Evaluation of habitat patches importance to desert landscape connectivity for three fox species, using resistance kernel and graph network

Document Type : Research Paper

Authors

1 Faculty of Natural Resources, Isfahan University of Technology, Esfahan, Iran

2 Associate Professor, Faculty of Natural Resources, Isfahan University of Technology, Esfahan, Iran

Abstract

Despite the importance of central Iranian desert as a refuge for threatened carnivores, conservation strategies for these species have been hindered by scarcity of data on distribution and habitat connectivity. The present study aimed to 1) determine the ecogeographical variables affecting distribution of three fox species including sand fox (Vulpes rueppellii), Blandford’s fox (Vulpes cana), and red fox (Vulpes vulpes); 2) identify important core habitats and dispersal corridors of the three fox species; and 3) evaluate habitat patch importance to landscape connectivity. At first step, ensemble models were built for each species using three distribution algorithms and 12 ecogeographic variables. Distribution of the foxes was affected by annual precipitation, shrubland density, human settlement density and topographic roughness. In the next step, negative exponential function was used to convert ensemble distribution maps to resistance surfaces. Core patches and significant corridors were predicted using resistance kernel approach. Most of the habitat core patches were located in Protected Areas (PAs), which could be related to high landscape resistance outside the areas under protection. Finally, the importance of identified core patches was evaluated using graph network. The patch importance to connectivity was significantly correlated with core extent, mean of relative density of dispersing individuals and probability of occurrence in patch. Results showed that the habitat quality indices are more effective than habitat quantity in predicting landscape connectivity. The obtaining results suggest that effective conservation of carnivores demands for an integrated landscape management aiming at functional connectivity among habitat patches.

