Impending anthropogenic threats and protected area prioritization for jaguars in the Brazilian Amazon

Jaguars (Panthera onca) exert critical top-down control over large vertebrates across the Neotropics. Yet, this iconic species have been declining due to multiple threats, such as habitat loss and hunting, which are rapidly increasing across the New World tropics. Based on geospatial layers, we extracted socio-environmental variables for 447 protected areas across the Brazilian Amazon to identify those that merit short-term high-priority efforts to maximize jaguar persistence. Data were analyzed using descriptive statistics and comparisons of measures of central tendency. Our results reveal that areas containing the largest jaguar densities and the largest estimated population sizes are precisely among those confronting most anthropogenic threats. Jaguars are threatened in the world’s largest tropical forest biome by deforestation associated with anthropogenic fires, and the subsequent establishment of pastures. By contrasting the highest threats with the highest jaguar population sizes in a bivariate plot, we provide a shortlist of the top-10 protected areas that should be prioritized for immediate jaguar conservation efforts and 74 for short-term action. Many of these are located at the deforestation frontier or in important boundaries with neighboring countries (e.g., Peruvian, Colombian and Venezuelan Amazon). The predicament of a safe future for jaguars can only be ensured if protected areas persist and resist downgrading and downsizing due to both external anthropogenic threats and geopolitical pressures (e.g., infrastructure development and frail law enforcement)

Nucleases as a barrier to gene silencing in the cotton boll weevil, Anthonomus grandis.

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Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.Peer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rotifera das zonas limnética e litorânea do reservatório de Tapacurá, Pernambuco, Brasil Rotifera from littoral and pelagic zones of Tapacurá reservoir, Pernambuco, Brazil

Os rotíferos do reservatório de Tapacurá, Pernambuco, Brasil foram estudados quanto à distribuição horizontal nas zonas limnética e litorânea, nos períodos chuvoso (agosto de 2003) e seco (janeiro de 2004). Amostras quali-quantitativas foram obtidas através de coletas em ritmo nictimeral nas distintas zonas do reservatório, com intervalo de seis horas, em duas profundidades. Os parâmetros bióticos riqueza, densidade, diversidade e equitabilidade foram avaliados. Análises de similaridade e variância (ANOVA) também foram utilizadas. Vinte e oito espécies e duas subespécies de Rotifera foram encontradas, das quais cinco espécies são novas ocorrências para Pernambuco. A zona litorânea apresentou maior riqueza que a limnética em ambos ao período sazonais, com nove espécies exclusivas. As diferenças de densidade e equitabilidade entre as zonas do reservatório não foram significativas, ao contrário da diversidade, que apresentou-se mais elevada na zona litorânea no período seco (p<0,01). A análise de similaridade revelou homogeneidade horizontal da comunidade de Rotifera no período chuvoso, caracterizado por menores temperaturas e maior velocidade do vento, e tendência à estratificação horizontal no período seco, caracterizado por temperaturas mais elevadas e menor intensidade dos ventos. A presença de macrófitas propiciou características peculiares na zona litorânea.<br>The rotifers of Tapacurá reservoir, State of Pernambuco, Brazil, were studied concerning the horizontal distribution in pelagic and littoral zones, in the rainy (August/2003) and dry seasons (January/2004). Qualitative and quantitative samples were carried out in nictimeral samplings in the different zones of the reservoir, with an interval of six hours in the two depths. Biotic parameters as richness, density, diversity and equitability were estimated. Similarity and Analysis of Variance (ANOVA) were also used. Twenty-eight species and two subspecies of Rotifer were found from which five species are considered new occurrences in State of Pernambuco. The littoral zone presented higher richness than the limnetic zone in both seasonal periods with nine exclusive species. The density and equitability differences between zones of the reservoir were not expressive in opposite to diversity that was higher in the littoral zone in the dry season (p<0.01). The similarity analysis indicated horizontal homogeneity of Rotifer community in the rainy season characterized by lower temperatures and higher wind velocity, and a tendency to horizontal stratification in the dry season characterized by higher temperatures and lower wind intensity. Presence of aquatic vegetation yielded peculiar features in the littoral zone