Canopy height variation and environmental heterogeneity in the tropical dry forests of coastal Oaxaca,Mexico

Despite its importance for carbon storage and other ecosystem functions, the variation in vegetation canopy height is not yet well understood. We examined the relationship between this community attribute and environmental heterogeneity in a tropical dry forest of southern Mexico. We sampled vegetation in 15 sites along a 100‐km coastal stretch of Oaxaca State, and measured the heights of all woody plants (excluding lianas). The majority of the ca. 4000 individuals recorded concentrated in the 4–8 m height range. We defined three plant sets to describe overall community canopy height at each site: a set including all plants, a set made up by the tallest plants representing 10 percent of all individuals, and a set comprising the 10 tallest plants. For each site we computed maximum height and the mean and median heights of the three sets. Significant collinearity was observed between the seven resulting height variables, but null distributions constructed through bootstrap revealed their different behaviors as functions of species richness and density of individuals. Through linear modeling and a model selection procedure, we identified 21 models that best described the variation in canopy height variables. These models pointed out to soil (measured as PC1 of a principal component analysis performed on 10 soil variables), water stress, and elevation as the main drivers of canopy height variation in the region. In the event of increasing water stress resulting from global climate change, the studied tropical dry forests could become shorter and thus decrease their carbon storage potential.     

Using species‐environmental amplitudes to predict pH values from vegetation

Question: Species optima or indicator values are frequently used to predict environmental variables from species composition. The present study focuses on the question whether predictions can be improved by using species environmental amplitudes instead of single values representing species optima. Location: Semi‐natural, deciduous hardwood forests of northwestern Germany. Methods: Based on a data set of 558 relevés, species responses (presence/absence) to pH were modelled with Huisman‐Olff‐Fresco (HOF) regression models. Species amplitudes were derived from response curves using three different methods. To predict the pH from vegetation, a maximum amplitude overlap method was applied. For comparison, predictions resulting from several established methods, i. e. maximum likelihood/present and absent species, maximum likelihood/present species only, mean weighted averages and mean Ellenberg indicator values were calculated. The predictive success (squared Pearson's r and root mean square error of prediction) was evaluated using an independent data set of 151 relevés. Results: Predictions based upon amplitudes defined by maximum Cohen's x probability threshold yield the best results of all amplitude definitions (R²= 0.75, RMSEP = 0.52). Provided there is an even distribution of the environmental variable, amplitudes defined by predicted probability exceeding prevalence are also suitable (R²= 0.76, RMSEP = 0.55). The prediction success is comparable to maximum likelihood (present species only) and – after rescaling – to mean weighted averages. Predicted values show a good linearity to observed pH values as opposed to a curvilinear relationship of mean Ellenberg indicator values. Transformation or rescaling of the predicted values is not required. Conclusions: Species amplitudes given by a minimum and maximum boundary for each species can be used to efficiently predict environmental variables from species composition. The predictive success is superior to mean Ellenberg indicator values and comparable to mean indicator values based on species weighted averages.     

Prediction of herbage yield in grassland: How well do Ellenberg N‐values perform?

Question: How useful are Ellenberg N‐values for predicting the herbage yield of Central European grasslands in comparison to approaches based on ordination scores of plant species composition or on soil parameters? Location: Central Germany (11°00′‐11°37’E, 50°21‐50°34’N, 500–840 m a.s.l.). Methods: Based on data from a field survey in 2001, the following models were constructed for predicting herbage yield in montane Central European grasslands: (1) Linear regression of mean Ellenberg N‐, R‐ and F‐values; (2) Linear regression of ordination scores derived from Non‐metric Multidimensional Scaling (NMDS) of vegetation data; and (3) Multiple linear regression (MLR) of soil variables. Models were evaluated by cross‐validation and validation with additional data collected in 2002. Results: Best predictions were obtained with models based on species composition. Ellenberg N‐values and NMDS scores performed equally well and better than models based on Ellenberg R‐ or F‐values. Predictions based on soil variables were least accurate. When tested with data from 2002, models based on Ellenberg N‐values or on NMDS scores accurately predicted productivity rank order of sites, but not the actual herbage yield of particular sites. Conclusions: Mean Ellenberg N‐values, which are easy to calculate, are as accurate as ordination scores in predicting herbage yield from plant species composition. In contrast, models based on soil variables may be useful for generating hypotheses about the factors limiting herbage yield, but not for prediction. We support the view that Ellenberg N‐values should be called productivity values rather than nitrogen values.     

