Windstorms are one of the most important disturbance factors in European forest ecosystems. An understanding of the major drivers causing observed changes in forests is essential to improve ...prediction models and as a basis for forest management. In the present study, we use machine learning techniques in combination with data sets on tree properties, bioclimatic and geomorphic conditions, to analyse the level of forest damage by windstorms in the Sudety Mountains over the period 2004–2010. We tested four scenarios under five classification model frameworks: logistic regression, random forest, support vector machines, neural networks, and gradient boosted modelling. Gradient boosted modelling and random forest have the best predictive power. Tree volume and age are the most important predictors of windstorm damage; climate and geomorphic variables are less important. Forest damage maps based on forest data from 2020 show lower probabilities of damage compared to the end of 20th and the beginning of 21st century.
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•Five machine learning models give consistent predictions of what controls wind damage.•Tree volume and age are the most important predictors of forest damage caused by wind.•Geomorphic and climate predictors are less important.•Random forest algorithm and gradient boosting modelling offer the best accuracy of prediction.•Forest stand features might significantly influence probability of forest damage.
Abstract
Abrupt events are a feature of many palaeoclimate records during the Holocene. The best example is the 8.2 ka event, which was triggered by a release of meltwater into the Labrador Sea and ...resulted in a weakening of poleward heat transport in the North Atlantic. We use an objective method to identify rapid climate events in globally distributed speleothem oxygen isotope records during the Holocene. We show that the 8.2 ka event can be identified in >70% of the speleothem records and is the most coherent signal of abrupt climate change during the last 12,000 years. The isotopic changes during the event are regionally homogenous: positive oxygen isotope anomalies are observed across Asia and negative anomalies are seen across Europe, the Mediterranean, South America and southern Africa. The magnitude of the isotopic excursions in Europe and Asia are statistically indistinguishable. There is no significant difference in the duration and timing of the 8.2 ka event between regions, or between the speleothem records and Greenland ice core records. Our study supports a rapid and global climate response to the 8.2 ka freshwater pulse into the North Atlantic, likely transmitted globally via atmospheric teleconnections.
Leaf morphological traits vary systematically along climatic gradients. However, recent studies in plant functional ecology have mainly analysed quantitative traits, while numerical models of species ...distributions and vegetation function have focused on traits associated with resource acquisition; both ignore the wider functional significance of leaf morphology.
A dataset comprising 22 leaf morphological traits for 662 woody species from 92 sites, representing all biomes present in China, was subjected to multivariate analysis in order to identify leading dimensions of trait covariation (correspondence analysis), quantify climatic and phylogenetic contributions (canonical correspondence analysis with variation partitioning) and characterise co‐occurring trait syndromes (k‐means clustering) and their climatic preferences.
Three axes accounted for >20% of trait variation in both evergreen and deciduous species. Moisture index, precipitation seasonality and growing‐season temperature explained 8%–10% of trait variation; family 15%–32%. Microphyll or larger, mid‐ to dark green leaves with drip tips in wetter climates contrasted with nanophyll or smaller glaucous leaves without drip tips in drier climates. Thick, entire leaves in less seasonal climates contrasted with thin, marginal dissected, aromatic and involute/revolute leaves in more seasonal climates. Thick, involute, hairy leaves in colder climates contrasted with thin leaves with marked surface structures (surface patterning) in warmer climates. Distinctive trait clusters were linked to the driest and most seasonal climates, for example the clustering of picophyll, fleshy and succulent leaves in the driest climates and leptophyll, linear, dissected, revolute or involute and aromatic leaves in regions with highly seasonal rainfall. Several trait clusters co‐occurred in wetter climates, including clusters characterised by microphyll, moderately thick, patent and entire leaves or notophyll, waxy, dark green leaves.
Synthesis. The plastic response of size, shape, colour and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function.
摘要
叶片形态特征沿气候梯度呈现规律性变化。近年来植物功能生态学的研究以数量性状的分析为主，而物种分布和植被功能的数值模型则聚焦于资源获取相关的性状。这两者都忽视了叶片形态更 广泛的功能意义。
中国植物性状数据库包含了来自中国92个站点的662种木本植物的22个叶片形态性状，覆盖了中国主要的生物群系。基于该数据库，本研究采用多元变量分析以确定这些性状协同变化的主要维度(对应分析)、量化气候和系统发育的贡献(典型对应分析和方差分解)，并表征共同出现的性状综合征(k‐means聚类分析)及其气候偏好。
多变量分析的三个主轴解释了常绿和落叶树种性状20%以上的变化。湿润指数、降水季节性和生长季平均温度解释了性状变化的8‐10%，科级阶元间差异则解释了15‐32%。在气候较湿润的地区，叶片为小型叶或更大型叶、颜色呈较绿至深绿且有滴水叶尖，而在气候较干燥的地区，叶片为微小型叶或更小型叶、叶表面覆白霜且无滴水叶尖。降水季节性变化较大地区的叶片，具有较薄、叶缘深裂、有芳香且内卷或外卷的特征，与季节性变化较小地区的厚实全缘叶形成对比。较冷的气候下的叶片厚而内卷且具毛，与较暖的气候下具有明显表面结构的薄叶形成对比。独特的性状簇与极端干燥和降水季节性强的气候紧密相关。例如在气候最干旱的地区，叶片聚类特征为鳞叶、肉质和多汁叶片；在降水季节性较强的地区则有极微小型叶、长叶、深裂、外卷或内卷和有芳香的叶片特征。某些叶片性状簇在潮湿的气候下同时出现，包括小型叶、中等厚度、平展、全缘叶的聚类特征或亚中型、蜡质、深绿色叶片的聚类特征。
综合而言，叶片大小、形状、颜色和其他形态特征响应气候变化的可塑性较弱，因此它们沿气候梯度所呈现出的更替，反映了由物种替代所引起的植物在群落水平上对环境的适应。植物区系中广泛存在的叶片形态特征信息可用于加强物种分布和植被功能的预测模型。
The plastic response of size, shape, colour and other leaf morphological traits to climate is muted, thus their apparent shift along climate gradients reflects plant adaptations to environment at a community level as determined by species replacement. Information on leaf morphological traits, widely available in floras, could be used to strengthen predictive models of species distribution and vegetation function.
