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Supplementary Materials for "Airborne Laser-guided Imaging Spectroscopy to Map Forest Trait Diversity and Guide Conservation

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Abstract

Airborne spectroscopy for forest traits The development of conservation priorities in the tropics is often hampered by the sparseness of ground data on biological diversity and the relative crudeness of larger-scale remote sensing data. Asner et al. developed airborne instrumentation to make large-scale maps of forest functional diversity across 72 million hectares of the Peruvian Andes and Amazon basin (see the Perspective by Kapos). They generated a suite of forest canopy functional trait maps from laser-guided imaging spectroscopy and used them to define distinct forest functional classes. These were then compared with government deforestation and land allocation data, which enabled an analysis of conservation threats and opportunities across the region. Science , this issue p. 385 ; see also p. 347

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... Empirical multivariate methods such as partial least squares regression (PLSR) and gaussian process regression (GPR) link the target variable with the full spectral information and these have been widely used in predicting functional traits in grassland ecosystems [40,41]. One advantage of plant functional trait mapping is the capturing of variation (volume, shape, and boundaries) in functional trait space, which enables the detection of species diversity or functional diversity without a priori species discrimination [10,34,35,42]. Analogously to spectral species, some species often exhibit covarying structural and physiological traits, driven by resource and environmental limitations based on the 'functional convergence hypothesis' [43], and are therefore usually grouped or clustered as plant functional types (PFTs) [35]. ...
... Analogously to spectral species, some species often exhibit covarying structural and physiological traits, driven by resource and environmental limitations based on the 'functional convergence hypothesis' [43], and are therefore usually grouped or clustered as plant functional types (PFTs) [35]. Compared to the classification of plant life/growth forms and actual species, the capability of imaging spectroscopy to causally link biochemical and structural traits to PFT with higher prediction accuracy and greater consistency has been validated in forest ecosystems [41,42]. However, few studies have explored functional trait-based grassland plant diversity monitoring [11]. ...
... The relationships among species diversity, spectral diversity, and functional trait diversity (or biochemical diversity) have been explored in tropical forests (e.g., in the Amazon and Hawaii) [42,44] and in the subtropical forest of Shennongjia [10,45]. However, the suitability of these links for grassland ecosystems needs to be further determined. ...
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Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.
... Diverse sets of observations are needed to assess both the magnitude and seasonal and interannual variability of modeled outputs. 82 Specialized experiments, such as free-air carbon enrichment studies, herbivore exclosures, or remotely sensed trait information [90][91][92] can also be used to test the realism of specific simulated processes. Taken together, these datasets can be used to test whether models correctly capture existing relationships between variables (or incorrectly assume existing relationships, which are not supported by observations). ...
... 82 Observational data for benchmarking include multiple-site and remote-sensing products of, e.g., fraction of absorbed photosynthetically active radiation, gross primary productivity, net primary productivity, burnt area, river discharge, or atmospheric CO 2 concentration. Specialized experiments or datasets, such as free-air carbon enrichment studies, herbivore exclosures, or remotely sensed trait information [90][91][92] can also be used to test the realism of specific simulated processes. Diverse data are needed to assess both magnitude and seasonal and interannual variability of modeled processes. ...
... [157][158][159][160] With these methods, parameter distributions provided by the available data (prior parameter estimate) are iteratively adjusted (posterior parameter estimate) by comparing simulation outputs with observed data at different scales, e.g., element fluxes derived from eddy-flux measurements, 161 tree size distribution derived from inventory data, 162 or remote-sensing products. 163 A promising avenue in terms of data assimilation is the spectrometry imagery of functional diversity, 90,164 which, at least for terrestrial ecosystems, can help to bridge the gap between biodiversity data available from field surveys and the amount of data required to better control for uncertainty in continentaland global-scale models. This raises new technical challenges in terms of data standardization (corrections and inter-calibration of remote-sensing images) and methods for data extraction. ...
Article
There are many sources of uncertainty in scenarios and models of socio-ecological systems, and understanding these uncertainties is critical in supporting informed decision-making about the management of natural resources. Here, we review uncertainty across the steps needed to create socio-ecological scenarios, from narrative storylines to the representation of human and biological processes in models and the estimation of scenario and model parameters. We find that socio-ecological scenarios and models would benefit from moving away from “stylized” approaches that do not consider a wide range of direct drivers and their dependency on indirect drivers. Indeed, a greater focus on the social phenomena is fundamental in understanding the functioning of nature on a human-dominated planet. There is no panacea for dealing with uncertainty, but several approaches to evaluating uncertainty are still not routinely applied in scenario modeling, and this is becoming increasingly unacceptable. However, it is important to avoid uncertainties becoming an excuse for inaction in decision-making when facing environmental challenges.
... Some of these needs are fulfilled by remote sensing information (Schweiger & Laliberté, 2022). Over the past decades, remote sensing has opened possibilities for Earth observation from air and space, allowing us to monitor ecological change, primarily expressed by changes in vegetation cover, distribution, and functioning, which can be subsequently linked to drivers of change in space and time, from local to global scale (Asner et al., 2017;Skidmore et al., 2015). Recent technological advances in remote sensing data acquisition and processing now open new perspectives for monitoring changes in biodiversity at unprecedented details over large geographic areas, and ultimately over the entire Earth Randin et al., 2020). ...
... Understanding the interactions between biodiversity and ecological/environmental drivers is difficult (Kreft & Jetz, 2007). From this point of view, collecting exploratory remote sensing data on environmental heterogeneity across large geographical extents is relatively simple and combining that information with changing patterns of functional traits could improve species identification, mapping, and monitoring of potential diversity hotspots (Asner et al., 2017;Skidmore et al., 2015). ...
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Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the ‘spectral species’ concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species‐specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.
... Additionally, two different vegetation may present the same spectral characteristics or mixed spectral phenomenon in a certain spectral segment, which makes it difficult to identify wetland types well by only using spectral response curves. These two phenomena greatly influence the classification algorithm based on spectral information and easily cause misclassification [18]. The particularity of wetlands makes wetland classification a challenging topic in remote sensing study. ...
... The Kappa coefficient is the ratio of agreement between the classification results and the validation samples, and the formula is shown as follows [22]. (18) where r represents the total number of the rows in the confusion matrix, N is the total number of samples, X ii is on the i diagonal of the confusion matrix, X i+ is the total number of observations in the i row, and X +i is the total number of observations in the i column. ...
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The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.
... In the last decade, functional analysis of ecosystems has gained attention because it is a useful perspective for assessing and monitoring the effects of global change on diversity (Cabello et al., 2012;Pereira et al., 2013). Furthermore, incorporating functional aspects into regionalization practice offers a great potential for improving our understanding of spatial and temporal diversity patterns (Garnier et al., 2016); and implementing new programs for the conservation of ecological processes (Asner et al., 2017). EFT concept has been highlighted as "the first serious attempt to group ecosystems (at large scales) on the basis of shared functional behavior" (Mucina, 2019), and its strength for a better understanding of ecological systems providing new information derives from its ability to capture ecosystem functioning into discrete entities that can be mapped. ...
