DOI: 10.5061/dryad.c59zw3rfg
title: The road to success and the fences to be crossed: Considering multiple infrastructure in landscape connectivity modelling (Dataset and script)

description: Linear infrastructure represent a barrier to movement for many species,
 reducing the connectivity of the landscapes in which they reside. Of all
 linear infrastructure, roads and fences are two of the most ubiquitous,
 and are understood to reduce landscape connectivity for wildlife. However,
 what is often neglected consideration is a holistic approach of modelling
 the effects of multiple types of linear infrastructure simultaneously. Few
 studies have examined this, typically assessing the impacts of a singular
 kind of infrastructure on landscape connectivity. Therefore, the aim of
 this study is to address the relative importance of considering multiple
 kinds of linear infrastructure in landscape connectivity modelling. We
 utilised presence data of red deer Cervus elaphus and
 wild boar Sus scrofa in Doñana Biosphere Reserve (Spain) to
 generate a sequential approach of scenarios of landscape connectivity;
 firstly only with environmental variables, secondly with roads as the sole
 infrastructure, thirdly with the addition of fences, and finally with the
 further addition of fences and wildlife road-crossing structures. We found
 that the connectivity of the landscape was greatly affected by the
 addition of fences and wildlife road-crossing structures in both species,
 with fences in particular causing considerable alterations to estimated
 movement pathways. Our finding impresses a need to consider multiple
 different types of linear infrastructure when modelling landscape
 connectivity to enable a more realistic view of wildlife movement and
 inform mitigation and conservation measures more accurately.
description: <strong>Species occurrence
 information</strong> Red deer and wild boar are
 ungulates commonly found in our study area. Presence–absence data
 used here was collected by D’Amico et al. (2016) using 40 randomly
 distributed transects, each 200 m long, perpendicular to and beginning
 from major paved or unpaved roads. The transects were positioned a minimal
 distance of 2 km apart and were divided into twenty 10 m-long segments.
 Presence of the focal species was determined by walking along the transect
 and georeferencing pellets found within a 1 m-wide buffer zone. For more
 detail, see methods in D’Amico et al. (2016).
 <strong contenteditable="true">Modelling </strong><strong contenteditable="true">l</strong><strong contenteditable="true">andscape </strong><strong contenteditable="true">c</strong><strong contenteditable="true">onnectivity</strong>
 Landscape connectivity models were built using CIRCUITSCAPE
 software (McRae et al. 2008). CIRCUITSCAPE relies on electrical circuit
 theory, creating multiple random movement pathways between focal nodes
 over a landscape permeability map. D’Amico et al. (2016) calculated the
 probability of occurrence of both species to the entire study area, using
 first environmental variables alone, and then environmental and
 road-related variables together. Here, we used the probability of
 occurrence maps from D’Amico et al. (2016) in CIRCUITSCAPE (i.e. we
 assumed that higher likelihood of occurrence is related to higher
 landscape permeability). The first layers of presence
 probability from D’Amico et al. (2016) (without considering roads) fed the
 baseline scenario of connectivity (i.e. environmental variables only),
 where movement was assumed to be conditioned by habitat quality, without
 considering roads, fences or passages. We then created three other
 scenarios for each species, where in the first one we overlaid roads
 (hereafter road scenario), in the second one we overlaid both roads and
 fences (hereafter fence scenario), and a third scenario where we overlaid
 roads, fences and wildlife passages (hereafter mitigated fence
 scenario). The second layers of presence probability
 from D’Amico et al. (2016) (also considering roads) fed the road scenario,
 where movement was assumed to be conditioned by environmental variables
 and roads. For the fence scenario, we assumed that fences bordering main
 roads were impermeable to our study species, functioning as a barrier.
 Well managed fences, such as those paralleling roads, are highly
 impermeable to ungulates, whereas livestock fences can be more readily
 permeable (Burkholder et al. 2018, Laguna et al. 2022). In fact,
 throughout seven years of roadkill fieldwork in this region, we have
 recorded no roadkills of these species in fenced roads (D’Amico et al.
 2015), suggesting that road fences do prevent access by ungulates. As for
 the livestock fences, scattered throughout the study area, we assumed that
 they represent a less restrictive movement filter. As there is no detailed
 information on the degree of barrier they represent, we assumed, based on
 personal observations, a conservative value in which they reduce the
 permeability value of the habitat they cross by 20%.
 The mitigated fence scenario is similar to the fence scenario,
 except that we increase the permeability in places where wildlife passages
 exist, allowing movement through these areas. Overpasses were assumed to
 be completely permeable, but underpasses are likely to differ in their
 level of permeability. Nevřelová et al. (2022) defined levels of usability
 for red deer and wild boar to underpasses based on their OI, but otherwise
 there is limited evidence as to the level of underpass permeability to
 ungulates. Therefore, we assigned permeability values for road underpasses
 and red deer and wild boar based on their OI. We
 utilised CIRCUITSCAPE using the wall-to-wall omnidirectional approach
 (Pelletier et al. 2014) for producing regional-scale maps of connectivity.
 Omnidirectional methods use the circuit theory algorithm to model the flow
 of electric current across a resistance grid from all directions,
 originating from the perimeter of the study area (Koen et al. 2014,
 Pelletier et al. 2014). The wall-to-wall models allow the flow of
 electrical current between thin, parallel source and ground strips placed
 on opposite sides of a buffered study region. Here, we used a buffer
 distance of 10 km, where the land use outside our study area was randomly
 assigned for each grid cell, to allow the diffusion of current before
 entering the study area. The area occupied by the ocean was ignored (NA
 value). The flow of current is modelled using the
 ’advanced mode’ in CIRCUITSCAPE across the region from North to South,
 South to North, East to West and West to East. The resulting current maps
 in each of the four directions are then averaged together for a final map
 of current density, which we used as maps of expected use intensity. Each
 map of use intensity was scaled to range between 0 and 100, to allow
 comparisons across species and scenarios of use intensity.
