Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands.

Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. Here we evaluated the relative importance of grazing pressure and herbivore type, climate and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this, we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.

Dryland microbiomes reveal community adaptations to desertification and climate change.

Drylands account for 45% of the Earth's land area, supporting ~40% of the global population. These regions support some of the most extreme environments on Earth, characterized by extreme temperatures, low and variable rainfall, and low soil fertility. In these biomes, microorganisms provide vital ecosystem services and have evolved distinctive adaptation strategies to endure and flourish in the extreme. However, dryland microbiomes and the ecosystem services they provide are under threat due to intensifying desertification and climate change. In this review, we provide a synthesis of our current understanding of microbial life in drylands, emphasizing the remarkable diversity and adaptations of these communities. We then discuss anthropogenic threats, including the influence of climate change on dryland microbiomes and outline current knowledge gaps. Finally, we propose research priorities to address those gaps and safeguard the sustainability of these fragile biomes.

Self-organization as a mechanism of resilience in dryland ecosystems.

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.

The global biogeography and environmental drivers of fairy circles.

Fairy circles (FCs) are regular vegetation patterns found in drylands of Namibia and Western Australia. It is virtually unknown whether they are also present in other regions of the world and which environmental factors determine their distribution. We conducted a global systematic survey and found FC-like vegetation patterns in 263 sites from 15 countries and three continents, including the Sahel, Madagascar, and Middle-West Asia. FC-like vegetation patterns are found in environments characterized by a unique combination of soil (including low nutrient levels and high sand content) and climatic (arid regions with high temperatures and high precipitation seasonality) conditions. In addition to these factors, the presence of specific biological elements (termite nests) in certain regions also plays a role in the presence of these patterns. Furthermore, areas with FC-like vegetation patterns also showed more stable temporal productivity patterns than those of surrounding areas. Our study presents a global atlas of FCs and provides unique insights into the ecology and biogeography of these fascinating vegetation patterns.

Climate change impacts on plant pathogens, food security and paths forward.

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Soils in warmer and less developed countries have less micronutrients globally.

Soil micronutrients are capital for the delivery of ecosystem functioning and food provision worldwide. Yet, despite their importance, the global biogeography and ecological drivers of soil micronutrients remain virtually unknown, limiting our capacity to anticipate abrupt unexpected changes in soil micronutrients in the face of climate change. Here, we analyzed >1300 topsoil samples to examine the global distribution of six metallic micronutrients (Cu, Fe, Mn, Zn, Co and Ni) across all continents, climates and vegetation types. We found that warmer arid and tropical ecosystems, present in the least developed countries, sustain the lowest contents of multiple soil micronutrients. We further provide evidence that temperature increases may potentially result in abrupt and simultaneous reductions in the content of multiple soil micronutrients when a temperature threshold of 12-14°C is crossed, which may be occurring on 3% of the planet over the next century. Altogether, our findings provide fundamental understanding of the global distribution of soil micronutrients, with direct implications for the maintenance of ecosystem functioning, rangeland management and food production in the warmest and poorest regions of the planet.

Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning.

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.

Grazing and ecosystem service delivery in global drylands.

Grazing represents the most extensive use of land worldwide. Yet its impacts on ecosystem services remain uncertain because pervasive interactions between grazing pressure, climate, soil properties, and biodiversity may occur but have never been addressed simultaneously. Using a standardized survey at 98 sites across six continents, we show that interactions between grazing pressure, climate, soil, and biodiversity are critical to explain the delivery of fundamental ecosystem services across drylands worldwide. Increasing grazing pressure reduced ecosystem service delivery in warmer and species-poor drylands, whereas positive effects of grazing were observed in colder and species-rich areas. Considering interactions between grazing and local abiotic and biotic factors is key for understanding the fate of dryland ecosystems under climate change and increasing human pressure.

Climate legacies drive the distribution and future restoration potential of dryland forests.