Abstract
Fire is an important influence on the global patterns of vegetation structure and composition. Wildfire is included as a distinct process in many dynamic global vegetation models but limited ...current understanding of fire regimes restricts these models’ ability to reproduce more than the broadest geographic patterns. Here we present a statistical analysis of the global controls of remotely sensed burnt area (BA), fire size (FS), and a derived metric related to fire intensity (FI). Separate generalized linear models were fitted to observed monthly fractional BA from the Global Fire Emissions Database (GFEDv4), median FS from the Global Fire Atlas, and median fire radiative power from the MCD14ML dataset normalized by the square root of median FS. The three models were initially constructed from a common set of 16 predictors; only the strongest predictors for each model were retained in the final models. It is shown that BA is primarily driven by fuel availability and dryness; FS by conditions promoting fire spread; and FI by fractional tree cover and road density. Both BA and FS are constrained by landscape fragmentation, whereas FI is constrained by fuel moisture. Ignition sources (lightning and human population) were positively related to BA (after accounting for road density), but negatively to FI. These findings imply that the different controls on BA, FS and FI need to be considered in process-based models. They highlight the need to include measures of landscape fragmentation as well as fuel load and dryness, and to pay close attention to the controls of fire spread.
Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key ...for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr−1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
We explore the large spatial variation in the relationship between population density and burned area, using continental-scale Geographically Weighted Regression (GWR) based on 13 years of ...satellite-derived burned area maps from the global fire emissions database (GFED) and the human population density from the gridded population of the world (GPW 2005). Significant relationships are observed over 51.5% of the global land area, and the area affected varies from continent to continent: population density has a significant impact on fire over most of Asia and Africa but is important in explaining fire over < 22% of Europe and Australia. Increasing population density is associated with both increased and decreased in fire. The nature of the relationship depends on land-use: increasing population density is associated with increased burned are in rangelands but with decreased burned area in croplands. Overall, the relationship between population density and burned area is non-monotonic: burned area initially increases with population density and then decreases when population density exceeds a threshold. These thresholds vary regionally. Our study contributes to improved understanding of how human activities relate to burned area, and should contribute to a better estimate of atmospheric emissions from biomass burning.
Recent climate changes have increased fire-prone weather conditions in many
regions and have likely affected fire occurrence, which might impact ecosystem
functioning, biogeochemical cycles, and ...society. Prediction of how fire
impacts may change in the future is difficult because of the complexity of
the controls on fire occurrence and burned area. Here we aim to assess how
process-based fire-enabled dynamic global vegetation models (DGVMs) represent
relationships between controlling factors and burned area. We developed a
pattern-oriented model evaluation approach using the random forest (RF)
algorithm to identify emergent relationships between climate, vegetation, and
socio-economic predictor variables and burned area. We applied this approach
to monthly burned area time series for the period from 2005 to 2011 from satellite
observations and from DGVMs from the “Fire Modeling Intercomparison
Project” (FireMIP) that were run using a common protocol and forcing data sets. The
satellite-derived relationships indicate strong sensitivity to climate
variables (e.g. maximum temperature, number of wet days), vegetation
properties (e.g. vegetation type, previous-season plant productivity and leaf
area, woody litter), and to socio-economic variables (e.g. human population
density). DGVMs broadly reproduce the relationships with climate variables
and, for some models, with population density. Interestingly, satellite-derived responses
show a strong increase in burned area with an increase in previous-season leaf area index
and plant productivity in most fire-prone ecosystems, which was largely
underestimated by most DGVMs. Hence, our pattern-oriented model evaluation
approach allowed us to diagnose that vegetation effects on fire are a main
deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately
simulate the role of fire under global environmental change.
Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and ...assess their predictability.
Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership.
Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internalto-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (N
area)), and photosynthetic capacities (V
cmax, J
max at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small.
Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.
Summary
Global vegetation and land‐surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of ...environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco‐evolutionary optimality (EEO) principles can provide novel, parameter‐sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf‐level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.
In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and ...anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.
This is the first multi‐model assessment of the influence of fire on vegetation and the carbon cycle under current climate and land use conditions. The influence of fire is derived by comparing a model simulation with fire to a simulation without fire. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to vegetation productivity, total carbon storage and turnover times.
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