 Patterns of landscape connectivity were visually inspected
 considering the use intensity across the landscape and the different
 scenarios (i.e. considering the role of the roads, fences and passages).
 Additionally, in order to better visualise the results, we divided our
 study area into three distinct sub-areas: the North sub-area (i.e. north
 of the El Rocío lagoon, including both National and Natural Park), the
 South sub-area (i.e. the core of the National Park, south of the El Rocío
 lagoon and east of the paved road road A-483 El Rocío-Matalascañas), and
 the West sub-area (i.e. mostly including Natural Park, west of the paved
 road A-483 El Rocío-Matalascañas, Fig. 1). A two-way analysis of variance
 (ANOVA) was employed to determine whether there were significant
 differences in estimated use intensity among areas and among scenarios,
 for each focal species. Tukey’s honestly significant difference (HSD) test
 was applied to identify the specific groups that differed significantly
 from one another. <strong contenteditable="true">References</strong>
 Burkholder, E. N., Jakes, A. F., Jones, P. F., Hebblewhite, M.
 and Bishop, C. J. 2018. To jump or not to jump: mule deer and white-tailed
 deer fence crossing decisions. – Wildl. Soc. Bull. 42: 420–429.
 D’Amico, M., Périquet, S., Román, J. and Revilla, E. 2016. Road
 avoidance responses determine the impact of heterogeneous road networks at
 a regional scale. – J. Appl. Ecol. 53: 181–190. Koen,
 E. L., Bowman, J., Sadowski, C. and Walpole, A. A. 2014. Landscape
 connectivity for wildlife: development and validation of multispecies
 linkage maps. – Methods Ecol. Evol. 5: 626–633. Laguna,
 E., Barasona, J. A., Carpio, A. J., Vicente, J. and Acevedo, P. 2022.
 Permeability of artificial barriers (fences) for wild boar (<em contenteditable="true">Sus scrofa</em>) in
 Mediterranean mixed landscapes. – Pest Manage. Sci. 78:
 2277–2286. McRae, B. H., Dickson, B. G., Keitt, T. H.
 and Shah, V. B. 2008. Using circuit theory to model connectivity in
 ecology, evolution, and conservation. – Ecology 89: 2712–2724.
 Nevřelová, M., Lehotská, B. and Ružičková, J. 2022. Methodology
 of wildlife underpasses attractiveness assessment. – Ekológia
 (Bratislava) 41: 172–182. Pelletier, D.,
 Clark, M., Anderson, M. G., Rayfield, B., Wulder, M. A. and Cardille, J.
 A. 2014. Applying circuit theory for corridor expansion and management at
 regional scales: tiling, pinch points, and omnidirectional connectivity. –
 PLoS One 9: e84135.
description: # The road to success and the fences to be crossed: considering multiple
 infrastructure in landscape connectivity modelling (Dataset and script)
 [https://doi.org/10.5061/dryad.c59zw3rfg](https://doi.org/10.5061/dryad.c59zw3rfg) Images showing presence probability for both study species, and R script implemented to build landscape connectivity models in CIRCUITSCAPE. ## Description of the data Landscape connectivity models were built using CIRCUITSCAPE software. This software relies on electrical circuit theory, creating multiple random movement pathways between focal nodes over a landscape permeability map. D’Amico et al. (2016) calculated the probability of occurrence of both species to the entire study area, using first environmental variables alone, and then environmental and road-related variables together. Here, we used the probability of occurrence maps from D’Amico et al. (2016) in CIRCUITSCAPE (i.e. we assumed that higher likelihoodof occurrence is related to higher landscape permeability). The first layers of presence probability from D’Amico et al. (2016) (without considering roads) fed thebaseline scenario of connectivity (i.e. environmental variables only), where movement was assumed tobe conditioned by habitat quality, without considering roads, fences or passages. We then created three other scenarios for each species, where in the first one we overlaid roads (hereafter road scenario), in the second one we overlaid both roads and fences (hereafter fence scenario), and a thirdscenario where we overlaid roads, fences and wildlife passages (hereafter mitigated fence scenario). The second layers of presence probability from D’Amico et al. (2016) (also considering roads) fed the road scenario, where movement was assumed to be conditioned by environmental variables and roads. BottingetalDatasetRDenv = TIF image showing red deer presence probability in our study area considering only environmental variables. BottingetalDatasetRDroads = TIF image showing red deer presence probability in our study area considering only environmental variables and road-related variables. BottingetalDatasetWBenv = TIF image showing wild boar presence probability in our study area considering only environmental variables. BottingetalDatasetWBroads = TIF image showing wild boar presence probability in our study area considering only environmental variables and road-related variables. ## Sharing/Access information Data was derived from the following sources: D'Amico M., Périquet S., Román J., Revilla E. (2016). [Road avoidance responses determine the impact of heterogeneous road-networks at a regional scale](http://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12572/full). Journal of Applied Ecology 53(1): 181-190. DOI: 10.1111/1365-2664.12572. ## Code/Software BottingetalRScript = R script implemented to build landscape connectivity models in CIRCUITSCAPE.