338 E. Mizsei et al.
Potential for altitudinal range shift Upward altitudinal shifts of species’ ranges are a common response to climate change but are limited by mountain height.We considered this by calculating a variable describing potential altitu- dinal shift based on elevation data from the Shuttle Radar Topography Mission (Jarvis et al., 2008) at a resolution of 30 × 30 m. We calculated the ratio of the elevation of the highest site in the species distribution model patch and the elevation of individual raster cells using the calc function from the R package raster (Hijmans, 2016)and assigned this value to characterize the opportunity for altitudinal shift for each raster cell.
Habitat alteration by humans We inferred habitat alter- ation from the presence and density of artificial objects in the landscape (Plieninger, 2006). We identified 3,680 ob- jects using satellite imagery from Google Earth (Google, Mountain View, USA) from 2017. These were mainly roads (2,079), livestock folds (768), shepherds’ buildings (494), drinking troughs for livestock (325), other buildings (e.g. ski resort infrastructure, 313) and open-pit mines (194). We performed kernel smoothing to estimate spatial density of these objects using the bkd2D function of the R package Kernsmooth (Wand, 2015). Conservation values were speci- fied to be inversely related to the density of artificial objects in the raster cells.
Habitat degradation by grazing Because grazing by live- stock (sheep and cattle) is a major driver of habitat quality in V. graeca habitats (Mizsei et al., 2016), we estimated the degree of habitat degradation by quantifying grazing pressure from the mean annual change in phytomass. We used two seasonal intervals: (1) after snowmelt but before the start of livestock grazing (summer, 1–15 July) and (2) at the end of grazing but before snowfall (autumn, 1–15 October), for 2003, 2007 and 2013. We estimated grazing pressure by changes in phytomass between summer and au- tumn, as quantified by NDVI values taken before and after the livestock grazing period. We estimated the phytomass decrease fromsummer to autumn by subtracting the summer NDVI values from the autumn values.We then upscaled the 30 × 30 m resolution to 100 × 100 m using the resample function of the R package raster (Hijmans, 2016).
Disturbance by traditional grazing Grazing pressure is not homogeneously distributed in these high mountain land- scapes and is concentrated near livestock folds. To account for this spatial heterogeneity we calculated a proxy for disturbance by livestock grazing (Blanco et al., 2008). We selected 768 livestock folds from the artificial objects data- set and weighted them by phytomass decrease values. We then performed an inverse weighted distance interpolation
using the spatial linear model fit of grazing pressure values over distance from the coordinates of the folds using the R packages gstat and spatstat (Baddeley et al., 2015).
Spatial prioritization We used the Zonation systematic conservation planning tool for spatial prioritization, with our raster data layers as inputs, because this tool primarily uses raster data (Lehtomäki & Moilanen, 2013). We per- formed the prioritization separately for both future cli- mate scenarios (A1B, B1).We used the R package rzonation (Morris, 2016), which runs Zonation 1.0 (Moilanen, 2007). Finally, we calculated the mean conservation value of each cell, to compare the results of the spatial priorities.
Evaluation of current protection and opportunities for future conservation We used the high-priority raster cell net- works obtained by the spatial prioritization to evaluate the degree of overlap with current protected areas. We aimed to identify areas where current protection ensures the long- term persistence of V. graeca populations and areas where further conservation actions such as protection, habitat management and/or restoration are necessary to ensure long-term persistence. We first converted the high-priority networks identified in the spatial prioritization into poly- gons and then intersected them with polygons of current protected areas. Spatial data on current protected areas were obtained from the World Database of Protected Areas (UNEP-WCMC, 2018), which included areas pro- tected by both national and European Union laws (Natura 2000 sites). Finally, we examined the interrelationships of the conservation value variables using a cluster analysis, to identify closely related variables that can identify targets for future conservation.
Results
Spatial priorities for long-term persistence The consensus of Zonation solutions for the two future climate scenarios identified eight separate mountain ranges as high priority areas (priority rank .0.8; Fig. 3). However, only three of these mountain ranges are known to hold at least 10 km2 of contiguous suitable habitat that will also be suitable by the 2080s (Nemercke in Albania, Tymfi and the Lakmos- Tzoumerka chain in Greece) and can thus be considered as key areas for the persistence of V. graeca (Fig. 4). Other known populations have high probability of extinction in the future, mainly as a result of climate change. Some mountain ranges that are not known to hold V. graeca po- pulations are high-priority areas (Fig. 3). The Zonation so- lutions for both climate scenarios protected a larger area than the sum of the areas with high mean conservation value (Fig. 3). This was because core sites with high spatial
Oryx, 2021, 55(3), 334–343 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000322
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