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Africa’s ecosystems have an important role in global carbon dynamics, yet consensus is lacking regarding the amount of carbon stored in woody vegetation and the potential impacts to carbon storage in response to changes in climate, land use and other Anthropocene risks. In this study, we explore the socioenvironmental conditions that have shaped the contemporary distribution of woody vegetation across sub-Saharan Africa and evaluate ecosystem response to multiple scenarios of climate change, anthropogenic pressures and fire disturbance. Our projections suggest climate change will have a small but negative effect on above-ground woody biomass at the continental scale, and the compounding effects of population growth, increasing human pressures and socioclimatic-driven changes in fire behaviour further exacerbate climate-driven trends. Relatively modest continental-scale trends obscure much larger regional perturbations, with climatic and anthropogenic factors leading to increased carbon storage potential in East Africa, offset by large deficits in West, Central and Southern Africa. The amount of carbon stored in African ecosystems and how climate change will affect this is uncertain. Projections indicate that carbon storage will increase in East Africa, climate change will have an overall negative impact on woody biomass and that other human pressures will amplify the trend.

Black-tailed prairie dogs (Cynomys ludovicianus) have been described as a keystone species and are important for grassland conservation, yet many concerns exist over the impact of prairie dogs on plant biomass production and consequently livestock production. The ability to map plant communities in pastures colonized by prairie dogs can provide land managers with an opportunity to optimize rangeland production while balancing conservation goals. The aim of this study was to test the ability of random forest (RF) models to classify five plant communities located on and off prairie dog towns in mixed-grass prairie landscapes of north central South Dakota, assess the stability of RF models among different years, and determine the utility of utilizing remote sensing techniques to identify prairie dog colony extent. During 2015 and 2016, Pleiades satellites were tasked to image the study site for a total of five monthly collections each summer (June–October). Training polygons were mapped in 2016 for the five plant communities and used to train RF models. Both the 2015 and 2016 RF models had low (1 %) out-of-bag error rates. However, comparisons between the predicted plant community maps using the 2015 imagery and one created with the 2016 imagery indicate over 32.9 % of pixels changed plant community class between 2015 and 2016. The results show that while RF models may predict with a high degree of accuracy, overlap of plant communities and interannual differences in rainfall may cause instability in fitted models. A final RF model combining both 2015 and 2016 data yielded the lowest error rates and was also highly accurate in determining prairie dog colony boundaries.

Drylands are a critical part of the earth system in terms of total area, socioeconomic and ecological importance. However, while drylands are known for their contribution to inter-annual atmospheric CO2 variability, they are sometimes overlooked in discussions of global carbon stocks. Here, in preparation for the November 2021 UN Climate Change Conference (COP26), we review dryland systems with emphasis on their role in current and future carbon storage, response to climate change and potential to contribute to a carbon neutral future. Current estimates of carbon in dryland soils and vegetation suggest they are significant at global scale, containing approximately 30% of global carbon in above and below-ground biomass, and surface-layer soil carbon (top 30 cm). As ecosystems that are limited by water, the drylands are vulnerable to climate change. Climate change impacts are, however, dependent on future trends in rainfall that include both drying and wetting trends at regional scales. Regional rainfall trends will initiate trends in dryland productivity, vegetation structure and soil carbon storage. However, while management of fire and herbivory can contribute to increased carbon sequestration, impacts are dependent on locally unique ecosystem responses and climate-soil-plant interactions. Similarly, while community based agroforestry initiatives have been successful in some areas, large-scale afforestation programs are logistically infeasible and sometimes ecologically inappropriate at larger scales. As climate changes, top-down prescriptive measures designed to increase carbon storage should be avoided in favour of locally-adapted approaches that balance carbon management priorities with local livelihoods, ecosystem function, biodiversity and cultural, social and economic priorities.

Ecosystems across the United States are changing in complex and surprising ways. Ongoing demand for critical ecosystem services requires an understanding of the populations and communities in these ecosystems in the future. This paper represents a synthesis effort of the U.S. National Science Foundation-funded Long-Term Ecological Research (LTER) network addressing the core research area of "populations and communities." The objective of this effort was to show the importance of long-term data collection and experiments for addressing the hardest questions in scientific ecology that have significant implications for environmental policy and management. Each LTER site developed at least one compelling case study about what their site could look like in 50-100 yr as human and environmental drivers influencing specific ecosystems change. As the case studies were prepared, five themes emerged, and the studies were grouped into papers in this LTER Futures Special Feature addressing state change, connectivity, resilience, time lags, and cascading effects. This paper addresses the "connectivity" theme and has examples from the Phoenix (urban), Niwot Ridge (alpine tundra), McMurdo Dry Valleys (polar desert), Plum Island (coastal), Santa Barbara Coastal (coastal), and Jornada (arid grassland and shrubland) sites. Connectivity has multiple dimensions, ranging from multi-scalar interactions in space to complex interactions over time that govern the transport of materials and the distribution and movement of organisms. The case studies presented here range widely, showing how land-use legacies interact with climate to alter the structure and function of arid ecosystems and flows of resources and organisms in Antarctic polar desert, alpine, urban, and coastal marine ecosystems. Long-term ecological research demonstrates that connectivity can, in some circumstances, sustain valuable ecosystem functions, such as the persistence of foundation species and their associated biodiversity or, it can be an agent of state change, as when it increases wind and water erosion. Increased connectivity due to warming can also lead to species range expansions or contractions and the introduction of undesirable species. Continued long-term studies are essential for addressing the complexities of connectivity. The diversity of ecosystems within the LTER network is a strong platform for these studies. National Science Foundation Long-Term Ecological Research programNational Science Foundation (NSF) [DEB-1637590, 1832016, DEB-0213767, 0816963, 1354494, 1902712, NSF OCE-0423565, 1058747, 1637630, OPP-1637708, OCE-1232779, DEB-1832194, DEB-1637686] Published version This research was supported by the National Science Foundation Long-Term Ecological Research program grants to the Central Arizona-Phoenix (DEB-1637590, 1832016), Plum Island (DEB-0213767, 0816963, 1354494, 1902712, NSF OCE-0423565, 1058747, 1637630), McMurdo (OPP-1637708), Santa Barbara (OCE-1232779), Jornada (DEB-1832194), and Niwot (DEB-1637686) LTER sites.