Knowing the extent and environmental drivers of forests is key to successfully restore degraded ecosystems, and to mitigate climate change and desertification impacts using tree planting. Water availability is the main limiting factor for the development of forests in drylands, yet the importance of groundwater resources and palaeoclimate as drivers of their current distribution has been neglected. Here we report that mid-Holocene climates and aquifer trends are key predictors of the distribution of dryland forests worldwide. We also updated the global extent of dryland forests to 1,283 million hectares and showed that failing to consider past climates and aquifers has resulted in ignoring or misplacing up to 130 million hectares of forests in drylands. Our findings highlight the importance of a wetter past and well-preserved aquifers to explain the current distribution of dryland forests, and can guide restoration actions by avoiding unsuitable areas for tree establishment in a drier world.

Phylotype diversity within soil fungal functional groups drives ecosystem stability.

Soil fungi are fundamental to plant productivity, yet their influence on the temporal stability of global terrestrial ecosystems, and their capacity to buffer plant productivity against extreme drought events, remain uncertain. Here we combined three independent global field surveys of soil fungi with a satellite-derived temporal assessment of plant productivity, and report that phylotype richness within particular fungal functional groups drives the stability of terrestrial ecosystems. The richness of fungal decomposers was consistently and positively associated with ecosystem stability worldwide, while the opposite pattern was found for the richness of fungal plant pathogens, particularly in grasslands. We further demonstrated that the richness of soil decomposers was consistently positively linked with higher resistance of plant productivity in response to extreme drought events, while that of fungal plant pathogens showed a general negative relationship with plant productivity resilience/resistance patterns. Together, our work provides evidence supporting the critical role of soil fungal diversity to secure stable plant production over time in global ecosystems, and to buffer against extreme climate events.

The potential of groundwater-dependent ecosystems to enhance soil biological activity and soil fertility in drylands.

Water availability controls the functioning of dryland ecosystems, driving a patchy vegetation distribution, unequal nutrient availability, soil respiration in pulses, and limited productivity. Groundwater-dependent ecosystems (GDEs) are acknowledged to be decoupled from precipitation, since their vegetation relies on groundwater sources. Despite their relevance to enhance productivity in drylands, our understanding of how different components of GDEs interconnect (i.e., soil, vegetation, water) remains limited. We studied the GDE dominated by the deep-rooted phreatophyte Ziziphus lotus, a winter-deciduous shrub adapted to arid conditions along the Mediterranean basin. We aimed to disentangle whether the groundwater connection established by Z. lotus will foster soil biological activity and therefore soil fertility in drylands. We assessed (1) soil and vegetation dynamics over seasons (soil CO2 efflux and plant activity), (2) the effect of the patchy distribution on soil quality (properties and nutrient availability), and soil biological activity (microbial biomass and mineralization rates) as essential elements of biogeochemical cycles, and (3) the implications for preserving GDEs and their biogeochemical processes under climate change effects. We found that soil and vegetation dynamics respond to water availability. Whereas soil biological activity promptly responded to precipitation events, vegetation functioning relies on less superficial water and responded on different time scales. Soil quality was higher under the vegetation patches, as was soil biological activity. Our findings highlight the importance of groundwater connections and phreatophytic vegetation to increase litter inputs and organic matter into the soils, which in turn enhances soil quality and decomposition processes in drylands. However, biogeochemical processes are jeopardized in GDEs by climate change effects and land degradation due to the dependence of soil activity on: (1) precipitation for activation, and (2) phreatophytic vegetation for substrate accumulation. Therefore, desertification might modify biogeochemical cycles by disrupting key ecosystem processes such as soil microbial activity, organic matter mineralization, and plant productivity.

Temperature thresholds drive the global distribution of soil fungal decomposers.

Unraveling the biogeographic pattern of soil fungal decomposers along temperature gradients-in smooth linearity or an abrupt jump-can help us connect the global carbon cycle to global warming. Through a standardized global field survey, we identify the existence of temperature thresholds that control the global distribution of soil fungal decomposers, leading to abrupt reductions in their proportion (i.e., the relative abundance in the fungal community) immediately after crossing particular air and soil temperature thresholds. For example, small increases over the mean annual temperature threshold of ~9°C result in abrupt reductions in their proportion, paralleling a similar temperature threshold for soil carbon content. We further find that the proportion of soil fungal decomposers is more sensitive to temperature increases under arid conditions. Given the positive correlation between the global distributions of fungal decomposers and soil heterotrophic respiration, the reported temperature-driven abrupt reductions in fungal decomposers could further suppress their driven soil decomposition processes and reduce carbon fluxes from soils to the atmosphere with implications for climate change feedback. This work not only advances the current knowledge on the global distribution of soil fungal decomposers, but also highlights that small changes in temperature around certain thresholds can lead to potential unexpected consequences in global carbon cycling under projected climate change.

