Abstract
Species distribution models (SDMs) generate predicted distribution maps which can be used as effective tools for conservation purposes. The persistence of isolated populations at the margin of a large distributional area depends on local threats which may differ from those faced by the main population. Environmental predictors can indicate suitable areas for these species and, indirectly, evaluate the impact on peripheral populations due to fragmentation and isolation. The Lanner Falcon (Falco biarmicus) is an Afro-tropical and Mediterranean polytypic species considered critically endangered (CR) in Arabian Peninsula by IUCN, but a lack of published information about its distribution persists. Here, we model the distribution of the Lanner Falcon in the Arabian Peninsula using nest-site data and map its core area and their habitat suitability using a robust algorithm with good prediction accuracy even at low sample sizes (MaxEnt). The predictive map suggests a potential distribution of the Lanner Falcons that runs from north to south along the eastern coast of the Red Sea. The Terrain Roughness Index contributed the most to the breeding range model predictions (57.6%), followed by isothermality (Bio3, 15.3%). The model suggests a tendency by Lanner Falcons to occupy areas with a low terrain complexity according to their behavioural patterns and breeding strategies. In addition, this falcon is highly sensitive to climate occupying high isothermal regions in order to avoid extreme heating events. Overall, predictive models indicate a narrow range of suitable environmental conditions for breeding as well restricted favourable areas during dispersal and migration. Thus, these small and fragmented populations are more likely prone to anthropogenic factors and must be buffered against them.
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References
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19, 716–723. https://doi.org/10.1109/TAC.1974.1100705
Albuquerque, F., Astudillo-Scalia, Y., Loyola, R., & Beier, P. (2019). Towards an understanding of the drivers of broad-scale patterns of rarity-weighted richness for vertebrates. Biodiversity & Conservation, 28(14), 3733–3747. https://doi.org/10.1007/s10531-019-01847-z
Allouche, O., Steinitz, O., Rotem, D., Rosenfeld, A., & Kadmon, R. (2008). Incorporating distance constraints into species distribution models. Journal of Applied Ecology, 45, 599–609.
Almpanidou, V., Tsapalou, V., Tsavdaridou, A. I., & Mazaris, A. D. (2020). The dark side of raptors’ distribution ranges under climate change. Landscape Ecology, 35, 1435–1443. https://doi.org/10.1007/s10980-020-01025-5
Al Zoubi, M., El-Halah, A., & Hamidan, N. A. (2019). A recovery of a Lanner Falcon Falco biarmicus suggesting a possible movement pattern in the Jordan’s breeding population. Jordan Journal of Natural History, 6, 54–57.
Amato, M., Ossino, A., Brogna, A., Cipriano, M., D’Angelo, R., Dipasquale, G., Mannino, V., Andreotti, A., & Leonardi, G. (2014). Influence of habitat and nest-site quality on the breeding performance of Lanner Falcons Falco biarmicus. Acta Ornithologica, 49, 1–7. https://doi.org/10.3161/000164514X682841
Andrews, I. (1995). The Birds of the Hashemite Kingdom of Jordan. Andrews.
Aragón, P., & Sánchez-Fernández, D. (2013). Can we disentangle predator-prey interactions from species distributions at a macro-scale? A case study with a raptor species. Oikos, 122, 64–72. https://doi.org/10.1111/j.1600-0706.2012.20348.x
Binothman, A. M. (2016). Current status of falcon populations in Saudi Arabia. MSc thesis, University of South Dakota, Paper 976.
Boland, C. R. J., & Burwell, B. O. (2020). Ranking and mapping the high conservation priority bird species of Saudi Arabia. Avian Conservation and Ecology, 15, 18. https://doi.org/10.5751/ACE-01705-150218
Boyce, M. S., Vernier, P. R., Nielsen, S. E., & Schmiegelow, F. K. (2002). Evaluating resource selection functions. Ecological Modelling, 157, 281–300. https://doi.org/10.1016/S0304-3800(02)00200-4
Brochet, A.-L., Jbour, S., Sheldon, R. D., Porter, R., Jones, V. R., Al Fazari, W., Al Saghier, O., Alkhuzai, S., Al-Obeidi, L. A., Angwin, R., Ararat, K., Pope, M., Shobrak, M. Y., Willson, M. S., Zadegan, S. S., & Butchart, S. H. M. (2019). A preliminary assessment of the scope and scale of illegal killing and taking of wild birds in the Arabian Peninsula, Iran and Iraq. Sandgrouse, 41, 154–175.