Short-term and long-term variation in seed bank/vegetation relations along an environmental and successional gradient

The seed bank along a successional and environmental gradient was analysed. Soil was collected in 3-cm thick horizons from permanent plots along two transects across a land uplift seashore, spanning several centuries of succession from shoreline to mature forest. Vegetation in the plots was recorded when the soil was sampled and also 9 and 15 yr before that. Within- and between-plot effects on seed bank./vegetation relationships were analysed using estimates of seed longevity. Sorensen's similarity index and mean Ellenberg indicator values.
A seed bank longevity index was constructed by using the database by Thompson et al, (1997 The soil seed banks of north west Europe. Methodology, density and longevity, Cambridge Univ Press), for all species with more than one entry in the database. For species with one or no entry, an internal Index was constructed. The two indices were correlated and it was suggested that the internal index should be used where the Thompson database is insufficient.
There were small differences between the upper three soil horizons in seed density, in similarity with the vegetation and in mean Ellenberg values. The highest seed densities and seed bank/vegetation similarities were found at the shoreline, after that the density and the similarity decreased with increasing successional age, with the mature forest having very low seed density and similarity values. Weighted mean Ellenberg indicator values for light, nitrogen, salt and moisture differed between vegetation and seed bank. For the seed bank, the mean Ellenberg values for light, moisture and nitrogen and weighted mean of seed bank longevity indices showed a trend along one of the transects.     

The contribution of nitrogen deposition to the eutrophication signal in understorey plant communities of European forests

We evaluated effects of atmospheric deposition of nitrogen on the composition of forest understorey vegetation both in space and time, using repeated data from the European wide monitoring program ICP‐Forests, which focuses on normally managed forest. Our aim was to assess whether both spatial and temporal effects of deposition can be detected by a multiple regression approach using data from managed forests over a relatively short time interval, in which changes in the tree layer are limited. To characterize the vegetation, we used indicators derived from cover percentages per species using multivariate statistics and indicators derived from the presence/absence, that is, species numbers and Ellenberg's indicator values. As explanatory variables, we used climate, altitude, tree species, stand age, and soil chemistry, besides deposition of nitrate, ammonia and sulfate. We analyzed the effects of abiotic conditions at a single point in time by canonical correspondence analysis and multiple regression. The relation between the change in vegetation and abiotic conditions was analyzed using redundancy analysis and multiple regression, for a subset of the plots that had both abiotic data and enough species to compute a mean Ellenberg N value per plot using a minimum of three species. Results showed that the spatial variation in the vegetation is mainly due to “traditional” factors such as soil type and climate, but a statistically significant part of the variation could be ascribed to atmospheric deposition of nitrate. The change in the vegetation over the past c. 10 years was also significantly correlated to nitrate deposition. Although the effect of deposition on the individual species could not be clearly defined, the effect on the vegetation as a whole was a shift toward nitrophytic species as witnessed by an increase in mean Ellenberg's indicator value.     

Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France

Species distribution modelling is an easy, persuasive and useful tool for anticipating species distribution shifts under global change. Numerous studies have used only climate variables to predict future potential species range shifts and have omitted environmental factors important for determining species distribution. Here, we assessed the importance of the edaphic dimension in the niche‐space definition of Quercus pubescens and in future spatial projections under global change over the metropolitan French forest territory. We fitted two species distribution models (SDM) based on presence/absence data (111 013 plots), one calibrated from climate variables only (mean temperature of January and climatic water balance of July) and the other one from both climate and edaphic (soil pH inferred from plants) variables. Future predictions were conducted under two climate scenarios (PCM B2 and HadCM3 A2) and based on 100 simulations using a cellular automaton that accounted for seed dispersal distance, landscape barriers preventing migration and unsuitable land cover. Adding the edaphic dimension to the climate‐only SDM substantially improved the niche‐space definition of Q. pubescens, highlighting an increase in species tolerance in confronting climate constraints as the soil pH increased. Future predictions over the 21st century showed that disregarding the edaphic dimension in SDM led to an overestimation of the potential distribution area, an underestimation of the spatial fragmentation of this area, and prevented the identification of local refugia, leading to an underestimation of the northward shift capacity of Q. pubescens and its persistence in its current distribution area. Spatial discrepancies between climate‐only and climate‐plus‐edaphic models are strengthened when seed dispersal and forest fragmentation are accounted for in predicting a future species distribution area. These discrepancies highlight some imprecision in spatial predictions of potential distribution area of species under climate change scenarios and possibly wrong conclusions for conservation and management perspectives when climate‐only models are used.     