The ecological and environmental science communities have embraced machine learning (ML) for empirical modelling and prediction. However, going beyond prediction to draw insights into underlying functional relationships between response variables and environmental 'drivers' is less straightforward. Deriving ecological insights from fitted ML models requires techniques to extract the 'learning' hidden in the ML models.We revisit the theoretical background and effectiveness of four approaches for deriving insights from ML: ranking independent variable importance (Gini importance, GI; permutation importance, PI; split importance, SI; and conditional permutation importance, CPI), and two approaches for inference of bivariate functional relationships (partial dependence plots, PDP; and accumulated local effect plots, ALE). We also explore the use of a surrogate model for visualization and interpretation of complex multi-variate relationships between response variables and environmental drivers. We examine the challenges and opportunities for extracting ecological insights with these interpretation approaches. Specifically, we aim to improve interpretation of ML models by investigating how effectiveness relates to (a) interpretation algorithm, (b) sample size and (c) the presence of spurious explanatory variables.We base the analysis on simulations with known underlying functional relationships between response and predictor variables, with added white noise and the presence of correlated but non-influential variables. The results indicate that deriving ecological insight is strongly affected by interpretation algorithm and spurious variables, and moderately impacted by sample size. Removing spurious variables improves interpretation of ML models. Meanwhile, increasing sample size has limited value in the presence of spurious variables, but increasing sample size does improves performance once spurious variables are omitted. Among the four ranking methods, SI is slightly more effective than the other methods in the presence of spurious variables, while GI and SI yield higher accuracy when spurious variables are removed. PDP is more effective in retrieving underlying functional relationships than ALE, but its reliability declines sharply in the presence of spurious variables. Visualization and interpretation of the interactive effects of predictors and the response variable can be enhanced using surrogate models, including three-dimensional visualizations and use of loess planes to represent independent variable effects and interactions.Machine learning analysts should be aware that including correlated independent variables in ML models with no clear causal relationship to response variables can interfere with ecological inference. When ecological inference is important, ML models should be constructed with independent variables that have clear causal effects on response variables. While interpreting ML models for ecological inference remains challenging, we show that careful choice of interpretation methods, exclusion of spurious variables and adequate sample size can provide more and better opportunities to 'learn from machine learning'.机器学习已经被广泛应用于生态和环境科学中进行经验建模和预测, 然而我们并不能直接有效地从机器学习模型中获取重要的生态机制关系。因此从机器学习模型中获取生态机制关系亟需有效的方法提取模型中的潜在关系。在本研究中, 我们主要考察了以下相关方法的理论背景和有效性。其中四种方法用来估计环境解释变量的重要性, 包括: 基尼系数, 排列特征重要性, 基于增长回归树模型分裂树的特征重要性, 以及条件排列重要性。两种方法来获取解释变量与目标变量之间的功能关系: 部份依赖图和累积局部效应。此外, 我们采用一个代理模型来可视化并解释复杂的多变量关系。本文旨在检验应用以上方法获取生态解释的机遇和挑战, 尤其是他们受样本大小和冗余特征变量的影响。本研究基于一组已知潜在生态机制的全球物种丰富度的模拟数据, 并添加白噪声和相关但没有直接因果关系的冗余特征。研究结果显示从机器学习中获取生态机制最主要受解释方法以及冗余特征的影响, 其次是样本大小。剔除冗余变量可以显著提高模型的生态解释能力; 并且当冗余变量去除后, 增大样本大小可以提高解释效果。在四个特征重要性排列的方法中, 当模型中存在冗余特征时, 分裂特征重要性比其他方法稍显优势; 当冗余特征去除后, 基尼系数和分裂特征重要性都能获得更准确的结果。在任何情况下, 部份依赖图比都比累计局部效应更有效提取功能关系; 冗余特征的存在同样影响了部份依赖图的有效性。应用三维可视化和局部多项式回归的组合作为代理模型可以有效表达多个环境特征的交互作用。本研究结果显示我们需要关注冗余变量对获取生态机制的影响。如果我们需要从机器学习模型中获取潜在的生态机制, 机器学习模型中最好只包括与反应变量有清晰因果关系的特征变量。从机器学习中获取生态机制解释一直是一个关键的挑战, 本研究显示应用合适的解释方法, 剔除冗余变量以及应用充足的样本量可以显著提高从机器学习中“学习”的机会。.