Calosi, P., Bilton, D. T., Spicer, J. I., et al. (2010). What determines a species’ geographical range? Thermal biology and latitudinal range size relationships in European diving beetles (Coleoptera, Dytiscidae). Journal of Animal Ecology, 79, 194–204. https://doi.org/10.1111/j.1365-2656.2009.01611.x
Chevin, L. M., Lande, R., & Mace, G. M. (2010). Adaptation, plasticity, and extinction in a changing environment: Towards a predictive theory. PLoS Biology, 8, e1000357. https://doi.org/10.1371/journal.pbio.1000357
Cox, N. A., Mallon, D., Bowles, P., Els, J., & Tognelli, M. F. (2012). The conservation status and distribution of reptiles of the Arabian Peninsula. IUCN, and Sharjah, UAE: The Environment and Protected Areas Authority.
Delany, M. J. (1989). The zoogeography of the mammal fauna of southern Arabia. Mammal Review, 19(4), 133–152. https://doi.org/10.1111/j.1365-2907.1989.tb00408.x
Di Cola, V., Broennimann, O., Petitpierre, B., Breiner, F. T., D’Amen, M., Randin, C., Engler, R., Pottier, J., Pio, D., Dubuis, A., & Pellissier, L. (2017). ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography, 40, 774–787. https://doi.org/10.1111/ecog.02671
Dormann, C. F., Elith, J., Bacher, S., et al. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Duan, R. Y., Kong, X. Q., Huang, M. Y., et al. (2014). The predictive performance and stability of six species distribution models. PLoS ONE, 9, e112764. https://doi.org/10.1371/journal.pone.0112764
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
Franklin, J. (2010). Mapping species distributions: Spatial inference and prediction. Cambridge University Press.
Frei, B., Fyles, J. W., Berl, J. L., et al. (2015). Low fecundity of Red-headed Woodpeckers (Melanerpes erythrocephalus) at the northern edge of the range. The Wilson Journal of Ornithology, 127, 639–645. https://doi.org/10.1676/14-167.1
Galante, P. J., Alade, B., Muscarella, R., et al. (2018). The challenge of modeling niches and distributions for data-poor species: A comprehensive approach to model complexity. Ecography, 41, 726–736.
Gaston, K. J. (2009). Geographic range limits: Achieving synthesis. Proceedings. Biological Sciences, 276, 1395–1406. https://doi.org/10.1098/rspb.2008.1480
Guisan, A., Lehmann, A., Ferrier, S., et al. (2006). Making better biogeographical predictions of species’ distributions. Journal of Applied Ecology, 43, 386–392. https://doi.org/10.1111/j.1365-2664.2006.01164.x
Hamidi, K., Matin, M. M., Kilpatrick, C. W., et al. (2019). Landscape and niche specialisation of two brush-tailed mice species Calomyscus elburzensis and C. hotsoni in Iran: A case of the role of ecological niche modelling in finding area(s) of contact. Ethology Ecology & Evolution, 31, 435–456. https://doi.org/10.1080/03949370.2019.1621390
Hatzofe, O. (2001). Reintroduction of raptors to Ramat HaNadiv. In Y. Leshem (Ed.), Wings over Africa (pp. 190–202). Latrum: International Center for the Study of Bird Migration.
Hurvich, C. M., & Tsai, C. L. (1989). Regression and time-series model selection in small sample sizes. Biometrika, 76, 297–307. https://doi.org/10.1093/biomet/76.2.297
Jenkins, A. R. (1995). Morphometrics and flight performance of southern African Peregrine and Lanner Falcons. Journal of Avian Biology, 26, 49–58. https://doi.org/10.2307/3677212
Jennings, M. C. (2010). Atlas of the Breeding birds of Arabia. Fauna of Saudi Arabia 25. Senckenberg Institute Frankfurt and the King Abdulaziz City for Science and Technology. Frankfurt and Riyadh.
