Contrasting wolf responses to different paved roads and traffic volume levels

Vol.:(0123456789) Biodiversity and Conservation https://doi.org/10.1007/s10531-021-02239-y 1 3 ORIGINAL PAPER Contrasting wolf responses todifferent paved roads andtraffic volume levels EmmaDennehy1· LuisLlaneza2· JoséVicenteLópez‑Bao1 Received: 1 June 2020 / Revised: 7 June 2021 / Accepted: 28 June 2021 © The Author(s) 2021 Abstract In some regions of the world, large carnivores, such as wolves, persist in landscapes with dense networks of paved roads. However, beyond the general impacts of roads on wildlife, we still lack information on carnivore responses to different types of roads and traffic volume levels. Using wolves in NW Spain as a case study, we show how wolves respond differently to paved road classes depending on road size, speed limit and traffic volume. All wolves evaluated (25 GPS collared wolves) crossed paved roads. Overall, during 3,915 sampling days, we recorded 29,859 wolf crossings. Wolf crossings of all paved road classes were recorded at a mean rate of 0.022 crossings/day/km (95% CI 0.016–0.027). Wolves crossed low speed and low traffic volume roads more frequently, and more often during the night, in order to lessen the chances of encountering traffic. We found mortality to be highest on roads with high speed and high traffic volume. How wolves interact with paved roads should be considered in landscape planning strategies in order to guarantee wolf long-term persistence in human-dominated landscapes. In our case, our results support an increasing focus on primary roads (class II) to identify segments of these roads where road mitigation efforts should be prioritised. Our study also highlights the importance of considering paved road classes when studying the impact of roads on wildlife. Keywords Canis lupus· Large carnivore conservation· Human-dominated landscapes· Wolf persistence· Movement· Paved roads Introduction The magnitude and rate of change in land-use cover presents a challenge in endeavouring to fully understand the responses of wildlife to ever-encroaching human environments (Torres etal. 2016; Watson etal. 2016). While concern is rightly given on the Communicated by Adeline Loyau. * José Vicente López-Bao jv[email protected]; [email protected] 1 Biodiversity Research Institute (CSIC/Oviedo University/Regional Government ofAsturias), Oviedo University, 33600Mieres, Spain 2 ARENA, Asesores en Recursos Naturales, SL, 27003Lugo, Spain Biodiversity and Conservation 1 3 conservation of roadless areas across the globe (Selva etal. 2011; Ibisch etal. 2016; Ascensão etal. 2018), there is increasing interest on how already existing and growing road networks may impact wildlife persistence (Bennett 2017). The expansion of roads in human-dominated landscapes can exacerbate habitat loss and fragmentation, lead to direct mortality by vehicle collisions and increase disturbance and human pressures on wildlife (e.g., Mech etal. 1988; Fahrig etal. 1995; Saunders etal. 2002; van Langevelde and Jaarsma 2004; Shephard etal. 2008; Ceia-Hasse etal. 2017; Laurance etal. 2017). By studying wildlife responses to growing road networks, conservationists are better able to delineate and prioritize effective mitigation measures to ensure the persistence of wildlife and functional ecosystems, in an increasingly human-dominated world (Forman 2000; Boutin and Hebert 2002; Dellinger etal. 2013; Wadey etal. 2018). Evidence shows that large carnivores have an ability to persist in human-dominated landscapes, therefore indicating that human-large carnivore separation is not a necessary condition for their conservation (López-Bao et al. 2017). The impact of linear infrastructures on large carnivores, and their behavioural responses, has been the focus of attention in recent times (e.g., Whittington 2005; Llaneza etal. 2012; Basille etal. 2013; Dellinger etal. 2013; Boulanger and Stenhouse 2014; Ordiz etal. 2014; Riley etal. 2014; Ceia-Hasse etal. 2017; Find’o 2019; López-Bao etal. 2019; Zeller etal. 2021; Proctor etal. 2019). For example, roads decrease Amur tiger (Panthera tigris altaica) survivorship and reproductive success (Kerley etal. 2002), isolate lynx (Lynx lynx) habitat patches (Kramer-Schadt etal. 2004), and lead to grizzly bear (Ursus arctos) avoidance of landscapes with high road densities (Mace etal. 1996). Furthermore, studies have shown how carnivores can perceive risks of crossing roads, with black