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe.

The West African Sahel Cropland map (WASC30) is a new 30-m cropland extent product for the nominal year of 2015. We used the computing resources provided by Google Earth Engine (GEE) to fit and apply Random Forest models for cropland detection in each of 189 grid cells (composed of 100 km2, hence a total of ~1.9 × 106 km2) across five countries of the West African Sahel (Burkina Faso, Mauritania, Mali, Niger, and Senegal). Landsat-8 surface reflectance (Bands 2–7) and vegetation indices (NDVI, EVI, SAVI, and MSAVI), organized to include dry-season and growing-season band reflectances and vegetation indices for the years 2013–2015, were used as predictors. Training data were derived from an independent, high-resolution, visually interpreted sample dataset that classifies sample points across West Africa using a 2-km grid (~380,000 points were used in this study, with 50% used for model training and 50% used for model validation). Analysis of the new cropland dataset indicates a summed cropland area of ~316 × 103 km2 across the 5 countries, primarily in rainfed cropland (309 × 103 km2), with irrigated cropland area (7 × 103 km2) representing 2% of the total cropland area. At regional scale, the cropland dataset has an overall accuracy of 90.1% and a cropland class (rainfed and irrigated) user’s accuracy of 79%. At bioclimatic zones scale, results show that land proportion occupied by rainfed agriculture increases with annual precipitation up to 1000 mm. The Sudanian zone (600–1200 mm) has the highest proportion of land in agriculture (24%), followed by the Sahelian (200–600 mm) and the Guinean (1200 +) zones for 15% and 4%, respectively. The new West African Sahel dataset is made freely available for applications requiring improved cropland area information for agricultural monitoring and food security applications.

Savanna woody plants can store significant amounts of carbon while also providing numerous other ecological and socio-economic benefits. However, they are significantly under-represented in widely used tree cover datasets, due to mapping challenges presented by their complex landscapes, and the underestimation of woody plants by methods that exclude short stature trees and shrubs. In this study, we describe a Google Earth Engine (GEE) application and present test case results for mapping percent woody canopy cover (%WCC) over a large savanna area. Relevant predictors of %WCC include information derived from radar backscatter (Sentinel-1) and optical reflectance (Sentinel-2), which are used in conjunction with plot level %WCC measurements to train and evaluate random forest models. We can predict %WCC at 40 m pixel resolution for the full extent of Senegal with a root mean square error of ∼8% (based on independent sample evaluation). Further examination of model results provides insights into method stability and potential generalizability. Annual median radar backscatter intensity is determined to be the most important satellite-based predictor of %WCC in savannas, likely due to its relatively strong response to non-leaf structural components of small woody plants which remain mostly constant across the wet and dry season. However, the best performing model combines radar backscatter metrics with optical reflectance indices that serve as proxies for greenness, dry biomass, burn incidence, plant water content, chlorophyll content, and seasonality. The primary use of GEE in the methodology makes it scalable and replicable by end-users with limited infrastructure for processing large remote sensing data.

Savannas are widespread global biomes covering ~20% of terrestrial ecosystems on all continents except Antarctica. These ecosystems play a critical role in regulating terrestrial carbon cycle, ecosystem productivity, and the hydrological cycle and contribute to human livelihoods and biodiversity conservation. Despite the importance of savannas in ecosystem processes and human well-being, the presence of mixed woody and herbaceous components at scales much finer than most medium and coarse resolution satellite imagery poses significant challenges to their effective representation in remote sensing and modeling of vegetation dynamics. Although previous studies have attempted to separate woody and herbaceous components, the focus on greenness indices and fractional cover provides little insight into spatio-temporal variability in woody and herbaceous vegetation structure, in particular, leaf area index (LAI). This paper presents a method to partition 1km spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) aggregate green leaf area index (LAIA) from 2003-2015, into separate woody (LAIW) and herbaceous (LAIH) constituents in both drought seasonal savannas and moist tropical forests of Sub-Saharan Africa (SSA). In our analysis, we use an allometric relationship describing the variation in peak within-canopy woody LAI of dominant tree species (LAIWpinc) across gradients in mean annual precipitation (MAP), coupled with independent estimates of woody canopy cover (τw), to constrain seasonally changing LAIW. We present the LAI partitioning approach and highlight the broad spatial and temporal patterns of woody and herbaceous LAI across SSA. The long-term average 8-day phenologies of woody and herbaceous LAI (averaged across 2003-2015) are available for evaluation, research and application purposes.

