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Global suicide mortality rates (2000-2019) and bibliographic data
<p>The dataset contains World Bank Suicide mortality rate WDI (world development indicator) (2000-2019) world-wide data in original and processed form. In addition to the statistical data this dataset also contains bibliographic records of articles published on the topic of suicide in relation to individual countries during (2000-2019) in original and processed form. </p> <p>The data consists of six archives:</p> <ol> <li>World development indicator suicide mortality rate SH.STA.SUIC.P5. This archive contains suicide mortality rate of 159 countries during the period of 2000-2019 per 100,000 population including males and females as of November, 2023.</li> <li>Web of science records country and suicide. This archive contains bibliographic records organized by country on the topic of suicide related to that country published during 2000-2019 as of November, 2023.</li> <li>Suicide mortality rate statistics and keywords. This archive contains processed data of 1 and 2 archives in three files. The 'Countries suicide rates and WOS records' contains organized temporal suicide mortality rate data for each country and each year for males and females including counts of articles on suicide related in that country. The 'words and countries matrix' file contains information about how many times author and paper keywords from suicide related publications were seen in articles associated with each country. This data is organized as matrix in which rows are keywords, columns are countries and cells are counts of the keyword. The 'words and countries pairs' file contains same information only organized as keyword country pairs.</li> <li>Suicide mortality rate clusters countries keywords titles. This archive contains bibliographic data organized by country clusters. These clusters group countries with similar suicide mortality rate dynamics in males and females shown in two included figures. Each folder of the cluster contains a section with bibliographic records; a section with keywords associated with each country; and a section in which each publication associated with the country has a separate filecontaining its title and keywords.</li> <li>Suicide keywords embedding data. This archive contains word embedding vectors and metadata learned by recurrent neural network trained to classify countries from suicide related keywords of articles associated with those countries. Folder 'trained with keywords' contains embeddings learned in classifying countries in which training samples are keyword strings of publications. Folder 'trained with titles' contains embeddings learned in classifying countries in which training samples are strings containing titles of publication plus keywords.</li> <li>Suicide keywords association rule mining. This archive contains files of subsets of keywords frequently mentioned together in suicide related publications. Folder 'Mining in clusters' has frequent keyword itemsets in country clusters. Folder 'Mining in individual countries' has frequent keyword itemsets in countries. Examples of keyword networks connecting clusters and networks connecting countries in individual clusters are included which helps to identify specific and shared keywords by country clusters and by countries in the individual clusters. </li> </ol> <p>These datasets support a data availability statements for upcoming articles.</p>
Mass mortality among colony-breeding seabirds in the German Wadden Sea in 2022 due to distinct genotypes of HPAIV H5N1 clade 2.3.4.4b: data sets on phylogeographic analyses
<p>Highly pathogenic avian influenza viruses (HPAIV) of clade 2.3.4.4b of the H5 goose/Guangdong (gs/GD) lineage have repeatedly emerged in Germany since 2016. Both poultry holdings and wild birds have been heavily hit but the 2020-2021 and 2021-2022 HPAI winter seasons exceeded all previously recorded epizootics in Germany in terms of number of wild bird cases recorded, genetic diversity of viruses, and duration of virus activity. In past seasons regional massing of wild bird cases were seen at the German coasts of the Baltic and North Sea, but species mainly affected varied from season to season. In 2022 a new and, in Europe, unprecedented aspect was observed when several cormorant and seabird breeding colonies became affected since May at the Baltic Sea coast and in the Wadden Sea, respectively by HPAI H5N1 viruses.</p> <p> </p>
The Effect of Prescription Drug Monitoring Programs (PDMPs) and Overdose Reversal Drug Accessibility Laws on Opioid Analgesic Mortality in the United States
