Dissertação - Thálita Cardoso by Centro Brasileiro de Estudos em Ecologia de Estradas

Centro Brasileiro de Estudos em Ecologia de Estradas Universidade Federal de Lavras - Lavras - MG - Brasil

THÁLITA DE RESENDE CARDOSO

Dissertação | 2013

DOES SHAPE AND SCALE OF ANALYSIS AFFECT THE CONSISTENCY OF THE FACTORS INFLUENCING SMALL VERTEBRATE ROAD-KILLS? Thálita de Resende Cardoso1,*, Clara Grilo2 Alex Bager1 1

Centro Brasileiro de Estudos em Ecologia de Estradas, Universidade Federal de

Lavras. Campus Universitário. 37200-000, Lavras, Minas Gerais, Brasil. 2

Departamento de Biologia & CESAM, Universidade de Aveiro, 3810-193

Aveiro, Portugal *

Endereço para correspondência: thalitarcardoso@yahoo.com.br

ABSTRACT KEYWORDS: Helicops infrataeniatus, Trachemys dorbgni, Didelphis albiventris, Myocastor coypus, Conepatus coypus, road segments, buffer analysis, road mortality, GLM, HP. 1.

INTRODUCTION Wildlife-vehicle collisions (WVC) are considered the most noticeable impact caused

by roads. It is frequently pointed as the main cause of vertebrate mortality by direct human influence (FORMAN; ALEXANDER, 1998; FAHRIG; RYTWINSKI, 2009). In the last decades, researchers have used available locations of WVC to model distribution patterns along roads in order to implement measures to minimize the road mortality rate (Gunson et al. 2011). These analyses have indicated that WVC along roads are not randomly distributed but are spatially clustered and therefore, some factors may promote the road-kills likelihood (e.g. Joyce and Mahoney, 2001). It is generally agreed that ecological patterns result from processes occurring at multiple spatial and temporal scales (Collinge, 2001). Integrate knowledge across scales is needed to characterize the full context of roads and wildlife interactions: from sites/individuals to landscape/population (e.g. DeCesare 2012). Mortality studies are mainly focus on one unit of analysis and there is a lack of understanding on the role of different spatial scales and shapes of units in determining the factors that explain the likelihood of WVC. This fact prevents accurate comparisons, assessments and

extrapolations of the patterns to other regions, and therefore creates problems in the location of measures to minimize WVC (DANKS AND PORTER, 2010). In general, differences in units of analysis are related mostly with two features: shape and size. Two types of shapes in the road-kill analysis are commonly used: a buffer area around the road-kill spot (e.g. LANGEN et al., 2009; COLINO-RABANAL et al., 2011) or the surveyed road is segmented in sections with pre-defined length where the presence of road-kills are assigned to each segment (e.g. GOMES et al., 2009; GRILO et al., 2011). Regarding the size of the units, the majority of studies used 1mile/km (e.g. LANGEN ET AL., 2009; CARVALHO E MIRA, 2011). Most of the unit sizes are chosen arbitrarily, ranging from 50m to 500m for buffer radius and from 50m to 1000m for road segments length (citação). The observation of some phenomena in different scales may deeply affect the values of prediction variable measured for the region a (Colino-rabanal, 2011, Danks e Porter, 2010, Ramp et al., 2005) and also its interpretation. The success of the analysis is condicioned to a careful selection of the best size and approach. However this corcern is not frequent. Researches have been using analysis and prediction scales with different sizes and kinds of approaches, sometimes related to the same species (e.g. Cureton II e Deaton, 2012; Shepard et al., 2008) or animal class (e.g. Caro et al., 2000; Malo et al., 2004) and often with no foundation to the use of an specific size (e.g. ). This way we have found results in different scales (e.g. Pinowski, 2005; Cáceres et al., 2010) and dealing different spatial characteristics related to the same species (e.g. Finder et al., 1999; Nielsen et al., 2003). Additionally, no research compared the results obtained by the use of different shapes and unit sizes in the road-kill analysis. Only a few studies compared the unit size with the buffer approach to identify the features that promote road-kills (e.g. FARMER ET AL. 2006; JANET ET AL., 2008, LANGEN ET AL., 2009, DANKS & PORTER 2010; COLINO-RABANAL ET AL., 2011) with mixed results: JANET ET AL (2008) with deer Odocoileus spp. and COLINO-RABANAL ET AL. (2011) with Iberian wolf Canis lupus signatus found better predictions with larger scales. On the other hand DANKS & PORTER (2010) with mouse Alces alces e LANGEN ET AL. (2009) with reptiles and amphibians found it in smaller sizes. We argue that the shape and size of unit analysis may influence the results. Thus, the evaluation of the results consistency over different approaches is needed to better understand the effect of shape and size on the road-kill analysis.

