Shedding light on the Holocephali taxonomy, the mitogenome of Chimaera opalescens

Cartilaginous fish (Chondrichthyes), i.e. sharks, rays, and chimaeras, are extremely interesting from a biological perspective as they represent one of the oldest and most ecologically diverse groups of jawed vertebrates. Their K-selective reproductive traits, make them vulnerable to overfishing. Chimaerid are also a frequent by-catch of deep-water fisheries. Holocephalans comprise a single surviving order, the Chimaeriformes, and are allocated into three different families: Callorhinchidae, Rhinochimaeridae and Chimaeridae (Weigmann 2016). Furthermore, the family Chimaeridae only includes two genera: Chimera and Hydrolagus. Recently, several new species have been described (Iglesias et al., 2022), including Chimera opalescens from deep-sea assemblages (Luchetti et al., 2011). However, previous records of C. opalescens were erroneously classified as Chimera monstrosa (Luchetti et al., 2011; Catarino et al., 2020), due to the similar morphology (Luchetti et al., 2011), which highlights the critical importance of molecular approaches to support species identification. Mitogenomes have been a powerful tool used to elucidate phylogenetic relationships, both at deep and at shallow evolutionary nodes. The development of long-read sequencing technologies a precise and reliable assembly of complete mtDNA genomes. The sequencing and characterization of the complete mitogenome of the opal chimera Chimera opalescens (Luchetti, Iglesias et al., 2011) was carried out, using the long-read technique PacBio HiFi. The entire mitogenome was 23,411 bp long and shows the same overall content, i.e. 13 protein-coding genes, 22 transfer RNA and 2 ribosomal RNA genes, as all other examined chondrichthyan mitogenomes. Phylogenetic reconstructions using all available Chondrichthyan mitogenomes, including 11 Holocephali (chimeras and ratfishes), places C. opalescens within the Chimaeridae family. Furthermore, the results reinforce previous findings, showing the genus Chimera as paraphyletic and thus highlighting the need to expand molecular approaches in this group of cartilaginous fishes

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

Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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