Kokko, H., & Lopez-Sepulcre, A. (2006). From individual dispersal to species ranges: Perspectives for a changing world. Science, 313, 789–791. https://doi.org/10.1126/science.1128566
Lawler, J. J., Wiersma, Y. F., & Huettman, F. (2011). Using species distribution models for conservation planning and ecological forecasting. In: Drew C A, Wiersma Y F, Huettmann F. Predictive Species and Habitat Modeling in Landscape Ecology. New York: Springer, 271–290. https://doi.org/10.1007/978-1-4419-7390-0_14
Leonardi, G. (2001). Falco biarmicus Lanner Falcon. Birds of Western Palearctic Update, 3, 157–174.
Leonardi, G. (2015). The Lanner Falcon. GLE, Catania, Italy.
Leonardi, G. (2020). Behavioural ecology of Western Palearctic Falcons. Springer Nature, Switzerland. https://doi.org/10.1007/978-3-030-60541-4
Liang, J., Xing, W., Zeng, G., et al. (2018). Where will threatened migratory birds go under climate change? Implications for China’s national nature reserves. Science of Total Environment, 645, 1040–1047. https://doi.org/10.1016/j.scitotenv.2018.07.196
Mallon, D. P. (2011). Global hotspots in the Arabian Peninsula. Zoology in the Middle East, 54(suppl. 3), 13–20. https://doi.org/10.1080/09397140.2011.10648896
Merow, C., Smith, M. J., Edwards, T. C., et al. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography, 37, 1267–1281. https://doi.org/10.1111/ecog.00845
Michailidou, D. E., Lazarina, M., & Sgardelis, S. P. (2021). Temperature and prey species richness drive the broad-scale distribution of a generalist predator. Diversity, 13, 169. https://doi.org/10.3390/d13040169
Moreau, R. E. (1966). The bird fauna of Africa and its islands. Academic Press.
Muscarella, R., Galante, P. J., Soley-Guardia, M., et al. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5, 1198–1205. https://doi.org/10.1111/2041-210X.12261
Naveda-Rodríguez, A., Bildstein, K. L., & Hernán-Vargas, F. (2016). Geographic patterns of species richness of diurnal raptors in Venezuela. Biodiversity and Conservation, 25(6), 1037–1052. https://doi.org/10.1007/s10531-016-1102-1
Noguera-Urbano, E. A., & Ferro, I. (2018). Environmental factors related to biogeographical transition zones of areas of endemism of Neotropical mammals. Australian Systematic Botany, 30(6), 485–494. https://doi.org/10.1071/SB16055
Osorio-Olvera, L., Lira-Noriega, A., Soberón, J., et al. (2020). ntbox: An R package with graphical user interface for modeling and evaluating multidimensional ecological niches. Methods in Ecology and Evolution, 11, 1199–1206. https://doi.org/10.1111/2041-210X.13452
Pearson, R. G., & Dawson, T. P. (2003). Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Global Ecology and Biogeography, 12, 361–371. https://doi.org/10.1046/j.1466-822X.2003.00042.x
Peterson, A. T., Papeş, M., & Soberón, J. (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling, 213, 63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographical distributions. Ecological Modelling, 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Phillips, S. J., Anderson, R. P., Dudík, M., et al. (2017). Opening the black box: An open-source release of Maxent. Ecography, 40, 887–893. https://doi.org/10.1111/ecog.03049
Porfirio, L. L., Harris, R. M. B., Lefroy, E. C., et al. (2014). Improving the use of species distribution models in conservation planning and management under climate change. PLoS ONE, 9(11), e113749. https://doi.org/10.1371/journal.pone.0113749
Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: Complexity, overfitting and evaluation. Journal of Biogeography, 41, 629–643. https://doi.org/10.1111/jbi.12227
Ramesh, T., Kalle, R., & Downs, C. T. (2016). Predictors of mammal species richness in KwaZulu-Natal, South Africa. Ecological Indicators, 60, 385–393. https://doi.org/10.1016/j.ecolind.2015.07.011
Sagarin, R. D., & Gaines, S. D. (2002). The ‘abundant centre’ distribution: To what extent is it a biogeographical rule? Ecology Letters, 5, 137–147. https://doi.org/10.1046/j.1461-0248.2002.00297.x
Schoenjahn, J., Pavey, C. R., & Walter, G. H. (2021). A true desert falcon with a delayed onset of heat dissipation behaviour. Journal of Arid Environments, 190, 104530. https://doi.org/10.1016/j.jaridenv.2021.104530
Shirihai, H., Yosef, R., Alon, D., et al. (2000). Raptor migration in Israel and the Middle East: A summary of 30 years of field research. Tech. Publ. Int. Birding & Res.