The main goal of this study is to examine consistency of the effect of the shape and the size of unit analysis in determining the mortality risk for small vertebrate species with different life-history traits. More specifically, we want to address the following issues: (1) Do variables related to the road-kill risk vary accordingly to the size and spatial scale used? (2) Does the weight of those variables are similar in units with different shape and size? With these insights we intend to contribute for a better understanding of the role of using different methodologies to determine the factor road mortality risks and therefore, contribute to a successful application of mitigation measures. 2. METHODS

2.1 STUDY SITE The study site is located in the Rio Grande do Sul, Brazil (Fig. 1). This region is characterized by coastal plains with low and rectilinear relief dominated mainly by wetlands associated with freshwater and saltwater lakes. This landscape also comprise extensive perennial and seasonal wet areas, seaside dunes and rice fields (TAGLIANI, 2001). The climate is humid with rainfall evenly distributed throughout the year. The temperature ranges between -3ºC and 18ºC most of the year, reaching 22ºC during the hottest months. A total of 137 km of two Brazilian Federal paved roads were surveyed (33km from BR392 and 104km from BR 471). The segment started in Pelotas city (52º19’ 38’’O, 31º48’18’’S) and the end is on Santa Marta’s Farm (52º41’27’’O, S 32º49’32’’ S), in Santa Vitória do Palmar town. BR 471 is connected an excerpt to Taim ecological station (ESEC Taim). The ecological station is a Protected Area with full protection and it is between Mirim Lake and Atlantic Ocean (Fig. 1). 2.2 TARGET SPECIES We selected five species vulnerable to road traffic which represent different lifehistory traits. We analyzed records of two reptiles - water snake (Helicops infrataeniatus) and the water tiger turtle (Trachemys dorbigni); and three mammal

species - white-eared opossum (Didelphis albiventris), nutria (Myocastor coypus) and skunk (Conepatus chinga). Water snake is an aquatic species, spread in southern Brazil (Rio Grande do Sul and Santa Catarina counties), Uruguay and Argentina (Buenos Aires, Santa Fe, Entre Rios, Corrientes, Chaco, Formosa, Misiones) (AGUIAR & DI-BERNARDO, 2004, KAWASHITA-RIBEIRO ET AL., 2013). It is a species particularly vulnerable to road mortality (KUNZ & GHIZONI-JR, 2009; MAINARDI & HARTMANN, 2009; BAGER & ROSA, 2011, AGUIAR & DI-BERNARDO, 2004; 2005). Water tiger turtle is a fresh water species, which inhabit swamps, lakes, and slow-moving rivers in Brazil, Uruguay and Argentina (BAGER ET AL., 2012). Because it moves slowly this species is a common victim of road traffic (LEMA, 2002, HENGEMÜHLE & CADEMARTORI, 2008), mostly females during reproduction season (LEMA, 2002). For example, BAGER & FONTOURA (2013) presented a rate of 0.23 individuals/100km/day in the Brazilian road BR471. White-eared opossum is a generalist (Vieira 2006), solitary and omnivorous species (CABRERA & YEPES 1960). Although this species occupy a broad ecological niche, it is mostly found in open deciduous forests (CERQUEIRA, 1985) from Colombia to central Argentina (EMMONS & FEER 1990). With the deforestation raise, individuals have been approaching urban areas and acquiring synanthropic habits (SANCHES ET AL., 2012). The high road-kill incidence must be related to the tolerant and opportunistic habit of the species (HENGEMÜHLE & CADEMARTORI, 2008; ROSA & MAUHS, 2004; CHEREM ET AL., 2007). Nutria is an aquatic rodent from South America, which was exported to Europe, Asia, Africa and North America for fur production (CARTER & LEONARD, 2002). Their occurrence in river sides and humid zones resulted from escapes and possible releases from nutria farms (LORI ET AL., 2013). BAGER & FONTOURA (2013) estimate a rate of 8.25 individuals/100km/day in BR471, Rio Grande do Sul, Brazil. Skunk is a carnivore widespread in northern Argentina, Uruguay, southern Brazil, Paraguay, southern Bolivia, South Peru and Northern Chile (EISENBERG & REDFORD 1999; CHEIDA ET AL., 2006). They inhabit mostly low vegetation areas, avoiding dense forests (MICHALSKI et al. 2007; LYRA-JORGE et al. 2008). BAGER & FONTOURA (2013) estimate a road-kill rate of 0.03 individuals/100km/day in BR471, Rio Grande do Sul, Brazil. 2.3 DATA COLLECTION

