Dataset Information

INaturalist insights illuminate COVID-19 effects on large mammals in urban centers.

ABSTRACT: Restricted human activity during the COVID-19 pandemic raised global attention to the presence of wildlife in cities. Here, we analyzed iNaturalist observations of prominent wildlife species around North-American urban centers, before and during the COVID-19 pandemic outbreak. We suggest that the popular notion of 'wildlife reclaiming cities' may have been exaggerated. We found that while pumas ventured deeper into urban habitats during the COVID-19 pandemic, bears, bobcats, coyotes, and moose did not. Species differential behavioral responses may highlight their evolutionary history cohabiting human habitats. Nevertheless, our results highlight the importance of urban nature for people during the pandemic. Our insights could help manage urban wildlife, better plan greenspaces, and promote positive nature engagements.

SUBMITTER: Vardi R

PROVIDER: S-EPMC7784545 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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iNaturalist insights illuminate COVID-19 effects on large mammals in urban centers.

Vardi Reut R Berger-Tal Oded O Roll Uri U

Biological Conservation. 20210105

Restricted human activity during the COVID-19 pandemic raised global attention to the presence of wildlife in cities. Here, we analyzed iNaturalist observations of prominent wildlife species around North-American urban centers, before and during the COVID-19 pandemic outbreak. We suggest that the popular notion of 'wildlife reclaiming cities' may have been exaggerated. We found that while pumas ventured deeper into urban habitats during the COVID-19 pandemic, bears, bobcats, coyotes, and moose d ...[more]

PMID: 33424027

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Dataset Information

INaturalist insights illuminate COVID-19 effects on large mammals in urban centers.

Publications

iNaturalist insights illuminate COVID-19 effects on large mammals in urban centers.

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