bears (Ursus americanus) having elevated heart rates when crossing high-traffic volume roads (Ditmer etal. 2018) or higher movement speeds when crossing roads than during other situations(Zeller etal. 2021), and grizzly bears (Ursus arctos horribilis) increasing their selection of roads closed to traffic (Whittington etal. 2019). For species such as wolves (Canis lupus), accumulating evidence is leading an interesting debate on the levels of wolf resilience towards landscape transformations and human disturbance (e.g. Llaneza etal. 2012, 2016; Lesmerises etal. 2013; Ahmadi etal. 2014; Chapron etal. 2014; Bojarska etal. 2020); which may be highly context-dependent (e.g., Eurasia vs. North America; Chapron etal. 2014; Sazatornil etal. 2016; Muhly etal. 2019). With an estimated wolf density around 2.55 wolves/100 km2 (López-Bao etal. 2018), and a mean paved road density at 2.7km/km2 (Llaneza etal. 2012), wolves in Galicia are expected to interact with roads frequently, as seen in other studies (e.g. Mladenoff etal. 1995; Whittingtonet al. 2004; Kaartinen etal. 2005; Lesmerises etal. 2013; Zimmerman etal. 2014; Ronnenberg etal. 2017). Forest roads and other trails can be advantageous to wolves, assisting movement through territories and consequently hunting and patrolling efficiency (Thurber etal. 1994; Musiani etal. 1998; James and Stuart-Smith 2000; Ciucci etal. 2003; Gurarie etal. 2011; Zimmerman etal. 2014; Dickie etal. 2017; Newton etal. 2017). On the other hand, roads and trails increase human accessibility (Thurber etal. 1994) and can lead to wolf mortality, either directly (i.e. vehicle collisions, Boyd and Pletscher 1999) or indirectly (i.e. hunting and poaching, Mech etal. 1988; Sazatornil etal. 2016; Suutarinen and Kojola 2018). Several studies have suggested an ability of wolves to perceive mortality risks associated with humans, and therefore adjust, for instance, their use of space and time accordingly (Thurber etal. 1994; Merrill and Mech 2000; Habib and Khumar 2007; Ahmadi etal. 2014; Zimmerman etal. 2014). In regions where human presence is low, temporal use of roads can span both day and night (e.g., Thurber etal. 1994). However, in Biodiversity and Conservation 1 3 human-dominated landscapes, wolves become more cryptic and nocturnal, decreasing the probability of encountering humans (Vilà etal. 1995; Ciucci etal. 1997; Theuerkauf etal. 2003a; Kusak etal. 2005; Zimmerman etal. 2014). This evidence supports the idea that wolf responses to roads may be largely influenced by their use and the levels of traffic intensity, rather than the presence of the roads themselves (Hebblewhite and Merrill 2008; Kohn etal. 2009; Lesmerises etal. 2013). Since traffic intensity varies between different road types, presumably so too would wolf activity (Kohn etal. 2009). Roads can be categorised into several classes depending on traffic volume, speed limit as well as physical attributes, such as number of lanes and fencing (van Langevelde and Jaarsma 2004; Colino-Rabanal et al. 2011). Consequently, distinguishing paved roads from one another would be a necessary step when studying wildlife responses to various road classes (Zeller etal. 2021). The Regional Government of Galicia has jurisdiction over most roads (although some of them are national jurisdiction), and they are classified into the following: (1) motorway (national and regional), (2) basic and complementary primary network (national and regional), (3) secondary network (regional) and (4) county roads network (regional). A number of studies investigating wolf, and large carnivore, movement patterns in relation to paved and forest roads grouped paved roads into one single class, namely primary roads, when analysing road network data (Theuerkauf etal. 2009; Gurarie etal. 2011; Zimmerman etal. 2014; Ordiz etal. 2015; López-Bao etal. 2019). Classification of paved road networks into more than one class has been described (e.g. primary and secondary roads; e.g., Theuerkauf et al. 2007; Ahmadi etal. 2014); but still the lack of differentiation between paved road classes can make wolf interactions to paved roads more difficult to interpret (see for example Find’o etal. 2019; Zeller etal. 2021). Presumably, wolves persisting in close proximity to roads may be able to distinguish between different traffic intensities (Kaartinen etal. 2005; Whittington etal. 2005). With that in mind, we aimed to evaluate contrasting responses of Iberian wolves to