The shortgrass steppe (SGS) occupies the southwestern part of the Great Plains. Half of the land is cultivated, but significant areas remain under natural vegetation. Despite previous studies of the SGS carbon cycle, not all aspects have been completely addressed, including gross productivity, ecosystem respiration, and ecophysiological parameters. Our analysis of 1998 — 2007 flux tower measurements at five Bowen ratio-energy balance (BREB) and three eddy covariance (EC) sites characterized seasonal and interannual variability of gross photosynthesis and ecosystem respiration. Identification of the nonrectangular hyperbolic equation for the diurnal CO2 exchange, with vapor pressure deficit (VPD) limitation and exponential temperature response, quantified quantum yield α, photosynthetic capacity Amax, and respiration rate rd with variation ranges (19 < α < 51 mmol mol-1, 0.48 < Amax < 2.1 mg CO2 m-2 s-1, 0.15 < rd < 0.49 mg CO2 m-2 s-1). Gross photosynthesis varied from 1 100 to 2 700 g CO2 m-2 yr-1, respiration from 900 to 3,000 g CO2 m-2 yr-1, and net ecosystem production from — 900 to 700 g CO2 m-2 yr-1, indicating that SGS may switch from a sink to a source depending on weather. Comparison of the 2004–2006 measurements at two BREB and two parallel EC flux towers located at comparable SGS sites showed moderately higher photosynthesis, lower respiration, and higher net production at the BREB than EC sites. However, the difference was not related only to methodologies, as the normalized difference vegetation index at the BREB sites was higher than at the EC sites. Overall magnitudes and seasonal patterns at the BREB and the EC sites during the 3-yr period were similar, with trajectories within the ± 1.5 standard deviation around the mean of the four sites and mostly reflecting the effects of meteorology.

An analysis of satellite images has pinpointed individual tree canopies over a large area of West Africa. The data suggest that it will soon be possible, with certain limitations, to map the location and size of every tree worldwide. Individual tree canopies pinpointed over a large area of West Africa.

Vegetation buffers local diurnal land surface temperatures, however, this effect has found limited applications for remote vegetation characterization. In this work, we parameterize diurnal temperature variations as the thermal decay rate derived by using satellite daytime and nighttime land surface temperatures and modeled using Newton’s law of cooling. The relationship between the thermal decay rate and vegetation depends on many factors including vegetation type, size, water content, location, and local conditions. The theoretical relationships are elucidated, and empirical relationships are presented. Results show that the decay rate summarizes both vegetation structure and function and exhibits a high correlation with other established vegetation-related observations. As proof of concept, we interpret 15-year spatially explicit trends in the annual thermal decay rates over Africa and discuss results. Given recent increases in availability of finer spatial resolution satellite thermal measurements, the thermal decay rate may be a useful index for monitoring vegetation.

The use of high resolution imagery in remote sensing has the potential to improve understanding of patch level variability in plant structure and community composition that may be lost at coarser scales. Random forest (RF) is a machine learning technique that has gained considerable traction in remote sensing applications due to its ability to produce accurate classifications with highly dimensional data and relatively efficient computing times. The aim of this study was to test the ability of RF to classify five plant communities located both on and off prairie dog towns in mixed grass prairie landscapes of north central South Dakota, and assess the stability of RF models among different years. During 2015 and 2016, Pleiades satellites were tasked to image the study site for a total of five monthly collections each summer (June–October). Training polygons were mapped in 2016 for the five plant communities and used to train separate 2015 and 2016 RF models. The RF models for 2015 and 2016 were highly effective at predicting different vegetation types associated with, and remote from, prairie dog towns (misclassification rates

Globally, the spatial distribution of vegetation is governed primarily by climatological factors (rainfall and temperature, seasonality, and inter-annual variability). The local distribution of vegetation, however, depends on local edaphic conditions (soils and topography) and disturbances (fire, herbivory, and anthropogenic activities). Abrupt spatial or temporal changes in vegetation distribution can occur if there are positive (i.e., amplifying) feedbacks favoring certain vegetation states under otherwise similar climatic and edaphic conditions. Previous studies in the tropical savannas of Africa and other continents using the MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF) satellite data product have focused on discontinuities in the distribution of tree cover at different rainfall levels, with bimodal distributions (e.g., concentrations of high and low tree cover) interpreted as alternative vegetation states. Such observed bimodalities over large spatial extents may not be evidence for alternate states, as they may include regions that have different edaphic conditions and disturbance histories. In this study, we conduct a systematic multi-scale analysis of diverse MODIS data streams to quantify the presence and spatial consistency of alternative vegetation states in Sub-Saharan Africa. The analysis is based on the premise that major discontinuities in vegetation structure should also manifest as consistent spatial patterns in a range of remote sensing data streams, including, for example, albedo and land surface temperature (LST). Our results confirm previous observations of bimodal and multimodal distributions of estimated tree cover in the MODIS VCF. However, strong disagreements in the location of multimodality between VCF and other data streams were observed at 1 km scale. Results suggest that the observed distribution of VCF over vast spatial extents are multimodal, not because of local-scale feedbacks and emergent bifurcations (the definition of alternative states), but likely because of other factors including regional scale differences in woody dynamics associated with edaphic, disturbance, and/or anthropogenic processes. These results suggest the need for more in-depth consideration of bifurcation mechanisms and thus the likely spatial and temporal scales at which alternative states driven by different positive feedback processes should manifest.