<p><strong>Purpose: </strong>This analysis focuses on the impact of Prescription Drug Monitoring Programs (PDMPs) and increased layperson access to the overdose reversal drug Naloxone on prescription opioid mortality rates. The prescription opioid mortality rate was analyzed against state laws governing PDMPs and Naloxone accessibility to laypersons to evaluate if there was a correlation between mortality reduction and implementation of the laws.</p> <p>Three main analyses were conducted:</p> <ol> <li>Does a state’s opioid mortality reduction correlate to an effective PDMP and/or Naloxone law?</li> <li>Do states with strong PDMP laws have a corresponding opioid overdose mortality rate reduction?</li> <li>Do states with strong Naloxone accessibility laws have a corresponding opioid overdose mortality rate reduction?</li> </ol> <p><strong>Conclusion:</strong> Patterns show that PDMP and Naloxone accessibility can be successful in reducing mortality rates, but implementations and results vary dramatically between states. Further changes to both PDMPs and Naloxone accessibility are needed for states to see reliable and consistent mortality reductions. Changes to regulations need time to implement and take effect, meaning longer term measurements and data will be required to see if positive impacts can be sustained.</p> <ol> </ol> <p><strong>Data and Datasets:</strong></p> <p>There are three main datasets used in the analysis.</p> <ul> <li>Opioid mortality rate by state from 2006 to 2016. <ul> <li>This datasetis from the CDC website (<a href="https://www.cdc.gov/drugoverdose/data/statedeaths.html">https://www.cdc.gov/drugoverdose/data/statedeaths.html</a>). I used web scraping and regular expression search to extract the data and calculated mortality reduction of each state.</li> <li><em>Mortality reduction formula: (Highest mortality rate) – (2016 mortality rate)</em><br> </li> </ul> </li> <li>PDMP law implementation in each state and its timelinefrom January 1, 1998 to July 1, 2016. <ul> <li>Dataset is from the Prescription Drug Abuse Policy System(<a href="http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372">http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372</a>). This dataset encompasses laws regulating which professions have access to the database and for what purpose, whether practitioners can delegate their access, whether patients can see their own information, and the extent to which access to individually-identified records may be granted for law enforcement purposes.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing the PDMP.I performed the following data preparation to allow for analysis and visualization:</li> <li><em>PDMP law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul> <ul> <li>Naloxone accessibility to laypersons law implementation in each state and its timeline from January 1, 2001 to December 31, 2016. <ul> <li>Dataset is also from the Prescription Drug Abuse Policy System (<a href="http://pdaps.org./datasets/laws-regulating-administration-of-naloxone-1501695139">http://pdaps.org./datasets/laws-regulating-administration-of-Naloxone-1501695139</a>).This dataset focuses on state laws that provide civil or criminal immunity to licensed healthcare providers or lay responders for opioid antagonist administration.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing Naloxone accessibility to laypersons. I performed the following data preparation to allow for analysis and visualization:</li> <li><em>Naloxone accessibility law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul>
Child mortality dataset (from the UN Inter-agency Group for Child Mortality Estimation database). June 2019
<p>This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (<a href="https://childmortality.org/data">https://childmortality.org/data</a>, downloaded June 2019).</p> <p>It includes:</p> <p><strong>Reference area: </strong>name of the country</p> <p><strong>Indicator:</strong> child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)</p> <p><strong>Sex: </strong>sex of the child (male, female and total)</p> <p><strong>Series name:</strong> name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]</p> <p><strong>Series year: </strong>year of survey/census/VR series</p> <p><strong>Observation value: </strong>value of indicator from survey/census/VR</p> <p><strong>Observation status:</strong> indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]</p> <p><strong>Series Category:</strong> category of survey/census/VR, and can be:</p> <ul> <li>DHS [Demographic and Health Survey]</li> <li>MIS [Malaria Indicator Survey]</li> <li>AIS [AIDS Indicator Survey]</li> <li>Interim DHS</li> <li>Special DHS</li> <li>NDHS [National DHS]</li> <li>WFS [World Fertility Survey]</li> <li>MICS [Multiple Indicator Cluster Survey]</li> <li>NMICS [National MICS]</li> <li>RHS [Reproductive Health Survey]</li> <li>PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]</li> <li>LSMS [Living Standard Measurement Survey]</li> <li>Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]</li> <li>Census</li> <li>VR [Vital Registration]</li> <li>SVR [Sample Vital Registration]</li> <li>Others [e.g. Life Tables]</li> </ul> <p><strong>Series type: </strong>the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)</p> <p><strong>Standard error: </strong>sampling standard error of the observation value</p> <p><strong>Series method: </strong>data collection method, and can be:</p> <ul> <li>Survey/census with Full Birth Histories</li> <li>Survey/census with Summary Birth Histories</li> <li>Survey/census with Household death</li> <li>Vital Registration</li> <li>Other</li> </ul> <p><strong>Lower and upper bound:</strong> the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).</p> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)</em></p>
Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population - Supporting Material
<p>Supporting material for the study:</p> <p><strong>"Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population."</strong></p> <p><strong>Authors:</strong></p> <p>Cathleen Petit-Cailleux1, Hendrik Davi1, François Lefèvre1, Joseph Garrigue<strong>2</strong>, Jean-André Magdalou<strong>2</strong>, Christophe Hurson<strong>2,3</strong><strong>, </strong>Elodie Magnanou<strong>2,4</strong>, and Sylvie Oddou-Muratorio1.</p> <p> </p> <p>Adresses</p> <p>1INRA, UR 629 Ecologie des Forêts Méditerranéennes, URFM, Avignon, France</p> <p><strong>2</strong>Réserve Naturelle Nationale de la Forêt de la Massane, France</p> <p><strong>3</strong>Fédération des Réserves Naturelles Catalanes, Prades, France</p> <p><strong>4</strong>Sorbonne Université, CNRS, Biologie Intégrative des Organismes Marins, BIOM, F-66650 Banyuls-sur-Mer, France</p> <p><strong>ORCID:</strong></p> <p>Cathleen Petit-Cailleux: <a href="https://orcid.org/0000-0001-7714-6583">https://orcid.org/0000-0001-7714-6583</a></p> <p>François Lefèvre : <a href="https://orcid.org/0000-0003-2242-7251">https://orcid.org/0000-0003-2242-7251</a></p> <p>Sylvie Oddou-Muratorio <a href="https://orcid.org/0000-0003-2374-8313">https://orcid.org/0000-0003-2374-8313</a></p> <p> </p> <p>-------------</p> <p>Raw data of the Table_Massane_moratlity_trees.csv and climate can be obtained from Joseph Garrigue, Jean-André Magdalou and Christophe Hurson.</p> <p>The inventories files and daily climate are the input dataset to run CASTANEA models.</p> <p>All details are provided in the article.</p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2021
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2021
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the fatal burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>More information</em></p> <p>For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>.</p> <p>Explore the estimates via <a href="https://burden.sciensano.be/shiny/mortality">https://burden.sciensano.be/shiny/mortality</a>.</p>
Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks
<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes' contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p> </p> <p>Abbreviation for column:</p> <p>X Longitude<br> Y Latitude<br> LAImax Leaf area index max reach<br> Nha Density per hectar<br> Vha Volume per hectar<br> NEE Net ecosystem exchange<br> NPP net primary production<br> Reco Respiration ecosystem<br> GPP Gross primary production<br> Etveg Evapotranspiration canopy<br> Etsol Evapotranspiration sol<br> TR tree transpiration<br> ETP evapotranspiration potentiel<br> BiomassOfReserves Biomass of reserve<br> rw ring width<br> dbh diameter at breast heast<br> height height<br> BBday Budburst date<br> rFD risk of frost<br> CRIM_max Maximum combined risk index of mortality reach<br> rNSC risk of carbon starvation<br> rPLC risk of embolism<br> rPLC_max Maximum risk of embolism reach<br> CRIM combined risk index of mortality<br> Climate Climatic model<br> rNSC_max maximum risk of carbon starvation reach<br> rFD_max Maximum risk of frost reach<br> Scenario_Sylvicol null means no silvulcture simulated<br> species species<br> Country Country<br> alt_watch altitude of climate simulated<br> grid_watch number of the pixel point of WATCH<br> grid_eurocordex number of the pixel point of Eurocordex<br> Pinus_sylvestris 0 abscence ; 1 presence<br> Fagus_sylvatica 0 abscence ; 1 presence<br> Quercus_petraea 0 abscence ; 1 presence<br> Picea_abies 0 abscence ; 1 presence<br> Pinus_pinaster 0 abscence ; 1 presence</p> <p> </p>
Coffee Consumption per Capita and Covid-19 Mortality Rate
<p>There is a correlation between average of "Coffee Consumption per capita" and average of "Covid-19 Mortality Rate" for countries with high coffee consumption per capita (the countries that has more than 5.4 kg per capita per year consumption).</p> <p>The Details of computations and data are provided in an attached supplementary file (Excel File Format).</p> <p>Data gathered on 10 Aug 2021</p> <p> </p>
Banana Per Capita Consumption and SARS-CoV-2 Mortality Rates