... Third, EFTs are identified by remote sensing tools from aggregated measurements of ecosystem functions at the pixel level, which in practice represents information of the performance of the whole ecosystem. Remote sensing tools can offer more integrative functional measures of the whole ecosystem performance (productivity, evapotranspiration, etc.) that complement our traditional view of ecosystems (Butchart et al., 2010;Asner et al., 2017). ...
Article
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Large-scale ecological variations across Earth have important consequences for biodiversity and, therefore, forbiological conservation. Despite the widespread use of ecological maps in conservation schemes, they have been based mainly on structural and compositional features but scarcely on functional dimensions of life. Incorporating functional variables complements and improves the descriptions of regionalizations and offers a new understanding of biodiversity patterns. The development of remote sensing measurement allows for the description of the functional patterns of ecosystems through Ecosystem Functional Types (EFTs), opening new opportunities to analyze the geography of life. This article aims to examine the relationships between ecological regionalization based on components and structure and patterns of ecosystem functioning. As proof of case, we chose the Baja California peninsula, whose singularity has generated a rich variety of ecological and biogeographical interpretations, mainly based on ecosystem components and structure. We hypothesize that patterns in ecosystem functioning reflect ecoregionalization based on composition and structure features. We identified Ecosystem Functional Types (EFTs), from three descriptors of the seasonal curves of MODIS Enhanced Vegetation Index (EVI) from 2001 to 2017. We characterized each ecoregion in terms of ecosystem functioning and we carried out a correspondence analysis between the EFTs classification and the ecoregions. At a large scale, EFTs showed a pattern with three general regions from northwest to south, capturing the north-south transition of climatic regimes shown in the ecoregions map, from the northwestern Mediterranean area to the southern tropical zone, with a desert transition area between them. However, differences between the functional characterization and some ecoregions were detected in ecoregions identified as discrepancy areas between authors. In particular, some ecoregions considered Mediterranean showed a Desert character in its functioning, and others considered as Desert were Tropical functionally. EFTs remotely sensed measured at regional scales provide the basis for a more comprehensive regionalization of geographical patterns of life and, therefore, an improvement for future conservation purposes.
... We used the scaled He values to investigate potential patterns in genetic structure using simple correlations with latitude and longitude, which provide little information about the historical and environmental drivers of observed relationships. Remote sensing and earth observation technologies constitute spatially resolved and contiguous approaches increasingly promising in linking genetic data to environmental information relevant to conservation of plant genetic resources, as is now established for the analysis of many plant traits (e.g., Asner et al. 2017;Wang et al. 2020). Additionally, models and simulation studies projecting possible future distributions and challenges for F. sylvatica under probable climatic scenarios are in continuous development (e.g., Capblancq et al. 2020a, b;Falk and Hempelmann 2013;Kramer et al. 2010). ...
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Unlabelled: Genetic diversity influences the evolutionary potential of forest trees under changing environmental conditions, thus indirectly the ecosystem services that forests provide. European beech (Fagus sylvatica L.) is a dominant European forest tree species that increasingly suffers from climate change-related die-back. Here, we conducted a systematic literature review of neutral genetic diversity in European beech and created a meta-data set of expected heterozygosity (He) from all past studies providing nuclear microsatellite data. We propose a novel approach, based on population genetic theory and a min-max scaling to make past studies comparable. Using a new microsatellite data set with unprecedented geographic coverage and various re-sampling schemes to mimic common sampling biases, we show the potential and limitations of the scaling approach. The scaled meta-dataset reveals the expected trend of decreasing genetic diversity from glacial refugia across the species range and also supports the hypothesis that different lineages met and admixed north of the European mountain ranges. As a result, we present a map of genetic diversity across the range of European beech which could help to identify seed source populations harboring greater diversity and guide sampling strategies for future genome-wide and functional investigations of genetic variation. Our approach illustrates how to combine information from several nuclear microsatellite data sets to describe patterns of genetic diversity extending beyond the geographic scale or mean number of loci used in each individual study, and thus is a proof-of-concept for synthesizing knowledge from existing studies also in other species. Supplementary information: The online version contains supplementary material available at 10.1007/s11295-022-01577-4.
... Cluster 5 (purple), "Remote sensing and monitoring", which contains 468 authors, deals with issues related to estimating carbon cycle products and changes in coverage visualized through satellite images. Gregory Asner (1580) studies estimates of biomass and greenhouse gas emissions [171,316], as well as land cover and its different uses, using satellite images [317,318]. For his part, Matthew Hansen (1352) focuses on cover change detection to determine an area's vulnerability [319,320]. ...
Article
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Tropical ecosystems play an important role in the environment. They provide multiple ecosystem services, such as carbon capture and sequestration, food supply, and climate regulation. Studying land use and land cover change makes it possible to understand the land's alterations associated with deforestation, degradation, erosion, soil desertification, and biodiversity loss. The objective of this study is to evaluate the different approaches to land use and land cover research in tropical forests based on the evolutionary and qualitative analysis of the last 44 years of scientific production. The data were collected using the Scopus database and was based on the PRISMA methodology's four phases: (i) identification, (ii) screening, (iii) eligibility, and (iv) included. The results showed a significant increase in the study of land use and land cover consolidated in 4557 articles, with contributions from 74 countries, revealing 14 themes and seven lines of research. Core research areas such as biodiversity, land use, and conservation exist due to the ongoing interest in the value of tropical forests and their response to climate change. The present research allowed us to consider future study topics such as the relationship between sustainable development goals and land use and cover in tropical forests, as well as the evaluation of the environmental impact of economic activities in forests.
... Today's applications of spectroscopy range from modelling and predicting leaf Serbin et al., 2014) and canopy traits (Asner et al., 2017;Singh et al., 2015), to detecting plant stress and natural enemies (Pontius et al., 2005;Sapes et al., 2022 ), to differentiating species and broader taxonomic clades (Féret & Asner, 2013;Meireles et al., 2020;Sapes et al., 2022). Indeed, maps of species (Roth et al., 2015), functional group composition (Schmidtlein et al., 2012;Schweiger et al., 2017), and traits of individual plants (Asner & Martin, 2009) or plant communities are highly valuable for investigating a plethora of ecological questions beyond the scale of individual research plots. ...
Article
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Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits and strategies—and the community‐ or ecosystem‐level processes they correlate with—at individual or community levels, covering more individuals and larger areas than traditional field surveys. Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra—radiative transfer and empirical models—and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow, and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits. While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past decades, there is also a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co‐occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait datasets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes. Synthesis. Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most.
