Forthcoming

Habitat Suitability Modelling for Feline Species in Jordan: A tool for Climate-Responsive Conservation Planning

Authors

  • Ehab Eid IUCN SSC Steering Committee member. Lutfi Queder Street. Al Yadodah. 11610 Amman. Jordan.
  • Alaaeldin Soultan Department of Ecology, Swedish University of Agricultural Sciences. Box 7044, 750 07 Uppsala, Sweden
  • Husam Elalqamy Ministry of Forests Land, Natural Resources Operations and Rural Development FLNRORD, Prince George, British Columbia, Canada

DOI:

https://doi.org/10.22120/jwb.2022.290322.1452

Keywords:

Caracal caracal, Felis chaus, Felis margarita, Felis silvestris, Habitat suitability, Jordan

Abstract

Three of the four known feline species in Jordan are categorized as critically endangered, according to the latest Red List assessment of mammals in Jordan, of which caracal: Caracal caracal, sand cat Felis margarita, and jungle cat Felis chaus. The fourth species, discussed within this paper – the wild cat Felis silvestris, is a species of least concern. Human activities such as hunting, poisoning, habitat destruction, and fragmentation are among the pressures seriously affecting the small and restricted populations of critically endangered felines. This study is the first to provide predictions on habitat suitability for the four species based on the two Representative Concentration Pathways (RCPs), predictions of how greenhouse gas concentration in the atmosphere, of 2.6 (representing “very stringent” corrections to the number of greenhouse gases accumulating in the atmosphere) and 8.5 (the “business-as-usual” or also known as the “worst-case scenario”). Results showed an alarming decline in suitable habitats for all species. The sand cat is predicted to lose its entire suitable habitats in 2050 and 2070 according to RCP 8.5, while both the caracal and jungle cat are to face the very precarious pressure of declined areas of suitable habitat. Jordan’s network of protected areas was deemed inadequate, according to this study, to protect the feline species and maintain their population. As potential solutions to counter the combined anticipated impacts occurring from both human activities and anticipated climate forecasts, it is necessary to strengthen the enforcement of environmental policies intended to protect reserves and natural areas, strengthen ex-situ conservation measures, minimize human pressures, to cope with the predicted habitats loss in the future, and to review the current network of protected areas.

References

Abu-Baker, M., Nassar, K., Rifai, L., Qarqaz, M., Al-Melhim, W. and Amr, Z. (2003). On the current status and distribution of the jungle cat Felis chaus in Jordan (Mammalia: Carnivora). Zoology in the Middle East. 30: 5-10.

Ahmed, K., Sachindra, D.A, Shahid, S., Demirel, M.C., Chung, E.S. (2019). Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences, 23: 4803–4824.

Aiello‐Lammens, E., Matthew, B., Robert, A., Radosavljevic, A., Vilela, B., Anderson, R.P. (2015). spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography, 38: 541–545. https://doi.org/10.1111/ecog.01132.

Akçakaya, H.R. and Atwood, J.L. (1997). A habitat-based metapopulation model of the California Gnatcatcher. Conservation Biololgy. 11: 422–434.

Allouche, O., Tsoar, A., and Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa, and the true skill statistic (TSS). Journal of Applied Ecology, 43: 1223–1232.

Al-Qinna, M. (2018). Analyses of Climate Variability in Jordan using Topographic Auxiliary Variables by the Cokriging Technique. Jordan Journal of Earth and Environmental Sciences. 9: 67-74.

Amr, Z. (2012). Mammals of Jordan. 2nd Edition. Al Rai Press. Amman, 308 pp.

Amr, Z.S., M. Abu Baker and L. Rifai (2004). Mammals of Jordan. Denisia 14: 437–465.

Araújo, M.B., and New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22: 42–47.

Bagchi, R., Crosby, M., Huntley, B., Hole, D.G., Butchart, S.H.M., Collingham, Y., Kalra, M., Rajkumar, J., Rahmani, A., Pandey, M., Gurung, H., Trai, L.T., Van Quang, N., and Willis, S.G. (2013). Evaluating the effectiveness of conservation site networks under climate change: accounting for uncertainty. Global Change Biology, 19: 1236–1248.

