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Habitat suitability mapping of stone pine ( Pinus pinea L.) under the effects of climate change
Biologia ( IF 1.5 ) Pub Date : 2020-09-17 , DOI: 10.2478/s11756-020-00594-9
Ayhan Akyol , Ömer K. Örücü , E. Seda Arslan

Climate models predict that the Mediterranean region will experience a particularly marked increase in aridity during the 21st century when compared to other regions. This study examines the current and future potential geographical distribution of the stone pine (Pinus pinea L.), a species of considerable ecological and economic importance, in the light of aridity predictions in the Mediterranean region. For this purpose, 19 bioclimatic variables taken from the Worldclim database in 30 arc-second spatial resolution (∼1000 m) were used. The bioclimatic variables that displayed high correlations with one another when applied to 125 pieces of presence data pertaining to the species were reduced using Principal Component Analysis (PCA) and the current and future potential distribution areas of the species were identified using MaxEnt 3.4.1 software. To determine the future geographical distribution of the species under the impact of climate change, the Community Climate System Model (CCSM ver. 4) was used to model the potential geographical distribution areas for two climate scenarios – Representative Concentration Pathways (RCP) 4.5 and 8.5 – and two time periods (2041–2060 and 2061–2080). According to the model data, the predicted potential geographical distribution areas of the stone pine increase under the 2041–2060 and 2061–2080 RCP 4.5–8.5 scenarios and move northwards and towards higher altitudes. Knowledge of the current and future potential distribution maps of the species, and especially of the suitable/very suitable distribution areas, can be used in work on land use, afforestation, ecological restoration and the protection of the species.



中文翻译:

气候变化影响下石松(Pinus pinea L.)的生境适宜性图

气候模型预测,与其他地区相比,在21世纪,地中海地区的干旱将特别明显。这项研究考察了石松(Pinus pinea)当前和将来的潜在地理分布L.),根据地中海地区的干旱预测,具有重要的生态和经济意义。为此,使用了从Worldclim数据库获取的19个生物气候变量,其空间分辨率为30弧秒(〜1000 m)。使用主成分分析(PCA)减少了应用于125种与该物种有关的存在数据时彼此之间具有高度相关性的生物气候变量,并使用MaxEnt 3.4.1软件识别了该物种的当前和未来潜在分布区域。要确定受气候变化影响的物种的未来地理分布,请使用社区气候系统模型(CCSM ver。4)被用来模拟两种气候情景的潜在地理分布区域–代表性浓度路径(RCP)4.5和8.5 –以及两个时间段(2041–2060和2061–2080)。根据模型数据,在2041-2060年和2061-2080 RCP 4.5-8.5情景下,石松的潜在潜在地理分布区域增加,并向北移动并向更高的高度移动。有关该物种当前和未来潜在分布图的知识,尤其是有关合适/非常合适的分布区域的知识,可用于土地利用,绿化,生态恢复和物种保护方面的工作。在2041-2060年和2061-2080 RCP 4.5-8.5情景下,石松的潜在潜在地理分布区域将增加,并向北移动并向更高的高度移动。有关该物种当前和未来潜在分布图的知识,尤其是有关合适/非常合适的分布区域的知识,可用于土地利用,绿化,生态恢复和物种保护方面的工作。在2041-2060年和2061-2080 RCP 4.5-8.5情景下,石松的潜在潜在地理分布区域将增加,并向北移动并向更高的高度移动。有关该物种当前和未来潜在分布图的知识,尤其是有关合适/非常合适的分布区域的知识,可用于土地利用,绿化,生态恢复和物种保护方面的工作。

更新日期:2020-09-18
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