transect for informed roadkill mitigation structure placement and ongoing ecopassage effectiveness monitoring. Automated camera trap monitoring of culverts within highly concentrated amphibian road hotspots during spring and summer 2011 (three culverts) and 2012 (two culverts) resulted in over eight hundred amphibian culvert events observed. Two sample Wilcoxon tests revealed differences between years in amphibian occurrence between 2010 and 2012 (W = 4679.5, p= 0.02), and mortalities among transect areas, with the largest differences between years within the Osoyoos passing lanes transect. Amphibian mortalities within the passing lanes transect were significantly reduced with the implementation of mitigation structures (x̅2010= 13.2 ± 32.5, x̅2011= 4.7 ± 12.8, x̅2012= 2.3 ± 7.3; 2010 vs. 2012: W= 1535.5, p< 0.001). Roadkill mitigation structures proved effective in observed amphibian occurrence of the entire passing lanes stretch as well as at distances 100 m and 200 m from observed culverts. Double fenced areas resulted in a 94% reduction in amphibian road occurrence. Five species of amphibians were observed over the three survey years (4051 road incidences over 657 survey hours): Pacific chorus frog (Pseudacris regilla), Western toad (Anaxyrus boreas), long-toed salamander (Ambystoma macrodactylum) plus blotched tiger salamander and Great Basin spadefoot. This study aims to provide a better understanding of amphibian hotspots on roadways and ecopassage use within the south Okanagan. It may act as a catalyst to further wildlife-vehicle interaction studies with improved mitigation solutions for amphibian roadway fatalities.

Roads can negatively impact the survival of wildlife populations through additional mortality from road kill and population fragmentation caused by road avoidance behaviors. The 11.9 mile section of State Route 101 between the towns of San Luis Obispo and Atascadero, CA, USA, cross a mountain lion movement corridor and an area important to maintaining ecological connectivity between protected lands in the Los Padres National Forest to the north and south. I examined the spatial patterns and landscape and roadway factors associated with road kill occurrence for six taxa; large mammals, mesocarnivores, squirrels, rabbits, birds and raptors. Between 1 May 2009 and 30 June 2010 road kills were documented using vehicle-based surveys. Small mammals were the most common road kill (58.3%), followed by mesocarnivores (10.9%), birds (10.6%), rabbits (5.1%), large mammals (3.3%) and raptors (3.2%). Twenty-nine large mammal road kills were observed during the survey period; eighteen mule deer, six black bears and five feral pigs. Road kill was highest in the middle of the survey area between the top of Cuesta Grade and the southern edge of Atascadero and lowest along the Cuesta Grade. I modeled road kill occurrence using logistic regression to determine which landscape and roadway characteristics were associated with road kill locations. Large mammal and mesocarnivore road kills were more likely to occur near riparian corridors. Mesocarnivore and squirrel road kills were associated with locations with greater roadside tree cover. Squirrel and rabbit road kills were more likely to occur along sections of the road with large grassy center medians. I documented animal activity patterns around the roadway during three survey periods (summer 2009, fall 2009 and spring 2010) using remote cameras placed on game trails and underpasses along the roadway. Mule deer displayed crepuscular activity patterns with peaks in activity in the morning between 05:00h and 07:00h and in the evening between 16:00h and 18:00h. Mesocarnivores generally displayed a nocturnal activity patterns with the majority of activity occurring between 18:00h and 06:00h. I used logistic regression to determine if there was a relationship between animal activity patterns and traffic patterns while controlling for time of day, day of the week, and season. Mule deer and mesocarnivore activity patterns varied significantly by time of day and mule deer activity also varied significantly by season; however only mesocarnivore activity varied significantly in relation to traffic volume suggesting that mesocarnivores are less activity when traffic volume is high. Using traffic volume and animal activity patterns I calculated a collision potential value for both mule deer and mesocarnivores. Collision potential for mule deer was high in the morning, between 06:00h and 08:00h, and in the evening, between 16:00h and 18:00h in all three seasons. Collision potential for mesocarnivores was high in the evening in fall 2009 (18:00h and 21:00) and spring 2010 (17:00h), and high in the morning in summer 2009 (09:00h).

