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Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged 1 , 2 . However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking 1 , 3 . Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots 4 . GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them. Mapping of groundwater-dependent ecosystems, which support biodiversity and rural livelihoods, shows they occur on more than one-third of global drylands analysed, but lack protections to safeguard these critical ecosystems and the societies dependent upon them from groundwater depletion.

The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine ( http://ClimateEngine.org ) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

Clostridium difficile is the etiological agent of antibiotic-associated diarrhoea (AAD) and pseudomembranous colitis in humans. The role of the surface layer proteins (SLPs) in this disease has not yet been fully explored. The aim of this study was to investigate a role for SLPs in the recognition of C. difficile and the subsequent activation of the immune system. Bone marrow derived dendritic cells (DCs) exposed to SLPs were assessed for production of inflammatory cytokines, expression of cell surface markers and their ability to generate T helper (Th) cell responses. DCs isolated from C3H/HeN and C3H/HeJ mice were used in order to examine whether SLPs are recognised by TLR4. The role of TLR4 in infection was examined in TLR4-deficient mice. SLPs induced maturation of DCs characterised by production of IL-12, TNFα and IL-10 and expression of MHC class II, CD40, CD80 and CD86. Furthermore, SLP-activated DCs generated Th cells producing IFNγ and IL-17. SLPs were unable to activate DCs isolated from TLR4-mutant C3H/HeJ mice and failed to induce a subsequent Th cell response. TLR4⁻/⁻ and Myd88⁻/⁻, but not TRIF⁻/⁻ mice were more susceptible than wild-type mice to C. difficile infection. Furthermore, SLPs activated NFκB, but not IRF3, downstream of TLR4. Our results indicate that SLPs isolated from C. difficile can activate innate and adaptive immunity and that these effects are mediated by TLR4, with TLR4 having a functional role in experimental C. difficile infection. This suggests an important role for SLPs in the recognition of C. difficile by the immune system.

Phenology of plants is important for ecological interactions. The timing and development of green leaves, plant maturity, and senescence affects biophysical interactions of plants with the environment. In this study we explored the agreement between land-based camera and satellite-based phenology metrics to quantify plant phenology and phenophases dates in five plant community types characteristic of the semi-arid cold desert region of the Great Basin. Three years of data were analyzed. We calculated the Normalized Difference Vegetation Index (NDVI) for both land-based cameras (i.e., phenocams) and Landsat imagery. NDVI from camera images was calculated by taking a standard RGB (red, green, and blue) image and then a near infrared (NIR) plus RGB image. Phenocam NDVI was calculated by extracting the red digital number (DN) and the NIR DN from images taken a few seconds apart. Landsat has a spatial resolution of 30 m2, while phenocam spatial resolution can be analyzed at the single pixel level at the scale of cm2 or area averaged regions can be analyzed with scales up to 1 km2. For this study, phenocam regions of interest were used that approximated the scale of at least one Landsat pixel. In the tall-statured pinyon and juniper woodland sites, there was a lack of agreement in NDVI between phenocam and Landsat NDVI, even after using National Agricultural Imagery Program (NAIP) imagery to account for fractional coverage of pinyon and juniper versus interspace in the phenocam data. Landsat NDVI appeared to be dominated by the signal from the interspace and was insensitive to subtle changes in the pinyon and juniper tree canopy. However, for short-statured sagebrush shrub and meadow communities, there was good agreement between the phenocam and Landsat NDVI as reflected in high Pearson’s correlation coefficients (r > 0.75). Due to greater temporal resolution of the phenocams with images taken daily, versus the 16-day return interval of Landsat, phenocam data provided more utility in determining important phenophase dates: start of season, peak of season, and end of season. More specific species-level information can be obtained with the high temporal resolution of phenocams, but only for a limited number of sites, while Landsat can provide the multi-decadal history and spatial coverage that is unmatched by other platforms. The agreement between Landsat and phenocam NDVI for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.

