A worldwide perspective on large carnivore attacks on humans.

Large carnivores have long fascinated human societies and have profound influences on ecosystems. However, their conservation represents one of the greatest challenges of our time, particularly where attacks on humans occur. Where human recreational and/or livelihood activities overlap with large carnivore ranges, conflicts can become particularly serious. Two different scenarios are responsible for such overlap: In some regions of the world, increasing human populations lead to extended encroachment into large carnivore ranges, which are subject to increasing contraction, fragmentation, and degradation. In other regions, human and large carnivore populations are expanding, thus exacerbating conflicts, especially in those areas where these species were extirpated and are now returning. We thus face the problem of learning how to live with species that can pose serious threats to humans. We collected a total of 5,440 large carnivore (Felidae, Canidae, and Ursidae; 12 species) attacks worldwide between 1950 and 2019. The number of reported attacks increased over time, especially in lower-income countries. Most attacks (68%) resulted in human injuries, whereas 32% were fatal. Although attack scenarios varied greatly within and among species, as well as in different areas of the world, factors triggering large carnivore attacks on humans largely depend on the socioeconomic context, with people being at risk mainly during recreational activities in high-income countries and during livelihood activities in low-income countries. The specific combination of local socioeconomic and ecological factors is thus a risky mix triggering large carnivore attacks on humans, whose circumstances and frequencies cannot only be ascribed to the animal species. This also implies that effective measures to reduce large carnivore attacks must also consider the diverse local ecological and social contexts.

Future threats to biodiversity and pathways to their prevention.

Tens of thousands of species are threatened with extinction as a result of human activities. Here we explore how the extinction risks of terrestrial mammals and birds might change in the next 50 years. Future population growth and economic development are forecasted to impose unprecedented levels of extinction risk on many more species worldwide, especially the large mammals of tropical Africa, Asia and South America. Yet these threats are not inevitable. Proactive international efforts to increase crop yields, minimize land clearing and habitat fragmentation, and protect natural lands could increase food security in developing nations and preserve much of Earth's remaining biodiversity.

Reconciling corruption with conservation triage: Should investments shift from the last best places?

Considerable outside funding will be required to overcome the financial shortfalls faced by most of Africa's protected areas. Given limited levels of external support, it will be essential to allocate these funds wisely. While most recent studies on conservation triage have recommended prioritizing reserves with the highest remaining conservation value (the "last best places"), such investments are complicated by the fact that these same reserves often attract the greatest revenues from ecotourism and thus the most attention from corrupt local governments. Alternatively, philanthropic organizations might achieve greater returns from investing in the management of neglected areas with lower current conservation value but with less financial leakage from corruption. We outline here how high levels of corruption could favor a strategy that shifts investments away from the last best places.

Group density, disease, and season shape territory size and overlap of social carnivores.

The spatial organization of a population can influence the spread of information, behaviour and pathogens. Group territory size and territory overlap and components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates and environmental risk (e.g. indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography. Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We further aim to explore the mechanisms by which disease perturbations could cause changes in spatial organization. Here we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We use network analysis to describe spatial organization and connectivity of social groups. We then examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models. We demonstrate that lions and wolves were similar in that group-level factors, such as number of groups and shaped spatial organization more than population-level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may have altered lion spatial organization, highlighting the importance of including infectious disease epizootics in studies of behavioural and movement ecology. We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the complex relationships between demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.

Cross-species transmission and evolutionary dynamics of canine distemper virus during a spillover in African lions of Serengeti National Park.

The outcome of pathogen spillover from a reservoir to a novel host population can range from a "dead-end" when there is no onward transmission in the recipient population, to epidemic spread and even establishment in new hosts. Understanding the evolutionary epidemiology of spillover events leading to discrete outcomes in novel hosts is key to predicting risk and can lead to a better understanding of the mechanisms of emergence. Here we use a Bayesian phylodynamic approach to examine cross-species transmission and evolutionary dynamics during a canine distemper virus (CDV) spillover event causing clinical disease and population decline in an African lion population (Panthera leo) in the Serengeti Ecological Region between 1993 and 1994. Using 21 near-complete viral genomes from four species we found that this large-scale outbreak was likely  ignited by a single cross-species spillover event from a canid reservoir to noncanid hosts <1 year before disease detection and explosive spread of CDV in lions. Cross-species transmission from other noncanid species probably fuelled the high prevalence of CDV across spatially structured lion prides. Multiple lines of evidence suggest that spotted hyenas (Crocuta crocuta) could have acted as the proximate source of CDV exposure in lions. We report 13 nucleotide substitutions segregating CDV strains found in canids and noncanids. Our results are consistent with the hypothesis that virus evolution played a role in CDV emergence in noncanid hosts following spillover during the outbreak, suggest that host barriers to clinical infection can limit outcomes of CDV spillover in novel host species.

Endemic infection can shape exposure to novel pathogens: Pathogen co-occurrence networks in the Serengeti lions.

Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Endemic pathogens that infect an individual in the first years of life, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating or ameliorating morbidity and mortality. Pathogen associations are ubiquitous but poorly understood, particularly in wild populations. We report here on 10 years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioural data to test if endemic pathogens shape subsequent infection by epidemic pathogens. We combine network and community ecology approaches to assess broad network structure and characterise associations between pathogens across spatial and temporal scales. We found significant non-random structure in the lion-pathogen co-occurrence network and identified both positive and negative associations between endemic and epidemic pathogens. Our results provide novel insights on the complex associations underlying pathogen co-occurrence networks.

The diversity of population responses to environmental change.

The current extinction and climate change crises pressure us to predict population dynamics with ever-greater accuracy. Although predictions rest on the well-advanced theory of age-structured populations, two key issues remain poorly explored. Specifically, how the age-dependency in demographic rates and the year-to-year interactions between survival and fecundity affect stochastic population growth rates. We use inference, simulations and mathematical derivations to explore how environmental perturbations determine population growth rates for populations with different age-specific demographic rates and when ages are reduced to stages. We find that stage- vs. age-based models can produce markedly divergent stochastic population growth rates. The differences are most pronounced when there are survival-fecundity-trade-offs, which reduce the variance in the population growth rate. Finally, the expected value and variance of the stochastic growth rates of populations with different age-specific demographic rates can diverge to the extent that, while some populations may thrive, others will inevitably go extinct.

Transmission ecology of canine parvovirus in a multi-host, multi-pathogen system.

Understanding multi-host pathogen maintenance and transmission dynamics is critical for disease control. However, transmission dynamics remain enigmatic largely because they are difficult to observe directly, particularly in wildlife. Here, we investigate the transmission dynamics of canine parvovirus (CPV) using state-space modelling of 20 years of CPV serology data from domestic dogs and African lions in the Serengeti ecosystem. We show that, although vaccination reduces the probability of infection in dogs, and despite indirect enhancement of population seropositivity as a result of vaccine shedding, the vaccination coverage achieved has been insufficient to prevent CPV from becoming widespread. CPV is maintained by the dog population and has become endemic with approximately 3.5-year cycles and prevalence reaching approximately 80%. While the estimated prevalence in lions is lower, peaks of infection consistently follow those in dogs. Dogs exposed to CPV are also more likely to become infected with a second multi-host pathogen, canine distemper virus. However, vaccination can weaken this coupling, raising questions about the value of monovalent versus polyvalent vaccines against these two pathogens. Our findings highlight the need to consider both pathogen- and host-level community interactions when seeking to understand the dynamics of multi-host pathogens and their implications for conservation, disease surveillance and control programmes.

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure.

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common. Here we provide a guide to the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus. Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years. When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.

Novel smacoviruses identified in the faeces of two wild felids: North American bobcat and African lion.

Smacoviruses are small circular single-stranded DNA viruses that appear to be prevalent in faeces of a range of animals and have also been found in a few insect species. In this study, we report the first viral genomes from faeces of free-roaming wild felids on two continents. Two smacoviruses were recovered from the faeces of two North American bobcats (Lynx rufus), and one was recovered from an African lion (Panthera leo). All three genomes are genetically different, sharing 59-69% genome-wide sequence identity to other smacoviruses. These are the first full smacovirus genome sequences associated with a large top-end feline predator, and their presence in these samples suggests that feline faeces are a natural niche for the organisms that these viruses infect.

Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas.

We compiled all credible repeated lion surveys and present time series data for 47 lion (Panthera leo) populations. We used a Bayesian state space model to estimate growth rate-λ for each population and summed these into three regional sets to provide conservation-relevant estimates of trends since 1990. We found a striking geographical pattern: African lion populations are declining everywhere, except in four southern countries (Botswana, Namibia, South Africa, and Zimbabwe). Population models indicate a 67% chance that lions in West and Central Africa decline by one-half, while estimating a 37% chance that lions in East Africa also decline by one-half over two decades. We recommend separate regional assessments of the lion in the World Conservation Union (IUCN) Red List of Threatened Species: already recognized as critically endangered in West Africa, our analysis supports listing as regionally endangered in Central and East Africa and least concern in southern Africa. Almost all lion populations that historically exceeded ∼ 500 individuals are declining, but lion conservation is successful in southern Africa, in part because of the proliferation of reintroduced lions in small, fenced, intensively managed, and funded reserves. If management budgets for wild lands cannot keep pace with mounting levels of threat, the species may rely increasingly on these southern African areas and may no longer be a flagship species of the once vast natural ecosystems across the rest of the continent.

Dynamics of a morbillivirus at the domestic-wildlife interface: Canine distemper virus in domestic dogs and lions.