A construção e ampliação de rodovias gera impactos significativos ao meio ambiente. Os principais impactos ao meio biótico são a supressão de vegetação, redução da riqueza e abundância de espécies de fauna como decorrência da fragmentação de habitats e aumento dos riscos de atropelamento de animais silvestres e domésticos. O objetivo geral do trabalho foi identificar padrões espaciais nos atropelamentos de fauna silvestre por espécie (nome popular) utilizando ferramentas de análise espacial e machine learning. Especificamente, buscou-se compreender a relação entre atropelamentos de animais silvestres e variáveis que representam características de uso e cobertura do solo e caracterização da rodovia, tais como formação florestal, corpos d\'água, silvicultura, áreas edificadas, velocidade máxima permitida, volume de tráfego, entre outras. Os atropelamentos de fauna silvestre foram analisados por espécie atropelada, a fim de identificar os padrões espaciais dos atropelamentos específicos para cada espécie. As ferramentas de análise espacial empregadas foram a Função K - para determinar o padrão de distribuição dos registros de atropelamento de fauna, o Estimador de Densidade de Kernel - para gerar estimativas de densidade de pontos sobre a rodovia, a Análise de Hotspots - para identificar os trechos mais críticos de atropelamento de fauna e, por fim, o Self-Organizing Maps (SOM), um tipo de rede neural artificial, que reorganiza amostras de dados n-dimensionais de acordo com a similaridade entre elas. Os resultados das análises de padrões pontuais foram importantes para entender que os pontos de atropelamento possuem padrões de distribuição espacial que variam por espécie. Os eventos ocorrem espacialmente agrupados e não estão homogeneamente distribuídos ao longo da rodovia. De maneira geral, os animais apresentam trechos de maior intensidade de atropelamento em locais distintos. O SOM permitiu analisar as relações entre múltiplas variáveis, lineares e não-lineares, tais como são os dados ecológicos, e encontrar padrões espaciais distintos por espécie. A maior parte dos animais foi atropelada próxima de fragmentos florestais e de corpos d\'água, e distante de cultivo de cana-de-açúcar, silvicultura e área edificada. Porém, uma parte considerável das mortes de animais dos tipos com maior número de atropelamentos ocorreu em áreas com paisagem diversificada, incluindo alta densidade de drenagem, fragmentos florestais, silvicultura e áreas edificadas. / The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the abundance and richness of species due to forest fragmentation and increase of animal (domestic and wildlife) vehicle collisions. The general objective of this work was to identify spatial patterns in wildlife-vehicle collisions individually per species by using spatial analysis and machine learning. Specifically, the relationship between wildlife-vehicle collisions and variables that represent land use and road characterization features - such as forests, water bodies, silviculture, sugarcane fields, built environment, speed limit and traffic volume - was investigated. The wildlife-vehicle collisions were analyzed per species, in order to identify the spatial patterns for each species separately. The spatial analysis tools used in this study were K-Function - to determine the distribution pattern of roadkill, Kernel Density Estimator (KDE) - to identify the location and intensity of hotspots and hotzones. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies between species. The events occur spatially clustered and are not uniformly distributed along the highway. In general, wildlife-vehicle collsions have their hotzones in different locations. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each species. Most of the wildlife was run over close to forest area and water bodies, and distant from sugarcane, silviculture and built environments. But a considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape, including high density of water bodies, silviculture and built environments.

A construção e ampliação de rodovias gera impactos significativos ao meio ambiente. Os principais impactos ao meio biótico são a supressão de vegetação, redução da riqueza e abundância de espécies de fauna como decorrência da fragmentação de habitats e aumento dos riscos de atropelamento de animais silvestres e domésticos. O objetivo geral do trabalho foi identificar padrões espaciais nos atropelamentos de fauna silvestre por espécie (nome popular) utilizando ferramentas de análise espacial e machine learning. Especificamente, buscou-se compreender a relação entre atropelamentos de animais silvestres e variáveis que representam características de uso e cobertura do solo e caracterização da rodovia, tais como formação florestal, corpos d\'água, silvicultura, áreas edificadas, velocidade máxima permitida, volume de tráfego, entre outras. Os atropelamentos de fauna silvestre foram analisados por espécie atropelada, a fim de identificar os padrões espaciais dos atropelamentos específicos para cada espécie. As ferramentas de análise espacial empregadas foram a Função K - para determinar o padrão de distribuição dos registros de atropelamento de fauna, o Estimador de Densidade de Kernel - para gerar estimativas de densidade de pontos sobre a rodovia, a Análise de Hotspots - para identificar os trechos mais críticos de atropelamento de fauna e, por fim, o Self-Organizing Maps (SOM), um tipo de rede neural artificial, que reorganiza amostras de dados n-dimensionais de acordo com a similaridade entre elas. Os resultados das análises de padrões pontuais foram importantes para entender que os pontos de atropelamento possuem padrões de distribuição espacial que variam por espécie. Os eventos ocorrem espacialmente agrupados e não estão homogeneamente distribuídos ao longo da rodovia. De maneira geral, os animais apresentam trechos de maior intensidade de atropelamento em locais distintos. O SOM permitiu analisar as relações entre múltiplas variáveis, lineares e não-lineares, tais como são os dados ecológicos, e encontrar padrões espaciais distintos por espécie. A maior parte dos animais foi atropelada próxima de fragmentos florestais e de corpos d\'água, e distante de cultivo de cana-de-açúcar, silvicultura e área edificada. Porém, uma parte considerável das mortes de animais dos tipos com maior número de atropelamentos ocorreu em áreas com paisagem diversificada, incluindo alta densidade de drenagem, fragmentos florestais, silvicultura e áreas edificadas. / The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the abundance and richness of species due to forest fragmentation and increase of animal (domestic and wildlife) vehicle collisions. The general objective of this work was to identify spatial patterns in wildlife-vehicle collisions individually per species by using spatial analysis and machine learning. Specifically, the relationship between wildlife-vehicle collisions and variables that represent land use and road characterization features - such as forests, water bodies, silviculture, sugarcane fields, built environment, speed limit and traffic volume - was investigated. The wildlife-vehicle collisions were analyzed per species, in order to identify the spatial patterns for each species separately. The spatial analysis tools used in this study were K-Function - to determine the distribution pattern of roadkill, Kernel Density Estimator (KDE) - to identify the location and intensity of hotspots and hotzones. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies between species. The events occur spatially clustered and are not uniformly distributed along the highway. In general, wildlife-vehicle collsions have their hotzones in different locations. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each species. Most of the wildlife was run over close to forest area and water bodies, and distant from sugarcane, silviculture and built environments. But a considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape, including high density of water bodies, silviculture and built environments.