different paved roads and traffic volume levels in a high road density region by employing data of 25 GPS collared wolves. Iberian wolves in NW Iberia have traditionally persisted in areas with high levels of human activity, and thus provide a good opportunity to investigate wolf responses to paved roads. We considered paved roads as separate road classes, according to their physical characteristics and traffic volume. Based on estimated crossing rates (which accounted for different lengths of road classes and wolf monitoring periods) made by GPS-collared wolves, we hypothesised that (1) wolves cross all paved road classes, (2) but crossing rates will be higher in low speed and low traffic volume roads compared with high speed and high traffic volume roads, (3) adults and sub-adults will cross roads more frequently than juveniles, due to adults and sub-adults moving further distances to hunt, patrol or disperse, and (4) wolves should cross all road classes more frequently during night hours, thereby reducing encounters with humans. We also hypothesised that the level of human activity, i.e. traffic volume, being different across road classes, influenced road use. We also expected mortality by vehicle collision to be highest on high-use road classes than on low-use classes, mainly because of the higher speed limit and traffic volume experienced on these road classes and therefore increasing the chances of a wolf being road-killed. Biodiversity and Conservation 1 3 Methods Study area Fieldwork was carried out in Galicia (NW Spain, ca. 42.5°N 8.1°W; Fig.1, highlighted in black box). The high human population in Galicia, mean population density of ca. 93 inhabitants/km2 (INE 2010), scattered in many settlements across the countryside, has resulted in a vast paved road network (mean paved road density 2.7km/km2, Llaneza etal. 2012; Fig.1b). There also exists a large network of dirt gravel roads (passable by 4 × 4 wheel drive, Fig.1c), created to serve as access mainly for forestry and agricultural Fig. 1 Galicia, north-western region of Iberian Peninsula, highlighted in black box. A Individual wolf home ranges used in this study (estimated as Minimum Convex Polygons using 100% of GPS locations; n wolves = 25. B Paved road network in Galicia, bold black lines representing motorways, national and regional roads, light roads representing secondary and county maintained roads. C Humanised landscapes of Galicia Biodiversity and Conservation 1 3 purposes. Consequently, the landscape of Galicia is heterogeneous and intensively managed by human activities comprised mainly of cropland (32%), scrubland (11%), and forestry plantations comprising of Eucalyptus spp. and Pinus spp. (43%). The last estimate of the status of wolves in Galicia was carried out between 2013 and 2015, resulting in the estimate of a continuous range distribution of wolves in the area, and 90 reproductive packs (Llaneza etal. 2015). The studied wolves We investigated wolf response to paved roads by studying the spatial behaviour of 25 individuals equipped with GPS-GSM collars (Followit, Sweden). Between 2006 and 2013, wolves were captured with Belisle© leg-hold snares (Edouard Belisle, Saint Veronique, PQ, Canada) and chemically immobilised by intramuscular injection of medetomidine (Domitor®, Merial, Lyon, France; 0.10mg/kg) using a blowpipe. All wolves were evaluated as clinically healthy at the moment of capture, and they only had minor lesions associated with trapping (i.e., skin abrasions). In Spain, wolves north of the Duero River are listed in Annex V of the European Habitats Directive (92/43/EEC). Fieldwork procedures were specifically approved by the Regional Government of Galicia. The wolves included in this study were captured under permits 19/2006, 71/2009, 86/2011, and 095/2013 from the Regional Government of Galicia (Spain). All fieldwork procedures adhered to the Spanish animal welfare regulations (Spanish Decree 53/2013). Sex and age were determined insitu. Age was estimated by dental pattern and tooth wear (Gipson etal. 2000) and wolves were classified into three categories: juveniles (< 1yr, n = 6), sub-adults (1–2 yrs, n = 10) and adults (> 2 yrs, n = 9). All wolves, except 4 pairs, belonged to different packs. We used the locations of GPS collars which were obtained every two hours for this study. Wolves were monitored an average of 156days (range 23–390days). For the purposes of this study, we estimated the home range of