Large-scale monitoring schemes are essential in assessing global mammalian biodiversity, and in this framework, leeches have recently been promoted as an indirect source of DNA from terrestrial mammal species. Carrion feeding flies are ubiquitous and can be expected to feed on many vertebrate carcasses. Hence, we tested whether fly-derived DNA analysis may also serve as a novel tool for mammalian diversity surveys. We screened DNA extracted from 201 carrion flies collected in tropical habitats of Côte d'Ivoire and Madagascar for mammal DNA using multiple PCR systems and retrieved DNA sequences from a diverse set of species (22 in Côte d'Ivoire, four in Madagascar) exploiting distinct forest strata and displaying a broad range of body sizes. Deep sequencing of amplicons generated from pools of flies performed equally well as individual sequencing approaches. We conclude that the analysis of fly-derived DNA can be implemented in a very rapid and cost-effective manner and will give a relatively unbiased picture of local mammal diversity. Carrion flies therefore represent an extraordinary and thus far unexploited resource of mammal DNA, which will probably prove useful for future inventories of wild mammal communities.

We identified a novel human polyomavirus from a kidney transplant patient under immunosuppressive treatment, by use of a generic PCR. The genome of the virus was completely amplified and sequenced. In phylogenetic analyses, it appeared as the closest relative to the African green monkey-derived lymphotropic polyomavirus (LPV). Further investigation of clinical samples from immunocompromised patients with specific nested PCR revealed additional positive samples, indicating that the virus naturally infects humans. The virus was tentatively named human polyomavirus 9 (HPyV9). The previously observed seroreactivity to LPV in human populations might find a partial explanation in the circulation of HPyV9.

The severe Ebola virus disease epidemic occurring in West Africa stems from a single zoonotic transmission event to a 2-year-old boy in Meliandou, Guinea. We investigated the zoonotic origins of the epidemic using wildlife surveys, interviews, and molecular analyses of bat and environmental samples. We found no evidence for a concurrent outbreak in larger wildlife. Exposure to fruit bats is common in the region, but the index case may have been infected by playing in a hollow tree housing a colony of insectivorous free-tailed bats (Mops condylurus). Bats in this family have previously been discussed as potential sources for Ebola virus outbreaks, and experimental data have shown that this species can survive experimental infection. These analyses expand the range of possible Ebola virus sources to include insectivorous bats and reiterate the importance of broader sampling efforts for understanding Ebola virus ecology.

Anthrax is a disease of wildlife, livestock and humans predominantly affecting low and 68 middle-income countries 2,4,5 . , suggesting a broad sub-Saharan distribution (Fig. 1) Table S1). 106In TNP we detected Bcbva DNA in 81 carcasses (40%; Fig ( Fig. 2A). We determined Bcbva prevalence within and outside the anthrax invariably led to a clearly reduced survival probability of communities (Fig. 4). 199For example, 76/84 models resulted in extirpation probability higher than 50%, while the 200 model which we consider the most realistic (community size 60, maximum age 46 years 201 and inter birth interval 6 years) resulted in an extirpation probability of 89% (Fig. 4). Our human-borne infectious diseases continues to rise 23,26,27 . 206

Preface 54There is much interest in using Earth Observation (EO) technology to track biodiversity, 55 ecosystem functions, and ecosystem services, understandable given the fast pace of 56 biodiversity loss. However, because most biodiversity is invisible to EO, EO-based 57 indicators could be misleading, which can reduce the effectiveness of nature 58 conservation and even unintentionally decrease conservation effort. We describe an 59 approach that combines automated recording devices, high-throughput DNA Meeting the Aichi Biodiversity Targets 64From Google Earth to airborne sensors, the Copernicus Sentinels, and cube satellites, 65Earth Observation is undergoing a rapid expansion in capacity, accessibility, resolution, 66and signal-to-noise ratio, resulting in a recognised shift in our capability for using 67 remote-sensing technologies to monitor biophysical processes on land and water [1][2][3] . 68These advances are motivating calls to use Earth Observation products to manage our 69 natural environment and to track progress toward global and national policy targets on 70 biodiversity and ecosystem services [4][5][6] . Foremost among these policies are the Strategic 71Plan for Biodiversity and the Aichi Biodiversity Targets, which were adopted in 2010 by products (net primary productivity and fire incidence) that could serve as Essential 108Biodiversity Variables for the Sahara, despite this biome's suitability for remote sensing 109 due to its visible biodiversity hotspots, remoteness, and availability of long time series. 110Many of the Aichi Targets require data with species-level resolution, either because some 111 species are direct policy targets (e.g. Target 9: "invasive species controlled or eradicated") 112 or because species compositional data define the metric (e.g. Target 11: "protected areas 113 are ecologically representative and conserved effectively"). species, but information could be 'borrowed' from data-rich species to increase the 294 precision of predictions for rare species. These procedures were able to compensate for 295 the fact that only 134 total bird species had been detected in the survey, which is less The GDM was parameterised with a training dataset of 2280 surveys and fourteen 303 environmental variables and explained 57% of the variation in beta diversity. In addition, for linking pure-EO data to biodiversity. 382The major remaining components of uncertainty relate to generalisability, because only a 383 single FSC-certified reserve was sampled; the applicability of results to arboreal species, 384 which tend to be detected more frequently in forests with disturbed canopy but are not 385 necessarily more widespread in these forests; and wide confidence intervals around 386 parameter estimates for some species as a consequence of sparse data and a fairly 394Another example of the CEOBE approach is the use of Generalised Dissimilarity 395Modelling to connect EO-derived metrics of habitat degradation and fragmentation 89,90 396 to over 300 million records of more ...