Mapping soil water holding capacity over large areas to predict the potential production of forest stands

International audienceEcological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected. We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm3 cm-3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict Ecological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected. We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm3 cm-3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict height growth for Fagus sylvatica, Picea abies and Quercus petraea is discussed. We show a much better predictive ability for SWHC" compared to SWHC'. The values of SWHC" extracted from the map were significantly related to tree height growth. They explained 10.7% of the height growth index variance for Fagus sylvatica (n = 866), 14.1% for Quercus petraea (n = 877) and 10.3% for Picea abies (n = 2067). The proportions of variance accounted by SWHC" were close to those recorded with SWHC" values estimated from the plots (11.5, 11.7, and 18.6% for Fagus sylvatica, Quercus petraea and Picea abies, respectively). We conclude that SWHC" can be mapped using basic soil parameters collected from plots, the predictive ability of the map and of data derived from the plot being close. Thus, the map could be used just as well for small areas as for large areas, directly or indirectly through water balance indices, to predict forest growth and thus production, today or in the future, in the context of an increasing drought period linked to a global change of climatic conditions

Evidence of climate effects on the height-diameter relationships of tree species

International audienceAbstractKey messageThe mean temperature from March to September affects the height-diameter relationship of many tree species in France. For most of these species, the temperature effect is nonlinear, which makes the identification of an optimal temperature possible. Increases in mean temperature could impact the volume supply of commercial species by the end of the twenty-first century.ContextHeight-diameter (HD) relationships are central in forestry since they are essential to estimate tree volume and biomass. Since the late 1960s, efforts have been made to generalize models of HD relationships through the inclusion of plot- and tree-level explanatory variables. In some recent studies, climate variables such as mean annual temperature and precipitation have been found to have a significant effect on HD allometry. However, in these studies, the effects were all considered to be linear or almost linear, which supposes that there is no optimal temperature and no optimal precipitation.AimsIn this study, we tested the hypothesis that an optimum effect of temperature and precipitation exists on tree heights.MethodsWe fitted generalized models of HD relationships to 44 tree species distributed across France. To make sure that the climate variables would not hide some differences in terms of the local environment, the models included explanatory variables accounting for competition, tree social status and other plot-level factors such as slope inclination and the occurrence of harvesting in the last five years.ResultsIt turned out that the temperature effect was significant for 33 out of 44 species and an optimum was found in 26 cases. The precipitation effect was linear and was found to be significant for only seven species. Although the two climate variables did not contribute as much as the competition and the social status indices to the model fit, they were still important contributors. Under the representative concentration pathway (RCP) 2.6 and the assumptions of constant form factors and forest conditions in terms of competition and social statuses, it is expected that approximately two thirds of the species with climate-sensitive HD relationships will generally be shorter. This would induce a decrease in volume ranging from 1 to 5% for most of these species.ConclusionForest practitioners should be aware that the volume supply of some commercial species could decrease by the end of the twenty-first century. However, these losses could be partly compensated for by changes in the form factors and the species distributions

Background mortality drivers of European tree species: climate change matters

International audienceIncreases in tree mortality rates have been highlighted in different biomes over the past decades. However, disentangling the effects of climate change on the temporal increase in tree mortality from those of management and forest dynamics remains a challenge. Using a modelling approach taking tree and stand characteristics into account, we sought to evaluate the impact of climate change on background mortality for the most common European tree species. We focused on background mortality, which is the mortality observed in a stand in the absence of abrupt disturbances, to avoid confusion with mortality events unrelated to long-term changes in temperature and rainfall. We studied 372 974 trees including 7312 dead trees from forest inventory data surveyed across France between 2009 and 2015. Factors related to competition, stand characteristics, management intensity, and site conditions were the expected preponderant drivers of mortality. Taking these main drivers into account, we detected a climate change signal on 45% of the 43 studied species, explaining an average 6% of the total modelled mortality. For 18 out of the 19 species sensitive to climate change, we evidenced greater mortality with increasing temperature or decreasing rainfall. By quantifying the mortality excess linked to the current climate change for European temperate forest tree species, we provide new insights into forest vulnerability that will prove useful for adapting forest management to future conditions