The original version of this Data Descriptor incorrectly referenced the “United Nations (UN) Food and Agriculture Organization (FAO) soilGrids250m system”. This has been corrected to “SoilGrids predictions” throughout the text in both the HTML and PDF versions.

Herbivores alter plant biodiversity (species richness) in many of the world’s ecosystems, but the magnitude and the direction of herbivore effects on biodiversity vary widely within and among ecosystems. One current theory predicts that herbivores enhance plant biodiversity at high productivity but have the opposite effect at low productivity. Yet, empirical support for the importance of site productivity as a mediator of these herbivore impacts is equivocal. Here, we synthesize data from 252 large-herbivore exclusion studies, spanning a 20-fold range in site productivity, to test an alternative hypothesis—that herbivore-induced changes in the competitive environment determine the response of plant biodiversity to herbivory irrespective of productivity. Under this hypothesis, when herbivores reduce the abundance (biomass, cover) of dominant species (for example, because the dominant plant is palatable), additional resources become available to support new species, thereby increasing biodiversity. By contrast, if herbivores promote high dominance by increasing the abundance of herbivory-resistant, unpalatable species, then resource availability for other species decreases reducing biodiversity. We show that herbivore-induced change in dominance, independent of site productivity or precipitation (a proxy for productivity), is the best predictor of herbivore effects on biodiversity in grassland and savannah sites. Given that most herbaceous ecosystems are dominated by one or a few species, altering the competitive environment via herbivores or by other means may be an effective strategy for conserving biodiversity in grasslands and savannahs globally. Fil: Koerner, Sally E.. University of North Carolina at Greensboro. Department of Biology; Estados Unidos Fil: Smith, Melinda D.. State University of Colorado - Fort Collins; Estados Unidos Fil: Burkepile, Deron E.. University of California; Estados Unidos Fil: Hanan, Niall P.. New Mexico State University; Estados Unidos Fil: Avolio, Meghan L.. University Johns Hopkins; Estados Unidos Fil: Collins, Scott L.. University of New Mexico; Estados Unidos Fil: Knapp, Alan K.. State University of Colorado - Fort Collins; Estados Unidos Fil: Lemoine, Nathan P.. State University of Colorado - Fort Collins; Estados Unidos Fil: Forrestel, Elisabeth J.. University of California at Davis; Estados Unidos Fil: Eby, Stephanie. Northeastern University; Estados Unidos Fil: Thompson, Dave I.. University of the Witwatersrand; Sudáfrica. Kruger National Park. South African Environmental Observation Network. Ndlovu Node. Scientific Services; Sudáfrica Fil: Aguado-Santacruz, Gerardo A.. Tecnológico Nacional de México; México Fil: Anderson, John P.. New Mexico State University; Estados Unidos Fil: Anderson, T. Michael. University Wake Forest; Estados Unidos Fil: Angassa, Ayana. Botswana University of Agriculture and Natural Resources; Sudáfrica. Hawassa University; Etiopía Fil: Bagchi, Sumanta. Indian Institute of Science; India Fil: Bakker, Elisabeth S.. Netherlands Institute of Ecology; Países Bajos Fil: Bastin, Gary. Alice Springs; Australia Fil: Baur, Lauren E.. University of New Mexico; Estados Unidos Fil: Beard, Karen H.. State University of Utah; Estados Unidos Fil: Beever, Erik A.. United States Geological Survey; Estados Unidos. State University of Montana; Estados Unidos Fil: Bohlen, Patrick J.. University of Central Florida; Estados Unidos Fil: Boughton, Elizabeth H.. MacArthur Agroecology Research Center; Estados Unidos Fil: Canestro, Don. University of California; Estados Unidos Fil: Cesa, Ariela. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina Fil: Chaneton, Enrique Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Cheng, Jimin. Northwest A&F University; China Fil: D?Antonio, Carla M.. University of California; Estados Unidos Fil: Deleglise, Claire. Université Grenoble Alpes; Francia Fil: Dembélé, Fadiala. Institut de Formation et de Recherche Appliquee; Sudáfrica Fil: Dorrough, Josh. Ecosystem Mangement Science; Austria Fil: Eldridge, David J.. University of New South Wales; Australia Fil: Fernandez Going, Barbara. University of Texas at Austin; Estados Unidos Fil: Fernández Lugo, Silvia. Universidad de La Laguna; España Fil: Fraser, Lauchlan H.. Thompson Rivers University; Canadá Fil: Freedman, Bill. Dalhousie University Halifax; Canadá Fil: García Salgado, Gonzalo. Dalhousie University Halifax; Canadá Fil: Goheen, Jacob R.. University of Wyoming; Estados Unidos Fil: Guo, Liang. Northwest A&F University; China Fil: Husheer, Sean. New Zealand Forest Surveys; Nueva Zelanda Fil: Karembé, Moussa. Université des Sciences; Malí Fil: Knops, Johannes M. H.. Universidad de Nebraska - Lincoln; Estados Unidos Fil: Kraaij, Tineke. Nelson Mandela University; Sudáfrica Fil: Kulmatiski, Andrew. State University of Utah; Estados Unidos Fil: Kytöviita, Minna Maarit. Universidad de Jyvaskyla; Finlandia Fil: Lezama, Felipe. Universidad de la República; Uruguay Fil: Loucougaray, Gregory. Université Grenoble Alpes; Francia Fil: Loydi, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina Fil: Milchunas, Dan G.. State University of Colorado - Fort Collins; Estados Unidos Fil: Milton, Suzanne J.. South African Environmental Observation Network; Sudáfrica Fil: Morgan, John W.. La Trobe University; Australia Fil: Moxham, Claire. Arthur Rylah Institute for Environmental Research; Australia Fil: Nehring, Kyle C.. State University of Utah; Estados Unidos Fil: Olff, Han. University of Groningen; Países Bajos Fil: Palmer, Todd M.. University of Florida; Estados Unidos Fil: Rebollo, Salvador. Universidad de Alcalá; España Fil: Riginos, Corinna. The Nature Conservancy; Estados Unidos Fil: Risch, Anita C.. Swiss Federal Institute for Forest, Snow and Landscape Research; Suiza Fil: Rueda, Marta. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; España Fil: Sankaran, Mahesh. National Centre For Biological Sciences; India. University of Leeds; Reino Unido Fil: Sasaki, Takehiro. Yokohama National University; Japón Fil: Schoenecker, Kathryn A.. Fort Collins Science Center and Colorado State University; Estados Unidos Fil: Schultz, Nick L.. Federation University; Australia Fil: Schütz, Martin. Swiss Federal Institute for Forest, Snow and Landscape Research; Suiza Fil: Schwabe, Angelika. Universitat Technische Darmstadt; Alemania Fil: Siebert, Frances. North West University; Sudáfrica Fil: Smit, Christian. University of Groningen; Países Bajos Fil: Stahlheber, Karen A.. University of Wisconsin Green Bay; Estados Unidos Fil: Storm, Christian. Universitat Technische Darmstadt; Alemania Fil: Strong, Dustin J.. United States Department of Agriculture. Agriculture Research Service; Estados Unidos Fil: Su, Jishuai. Northwest A&F University; China Fil: Tiruvaimozhi, Yadugiri V.. National Centre for Biological Sciences; India Fil: Tyler, Claudia. University of California; Estados Unidos Fil: Val, James. Office of Environment and Heritage; Australia Fil: Vandegehuchte, Martijn L.. University of Ghent; Bélgica. Swiss Federal Institute for Forest, Snow and Landscape Research; Suiza Fil: Veblen, Kari E.. State University of Utah; Estados Unidos Fil: Vermeire, Lance T.. United States Department of Agriculture. Agriculture Research Service; Estados Unidos Fil: Ward, David. Kent State University; Estados Unidos Fil: Wu, Jianshuang. Lhasa National Ecological Research Station; China Fil: Young, Truman P.. University of California at Davis; Estados Unidos. Mpala Research Centre; Kenia Fil: Yu, Qiang. Chinese Academy Of Agricultural Sciences; China. Institute of Agricultural Resources and Regional Planning; China Fil: Zelikova, Tamara Jane. University Of Wyoming; . University of Wyoming; Estados Unidos