2.3.1 ROAD-KILL DATA Road-kill data were obtained from “Estrada Viva” project database. Locations were recorded weekly from January to December 2005, by car, at low speed (approximately 50 km/h), with at least two observers, avoiding weekends, holidays and rainy days. Road surveys were performed between 7am and 3pm and road-kills were recorded with a handheld GPS (maximum error 5 m). 2.3.2 LAND USE DATA We built a land use map based on an earlier database and maps from TAGLIANI (2001). Tagliani’s map was created from Landsat 7 satellite images (2000) using SPRING software. The size of the imaged scene is 185km x 185km, with spatial resolution of 30x30m, 15x15m (panchromatic band), 60x60 (thermal band). The representative land use classes analyzed in the study site were: Predominant Rice Field, Sandbank vegetation, Seaside fields, Wetlands and Non-native vegetation. Predominant rice field are areas predominantly covered by irrigates rice culture; Sandbank vegetation are community’s placed high on dunes and slopes, on dry soil; Seaside fields are flood fields of low grass; Wetlands are floodplains with fertile clay soils and Non-native vegetation are forestation of eucalyptus and pine (Eucalyptus sp. and Pinus sp.). 2.3.3 SAMPLE UNITS For this analysis we used two shapes (buffer and segment) and three unit sizes related with daily movement, standard scale and dispersal ability (Table 1). We used a buffer area around the road-kill location and divided the road surveyed in segments with pre-defined length. Daily movement was estimated using information of the average home-range size for each species accordingly to BISSONETTE & ADAIR (2008). According to this study daily movement is the square root of the home-range size. The standard scale was the measure used in the majority of studies, which was 1000m length. Dispersal ability is seven times the length of the daily movement (BISSONETTE & ADAIR 2008). For water snake, no information was available in terms of home-range size. Thus we used the data of a research on a species of the same family - liophis (Liophis poecilogyrus) (Hartz et al., 2001) which is present in a site