Bagchi, R., Hole, D.G., Butchart, S.H.M., Collingham, Y.C., Fishpool, L.D., Plumptre, A.J., Owiunji, I., Mugabe, H., and Willis, S.G. (2018). Forecasting potential routes for movement of endemic birds among important sites for biodiversity in the Albertine Rift under projected climate change. Ecography (Cop.),41: 401–413.

Baker, D.J., Hartley, A.J., Burgess, N.D., Butchart, S.H.M., Carr, J.A., Smith, R.J., Belle, E., and Willis, S.G. (2015). Assessing climate change impacts for vertebrate fauna across the West African protected area network using regionally appropriate climate projections. Diversity and Distribution, 21: 991–1003.

Baker, D.J., Hartley, A.J., Butchart, S.H.M., and Willis, S.G. (2016). Choice of baseline climate data impacts projected species’ responses to climate change. Global Change Biology, 22: 2392–2404.

Barbet-Massin, M., Thuiller, W., and Jiguet, F. (2010). How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models? Ecography (Cop.), 33: 878–886.

Beale, C.M., Lennon, J.J., Gaston, K.J., Blackburn, T.M., Kearney, M., Porter, W.P., Williams, C., Ritchie, S., Hoffmann, A.A., Rouget, M., Richardson, D.M., Milton, S.J., Polakow, D., Loiselle, B.A., Howell, C.A., Graham, C.H., Goerck, J.M., Brooks, T., Smith, K.G., Burgman, M.A. (2012). Incorporating uncertainty in predictive species distribution modeling. Philos. Transactions of the Royal Society London B: Biological Sciences, 367: 247–258.

Bowker, G. C. (2000). Biodiversity datadiversity. Social Studies of Science, 30(5): 643–683.

Breiman, L. (2001). Random Forests. Machine Learning, 45: 5–32.

Buisson, L., Thuiller, W., Casajus, N., Lek, S., and Grenouillet, G. (2010). Uncertainty in ensemble forecasting of species distribution. Global Change Biology. 16: 1145–1157.

Cianfrani, C., Broennimann, O., Loy, A., and Guisan, A. (2018). More than range exposure: Global otter vulnerability to climate change. Biology Conservation, 221: 103–113.

Cowles, H. C. (1899). The Ecological relations of vegetation on the sand dune of Lake Michgan. Bot. Gazette, 27, 95-117, 167-202, 281-308, 361-91.

Digeronimo P, Pas MA, Edmonds J. (2010). 11th Conservation Workshop for the Fauna of Arabia - Environment and Protected Areas Authority (EPAA).

Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Garre, G., Jaime, R., García, M., Bernd, G., Bruno, L., Pedro, J.L., Tamara, M., Colin, M., Patrick E.O., Björn, R., Boris, S., Andrew, K.S., Damaris, Z., Sven, L. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop), 36: 27–46.

Eid, E. & R. Handal (2018). Illegal hunting in Jordan: Using social media to assess impacts on wildlife. Oryx. 52: 730-735. DOI: https://doi.org/10.1017/S0030605316001629.

Eid, E., Abu Baker, M., and Amr, Z. (2020). National Red data book of mammals in Jordan. Amman, Jordan: IUCN Regional Office for West Asia Amman. , ISBN: 978-2-8317-2076-0 (PDF), DOI: https://doi.org/10.2305/IUCN.CH.2020.12.en, pp-131.

El-Gabbas, A., and Dormann, C.F. (2018). Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling. Ecology and Evolution, 8, 2196–2206.

Elith, J. (2017). Predicting Distributions of Invasive Species. In A. Robinson, T. Walshe, M. Burgman, & M. Nunn (Eds.), Invasive Species: Risk Assessment and Management (pp. 93-129). Cambridge: Cambridge University Press. doi:10.1017/9781139019606.006

Elith, J., Phillips, S.J., Hastie, T., Dudk, M., Chee, Y.E., Yates, C.J., (2011). A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17: 43–57.

