Francis Petter

The distribution and transmission of Yersinia pestis , the bacterial agent of plague, responds dynamically to climate, both within wildlife reservoirs and human populations. The exact mechanisms mediating plague's response to climate are still poorly understood, particularly across large environmentally heterogeneous regions encompassing several reservoir species. A heterogeneous response to precipitation was observed in plague intensity across northern and southern China during the Third Pandemic. This has been attributed to the response of reservoir species in each region. We use environmental niche modelling and hindcasting methods to test the response of a broad range of reservoir species to precipitation. We find little support for the hypothesis that the response of reservoir species to precipitation mediated the impact of precipitation on plague intensity. We instead observed that precipitation variables were of limited importance in defining species niches and rarely showed the expected response to precipitation across northern and southern China. These findings do not suggest that precipitation–reservoir species dynamics never influence plague intensity but that instead, the response of reservoir species to precipitation across a single biome cannot be assumed and that limited numbers of reservoir species may have a disproportional impact upon plague intensity.

Abiotic factors are usually considered key drivers of species distribution at macro scales, while biotic interactions are mostly used at local scales. A few studies have explored the role of biotic interactions at macro scales, but all considered a limited number of species and obligate interactions. We examine the role of biotic interactions in large‐scale SDMs by testing two main hypotheses: (1) biotic factors in SDMs can have an important role at continental scale; (2) the inclusion of biotic factors in large‐scale SDMs is important also for generalist species. We used a maximum entropy algorithm to model the distribution of 177 bat species in Africa calibrating two SDMs for each species: one considering only abiotic variables (noBIO‐SDMs) and the other (BIO‐SDMs) including also biotic variables (trophic resource richness). We focused the interpretation of our results on variable importance and response curves. For each species, we also compared the potential distribution measuring the percentage of change between the two models in each pixel of the study area. All models gave AUC >0.7, with values on average higher in BIO‐SDMs compared to noBIO‐SDMs. Trophic resources showed an importance overall higher level than all abiotic predictors in most of the species (~68%), including generalist species. Response curves were highly interpretable in all models, confirming the ecological reliability of our models. Model comparison between the two models showed a change in potential distribution for more than 80% of the species, particularly in tropical forests and shrublands. Our results highlight the importance of considering biotic interactions in SDMs at macro scales. We demonstrated that a generic biotic proxy can be important for modeling species distribution when species‐specific data are not available, but we envision that a multi‐scale analysis combined with a better knowledge of the species might provide a better understanding of the role of biotic interactions.

Rodents, a diverse, globally distributed and ecologically important order of mammals are nevertheless important reservoirs of known and novel zoonotic pathogens. Ongoing anthropogenic land use change is altering these species’ abundance and distribution, which among zoonotic host species may increase the risk of zoonoses spillover events. A better understanding of the current distribution of rodent species is required to guide attempts to mitigate against potentially increased zoonotic disease hazard and risk. However, available species distribution and host-pathogen association datasets (e.g. IUCN, GBIF, CLOVER) are often taxonomically and spatially biased. Here, we synthesise data from West Africa from 127 rodent trapping studies, published between 1964–2022, as an additional source of information to characterise the range and presence of rodent species and identify the subgroup of species that are potential or known pathogen hosts. We identify that these rodent trapping studies, although biased towards human dominated landscapes across West Africa, can usefully complement current rodent species distribution datasets and we calculate the discrepancies between these datasets. For five regionally important zoonotic pathogens (Arenaviridae spp., Borrelia spp., Lassa mammarenavirus, Leptospira spp. and Toxoplasma gondii), we identify host-pathogen associations that have not been previously reported in host-association datasets. Finally, for these five pathogen groups, we find that the proportion of a rodent hosts range that have been sampled remains small with geographic clustering. A priority should be to sample rodent hosts across a greater geographic range to better characterise current and future risk of zoonotic spillover events. In the interim, studies of spatial pathogen risk informed by rodent distributions must incorporate a measure of the current sampling biases. The current synthesis of contextually rich rodent trapping data enriches available information from IUCN, GBIF and CLOVER which can support a more complete understanding of the hazard of zoonotic spillover events.

Population fluctuations are widespread across the animal kingdom, especially in the order Rodentia, which includes many globally important reservoir species for zoonotic pathogens. The implications of these fluctuations for zoonotic spillover remain poorly understood. Here, we report a global empirical analysis of data describing the linkages between habitat use, population fluctuations and zoonotic reservoir status in rodents. Our quantitative synthesis is based on data collated from papers and databases. We show that the magnitude of population fluctuations combined with species’ synanthropy and degree of human exploitation together distinguish most rodent reservoirs at a global scale, a result that was consistent across all pathogen types and pathogen transmission modes. Our spatial analyses identified hotspots of high transmission risk, including regions where reservoir species dominate the rodent community. Beyond rodents, these generalities inform our understanding of how natural and anthropogenic factors interact to increase the risk of zoonotic spillover in a rapidly changing world. Many rodent species are known as hosts of zoonotic pathogens, but the ecological conditions that trigger spillover are not well-understood. Here, the authors show that population fluctuations and association with human-dominated habitats explain the zoonotic reservoir status of rodents globally.

