Climate models predict increases in drought frequency and severity worldwide, with potential impacts on diverse systems, including African savannas. These droughts pose a concern for the conservation of savanna mammal communities, such that understanding how different species respond to drought is vital. Because grass decreases so consistently during droughts, we predict that grass‐dependent species (grazers and mixed feeders) will respond strongly to drought, whether by changing diets, seeking drought refugia, or suffering mortality. A recent severe but heterogeneous drought in Kruger National Park, South Africa, afforded a rare opportunity to test these hypotheses in situ—crucial, given the central role of landscape‐scale movement as a potential herbivore strategy. We used herbivore dung as a proxy, integrating spatial distributions (dung counts) with diet composition (carbon isotope analysis of dung). As predicted, browsers showed little response to drought. However, mixed feeders switched their diets to incorporate more C3 trees/forbs, but did not move. Meanwhile, grazers and megaherbivores instead moved toward drought refugia. Synthesis and applications: The responses we observed by savanna herbivores are largely amplifications of typical dry season strategies and reflect constraints imposed by body size and feeding ecology. Grazers may be at particular risk from increased drought frequency and spatial extent if drought refugia become decreasingly available. Conservation strategies should recognize these constraints and work to facilitate the diverse responses of herbivores to drought.

Public parks serve an important societal function as recreational spaces for diverse communities of people, with well documented physical and mental health benefits. As such, parks may be crucial for how people have handled effects of the COVID-19 pandemic, particularly the increasingly limited recreational opportunities, widespread financial uncertainty, and consequent heightened anxiety. Despite the documented benefits of parks, however, many states have instituted park shutdown orders due to fears that public parks could facilitate SARS-CoV-2 transmission. Here we use geotagged social media data from state, county, and local parks throughout New Jersey to examine whether park visitation increased when the COVID-19 pandemic began and whether park shutdown orders were effective at deterring park usage. We compare park usage during four discrete stages of spring 2020: (1) before the pandemic began, (2) during the beginning of the pandemic, (3) during the New Jersey governor’s state-wide park shutdown order, and (4) following the lifting of the shutdown. We find that park visitation increased by 63.4% with the onset of the pandemic. The subsequent park shutdown order caused visitation in closed parks to decline by 76.1% while parks that remained open continued to experience elevated visitation levels. Visitation then returned to elevated pre-shutdown levels when closed parks were allowed to reopen. Altogether, our results indicate that parks continue to provide crucial services to society, particularly in stressful times when opportunities for recreation are limited. Furthermore, our results suggest that policies targeting human behavior can be effective and are largely reversible. As such, we should continue to invest in public parks and to explore the role of parks in managing public health and psychological well-being.

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Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in coronavirus disease 2019 .We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Methods:We performed a single-center prospective study of 14 adult COVID-19(+) intensive care unit patients who were age-and sex-matched to 14 COVID-19(−) intensive care unit patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Baseline values for 10 biomarkers of interest were compared between the three groups, and visualized using Fisher's linear discriminant function. Linear repeated-measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts.Results: Elevated D-dimer was the strongest contributor in distinguishing COVID-19 status; however, D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death.Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients: low (30%) and high (100%) mortality groups. Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels. Conclusions: Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19.| 1547 JUNEJA Et Al.