Wild meat consumption in tropical forests spares a significant carbon footprint from the livestock production sector

Whether sustainable or not, wild meat consumption is a reality for millions of tropical forest dwellers. Yet estimates of spared greenhouse gas (GHG) emissions from consuming wild meat, rather than protein from the livestock sector, have not been quantified. We show that a mean per capita wild meat consumption of 41.7 kg yr−1 for a population of ~ 150,000 residents at 49 Amazonian and Afrotropical forest sites can spare ~ 71 MtCO2-eq annually under a bovine beef substitution scenario, but only ~ 3 MtCO2-eq yr−1 if this demand is replaced by poultry. Wild meat offtake by these communities could generate US$3M or US$185K in carbon credit revenues under an optimistic scenario (full compliance with the Paris Agreement by 2030; based on a carbon price of US$50/tCO2-eq) and US$1M or US$77K under a conservative scenario (conservative carbon price of US$20.81/tCO2-eq), representing considerable incentives for forest conservation and potential revenues for local communities. However, the wild animal protein consumption of ~ 43% of all consumers in our sample was below the annual minimum per capita rate required to prevent human malnutrition. We argue that managing wild meat consumption can serve the interests of climate change mitigation efforts in REDD + accords through avoided GHG emissions from the livestock sector, but this requires wildlife management that can be defined as verifiably sustainable.

Quantifying and communicating the burden of COVID-19

Background An essential aspect of preventing further COVID-19 outbreaks and to learn for future pandemics is the evaluation of different political strategies, which aim at reducing transmission of and mortality due to COVID-19. One important aspect in this context is the comparison of attributable mortality. Methods We give a comprehensive overview of six epidemiological measures that are used to quantify COVID-19 attributable mortality (p-score, standardized mortality ratio, absolute number of excess deaths, per capita rate, z-score and the population attributable fraction). Results By defining the six measures based on observed and expected deaths, we explain their relationship. Moreover, three publicly available data examples serve to illustrate the interpretational strengths and weaknesses of the various measures. Finally, we give recommendation which measures are suitable for an evaluation of public health strategies against COVID-19. The R code to reproduce the results is available as online supplementary material. Conclusion The number of excess deaths should be always reported together with the population attributable fraction, the p-score or the standardized mortality ratio instead of a per capita rate. For a complete picture of COVID-19 attributable mortality, quantifying and communicating its relative burden also to a lay audience is of major importance.

Per capita COVID-19 Case Rates are Lower in U.S. Counties Voting more Heavily Democratic in the 2016 Presidential Election, except not in States with a Republican Governor and Legislature

In our recent paper Why do per capita COVID-19 Case Rates Differ Between U.S. States? we established that U.S. states with a Democratic governor and a Democratic legislature have lower COVID-19 per capita case rates than states with a Republican governor and a Republican legislature, and case rates of states with a mixed government fall between the two. This difference remained after accounting for differences between states in several demographic and socio-economic variables. In a recent working paper The Changing Political Geographies of COVID-19 in the U.S. it was found that that early in the pandemic U.S. counties at higher levels of percentage Democratic vote in the 2016 presidential election had higher weekly per capita COVID-19 rates, but that the situation was in the opposite direction by August 2020. We show here that counties with a higher percentage of Democratic vote in the 2016 presidential election have a lower mean cumulative per capita rate of COVID-19 cases and of COVID-19 deaths, adjusted for county demographic and socio-economic characteristics, but only for counties in states that currently have a Democratic governor and both chambers of the legislature Democratic or in states that have a mixed government, but not for states that currently have a Republican governor and both chambers in the legislature Republican. One possible contributor to this difference is that some state Republican governments have restricted local action to fight the spread of COVID-19.

Detecting regime shifts in communities using estimated rates of change

Regime shifts (periods of rapid change punctuating longer periods of lower variability) are observed in a wide range of ecosystems, and effective fisheries management requires the ability to detect these shifts. Detecting shifts is straightforward in single-species time series when transitions are detectable as periods of rapid change. However, shifts in complex and spatially structured communities may be harder to detect. We propose an approach to characterize community regime shifts, using nonparametric spatiotemporal regression models to estimate three indicators of community change (the among-species mean rate of change, mean per-capita rate of change, and standard deviation of per-capita rate of change). These indicators can detect shifts that result in either changes in abundance or composition. We applied our approach to a 37-year community biomass time series that spans the Newfoundland Shelf groundfish collapse. Our method detected a community shift earlier than alternative single-indicator breakpoint approaches and gave additional insight into the spatiotemporal pattern of the change, including detecting three separate periods of change and characterizing the first locations to show signs of recovery. The indicators applied in this study provide novel insights into Newfoundland groundfish dynamics and should be useful in the characterization of other abrupt ecological changes.