Land surface phenology (LSP) in the Sahara desert is poorly understood due to the difficulty in detecting subtle variations in vegetation greenness. This study examined the spatial and temporal patterns of LSP and its responses to rainfall seasonality in the Sahara desert. We first generated daily two-band enhanced vegetation index (EVI2) from half-hourly observations acquired by the Spinning Enhanced Visible and Infrared Imager on board the Meteosat Second Generation series of geostationary satellites from 2006 to 2012. The EVI2 time series was used to retrieve LSP based on the Hybrid Piecewise Logistic Model. We further investigated the associations of spatial and temporal patterns in LSP with those in rainfall seasonality derived from the daily rainfall time series of the Tropical Rainfall Measurement Mission. Results show that the spatial shifts in the start of the vegetation growing season generally follow the rainy season onset that is controlled by the summer rainfall regime in the southern Sahara desert. In contrast, the end of the growing season significantly lags the end of the rainy season without any significant dependence. Vegetation growing season can unfold during the dry seasons after onset is triggered during rainy seasons. Vegetation growing season can be as long as 300 days or more in some areas and years. However, the EVI2 amplitude and accumulation across the Sahara region was very low indicating sparse vegetation as expected in desert regions. EVI2 amplitude and accumulated EVI2 strongly depended on rainfall received during the growing season and the preceding dormancy period.