Shobrak, M. Y. (2015). Trapping of Saker Falcon Falco cherrug and Peregrine Falcon Falco peregrinus in Saudi Arabia: Implications for biodiversity conservation. Saudi Journal of Biological Sciences, 22(4), 491–502. https://doi.org/10.1016/j.sjbs.2014.11.024
Sillero, N. (2011). What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling, 222, 1343–1346. https://doi.org/10.1016/j.ecolmodel.2011.01.018
Simberloff, D. (1995). Habitat fragmentation and population extinction of birds. Ibis, 137, 105–111. https://doi.org/10.1111/j.1474-919X.1995.tb08430.x
Soberon, J., & Nakamura, M. (2009). Niches and distributional areas: Concepts, methods, and assumptions. Proceedings of the National Academy of Sciences USA, 106(suppl. S2), 19644–19650. https://doi.org/10.1073/pnas.0901637106
Souttou, K., Boukhemza, M., Baziz, B., et al. (2005). Régime alimentaire du Faucon Lanier Falco biarmicus en Algerie. Alauda, 73, 357–360.
Sutton, L. J., & Puschendorf, R. (2020). Climatic niche of the Saker Falcon Falco cherrug: Predicted new areas to direct population surveys in Central Asia. Ibis, 162(1), 27–41. https://doi.org/10.1111/ibi.12700
Sutton, L. J., Anderson, D. L., Franco, M., et al. (2021a). Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution. Ecology and Evolution, 11, 481–497. https://doi.org/10.1002/ece3.7068
Sutton, L. J., McClure, C. J. W., Kini, S., et al. (2021b). Climatic constraints on Laggar falcon (Falco jugger) distribution predicts multidirectional range movements under future climate change scenarios. Journal of Raptor Research, 54, 1–17. https://doi.org/10.3356/0892-1016-54.1.1
Symes, A., Taylor, J., Mallon, D., et al. (2015). The conservation status and distribution of the breeding birds of the Arabian Peninsula. IUCN, and Sharjah, UAE: Environment and Protected Areas Authority. Cambridge, UK and Gland, Switzerland.
Syphard, A. D., & Franklin, J. (2009). Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors. Ecography, 32, 907–918.
Thakur, M., Wullschleger, E., Schättin, E., et al. (2018). Globally common, locally rare: Revisiting disregarded genetic diversity for conservation planning of widespread species. Biodiversity and Conservation, 27, 3031–3035. https://doi.org/10.1007/s10531-018-1579-x
Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bio-climatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41, 291–307. https://doi.org/10.1111/ecog.02880
Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21, 335–342. https://doi.org/10.1890/10-1171.1
Wiens, J. D., Schumaker, N. H., Inman, R. D., et al. (2017). Spatial demographic models to inform conservation planning of Golden eagles in renewable energy landscapes. Journal of Raptor Research, 51, 234–257. https://doi.org/10.3356/JRR-16-77.1
Wisz, M. S., Hijmans, R. J., Li, J., et al. (2008). Effects of sample size on the performance of species distribution models. Diversity and Distribution, 14, 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x
Zeng, Q., Zhang, Y., Sun, G., et al. (2015). Using species distribution model to estimate the wintering population size of the endangered scaly-sided Merganser in China. PLoS ONE, 10, e0117307. https://doi.org/10.1371/journal.pone.0117307
Zhang, J., Jiang, F., Li, G., et al. (2019). Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China. Ecology and Evolution, 9, 6643–6654. https://doi.org/10.1002/ece3.5243
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This research was supported by the Saudi Falcon Club, the Special Forces for Environmental Safety, the National Wildlife Center, the Ministry of Environment, Water, and Agriculture, Tilad Environmental Consultancy and IUCN. We thank two anonymous reviewers for their comments on a previous version of this manuscript.
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Alabdulhafith, B., Binothman, A., Alwahiby, A. et al. Predicting the potential distribution of a near-extinct avian predator on the Arabian Peninsula: implications for its conservation management. Environ Monit Assess 194, 535 (2022). https://doi.org/10.1007/s10661-022-10225-2
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DOI: https://doi.org/10.1007/s10661-022-10225-2