... The magnitude and shape of the reflectance at any given point on the surface is a complex combination of material structure and molecular composition. Consequently, these instruments can be used for a plethora of Earth Science applications, ranging from surface classification, estimating vegetation traits and genetics, mapping of soil properties and snow conditions, and many others (Clark et al., 2003;Painter et al., 2003;Asner et al., 2017;Carmon and Ben-Dor, 2017;Gholizadeh et al., 2017;Pelta et al., 2019;Chadwick et al., 2020;Blonder et al., 2021;Bohn et al., 2021;Cawse-Nicholson et al., 2021). However all of these biogeophysical retrievals are predicated on the accurate characterization of surface reflectance, which in the context of remote sensing must be estimated from at-sensor radiance using a coupled surface and atmospheric model, a routine colloquially referred to as atmospheric correction. ...
Article
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Models linking surface characteristics within incident solar radiation are inexorably dependent on the topography of the given region. To date, however, most operational surface reflectance retrievals treat this dependence by assuming a flat terrain, leading to significant deviations in the estimated reflectance. Here, we demonstrate that incorporating dynamic topography directly into the joint surface and atmospheric model during retrievals has several advantages. First, it allows for a more complete physical accounting of downwelling illumination, providing more accurate estimates of the absolute magnitude of reflectance. Second, it facilitates a superior resolution of the atmospheric state, most notably due to the confounding influence of atmospheric aerosols and unresolved topographic effects. Our methodology utilizes a practical, high-fidelity, model-driven approach to separate out diffuse and direct irradiation and account for topographic effects during the joint inversion of atmosphere and surface properties. We achieve this by enhancing the atmosphere/surface inversion to account for the radiative transfer effects of surface slope. We further demonstrate how uncertainties in topographic features can be quantified and leveraged within our formulation for a more realistic posterior uncertainty estimates. Our results demonstrate that the inclusion of topographic effects into the retrieval model reduces errors in the reflectance of an only moderately rugged terrain by more than 15%, and that a post hoc accounting of topography cannot achieve these same results.
... Models are based on training data with an implementation of matrix and formula interface with a factor dependent variable type (y) for classification [99,100]. A linear SVM model was created using the "e1071" package with parameters best fit for desert vegetation based on Ge et al. [101] which are 0.125 cost and 128 kappa [102][103][104][105][106]. ...
Article
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Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.
... Plant physiology and ecological processes are scale-dependent. Due to the lack of the ground hyperspectral observation platforms, previous studies have been limited to the scale of landscape, field plot, and tree canopy based on airborne hyperspectral data (Asner et al. 2017;Markiet and Mõttus 2020;Shen et al. 2020). Most efforts to date have involved two-dimensional mapping of traits that typically represent the upper canopy conditions (Chlus, Kruger, and Townsend 2020). ...
Article
The spectral characteristics of sunlit and shaded leaves are critical to improving the utilization of remote sensing methodology to quantify forest physiology. However, spectral characteristics within the tree canopies, especially normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI), are poorly understood. Our study used an imaging observation platform to obtain hyperspectral imagery of a Manchurian Ash canopy on Changbai Mountain. A non-imaging spectrometer was employed for an assisted analysis. The study results of the corresponding spectrum obtained at two observation spatial scales were significantly different between sunlit and shaded leaves. For imaging spectral observations, there were significant differences in NDVI and PRI between sunlit and shaded leaves (P < 0.001). PRI near the petiole was significantly lower than in other parts of leaves (P = 0.049). Non-imaging spectral observations of the reflectance of sunlit and shaded leaves were different only in the visible light region. The PRI of the shaded leaves were higher than that of sunlit leaves, which was consistent with the imaging spectral observations. The complexity of light environment within the canopy, especially the differences in incident irradiance, contributed to the range of leaf attribute measurements, which resulted in the variability of spectral characteristics.
... Finally, understanding soil and climate drivers of floristic composition is fundamental to support conservation policies aiming to protect species and their ecosystem services. In this sense, developing more accurate soil and climate maps will not only help us to envision toward which floristic composition patterns could global change reshape tropical forests (Colwell et al., 2008;Feeley et al., 2020), but also to map how environmental gradients cause variations in plant functional strategies and forest functioning across space (Asner et al., 2017) and time (NGEE-Tropics Project, 2022). ...
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A vast literature indicates that environment plays a paramount role in determining floristic composition in tropical forests. However, it remains unclear which are the most important environmental factors and their relative effect across different spatial scales, plant life forms or forest types. This study reviews the state of knowledge on the effect of soil and climate on floristic composition in tropical forests. From 137 publications, we collated information regarding: (1) spatial scale, continent, country, life form, and forest type; (2) proportion of variance in floristic composition explained by soil and climatic variables and how it varies across spatial scales; and (3) which soil and climate variables had a significant relationship on community composition for each life form and forest type. Most studies were conducted at landscape spatial scales (67%) and mainly in South America (74%), particularly in Brazil (40%). Studies majorly focused on trees (82%) and on lowland evergreen tropical forests (74%). Both soil and climate variables explained in average the same amount (14% each) of the variation observed in plant species composition, although soils appear to exert a stronger influence at smaller spatial scales while climate effect increases toward larger ones. Temperature, precipitation, seasonality, soil moisture, soil texture, aluminum, and base cations-calcium and magnesium-and their related variables (e.g., cation exchange capacity, or base saturation) were frequently reported as important variables in structuring plant communities. Yet there was variability when comparing different life forms or forest types, which renders clues about certain ecological peculiarities. We recommend the use of standardized protocols for collecting environmental and floristic information in as much as possible, and to fill knowledge gaps in certain geographic regions. These actions will be especially beneficial to share uniform data between researchers, conduct analysis at large spatial scales and get a better understanding of the link between soils and climate gradients and plant strategies, which is key to propose better conservation policies under the light of global change.
... A number of studies have demonstrated the capability of spectroscopy to characterise vegetation traits from optical properties acquired at different spatio-temporal scales [19][20][21][22][23][24][25]. Leaf reflectance and transmittance offer an opportunity for non-invasive and rapid quantification of several plant functional traits related to leaf internal anatomical structure and their biochemical composition when using appropriate inversion methods [26][27][28][29][30]. ...
