Global maps of bioclimatic variables currently exist only at very coarse spatial resolution(e.g.WorldClim).For ecological studies requiring higher resolved information,this spatial resolution is often insufficient.The...Global maps of bioclimatic variables currently exist only at very coarse spatial resolution(e.g.WorldClim).For ecological studies requiring higher resolved information,this spatial resolution is often insufficient.The aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation(EO)data at a higher spatial resolution of 1 km.The analysis used two different satellite time series data sets:land surface temperature(LST)from Moderate Resolution Imaging Spectroradiometer(MODIS),and precipitation(P)from Climate Hazards Group Infrared Precipitation with Stations(CHIRPS).Monthly maximum,mean,and minimum air temperature were estimated from Terra MODIS satellite(collection 6)LST time series product using the random forest(RF)regression model.Monthly total precipitation data were obtained from CHIRPS version 2.0.Based on this primary data,spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated,representing the period 2002-2017.We tested the relationship between estimated bioclimatic variables(SatClim)and WorldClim bioclimatic variables version 2.0(WorldClim)using determination coefficient(R^(2)),root mean square error(RMSE),and normalized root mean square error(nRMSE)and found overall good agreement.Among the set of 19 WorldClim bioclimatic variables,17 were estimated with a coefficient of determination(R^(2))higher than 0.7 and normalized RMSE(nRMSE)lower than 8%,confirming that the spatial pattern and value ranges can be retrieved from satellite data with much higher spatial resolution compared to WorldClim.Only the two bioclimatic variables related to temperature extremes(i.e.,annual mean diurnal range and isothermality)were modeled with only moderate accuracy(R^(2) of about 0.4 with nRMSE of about 11%).Generally,precipitation-related bioclimatic variables were closer correlated with WorldClim compared to temperature-related bioclimatic variables.The overall success of the modeling was attributed to the fact that satellite-derived data are well suited to generated spatial fields of precipitation and temperature variables,especially at high altitudes and high latitudes.As a consequence of the successful retrieval of the bioclimatic variables at 1 km spatial resolution,we are confident that the estimated 19 bioclimatic variables will be very useful for a range of applications,including species distribution modeling.展开更多
Pinus koraiensis(Sieb.et Zucc.) is a coniferous tree species naturally distributed in northeastern China.However,the effects of gene flow on its genetic diversity and structure remain unclear.This study investigates t...Pinus koraiensis(Sieb.et Zucc.) is a coniferous tree species naturally distributed in northeastern China.However,the effects of gene flow on its genetic diversity and structure remain unclear.This study investigates these dynamics in seven populations using ten microsatellite markers.The results show a high level of genetic diversity within the populations(Ho=0.633,He=0.746).In addition,molecular analysis of variance(AMOVA) shows that 98% of genetic diversity occurs within populations,with minimal differentiation between populations(Fst=0.009-0.033).Gene flow analysis shows significant migration rates between specific population pairs,particularly C-TH(87%),LS-Y(69%) and TH-LS(69%),suggesting genetic homogenization.Bayesian clustering(STRUCTURE) supports admixture and weak population differentiation.Environmental factors,especially temperature-related variables,significantly influence genetic patterns.Partial Mantel tests and multiple matrix regression show strong correlations between genetic distance and adaptations to cold temperatures(bio6 and bio11).Overall,this study emphasizes the robust genetic diversification and high migration rates in the populations of P.koraiensis and highlights their resilience.These results emphasize the importance of incorporating genetic and ecological factors into conservation strategies for sustainable forest management.This research provides valuable insights into the complex interplay of genetic variation,gene flow and environmental influences in forest tree species and improves our understanding of their adaptive mechanisms.展开更多
Climate change is thought to have a greater impact on crops that require particular conditions for their productivity.Southwest Ethiopia is a region where important cash crops such as Coffea arabica and Aframomum corr...Climate change is thought to have a greater impact on crops that require particular conditions for their productivity.Southwest Ethiopia is a region where important cash crops such as Coffea arabica and Aframomum corrorima(korerima)originate.These crops are known to require shade for their growth and productivity.This study was conducted to assess the impacts of climate change on an important but neglected cash crop of A.corrorima using GIS-based species distribution approaches.Local meteorological data and bioclimatic data from WorldClim were used to map past,present,and future distribution of the crop in the Coffee Forest System of Southwest Ethiopia.Moreover,96 key informants were interviewed and completed questionnaires to complement the distribution modeling.The key informants mapped the history and present occurrences of A.corrorima and based on this,ground-truthing survey was conducted.The interpolation method of the Inverse Distance Weighted was used in ArcGIS 10.5 to develop bioclimatic variables for modeling past and present distribution while data from IPCC(AR4)Emissions Scenarios was used for the