2019
DOI: 10.1590/01047760201925042643
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INVESTIGATION AND EVALUATION OF STONE PINE ( Pinus pinea L.) CURRENT AND FUTURE POTENTIAL DISTRIBUTION UNDER CLIMATE CHANGE IN TURKEY

Abstract: AKYOL, A.; ORUCU, O. K. Investigation and evaluation of stone pine (Pinus pinea L.) current and future potential distribution under climate change in Turkey. CERNE, v. 25, n. 4, p.415-423, 2019. HIGHLIGHTS The most effective bioclimatic variable in the potential distribution of stone pine was minimum temperature of coldest month (Bio6). The prediction model in 2050-2070 and RCP4.5-8.5 showed that the stone pine habitat would decrease. According to prediction model, stone pine will shift geographical distributi… Show more

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Cited by 23 publications
(7 citation statements)
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References 45 publications
(42 reference statements)
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“…Further, Natalini et al (2016) suggested that the Pinus pinea species has a plastic response to warmer and drier climates that can vary among populations, and some measure of such variability should be considered in long-term forecasts of vegetation dynamics. Akyol & Orucu (2019) found that the most important bioclimatic variables affecting the potential distribution of P. pinea are the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), and precipitation of the wettest quarter (Bio16); whereas, Serra Varela (2018) found that annual mean temperature (Bio1), temperature seasonality (Bio4), precipitation seasonality (Bio15), precipitation of the warmest quarter (Bio 18), and precipitation of the coldest quarter (Bio 19) are the main drivers of stone pine distribution. Simulation models under two different future climate change scenarios predicted that P. Pinea will lose suitable habitats and will shift toward northern and higher elevation sites.…”
Section: Iforest -Biogeosciences and Forestrymentioning
confidence: 99%
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“…Further, Natalini et al (2016) suggested that the Pinus pinea species has a plastic response to warmer and drier climates that can vary among populations, and some measure of such variability should be considered in long-term forecasts of vegetation dynamics. Akyol & Orucu (2019) found that the most important bioclimatic variables affecting the potential distribution of P. pinea are the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), and precipitation of the wettest quarter (Bio16); whereas, Serra Varela (2018) found that annual mean temperature (Bio1), temperature seasonality (Bio4), precipitation seasonality (Bio15), precipitation of the warmest quarter (Bio 18), and precipitation of the coldest quarter (Bio 19) are the main drivers of stone pine distribution. Simulation models under two different future climate change scenarios predicted that P. Pinea will lose suitable habitats and will shift toward northern and higher elevation sites.…”
Section: Iforest -Biogeosciences and Forestrymentioning
confidence: 99%
“…Simulation models under two different future climate change scenarios predicted that P. Pinea will lose suitable habitats and will shift toward northern and higher elevation sites. For example, Akyol & Orucu (2019) showed that P. pinea in Turkey will shift its geographical distribution in the future and experience losses of habitat, particularly in the western and southern parts of Turkey. According to Akyol & Orucu (2019), the potential distribution of P. pinea in the years 2050 and 2070 will decrease under the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios.…”
Section: Iforest -Biogeosciences and Forestrymentioning
confidence: 99%
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“…MaxEnt is a software that predicts the distribution of species using by habitats feature and maximum entropy algorithm (Phillips et al, 2017). MaxEnt has been used in recent years to reveal how climate change affects species distribution (Handrick and McGarvey, 2019;Akyol and Örücü, 2019;Mahdavi et al, 2020;Qian et al, 2020). MaxEnt is also a practicable and valid method for all species (Untalan et al, 2019).…”
Section: Climatic Habitat Suitability (Climate Envelope Model) Models and Mapsmentioning
confidence: 99%