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Leaf biochemical traits indicating early symptoms of plant stress can be assessed using imaging spectroscopy combined with radiative transfer modelling (RTM). In this study, we assessed the potential applicability of the leaf radiative transfer model Fluspect-Cx to simulate optical properties and estimate leaf biochemical traits through inversion of two native Australian eucalypt species: Eucalyptus dalrympleana and E. delegetensis. The comparison of measured and simulated optical properties revealed the necessity to recalibrate the refractive index and specific absorption coefficients of the eucalypt leaves’ biochemical constituents. Subsequent validation of the modified Fluspect-Cx showed a closer agreement with the spectral measurements. The average root mean square error (RMSE) of reflectance, transmittance and absorptance values within the wavelength interval of 450–1600 nm was smaller than 1%. We compared the performance of both the original and recalibrated Fluspect-Cx versions through inversions aiming to simultaneously retrieve all model inputs from leaf optical properties with and without prior information. The inversion of recalibrated Fluspect-Cx constrained by laboratory-based measurements produced a superior accuracy in estimations of leaf water content (RMSE = 0.0013 cm, NRMSE = 6.55%) and dry matter content (RMSE = 0.0036 g·cm−2, NRMSE = 21.28%). The estimation accuracies of chlorophyll content (RMSE = 8.46 µg·cm−2, NRMSE = 24.73%), carotenoid content (RMSE = 3.83 µg·cm−2, NRMSE = 30.82%) and anthocyanin content (RMSE = 1.69 µg·cm−2, NRMSE = 37.12%) were only marginally better than for the inversion without any constraints. Additionally, we investigated the possibility to substitute the prior information derived in the laboratory by non-destructive reflectance-based spectral indices sensitive to the retrieved biochemical traits, resulting in the most accurate estimation of carotenoid content (RMSE = 3.65 µg·cm−2, NRMSE = 29%). Future coupling of the recalibrated Fluspect with a forest canopy RTM is expected to facilitate retrieval of biophysical traits from spectral air/space-borne image data, allowing for assessing the actual physiological status and health of eucalypt forest canopies.
... This helps quantify aboveground biomass and diversity (e.g. Asner et al., 2017;Saatchi et al., 2011), as well as responses of tropical forests to environmental change and human disturbances (e.g. Reiche et al., 2015;Wigneron et al., 2020). ...
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Lianas (woody vines) are abundant and diverse, particularly in tropical ecosystems. Lianas use trees for structural support to reach the forest canopy, often putting leaves above their host tree. Thus they are major parts of many forest canopies. Yet, relatively little is known about distributions of lianas in tropical forest canopies, because studying those canopies is challenging. This knowledge gap is urgent to address because lianas compete strongly with trees, reduce forest carbon uptake and are thought to be increasing, at least in the Neotropics. Lianas can be difficult to study using traditional field methods. Their pliable stems often twist and loop through the understory, making it difficult to assess their structure and biomass, and the sizes and locations of their crowns. Furthermore, liana stems are commonly omitted from standard field surveys. Remote sensing of lianas can help overcome some of these obstacles and can provide critical insights into liana ecology, but to date there has been no systematic assessment of that contribution. We review progress in studying liana ecology using ground‐based, air‐borne and space‐borne remote sensing in four key areas: (i) spatial and temporal distributions, (ii) structure and biomass, (iii) responses to environmental conditions, and (iv) diversity. This demonstrates the great potential of remote sensing for rapid advances in our knowledge and understanding of liana ecology. We then look ahead, to the possibilities offered by new and future advances. We specifically consider the data requirements, the role of technological advances and the types of methods and experimental designs that should be prioritised. Synthesis. The particular characteristics of the liana growth form make lianas difficult to study by ground‐based field methods. However, remote sensing is well suited to collecting data on lianas. Our review shows that remote sensing is an emerging tool for the study of lianas, and will continue to improve with recent developments in sensor and platform technology. It is surprising, therefore, how little liana ecology research has utilised remote sensing to date – this should rapidly change if urgent knowledge gaps are to be addressed. In short, liana ecology needs remote sensing.
... Cependant, des insuffisances dans la discrimination d'espèces végétales sont souvent mentionnées en utilisant des données satellitaires de résolution spatiale et spectrale modérées(Harvey and Hill, 2001;McCarthy et al., 2005).De ce fait, d'autres études ont porté sur l'imagerie hyperspectrale aéroportée. L'utilisation de l'imagerie hyperspectrale aéroportée à haute résolution spatiale a démontré un potentiel pour obtenir des informations variées permettant d'étudier différents aspects de la biodiversité des forêts tropicales.Baldeck et al. (2014) ont pu discriminer la variabilité de la composition floristique des savanes africaines en utilisant des données hyperspectrales aéroportées de la plateforme Carnegie Airborne Observatory (CAO)(Asner et al., 2017). Toujours avec le CAO,Nécessité de comprendre et interpréter le signal grâce aux outils de modélisationQuoiqu'ayant montré un potentiel pour la cartographie de la biodiversité, les données hyperspectrales aéroportées ne sont cependant utilisables qu'à l'échelle locale : leur utilisation à des échelles régionales ou globales s'avère impossible pour des raisons logistiques et financières. ...
Thesis
La préservation de la biodiversité est un enjeu majeur pour le développement durable. Face aux besoins de conservation à l’échelle globale, la définition des méthodes opérationnelles qui permettent d’évaluer la diversité biologique est nécessaire pour l’orientation des différentes politiques environnementales. La télédétection optique a montré un potentiel pour étudier la biodiversité. L’imagerie hyperspectrale aéroportée a été largement utilisée avec succès. Malgré son potentiel, l’imagerie hyperspectrale aéroportée ne permet pas de couvrir des vastes étendues (échelle régionale ou globale) suite à des contraintes logistique et financière. Les missions satellites hyperspectrales actuelles et futures (PRISMA, EnMAP, Biodiversity, CHIME, SBG…) offrent la possibilité d’étudier la biodiversité à grande échelle. Il existe cependant un besoin d’améliorer l’interprétation physique des méthodes existantes, basées sur les données aéroportées, pour évaluer leurs potentiels. Les outils de modélisation du transfert radiatif permettent de mieux comprendre l’interaction entre un rayonnement incident et les milieux physiques qu’ils traversent et de ce fait d’interpréter le signal. Ce projet de thèse vise à définir un cadre pour produire des simulations réalistes à l’aide du modèle de transfert radiatif 3D DART (Discrete Anisotropic Radiative Transfer) dans une perspective de soutien au développement méthodologique pour l'évaluation de la biodiversité et la préparation de futures missions satellites à l'aide de la modélisation 3D (adapté aux milieux complexes tels que les forêts tropicales). Pour ce faire, nous avons réalisé des études de sensibilité pour comprendre l’influence de deux facteurs sur la réflectance simulée par DART : la variabilité spatiale des propriétés optiques foliaires, la prise en compte des éléments non photosynthétiques de la végétation. Puis nous avons comparé ces simulations à des données hyperspectrales aéroportées expérimentales en décrivant les scènes forestières correspondantes de la manière la plus fine à l’aide d’information relatives à la structure, à la composition en espèces et à une sélection de traits fonctionnels foliaires. Plusieurs approches s’appuyant sur les propriétés optiques foliaires, et sur la prise en compte d’une fraction ligneuse ont été testées pour l’intégration des éléments non photosynthétique dans la scène. La variabilité spatiale des propriétés optiques foliaires a été testée en s’appuyant sur les données d’inventaires spatialisées, permettant de prendre en compte la variabilité à l’échelle du pixel, ou en opérant une uniformisation des propriétés optiques à l’échelle de la couronne de chaque individu, ou à l’échelle des espèces. Nos résultats ont montré que les simulations les plus proches des données expérimentales, jugées les plus réalistes, étaient obtenues par l’intégration des éléments non photosynthétiques par le biais d’une famille de constituants chimiques foliaires, les pigments bruns, combinée à une prise en compte de la variabilité des propriétés optiques à l’échelle du pixel. Les différences entre données expérimentales et simulations ont été étudiées en s’appuyant sur différents critères, comme la différence spectrale, la dissimilarité spectrale interspécifique et interspécifique et la capacité de discrimination spectrale des espèces. Nous avons obtenu une bonne concordance entre les simulations issues du scénario le plus réaliste et les données expérimentales.