future occurrence prediction using Principal Component Analysis.Eleven best bioclimatic variables were selected and MaxEnt was used to model past,present and future distribution of A.corrorima.The output of our model was validated using Area Under the Curve(AUC)approach.Temperature and precipitation are the most important environmental variable,then temperature increased by 1.3°C in the past(from 1988 to 2018)while it is predicted to increase further by at least 1.4°C before 2050.On the contrary,precipitation decreased by an average of 10.1 mm from the past while it is predicted to decrease further by 12.5 mm before 2050.Our model shows that the area suitable for korerima in 1988 was 20,638.2 ha and it was reduced by half and became 10,545.3 ha in 2018,similarly predicted to shrink into 3225.5 ha by 2050.The findings from the key informants confirm the model results whereby 89.1%of the respondent replied korerima producing area has been shifted into the mountains over the last 30 years(by 150 m a.s.l.from 1988 to 2018)and thus expected to be pushing up in the next 32 years(by 133 m before 2050).The community claims that the length of the rainy season of the area has been shortening from 9 months in the past to an average of 5.5 months recently which also coincides with increasing temperature.We conclude that with the changing climatic condition,the suitable habitat of korerima has already shrank by 48.9%(from 1988 to 2018)and the trend may lead to a shrink by 84.38%before 2050(from 1988 to 2050).Therefore,it is important to develop site-specific climate adaptation strategies for the region such as promoting alternative livelihoods and avoiding further coffee forest degradation and deforestation.展开更多
Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, touri...Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, tourism riches, as well as ecological benefits, such as nutrient regulation and carbon sequestration. Thus, this work aimed to generate potential distribution modeling for the Brazilian forest species, to provide information that will serve as a strategy for conservation, restoration and commercial plantation of them, that is, encouraging the use of legal native species in the forest sector. Eleven tree species and 19 bioclimatic variables were selected. The software Maxent 3.3.3 was applied in the generation of the distribution models and the area under the curve of receiver operating characteristic （AUC） was used to analyze the model. The Jackknife test contributed to identify which bioclimatic variables are most important or influential in the model. The models showed AUC values ranged from 0.857 to 0.983. The species with higher AUC values were Araucaria angustifolia, Mimosa scabrella and Euterpe edulis, respectively. The maximum temperature of warmest month showed the highest influence for the most species, followed by the mean diurnal range and annual precipitation. It was observed that for some species, there were restricted areas of environmental suitability, such as Araucaria angustifolia, Ilex paraguariensis and Mimosa scabrella. The models used could trace the potential distribution areas using the environmental variables, and these models contribute significantly to sustainable forest management.展开更多
In Benin, Shea tree (Vitellaria paradoxa) is one of the agroforestry species of great socio-economic importance for local populations. Given the actual variation in the climate parameters, it is necessary to anticipat...In Benin, Shea tree (Vitellaria paradoxa) is one of the agroforestry species of great socio-economic importance for local populations. Given the actual variation in the climate parameters, it is necessary to anticipate the future spatial distribution of Shea trees as an adaptation strategy and for designing relevant conservation strategies. The aim of the present research was to evaluate the influence of climate change on the distribution areas of Shea trees in Benin. Occurrence data consisting of geographic coordinates of Shea trees in Benin as well as bioclimatic variables were recorded. Furthemore, additional presence points were collected from the Global Biodiversity Information Facility database website. Current and future environmental data for the study area were obtained from the Africlim website. Bioclimatic variables (moisture and temperature), monthly maximum and minimum temperatures and annual rainfall were collected from Worldclim synoptic stations website for the period 1970-2000. The aridity index was created from the potential evapotranspiration (PET) and annual rainfall, using spatial analysis tools of ArcGIS. The impact of current and future environmental conditions on favourable Shea trees’ growing area was assessed following the maximum entropy (MaxEnt) approach under two climate scenarios (RCP 4.5 and RCP 8.5). Under the current climate conditions, 80% of Benin territory and 79% of the protected areas were highly favourable for Shea trees growing and conservation. However, all climate scenarios projected the significant decrease of 14% to 19% of the distribution of favourable for Shea tree growing area and 26% to 30% of the protected areas by 2055 in favour of non-favourable for the trees’ distribution. The protection of habitats favourable for the species development, coupled with a quick restoration of the species through the use of appropriate vegetative propagation techniques are required to sustain the species’ conservation in Benin and maintain farmers’ livelihood.展开更多
基金The authors appreciate the providers of temperature and precipitation products to allow us to download and use these data sets.We thank two anonymous referees for their comments on the manuscript.