Biogeography of global drylands.

Despite their extent and socio-ecological importance, a comprehensive biogeographical synthesis of drylands is lacking. Here we synthesize the biogeography of key organisms (vascular and nonvascular vegetation and soil microorganisms), attributes (functional traits, spatial patterns, plant-plant and plant-soil interactions) and processes (productivity and land cover) across global drylands. These areas have a long evolutionary history, are centers of diversification for many plant lineages and include important plant diversity hotspots. This diversity captures a strikingly high portion of the variation in leaf functional diversity observed globally. Part of this functional diversity is associated with the large variation in response and effect traits in the shrubs encroaching dryland grasslands. Aridity and its interplay with the traits of interacting plant species largely shape biogeographical patterns in plant-plant and plant-soil interactions, and in plant spatial patterns. Aridity also drives the composition of biocrust communities and vegetation productivity, which shows large geographical variation. We finish our review by discussing major research gaps, which include: studying regular vegetation spatial patterns; establishing large-scale plant and biocrust field surveys assessing individual-level trait measurements; knowing whether the impacts of plant-plant and plant-soil interactions on biodiversity are predictable; and assessing how elevated CO2 modulates future aridity conditions and plant productivity.

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN.

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world's olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index-NDVI-and green normalized difference vegetation index-GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors.

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

Soil Application of a Formulated Biocontrol Rhizobacterium, Pseudomonas chlororaphis PCL1606, Induces Soil Suppressiveness by Impacting Specific Microbial Communities.

Biocontrol bacteria can be used for plant protection against some plant diseases. Pseudomonas chlororaphis PCL1606 (PcPCL1606) is a model bacterium isolated from the avocado rhizosphere with strong antifungal antagonism mediated by the production of 2-hexyl, 5-propil resorcinol (HPR). Additionally, PcPCL1606 has biological control against different soil-borne fungal pathogens, including the causal agent of the white root rot of many woody crops and avocado in the Mediterranean area, Rosellinia necatrix. The objective of this study was to assess whether the semicommercial application of PcPCL1606 to soil can potentially affect avocado soil and rhizosphere microbial communities and their activities in natural conditions and under R. necatrix infection. To test the putative effects of PcPCL1606 on soil eukaryotic and prokaryotic communities, a formulated PcPCL1606 was prepared and applied to the soil of avocado plants growing in mesocosm experiments, and the communities were analyzed by using 16S/ITS metagenomics. PcPCL1606 survived until the end of the experiments. The effect of PcPCL1606 application on prokaryotic communities in soil and rhizosphere samples from natural soil was not detectable, and very minor changes were observed in eukaryotic communities. In the infested soils, the presence of R. necatrix strongly impacted the soil and rhizosphere microbial communities. However, after PcPCL1606 was applied to soil infested with R. necatrix, the prokaryotic community reacted by increasing the relative abundance of few families with protective features against fungal soilborne pathogens and organic matter decomposition (Chitinophagaceae, Cytophagaceae), but no new prokaryotic families were detected. The treatment of PcPCL1606 impacted the fungal profile, which strongly reduced the presence of R. necatrix in avocado soil and rhizosphere, minimizing its effect on the rest of the microbial communities. The bacterial treatment of formulated PcPCL1606 on avocado soils infested with R. necatrix resulted in biological control of the pathogen. This suppressiveness phenotype was analyzed, and PcPCL1606 has a key role in suppressiveness induction; in addition, this phenotype was strongly dependent on the production of HPR.

Whale counting in satellite and aerial images with deep learning.

Despite their interest and threat status, the number of whales in world's oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution images. Since deep convolutional neural networks (CNNs) are achieving great performance in several computer vision tasks, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales in satellite and aerial images based on open data and tools. In particular, we designed a two-step whale counting approach, where the first CNN finds the input images with whale presence, and the second CNN locates and counts each whale in those images. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 81% in detecting and 94% in counting whales. Combining these two steps increased accuracy by 36% compared to a baseline detection model alone. Applying this cost-effective method worldwide could contribute to the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process.