... Notably, given the strong correlation between remote sensing-based vegetation index (e.g. NDVI) and these functional traits (Asner et al., 2017), the aforementioned positive effects of NDVI-based diversity in Koontz et al. (2020) may result more from functional diversity than structural diversity. ...
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Structural diversity is an emerging dimension of biodiversity that accounts for size variations in organs among individuals in a community. Previous studies show significant effects of structural diversity on forest growth, but its effects on forest mortality are not known, particularly at a large scale. To address this knowledge gap, we quantified structural diversity using stem structural diversity (SSD) based on both tree diameter and height. We obtained U.S. Forest Service Forest Inventory and Analysis (FIA) data from over 2400 plots across southcentral U.S. forests that have suffered a recent drought. Using data from multiple sampling times, we calculated SSD and compared the relative importance of SSD, species diversity, functional diversity and other stand attributes in determining tree mortalities caused by fire, insects and diseases. We also used FIRETEC, a physics-based fire model, to test the effect of SSD on canopy consumption by fire. Our results showed that (1) SSD was positively associated with tree mortalities caused by all three disturbances; (2) species richness was negatively associated with insect- and disease-caused mortalities; (3) functional diversity was negatively associated with fire- and disease-caused mortalities and (4) more phylogenetically related species had more similar mortality rates by insect and disease but not fire. Moreover, the FIRETEC model showed increasing canopy consumption by fire in stands with greater SSD. Together, the different tree mortalities during drought associated with SSD more consistently than the other biodiversity metrics were evaluated. Synthesis. Our results suggest that SSD could be considered in modelling forest dynamics and planning management to sustain forest health under disturbances. © 2022 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.
... For example, current Landsat or Sentinel satellites capture reflectance in 30 × 30 or 20 × 20 m resolution, respectively. Consequently, they cannot provide information on spectral variability at the scale of individual trees, at which arboreal folivore feeding decisions are made (Asner et al., 2017;Attiwill & Adams, 1996;Baldeck et al., 2015;Futuyma & Moreno, 1988;Hume, 1999). High-resolution alternatives from air-or spaceborne hyper-and multispectral imagery can be costly, but have been successfully applied in mapping potential favorable feeding habitat for southeastern Australian folivores (Youngentob et al., 2012), as well as koalas in western Queensland (H. ...
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Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.
... Advances in monitoring techniques have allowed an understanding of the factors driving generalization and specialization from the cellular and molecular level to the factors governing community composition, ecosystem functioning and ecosystem service provision. Advanced remote sensing technologies allow almost real-time spatially continuous largescale monitoring of ecosystems using different sensors to explore leaf chemical composition, biodiversity and functional traits [199,200]. China has the technology to implement these approaches, but frameworks to promote more interdisciplinary research are still needed. ...
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Biodiversity science in China has seen rapid growth over recent decades, ranging from baseline biodiversity studies to understanding the processes behind evolution across dynamic regions such as the Qinghai-Tibetan Plateau. We review research, including species catalogues; biodiversity monitoring; the origins, distributions, maintenance and threats to biodiversity; biodiversity-related ecosystem function and services; and species and ecosystems' responses to global change. Next, we identify priority topics and offer suggestions and priorities for future biodiversity research in China. These priorities include (i) the ecology and biogeography of the Qinghai-Tibetan Plateau and surrounding mountains, and that of subtropical and tropical forests across China; (ii) marine and inland aquatic biodiversity; and (iii) effective conservation and management to identify and maintain synergies between biodiversity and socio-economic development to fulfil China's vision for becoming an ecological civilization. In addition, we propose three future strategies: (i) translate advanced biodiversity science into practice for biodiversity conservation; (ii) strengthen capacity building and application of advanced technologies, including high-throughput sequencing, genomics and remote sensing; and (iii) strengthen and expand international collaborations. Based on the recent rapid progress of biodiversity research, China is well positioned to become a global leader in biodiversity research in the near future.
... The advent of RTK-PPK enabled DAP systems to provide accurate geolocation (<20 cm), which provides a tool to perform high-temporalresolution monitoring of tropical forests. The DAP workflow presented here could be deployed quickly after a disturbance event, such as a storm, drought or fire, to measure its effects and also assist in a wide range of conservation-related projects and programs to promote an interest in canopy processes outside of the academic community (e.g., [39,40]). ...
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Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.
... IS includes hundreds of narrow contiguous spectral bands throughout the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) spectral regions, ranging from 400 to 2500 nm, which provides accurate and valuable spectral information on plant conditions (Goetz et al. 1985;Gillespie et al. 2008;Pena and Altmann 2009). Specific absorption features can capture leaf or canopy chemistry, enabling species identification (Ustin 2013;Asner et al. 2017). In general, plant spectral signals depend on photosynthetic pigment concentration in the VIS region, leaf structure in the NIR spectral regions, and canopy water content, lignin, cellulose, and non-structural carbohydrates in the SWIR spectral regions (Rosso, et al. 2005; Van der Meer and De Jong 2011). ...
