DOI: 10.1016/j.foreco.2017.06.044
Scopus记录号: 2-s2.0-85021632648
论文题名: Predicting current and future disease outbreaks of Diplodia sapinea shoot blight in Italy: species distribution models as a tool for forest management planning
作者: Bosso L. ; Luchi N. ; Maresi G. ; Cristinzio G. ; Smeraldo S. ; Russo D.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2017
卷: 400 起始页码: 655
结束页码: 664
语种: 英语
英文关键词: Climate change
; Forest ecology
; Fungus
; GIS
; Maxent
; Pine
Scopus关键词: Climate models
; Ecology
; Forestry
; Fungi
; Geographic information systems
; Geographical distribution
; Maximum entropy methods
; Population distribution
; Seed
; Climate change scenarios
; Forest ecology
; Forest management planning
; Future climate projections
; Maxent
; Maximum entropy models
; Pine
; Species distribution models
; Climate change
; climate change
; climate effect
; climate modeling
; disease prevalence
; ecological modeling
; economic impact
; forest ecosystem
; forest management
; fungal disease
; future prospect
; geographical distribution
; GIS
; host-pathogen interaction
; management practice
; prediction
; spatial distribution
; Italy
; Diplodia
; Fungi
英文摘要: Species distribution models (SDMs) provide realistic scenarios to explain the influence of bioclimatic variables on plant pathogen distribution. Diplodia sapinea is most harmful to plantations of both exotic and native pine species in Italy, causing economic consequences expecially to edible seed production. In this study, we developed maximum entropy models for D. sapinea in Italy to reach the following goals: (i) to carry out the pathogen's first geographical distribution analysis in Italy and determine which eco-geographical variables (EGVs) may influence its outbreaks; (ii) to detect the effect of climate change on the potential occurrence of disease outbreaks by 2050 and 2070. We used Maxent ver. 3.4.0 to develop SDMs. We used six global climate models (BCC-CSM1-1, CCSM4, GISS-E2-R, MIROC5, HadGEM2-ES and MPI-ESM-LR) for two representative concentration pathways (4.5 and 8.5) and two time projections (2050 and 2070) to detect future climate projections of D. sapinea. The most important EGVs influencing outbreaks were land cover, altitude, mean temperature of driest and wettest quarter, precipitation of wettest quarter, precipitation seasonality and minimum temperature of coldest month. The distribution of D. sapinea mostly expanded in central and southern Italy and shifted in altitude upwards on average by ca. 93m a.s.l. Moreover the fungus expanded the range where disease outbreaks may be recorded in response to an increase in the mean temperature of wettest and driest quarter by ca. 1.9 °C and 5.8 °C, respectively in all climate change scenarios. Precipitation of wettest quarter did not differ between current and any of future models. Under different climate change scenarios D. sapinea's disease outbreaks will be likely to affect larger areas of pine forests in the country, probably causing heavy effects on the dynamics and evolution of these stands or perhaps constraining their survival. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64228
Appears in Collections: 影响、适应和脆弱性
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作者单位: Wildlife Research Unit, Dipartimento di Agraria, Università degli Studi di Napoli Federico II, Via Università 100, 80055 Portici, Napoli, Italy; Institute for Sustainable Plant Protection – National Research Council (IPSP-CNR), Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Firenze, Italy; Technology Transfer Centre, Fondazione Edmund Mach, Via E. Mach 1, I-38010, San Michele all'Adige, Trento, Italy; School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, United Kingdom
Recommended Citation:
Bosso L.,Luchi N.,Maresi G.,et al. Predicting current and future disease outbreaks of Diplodia sapinea shoot blight in Italy: species distribution models as a tool for forest management planning[J]. Forest Ecology and Management,2017-01-01,400