close to our study site. For Nutria the home range values presented in the literature vary widely, thus we used the average area estimated resulting in 375000m². Thus, we used the daily movement and dispersal ability of each species as well the standard scale to define length of road segments and the diameter of buffers (Table 1). We estimate the number of buffers and segments with road-kills and select a similar number of random buffers and segments without road-kills, using "Hawth's analysis tools" of ArcGis 9.3 (Environmental Systems Research Institute, Inc., Redlands, CA). For both approaches we extracted the area of each land use class. 2.4 DATA ANALYSIS The analysis of consistency of results were performed through Generalized Linear models (GLM) and hierarchical partitioning (HP). With GLM analysis we obtained the best model that explain the relationship between presence/absence of roadkills and land use classes. The proportion of variance explained independently and jointly by each land use was determined using HP (OLEA, MATEO-TOMAS & FRUTOS, 2010; MAC NALLY 2000; 2002). We used HP procedures due to the tendency of linear models being seriously affected by multicolinearity among several explanatory variables (GRAHAN, 2003). Therefore, GLM and HP were considered complementary methods since GLM allow the selection of best models and HP identify the contribution (weight) of each isolated (I’s) and joint (J’s) land use class to analyze the effect of scales and shape of units to explain the road-kills likelihood. Firstly, we tested the multicollinearity among land use variables, confronting them via pair-plots (ZUUR, 2010). From pair of variables with correlation value higher than 0.7, we just kept the land use variable with higher biological meaning in the GLM analysis. We then run GLM using all possible combinations of the selected land use variables. We considered the best model the one with the lowest Akaike Information Criteria (AIC) (BURNHAM & ANDERSON, 2002). We used log-likelihood as a goodness-of-fit measure in HP (MAC NALLY, 2002). To assess the statistical significance of variables, 1000 randomizations of the data matrix were generated to compute I’s distributions for each predictor. Results were expressed with α=0.05 (Z-score>1.96) (MAC NALLY, 2002). All statistical analyses were performed using the R statistical software, version 2.9.1 (R DEVELOPMENT CORE TEAM 2006, VIENNA, AUSTRIA). For GLM analyses we used MuMIn

package (Barton, 2012) and Hierarchical partitioning was performed using the “hiert.part package” Version 1.0 (Walsh and Mac Nally, 2004) . 3. RESULTS

With the road survey, we recorded 186 water snakes, 67 water tiger turtles, 121 white eared opossums, 68 nutrias and 54 skunks. Thus, we defined buffers around the road-kills and select the same number of random buffers without road-kills. The number of road segments for the daily movement, standard and dispersal scales were respectively: 164, 67 and 98 for water snake; 108, 72 and 64 for water tiger turtle; 174, 110 and 114 for white-eared opossum; 68, 64 and 30 for nutria and 64, 72 and 10 for skunk. Half of these numbers represents the number of segments for each species with road-kills. The effect of size and spatial scale on the identification of the factors influencing the small vertebrate road-kills Based on the GLM analysis, small variation was found to explain road mortality likelihood over two approaches and three scales (Table 2). Regarding the shape of units, we reported that the buffer approach presented more consistent results than the segment approach, since the number and type of variables in the models did not differ much over the three scale of analysis (daily movement, standard and dispersal). However, the segment approach presented better results regarding the correct classification of observations and the area under curve. The best models were performed under the standard and dispersal scale: the dispersal scale performed the most accurate model for water tiger turtle, white-eared opossum and skunk road-kills whereas standard scale obtained the most accurate model for water snake, nutria and also white eared opossum road-kills. In terms of AIC weight, all best models did not differ much from the all combination of variables run, ranging from 0.13 to 0.33, except for nutria at buffer/daily movement scale (AICweight = 0.97). Analyzing at which scale unit was the best for each shape approach in terms of model accuracy, we also found few differences. However, for buffer approach, the standard scale fitted better for all species except for water tiger turtle, whereas for segment approach the best scale of analysis was: daily movement for water snake;

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standard for nutria, dispersal for skunk, both standard and dispersal for white-eared opossum and all scales showed similar results for water tiger turtle. In the assessment of consistency among GLMs, we observed that several models showed the same variables. The majority of models with the same variables were between standard and dispersal scales for the following species: water tiger turtle (two models), white-eared opossum (one model), nutria (one model), and skunk (one model). We reported similar models between daily-movement and standard scales for all species (four models) except skunk. Equally, for daily-movement and dispersal we found similarities for water tiger turtle (one model) and white-eared opossum (two models). At least one variable was present throughout all the best models for each species, except for nutria where the seaside field variable occurred in four of six models. The variables that enter in every model were: seaside fields for water snake; predominant rice field for water tiger turtle; seaside fields, sandbank vegetation and non-native vegetation for white eared opossum; and predominant rice field for skunk. The effect of size and spatial scale on the weight of variables to explain road-kills When analyzing the independent and the joint effect in terms of variance explained of variables for road-kill likelihood, we reported that the effect of independent variables was consistent but the joint effects varied considerably. In fact, standard scale and segment approach were the most consistent units of analysis. For water snake, we found that variables have similar importance, except the joint effect of the sandbank vegetation and non-native vegetation at segment approach, which explained most of the variance in the standard and dispersal scale, respectively. No variation on the effect of variables was found among the variance explained for water tiger turtle among the shape and size of units. For white-eared opossum, we found joint differences between buffer and segment approach regarding the daily movement scale, namely for predominant rice filed and sandbank vegetation. For nutria there were no huge differences on the independent and joint effects, except for the joint effect of sandbank vegetation and wetlands in dispersal scale at buffer and segment approaches. For skunk, the effects of variables did not changed along the scale and shape of analysis, except the wetlands and sandbank vegetation joint effect at segment for the daily movement scale and, the independent effect of non-native vegetation at buffer approach in the standard scale.