<p>Plant Lectins are natural Antiviral agents and Banana is rich in them. Bananas are also a rich source of magnesium. Magnesium has a positive and effective role in increasing the body's immunity and stimulating the production of antibodies in the body.</p> <p>R2=0.99</p>
Valuation of heat related mortality risk and tick-borne diseases
<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>
Improving Methods to Measure Comparable Mortality by Cause - Gold Standard Verbal Autopsy Data 2011-2014
<p>These data were collected and compiled as part of the Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) project, funded by Australia's National Health and Medical Research Council (NHMRC). Verbal autopsies (VAs) were conducted between 2011 and 2014 in three sites: Bohol, Philippines; Chandpur and Comila Districts, Bangladesh; and Central and Eastern Highlands Provinces, Papua New Guinea. Diagnostic criteria and cause lists similar to those employed in the Population Health Metrics Research Consortium (PHMRC) study were used to identify gold standard (GS) deaths. This study added 3512 deaths (2491 adults, 320 children, and 701 neonates) to the GS VA database created from the PHMRC study. This dataset contains the combined PHMRC and IMMCMC data for an updated GS VA database.</p>
BeBOD estimates of mortality and years of life lost, 2004-2019
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the fatal burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 130 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p>
BeBOD estimates of mortality and years of life lost for 131 causes of death, 2004-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the fatal burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>More information</em></p> <p>For additional background on BeBOD, please visit <a href="https://www.sciensano.be/en/projects/belgian-national-burden-disease-study">https://www.sciensano.be/en/projects/belgian-national-burden-disease-study</a>.</p> <p>Explore the estimates via <a href="https://burden.sciensano.be/shiny/mortality">https://burden.sciensano.be/shiny/mortality</a>.</p>
New effects of Roundup on amphibians: Predators reduce herbicide mortality while herbicides induce anti-predator morphology, 2006.
The use of pesticides is important for growing crops and protecting human health by reducing the prevalence of targeted pest species. However, less attention is given to the potential unintended effects on nontarget species, including taxonomic groups that are of current conservation concern. One issue raised in recent years is the potential for pesticides to become more lethal in the presence of predatory cues, a phenomenon observed thus far only in the laboratory. A second issue is whether pesticides can induce unintended trait changes in nontarget species, particularly trait changes that might mimic adaptive responses to natural environmental stressors. Using outdoor mesocosms, I created simple wetland communities containing leaf litter, algae, zooplankton, and three species of tadpoles (wood frogs [Rana sylvatica or Lithobates sylvaticus], leopard frogs [R. pipiens or L. pipiens], and American toads [Bufo americanus or Anaxyrus americanus]). I exposed the communities to a factorial combination of environmentally relevant herbicide concentrations (0, 1, 2, or 3 mg acid equivalents [a.e.]/L of Roundup Original MAX) crossed with three predator-cue treatments (no predators, adult newts [Notophthalmus viridescens], or larval dragonflies [Anax junius]). Without predator cues, mortality rates from Roundup were consistent with past studies. Combined with cues from the most risky predator (i.e., dragonflies), Roundup became less lethal (in direct contrast to past laboratory studies). This reduction in mortality was likely caused by the herbicide stratifying in the water column and predator cues scaring the tadpoles down to the benthos where herbicide concentrations were lower. Even more striking was the discovery that Roundup induced morphological changes in the tadpoles. In wood frog and leopard frog tadpoles, Roundup induced relatively deeper tails in the same direction and of the same magnitude as the adaptive changes induced by dragonfly cues. To my knowledge, this i
First-order vertebrated mortality due the 2020 wildfires in the Pantanal wetland, Brazil
We conducted ground surveys along line transects to estimate the first-order impact of the 2020 wildfires on vertebrates in the Pantanal wetlands, Brazil. We adopted the distance sampling technique (Burnham et al 1980) to estimate the densities and the number of dead vertebrates in the 39,030 square kilometers affected by fire. We covered 123 transects scattered in the floodplain, up to 72 hours after the fires, mostly within 24 or 48 hours. We recorded the perpendicular distance between each carcass found in the field and the line transect. The carcasses were identified at least at Order level, down to species level when possible. The surveys were conducted from August to November 2020.