every individual as the Minimum Convex Polygon (MCP) using 100% of locations during the entire monitoring period for each wolf (Fig.1a). Accordingly, the mean (± SD) wolf MCP of the 25 wolves studied was 262.05 ± 253.46 km2 (Appendix B). The paved road network withinwolf home ranges: Paved road classes Firstly, we categorised paved roads into four classes depending on their purpose, speed limit and traffic volume (Table 1). We used official information on the Galician paved road network (information provided by the Regional Government of Galicia and Fomento Ministry of Government of Spain) to quantify the extent of the paved road network within every wolf home range (MCP). In addition, we also reviewed this paved road network within each wolf MCP by overlapping the official spatial information provided with highresolution digital orthoimages, since some roads may not be included in the official data (Llaneza etal. 2018). All paved roads that were missing were added to the network manually using ArcGIS (ESRI, California, USA). Paved roads were classified in accordance to their purpose, speed limit and traffic volume, with number of lanes and fencing details given to describe wolf accessibility (Table1). The length of each paved road class within each MCP was measured in km to calculate road class densities (km/km2). Biodiversity and Conservation 1 3 Table 1 Main characteristics of the four paved road classes within Galicia and considered in this study Individual home ranges (estimated as Minimum Convex Polygons using 100% of GPS locations) of the studied wolves were located in A Coruña, Lugo, and Pontevedra provinces DAI daily average intensity Road class Purpose Number of lanes Speed limit (km/h) Fencing Traffic volume (Vehicles D.A.I.) I Motorway Connect main cities across country 4 or more 120 Always fenced A Coruña 10,735 Lugo 3,927 Pontevedra 9,362 II Basic primary (National) Connect areas at a national scale 2 100–90 Rare A Coruña 5,911 Lugo 4,158 Pontevedra 8,058 Additional primary (Regional) Connect areas at a regional scale 2 100–90 Rare A Coruña 4,028 Lugo 1,486 Pontevedra 4,439 III Secondary road Connect towns or villages 2 100–90 Rare A Coruña 1,866 Lugo 674 Pontevedra 1,824 IV County road Connect villages and less frequented areas 2 80 No No traffic statistics available Biodiversity and Conservation 1 3 Traffic flow We obtained data of traffic flow from the official transport records of the Regional Government of Galicia (Autonomous Community maintained roads) and from the Spanish Ministry of Development (motorways and national roads, references in Appendix A). Traffic flow for motorways, national and regional roads, as well as a few secondary roads were obtained from those sources. No data on traffic flow was available for county roads and therefore this class was removed for this specific analysis. For the other three road types within wolf MCPs, traffic flow stations on roads crossed by wolves were utilised, and the mean traffic flow on a daily and every 2h time interval were used to test the influence of traffic volume on wolf response to paved roads. Wolf road‑kill dataset We compiled official records on wolf mortality caused by vehicle collisions between 1991 and 2012 from the Regional Government of Galicia (Appendix F). The sex was known for all 54 dead wolves: 25 (46.3%) female and 29 (53.7%) male. Estimation of age was possible in 48 cases: 27 (50%) adults, 12 (22.2%) sub-adults and 9 (16.7%) juveniles. No age classification was given for 6 individuals (11.1%). Monthly distribution of vehicle collision mortalities was graphed (Appendix G). Data analysis For each individual, consecutive GPS locations were connected via straight-line segments, and these line segments were used to determine crossing rates, by overlapping them with the paved road network. Only line segments derived from effective locations every 2h were considered, removing the small number of cases of missing locations. GPS success rate was very high in this area. For example, for 5 wolves in our dataset and 36,104 locations (range 3654–11,861 per wolf), GPS success rate was, on average, 98% per individual (Planella etal. 2016). When a line segment from the initial GPS location to the successive location was bisected by a road an intersect point was created using ArcGIS. Since we considered a 2h time interval, our approach allowed us to derive a minimum number of crossings. To test for differences in the frequency of wolf crossings in relation to the road class, we analysed the number of crossings on each road class by calculating an “Index Cross” for each wolf, accounting for sampling effort and road length. Thus, we took into account that the number of crosses depended on the number of days that each wolf was sampled (there was a significant and positive correlation between the number of crosses of paved roads and the number of sampling days, Pearson correlation analysis, rp = 0.856, P < 0.0001, n = 25). We also considered the length (km) of every road class within wolf MCPs (there was a significant and positive correlation between the number of crosses and the length of each paved road class, Pearson correlation, rp = 0.474, P = 0.017, n = 25). The Index Cross for each paved road class within a wolf MCP was calculated as follows (Eq.1): (1) Index cross = Number of crosses∕(Number of monitoring days∕Length of road(km)) Biodiversity and Conservation 1 3 where the number of crosses were standardised for all wolves by dividing this figure by the ratio between the number of wolf monitoring days and road length (km). We log-transformed the Index Cross for subsequent analysis. For each wolf, we also separated every Index Cross into three time periods within the circadian cycle. We split the 24-h period into: (i) night (only star and lunar light, i.e., difficult to make out horizon), (ii) twilight (the sun is 6 degrees below the horizon) and (iii) day, the remaining time. In order to account for wolf crepuscular activity (Merrill and Mech 2003; Theuerkauf 2009), we buffered twilight 2h before sunrise and 2h after sunset. Daylight Savings Time was also accounted for. We built general linear mixed models (GLMMs) with Gaussian error distribution and identity link, to compare a set of seven competing models explaining wolf response to paved roads, using as a proxy the Index Cross (response variable), according to age, road class, time period and their interactions. Firstly, we considered (i) the null model, (ii) a model containing the variable wolf age (3 levels), (iii) a model considering the road class (4 levels, Table1), and (iv) a model considering time period (3 levels). Three interaction models were also employed, including the interaction between road class and time period, in order to explore different temporal use of paved roads according to the road class, the interaction between age and road class and the interaction between age and time period. The interactions with age were considered to test for individual differences in crossing rates according to individual attributes. As repeated measurements of the wolf responses to different paved roads are unlikely to be independent for the same individual, the identity of the individual was treated as a random factor in these models. Sex was not considered in the set of competing models since we did not detect differences in crossing rates between the sexes (GLMM using Index Cross as response variable, sex as predictor, and wolf identity as random factor, P = 0.984). In addition, for the small number of roads within wolf MCPs where data on traffic flow was available (n roads = 54), we built another GLMM with Negative Binomial error distribution and log link to evaluate how traffic flow (mean daily number of vehicles) influenced the number of crossings. We used asa response variable the mean daily number of crossings overa given paved road madeby a wolf (i.e., count data, not distributed following a Poisson distribution). The identity of the individual was also treated as a random factor in this model. For this dataset, we compared this model against a model considering road class (i.e., the road class according to Table1), and a model considering both predictors (traffic flow and road class), in order to get insights into the idea of whether traffic flow is more important than the type of road considered. Akaike Information Criterion with a second order correction for small sample size (AICc) was used for model selection (Burnham and Anderson 2010). We also used the Akaike weights (wi) to determine the relative strength of support for each competing model (Burnham and Anderson 2010). We used the “glmmADMB” package for R software (Skaug etal. 2014) to run GLMMs, the ‘‘car” package (Fox etal. 2015) to evaluate the significance levels for model parameters, and the “bbmle” package to calculate Akaike weights (Bolker 2017). Additionally, we also correlated the mean