Satellite observation to assess dengue risk due to Aedes aegypti and Aedes albopictus in a subtropical city of Argentina

Earth observation environmental features measured through remote sensing and models of vector mosquitoes species Aedes aegypti and Ae. albopictus provide an advancement with regards to dengue risk in urban environments of subtropical areas of Argentina. The authors aim to estimate the effect of landscape coverage and spectral indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Water Index [NDWI] and Normalized Difference Built-up Index [NDBI]) on the larvae abundance of Ae. aegypti and Ae. albopictus in Eldorado, Misiones, Argentina using remote satellite sensors. Larvae of these species were collected monthly (June 2016 to April 2018), in four environments: tire repair shops, cemeteries, dwellings and an urban natural park. The proportion of landscape coverage (water, urban areas, bare soil, low vegetation and high vegetation) was determined from the supervised classification of Sentinel-2 images and spectral indices, calculated. The authors developed spatial models of both vector species by generalized linear mixed models. The model's results showed that Ae. aegypti larvae abundance was better modelled by NDVI minimum values, NDBI maximum values and the interaction between them. For Ae. albopictus proportion of bare soil, low vegetation and the interaction between both variables explained better the abundance.     

Novelty predictors for shrub (and climbers) ecological niche modeling,based on their successional stage

The identification of species' environmental predictors constitute a key challenge for decision making, especially when using ecological niche modeling based on these drivers and when presence points are limited. More specifically, shrub species are affected by ecosystem dynamics, and appear in degraded formations, in dense mid-stage vegetation formations, or under late climax-forest canopy. In this study, we tested novelty predictors to understand the drivers that affect the selected species distribution in the Mediterranean biome, targeting different vegetation successional stages, and further improve ecological models' performance, when presence points are limited. Land surface temperature (LST) in association with temperature related predictors, allowed differentiating between species thriving in the understory of the forest canopy, from those that are co-dominant with dense vegetation cover and a third group/species, thriving in degraded vegetation. In addition, the Normalized Difference Vegetation Level Index (NDVI) played a key role in the models for species growing in highly degraded ecological niches such as Spartium junceum, Calicotome villosa, but also forest-fringe vegetation like the climber Hedera helix. Our study highlights the importance of integrating remote sensed predictors, combined with appropriate climate drivers, when using ecological niche modeling.     

Interactive effects between plant functional types and soil factors on tundra species diversity and community composition

Plant communities are coupled with abiotic factors, as species diversity and community composition both respond to and influence climate and soil characteristics. Interactions between vegetation and abiotic factors depend on plant functional types (PFT) as different growth forms will have differential responses to and effects on site characteristics. However, despite the importance of different PFT for community assembly and ecosystem functioning, research has mainly focused on vascular plants. Here, we established a set of observational plots in two contrasting habitats in northeastern Siberia in order to assess the relationship between species diversity and community composition with soil variables, as well as the relationship between vegetation cover and species diversity for two PFT (nonvascular and vascular). We found that nonvascular species diversity decreased with soil acidity and moisture and, to a lesser extent, with soil temperature and active layer thickness. In contrast, no such correlation was found for vascular species diversity. Differences in community composition were found mainly along soil acidity and moisture gradients. However, the proportion of variation in composition explained by the measured soil variables was much lower for nonvascular than for vascular species when considering the PFT separately. We also found different relationships between vegetation cover and species diversity according the PFT and habitat. In support of niche differentiation theory, species diversity and community composition were related to edaphic factors. The distinct relationships found for nonvascular and vascular species suggest the importance of considering multiple PFT when assessing species diversity and composition and their interaction with edaphic factors. Synthesis: Identifying vegetation responses to edaphic factors is a first step toward a better understanding of vegetation–soil feedbacks under climate change. Our results suggest that incorporating differential responses of PFT is important for predicting vegetation shifts, primary productivity, and in turn, ecosystem functioning in a changing climate.     

