Increasing temperatures and severe droughts threaten forest vitality globally.Prediction of forest response to climate change requires knowledge of the spatiotemporal patterns of monthly or seasonal climatic impacts o...Increasing temperatures and severe droughts threaten forest vitality globally.Prediction of forest response to climate change requires knowledge of the spatiotemporal patterns of monthly or seasonal climatic impacts on the growth of tree species,likely driven by local climatic aridity,climate trends,edaphic conditions,and the climatic adaption of tree species.The ability of tree species to cope with changing climate and the effects of environmental variables on growth trends and growth-climate relationships across diverse bioclimatic regions are still poorly understood for many species.This study investigated radial growth trends,interannual growth variability,and growth-climate sensitivity of two dominant tree species,Pinus tabulaeformis(PT)and Pinus sylvestris var.mongolica(PS),across a broad climatic gradient with a variety of soil properties in temperate Northern China.Using a network of 83 tree ring chronologies(54 for PT and 29 for PS)from 1971 to 2010,we documented that both species maintained constant growth trends at wet sites,while both displayed rapid declines at dry sites.We reported the species-specific drivers of spatial heterogeneity in growth trends,interannual growth variability,and growth-climate relationships.Calculated climatic variables and soil properties were identified as the most critical factors affecting the growth trends and growth-climate relationships.However,climatic variables play more essential roles than soil properties in determining the spatial heterogeneity of the growth-climate relationship.Lower clay content and higher soil nutrient regimes can exacerbate the moisture-related susceptibility of tree growth.Our findings highlight that soil properties emerged as important modulating factors to predict the drought vulnerability of forests in addition to climatic variables.Considering the continued climate warmingdrying trend in the future,both pines will face a more severe growth decline and increase in drought vulnerability at drier sites with lower clayed soil or higher nutrient regimes.展开更多
Chinese tallow tree (Triadica sebifera (L.) Small, Sapium sebiferum (L.) Roxb) is an invasive species that is replacing native ecosystems in areas of eastern Texas. It is imperative that the spatial pattern of the spr...Chinese tallow tree (Triadica sebifera (L.) Small, Sapium sebiferum (L.) Roxb) is an invasive species that is replacing native ecosystems in areas of eastern Texas. It is imperative that the spatial pattern of the spread of this species be identified, as well as causal mechanisms. To that end, we seek to determine factors that contribute to the spread of Chinese tallow using classification and regression tree (CART) and logistic regression. We also attempt to identify current locations and spread rates across eastern Texas using Forest Inventory Analysis (FIA) data within major forest types. Distance to formerly infested plots and roads, slope, and disturbances (natural and anthropogenic) were identified as major (either facilitating or impeding) factors for the spread of Chinese tallow across the landscape. The highest probability of occurrence and spread rate of Chinese tallow were found in the oak/ gum/cypress forest type. Continued disturbance, from harvest events or natural disasters will allow the species to continue to spread throughout the region and could threaten overall forest productivity. We also discuss some implications of the continued spread of Chinese tallow on forest management. Forest managers could benefit from this analysis and use it as a guide for monitoring forest types with the highest risk of invasion.展开更多
Cavity trees are integral components of healthy forest ecosystems and provide habitat and shelter for a wide variety of wildlife species. Thus, monitoring and predicting cavity tree abundance is an important part of f...Cavity trees are integral components of healthy forest ecosystems and provide habitat and shelter for a wide variety of wildlife species. Thus, monitoring and predicting cavity tree abundance is an important part of forest management and wildlife conservation. However, cavity trees are relatively rare and their abundance can vary dramatically among forest stands, even when the stands are similar in most other respects. This makes it difficult to model and predict cavity tree density. We utilized data from the Missouri Ozark Forest Ecosystem Project to show that it is virtually impossible to accurately predict cavity tree occurrence for individual trees or to predict mean cavity tree abundance for individual forest stands. However, we further show that it is possible to model and predict mean cavity tree density for larger spatial areas. We illustrate the prediction error monotonically decreases as the spatial scale of predictions in-creases. We successfully explored the utility of three classes of models for predicting cavity tree probability/density: logistic regression, neural network, and classification and regression tree (CART). The results provide valuable insights into methods for landscape-scale mapping of cavity trees for wildlife habitat management, and also on sample size determination for cavity tree surveys and monitoring.展开更多