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Stochastic Models for Solar Power

International audienceIn this work we develop a stochastic model for the solar power at the surface of the earth. We combine a deterministic model of the clear sky irradiance with a stochastic model for the so-called clear sky index to obtain a stochastic model for the actual irradiance hitting the surface of the earth. Our clear sky index model is a 4-state semi-Markov process where state durations and clear sky index values in each state have phase-type distributions. We use per-minute solar irradiance data to tune the model, hence we are able to capture small time scales fluctuations. We compare our model with the on-off power source model developed by Miozzo et al. (2014) for the power generated by photovoltaic panels, and to a modified version that we propose. In our on-off model the output current is frequently resampled instead of being a constant during the duration of the " on " state. Computing the autocorrelation functions for all proposed models, we find that the irradiance model surpasses the on-off models and it is able to capture the multiscale correlations that are inherently present in the solar irradiance. The power spectrum density of generated trajectories matches closely that of measurements. We believe our irradiance model can be used not only in the mathematical analysis of energy harvesting systems but also in their simulation

What we use is not what we know: environmental predictors in plant distribution models

Questions: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter plant SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing), and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods: We carried out an extensive, systematic review of recently published plant SDM studies (years 2010-2015; n = 200) to determine the predictors used in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000-2015; n = 40). Results: Except for the pure climatic studies, a large majority of plant SDM studies neglected several ecophysiologically-meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 years. Conclusions: Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g., soil moisture). However, others are not yet easily obtainable across whole study extents (e.g., soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental science

Predicting species dominance shifts across elevation gradients in mountain forests in Greece under a warmer and drier climate

The Mediterranean Basin is expected to face warmer and drier conditions in the future, following projected increases in temperature and declines in precipitation. The aim of this study is to explore how forests dominated by Abies borisii-regis, Abies cephalonica, Fagus sylvatica, Pinus nigra and Quercus frainetto will respond under such conditions. We combined an individual-based model (GREFOS), with a novel tree ring data set in order to constrain tree diameter growth and to account for inter- and intraspecific growth variability. We used wood density data to infer tree longevity, taking into account inter- and intraspecific variability. The model was applied at three 500-m-wide elevation gradients at Taygetos in Peloponnese, at Agrafa on Southern Pindos and at Valia Kalda on Northern Pindos in Greece. Simulations adequately represented species distribution and abundance across the elevation gradients under current climate. We subsequently used the model to estimate species and functional trait shifts under warmer and drier future conditions based on the IPCC A1B scenario. In all three sites, a retreat of less drought-tolerant species and an upward shift of more drought-tolerant species were simulated. These shifts were also associated with changes in two key functional traits, in particular maximum radial growth rate and wood density. Drought-tolerant species presented an increase in their average maximal growth and decrease in their average wood density, in contrast to less drought-tolerant species

Climatic predictors of species distributions neglect biophysiologically meaningful variables

This is the final version. Available on open access from Wiley via the DOI in this record.Aim: Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models requires the use of climate variables that adequately capture physiological responses of species to climate and therefore provide a proximal link between climate and their distributions. Here, we examine whether the climate variables used in plant SDMs are different from those known to influence directly plant physiology. Location: Global. Methods: We carry out an extensive, systematic review of the climate variables used to model the distributions of plant species and provide comparison to the climate variables identified as important in the plant physiology literature. We calculate the top ten SDM and physiology variables at 2.5 degree spatial resolution for the globe and use principal component analyses and multiple regression to assess similarity between the climatic variation described by both variable sets. Results: We find that the most commonly used SDM variables do not reflect the most important physiological variables and differ in two main ways: (i) SDM variables rely on seasonal or annual rainfall as simple proxies of water available to plants and neglect more direct measures such as soil water content; and (ii) SDM variables are typically averaged across seasons or years and overlook the importance of climatic events within the critical growth period of plants. We identify notable differences in their spatial gradients globally and show where distal variables may be less reliable proxies for the variables to which species are known to respond. Main conclusions: There is a growing need for the development of accessible, fine-resolution global climate surfaces of physiological variables. This would provide a means to improve the reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a changing climate