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Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy for species diversity based on remote sensing. Thus, the research aim was to develop a comprehensive methodology based on spectral diversity for SD and richness mapping and to study their relations with environmental and human-derived factors, demonstrated along Mediterranean to semi-arid climatic gradient. The study addresses two main knowledge gaps regarding spectral diversity: (1) improving the accuracy of woody species classification by features extraction and selection, and by using texture analysis in an ecosystem characterized by high spatial variability and relatively small-sized and sparse woody vegetation; and (2) developing a better estimate of the local species ‎richness and their response to environmental and human-derived factors (i.e. climate, topography, substrate, and land cover factors) across a transition zone between Mediterranean woodlands and semi-arid dwarf shrublands. A hyperspectral image was acquired for a 43-km strip along the study area using an airborne flight of AISA-FENIX (380–2500 nm, 420 bands) at the end of the 2017 rainy season. The dominant species were surveyed, with a total number of 247 trees and shrubs, to train a machine learning support vector machine (SVM) classification for species distribution mapping, which yielded an overall accuracy of 86.1%. A feature extraction and selection methodology was developed, combining principal component analysis and neighborhood component analysis techniques, facilitating the identification of 33 spectral diagnostic bands out of 330 spectral bands. The classification accuracy was decreased by about 2% to 84.2% using only 33 spectral bands. The classification accuracy improved by about 7.1% for the seven large crown species (93.3%) by adding texture information. Later, the local species richness was calculated by utilizing the alpha diversity index (i.e. the Shannon Index) for 30-m grid cells and was tested in response to environmental (i.e. climate, substrate, and topography) and human-derived factors (i.e. land cover). The highest sensitivity to alpha diversity factors was mean annual precipitation, slope, and land surface temperature. The alpha diversity showed higher richness in the natural Mediterranean shrubland and the guarrigue located in the northern part of the climate gradient. We suggest that the approach presented here significantly improves the estimation of woody species distribution and diversity in areas characterized by high spatial heterogeneity along steep climatic gradients.
... Here we build off a past approach using a large-scale ecosystem mapping and sampling scheme, driven by airborne hyperspectral remote sensing data (Asner et al., 2017), to scale surveys of reef fish over an extensive, ecologically complex reef ecosystem in the Hawaiian Islands. The method provides detailed maps of the most important habitat-generating organisms in an ecosystem (e.g., trees in forests, corals on reefs), and utilizes these maps along with other geospatial information to sample and then upscale field-based surveys of habitat occupants (e.g., birds in forests, fish on reefs) to the regional level. ...
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Coral reefs are undergoing changes caused by coastal development, resource use, and climate change. The extent and rate of reef change demand robust and spatially explicit monitoring to support management and conservation decision-making. We developed and demonstrated an airborne-assisted approach to design and upscale field surveys of reef fish over an ecologically complex reef ecosystem along Hawai‘i Island. We also determined the minimal set of mapped variables, mapped reef strata, and field survey sites needed to meet three goals: (i) increase field survey efficiency, (ii) reduce field sampling costs, and (iii) ensure field sampling is geostatistically robust for upscaling to regional estimates of reef fish composition. Variability in reef habitat was best described by a combination of water depth, live coral and macroalgal cover, fine-scale reef rugosity, reef curvature, and latitude as a proxy for a regional climate-ecosystem gradient. In combination, these factors yielded 18 distinct reef habitats, or strata, throughout the study region, which subsequently required 117 field survey sites to quantify fish diversity and biomass with minimal uncertainty. The distribution of field sites was proportional to stratum size and the variation in benthic habitat properties within each stratum. Upscaled maps of reef survey data indicated that fish diversity is spatially more uniform than fish biomass, which was lowest in embayments and near land-based access points. Decreasing the number of field sites from 117 to 45 and 75 sites for diversity and biomass, respectively, resulted in a manageable increase of statistical uncertainty, but would still yield actionable trend data over time for the 60 km reef study region on Hawai‘i Island. Our findings suggest that high-resolution benthic mapping can be combined with stratified-random field sampling to generate spatially explicit estimates of fish diversity and biomass. Future expansions of the methodology can also incorporate temporal shifts in benthic composition to drive continuously evolving fish monitoring for sampling and upscaling. Doing so reduces field-based labor and costs while increasing the geostatistical power and ecological representativeness of field work.
... To overcome this challenge, an increasing number of studies has explored the applicability of remote sensing techniques in assessing regional plant functional diversity for different ecosystems to scale up our biodiversity monitoring capabilities (Aguirre-gutiérrez et al., 2021;Jetz et al., 2016;Wang and Gamon, 2019). State-of-the-art studies used airborne data to map multivariate forest functional types (Asner et al., 2017) and plant functional diversity using both optical and LiDAR observations in combination with statistical approaches (Durán et al., 2019) and spectral indices (Schneider et al., 2017). Despite the value of these airborne remote sensing observations, its potential for application at larger extents is limited as airborne campaigns remain costly to organize and are bound in spatial extent and repeatability. ...
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Large-scale high-resolution satellite observations of plant functional diversity patterns will greatly benefit our ability to study ecosystem functioning. Here, we demonstrate a potentially scalable approach that uses aggregate plant traits estimated from radiative transfer model (RTM) inversion of Sentinel-2 satellite images to calculate community patterns of plant functional diversity. Trait retrieval relied on simulations and Look-up Tables (LUTs) generated by a RTM rather than heavily depending on a priori field data and data-driven statistical learning. This independence from in-situ training data benefits its scalability as relevant field data remains scarce and difficult to acquire. We ran a total of three different inversion algorithms that are representative of commonly applied approaches and we used two different metrics to calculate functional diversity. In tandem with Sentinel-2 image-based estimation of plant traits, we measured Leaf Area Index (LAI), leaf Chlorophyll content (CAB), and Leaf Mass per Area (LMA) in-situ in a (semi-)natural heterogeneous landscape (Montesinho region) located in northern Portugal. Sampling plots were scaled and georeferenced to match the satellite observed pixels and thereby allowed for a direct one-to-one posterior ground truth validation of individual traits and functional diversity. Across approaches, we observe a reasonable correspondence between the satellite-based retrievals and the in-situ observations in terms of the relative distribution of individual trait means and plant functional diversity across locations despite the heterogeneity of the landscape and canopies. The functional diversity estimates, based on a combination of canopy and leaf traits, were robust against estimation biases in trait means. Particularly, the convex hull volume estimate of functional diversity showed strong concordance with in-situ observations across all three inversion methods (Spearman's ρ: 0.67–0.80). The remotely sensed estimates of functional diversity also related to in-situ taxonomic diversity (Spearman's ρ: 0.55–0.63). Our work highlights the potential and challenges of RTM-based functional diversity metrics to study spatial community-level ecological patterns using currently operational and publicly available Sentinel-2 imagery. While further validation and assessment across different ecosystems and larger datasets are needed, the study contributes towards a further maturation of scalable, spatially, and temporally explicit methods for functional diversity assessments from space.
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Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (λ), Shannon (H′), and Pielou (J′), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R2 = 0.79) and SVM (averaged R2 = 0.76) models outperformed the other models (R2 of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems.