Increased atmospheric evaporative demand has important implications for humans and ecosystems in water-scarce lands. While temperature plays a significant role in driving evaporative demand and its trend, other climate variables are also influential and their contributions to recent trends in evaporative demand are unknown. We address this gap with an assessment of recent (1980–2020) trends in annual reference evapotranspiration (ETo) and its drivers across the continental United States based on five gridded datasets. In doing so, we characterize the structural uncertainty of ETo trends and decompose the relative influences of temperature, wind speed, solar radiation, and humidity. Results highlight large and robust changes in ETo across much of the western United States, centered on the Rio Grande region where ETo increased 135–235 mm during 1980–2020. The largest uncertainties in ETo trends are in the central and eastern United States and surrounding the Upper Colorado River. Trend decomposition highlights the strong and widespread influence of temperature, which contributes to 57% of observed ETo trends, on average. ETo increases are mitigated by increases in specific humidity in non-water-limited regions, while small decreases in specific humidity and increases in wind speed and solar radiation magnify ETo increases across the West. Our results show increases in ETo across the West that are already emerging outside the range of variability observed 20–40 years ago. Our results suggest that twenty-first-century land and water managers need to plan for an already increasing influence of evaporative demand on water availability and wildfire risks.

Dryland riparian areas are under increasing stress due to expanding human water demands and a warming climate. Quantifying responses of dryland riparian vegetation to these pressures is complicated by high climatic variability, which can create strong, transient changes in vegetation vigor that could mask other disturbance events. In this study, we utilize a 34-year archive of Landsat satellite data to (1) quantify the strength and timescales of vegetation responses to interannual variability in drought status and (2) isolate and remove this influence to assess resultant trends in vegetation vigor for riparian areas across the state of Nevada, the driest state in the USA. Correlations between annual late-summer Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation–Evapotranspiration Index (SPEI) were calculated across a range of time periods (varying timing and durations) for all riparian pixels within each of the 45 ecoregions, and the variability of these values across the study area is shown. We then applied a novel drought adjustment method that used the strongest SPEI–NDVI timescale relationships for each ecoregion to remove the influence of interannual drought status. Our key result is a 30 m resolution map of drought-adjusted riparian NDVI trends (1985–2018). We highlight and describe locations where impacts of invasive species biocontrol, mine water management, agriculture, changing water levels, and fire are readily visualized with our results. We found more negatively trending riparian areas in association with wide valley bottoms, low-intensity agricultural land uses, and private land ownerships and more positive trends in association with narrow drainages, public lands, and surrounding perennial water bodies (an indication of declining water levels allowing increased vegetative cover). The drought-adjusted NDVI improved the statistical significance of trend estimates, thereby improving the ability to detect such changes. Results from this study provide insight into the strength and timescales of riparian vegetation responses to drought and can provide important information for managing riparian areas within the study area. The novel approach to drought adjustment is readily transferrable to other regions.

The Complementary Relationship of evaporation (CR) theory has gained attention in recent years, in part because it relies solely on standard weather data for estimating evaporation, eliminating the need for land surface moisture and resistance information. CR models show varied skill across diverse landscapes and climates. However, inconsistent parameterization, calibration strategies, station data sets, and geographic coverage have hindered consistent and systematic intercomparison of model performance. This study intercompares four widely used CR models, uncalibrated and calibrated, with eddy covariance data worldwide, and assesses how environmental conditions affect model skill and calibration. Eddy covariance data from 227 FLUXNET and AmeriFLUX stations were quality assured and controlled, filtered, and energy balance closure corrected using open‐source software and benchmarking procedures for reproducibility, after which 82 sites remained. Systematic intercomparison showed that, overall, the Rescaled Power function (Szilagyi et al., 2022, https://doi.org/10.1029/2022wr033095) had the highest skill followed by Sigmoid (Han & Tian, 2018, https://doi.org/10.1029/2017wr021755), while Polynomial model (Brutsaert, 2015, https://doi.org/10.1002/2015wr017720) generally biased high, and Advection‐Aridity (Brutsaert & Stricker, 1979, https://doi.org/10.1029/wr015i002p00443) often produced unrealistic negative values when observed evaporation was low. Two‐parameter calibration greatly improved model skill, with single‐parameter PT‐α calibration performing comparably, suggesting potential to reduce model non‐uniqueness. Calibration and model skill rankings were consistent using energy balance closure corrected and uncorrected data sets. Results indicate that environmental conditions such as aridity index, relative humidity, vapor pressure deficit, and the ratio between radiative and apparent potential evaporation can explain spatial patterns of CR model parameters, revealing opportunities for future development of improved, calibration‐free CR evaporation modeling.

