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Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive "wastelands." Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements. Plain Language Summary: Drylands are landscapes with limited water availability, which cover 40% of Earth's land surfaces, support billions of humans, and play a substantial role in Earth's weather and climate systems. However, these ecosystems are under threat from droughts and heatwaves. They are also poorly understood because of challenges measuring their highly diverse vegetation types and interspersed vegetation cover and because of incorrect perceptions that they are unimportant "wastelands." These limitations make it challenging to manage their landscapes and quantify how drylands are driving Earth's weather and climate. NASA solicited proposals for a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. The ARID scoping study aims to design a plan for how NASA satellite, aircraft, and field instruments can be used to better understand dryland ecosystems and their response to change. A primary scoping goal is to engage with scientists and data‐users, especially those who manage land, to understand research and management priorities in drylands. Here, we discuss details of our meeting with over 300 scientists and data‐users in Tucson, AZ in October 2023. We also highlight feedback from our tribal‐focused workshop in New Mexico, conference town halls, and international meetings. Key Points: Adaptation and Response in Drylands (ARID) is a 1‐year scoping study for a multi‐year NASA Terrestrial Ecology dryland field campaignAn ARID workshop was held in Tucson, Arizona in October 2023 with more than 30 data end‐users and 300 scientists in attendanceFurther input from hundreds of researchers and end‐users was obtained through workshops, conference townhalls, and tribal engagement [ABSTRACT FROM AUTHOR]

Summary: The Jornada Basin Long‐Term Ecological Research Site (JRN‐LTER, or JRN) is a semiarid grassland–shrubland in southern New Mexico, USA. The role of intraspecific competition in constraining shrub growth and establishment at the JRN and in arid systems, in general, is an important question in dryland studies.Using information on shrub distributions and growth habits at the JRN, we present a novel landscape‐scale (c. 1 ha) metric (the 'competition index', CI), which quantifies the potential intensity of competitive interactions. We map and compare the intensity of honey mesquite (Prosopis glandulosa, Torr.) competition spatially and temporally across the JRN‐LTER, investigating associations of CI with shrub distribution, density, and soil types.The CI metric shows strong correlation with values of percent cover. Mapping CI across the Jornada Basin shows that high‐intensity intraspecific competition is not prevalent, with few locations where intense competition is likely to be limiting further honey mesquite expansion.Comparison of CI among physiographic provinces shows differences in average CI values associated with geomorphology, topography, and soil type, suggesting that edaphic conditions may impose important constraints on honey mesquite and growth. However, declining and negative growth rates with increasing CI suggest that intraspecific competition constrains growth rates when CI increases above c. 0.5. [ABSTRACT FROM AUTHOR]

Although it is widely known that observations of aeolian sediment transport are susceptible to large sampling errors, sample designs are frequently used that do not sufficiently reduce the measurement uncertainties inherent in the study of aeolian processes. Here, we examine the influence of sample size (n) and sampling location on uncertainty in models of aeolian sediment transport responses to vegetation cover change. We compare measurements from a stratified random array of 27 horizontal sediment mass flux samplers to vegetative cover data collected at a 1 ha site over a period of nearly 6 years. To assess the sensitivity of modeled relationships between aeolian transport and vegetative cover to sample design, we analyze statistical regressions for all possible combinations of sample size and sampler locations. We show that at least 17 randomly located samplers are needed to consistently capture the sediment mass flux response to vegetative cover change. We found that multiple statistically significant models can describe the sediment flux‐vegetative cover relationship when using smaller sample sizes, demonstrating the risks of inferring sediment transport response from an underpowered sample design. Across vegetative functional groups, we found that woody cover generally influenced aeolian sediment transport rates more than herbaceous cover, while model uncertainty at large sample sizes (n > 17) showed the limitation of using vegetative cover as an indicator of aeolian sediment transport. Our results suggest an evaluation of sampling practices in aeolian sediment transport studies may be needed to avoid inferential errors that are likely pervasive in this field of study. Plain Language Summary: Studies of aeolian sediment transport are prone to sampling errors, largely due to the many factors that control the transport process. However, researchers often look to simplify sample designs and monitoring methods as monitoring is time consuming and expensive. To that end, vegetative cover is an easily collected and interpreted environmental indicator that affects the rates of aeolian sediment transport. However, little consideration has been given to how many samples are needed to confidently model the relationship between changes in vegetative cover and sediment transport. We investigate how changes in sample size, modeling approaches, and vegetative cover may affect inferences made about aeolian sediment transport. We show that using traditional approaches in aeolian research (using small sample sizes and relatively simple environmental data) could risk false conclusions being drawn about aeolian transport processes, even with models that appear to be accurate. If sampling does not adequately describe the sediment transport "population" response, variability in field measurements can be so large that it undermines our understanding of how and why aeolian sediment transport rates change over time. These findings may require a re‐evaluation of typical sediment transport sampling practices. Key Points: Heterogeneity in soil, vegetation, and wind cause aeolian sediment flux to have large spatiotemporal variabilityModels describing change in aeolian sediment fluxes with ground cover are highly sensitive to sampling errorLarge sample sizes and a variety of environmental indicators are needed to produce consistent models that can inform wind erosion management [ABSTRACT FROM AUTHOR]