文摘Global maps of bioclimatic variables currently exist only at very coarse spatial resolution(e.g.WorldClim).For ecological studies requiring higher resolved information,this spatial resolution is often insufficient.The aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation(EO)data at a higher spatial resolution of 1 km.The analysis used two different satellite time series data sets:land surface temperature(LST)from Moderate Resolution Imaging Spectroradiometer(MODIS),and precipitation(P)from Climate Hazards Group Infrared Precipitation with Stations(CHIRPS).Monthly maximum,mean,and minimum air temperature were estimated from Terra MODIS satellite(collection 6)LST time series product using the random forest(RF)regression model.Monthly total precipitation data were obtained from CHIRPS version 2.0.Based on this primary data,spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated,representing the period 2002-2017.We tested the relationship between estimated bioclimatic variables(SatClim)and WorldClim bioclimatic variables version 2.0(WorldClim)using determination coefficient(R^(2)),root mean square error(RMSE),and normalized root mean square error(nRMSE)and found overall good agreement.Among the set of 19 WorldClim bioclimatic variables,17 were estimated with a coefficient of determination(R^(2))higher than 0.7 and normalized RMSE(nRMSE)lower than 8%,confirming that the spatial pattern and value ranges can be retrieved from satellite data with much higher spatial resolution compared to WorldClim.Only the two bioclimatic variables related to temperature extremes(i.e.,annual mean diurnal range and isothermality)were modeled with only moderate accuracy(R^(2) of about 0.4 with nRMSE of about 11%).Generally,precipitation-related bioclimatic variables were closer correlated with WorldClim compared to temperature-related bioclimatic variables.The overall success of the modeling was attributed to the fact that satellite-derived data are well suited to generated spatial fields of precipitation and temperature variables,especially at high altitudes and high latitudes.As a consequence of the successful retrieval of the bioclimatic variables at 1 km spatial resolution,we are confident that the estimated 19 bioclimatic variables will be very useful for a range of applications,including species distribution modeling.
基金funded by grants from the Innovation Project of the State Key Laboratory of Tree Genetics and Breeding(2021B01).
文摘Pinus koraiensis(Sieb.et Zucc.) is a coniferous tree species naturally distributed in northeastern China.However,the effects of gene flow on its genetic diversity and structure remain unclear.This study investigates these dynamics in seven populations using ten microsatellite markers.The results show a high level of genetic diversity within the populations(Ho=0.633,He=0.746).In addition,molecular analysis of variance(AMOVA) shows that 98% of genetic diversity occurs within populations,with minimal differentiation between populations(Fst=0.009-0.033).Gene flow analysis shows significant migration rates between specific population pairs,particularly C-TH(87%),LS-Y(69%) and TH-LS(69%),suggesting genetic homogenization.Bayesian clustering(STRUCTURE) supports admixture and weak population differentiation.Environmental factors,especially temperature-related variables,significantly influence genetic patterns.Partial Mantel tests and multiple matrix regression show strong correlations between genetic distance and adaptations to cold temperatures(bio6 and bio11).Overall,this study emphasizes the robust genetic diversification and high migration rates in the populations of P.koraiensis and highlights their resilience.These results emphasize the importance of incorporating genetic and ecological factors into conservation strategies for sustainable forest management.This research provides valuable insights into the complex interplay of genetic variation,gene flow and environmental influences in forest tree species and improves our understanding of their adaptive mechanisms.