Factors affecting Sciomyzidae (Diptera) across a transect at Skealoghan Turlough (Co. Mayo,Ireland)

Sciomyzid flies, which have potential as bio-indicators, were sampled by sweep-net surveys at a turlough in the west of Ireland. Turloughs are ephemeral wetlands (unique to Ireland), which flood from groundwater in winter and empty in the summer, during that time, they are frequently grazed. The weekly survey consisted of ten linear sweeps (5 m × 1 m) in each of six homogeneous and contiguous vegetation zones throughout the summer of 2004. The fauna was dominated by univoltine species with Ilione albiseta being particularly abundant, though species displaying a range of phenologies and life histories were also present. Species richness and total abundance were significantly higher in the two zones with the highest hydroperiods. Mantel tests showed that the species matrix was significantly co-structured with permanent features of the physical environment, but not with stochastic sampling variables related to weather conditions. Mantel correllograms displayed typical patterns of autocorrelation for hydroperiod, soil moisture and soil pH in each zone and vegetation height, vegetation length and Ellenberg moisture index (weighted for vegetation composition) in each sweep-path. No patterns of autocorrelation were evident for distance among zones, area of patch of vegetation zone sampled, area of the vegetation zone on the whole turlough, soil mass-loss-on-ignition and Ellenberg N and reaction indices. These results provide strong evidence for high microhabitat specificity in Sciomyzidae at this site and indicate a major influence of vegetation structure and hydrological regime on their ecology.

The contribution of vegetation and landscape configuration for predicting environmental change impacts on Iberian birds

Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26-40% of the cases with BRT, and in 1-18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species.     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Ecological modeling and distribution analysis of digger scorpions: Odontobuthus doriae,Odonthubutus bidentatus (Scorpiones: Buthidae) and Scorpio maurus (Scorpiones: Scorpionidae) in Iran using the maximum entropy method

Prediction models are essential for the potential geographic distribution of scorpions, prevention of scorpion stings and diverse applications in conservation biology. There is limited information about habitat suitability and the factors affecting the distribution of Iranian digger scorpions. This study was undertaken to model the distribution of three types of digger scorpion in Iran, Odontobuthus doriae Thorell, Odonthubutus bidentatus Lourenco (Scorpiones: Buthidae) and Scorpio maurus Pocockin (Scorpiones: Scorpionidae), and investigate the factors affecting its distribution using the maximum entropy method. A total of 20 environmental and climate variables were used for modeling and evaluation of the ecological niche. The similarities and differences between the ecological overlap of the digger scorpions were evaluated using comparative environmental niche model (ENM Tools software). The results showed that the main factors for habitat suitability of O. doriae were soil type, mean temperature of the wettest quarter and slope. The variables for S. maurus were soil type, precipitation of the coldest quarter and slope. Annual temperature range, mean temperature of the driest quarter and land use had the greatest influence on the distribution of O. bidentatus. The ecological niches for O. doriae and O. bidentatus overlapped. The niche of these species differed from the niche of S. maurus. This approach could be helpful for the prediction of the potential distribution of three digger scorpion species and this model can be an effective for the promotion of health.

     

What makes a plant species specialist in mixed broad-leaved deciduous forests?

Knowledge about relationships between specialization degree of species, i.e. the width of their realized niche and functional traits, may have important implications for the assessment of future population developments under environmental change. In this study, we used a recently introduced method to calculate ecological niche widths of plant species in mixed broad-leaved deciduous forests and to investigate the dependence between niche widths of plants and their functional traits and Ellenberg indicator values. The research is based on a dataset of 4556 phytosociological relevés of mixed broad-leaved deciduous forests in Slovenia. We calculated theta indices for 326 species, which ranks them along a continuous gradient of habitat specialization. For 272 species, we compiled 26 functional traits and Ellenberg indicator values. We found some significant correlations between theta indices of species and their functional traits and Ellenberg indicator values; habitat specialists thrive primarily on the highest altitudes, on colder, dry sites and achieve the age of first flowering later than generalists. They also have smaller seed diameter, lower leaf dry matter content, lower mean canopy height and bigger specific leaf area than generalists. Two species groups, chamaephytes and spring green species, are particularly characterized as specialist species. The added value of our work is in complementing the knowledge about the niche differentiating along different environmental gradients and species coexistence in mixed broad-leaved deciduous forests.     

