Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger-collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% Bayesian credible interval [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas but by itself is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.

Locust and grasshopper (family: Acrididae) outbreaks are ancient agricultural challenges and can be difficult to manage due to their fast population growth and high mobility. The Senegalese grasshopper (Oedaleus senegalensis Krauss, 1877) is a major Sahelian pest distributed from West Africa to India that attacks cereal crops. It typically has three generations during the rainy season, which sequentially migrate north to south with prevailing rains, and is highly responsive to land use and land cover change, preferring fallow fields. Depending on environmental conditions and the characteristics of the rainy season, outbreaks are observed throughout Sahelian countries from Senegal to Sudan. The National Plant Protection Organizations (NPPOs) of many Sahelian countries consider O. senegalensis a significant pest and include it in regular monitoring and treatment programs; however, resources are often limited. Community-based management in collaboration with NPPOs, as well as development of improved decision-making and management tools, can leverage existing infrastructures to advance sustainable pest management and food security.

Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% BCI [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas, but by itself, is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.

Locusts are among the most feared agricultural pests. Spatiotemporal forecasting is a key process in their management. The present review aims to 1) set a common language on the subject, 2) evaluate the current methodologies, and 3) identify opportunities to improve forecasting tools. Forecasts can be used to provide reliable predictions on locust presence, reproduction events, gregarization areas, population outbreaks, and potential impacts on agriculture. Statistical approaches are used for the first four objectives, whereas mechanistic approaches are used for the latter. We advocate 1) to build reliable and reproducible spatiotemporal forecasting systems for the impacts on agriculture, 2) to turn scientific studies into operational forecasting systems, and 3) to evaluate the performance of these systems.

Abstract To document and halt biodiversity loss, monitoring, quantifying trends and assessing management and conservation strategies on wildlife populations and communities are crucial steps. With increasing technological innovations, more and more data are collected and new quantitative methods are constantly developed. These rapid developments come with an increasing need for analytical skills, which are hardly accessible to managers. On the other hand, researchers spend more and more time on research grant applications and administrative tasks, which leaves fewer opportunities for knowledge transfer. This situation tends to increase the gap between researchers and managers. Here, we illustrate how to fill this gap by presenting two long‐term collaborations between a research unit—Centre for Functional and Evolutionary Ecology; CEFE—and a national agency—French Biodiversity Agency; OFB. The first example is a collaboration providing statistical support to national parks for the design and implementation of scientific monitoring protocols. It relies on the recruitment of a research engineer funded by OFB and physically based at CEFE, who works closely with OFB and managers. The second example is a collaboration on the management of large carnivores. For more than 10 years, it has involved several PhD students and post‐doctoral fellows co‐supervised by CEFE and OFB, and has recently resulted in the recruitment of a permanent OFB researcher who works half‐time at CEFE and half‐time at OFB. These case studies illustrate the modalities of collaborative work between public institutions acting at different levels of biodiversity conservation for the co‐construction of research agendas and the exchange of knowledge. These collaborations also bring out some challenges. Inter‐knowledge and mutual learning remain difficult at scales larger than that of the teams concerned. The staff working at this interface needs to possess good listening skills, respect all partners' needs and demonstrate flexibility. Knowledge exchanges require time, thus reducing productivity according to quantitative metrics such as scientific publications or institutional reports. These collaborations can therefore be difficult to assume socially, and remain tenuous because they rely on a good understanding of the differences in governance of the various partners. Based on our experience, success is favoured by long‐term and close relationships, and by co‐construction of projects at early stage. Sharing a space (i.e. office or building) facilitates face‐to‐face interactions during planned work sessions and casual meetings that build up a shared scientific culture and mutual trust.

Four field seasons of snow leopard (Panthera uncia) camera trapping inside Naryn State Nature Reserve, Kyrgyzstan, performed thanks to citizen science expeditions, allowed detecting a minimal population of five adults, caught every year with an equilibrated sex ratio (1.5:1) and reproduction: five cubs or subadults have been identified from three litters of two different females. Crossings were observed one to three times a year, in front of most camera traps, and several times a month in front of one of them. Overlap of adults’ minimal territories was observed in front of several camera traps, regardless of their sex. Significant snow leopard presence was detected in the buffer area and at Ulan area which is situated at the reserve border. To avoid poaching on this apex predator and its preys, extending the more stringent protection measures of the core zone to both the Southern buffer area and land adjacent to Ulan is recommended.