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Efforts to assess and understand changes in plant diversity and ecosystem functioning focus on the analysis of taxonomic diversity. However, the resilience of ecosystems depends not only on species richness but also on the functions (responses and effects) of species within communities and ecosystems. Therefore, a functional approach is required to estimate functional diversity through functional traits and to model its changes in space and time. This study aims to: (i) as-sess the accuracy of estimates of species richness and tree functional richness obtained from field data and Sentinel-2 imagery in tropical dry forests of the Yucatan Peninsula; (ii) map and ana-lyze the relationships between these two variables. We calculated species richness and functional richness (from six functional traits) of trees from 87 plots of the National Forest Inventory in a semi-deciduous tropical forest and 107 in a semi-evergreen tropical forest. Species richness and functional richness were mapped using reflectance values, vegetation indices, and texture meas-urements from Sentinel-2 imagery as explanatory variables. Validation of the models to map these two variables yielded a coefficient of determination (R2) of 0.43 and 0.50, and a mean squared relative error of 25.4% and 48.8%, for tree species richness and functional richness, re-spectively. For both response variables, the most important explanatory variables were Senti-nel-2 texture measurements and spectral bands. Tree species richness and functional richness were positively correlated in both forest types. Bivariate maps showed that 44.9% and 26.5% of the forests studied had high species richness and functional richness values. Our findings high-light the importance of integrating field data and remotely sensed variables for estimating tree species richness and functional richness. In addition, the combination of species richness and functional richness maps presented here is potentially valuable for planning, conservation, and restoration strategies by identifying areas that maximize ecosystem service provisioning, car-bon storage, and biodiversity conservation.
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In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the "spectral spe-cies"-sets of pixels with a similar spectral signal-and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept.
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Although tropical forests differ substantially in form and function, they are often represented as a single biome in global change models, hindering understanding of how different tropical forests will respond to environmental change. The response of the tropical forest biome to environmental change is strongly influenced by forest type. Forest types differ based on functional traits and forest structure, which are readily derived from high resolution airborne remotely sensed data. Whether the spatial resolution of emerging satellite-derived hyperspectral data is sufficient to identify different tropical forest types is unclear. Here, we resample airborne remotely sensed forest data at spatial resolutions relevant to satellite remote sensing (30 m) across two sites in Malaysian Borneo. Using principal component and cluster analysis, we derive and map seven forest types. We find ecologically relevant variations in forest type that correspond to substantial differences in carbon stock, growth, and mortality rate. We find leaf mass per area and canopy phosphorus are critical traits for distinguishing forest type. Our findings highlight the importance of these parameters for accurately mapping tropical forest types using space borne observations.
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In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is emerging as a promising tool to map plant biodiversity from space. Proposed approaches often rely on the Spectral Variation Hypothesis (SVH), linking the heterogeneity of terrestrial vegetation to the variability of the spectroradiometric signals. Yet, due to observational limitations, the SVH has been insufficiently tested, remaining unclear which metrics, methods, and sensors could provide the most reliable estimates of plant biodiversity. Here we assessed the potential of RS to infer plant biodiversity using radiative transfer simulations and inversion. We focused specifically on “functional diversity,” which represents the spatial variability in plant functional traits. First, we simulated vegetation communities and evaluated the information content of different functional diversity metrics (FDMs) derived from their optical reflectance factors (R) or the corresponding vegetation “optical traits,” estimated via radiative transfer model inversion. Second, we assessed the effect of the spatial resolution, the spectral characteristics of the sensor, and signal noise on the relationships between FDMs derived from field and remote sensing datasets. Finally, we evaluated the plausibility of the simulations using Sentinel-2 (multispectral, 10 m pixel) and DESIS (hyperspectral, 30 m pixel) imagery acquired over sites of the Functional Significance of Forest Biodiversity in Europe (FunDivEUROPE) network. We demonstrate that functional diversity can be inferred both by reflectance and optical traits. However, not all the FDMs tested were suited for assessing plant functional diversity from RS. Rao's Q index, functional dispersion, and functional richness were the best-performing metrics. Furthermore, we demonstrated that spatial resolution is the most limiting RS feature. In agreement with simulations, Sentinel-2 imagery provided better estimates of plant diversity than DESIS, despite the coarser spectral resolution. However, Sentinel-2 offered inaccurate results at DESIS spatial resolution. Overall, our results identify the strengths and weaknesses of optical RS to monitor plant functional diversity. Future missions and biodiversity products should consider and benefit from the identified potentials and limitations of the SVH.
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Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate high-resolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data-canopy height, leaf area index (LAI), and understory LAI-and eighteen variables derived from hyperspectral data-15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m 2 /m 2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the 2 fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale.
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During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi‐resolution information for analysis. Climate change, land‐use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high‐density, and high‐resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio‐Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross‐site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio‐environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.
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Biodiversity monitoring and understanding ecological processes on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.
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Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.
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Aims Trees dominate the biomass in many ecosystems and are essential for ecosystem functioning and human well‐being. They are also one of the best studied functional groups of plants, with vast amounts of biodiversity data available in scattered sources. We here aim to illustrate that an efficient integration of this data could produce a more holistic understanding of vegetation. Methods To assess the extent of potential data integration, we use key databases of plant biodiversity to 1) obtain a list of tree species and their distributions, 2) identify coverage and gaps of different aspects of tree biodiversity data, and 3) discuss large‐scale patterns of tree biodiversity in relation to vegetation. Results Our global list of trees included 58,044 species. Taxonomic coverage varies in three key databases, with data on the distribution, functional traits, and molecular sequences for about 84%, 45% and 44% of all tree species, which is > 10% greater than for plants overall. For 28% of all tree species, data are available in all three databases. However, less data are digitally accessible about the demography, ecological interactions, and socio‐economic role of tree species. Integrating and imputing existing tree biodiversity data, mobilization of non‐digitized resources and targeted data collection, especially in tropical countries, could help closing some of the remaining data gaps. Conclusions Due to their key ecosystem roles and having large amounts of accessible data, trees are a good model group for understanding vegetation patterns. Indeed, tree biodiversity data are already beginning to elucidate the community dynamics, functional diversity, evolutionary history and ecological interactions of vegetation, with great potential for future applications. An interoperable and openly accessible framework linking various databases would greatly benefit future macroecological studies, and should be linked to a platform that makes information readily accessible to end users in biodiversity conservation and management.
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Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R2: 0.45-0.67, relative RMSE: 11%-14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems.
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The world's greatest terrestrial stores of biodiversity and carbon are found in the forests of northern South America, where large-scale biogeographic patterns and processes have recently begun to be described(1-4). Seven of the nine countries with territory in the Amazon basin and the Guiana shield have carried out large-scale forest inventories, but such massive data sets have been little exploited by tropical plant ecologists(5-8). Although forest inventories often lack the species-level identifications favoured by tropical plant ecologists, their consistency of measurement and vast spatial coverage make them ideally suited for numerical analyses at large scales, and a valuable resource to describe the still poorly understood spatial variation of biomass, diversity, community composition and forest functioning across the South American tropics(9). Here we show, by using the seven forest inventories complemented with trait and inventory data collected elsewhere, two dominant gradients in tree composition and function across the Amazon, one paralleling a major gradient in soil fertility and the other paralleling a gradient in dry season length. The data set also indicates that the dominance of Fabaceae in the Guiana shield is not necessarily the result of root adaptations to poor soils ( nodulation or ectomycorrhizal associations) but perhaps also the result of their remarkably high seed mass there as a potential adaptation to low rates of disturbance.