Soil carbon cycling response to hemlock mortality at the Coweeta Hydrologic Laboratory
We studied the impacts of hemlock mortality from infestation by the hemlock woolly adlegid (HWA) on soil carbon cycling at the Coweeta Hydrologic Laboratory. The HWA was first found at Coweeta in 2003. In 2013 and 2014, we re-sampled plots established in an earlier study by Elliott and others. There were 12 20 x 20 m plots: 4 were control hardwood stands, 4 were untreated hemlock communities, and 4 were hemlock that were girdled. We measured soil C and N concentration, soil delta 13 C, exoemzyme activities, root biomass, soil respiration, forest floor mass, and fungal hyphal biomass.
Changes in water temperature in streams with progressive hemlock mortality at 8 Coweeta Hydrologic Laboratory study sites from 2004 to 2013
The purpose of this study was to document changes in water temperature in 8 streams in areas affected by hemlock mortaity. Beginning in August 2004, one temperature logger was submerged at the downstream end of each of 8 small stream sites. Temperature was recorded a minimum of every 4 h. This study was conducted at Coweeta Hydrologic Laboratory, Otto, North Carolina. Stream sites were located on 1st and 2nd order streams reaches affected by hemlock death. Six sites were located in areas that have not been logged since the area became National Forest in the late 1920s and where streams passed through or were adjacent to permanent vegetation plots in which trees were measured in 1934-35, 1969-73 and 1988-93 (Elliott and Swank, 2008). Also included are data from two sites on WS 7 (Big Hurricane Branch), which was logged in 1977.
MCR LTER: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality
Data are from an 18-month field experiment on the fore reef of Moorea, testing how different forms of nitrogen (nitrate vs. urea) impact coral bleaching and mortality during two mild thermal stress events in the Austral summers of 2016 and 2017. These data are associated with a manuscript currently in review at Ecosystems. Tentative mansucript title and author list are: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality Deron E. Burkepile, Andrew A. Shantz, Thomas C. Adam, Katrina S. Munsterman, Kelly E. Speare, Mark C. Ladd, Mallory M. Rice, Shelby McIlroy, Andrew J. Brooks, Russell J. Schmitt, and Sally J. Holbrook These data are part of the NSF project: RAPID: How does nutrient availability alter coral bleaching, mortality, and recovery on Moorea coral reefs? (funded wholly or part by NSF Awards OCE-1619697).
Data from: Differential impact of severe drought on infant mortality in two sympatric neotropical primates
<p>Extreme climate events can have important consequences for the dynamics of natural populations, and severe droughts are predicted to become more common and intense due to climate change. We analysed infant mortality in relation to drought in two primate species (white-faced capuchins, <i>Cebus capucinus imitator,</i> and Geoffroy's spider monkeys, <i>Ateles geoffroyi</i>) in a tropical dry forest in north-western Costa Rica. Our survival analyses combine several rare and valuable long-term data sets, including long-term primate life-history, landscape-scale fruit abundance, food-tree mortality, and climate conditions. Infant capuchins showed a threshold mortality response to drought, with exceptionally high mortality during a period of intense drought, but not during periods of moderate water shortage. In contrast, spider monkey females stopped reproducing during severe drought, and the mortality of infant spider monkeys peaked later during a period of low fruit abundance and high food-tree mortality linked to the drought. These divergent patterns implicate differing physiology, behaviour, or associated factors in shaping species-specific drought responses. Our findings link predictions about the Earth's changing climate to environmental influences on primate mortality risk and thereby improve our understanding of how the increasing severity and frequency of droughts will affect the dynamics and conservation of wild primates.</p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.