基金the Tapi Spice Research Center for their support。
文摘Climate change is thought to have a greater impact on crops that require particular conditions for their productivity.Southwest Ethiopia is a region where important cash crops such as Coffea arabica and Aframomum corrorima(korerima)originate.These crops are known to require shade for their growth and productivity.This study was conducted to assess the impacts of climate change on an important but neglected cash crop of A.corrorima using GIS-based species distribution approaches.Local meteorological data and bioclimatic data from WorldClim were used to map past,present,and future distribution of the crop in the Coffee Forest System of Southwest Ethiopia.Moreover,96 key informants were interviewed and completed questionnaires to complement the distribution modeling.The key informants mapped the history and present occurrences of A.corrorima and based on this,ground-truthing survey was conducted.The interpolation method of the Inverse Distance Weighted was used in ArcGIS 10.5 to develop bioclimatic variables for modeling past and present distribution while data from IPCC(AR4)Emissions Scenarios was used for the future occurrence prediction using Principal Component Analysis.Eleven best bioclimatic variables were selected and MaxEnt was used to model past,present and future distribution of A.corrorima.The output of our model was validated using Area Under the Curve(AUC)approach.Temperature and precipitation are the most important environmental variable,then temperature increased by 1.3°C in the past(from 1988 to 2018)while it is predicted to increase further by at least 1.4°C before 2050.On the contrary,precipitation decreased by an average of 10.1 mm from the past while it is predicted to decrease further by 12.5 mm before 2050.Our model shows that the area suitable for korerima in 1988 was 20,638.2 ha and it was reduced by half and became 10,545.3 ha in 2018,similarly predicted to shrink into 3225.5 ha by 2050.The findings from the key informants confirm the model results whereby 89.1%of the respondent replied korerima producing area has been shifted into the mountains over the last 30 years(by 150 m a.s.l.from 1988 to 2018)and thus expected to be pushing up in the next 32 years(by 133 m before 2050).The community claims that the length of the rainy season of the area has been shortening from 9 months in the past to an average of 5.5 months recently which also coincides with increasing temperature.We conclude that with the changing climatic condition,the suitable habitat of korerima has already shrank by 48.9%(from 1988 to 2018)and the trend may lead to a shrink by 84.38%before 2050(from 1988 to 2050).Therefore,it is important to develop site-specific climate adaptation strategies for the region such as promoting alternative livelihoods and avoiding further coffee forest degradation and deforestation.
文摘Brazil is one of the countries with the greatest biodiversity, being covered by diverse ecosystems. Native trees commercially planted generate numerous benefits for communities, providing cultural, recreational, tourism riches, as well as ecological benefits, such as nutrient regulation and carbon sequestration. Thus, this work aimed to generate potential distribution modeling for the Brazilian forest species, to provide information that will serve as a strategy for conservation, restoration and commercial plantation of them, that is, encouraging the use of legal native species in the forest sector. Eleven tree species and 19 bioclimatic variables were selected. The software Maxent 3.3.3 was applied in the generation of the distribution models and the area under the curve of receiver operating characteristic （AUC） was used to analyze the model. The Jackknife test contributed to identify which bioclimatic variables are most important or influential in the model. The models showed AUC values ranged from 0.857 to 0.983. The species with higher AUC values were Araucaria angustifolia, Mimosa scabrella and Euterpe edulis, respectively. The maximum temperature of warmest month showed the highest influence for the most species, followed by the mean diurnal range and annual precipitation. It was observed that for some species, there were restricted areas of environmental suitability, such as Araucaria angustifolia, Ilex paraguariensis and Mimosa scabrella. The models used could trace the potential distribution areas using the environmental variables, and these models contribute significantly to sustainable forest management.
文摘In Benin, Shea tree (Vitellaria paradoxa) is one of the agroforestry species of great socio-economic importance for local populations. Given the actual variation in the climate parameters, it is necessary to anticipate the future spatial distribution of Shea trees as an adaptation strategy and for designing relevant conservation strategies. The aim of the present research was to evaluate the influence of climate change on the distribution areas of Shea trees in Benin. Occurrence data consisting of geographic coordinates of Shea trees in Benin as well as bioclimatic variables were recorded. Furthemore, additional presence points were collected from the Global Biodiversity Information Facility database website. Current and future environmental data for the study area were obtained from the Africlim website. Bioclimatic variables (moisture and temperature), monthly maximum and minimum temperatures and annual rainfall were collected from Worldclim synoptic stations website for the period 1970-2000. The aridity index was created from the potential evapotranspiration (PET) and annual rainfall, using spatial analysis tools of ArcGIS. The impact of current and future environmental conditions on favourable Shea trees’ growing area was assessed following the maximum entropy (MaxEnt) approach under two climate scenarios (RCP 4.5 and RCP 8.5). Under the current climate conditions, 80% of Benin territory and 79% of the protected areas were highly favourable for Shea trees growing and conservation. However, all climate scenarios projected the significant decrease of 14% to 19% of the distribution of favourable for Shea tree growing area and 26% to 30% of the protected areas by 2055 in favour of non-favourable for the trees’ distribution. The protection of habitats favourable for the species development, coupled with a quick restoration of the species through the use of appropriate vegetative propagation techniques are required to sustain the species’ conservation in Benin and maintain farmers’ livelihood.
