Gaps and weaknesses in the global protected area network for safeguarding at-risk species

Protected areas are essential to biodiversity conservation. Creating new parks can protect larger populations and more species, yet strengthening existing parks, particularly those vulnerable to harmful human activities, is a critical but underappreciated step for safeguarding at-risk species. Here, we model the area of habitat that terrestrial mammals, amphibians, and birds have within park networks and their vulnerability to current downgrading, downsizing, or degazettement events and future land-use change. We find that roughly 70% of species analyzed have scant representation in parks, or occur within parks that are affected by shifts in formal legal protections or are vulnerable to increased human pressures. Our results also show that expanding and strengthening park networks across just 1% of the world’s land area could preserve irreplaceable habitats of 1191 species that are particularly vulnerable to extinction

A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of ‘missingness’

Natural History of Patients with Ischemia and No Obstructive Coronary Artery Disease: The CIAO-ISCHEMIA Study

Background: Ischemia with no obstructive coronary artery disease (INOCA) is common and has an adverse prognosis. We set out to describe the natural history of symptoms and ischemia in INOCA. Methods: CIAO-ISCHEMIA (Changes in Ischemia and Angina over One year in ISCHEMIA trial screen failures with INOCA) was an international cohort study conducted from 2014-2019 involving angina assessments (Seattle Angina Questionnaire [SAQ]) and stress echocardiograms 1-year apart. This was an ancillary study that included patients with history of angina who were not randomized in the ISCHEMIA trial. Stress-induced wall motion abnormalities were determined by an echocardiographic core laboratory blinded to symptoms, coronary artery disease (CAD) status and test timing. Medical therapy was at the discretion of treating physicians. The primary outcome was the correlation between changes in SAQ Angina Frequency score and change in echocardiographic ischemia. We also analyzed predictors of 1-year changes in both angina and ischemia, and compared CIAO participants with ISCHEMIA participants with obstructive CAD who had stress echocardiography before enrollment, as CIAO participants did. Results: INOCA participants in CIAO were more often female (66% of 208 vs. 26% of 865 ISCHEMIA participants with obstructive CAD, p\u3c0.001), but the magnitude of ischemia was similar (median 4 ischemic segments [IQR 3-5] both groups). Ischemia and angina were not significantly correlated at enrollment in CIAO (p=0.46) or ISCHEMIA stress echocardiography participants (p=0.35). At 1 year, the stress echocardiogram was normal in half of CIAO participants and 23% had moderate or severe ischemia (≥3 ischemic segments). Angina improved in 43% and worsened in 14%. Change in ischemia over one year was not significantly correlated with change in angina (rho=0.029). Conclusions: Improvement in ischemia and improvement in angina were common in INOCA, but not correlated. Our INOCA cohort had a similar degree of inducible wall motion abnormalities to concurrently enrolled ISCHEMIA participants with obstructive CAD. Our results highlight the complex nature of INOCA pathophysiology and the multifactorial nature of angina

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

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Interleukin-6 Contributes to Inflammation and Remodeling in a Model of Adenosine Mediated Lung Injury

Chronic lung diseases are the third leading cause of death in the United States due in part to an incomplete understanding of pathways that govern the progressive tissue remodeling that occurs in these disorders. Adenosine is elevated in the lungs of animal models and humans with chronic lung disease where it promotes air-space destruction and fibrosis. Adenosine signaling increases the production of the pro-fibrotic cytokine interleukin-6 (IL-6). Based on these observations, we hypothesized that IL-6 signaling contributes to tissue destruction and remodeling in a model of chronic lung disease where adenosine levels are elevated.We tested this hypothesis by neutralizing or genetically removing IL-6 in adenosine deaminase (ADA)-deficient mice that develop adenosine dependent pulmonary inflammation and remodeling. Results demonstrated that both pharmacologic blockade and genetic removal of IL-6 attenuated pulmonary inflammation, remodeling and fibrosis in this model. The pursuit of mechanisms involved revealed adenosine and IL-6 dependent activation of STAT-3 in airway epithelial cells.These findings demonstrate that adenosine enhances IL-6 signaling pathways to promote aspects of chronic lung disease. This suggests that blocking IL-6 signaling during chronic stages of disease may provide benefit in halting remodeling processes such as fibrosis and air-space destruction

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications