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Evaluating Methods for Identifying Large Mammal Road Crossing Locations: Black Bears As a Case Study

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Evaluating Methods for Identifying Large Mammal Road Crossing Locations: Black Bears As a Case Study Landscape Ecol (2020) 35:1799–1808 https://doi.org/10.1007/s10980-020-01057-x (0123456789().,-volV)( 0123456789().,-volV) RESEARCH ARTICLE Evaluating methods for identifying large mammal road crossing locations: black bears as a case study Katherine A. Zeller . David W. Wattles . Stephen Destefano Received: 26 March 2020 / Accepted: 11 June 2020 / Published online: 20 June 2020 Ó This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2020 Abstract methods in identifying wildlife road crossing and Context Roads have several negative effects on large mitigation locations. mammals including restricting movements, isolating Objectives We examined five commonly used meth- populations, and mortality due to vehicle collisions. ods to identify road crossing locations for wildlife to Where large mammals regularly cross roads, driver determine which method best captured empirical safety is also a concern. Wildlife road crossing crossing locations. structures are often proposed to mitigate the negative Methods We used GPS collar data on black bears in effects on wildlife and human safety. However, few Massachusetts, USA, to estimate road crossing loca- studies have compared the efficacy of different tions with a road crossing resource selection function, factorial least-cost paths, resistant kernels, Cir- cuitscape, and an individual based movement model. We evaluated model performance on each road class Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10980-020-01057-x) con- and all road classes combined with a hold out road tains supplementary material, which is available to authorized crossing data set. users. Results All methods performed well, but Cir- K. A. Zeller (&) Á S. Destefano cuitscape consistently outperformed the other meth- Massachusetts Cooperative Fish and Wildlife Research ods, both for each road class and all road classes Unit, University of Massachusetts, 160 Holdsworth Way, combined. Amherst, MA 01003-9285, USA Conclusions Road mitigation efforts for wildlife are e-mail: [email protected] often costly and have a long-term footprint on the S. Destefano landscape, therefore, it is important to select locations e-mail: [email protected] for these efforts that provide functional connectivity Present Address: for wildlife. We are the first to compare different road K. A. Zeller crossing models for large mammals across a large Aldo Leopold Wilderness Research Institute, Rocky study area and on different road types. Our results Mountain Research Station, US Forest Service, 790 E Beckwith Avenue, Missoula, MT 59801, USA provide information to assist researchers and man- agers in selecting an analytical method for identifying D. W. Wattles potential road mitigation locations for large mammals. Massachusetts Division of Fisheries and Wildlife, 1 Rabbit Hill Road, Westborough, MA 01581, USA e-mail: [email protected] 123 1800 Landscape Ecol (2020) 35:1799–1808 Keywords Road ecology Á Connectivity Á Wildlife Á telemetry data to identify consistent road crossing Massachusetts Á Ursus americanus Á Road mitigation Á locations from collared individuals (e.g. Bastille- Road crossing Rousseau et al. 2018). While others use road crossing locations identified by collar data to model road crossing probability across an entire road network (Lewis et al. 2011; Zeller et al. 2018b). There are also Introduction studies that identify probable crossing locations with connectivity models such as least-cost path (Adri- Roads cover about 20% of the earth’s surface and aensen et al. 2003; Loro et al. 2015), factorial least- fragment natural areas into disjunct patches, only 7% cost path (FLCP Cushman et al. 2009, 2013), resistant of which are larger than 100 km2 (Ibisch et al. 2016). kernel (Compton et al. 2007; Cushman et al. 2014), or Large mammals, due to their large home range Circuitscape models (McRae et al. 2008, 2012). These requirements, must often cross roads to access critical connectivity models are typically run across a resis- resources and survive (Fahrig and Rytwinski 2009). tance surface derived from expert opinion, telemetry, Navigating the road and transportation network can or genetic data (Zeller et al. 2012) and sites along cause direct mortality due to vehicle collisions (Seiler roadways with high connectivity values are considered and Helldin 2006), but roads have also been shown to for mitigation. Lastly, high-use road crossing locations restrict movements, isolate populations, and reduce can be identified with individual-based movement gene flow, which can all have indirect negative effects models (IBMMs; e.g. Quaglietta et al. 2019). With on survival (Forman et al. 2003; Balkenhol and Waits IBMMS, it is assumed a location that has a high 2009; Gustafson et al. 2017). Large mammals crossing frequency of crossings by simulated individuals would roads also pose risks to human safety. Annually, in the also have a high frequency of crossings by real animals United States, there are over one million large and that these locations might be appropriate for mammal-vehicle collisions resulting in approximately mitigation efforts. 