世界自然保护联盟(IUCN)红色名录药用物种褐梭梭(Chloroxylon swetenia DC)生境适宜性预测。在印度泰米尔纳德邦使用集成模型

IF 2.9 Q1 FORESTRY
Rajendran Silambarasan , Kasthuri Nair A , Maniyan Gomathi , Hareendran Nair J , Nishanth Kumar S , Shan Sasidharan
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引用次数: 0

摘要

气候变化正在深刻地影响着生物多样性,因此需要全面了解气候变化对生态系统和物种的影响,从而为可持续的保护战略提供信息。本研究以甜氯梭兰(Chloroxylon sweetenia DC)为研究对象。作为世界自然保护联盟(IUCN)红色名录(标准A1c)中脆弱的特有物种,利用集合模型评估其在不同气候变化情景下的栖息地适宜性。采用BCC_CSM2-MR和EC-Earth3-Veg两种不同的气候预测模型,结合19个生物气候变量、4个土壤相关变量(容积密度)和5个土地利用/土地覆盖变量(树木覆盖、耕地、水和建成区),模拟了2021-2040、2041-2060、2061-2080和2081-2100年4个共享社会经济路径(ssp)上的物种栖息地适宜性。在测试的算法中,随机森林模型表现出最高的性能。对模型贡献最大的8个气候变量分别是Bio1(年平均温度)、Bio3(等温线)、Bio4(温度季节性)、Bio7(年温度范围)、Bio13(最湿月份降水)、Bio15(降水季节性)、Bio18(最暖季度降水)和Bio19(最冷季度降水),以及1个土壤变量(容重)和4个土地利用/土地覆盖变量(树木覆盖、耕地、建成区、植被)。和水)。整体模型确定了西高止山脉和东高止山脉,神圣的树林和泰米尔纳德邦的灌木森林是非常适合甜毛囊菌的栖息地。值得注意的是,在EC-Earth3-Veg投影中,甜草的生境适宜性范围增大,而在BCC_CSM2-MR投影中,甜草的适宜性范围在SSP5-8.5之间显著减小。相比之下,EC-Earth3-Veg预测在2041 - 2060年和2081-2100年期间,所有sps的栖息地扩张最大。值得注意的是,加权平均集合类型的最大增益比委员会平均的增益要大。此外,在西高止山脉及其邻近地区,该物种的种群范围有所增加,而在东高止山脉地区则有所减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting habitat suitability for conservation of IUCN- red listed medicinal species Chloroxylon swietenia DC., in Tamil Nadu, India using ensemble modeling
Climate change is profoundly affecting biodiversity, highlighting the need for a comprehensive understanding of its impacts on ecosystems and species to inform sustainable conservation strategies. This study focuses on Chloroxylon swietenia DC., an endemic species listed as Vulnerable under the IUCN Red List (criterion A1c), to assess its habitat suitability under various climate change scenarios using ensemble modeling. We incorporated 19 bioclimatic variables, four soil-related variables (bulk density), and five land use/land cover variables (tree cover, cultivated land, water, and built-up areas) to model the species' habitat suitability across four Shared Socio-economic Pathways (SSPs) for the periods 2021–2040, 2041–2060, 2061–2080, and 2081–2100, using two different climate projections: BCC_CSM2-MR and EC-Earth3-Veg. Among the algorithms tested, the Random Forest model exhibited the highest performance. The eight climatic variables that contributed most significantly to the model were Bio1 (annual mean temperature), Bio3 (isothermality), Bio4 (temperature seasonality), Bio7 (temperature annual range), Bio13 (precipitation of wettest month), Bio15 (precipitation seasonality), Bio18 (precipitation of warmest quarter), and Bio19 (precipitation of coldest quarter), along with one soil variable (bulk density) and four land use/land cover variables (tree cover, cultivated land, built-up areas, and water). The ensemble model identified the Western and Eastern Ghats, sacred groves, and scrub forests of Tamil Nadu as highly suitable habitats for C. swietenia. Notably, the habitat suitability range of C. swietenia increased in the EC-Earth3-Veg projection, whereas the BCC_CSM2-MR projection indicated a significant reduction in suitability at SSP5–8.5. In contrast, the EC-Earth3-Veg projection showed maximum habitat expansion between 2041 and 2060 and 2081–2100 across all SSPs. Notably maximum gain noticed in weighed mean ensemble type than the committee averaging. Furthermore, the results indicate an increase in the species' population range in the Western Ghats and its neighboring region and reduction noticed in the Eastern ghats region.
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来源期刊
Trees, Forests and People
Trees, Forests and People Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
4.30
自引率
7.40%
发文量
172
审稿时长
56 days
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