Fungi such as Aspergillus fumigatus, Candida albicans, and Cryptococcus neoformans are opportunistic pathogens in humans. They usually infect individuals whose immune system is compromised due to either a primary infection, e.g., HIV/AIDS, or as part of treatment for another condition, e.g., stem cell or solid organ transplant. In hosts with a weakened immune system, these fungi can cause life-threatening infections. Unlike true pathogens, opportunistic pathogens do not have specific mechanisms to overcome a healthy host, requiring a different approach to understand how they cause infection. The ability of fungi to adapt to various environmental conditions, including the human host, is critical for virulence. In humans, micronutrient metals, such as iron, are sequestered to reduce serum concentrations, which helps to inhibit microbial growth. Other human tissues may increase metal concentrations to toxic levels to prevent infection by pathogens. The ability of fungi to acquire or detoxify nutrients, such as iron or copper, from the host is essential for the establishment of infection. In this review, the role of fungal nutrition will be discussed in relation to opportunistic fungal pathogens. It will focus on the acquisition of micronutrients, e.g., iron, copper, and zinc, and how this enables these fungi to circumvent host nutritional immunity.

Dendritic cells (DC) are the most important antigen presenting cells and play a pivotal role in host immunity to infectious agents by acting as a bridge between the innate and adaptive immune systems. Monocyte-derived immature DCs (iDC) were infected with viable resting conidia of Aspergillus fumigatus (Af293) for 12 hours at an MOI of 5; cells were sampled every three hours. RNA was extracted from both organisms at each time point and hybridised to microarrays. iDC cell death increased at 6 h in the presence of A. fumigatus which coincided with fungal germ tube emergence; >80% of conidia were associated with iDC. Over the time course A. fumigatus differentially regulated 210 genes, FunCat analysis indicated significant up-regulation of genes involved in fermentation, drug transport, pathogenesis and response to oxidative stress. Genes related to cytotoxicity were differentially regulated but the gliotoxin biosynthesis genes were down regulated over the time course, while Aspf1 was up-regulated at 9 h and 12 h. There was an up-regulation of genes in the subtelomeric regions of the genome as the interaction progressed. The genes up-regulated by iDC in the presence of A. fumigatus indicated that they were producing a pro-inflammatory response which was consistent with previous transcriptome studies of iDC interacting with A. fumigatus germ tubes. This study shows that A. fumigatus adapts to phagocytosis by iDCs by utilising genes that allow it to survive the interaction rather than just up-regulation of specific virulence genes.

We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (mTBI) using multi-shell diffusion magnetic resonance imaging, deriving compartment-specific white matter tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of white matter (WM) changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with mTBI within 30 days of injury and 21 age- and sex-matched controls were imaged on a 3 Tesla magnetic resonance scanner. Multi-shell diffusion acquisition was performed with five b-values (250-2500 sec/mm2) along 6-60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrated focally lower intra-axonal diffusivity (Daxon) in mTBI patients in the splenium of the corpus callosum (sCC; p < 0.05, FWE-corrected). The area under the curve value for Daxon was 0.76 with a low sensitivity of 46.9% but 100% specificity. These results indicate that Daxon may be a useful imaging biomarker highly specific for mTBI-related WM injury. The observed decrease in Daxon suggests restriction of the diffusion along the axons occurring shortly after injury.