In the Great Plains, grassland carbon dynamics differ across broad gradients of precipitation and temperature, yet finer-scale variation in these variables may also affect grassland processes. Despite the importance of grasslands, there is little information on how fine-scale relationships compare between them regionally. We compared grassland C exchanges, energy partitioning and precipitation variability in eight sites in the eastern and western Great Plains using eddy covariance and meteorological data. During our study, both eastern and western grasslands varied between an average net carbon sink and a net source. Eastern grasslands had a moderate vapor pressure deficit (VPD = 0.95 kPa) and high growing season gross primary productivity (GPP = 1010 ± 218 g C m−2 yr−1). Western grasslands had a growing season with higher VPD (1.43 kPa) and lower GPP (360 ± 127 g C m−2 yr−1). Western grasslands were sensitive to precipitation at daily timescales, whereas eastern grasslands were sensitive at monthly and seasonal timescales. Our results support the expectation that C exchanges in these grasslands differ as a result of varying precipitation regimes. Because eastern grasslands are less influenced by short-term variability in rainfall than western grasslands, the effects of precipitation change are likely to be more predictable in eastern grasslands because the timescales of variability that must be resolved are relatively longer. We postulate increasing regional heterogeneity in grassland C exchanges in the Great Plains in coming decades.

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Theory and empirical evidence for the impacts of fire and herbivory in savannahs is well established – they are top-down disturbances that maintain savannahs in disequilibrium states away from potential tree cover. In African savannahs, the demand for fuelwood is extremely high, so tree harvest likely also has an impact, both directly and indirectly, on tree cover, density and biomass. Many savannah trees resprout vigorously from the base after harvest. However, harvested trees regenerate as saplings susceptible to fire and browsing, so harvest may have important demographic consequences. Here, we report the effects of tree harvest, and its interaction with fire and herbivory, on savannah dynamics by analysing woody regrowth following a harvest in arid Sahelian and mesic Guinean savannahs in Mali, West Africa. Tree harvest resulted in an overall reduction in wood production per tree compared to growth in nonharvested trees. Regrowth, either biomass or height, did not differ among fire and herbivory treatments. Our results suggest that the resprouting abilities that savannah trees have evolved to cope with frequent fire are essential for surviving tree harvest and subsequent disturbance. In these savannahs, regrowth is rapid enough in the first growing season to escape the impact of dry season fires. Resume La theorie et les preuves empiriques de l'impact des feux et de l'herbivorie dans les savanes sont bien etablies – ce sont des perturbations en cascade qui maintiennent les savanes dans un etat instable en l'empechant de former une couverture arboree. Dans les savanes africaines, la demande de bois de feu est extremement elevee, et la collecte d'arbres a donc probablement aussi un impact, direct et indirect, sur la couverture, la densite et la biomasse des arbres. De nombreux arbres de savane rejettent vigoureusement de la base apres la coupe. Mais les arbres coupes repoussent en jeunes plants susceptibles de bruler ou d'etre manges de sorte que la collecte peut avoir des consequences demographiques importantes. Nous signalons ici les effets de la collecte d'arbres et de son interaction avec les feux et l'herbivorie sur la dynamique des savanes en analysant la repousse ligneuse apres une collecte dans des savanes arides saheliennes et mesiques guineennes du Mali, en Afrique de l'Ouest. La collecte d'arbres aboutit a une reduction globale de la production de bois par arbre, comparee a la croissance chez les arbres non recoltes. La repousse, en biomasse et en hauteur, ne differait pas entre traitement par le feu ou par l'herbivorie. Nos resultats suggerent que les capacites de repousse que les arbres de la savane ont developpees pour repondre aux feux frequents sont essentielles pour survivre aux collectes de bois et aux perturbations qu'elles entrainent. Dans ces savanes, la repousse lors de la premiere saison de croissance est assez rapide pour echapper aux impacts des feux de saison seche.

"Over many decades our understanding of the impacts of intermittent drought in water-limited environments like the West African Sahel has been influenced by a narrative of overgrazing and human-induced desertification. The desertification narrative has persisted in both scientific and popular conception, such that recent regional-scale recovery ('regreening') and local success stories (community-led conservation efforts) in the Sahel, following the severe droughts of the 1970s–1980s, are sometimes ignored. Here we report a study of watershed-scale vegetation dynamics in 260 watersheds, sampled in four regions of Senegal, Mali, and Niger from 1983–2012, using satellite-derived vegetation indices as a proxy for net primary production. In response to earlier controversy, we first examine the shape of the rainfall–net primary production relationship and how it impacts conclusions regarding greening or degradation. We conclude that the choice of functional relationship has little quantitative impact on our ability to infer greening or degradation trends. We then present an approach to analyze changes in long-term (decade-scale) average rain-use efficiency (an indicator of slowly responding vegetation structural changes) relative to changes in interannual-scale rainfall sensitivity (an indicator of landscape ability to respond rapidly to rainfall variability) to infer trends in greening/degradation of the watersheds in our sample regions. The predominance of increasing rain-use efficiency in our data supports earlier reports of a 'greening' trend across the Sahel. However, there are strong regional differences in the extent and direction of change, and in the apparent role of changing woody and herbaceous components in driving those temporal trends."