Effects of Detritus on the Mosquito Culex pipiens: Phragmites and Schedonorus (Festuca) Invasion Affect Population Performance

Species interactions that influence the performance of the exotic mosquito Culex pipiens can have important effects on the transmission risk of West Nile virus (WNV). Invasive plants that alter the vegetation communities of ephemeral ground pools may facilitate or resist the spread of C. pipiens (L.) by altering allochthonous inputs of detritus in those pools. To test this hypothesis, we combined field surveys of roadside stormwater ditches with a laboratory microcosm experiment to examine relationships between C. pipiens performance and water quality in systems containing detritus from invasive Phragmites australis (Cav.) Trin. Ex Steud., introduced Schedonorus arundinaceus (Schreb.) Dumort., or native Juncus effusus L. or Typha latifolia L. In ditches, C. pipiens abundance was unrelated to detritus species but female C. pipiens were significantly larger from ditches with S. arundinaceus and smaller with J. effusus. Larger and smaller C. pipiens were also produced in microcosms provisioned with S. arundinaceus and J. effusus, respectively, yet the per capita rate of population of change did not vary. Larger females from habitats with S. arundinaceus were likely caused by faster decay rates of S. arundinaceus and resultant increases in microbial food, but lower survival as a result of fouling and higher tannin-lignin concentrations resulted in little changes to overall population performance. Larger female mosquitoes have been shown to have greater potential for transmitting arboviruses. Our findings suggest that changed community-level interactions from plant invasions in urban ephemeral ground pools can affect the fitness of C. pipiens and possibly increase WNV risk.

“A Taste for Associations”

This chapter explores how white male associations defined a new postwar identity for Natchez men, many of whom established local Lost Cause traditions. Freemasonry grew rapidly in the late antebellum era, with Mississippi showing the highest per capita rate of membership in the nation. Large numbers of white mostly middle-class men in search of a place to bond with other defeated Confederate warriors joined numerous Masonic orders and began forging new identities for themselves. The militia tradition in Natchez with its emphasis on competitive sports was another means of defining white manhood after the war. The Adams Light Infantry offered members a venue to bond with other men, while also initiating the white community’s first attempts to create a collective memory of the war. In their efforts to dominate public space and create memorial traditions, Natchez militia members also resorted to violent attacks against blacks they perceived as challenging white supremacy.

Estimating virulence from relative survival

A pathogen’s virulence is a key parameter in the mathematical models on which most epidemiological theory is based. In these models virulence generally has a very specific definition where it is the increased per capita rate of mortality of infected hosts due to infection. Empirical studies involving the experimental infection of hosts often estimate virulence with the aim of comparing these estimates to values or patterns predicted in the theoretical literature. However most empirical studies do not estimate virulence as it is defined in the theoretical literature, thus potentially confounding comparisons between the two approaches. Here the analysis of relative survival is applied to the type of data routinely generated in empirical studies to estimate virulence as it is defined in the theoretical literature. The theoretical grounds for approach are outlined, followed by worked examples estimating the virulence of different pathogens with data from published studies. Code allowing virulence to be estimated by maximum likelihood with R is provided.

Truck and Multivehicle Truck Accidents with Injuries Near Colorado Oil and Gas Operations

Unconventional and conventional oil and gas (O&G) operations raise public health concerns, such as the potential impacts from trucking activity in communities that host these operations. In this work, we used two approaches to evaluate accidents in relation to O&G activities in the State of Colorado. First, we calculated the rate of truck accidents by computing the ratio of accident count and county population. When comparing counties with increased O&G operations to counties with less activity, we found that counties with more activity have greater rates of truck traffic accidents per capita (Rate Ratio = 1.07, p < 0.05, 95% CI: 1.01–1.13). Second, we laid a grid over the eleven counties of interest and counted, for each cell, the number of truck accidents, the number of multivehicle accidents with injuries, the number of homes, and the number of O&G wells. We then applied hurdle count models, using the accident counts as the outcomes and the number of homes and number of wells as independent variables. We found that both independent variables are significant predictors of truck accidents and multivehicle truck accidents. These accidents are of concern since they can have an impact on the people who live near O&G operations.

On the origin and timing of Zika virus introduction in Brazil

SummaryThe timing and origin of Zika virus (ZIKV) introduction in Brazil has been the subject of controversy. Initially, it was assumed that the virus was introduced during the FIFA World Cup in June–July 2014. Then, it was speculated that ZIKV may have been introduced by athletes from French Polynesia (FP) who competed in a canoe race in Rio de Janeiro in August 2014. We attempted to apply mathematical models to determine the most likely time window of ZIKV introduction in Brazil. Given that the timing and origin of ZIKV introduction in Brazil may be a politically sensitive issue, its determination (or the provision of a plausible hypothesis) may help to prevent undeserved blame. We used a simple mathematical model to estimate the force of infection and the corresponding individual probability of being infected with ZIKV in FP. Taking into account the air travel volume from FP to Brazil between October 2013 and March 2014, we estimated the expected number of infected travellers arriving at Brazilian airports during that period. During the period between December 2013 and February 2014, 51 individuals travelled from FP airports to 11 Brazilian cities. Basing on the calculated force of ZIKV infection (the per capita rate of new infections per time unit) and risk of infection (probability of at least one new infection), we estimated that 18 (95% CI 12–22) individuals who arrived in seven of the evaluated cities were infected. When basic ZIKV reproduction numbers greater than one were assumed in the seven evaluated cities, ZIKV could have been introduced in any one of the cities. Based on the force of infection in FP, basic reproduction ZIKV number in selected Brazilian cities, and estimated travel volume, we concluded that ZIKV was most likely introduced and established in Brazil by infected travellers arriving from FP in the period between October 2013 and March 2014, which was prior to the two aforementioned sporting events.