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Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

High-resolution climatic data are essential to many questions and applications in environmental research and ecology. Here we develop and implement a new semi-mechanistic downscaling approach for daily precipitation estimate that incorporates high resolution (30 arcsec, ≈ 1 km) satellite-derived cloud frequency. The downscaling algorithm incorporates orographic predictors such as wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. We apply the method to the ERA5 precipitation archive and MODIS monthly cloud cover frequency to develop a daily gridded precipitation time series in 1 km resolution for the years 2003 onward. Comparison of the predictions with existing gridded products and station data from the Global Historical Climate Network indicates an improvement in the spatio-temporal performance of the downscaled data in predicting precipitation. Regional scrutiny of the cloud cover correction from the continental United States further indicates that CHELSA-EarthEnv performs well in comparison to other precipitation products. The CHELSA-EarthEnv daily precipitation product improves the temporal accuracy compared with a large improvement in the spatial accuracy especially in complex terrain. Measurement(s) hydrological precipitation process Technology Type(s) cloud-cover informed downscaling Factor Type(s) temporal interval • geographic location Sample Characteristic - Environment climate system • cloud Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16910344

Aim: Knowledge of species geographical distributions is critical for many ecological and evolutionary questions and underpins effective conservation decision-making, yet it is usually limited in spatial resolution or reliability. Over large spatial extents, range predictions are typically derived from expert knowledge or, increasingly, species distribution models based on individual occurrence records. Expert maps are useful at coarse resolution, where they are suitable for delineating unoccupied regions. In contrast, point records typically provide finerscale occurrence information that can be characterized for its environmental association, but usually suffers from observer biases and does not representatively or fully address the geographical or environmental range occupied by a species. Innovation: We develop a new modelling methodology to combine the complementary informative attributes of both data types to improve fine-scale, large-extent predictions. We use expert delineations to constrain predictions of a species distribution model parameterized with incidental point occurrence records. We introduce a maximum entropy approach for combining the two data types and generalize it to Poisson point process models. We illustrate critical decision making during model construction using two detailed case studies and illustrate features more generally with applications to species with vastly different range and data attributes. Our methods are illustrated in the Supporting Information and with a new R package, bossMaps, that integrates with existing generalized linear modelling and Maxent software. Main conclusions: Our modelling strategy flexibly accommodates expert maps with different levels of bias and precision. The approach can also be useful with other coarse sources of spatially explicit information, including habitat associations, elevational bands or vegetation types. The flexible nature of this methodological innovation can support improved characterization of species distributions for a variety of applications and is being implemented as a standard element underpinning integrative species distribution predictions in the Map of Life (https://mol.org/).

Wikipedia has quickly become one of the most frequently accessed encyclopedic references, despite the ease with which content can be changed and the potential for 'edit wars' surrounding controversial topics. Little is known about how this potential for controversy affects the accuracy and stability of information on scientific topics, especially those with associated political controversy. Here we present an analysis of the Wikipedia edit histories for seven scientific articles and show that topics we consider politically but not scientifically \"controversial\" (such as evolution and global warming) experience more frequent edits with more words changed per day than pages we consider \"noncontroversial\" (such as the standard model in physics or heliocentrism). For example, over the period we analyzed, the global warming page was edited on average (geometric mean ±SD) 1.9±2.7 times resulting in 110.9±10.3 words changed per day, while the standard model in physics was only edited 0.2±1.4 times resulting in 9.4±5.0 words changed per day. The high rate of change observed in these pages makes it difficult for experts to monitor accuracy and contribute time-consuming corrections, to the possible detriment of scientific accuracy. As our society turns to Wikipedia as a primary source of scientific information, it is vital we read it critically and with the understanding that the content is dynamic and vulnerable to vandalism and other shenanigans.

Despite being first described 45 years ago, the existence of a distinct diabetic cardiomyopathy remains controversial. Nonetheless, it is widely accepted that the diabetic heart undergoes characteristic structural and functional changes in the absence of ischaemia and hypertension, which are independently linked to heart failure progression and are likely to underlie enhanced susceptibility to stress. A prominent feature is marked collagen accumulation linked with inflammation and extensive extracellular matrix changes, which appears to be the main factor underlying cardiac stiffness and subclinical diastolic dysfunction, estimated to occur in as many as 75% of optimally controlled diabetics. Whether this characteristic remodelling phenotype is primarily driven by microvascular dysfunction or alterations in cardiomyocyte metabolism remains unclear. Although hyperglycaemia regulates multiple pathways in the diabetic heart, increased reactive oxygen species (ROS) generation is thought to represent a central mechanism underlying associated adverse remodelling. Indeed, experimental and clinical diabetes are linked with oxidative stress which plays a key role in cardiomyopathy, while key processes underlying diabetic cardiac remodelling, such as inflammation, angiogenesis, cardiomyocyte hypertrophy and apoptosis, fibrosis and contractile dysfunction, are redox sensitive. This review will explore the relative contributions of the major ROS sources (dysfunctional nitric oxide synthase, mitochondria, xanthine oxidase, nicotinamide adenine dinucleotide phosphate oxidases) in the diabetic heart and the potential for therapeutic targeting of ROS signalling using novel pharmacological and non-pharmacological approaches to modify specific aspects of the remodelling phenotype in order to prevent and/or delay heart failure development and progression.