The invasion of Chinese tallow(Triadica sebifera(L.)Small)is a serious threat to the endangered slash pine(Pinus elliottii)flatwood ecosystem in the Gulf of Mexico Coastal Plain,United States.Prescribed fire in combin...The invasion of Chinese tallow(Triadica sebifera(L.)Small)is a serious threat to the endangered slash pine(Pinus elliottii)flatwood ecosystem in the Gulf of Mexico Coastal Plain,United States.Prescribed fire in combination with vegetation management has been suggested as a preferred approach for mitigating Chinese tallow invasion and restoring this endangered ecosystem.A large plot of 0.86-ha with 281 nested contiguous 30-m^(2) quadrats was established in a tallow-invaded slash pine flatwood and all tallow trees,saplings,seedlings and associated factors in each quadrat were measured to study the community-level tallow invasion processes before and after a prescribed fire and by dispersal and community factors.Classification and regression tree models show that the dispersal factors(distances to the road and to the trail)and microtopography(elevation)determine the invasion probability of tallow,but the degree of invasion(abundance)of tallow depends on the interactions of both dispersal factors and community factors such as canopy closure and grass/herbaceous coverage.Areas nearer to roads and trails,dominated by native grass/herbaceous species,and with a low elevation and canopy closure are highly susceptible to tallow invasion and establishment.The effect of fire on tallow invasion varies with overstory and understory conditions.Density of tallow seedlings and saplings increased greatly after fire in the areas dominated by slash pines in the overstory and native grass/herbaceous species in the understory.To control tallow invasion and establishment,tallow seed trees/sources should be removed from the area and vicinity to be burned.展开更多
The abundance of cavity trees varies greatly due to the stochastic nature of cavity formation processes and involved disturbance agents.At small spatial scales such as a stand or plot,cavity tree abundance is extraord...The abundance of cavity trees varies greatly due to the stochastic nature of cavity formation processes and involved disturbance agents.At small spatial scales such as a stand or plot,cavity tree abundance is extraordinarily difficult to predict precisely using tree and stand factors.In this study we used resampling methods to simulate the effect of spatial scale on cavity tree density(CTD)estimation using cavity tree data collected from a long-term forest experimental project.More than 53,000 measured trees were randomly divided into two approximately equal parts:the construction and test datasets,to construct classification and regression tree(CART)and logistic regression(LR)models to predict cavity probability and to test the accuracy of CTD estimation across varying spatial scales,respectively.Simulation results showed that when the spatial scale was<10 ha,the predicted CTD varied dramatically,and with this specific dataset,CART tended to overestimate,whereas LR and the sample mean method underestimated the true CTD estimated by the construction dataset.Compared with the sample mean method,the use of tree characteristics in both CART and LR resulted in slight or moderate reduction of the relative error(RE)(<20%)when the spatial scale was<10 ha.However,CART and LR,particularly CART,could improve CTD prediction efficiency significantly at larger spatial scales.For instance,the RE of CART was only 17%of the sample mean method at a spatial scale of 50 ha.Resource managers could use this information for cavity tree sampling and monitoring.展开更多
基金funded by the National Key Research and Development Plan of China(No.2022YFE0127900)the National Natural Science Foundation of China(Nos.32071558,32171559)+2 种基金the Natural Science Foundation Key Project of Inner Mongolia Autonomous Region,China(No.2023ZD23)the Hulunbuir Science and Technology Plan Project(No.SF2022001)the Fundamental Research Funds of CAF(CAFYBB2023ZA002).
文摘Increasing temperatures and severe droughts threaten forest vitality globally.Prediction of forest response to climate change requires knowledge of the spatiotemporal patterns of monthly or seasonal climatic impacts on the growth of tree species,likely driven by local climatic aridity,climate trends,edaphic conditions,and the climatic adaption of tree species.The ability of tree species to cope with changing climate and the effects of environmental variables on growth trends and growth-climate relationships across diverse bioclimatic regions are still poorly understood for many species.This study investigated radial growth trends,interannual growth variability,and growth-climate sensitivity of two dominant tree species,Pinus tabulaeformis(PT)and Pinus sylvestris var.mongolica(PS),across a broad climatic gradient with a variety of soil properties in temperate Northern China.Using a network of 83 tree ring chronologies(54 for PT and 29 for PS)from 1971 to 2010,we documented that both species maintained constant growth trends at wet sites,while both displayed rapid declines at dry sites.We reported the species-specific drivers of spatial heterogeneity in growth trends,interannual growth variability,and growth-climate relationships.Calculated climatic variables and soil properties were identified as the most critical factors affecting the growth trends and growth-climate relationships.However,climatic variables play more essential roles than soil properties in determining the spatial heterogeneity of the growth-climate relationship.Lower clay content and higher soil nutrient regimes can exacerbate the moisture-related susceptibility of tree growth.Our findings highlight that soil properties emerged as important modulating factors to predict the drought vulnerability of forests in addition to climatic variables.Considering the continued climate warmingdrying trend in the future,both pines will face a more severe growth decline and increase in drought vulnerability at drier sites with lower clayed soil or higher nutrient regimes.