FAO (Food and Agricultural Organization). (2012). Assessment of the risks from climate change and water scarcity on food productivity in Jordan.

Fick, S.E., and Hijmans, R.J. (2017). WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37: 4302–4315.

Fielding, A.H., and Bell, J.F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24: 38–49.

Fordham, D. A., Wigley, T. M. L., & Brook, B. W. (2011). Multi-model climate projections for biodiversity risk assessments. Ecological Applications, 21(8), 3317–3331. doi:10.1890/11-0314.1

Gibson, L.A., Wilson, B.A., Cahill, D.M. & Hill, J. (2004) Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach. Journal of Applied Ecology, 41: 213– 223.

Goberville, E., Beaugrand, G., Hautekèete, N.-C., Piquot, Y., and Luczak, C. (2015). Uncertainties in the projection of species distributions related to general circulation models. Ecology and Evolution, 5: 1100–1116.

Grinnell, J. (1917). Field tests of theories concerning distributional control. American Naturalist 51: 115-128.

Guisan, A., Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology. Ecol. Model. 135: 147-186

Hannemann, H., Willis, K.J., and Macias-Fauria, M. (2016). The devil is in the detail: Unstable response functions in species distribution models challenge bulk ensemble modeling. Global Ecology and Biogeography, 25: 26–35.

Harrison, D.L., and Bates, P.J. (1991). The Mammals of Arabia. Harrison Zoological Museum, Kent, 354 pp.

IPCC, (2014): Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.4

Jiang, H., Liu, T., Li, L., Zhao, Y., Pei, L., and Zhao, J. (2016). Predicting the Potential Distribution of Polygala tenuifolia Willd. under Climate Change in China. PLoS One, 11, 0163718.

Kock, D., D. M. Shafie and Z. S. Amr.(1993). The Jungle cat, Felis chaus Güldenstaedt, 1776, in Jordan. – Zeitschrift für Säugetierkunde (Vol. 58). Berlin.

Kramer –Schadt, S. Niedballa, J., Pilgrim J., Boris Schröder, B., Lindenborn, J, Reinfelder, V., Stillfried, M., Heckmann, I., Scharf A., K., Augeri, D. M., Cheyne, S. M., Hearn, A. J.,Ross, J, Macdonald, D. A., Mathai, J., Eaton, J., Marshall, A. J., Semiadi, G., Rustam, R.,Bernard, H., Alfred, R., Samejima, H., Duckworth, J.W., Breitenmoser‐Wuersten, C., Belant, J. L., Hofer, H., Wilting, A. (2013)-The importance of correcting for sampling bias in MaxEnt species distribution models, Diversity and Distribution, Volume19, Issue11, pages 1366-1379, https://doi.org/10.1111/ddi.12096

Manly, B.F., McDonald, L.L., Thomas, D.L., McDonald, T.L., Erickson, W.P., (2002). Resource Selection by Animals: Statistical Design and Analysis for Field Studies, 2nd ed. Kluwer Academic Publishers, Dordrecht.

Margules, C.R. & Pressey, R.L. (2000) Systematic conservation planning. Nature, 405: 243–253.

Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K., and Thuiller, W. (2009). Evaluation of consensus methods in predictive species distribution modeling. Diversity and Distribution, 15: 59–69.

Naimi, B., Hamm, N.A.S., Groen, T.A., Skidmore, A.K., and Toxopeus, A.G. (2014). Where is positional uncertainty a problem for species distribution modeling?, Ecography 37: 191–203.

Ortega-Huerta, M. A., and A. T. Peterson. (2003). Effects of geographic scale on analyzing associations between regional habitats and distribution patterns of Mexican birds. Anales del Instituto de Biologia, U.N.A.M. 74: 203-210.

Ottaviani, D., Lasinio, G.J., Boitani, L., (2004). Two statistical methods to validate habitat suitability models using presence-only data. Ecol. Model. 179: 417–443.