Author(s): Mahesh Sankaran (corresponding author) [1]; Niall P. Hanan [1]; Robert J. Scholes [2]; Jayashree Ratnam [1]; David J. Augustine [3]; Brian S. Cade [4]; Jacques Gignoux [5]; Steven I. [...]

Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and 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 emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe. [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Abstract The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO2. Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century.

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. [ABSTRACT FROM AUTHOR]

The article discusses the study conducted at the Kruger National Park in South Africa on species abundance and diversity of termites in a single catena. Mentioned are results of the stabilization of termites' accumulation curves and confidence interval among genera samples. A table is presented on the classification of termite genera in the area under Rhinotermitidae and Termitidae families.

Vegetation structure in water-limited systems is to a large degree controlled by ecohydrological processes, including mean annual precipitation (MAP) modulated by the characteristics of precipitation and geomorphology that collectively determine how rainfall is distributed vertically into soils or horizontally in the landscape. We anticipate that woody canopy cover, crown density, crown size, and the level of spatial aggregation among woody plants in the landscape will vary across environmental gradients. A high level of woody plant aggregation is most distinct in periodic vegetation patterns (PVPs), which emerge as a result of ecohydrological processes such as runoff generation and increased infiltration close to plants. Similar, albeit weaker, forces may influence the spatial distribution of woody plants elsewhere in savannas. Exploring these trends can extend our knowledge of how semi-arid vegetation structure is constrained by rainfall regime, soil type, topography, and disturbance processes such as fire. Using high-spatial-resolution imagery, a flexible classification framework, and a crown delineation method, we extracted woody vegetation properties from 876 sites spread over African savannas. At each site, we estimated woody cover, mean crown size, crown density, and the degree of aggregation among woody plants. This enabled us to elucidate the effects of rainfall regimes (MAP and seasonality), soil texture, slope, and fire frequency on woody vegetation properties. We found that previously documented increases in woody cover with rainfall is more consistently a result of increasing crown size than increasing density of woody plants. Along a gradient of mean annual precipitation from the driest (< 200mmyr-1) to the wettest (1200-1400mmyr-1) end, mean estimates of crown size, crown density, and woody cover increased by 233, 73, and 491% respectively. We also found a unimodal relationship between mean crown size and sand content suggesting that maximal savanna tree sizes do not occur in either coarse sands or heavy clays. When examining the occurrence of PVPs, we found that the same factors that contribute to the formation of PVPs also correlate with higher levels of woody plant aggregation elsewhere in savannas and that rainfall seasonality plays a key role for the underlying processes. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Theoretical models of allometric scaling provide frameworks for understanding and predicting how and why the morphology and function of organisms vary with scale. It remains unclear, however, if the predictions of ‘universal’ scaling models for vascular plants hold across diverse species in variable environments. Phenomena such as competition and disturbance may drive allometric scaling relationships away from theoretical predictions based on an optimized tree. Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-specific, and ‘global’ (i.e. interspecific) scaling exponents for several allometric relationships using tree- and branch-level data harvested from three savanna sites across a rainfall gradient in Mali, West Africa. We use these exponents to provide a rigorous test of three plant scaling models (Metabolic Scaling Theory (MST), Geometric Similarity, and Stress Similarity) in savanna systems. For the allometric relationships we evaluated (diameter vs. length, aboveground mass, stem mass, and leaf mass) the empirically calculated exponents broadly overlapped among species from diverse environments, except for the scaling exponents for length, which increased with tree cover and density. When we compare empirical scaling exponents to the theoretical predictions from the three models we find MST predictions are most consistent with our observed allometries. In those situations where observations are inconsistent with MST we find that departure from theory corresponds with expected tradeoffs related to disturbance and competitive interactions. We hypothesize savanna trees have greater length-scaling exponents than predicted by MST due to an evolutionary tradeoff between fire escape and optimization of mechanical stability and internal resource transport. Future research on the drivers of systematic allometric variation could reconcile the differences between observed scaling relationships in variable ecosystems and those predicted by ideal models such as MST. [ABSTRACT FROM AUTHOR]