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When comparing the presence of variables in the best GLM with the percentage of the variance explained by the joint effect of variables in HP, we found some differences. In fact, we have reported many variables with high effect in the HP analysis which were absent in the GLM. For example, variables with high effects in the water snake HP analysis (predominant rice field and sandbank vegetation) were absent in the GLM except at the buffer /dispersal and buffer/standard approaches, respectively. For water tiger turtle, seaside field was an important variable in the HP analysis, which is only present in the segment/daily movement approach at GLM. Wetlands have a high effect on the HP analysis for white-eared opossum, but it was not selected in the best GLM. Similar observation occurred when run the GLM for skunk. Sandbank vegetation was an important variable in the HP analysis but only occurred in the buffer/dispersal approach for nutria. Summarizing, we found more consistency between GLM and HP analysis with the buffer approach at standard and dispersal scales, since they presented more variables with high variance explained in the HP and were also present in GLM. The best examples for this fact were water tiger turtle and white-eared-opossum models (Figure 2). 4. DISCUSSION

The definition of unit analysis is a critical issue to understand the ecological processes (McMahon & Diez, 2007). Inferences from an observation may be biased, if the scale and shape of analysis does not detect the spatial species requirements (Cadotte & Fukami 2005). There is little knowledge on the effect of scale and shape on the roadkill analysis. Such information is valuable for accomplishing mitigation objectives as the definition and placement of the most appropriate measure to prevent WVCs. In this study we wanted to clarify this issue by using different shapes and scale of analysis based on the spatial requirements of five small vertebrate species with different life-history traits. Overall, our results show that road-kills are better explained at broader scales (standard and dispersal scales) and segments approaches. However, our findings show that the definition of scale and shape of units is complex and may be species-specific. The similarity of results among GLMs indicates that scale and shape seems to have little influence on the identification of variables explaining the road-kill

occurrence of small vertebrates. However, Hierarchical Partitioning analyses show that the importance of variables to explain road-kills occurrence seems to be affected by the scales and shape of units. Moreover, in several cases, the most important variables in the HP analysis were not present in the best GLM models. O uso combinado de GLM e HP pode fornecer conclusões firmes sobre as relações espécie-ambiente (Frutos et al., 2007). The incongruences between GLM models and HP analyses may have occurred due to an undetected degree of multicollinearity, despite the removal of variables with a correlation above 0.7. In fact, HP analyses are used as a tool against multicollinearity (MacNally 2000; 2002; MacNally & Walsh, 2004), which prevent the exclusion of ecologically important variables whereas variables that statistically better explain the data are kept in the models (Graham, 2003). Therefore, we used HP to complement the analysis of GLM determinando a contribuição independente que cada variável tem sobre a variável resposta e separando da contribuição conjunta, resultante da correlação com outras variáveis (Frutos et al., 2007). For instance, we detected through GLM that the proximity with predominant rice field is the variable responsible for a high likelihood of skunk road-kills. However, wetlands emerged as the variable with highest effect on road-kills occurrence in the HP. This factor may have implication on the definition and location of roadkill mitigation measures. One explanation of this dissimilarity is the home-range size and the generalist character of the species. Home range is the area used by an individual to forage, reproduce and perform all of its daily activities (BURT 1943, citação). This area varies according to animal size and feeding habits (citação). Analyzing in the following sequence: water snake, water tiger turtle, white-eared opossum, nutria e skunk, we observed that while the home ranges increased, also increased the differences among the variables in the models (especially regarding HP results). Moreover, the species which presented models completely equal on scales and approaches were the two species with similar and intermediary home ranges (17424m² for water tiger turtle, 23300m² for white-eared opossum). Species with larger home ranges and dispersal capacities are generally less specialists regarding habitat, being able to explore distinct places according to resource supply (KREBS & DAVIES, 1996, citação). The result differences can be explained by a larger diversity of explored habitats by the species with larger home ranges (nutria e skunk). We observed that large home-ranges are realted with high variations among the most important variables which suggest that species with higher home ranges, the differences among scales and approaches may be more substantial. We also haven’t found any relation between the scale of analyses and