Pereira, J. M. C. and Itami, R. M. (1991) GIS-based habitat modelling using logistic multiple regression: a case study of the Mt Graham Red Squirrel. Photogrammetric Engineering and Remote Sensing 57(11): 1475-1486.

Peterson, A.T., and Robins, R. (2003). Using Ecological-Niche Modeling to Predict Barred Owl Invasions with Implications for Spotted Owl Conservation. Conservation Biology, 17: 1161–1165.

Phillips, S. J., Dudík, M. (2008) Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31 (2): 161-175

Phillips, S.J., and Dudík, M. (2008). Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography (Cop.).,31: 161–175.

Phillips, S.J., Anderson, R.P., and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190: 231–259.

Prendergast, J.R., Quinn, R.M. & Lawton, J.H. (1999) The gaps between theory and practice in selecting nature reserves. Conservation Biology, 13: 484–492.

Radosavljevic, A., and Anderson, R.P. (2014). Making better MaxEnt models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41: 629–643.

Riahi K, Krey V, Rao S, Chirkov V, Fischer G, Kolp P, Kindermann G, Nakicenovic N, Rafai P (2011) RCP-8.5: exploring the consequence of high emission trajectories. Climatic Change. doi: 10.1007/s10584-011-0149-y.

Sanchez-Cordero, V., Illoldi-Rangel, P., Linaje, M., Sarkar, S. & ´ Peterson, A.T. (2005) Deforestation and extant distributions of Mexican endemic mammals. Biological Conservation 126: 465–473.

Sánchez-Fernández, D., Jorge, M.L., and Olga, L. H-M (2010). Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles. 17, 163–171. DOI: 10.1111/j.1472-4642.2010.00716.

Sanderson, Benjamin, Knutti, Reto and Cladwell, Peter (2015). Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties. J. Climate (2015) 28 (13): 5150–5170. https://sci-hub.st/https://doi.org/10.1175/JCLI-D-14-00361.1

Segurado, P., Araújo, M. B. (2004) An evaluation of methods for modelling species distributions. J. Biogeogr. 31: 1555-1568.

The Ministry of Environment (2014). Jordan’s Third National Communication on Climate Change. United Nations Development Programme

Thuiller, W., Georges, D., and Engler, R. (2016). biomod2: Ensemble platform for species distribution modeling. R Packag. Version 3.3-13/R726., https://r-forge.r-project.org/projects/biomod/. https://r-forge.r-project.org/projects/biomod/.

Tisseuil, C., Leprieur, F., Grenouillet, G., Vrac, M., and Lek, S. (2012). Projected impacts of climate change on spatio-temporal patterns of freshwater fish beta diversity: a deconstructing approach. Global Ecology and Biogeography, 21: 1213–1222.

Titeux, N., Maes, D., Van Daele, T., Onkelinx, T., Heikkinen, R.K., Romo, H., García-Barros, E., Munguira, M.L., Thuiller, W., van Swaay, C.A.M., Schweiger, O., Settele, J., Harpke, A., Wiemers, M., Brotons, L., and Luoto, M. (2017). The need for large-scale distribution data to estimate regional changes in species richness under future climate change. Diversity and Distribution, 23: 1393–1407.

Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R (2007) A comparative evaluation of presence-only methods for modelling species distribution. Divers Distrib 13: 397–405.

Van Vuuren, D.M., den Elzen, P., Lucas, B., Eickhout, B., Strengers, B., van Ruijven, S., Wonink, R., van Houdt, D. (2007). Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Climatic Change, 81: 119–159 doi:10.1007/s10584-006-9172-9.

Downloads

Published

2022-03-29

How to Cite

Eid, E. ., Soultan, A. ., & Elalqamy, H. . (2022). Habitat Suitability Modelling for Feline Species in Jordan: A tool for Climate-Responsive Conservation Planning . Journal of Wildlife and Biodiversity, 6(X). https://doi.org/10.22120/jwb.2022.290322.1452

Issue

Section

Original Article