26,000 human injuries and 200 deaths (Huijser et al. When identifying locations for road mitigation 2008). measures, sometimes the choice of approach is limited Road mitigation efforts are often proposed to to the data at hand, but often, one can implement a alleviate the negative effects on wildlife and increase number of the methods outlined above. For example, if human safety. Mitigation measures may include GPS-telemetry data are available, those data can be warning signs, reduced speed limits, large animal used to model road crossing locations across a road detection systems, fencing, construction of crossing network. Or, GPS-telemetry data can be used to model structures (e.g. wildlife overpasses or underpasses), or resistance to movement for a study area, which can a combination of these measures (Clevenger and serve as the basis for running connectivity models or Huijser 2011). For example, combining fencing with IBMMs. The direct modeling of wildlife vehicle crossing structures can reduce large mammal roadkill collisions or road crossing locations is attractive by 83% (Rytwinski et al. 2016). However, retrofitting because the analysis is localized to the road network existing road networks with mitigation measures like and incorporates specific attributes along the roadway crossing structures can be costly, therefore, it is that may affect crossing behavior. However, the important to select locations along the roadway that connectivity models are also appealing because they will be the most successful at providing functional contextualize crossing locations in the larger land- connectivity for wildlife. scape by modeling movement and flow not only at Currently, there are myriad methods for identifying roadways, but also in areas leading up to and away locations for road mitigation efforts. One method is to from roadways. Therefore, these two different use wildlife-vehicle collision locations to create approaches represent two different scales for modeling models of collision risk for a species or groups of road crossings and potential mitigation locations. The species, and then identify mitigation locations from IBMM models might be considered a hybrid of these these results (Gunson et al. 2011). There are multiple scales since they model movement across the land- methods based on animal tracking data (e.g. Schuster scape, but the result is ultimately an iterative process et al. 2013). Some studies use high-frequency GPS- of fine scale movement decisions. 123 Landscape Ecol (2020) 35:1799–1808 1801 To our knowledge, there have been only a few Methods studies comparing the efficacy of different road crossing identification methods for wildlife. Cle- Black bear and road data venger et al. (2002) compared local-scale approaches for black bears (Ursus americanus) in Banff National The Massachusetts Division of Fisheries and Wildlife Park informed by expert opinion, literature, and almost exclusively collars females due to the impor- empirical data and found the empirical data outper- tance of females in driving black bear reproductive formed the other two methods at identifying highway output and population trends. Therefore, we used data crossing locations. Cushman et al. (2014), compared a from GPS collared female black bears with established local-scale approach and two landscape-scale home ranges in the central and western parts of the approaches for black bears in Idaho. They assessed Commonwealth of Massachusetts to develop and the ability of two resistance surfaces (one derived from evaluate the performance of road crossing models genetic data and one from telemetry data) and two (Fig. 1). The collar fix interval was either 15 or connectivity models (FLCPs and resistant kernels) to 45-min. Bear capture and handling procedures and predict black bear highway crossing locations. They GPS data post-processing methods are detailed in found that telemetry data outperformed genetic data Zeller et al. (2018a, b). for developing the resistance surface and that the Bears collared from 2012 to 2017 (36 individuals; FLCP method outperformed the resistant kernel 75 bear-years) were used in developing the road method. Bastille-Rousseau et al. (2018) compared crossing models and bears collared in 2018 (17 results of different samples of GPS data
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