Abstract: A land surface model (a modified version of the Simple Biosphere Model, Version 2; SiB2) was parameterized and tested against two years of eddy covariance flux measurements made over un-grazed tallgrass prairie and a winter wheat field in Oklahoma, USA. The land surface model computed 30-min estimates of sensible and latent heat flux and carbon dioxide flux that agree well with the patterns observed in the field, simulating in particular the contrasting seasonal timing of fluxes in the wheat, where leaf area and physiological activity peak in the spring, and prairie, where leaf area and physiological activity peak in summer. However, systematic errors in flux estimates were also identified for particular times of day and season. These systematic errors are sometimes related to difficulty in correct definition of vegetation structure (LAI) and physiological activity. This was observed particularly in the wheat site towards the end of the growing season when senescence, which reduces both the amount and the physiological activity of leaves, is difficult to parameterize. Simulation errors are also attributed to problems in the mathematical description of water stress, soil respiration, and the leaf-to-canopy scaling methodology. SiB2 tends to predict too much photosynthetic activity at low solar angles, while developing soil moisture stress before it is seen in the field. Systematic errors in energy balance terms (heat and water fluxes) occur for bare soil and dormant vegetation, related to simulation of soil heat flux. Daytime errors in sensible and latent heat fluxes average 20Wm−2, and can be more than 100Wm−2 at certain times. In regional and global climate models, the effect of land surface sub-model errors on atmospheric dynamics will depend in part on the magnitude of the systematic error, but also on the spatial extent and temporal duration over which the systematic error persists. [Copyright &y& Elsevier]

Several explanations for the persistence of tree–grass mixtures in savannas have been advanced thus far. In general, these either concentrate on competition-based mechanisms, where niche separation with respect to limiting resources such as water lead to tree–grass coexistence, or demographic mechanisms, where factors such as fire, herbivory and rainfall variability promote tree–grass persistence through their dissimilar effects on different life-history stages of trees. Tests of these models have been largely site-specific, and although different models find support in empirical data from some savanna sites, enough dissenting evidence exists from others to question their validity as general mechanisms of tree–grass coexistence. This lack of consensus on determinants of savanna structure and function arises because different models: (i) focus on different demographic stages of trees, (ii) focus on different limiting factors of tree establishment, and (iii) emphasize different subsets of the potential interactions between trees and grasses. Furthermore, models differ in terms of the most basic assumptions as to whether trees or grasses are the better competitors. We believe an integration of competition-based and demographic approaches is required if a comprehensive model that explains both coexistence and the relative productivity of the tree and grass components across the diverse savannas of the world is to emerge. As a first step towards this end, we outline a conceptual framework that integrates existing approaches and applies them explicitly to different life-history stage of trees. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]