hourly traffic flow against the number of wolf crossings, and the number of mortality events by type of paved road with the Index Cross using a Spearman’s Rank Correlation analysis. Finally, Kruskal–Wallis test was used to test for significant differences in the wolf response (i.e., Index Cross) to traffic flow across time periods. All statistical analyses were performed in R 3.0.2 (R Core Team 2015). Biodiversity and Conservation 1 3 Results On average, the studied wolves persisted in areas with densities of paved roads at the home range level (MCP) ranging from 0.75 to 2.95km/km2, with a mean paved road density of 1.92km/km2 (Appendix B). The majority of wolf MCPs (98%) contained primary and secondary roads (Appendix B), and eight out of twenty-five wolves (32%) had motorways within their MCPs, with 62% of these wolves having crossed them. Paved road length was positively and significantly correlated with MCP home range size (Spearman’s rank correlation, rs = 0.975, n = 25, P < 0.0001). Overall, in 3915 sampling days, we recorded 29,859 wolf crossings over paved roads (mean number of crosses by wolf = 1194, range 43–4172) (Appendix C). Wolf crosses of all paved road classes were recorded at a mean rate of 0.022 crossings/day/km (95% CI 0.016–0.027). Considering this mean rate, and the minimum length of paved roads observed within a wolf home range (39km; Appendix B), this means that the studied wolves were expected to cross a paved road at least once every day. Road class IV (county roads) and class II roads (primary roads) were crossed more frequently, followed by road class III (secondary roads) and road class I (motorways) (Fig.2). The median index cross over class I roads was 0.001 crossings/day/km, 0.018 in class II roads, 0.010 in class III roads and 0.019 in class IV roads (Fig.2). Crossings were most frequent at night (median, 0.014 crossings/day/km), then during dawn/dusk (0.003 crossings/day/km) and daylight (0.001 crossings/day/km) (Fig.2). Adult (0.018 crossings/day/km) and sub-adult (0.028 crossings/day/km) wolves crossed roads more frequently than juveniles (0.009 crossings/ day/km; Fig.2), and all wolves crossed more frequently during night hours (Appendix D). The most parsimonious model explaining the variation in wolf crossings over paved roads was the model considering road class, time period and their interaction (wi = 1; Table2). Wolf crossings over paved roads were significantly different across road types (Wald χ2 = 110.62, d.f. = 3, P < 0.0001; Appendix E); whereas time period and the interaction road class and time period did not show significant influence on wolf Index Cross (both P > 0.234; Appendix E). Traffic flow influenced the number of crossings registered by wolves. We detected a negative and marginal significant effect of traffic flow on the mean daily number of wolf crossings (Wald χ2 = 3.38, d.f. = 1, P = 0.066). The model containing traffic flow showed the highest support (wi = 0.50), compared to the model including road class (wi = 0.26) and the model with both predictors (wi = 0.25) (road class was not significant in both models (P > 0.382). The median response of mean traffic flow differed across time periods (Kruskal–Wallis, H = 367.50, d.f. = 11, P = 0.0001). Interestingly, when traffic flow and number of crossings were examined on a two hour basis, we observed a significant and negative correlation between mean traffic flow and the mean number of crossings (Spearman’s rank correlation, rs:−0.385, n = 708, P = 0.0001; Fig.3). Out of the 54 records of wolves being road-killed in the area between 1991 and 2012, the majority of cases occurred on primary roads (class II) (61%), followed by secondary roads (class III) (28%), county roads (class IV) (9%) and motorways (class I) (2%) (Appendix F). We did not detect a significant correlation between the mean Index Cross by road class and the number of road kills (Spearman’s rank correlation, rs = 0.634, n = 4, P = 0.365; Fig.4). But, the mean Index Cross for primary roads (class II) (0.026 crossings/day/km), and the number of road-killed wolves was the highest (Fig.4). By looking at the monthly distribution of vehicle collision mortalities, it was observed that Biodiversity and Conservation 1 3 Jȩdrzejewski W, Niedzialkowska M, Mysiajek RW, Nowak S, Jedrzejewska B (2005) Habitat selection by wolves Canis lupus in the uplands and mountains of southern Poland. 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