文摘Chinese tallow tree (Triadica sebifera (L.) Small, Sapium sebiferum (L.) Roxb) is an invasive species that is replacing native ecosystems in areas of eastern Texas. It is imperative that the spatial pattern of the spread of this species be identified, as well as causal mechanisms. To that end, we seek to determine factors that contribute to the spread of Chinese tallow using classification and regression tree (CART) and logistic regression. We also attempt to identify current locations and spread rates across eastern Texas using Forest Inventory Analysis (FIA) data within major forest types. Distance to formerly infested plots and roads, slope, and disturbances (natural and anthropogenic) were identified as major (either facilitating or impeding) factors for the spread of Chinese tallow across the landscape. The highest probability of occurrence and spread rate of Chinese tallow were found in the oak/ gum/cypress forest type. Continued disturbance, from harvest events or natural disasters will allow the species to continue to spread throughout the region and could threaten overall forest productivity. We also discuss some implications of the continued spread of Chinese tallow on forest management. Forest managers could benefit from this analysis and use it as a guide for monitoring forest types with the highest risk of invasion.
文摘Cavity trees are integral components of healthy forest ecosystems and provide habitat and shelter for a wide variety of wildlife species. Thus, monitoring and predicting cavity tree abundance is an important part of forest management and wildlife conservation. However, cavity trees are relatively rare and their abundance can vary dramatically among forest stands, even when the stands are similar in most other respects. This makes it difficult to model and predict cavity tree density. We utilized data from the Missouri Ozark Forest Ecosystem Project to show that it is virtually impossible to accurately predict cavity tree occurrence for individual trees or to predict mean cavity tree abundance for individual forest stands. However, we further show that it is possible to model and predict mean cavity tree density for larger spatial areas. We illustrate the prediction error monotonically decreases as the spatial scale of predictions in-creases. We successfully explored the utility of three classes of models for predicting cavity tree probability/density: logistic regression, neural network, and classification and regression tree (CART). The results provide valuable insights into methods for landscape-scale mapping of cavity trees for wildlife habitat management, and also on sample size determination for cavity tree surveys and monitoring.
基金the U.S.Department of Agriculture National Institute of Food and Agriculture’s McIntire Stennis fund(project#:ALAZ00065)Hatch fund(project#:ALA0031-1-17108)through the Alabama Agricultural Experi-mental Station.
文摘The invasion of Chinese tallow(Triadica sebifera(L.)Small)is a serious threat to the endangered slash pine(Pinus elliottii)flatwood ecosystem in the Gulf of Mexico Coastal Plain,United States.Prescribed fire in combination with vegetation management has been suggested as a preferred approach for mitigating Chinese tallow invasion and restoring this endangered ecosystem.A large plot of 0.86-ha with 281 nested contiguous 30-m^(2) quadrats was established in a tallow-invaded slash pine flatwood and all tallow trees,saplings,seedlings and associated factors in each quadrat were measured to study the community-level tallow invasion processes before and after a prescribed fire and by dispersal and community factors.Classification and regression tree models show that the dispersal factors(distances to the road and to the trail)and microtopography(elevation)determine the invasion probability of tallow,but the degree of invasion(abundance)of tallow depends on the interactions of both dispersal factors and community factors such as canopy closure and grass/herbaceous coverage.Areas nearer to roads and trails,dominated by native grass/herbaceous species,and with a low elevation and canopy closure are highly susceptible to tallow invasion and establishment.The effect of fire on tallow invasion varies with overstory and understory conditions.Density of tallow seedlings and saplings increased greatly after fire in the areas dominated by slash pines in the overstory and native grass/herbaceous species in the understory.To control tallow invasion and establishment,tallow seed trees/sources should be removed from the area and vicinity to be burned.
文摘The abundance of cavity trees varies greatly due to the stochastic nature of cavity formation processes and involved disturbance agents.At small spatial scales such as a stand or plot,cavity tree abundance is extraordinarily difficult to predict precisely using tree and stand factors.In this study we used resampling methods to simulate the effect of spatial scale on cavity tree density(CTD)estimation using cavity tree data collected from a long-term forest experimental project.More than 53,000 measured trees were randomly divided into two approximately equal parts:the construction and test datasets,to construct classification and regression tree(CART)and logistic regression(LR)models to predict cavity probability and to test the accuracy of CTD estimation across varying spatial scales,respectively.Simulation results showed that when the spatial scale was<10 ha,the predicted CTD varied dramatically,and with this specific dataset,CART tended to overestimate,whereas LR and the sample mean method underestimated the true CTD estimated by the construction dataset.Compared with the sample mean method,the use of tree characteristics in both CART and LR resulted in slight or moderate reduction of the relative error(RE)(<20%)when the spatial scale was<10 ha.However,CART and LR,particularly CART,could improve CTD prediction efficiency significantly at larger spatial scales.For instance,the RE of CART was only 17%of the sample mean method at a spatial scale of 50 ha.Resource managers could use this information for cavity tree sampling and monitoring.