Generally, biodiversity is higher in the tropics than at the poles. This pattern is present across taxa as diverse as plants and insects. Marine mammals and birds buck this trend, however, with more species and more individuals occurring at the poles than at the equator. Grady et al. asked why this is (see the Perspective by Pyenson). They analyzed a comprehensive dataset of nearly 1000 species of shark, fish, reptiles, mammals, and birds. They found that predation on ectothermic (“cold-blooded”) prey is easier where waters are colder, which generates a larger resource base for large endothermic (“warm-blooded”) predators in polar regions. Science , this issue p. eaat4220 ; see also p. 338 Marine mammal and bird diversity is highest in polar regions, owing to the availability of cold, slow prey. Species richness of marine mammals and birds is highest in cold, temperate seas—a conspicuous exception to the general latitudinal gradient of decreasing diversity from the tropics to the poles. We compiled a comprehensive dataset for 998 species of sharks, fish, reptiles, mammals, and birds to identify and quantify inverse latitudinal gradients in diversity, and derived a theory to explain these patterns. We found that richness, phylogenetic diversity, and abundance of marine predators diverge systematically with thermoregulatory strategy and water temperature, reflecting metabolic differences between endotherms and ectotherms that drive trophic and competitive interactions. Spatial patterns of foraging support theoretical predictions, with total prey consumption by mammals increasing by a factor of 80 from the equator to the poles after controlling for productivity.

Human milk (HM) is rich in bioactive factors promoting postnatal small intestinal development and maturation of the microbiome. HM is also protective against necrotizing enterocolitis (NEC), a devastating inflammatory condition predominantly affecting preterm infants. The HM glycosaminoglycan, hyaluronan (HA), is present at high levels in colostrum and early milk. Our group has demonstrated that HA with a molecular weight of 35 kDa (HA35) promotes maturation of the murine neonatal intestine and protects against two distinct models of NEC. However, the molecular mechanisms underpinning HA35-induced changes in the developing ileum are unclear. CD-1 mouse pups were treated with HA35 or vehicle control daily, from P7 to P14, and we used network and functional analyses of bulk RNA-seq ileal transcriptomes to further characterize molecular mechanisms through which HA35 likely influences intestinal maturation. HA35-treated pups separated well by principal component analysis, and cell deconvolution revealed increases in stromal, Paneth, and mature enterocyte and progenitor cells in HA35-treated pups. Gene set enrichment and pathway analyses demonstrated upregulation in key processes related to antioxidant and growth pathways, such as nuclear factor erythroid 2-related factor-mediated oxidative stress response, hypoxia inducible factor-1 alpha, mechanistic target of rapamycin, and downregulation of apoptotic signaling. Collectively, pro-growth and differentiation signals induced by HA35 may present novel mechanisms by which this HM bioactive factor may protect against NEC.

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth’s biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth’s biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration. This Perspective discusses how the latest advances in remote sensing can be used to answer basic ecological and evolutionary questions, as well as contribute to important biodiversity monitoring.

Chatbots powered by artificial intelligence, such as ChatGPT, are ready to speed up monotonous coding tasks and teach you new skills. We highlight, with worked examples, some advantages and limitations of using generative artificial intelligence for scientific coding and argue that if you are willing to debug, you can get a head start on more challenging tasks.

Freshwater ecosystems harbor specialized and vulnerable biodiversity, and the prediction of potential impacts of freshwater biodiversity to environmental change requires knowledge of the geographic and environmental distribution of taxa. To date, however, such quantitative information about freshwater species distributions remains limited. Major impediments include heterogeneity in available species occurrence data, varying detectability of species in their aquatic environment, scarcity of contiguous freshwater-specific predictors, and methods that support addressing these issues in a single framework. Here we demonstrate the use of a hierarchical Bayesian modeling (HBM) framework that combines disparate species occurrence information with newly-developed 1 km freshwater-specific predictors, to account for imperfect species detection and make fine-grain (1 km) estimates of distributions in freshwater organisms. The approach integrates a Bernoulli suitability and a Binomial observability process into a hierarchical zero-inflated Binomial model. The suitability process includes point presence observations, records of site visits, 1 km environmental predictors and expert-derived species range maps integrated with a distance-decay function along the within-stream distance as covariates. The observability process uses repeated observations to estimate a probability of observation given that the species was present. The HBM accounts for the spatial autocorrelation in species habitat suitability projections using an intrinsic Gaussian conditional autoregressive model. We used this framework for three fish species native to different regions and habitats in North America. Model comparison shows that HBMs significantly outperformed non-spatial GLMs in terms of AUC and TSS scores, and that expert information when appropriately included in the model can provide an important refinement. Such ancillary species information and an integrative, hierarchical Bayesian modeling framework can therefore be used to advance fine-grain habitat suitability predictions and range size estimates in the freshwater realm. Our approach is extendable in terms of data availability and generality and can be used on other freshwater organisms and regions.