dispersal ability of the species. Standard scale seems to be a reasonable solution as it worked well for species with both low and high home-range values. However, several studies with larger body sized species are in line with our findings (JANET ET AL., 2008, COLINO-RABANAL ET AL. 2011). For example, among three scale of analysis, COLINO-RABANAL et al. (2011) found better results at broader scale (buffer of 1000 and 5000 meter radius) for Iberian wolf. Equally, JANET ET AL. (2008) using road-kill data of North American deer identified, among the sizes of 100, 200, 400 and 800m radius, found better results with the highest radius. In fact, our results are based on data of small vertebrate species with territories ranging from 0.0003 to 1.65km². Despite this similarity with our results, both researches were done with large mammals which have high dispersal ability (according to Mattisson et al. (2013), from 259 to 1.676 km² home range for the Wolf, and according to Walter et al. (2013) from 8 to 16.2Km² for the deer).

There were also other

researches which obtained complementary results according to the analyses they intended to do. DANKS & PORTER (2010, used data from the moose species Alce alces, among scales with 0.25, 0.5, 1, 2.5 e 5 Km radius. They identified that the scale with 2.5km radius better predict the landscape variable composition, furthermore, the scale with 5.0km better predict the landscape variable configuration. In the other hand, FARMER et al. (2006), using data from Odocoileus hemionus sitkensis, among the scales (50, 500 and 1000m radius), the habitat factors in scales with 500 and 1000m scales had a higher effect on adult and young female roadkill, whilew the characterístics of nearby habitats had a higher effect on adult male roadkill. No research had as a result the best explanation in fine scale. Then we can conclude that the results are better as larger the area of the analysis unit is, regardless the dispersion capacity of the species. This factor shows the need of the species to use the landscape to explore different places according to the resource supply to satisfy different needs. (KREBS & DAVIES, 1996). Most studies do not present any scientific support to explain why some image resolution was chosen to generate the land-use map used to analyze data on a species (citação). In fact, we can raise the hypothesis that some important fine-scale differences may have not been detected since we used a land-use map generated from an image with 30m resolution (Tagliani, 2001). As smaller the animal’s dispersal capacity is, the larger should be the scale of the land use variables so the differences may be perceived (LANGEN ET AL. 2009). When analyzing the relationship between herpetological