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Significance Land use is a principal driver of carbon emissions, either directly through land change processes such as deforestation or indirectly via transportation and industries supporting natural resource use. To minimize the effects of land use on the climate system, natural ecosystems are needed to offset gross emissions through carbon sequestration. Managing this critically important service must be achieved tactically if it is to be cost-effective. We have developed a high-resolution carbon mapping approach that can identify biogeographically explicit targets for carbon storage enhancement among all landholders within a country. Applying our approach to Perú reveals carbon threats and protections, as well as major opportunities for using ecosystems to sequester carbon. Our approach is scalable to any tropical forest country.
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Download at : http://www.jstatsoft.org/v61/i06/paper Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity. The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them. The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform kmeans and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the dataset of interest.
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Understanding, modeling, and predicting the impact of global change on ecosystem functioning across biogeographical gradients can benefit from enhanced capacity to represent biota as a continuous distribution of traits. However, this is a challenge for the field of biogeography historically grounded on the species concept. Here we focus on the newly emergent field of functional biogeography: the study of the geographic distribution of trait diversity across organizational levels. We show how functional biogeography bridges species-based biogeography and earth science to provide ideas and tools to help explain gradients in multifaceted diversity (including species, functional, and phylogenetic diversities), predict ecosystem functioning and services worldwide, and infuse regional and global conservation programs with a functional basis. Although much recent progress has been made possible because of the rising of multiple data streams, new developments in ecoinformatics, and new methodological advances, future directions should provide a theoretical and comprehensive framework for the scaling of biotic interactions across trophic levels and its ecological implications.
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Significance Canopy trees are keystone organisms that create habitat for an enormous array of flora and fauna and dominate carbon storage in tropical forests. Determining the functional diversity of tree canopies is, therefore, critical to understanding how tropical forests are assembled and predicting ecosystem responses to environmental change. Across the megadiverse Andes-to-Amazon corridor of Peru, we discovered a large-scale nested pattern of canopy chemical assembly among thousands of trees. This nested geographic and phylogenetic pattern within and among forest communities provides a different perspective on current and future alterations to the functioning of western Amazonian forests resulting from land use and climate change.
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Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including-in the latter case-x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.
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The scientific community has come to a broad consensus on many aspects of the relationship between biodiversity and ecosystem functioning, including many points relevant to management of ecosystems. Further progress will require integration of knowledge about biotic and abiotic controls on ecosystem properties, how ecological communities are structured, and the forces driving species extinctions and invasions. To strengthen links to policy and management, we also need to integrate our ecological knowledge with understanding of the social and economic constraints of potential management practices. Understanding this complexity, while taking strong steps to minimize current losses of species, is necessary for responsible management of Earth's ecosystems and the diverse biota they contain. Based on our review of the scientific literature, we are certain of the following conclusions: 1)Species' functional characteristics strongly influence ecosystem properties. Functional characteristics operate in a variety of contexts, including effects of dominant species, keystone species, ecological engineers, and interactions among species (e.g., competition, facilitation, mutualism, disease, and predation). Relative abundance alone is not always a good predictor of the ecosystem-level importance of a species, as even relatively rare species (e.g., a keystone predator) can strongly influence pathways of energy and material flows. 2)Alteration of biota in ecosystems via species invasions and extinctions caused by human activities has altered ecosystem goods and services in many well-documented cases. Many of these changes are difficult, expensive, or impossible to reverse or fix with technological solutions. 3)The effects of species loss or changes in composition, and the mechanisms by which the effects manifest themselves, can differ among ecosystem properties, ecosystem types, and pathways of potential community change. 4)Some ecosystem properties are initially insensitive to species loss because (a) ecosystems may have multiple species that carry out similar functional roles, (b) some species may contribute relatively little to ecosystem properties, or (c) properties may be primarily controlled by abiotic environmental conditions. 5)More species are needed to insure a stable supply of ecosystem goods and services as spatial and temporal variability increases, which typically occurs as longer time periods and larger areas are considered. We have high confidence in the following conclusions: 1)Certain combinations of species are complementary in their patterns of resource use and can increase average rates of productivity and nutrient retention. At the same time, environmental conditions can influence the importance of complementarity in structuring communities. Identification of which and how many species act in a complementary way in complex communities is just beginning. 2)Susceptibility to invasion by exotic species is strongly influenced by species composition and, under similar environmental conditions, generally decreases with increasing species richness. However, several other factors, such as propagule pressure, disturbance regime, and resource availability also strongly influence invasion success and often override effects of species richness in comparisons across different sites or ecosystems. 3)Having a range of species that respond differently to different environmental perturbations can stabilize ecosystem process rates in response to disturbances and variation in abiotic conditions. Using practices that maintain a diversity of organisms of different functional effect and functional response types will help preserve a range of management options. Uncertainties remain and further research is necessary in the following areas: 1)Further resolution of the relationships among taxonomic diversity, functional diversity, and community structure is important for identifying mechanisms of biodiversity effects. 2)Multiple trophic levels are common to ecosystems but have been understudied in biodiversity/ecosystem functioning research. The response of ecosystem properties to varying composition and diversity of consumer organisms is much more complex than responses seen in experiments that vary only the diversity of primary producers. 3)Theoretical work on stability has outpaced experimental work, especially field research. We need long-term experiments to be able to assess temporal stability, as well as experimental perturbations to assess response to and recovery from a variety of disturbances. Design and analysis of such experiments must account for several factors that covary with species diversity. 4)Because biodiversity both responds to and influences ecosystem properties, understanding the feedbacks involved is necessary to integrate results from experimental communities with patterns seen at broader scales. Likely patterns of extinction and invasion need to be linked to different drivers of global change, the forces that structure communities, and controls on ecosystem properties for the development of effective management and conservation strategies. 5)This paper focuses primarily on terrestrial systems, with some coverage of freshwater systems, because that is where most empirical and theoretical study has focused. While the fundamental principles described here should apply to marine systems, further study of that realm is necessary. Despite some uncertainties about the mechanisms and circumstances under which diversity influences ecosystem properties, incorporating diversity effects into policy and management is essential, especially in making decisions involving large temporal and spatial scales. Sacrificing those aspects of ecosystems that are difficult or impossible to reconstruct, such as diversity, simply because we are not yet certain about the extent and mechanisms by which they affect ecosystem properties, will restrict future management options even further. It is incumbent upon ecologists to communicate this need, and the values that can derive from such a perspective, to those charged with economic and policy decision-making.
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