An understanding of rainfall characteristics at multiple spatiotemporal scales is of great importance for hydrological, biogeochemical, and land surface modeling studies. In the present study, patterns of rainfall are analyzed over the African continent based on 3 hourly 0.25° Tropical Rainfall Measuring Mission (TRMM) estimates between 1998 and 2012 to produce monthly statistical summaries. The selected rain event properties are multiyear means of precipitation total amount (mm), event frequency (number), rate (mm/h), and duration (h) calculated independently for each calendar month. Analysis of 3 hourly and daily events in the 1998–2012 period suggests that rainfall amount can be summarized using gamma probability density functions. Assuming stationarity, gamma probability density functions of the total depth of 3 hourly and daily events are estimated and then used for temporal downscaling of monthly rainfall estimates (past or future). As a result, we generate 3 hourly and daily rainfall estimates that are pixelwise statistically indistinguishable from the observations while preserving monthly totals. Example scripts are provided that can be used to access monthly statistics and implement downscaling using archival (or projected) monthly rainfall estimates. These statistics could also be utilized for the assessment of rainfall from atmospheric models.

Savannas cover 60% of the African continent and play an important role in the global carbon (C) emissions from fire and land use. To better characterize the biophysical controls over soil respiration in these settings, half-hourly observations of volumetric soil–water content, temperature, and the concentration of carbon dioxide (CO 2 ) at different soil depths were continually measured from 2005 to 2007 under trees (“sub-canopy”) and between trees (“inter-canopy”) in a savanna vegetation near Skukuza, Kruger National Park, South Africa. The measured soil climate and CO 2 concentration data were assimilated into a process-based model that estimates the CO 2 production and flux with coupled dynamics of dissolved organic C (DOC) and microbial biomass C. Our results show that temporal and spatial variations in CO 2 flux were strongly influenced by precipitation and vegetation cover, with two times greater CO 2 flux in the sub-canopy plots (∼2421 g CO 2 m −2 yr −1 ) than in the inter-canopy plots (∼1290 g CO 2 m −2 yr −1 ). Precipitation influenced soil respiration by changing soil temperature and moisture; however, our modeling analysis suggests that the pulsed response of soil respiration to precipitation events (known as “Birch effect”) is a key control on soil fluxes at this site. At this site, “Birch effect” contributed to approximately 50% and 65% of heterotrophic respiration or 20% and 39% of soil respiration in the sub-canopy and inter-canopy plots, respectively. These results suggest that pulsed response of respiration to precipitation events is an important component of the C cycle of savannas and should be considered in both measurement and modeling studies of carbon exchange in similar ecosystems.

Bastin et al . (Reports, 12 May 2017, p. 635) infer forest as more globally extensive than previously estimated using tree cover data. However, their forest definition does not reflect ecosystem function or biotic composition. These structural and climatic definitions inflate forest estimates across the tropics and undermine conservation goals, leading to inappropriate management policies and practices in tropical grassy ecosystems.

Surveying Savannas Savannas are structurally similar across the three major continents where they occur, leading to the assumption that the factors controlling vegetation structure and function are broadly similar, too. Lehmann et al. (p. 548 ) report the results of an extensive analysis of ground-based tree abundance in savannas, sampled at more than 2000 sites in Africa, Australia, and South America. All savannas, independent of region, shared a common functional property in the way that moisture and fire regulated tree abundance. However, despite qualitative similarity in the moisture–fire–tree-biomass relationships among continents, key quantitative differences exist among the three regions, presumably as a result of unique evolutionary histories and climatic domains.

We assess 32 years of vegetation change in the West African Sudano-Sahelian region following the drought events of the 1970s and 1980s. Change in decadal mean rain use efficiency is used to diagnose trends in woody vegetation that is expected to respond more slowly to post-drought rainfall gains, while change in the slope of the productivity–rainfall relationship is used to infer changing herbaceous conditions between early and late periods of the time series. The linearity/non-linearity of the productivity–rainfall relationship and its impact on the interpretation of overall greening trends, and specific woody and herbaceous vegetation trends, is also examined. Our results show a mostly positive association between productivity and rainfall (69% of pixels), which can be best described as linear (32%) or saturating (37%). Choosing the ‘best’ model at a specific location using Akaike Information Criterion has no discernible effect on the interpretation of overall greening or herbaceous trends, but does influence the detection of trends in woody vegetation. We conclude that widespread recovery in woody vegetation is responsible for the post-drought greening phenomenon reported elsewhere for the Sahel and Sudanian sub-regions. Meanwhile, trends in herbaceous vegetation are less pronounced, with no consistent indication towards either herbaceous degradation or recovery.

[1] Water is a precious resource since the lives and livelihoods of human societies depend on their ability to access this resource. This is particularly true in arid and semiarid regions like the Sahel where permanent surface waters are rare and hydrological solutions (e.g., accessing deep aquifers) are often not feasible. This work is devoted to the determination of the spatial and temporal variations of surface water extent at 250 m over 9 years (2003–2011) for the Soudan-Sahel region using visible and infrared imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) from both Terra and Aqua platforms. The methodology is automatic and could be applied to other regions and sensors. We utilized a combination of two MODIS spectral indices: the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index. Accuracy assessments carried out on 743 lakes across 37 Landsat images selected in Sahelian Africa on the basis of a stratified sampling show that the new product is (i) reliably able to detect surface water bodies of approximately 50 ha and larger, (ii) improves on existing surface water body products, and (iii) can be used to monitor the seasonal distribution of surface water in semiarid regions.