fauna and land use LANGEN ET AL. (2009) using a high resolution map of 1m, obtained better results with smaller scales (100m) than for 500 and 1000m. There is also another factor related to study area that may explain this disparity in results. Our area is inserted in a region of pioneer vegetal formations with seasonal floods and strong culture on irrigated rice. The rice culture in the area represents 79% of the country’s production (EMBRAPA, 2004; CORDEIRO E HASENACK, 2009). There are a few differences in the soil use of the region, having a strong relation with agriculture. Evidences of this agricultural use are irrigation/drainage channels and “taipas” resultants from the irrigated rice culture in wetlands and seaside fields, as well as in terraces on dry fields. (CORDEIRO E HASENACK, 2009). The studied species often showed use of these environments since they are well adapted to seasonal water conditions (citação). Seaside fields and predominant rice fields are the more frequent variables on the models. Both variables have a strong water factor, and it is a habitat characteristic limiting to the development and nesting of most of the studied species, which corroborates with the similarity found in the results. Management Implications In general we found out that broader scales and segments shown to be the best analysis units for small vertebrates. The best analysis scales are particular of each species and the ideal would be preliminary multi-scale studies. Complementarily, we sugest new researchs using land-use maps with better resolutions, analysing species with different home ranges and in a region with a very heterogeneous landscape. However, dealing with each species at a time, we saw more contrasting results with distinct variables on the models. Those variations become more important when we deal with the application of those results. The application of mitigation measures based on researches with arbitrary use of scales is fragile. We highlight with our research the importance of a criterion for the use of scale and shapes of analysis units on roadkill studies. Our research opens new grounds when we discuss different scales and shapes for the analysis units, considering species with different life-history traits. For this reason it was supposed to have complex results and a large raise of hypothesis and discussions. 5. ACKNOWLEDGEMENTS

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TABELAS E FIGURAS

Figure 1- Study area.

Table 1. Average home range size (m²) for each species obtained through the literature and the estimated daily movements, standard, and dispersion ability scales accordingly to BISSONETTE & ADAIR (2008). Common name Home range Water Snake 3000 Water tiger turtle 17424 White-eared opossum 23300 Nutria 375000 Skunk 1650000

Reference Hartz et al., 2001 Bager et al. (2012) Sanches et al. (2012) Nolfo-Clements (2009) Kasper et al. (2011)

Daily movement 62 150 172 692 1450

Standard 1000 1000 1000 1000 1000

Dispersal 383 924 1068 4286 8991

Table 3 – Best GLM models for buffer and segment approaches and for the three scales of analysis to estimate the road-kill likelihood of species Water Snake,Water tiger turtle, White-eared opossum, Nutria and Skunk. Species

Approach

Scale

Variable

Coefficients

S.E.

(Intercept) Seaside Fields Wetlands (Intercept) Seaside Fields Sandbank vegetation Non-native vegetation (Intercept) Predominant Rice Field Wetlands Seaside Fields Non-native vegetation

-1.611216 0.000898 0.000575 -2.111000 0.000005 -0.000006 0.000002 -3.031000 0.000011 0.000018 0.000037 0.000024

0.250351 0.000108 0.000124 0.291000 0.000000 0.000002 0.000002 0.797900 0.000008 0.000008 0.000007 0.000010

(Intercept) Seaside Fields (Intercept) Seaside Fields (Intercept) Seaside Fields Non-native vegetation

-0.737145 0.000493 -0.638300 0.000002 -0.710400 0.000010 0.000050

0.237514 0.000114 0.368600 0.000001 0.327000 0.000004 0.000032

(Intercept) Predominant Rice Field Wetlands Non-native vegetation (Intercept) Predominant Rice Field Wetlands Non-native vegetation (Intercept) Predominant Rice Field Wetlands Non-native vegetation

0.574300 -0.000136 -0.000043 -0.000204 0.737900 -0.000003 -0.000002 -0.000007 0.734900 -0.000004 -0.000002 -0.000008

0.237900 0.000040 0.000029 0.000104 0.270700 0.000001 0.000001 0.000004 0.269100 0.000001 0.000001 0.000004

(Intercept) Predominant Rice Field Seaside Fields (Intercept) Predominant Rice Field (Intercept) Predominant Rice Field

-0.146100 -0.000107 0.000065 0.489000 -0.000003 0.587400 -0.000004

0.322200 0.000043 0.000029 0.282900 0.000001 0.309400 0.000001

(Intercept) Seaside Fields Sandbank vegetation Non-native vegetation (Intercept) Seaside Fields Sandbank vegetation (Intercept) Seaside Fields Sandbank vegetation Non-native vegetation

-0.734700 0.000048 0.000114 0.000073 -0.845900 0.000002 0.000006 -1.011000 0.000002 0.000006 0.000002

0.218000 0.000013 0.000043 0.000042 0.232100 0.000000 0.000002 0.257700 0.000000 0.000002 0.000002