Morbilliviruses cause many diseases of medical and veterinary importance, and although some (e.g., measles and rinderpest) have been controlled successfully, others, such as canine distemper virus (CDV), are a growing concern. A propensity for host-switching has resulted in CDV emergence in new species, including endangered wildlife, posing challenges for controlling disease in multispecies communities. CDV is typically associated with domestic dogs, but little is known about its maintenance and transmission in species-rich areas or about the potential role of domestic dog vaccination as a means of reducing disease threats to wildlife. We address these questions by analyzing a long-term serological dataset of CDV in lions and domestic dogs from Tanzania's Serengeti ecosystem. Using a Bayesian state-space model, we show that dynamics of CDV have changed considerably over the past three decades. Initially, peaks of CDV infection in dogs preceded those in lions, suggesting that spill-over from dogs was the main driver of infection in wildlife. However, despite dog-to-lion transmission dominating cross-species transmission models, infection peaks in lions became more frequent and asynchronous from those in dogs, suggesting that other wildlife species may play a role in a potentially complex maintenance community. Widespread mass vaccination of domestic dogs reduced the probability of infection in dogs and the size of outbreaks but did not prevent transmission to or peaks of infection in lions. This study demonstrates the complexity of CDV dynamics in natural ecosystems and the value of long-term, large-scale datasets for investigating transmission patterns and evaluating disease control strategies.

Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions.

Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. Here, we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype-specific transmission pathways in a social carnivore. We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle ) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father- and/or mother-offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype-specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks or co-infection best structured the FIVPle network. While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype-specific. Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.

Estimating wildlife disease dynamics in complex systems using an Approximate Bayesian Computation framework.

Emerging infectious diseases of wildlife are of increasing concern to managers and conservation policy makers, but are often difficult to study and predict due to the complexity of host-disease systems and a paucity of empirical data. We demonstrate the use of an Approximate Bayesian Computation statistical framework to reconstruct the disease dynamics of bovine tuberculosis in Kruger National Park's lion population, despite limited empirical data on the disease's effects in lions. The modeling results suggest that, while a large proportion of the lion population will become infected with bovine tuberculosis, lions are a spillover host and long disease latency is common. In the absence of future aggravating factors, bovine tuberculosis is projected to cause a lion population decline of ~3% over the next 50 years, with the population stabilizing at this new equilibrium. The Approximate Bayesian Computation framework is a new tool for wildlife managers. It allows emerging infectious diseases to be modeled in complex systems by incorporating disparate knowledge about host demographics, behavior, and heterogeneous disease transmission, while allowing inference of unknown system parameters.

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images.

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics-level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported "nothing here" for an image that was ultimately classified as containing an animal (fraction blank)-to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.

Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania.

The random encounter model (REM) is a novel method for estimating animal density from camera trap data without the need for individual recognition. It has never been used to estimate the density of large carnivore species, despite these being the focus of most camera trap studies worldwide. In this context, we applied the REM to estimate the density of female lions (Panthera leo) from camera traps implemented in Serengeti National Park, Tanzania, comparing estimates to reference values derived from pride census data. More specifically, we attempted to account for bias resulting from non-random camera placement at lion resting sites under isolated trees by comparing estimates derived from night versus day photographs, between dry and wet seasons, and between habitats that differ in their amount of tree cover. Overall, we recorded 169 and 163 independent photographic events of female lions from 7,608 and 12,137 camera trap days carried out in the dry season of 2010 and the wet season of 2011, respectively. Although all REM models considered over-estimated female lion density, models that considered only night-time events resulted in estimates that were much less biased relative to those based on all photographic events. We conclude that restricting REM estimation to periods and habitats in which animal movement is more likely to be random with respect to cameras can help reduce bias in estimates of density for female Serengeti lions. We highlight that accurate REM estimates will nonetheless be dependent on reliable measures of average speed of animal movement and camera detection zone dimensions. © 2015 The Authors. Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

Cheetahs and wild dogs show contrasting patterns of suppression by lions.

Top predators can dramatically suppress populations of smaller predators, with cascading effects throughout communities, and this pressure is often unquestioningly accepted as a constraint on mesopredator populations. In this study, we reassess whether African lions suppress populations of cheetahs and African wild dogs and examine possible mechanisms for coexistence between these species. Using long-term records from Serengeti National Park, we tested 30 years of population data for evidence of mesopredator suppression, and we examined six years of concurrent radio-telemetry data for evidence of large-scale spatial displacement. The Serengeti lion population nearly tripled between 1966 and 1998; during this time, wild dogs declined but cheetah numbers remained largely unchanged. Prior to their local extinction, wild dogs primarily occupied low lion density areas and apparently abandoned the long-term study area as the lion population 'saturated' the region. In contrast, cheetahs mostly utilized areas of high lion density, and the stability of the cheetah population indicates that neither high levels of lion-inflicted mortality nor behavioural avoidance inflict sufficient demographic consequences to translate into population-level effects. Population data from fenced reserves in southern Africa revealed a similar contrast between wild dogs and cheetahs in their ability to coexist with lions. These findings demonstrate differential responses of subordinate species within the same guild and challenge a widespread perception that lions undermine cheetah conservation efforts. Paired with several recent studies that document fine-scale lion-avoidance by cheetahs, this study further highlights fine-scale spatial avoidance as a possible mechanism for mitigating mesopredator suppression.