Keywords


[1]. Abade, L., Macdonald, D.W., & Dickman, A.J. (2014). Using landscape and bioclimatic features to predict the distribution of lions, leopards and spotted hyaenas in Tanzania’s Ruaha landscape, PLOS ONE, 5 | e96261.
[2]. Ahmadi, M., Balouchi, B.N., Jowkar, H., Hemami, M.R., Fadakar, D., Malakouti-Khah, S., & Ostrowsk, S. (2017). Combining landscape suitability and habitat connectivity to conserve the last surviving population of cheetah in Asia. Diversity and Distribution, 23, 592–603.
[3]. Araujo, M.B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22, 42–47.
[4]. Chetkiewicz, C.L.B., Clair, C.C., & Boyce, M.S. (2006). Corridors for conservation: integrating pattern and process. Annual Reviews of Ecology and Evolutionary Systems, 37, 317–342.
[5]. Compton, B., McGarigal, K., Cushman, S.A., & Gamble, L. (2007). A resistant kernel model of connectivity for vernal pool breeding amphibians. Conservation Biology, 21, 788–799
[6]. Crooks, K.R., & Sanjayan, M. (2006). Connectivity conservation. Cambridge, U.K.: Cambridge University Press
[7]. Cushman, S.A., Landguth, E.L., & Flather, C.H. (2013). Evaluating population connectivity for species of conservation concern in the American Great Plains. Biodiversity Conservation, 22, 2583–2605.
[8]. Cushman, S.A., McKelvey, K.S., & Schwartz, M.K. (2009). Using empirically derived source-destination models to map regional conservation corridors. Conservation Biology, 23, 368–376.
[9]. Deleo, J.M. (1993). Receiver operating characteristic laboratory (ROCLAB): Software for developing decision strategies that account for uncertainty. In: Proceedings of the second international symposium on uncertainty modeling and analysis 318-325.
[10]. Di Minin, E., Hunter, L.T.B., Balme, G.A., Smith, R.J., Goodman, P.S., & Slotow, R. (2013). Creating larger and better connected protected areas enhances the persistence of big game species in the Maputaland- Pondoland-Albany biodiversity hotspot, PLOS ONE 8, 1–14
[11]. Dormann C.F.M., McPherson, J.B., Arau´jo, M., Bivand, R., & Bolliger, J. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30, 609–628.
[12]. Elith, J., Graham, H., Anderson, C. P., Dudı´k, R., & Ferrier, M. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.
[13]. Erfanian, B., Mirkarimi, S.B., Salman Mahini, A., & Rezaei, H.R. (2013). A presence-only habitat suitability model for Persian leopard Panthera pardus saxicolor in Golestan National Park, Iran. Wildlife Biology, 19, 170-178. 2013
[14]. Estrada, E., & Bodin, O. (2008). Using network centrality measures to manage landscape connectivity. Ecological Applications, 18, 1810–1825.
[15]. Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology and Systematic,34, 487–515.
[16]. Flint, L.E., & Flint, A.L. (2012). Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Ecological Processes, 12, 123-140.
[17]. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 12, 1965–1978.
[18]. Hobson, R.D. (1972). Surface roughness in topography: quantitative approach. In: Chorley, R.J. (ed). Spatial analysis in geomorphology. Metheur, London: 225-245.
[19]. Keeley, A.T.H., Beier, P., & Gagnon, J.W. (2016). Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities. Landscape Ecology, 31, 2151–2162.
[20]. Khosravi, R., Hemami, M., Malekian, M., Flint, A., & Flint, L. (2016). Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model. Turkish Journal of Zoology, 40, 574-585.
[21]. Khosravi, R., Hemami, M.R., & Cushman, S.A. (2018). Multispecies assessment of core areas and connectivity of desert carnivores in central Iran. Diversity and Distributions, 24, 193-207
[22]. Landguth, E.L., Hand, B.K., Glassy, J., & Cushman, S.A. (2012). UNICOR: a species connectivity and corridor network simulator. Ecography, 35, 9–14.
[23]. Landisj, R., & Koch, G.G. (1997). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.
[24]. Moqanaki, E.M., & Cushman, S.A. (2016). All roads lead to Iran: Predicting landscape connectivity of the last stronghold for the critically endangered Asiatic cheetah. Animal Conservation, 11, 1367-9430.
[25]. Omidi, M. (2008). Analyzing and modelling spatial distribution of leopard (Panthera pardus saxicolor) in Kolahghazi National Park, In Isfahan Province. M.Sc. thesis, College of Environmental Science, Islamic Azad University Science and Research Campus, Tehran, Iran (in Farsi).
[26]. Ray, N., Lehmann, A., & Joly, P. (2002). Modelling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability. Biodiversity Conservation, 11, 2143–2165
[27]. Revilla, E., Wiegand, T., Palomares, F., Ferreras, P., & Delibes, M. (2004) Effects of matrix heterogeneity on animal dispersal: from individual behavior to metapopulation-level parameters. The American Naturalist, 164, 130–153.
[28]. Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Ritchie, E.G., Hebblewhite, M., Berger, J., Elmhagen, B., Letnic, M., Nelson, M.P., Schmitz, O.J., Smith, D.W., Wallach, A.D., & Wirsing, A.J. (2014).  Status and ecological effects of the world’s largest carnivores. Science, 343, 124- 148.
[29]. Rodrıguez-Soto, C., Monroy-Vilchis, O.,  Maiorano, L., Boitani, L., Faller, J.C., Briones, M.A., Nunez, R., Rosas-Rosas, O., Ceballos, G., & Falcucci, A. (2011). Predicting potential distribution of the jaguar (Panthera onca) in Mexico: identification of priority areas for conservation. Diversity and Distributions, 17, 350–361.
[30]. Rosenberg, D.K., Noon, B.R., & Meslow, E.C. (1997). Biological corridors: form, function, and efficacy. Bioscience, 47, 677–687
[31]. Saura, S., & Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landscape Urban Plan, 83, 91–103
[32]. Saura, S., & Rubio, L. (2010). A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. Ecography, 33, 523–537.
[33]. Saura, S., & Torné, J. (2009). Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environmental Modeling Software, 24, 135–139
[34]. Taylor, P.D., Fahrig, L., Henein, K., & Merriam, G. (1993). Connectivity is a vital element of landscape structure. Oikos, 68, 571-573
[35]. Thuiller, W., Georges, D., Engler, R., & Breiner, F. (2016). Package ‘biomod2 Ensemble Platform for Species Distribution Modeling.
[36]. Urban, D., & Keitt, T.H. (2001). Landscape connectivity: a graph- theoretic perspective. Ecology, 82, 1205–1218.
[37]. Warren, D., Glor, R., & Turelli, M. (2010). ENMTools: a toolbox for comparative studies of environmental niche models. Ecography, 33, 607–611.
[38]. Wiens, J.A. (2001). The landscape concept of dispersal. In: Clobert J., Danchin, E., Dhondt, A.A., & Nichols, J.D (eds). Dispersal. Oxford University Press, New York: 96–109
[39]. Zarco-Gonzalez, M., Monroy-Vilchis, O., Rodrıguez-Soto, C., & Urios, V. (2012). Spatial factors and management associated with livestock predations by Puma concolor in Central Mexico. Human Ecology, 40, 631–638.
[40]. Zeller, K.A., McGarigal, K., & Whiteley, A.R. (2012). Estimating landscape resistance to movement: a review. Landscape Ecology, 27, 777–797
[41]. Zhao, H., Liu, S., Dong, S., Su, X., Liu, Q., & Deng, L. (2014). Characterizing the importance of habitat patches in maintaining landscape connectivity for Tibetan antelope in the Altun Mountain National Nature Reserve, China. Ecological research, 29, 1065–1075.
[42]. Zheng, B., & Agresti, A. (2000). Summarizing the predictive power of a generalized linear model. Statistics in Medicine, 19, 1771–1781.