(Intercept) Seaside Fields Sandbank vegetation Non-native vegetation (Intercept) Seaside Fields Sandbank vegetation Non-native vegetation (Intercept) Seaside Fields Sandbank vegetation Non-native vegetation

-0.723600 0.000057 0.000111 0.000054 -0.822500 0.000002 0.000005 0.000004 -0.826000 0.000001 0.000009 0.000004

0.236700 0.000016 0.000048 0.000037 0.329200 0.000001 0.000003 0.000003 0.327100 0.000001 0.000005 0.000003

P -value

AICweight

Correct AUC Classification

Water snake Buffer Daily movement

Standard

Dispersal

0.31

0.70

0.75

0.28

0.78

0.86

0.16

0.77

0.80

0.21

0.67

0.68

0.28

0.63

0.64

0.33

0.58

0.66

0.20

0.66

0.70

0.20

0.64

0.71

0.21

0.65

0.71

0.24

0.69

0.76

0.13

0.69

0.23

0.70

0.72

0.23

0.63

0.66

0.17

0.64

0.68

0.17

0.63

0.69

0.23

0.67

0.68

0.20

0.68

0.20

0.59

0.68

0.00 0.00 0.00 0.01 0.11 0.00 0.00 0.00 0.01

Segment Daily movement Standard Dispersal

0.00 0.02 0.02 0.05

Water tiger turtle Buffer Daily movement

Standard

Dispersal

0.00 0.19 0.01 0.00 0.13 0.03 0.00 0.15 0.02

Segment Daily movement

Standard Dispersal

0.00 0.02 0.00 0.00

White-eared opossum Buffer Daily movement

Standard

Dispersal

0.00 0.00 0.07 0.00 0.01 0.00 0.00 0.14

Segment Daily movement

Standard

Dispersal

0.00 0.01 0.14 0.02 0.14 0.07 0.03 0.01 0.12

19 Â Â Nutria Buffer Daily movement Standard Dispersal

(Intercept) Seaside Fields (Intercept) Seaside Fields (Intercept) Seaside Fields Sandbank vegetation

-0.862400 0.000005 -0.934800 0.000003 -0.773300 0.000000 -0.000001

0.278000 0.000001 0.286600 0.000001 0.357200 0.000000 0.000000

(Intercept) Seaside Fields (Intercept) Wetlands (Intercept) Wetlands

-0.293200 0.000002 -0.226400 0.000002 -0.457400 0.000000

0.345500 0.000002 0.289700 0.000002 0.497700 0.000000

(Intercept) Predominant Rice Field (Intercept) Predominant Rice Field Non-native vegetation (Intercept) Predominant Rice Field Non-native vegetation

-0.340900 0.000001 -0.524500 0.000001 0.000007 -0.137700 0.000000 0.000000

0.262400 0.000000 0.288200 0.000001 0.000004 0.380400 0.000000 0.000000

(Intercept) Predominant Rice Field (Intercept) Predominant Rice Field Sandbank vegetation (Intercept) Predominant Rice Field Non-native vegetation

-0.513900 0.000001 -0.826800 0.000002 0.000010 1.557000 0.000001 -0.000002

0.335400 0.000000 0.375000 0.000001 0.000007 1.383000 0.000002 0.000001

0.33

0.67

0.71

0.28

0.68

0.71

0.22

0.65

0.71

97.00

0.56

0.10

0.61

0.69

0.16

0.67

0.62

0.20

0.59

0.61

0.20

0.60

0.62

0.22

0.65

0.62

0.26

0.66

0.68

0.26

0.64

0.72

0.29

0.90

0.96

0.00 0.00 0.00 0.14

Segment Daily movement Standard Dispersal

0.23 0.11 0.15

Skunk Buffer Daily movement Standard

Dispersal

0.05 0.08 0.05 0.01 0.12

Segment Daily movement Standard

Dispersal

0.01 0.01 0.05 0.13 0.01

Figure 2 –Percentage of variance in the occurrence of road-kills for each species explained independently (I) and jointly (J) by the five land use variables.