Understanding the status and abundance of species is essential for effective conservation decision-making. However, the availability of species data varies across space, taxonomic groups and data types. A case study was therefore conducted in a high biodiversity region—East Africa—to evaluate data biases, the factors influencing data availability, and the consequences for conservation. In each of the eleven target countries, priority animal species were identified as threatened species that are protected by national governments, international conventions or conservation NGOs. We assessed data gaps and biases in the IUCN Red List of Threatened Species, the Global Biodiversity Information Facility and the Living Planet Index. A survey of practitioners and decision makers was conducted to confirm and assess consequences of these biases on biodiversity conservation efforts. Our results showed data on species occurrence and population trends were available for a significantly higher proportion of vertebrates than invertebrates. We observed a geographical bias, with higher tourism income countries having more priority species and more species with data than lower tourism income countries. Conservationists surveyed felt that, of the 40 types of data investigated, those data that are most important to conservation projects are the most difficult to access. The main challenges to data accessibility are excessive expense, technological challenges, and a lack of resources to process and analyse data. With this information, practitioners and decision makers can prioritise how and where to fill gaps to improve data availability and use, and ensure biodiversity monitoring is improved and conservation impacts enhanced.

Emerging diseases caused by coronaviruses of likely bat origin (e.g., SARS, MERS, SADS, COVID-19) have disrupted global health and economies for two decades. Evidence suggests that some bat SARS-related coronaviruses (SARSr-CoVs) could infect people directly, and that their spillover is more frequent than previously recognized. Each zoonotic spillover of a novel virus represents an opportunity for evolutionary adaptation and further spread; therefore, quantifying the extent of this spillover may help target prevention programs. We derive current range distributions for known bat SARSr-CoV hosts and quantify their overlap with human populations. We then use probabilistic risk assessment and data on human-bat contact, human viral seroprevalence, and antibody duration to estimate that a median of 66,280 people (95% CI: 65,351–67,131) are infected with SARSr-CoVs annually in Southeast Asia. These data on the geography and scale of spillover can be used to target surveillance and prevention programs for potential future bat-CoV emergence. Coronaviruses may spill over from bats to humans. This study uses epidemiological data, species distribution models, and probabilistic risk assessment to map overlap among people and SARSr-CoV bat hosts and estimate how many people are infected with bat-origin SARSr-CoVs in Southeast Asia annually.

Detailed knowledge about biodiversity distribution is critical for monitoring the biological effects of global change processes. Biodiversity knowledge gaps hamper the monitoring of conservation trends and they are especially evident in the desert biome. Mauritania constitutes a remarkable example on how remoteness and regional insecurity affect current knowledge gaps. Mammals remain one of the least studied groups in this country, without a concerted species checklist, the mapping of regions concentrating mammal diversity, or a national assessment of their conservation status. This work assessed the diversity, distribution, and conservation of land mammals in Mauritania. A total of 6,718 published and original observations were assembled in a spatial database and used to update the occurrence status, distribution area, and conservation status. The updated taxonomic list comprises 107 species, including 93 extant, 12 Regionally Extinct, and 2 Extinct in the Wild. Mapping of species distributions allowed locating concentrations of extant mammal species richness in coastal areas, along the Senegal River valley, and in mountain plateaus. Recent regional extinction of large-sized Artiodactyla and Carnivora has been very high (11% extinct species). From the extant mammals, 11% are threatened, including flagship species (e.g., Addax nasomaculatus and Panthera pardus). Species richness is poorly represented by the current protected areas. Despite the strong advances made, 23% of species categorise as Data Deficient. Persisting systematics and distribution uncertainties require further research. Field surveys in currently unexplored areas (northern and south-eastern regions) are urgently needed to increase knowledge about threatened mammals. The long-term conservation of land mammals in Mauritania is embedded in a complex web of socioeconomic and environmental factors that call for collaborative action and investment in sustainable human development. The current work sets the baseline for the future development of detailed research studies and to address the general challenges faced by mammals and biodiversity in the country.

Orthohantaviruses, family Hantaviridae , are globally distributed except for Antarctica where they are absent. In animals, orthohantaviruses are transmitted horizontally, either directly through aggressive interactions and grooming or by inhaling infectious particles shed from urine, feces, or saliva in the environment. Humans become infected by inhaling aerosols of the virus-contaminated excretions of small mammals. Orthohantaviral infections in humans cause severe hantavirus pulmonary syndrome (HPS) in the North American Artic and hemorrhagic fever with renal syndrome (HFRS) in the Eurasian Arctic. In the Arctic, 16 rodent species (order Rodentia) and five shrew species (order Eulipotyphla) have been identified as reservoirs of orthohantaviruses by RNA detection. The two most important reservoir rodents in the Arctic are the bank vole ( Myodes glareolus ) in Eurasia carrying Puumala orthohantavirus (PUUV) and North American deermouse ( Peromyscus maniculatus ) in the North American Arctic carrying Sin Nombre orthohantavirus (SNV); both rodents being habitat generalists occurring in natural and human-modified habitats. Global warming, either independently or in combination with onshore exploitation of natural resources, is expected to increase the distribution range of reservoirs (including bank vole and North American deermouse, rats ( Rattus rattus and R. norvegicus ), house mouse ( Mus musculus ) and field mice ( Apodemus spp.)), and their associated orthohantaviruses. These changes pose the risk of introducing New World orthohantaviruses (e.g., Jemez Springs virus (JMSV) and SNV) to areas where so far only Old World orthohantaviruses (e.g., Hantaan orthohantavirus (HTNV) and PUUV) occur and vice versa. Climate change in the Arctic will likely also promote transmission and prevalence of orthohantaviruses in their reservoirs and hence increase zoonotic risk. The expected environmental changes call for increased surveillance and preparedness to mitigate potential outbreaks of orthohantavirus diseases in humans.

1. Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete “risk‐of‐bias” assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.

Abstract Rodent biogeographic studies are disproportionately scarce in Iran, however, they are an ideal system to understand drivers of biodiversity distributions in the country. The aims of the present research are to determine (i) the pattern of rodent richness across the country, (ii) quantify th…