Adaptive distributional changes of Aegithalos caudatus in the Palearctic region during the Ice Age, present, and future periods
DOI:
https://doi.org/10.5281/zenodo.13835253Keywords:
climate change, habitat suitability modeling, Aegithalos caudatus, species distributionAbstract
Climate change alters their distribution across a wide geographical range by impacting habitats and bird populations. For the first time globally, this pioneering research integrates climate data, species presence points from field observations, and the international bird data registry. Utilizing various modeling algorithms, it scrutinizes the distribution changes of Aegithalos caudatus across a significant portion of its Palearctic biogeographical range across five distinct time periods: the Last Interglacial, the Last Glacial Maximum, the mid-Holocene, the present, and future scenarios, encompassing both an optimistic outlook with sustainable development policies and a pessimistic projection with ongoing greenhouse gas emissions. The findings revealed that three algorithms random forest, support vector machine, and maximum entropy outperformed other modeling methods in discerning suitable and unsuitable habitats for Aegithalos caudatus. Evaluation using these models highlighted that the peak of species distribution, reaching 40%, was observed during the Last Glacial Maximum period. Conversely, its favorable habitat decreased by 29% during the Last Interglacial period. Moreover, it appears that climate amelioration during the mid-Holocene and present times has increased the habitat suitability of Aegithalos caudatus t across the Palearctic region to 36% and 35%, respectively. In the optimistic scenario for the year 2080, where sustainable policies are adopted to mitigate climate change, there is a notable increase in the distribution and habitat suitability of the long-tailed tit, covering 47% of the Palearctic biogeographical range. Conversely, in the pessimistic scenario, the distribution of this species diminishes to 35%. Across various time periods, the annual temperature range emerges as the most influential climatic factor affecting the habitat suitability of this species.
References
Araújo, M. B., Pearson, R. G., Thuiller, W., & Erhard, M. (2005). Validation of species–climate impact models under climate change. Global Change Biology, 11(9), 1504-1513.
Beyer, R. M., Krapp, M., & Manica, A. (2020). High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Scientific Data, 7, 236. https://doi.org/10.1038/s41597-020-0552-1.
Drake, J. M., Randin, C., & Guisan, A. (2006). Modelling ecological niches with support vector machines. Journal of Applied Ecology, 43(3), 424-432.
Ebird. (2022). Aegithalos caudatus. Available from https://ebird.org/species/lottit1. (Accessed 18th 2023).
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315.
GBIF. (2023). Aegithalos caudatus. Available from https://www.gbif.org/species/2495000. (Accessed 18th November 2023).
Gasse, F. (2000). Hydrological changes in the African tropics since the Last Glacial Maximum. Quaternary Science Reviews, 19(1-5), 189-211.
Guisan, A., Edwards Jr, T. C., & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecological Modelling, 157(2-3), 89-100.
Haghani, A., Khoobdel, M., Dehghani, R., Adibzadeh, A., Sobati, H., & Aliabadian, M. (2019). Ecological modeling and distribution analysis of digger scorpions: Odontobuthus doriae, Odonthubutus bidentatus (Scorpiones: Buthidae) and Scorpio maurus (Scorpiones: Scorpionidae) in Iran using the maximum entropy method. Applied Entomology and Zoology, 55, 17 - 24.
Harrap, S. (2020). Long-tailed Tit (Aegithalos caudatus), version 1.0. In Birds of the World. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.lottit1.01.
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, 25(15).
Kehl, M. (2009). Quaternary climate change in Iran—the state of knowledge. Erdkunde, 63(1), 1-17.
Lister, A. M., Stuart, A. J., & Stuart, A. J. (2008). The impact of climate change on large mammal distribution and extinction: Evidence from the last glacial/interglacial transition. https://doi.org/10.1016/J.CRTE.2008.04.001.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., & Thuiller, W. (2009). Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15(1), 59-69.
Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784-2817.
Naderi, M., Kaboli, M., Koren, T., Karami, M., Zupan, S., Rezaei, H., & Krystufek, B. (2014). Mitochondrial evidence uncovers a refugium for the fat dormouse (Glis glis Linnaeus, 1766) in Hyrcanian forests of northern Iran. Mammalian Biology, 79, 10-17. https://doi.org/10.1016/j.mambio.2013.12.001.
Nikolova, I., Yin, Q., Berger, A., Singh, U. K., and Karami, M. P. (2013). The last interglacial (Eemian) climate simulated by LOVECLIM and CCSM3, Clim. Past, 9, 1789–1806.
Ntwari, N., Yocgo, R., Ndokoye, P., & Kagabo, D. (2024). Mapping and forecasting of fall armyworm pest distribution in East Africa using climate information. Sustainability and Biodiversity Conservation, 3 (1), 90–107. https://doi.org/10.5281/zenodo.11293684.
Nwoko, O. E., Manyangadze, T., & Chimbari, M. J. (2023). Predicted changes in habitat suitability for human schistosomiasis intermediate host snails for modelled future climatic conditions in KwaZulu-Natal, South Africa. Frontiers in Environmental Science, 11, 1243777.
Pacifici, M., Visconti, P., Butchart, S. H. M., Watson, J. E. M., Cassola, F. M., & Rondinini, C. (2022). Species' traits influenced their response to recent climate change. Nature Climate.
Radchuk, V., Kramer-Schadt, S., & Reed, T. E. (2023). Adaptive responses of wildlife to climate change are constrained by within-species variability and ecological context. Nature Communications, 14(1), 229.
Sántiz, E. C., Lorenzo, C., Carrillo-Reyes, A., Navarrete, D. A., & Islebe, G. A. (2016). Effect of climate change on the distribution of a critically threatened species. https://doi.org/10.12933/THERYA-16-358.
Sıkdokur, E., Naderi, M., Celtik, E., Kemahli Aytekin, M. C., Kusak, J., Saglam, I. K., & Sekercioglu, C. H. (2024). Human-brown bear conflicts in Türkiye are driven by increased human presence around protected areas. Ecological Informatics, 81, Article 102643. https://doi.org/10.1016/j.ecoinf.2024.102643.
Stevens, L. R., Wright Jr, H. E., & Ito, E., 2001. Changes in seasonality of climate during the Late-glacial and Holocene at Lake Zeribar, Iran. The Holocene, 11(6), 747-755.
Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., ... & Williams, S. E. (2004). Extinction risk from climate change. Nature, 427(6970), 145-148. https://doi.org/10.1038/nature02121.
Warren, D. L., Matzke, N. J., Cardillo, M., Baumgartner, J. B., Beaumont, L. J., Turelli, M., Glor, R. E., Huron, N. A., Simões, M., Iglesias, T. L., Piquet, J. C., & Dinnage, R. (2021). ENMTools 1.0: an R package for comparative ecological biogeography. Ecography, 44(1), 4-15.
WallisDeVries, M. F., & Van Swaay, C. A. M. (2006). Global warming and excess nitrogen may induce butterfly decline by microclimatic cooling. Global Change Biology, 12(9), 1620-1626. https://doi.org/10.1111/j.1365-2486.2006.01202.x.
WorldClim. (2022). Ocean. Available from https://worldclim.org/. (Accessed 15th November 2022).
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