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The Andean mountains of South America are the most species-rich biodiversity hotspot worldwide with c. 15% of the world's plant species, in only 1% of the world's land surface. Orchids are a key element of the Andean flora, and one of the most prominent components of the Neotropical epiphyte diversity, yet very little is known about their origin and diversification. We address this knowledge gap by inferring the biogeographical history and diversification dynamics of the two largest Neotropical orchid groups (Cymbidieae and Pleurothallidinae), using two unparalleled, densely sampled orchid phylogenies (including more than 400 newly generated DNA sequences), comparative phylogenetic methods, geological and biological datasets. We find that the majority of Andean orchid lineages only originated in the last 20-15 million yr. Andean lineages are derived from lowland Amazonian ancestors, with additional contributions from Central America and the Antilles. Species diversification is correlated with Andean orogeny, and multiple migrations and recolonizations across the Andes indicate that mountains do not constrain orchid dispersal over long timescales. Our study sheds new light on the timing and geography of a major Neotropical diversification, and suggests that mountain uplift promotes species diversification across all elevational zones.

Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique “emerging hotspot analysis tool” in South Asia. Daily MODIS active fire locations data of 15 years (2003–2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15 years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.

Land use and related pressures have reduced local terrestrial biodiversity, but it is unclear how the magnitude of change relates to the recently proposed planetary boundary (\"safe limit\"). We estimate that land use and related pressures have already reduced local biodiversity intactness–the average proportion of natural biodiversity remaining in local ecosystems–beyond its recently proposed planetary boundary across 58.1% of the world's land surface, where 71.4% of the human population live. Biodiversity intactness within most biomes (especially grassland biomes), most biodiversity hotspots, and even some wilderness areas is inferred to be beyond the boundary. Such widespread transgression of safe limits suggests that biodiversity loss, if unchecked, will undermine efforts toward long-term sustainable development.

A common hypothesis for the rich biodiversity found in mountains is uplift-driven diversification—that orogeny creates conditions favoring rapid in situ speciation of resident lineages. We tested this hypothesis in the context of the Qinghai–Tibetan Plateau (QTP) and adjoining mountain ranges, using the phylogenetic and geographic histories of multiple groups of plants to infer the tempo (rate) and mode (colonization versus in situ diversification) of biotic assembly through time and across regions. We focused on the Hengduan Mountains region, which in comparison with the QTP and Himalayas was uplifted more recently (since the late Miocene) and is smaller in area and richer in species. Time-calibrated phylogenetic analyses show that about 8 million y ago the rate of in situ diversification increased in the Hengduan Mountains, significantly exceeding that in the geologically older QTP and Himalayas. By contrast, in the QTP and Himalayas during the same period the rate of in situ diversification remained relatively flat, with colonization dominating lineage accumulation. The Hengduan Mountains flora was thus assembled disproportionately by recent in situ diversification, temporally congruent with independent estimates of orogeny. This study shows quantitative evidence for uplift-driven diversification in this region, and more generally, tests the hypothesis by comparing the rate and mode of biotic assembly jointly across time and space. It thus complements the more prevalent method of examining endemic radiations individually and could be used as a template to augment such studies in other biodiversity hotspots.

Demand for food products, often from international trade, has brought agricultural land use into direct competition with biodiversity. Where these potential conflicts occur and which consumers are responsible is poorly understood. By combining conservation priority (CP) maps with agricultural trade data, we estimate current potential conservation risk hotspots driven by 197 countries across 48 agricultural products. Globally, a third of agricultural production occurs in sites of high CP (CP > 0.75, max = 1.0). While cattle, maize, rice, and soybean pose the greatest threat to very high-CP sites, other low-conservation risk products (e.g., sugar beet, pearl millet, and sunflower) currently are less likely to be grown in sites of agri-culture–conservation conflict. Our analysis suggests that a commodity can cause dissimilar conservation threats in different production regions. Accordingly, some of the conservation risks posed by different countries depend on their demand and sourcing patterns of agricultural commodities. Our spatial analyses identify potential hotspots of competition between agriculture and high-conservation value sites (i.e., 0.5° resolution, or ~367 to 3,077km2, grid cells containing both agriculture and high-biodiversity priority habitat), thereby providing additional information that could help prioritize conservation activities and safeguard biodiversity in individual countries and globally. A web-based GIS tool at https://agriculture.spatialfootprint.com/biodiversity/ systematically visualizes the results of our analyses.

Coastal ecosystems release or absorb carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), but the net effects of these ecosystems on the radiative balance remain unknown. We compiled a dataset of observations from 738 sites from studies published between 1975 and 2020 to quantify CO2, CH4 and N2O fluxes in estuaries and coastal vegetation in ten global regions. We show that the CO2-equivalent (CO2e) uptake by coastal vegetation is decreased by 23–27% due to estuarine CO2e outgassing, resulting in a global median net sink of 391 or 444 TgCO2e yr−1 using the 20- or 100-year global warming potentials, respectively. Globally, total coastal CH4 and N2O emissions decrease the coastal CO2 sink by 9–20%. Southeast Asia, North America and Africa are critical regional hotspots of GHG sinks. Understanding these hotspots can guide our efforts to strengthen coastal CO2 uptake while effectively reducing CH4 and N2O emissions.The authors show that estuarine and coastal vegetation are collectively a greenhouse gas (GHG) sink for the atmosphere, but methane and nitrous oxide emissions counteract the carbon dioxide uptake. Critical coastal GHG sink hotspots are identified in Southeast Asia, North America and Africa.

Since its emergence in the 1990s, White Spot Disease (WSD) has had major economic and societal impact in the crustacean aquaculture sector. Over the years shrimp farming alone has experienced billion dollar losses through WSD. The disease is caused by the White Spot Syndrome Virus (WSSV), a large dsDNA virus and the only member of the Nimaviridae family. Susceptibility to WSSV in a wide range of crustacean hosts makes it a major risk factor in the translocation of live animals and in commodity products. Currently there are no effective treatments for this disease. Understanding the molecular basis of disease processes has contributed significantly to the treatment of many human and animal pathogens, and with a similar aim considerable efforts have been directed towards understanding host–pathogen molecular interactions for WSD. Work on the molecular mechanisms of pathogenesis in aquatic crustaceans has been restricted by a lack of sequenced and annotated genomes for host species. Nevertheless, some of the key host–pathogen interactions have been established: between viral envelope proteins and host cell receptors at initiation of infection, involvement of various immune system pathways in response to WSSV, and the roles of various host and virus miRNAs in mitigation or progression of disease. Despite these advances, many fundamental knowledge gaps remain; for example, the roles of the majority of WSSV proteins are still unknown. In this review we assess current knowledge of how WSSV infects and replicates in its host, and critique strategies for WSD treatment.

We analyse the relationship between population influx and the effective reproduction number in the 23 wards of Tokyo during the COVID-19 pandemic to estimate hotspots of infection. We identify some patterns of population influx via factor analysis and estimate specific areas as infection-related hotspots by focusing on influx patterns that are highly correlated with the effective reproduction number. As a result, several influx patterns are assumed to be directly related to the subsequent spread of the infection. This analytical method has the potential to detect unknown hotspots related to pandemics in the future.