林冠结构和地形对亚热带常绿落叶阔叶林林下幼苗物种多样性和功能多样性的影响

森林结构和地形是森林生态系统最明显的特点,也是影响林下幼苗存活和物种多样性的关键因子。该研究采用半球面摄影方法提取八大公山生长监测样地（共1.2 hm²）林冠结构参数,通过调查地表层木本植物幼苗的组成和多度,获取常见植物幼苗叶片功能性状,结合详细的地形信息,利用空间同步自回归模型探究林冠结构变量及地形因子对幼苗物种多样性及功能多样性的影响。结果表明：（1）八大公山亚热带山地常绿落叶阔叶林林冠结构复杂度较高,最大林冠高的平均值达到19.94 m,叶面积指数、平均叶倾角和林冠覆盖度分别为2.94、30.88°和0.87;（2）林冠结构变量和地形因子能够解释32.6%~48.4%的林下幼苗物种多样性指数变异和28.5%~70.2%的功能多样性变异,但地形因子对幼苗物种多样性的影响很小;（3）预测在亚热带常绿落叶阔叶林高海拔的山坡上,有较低的叶面积指数和平均叶倾角群落有较高的幼苗物种多样性;而在低海拔山脊上,较低的叶面积指数和平均叶倾角群落林下幼苗层有较高的功能多样性。此结果对科研人员和林业工作者开展野外森林更新情况评估和样方调查将有所帮助。     

Vegetation of beech forests in the Rychlebské Mountains,Czech Republic,re-inspected after 60 years with assessment of environmental changes

From 1941–;1944 nearly 30 phytosociological relevés were completed by F. K. Hartmann in the Rychlebské Mountains, a typical mountainous area in northeastern Czech Republic. Of the original plots still covered with adult grown beech (Fagus sylvatica) forest, 22 were resampled in 1998 and 1999. In order to describe the recent vegetation variability of the sites 57 relevés were recorded. Changes in vegetation were estimated using relative changes in species density and ordinations (PCA, RDA). Environmental changes were assessed using Ellenberg indicator values when no direct measurements were available. A decline in species diversity has been documented, particularly, many species occurring frequently in deciduous forests with nutrient and moisture well-supplied soils around neutral have decreased. In contrast, several light-demanding, acid- and soil desiccation-tolerant species have increased. Natural succession, quantified as forest age, contributed slightly to these changes. In Ellenberg indicator values, a decline in F (soil moisture), R (soil calcium) and N (ecosystem productivity), and an increase in L (understorey light) were shown. This is interpreted as the influence of modified forestry management and of airborne pollutants. Intensified logging caused the canopy to open and soil conditions to worsen. The latter is most likely also due to acid leaching of soil cations (Ca, K, Na). This caused a decline in soil productivity, thus the effect of nitrification could not be detected. The original relevés may have differed in size influencing the results. This revised version was published online in August 2006 with corrections to the Cover Date.     

Competition and regeneration in quaking aspen–white fir (Populus tremuloides–Abies concolor) forests in the Northern Sierra Nevada,USA

Question: How does competition between quaking aspen (Populus tremuloides) and white fir (Abies concolor) affect growth and spatial pattern of each species? Location: The northern Sierra Nevada, California, USA. Methods: In paired plots in mixed aspen‐ (n=3) or white fir‐dominated (n=2) stands, we mapped trees and saplings and recorded DBH, height, species, and condition and took increment cores. We tallied seedlings by species. Tree ring widths were used as a measure of basal area change over the last decade, and canopy openness was identified using hemispherical photographs. Linear mixed models were used to relate neighborhood indices of competition, stand, and tree‐level variables to diameter increment. Spatial patterns of stems were identified using the Neighborhood Density Function. Results: White fir radial growth was higher in aspen‐ than white fir‐dominated plots. Individual‐level variables were more important for white fir than for aspen growth, while variables representing competitive neighborhood were important only for aspen. The forest canopy was more open in aspen‐ than white fir‐dominated stands, but ample aspen seedlings were observed in all stands. Canopy stems of aspen and white fir were randomly distributed, but saplings and small trees were clumped. Aspen saplings were repelled by canopy aspen stems. Conclusions: Variation in canopy openness explained more stand–stand variation in white fir than aspen growth, but high light levels were correlated with recruitment of aspen seedlings to the sapling class. Radial growth of aspen was predicted by indices of neighborhood competition but not radial growth of white fir, indicating that spacing and stem arrangement was more important for aspen than white fir growth. Fire suppression has removed a major disturbance mechanism that promoted aspen persistence and reduced competition from encroaching conifers, and current forests favor species that regenerate best by advance regeneration (white fir).     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350–1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land‐atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of ~?7.5 g C m^?2 day^?1 during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350–1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models.