Social networks are considered to be 'highly modular' when individuals within one module are more connected to each other than they are to individuals in other modules. It is currently unclear how highly modular social networks influence the persistence of contagious pathogens that generate lifelong immunity in their hosts when between-group interactions are age dependent. This trait occurs in social species with communal nurseries, where juveniles are reared together for a substantial period in burrows or similar forms of containment and are thus in isolation from contact with individuals in other social groups. Our main objective was to determine whether, and to what extent, such age-dependent patterns of between-group interactions consistently increased the fade-out probability of contagious pathogens that generate lifelong immunity in their hosts. We hypothesised that in populations of species where juveniles are raised in communal nurseries, a high proportion of recovered adults in a group would form a 'protective barrier' around susceptible juveniles against pathogen transmission, thereby increasing the probability of epidemic fade-out in the population. To test this idea, we implemented a spatially implicit individual-based susceptible-infected-recovered (SIR) model for a large range of generic host and pathogen traits. Our results indicated that (a) the probability of epidemic fade-out was consistently higher in populations with communal nurseries, especially for highly contagious pathogens (high basic reproduction number, R0 ) and (b) communal nurseries can counteract the cost of group living in terms of infection risk to a greater extent than variation in other traits. We discuss our findings in relation to herd immunity and outline the importance of considering the network structure of a given host population before implementing management measures such as vaccinations, since interventions focused on individuals with high between-group contact should be particularly effective for controlling pathogen spread in hosts with communal nurseries.

Deep learning is used in computer vision problems with important applications in several scientific fields. In ecology for example, there is a growing interest in deep learning for automatizing repetitive analyses on large amounts of images, such as animal species identification. However, there are challenging issues toward the wide adoption of deep learning by the community of ecologists. First, there is a programming barrier as most algorithms are written in Python while most ecologists are versed in R. Second, recent applications of deep learning in ecology have focused on computational aspects and simple tasks without addressing the underlying ecological questions or carrying out the statistical data analysis to answer these questions. Here, we showcase a reproducible R workflow integrating both deep learning and statistical models using predator-prey relationships as a case study. We illustrate deep learning for the identification of animal species on images collected with camera traps, and quantify spatial co-occurrence using multispecies occupancy models. Despite average model classification performances, ecological inference was similar whether we analysed the ground truth dataset or the classified dataset. This result calls for further work on the trade-offs between time and resources allocated to train models with deep learning and our ability to properly address key ecological questions with biodiversity monitoring. We hope that our reproducible workflow will be useful to ecologists and applied statisticians. All material (source of the Rmarkdown notebook and auxiliary files) is available from https://github.com/oliviergimenez/computo-deeplearning-occupany-lynx.

To understand the dynamics of a pathogen in an animal population, one must assess how the infection status of individuals changes over time. With wild animals, this can be very challenging because individuals can be difficult to trap and sample, even more so since they are tested with imperfect diagnostic techniques. Multi-event capture-recapture models allow analysing longitudinal capture data of individuals whose infection status is assessed using imperfect tests. In this study, we used a two-year dataset from a longitudinal field study of peridomestic wild bird populations in the United Arab Emirates during which thousands of birds from various species were captured, sampled and tested for Newcastle disease virus exposure using a serological test. We developed a multi-event capture-recapture model to estimate important demographic and epidemiological parameters of the disease. The modelling outputs provided important insights into the understanding of Newcastle disease dynamics in peridomestics birds, which varies according to ecological and epidemiological parameters, and useful information in terms of surveillance strategies. To our knowledge, this study is the first attempt to model the dynamics of Newcastle disease in wild bird populations by combining longitudinal capture data and serological test results. Overall, it showcased that multi-event capture-recapture models represent a suitable method to analyse imperfect capture data and make reliable inferences on infectious disease dynamics in wild populations.

© 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Humans are primary drivers of declining abundances and extirpation of large carnivores worldwide. Management interventions to restore biodiversity patterns, however, include carnivore reintroductions, despite the many unresolved ecological consequences associated with such efforts. Using multistate capture–mark–recapture models, we explored age-specific survival and cause-specific mortality rates for 134 pumas (Puma concolor) monitored in the Greater Yellowstone Ecosystem during gray wolf (Canis lupus) recovery. We identified two top models explaining differences in puma survivorship, and our results suggested three management interventions (unsustainable puma hunting, reduction in a primary prey, and reintroduction of a dominant competitor) have unintentionally impacted puma survival. Specifically, puma survival across age classes was lower in the 6-month hunting season than the 6-month nonhunting season; human-caused mortality rates for juveniles and adults, and predation rates on puma kittens, were higher in the hunting season. Predation on puma kittens, and starvation rates for all pumas, also increased as managers reduced elk (Cervus elaphus) abundance in the system, highlighting direct and indirect effects of competition between recovering wolves and pumas over prey. Our results emphasize the importance of understanding the synergistic effects of existing management strategies and the recovery of large, dominant carnivores to effectively conserve subordinate, hunted carnivores in human-dominated landscapes.

Determining the ‘space race’ between sympatric species is crucial to understand the effects of interspecific interactions on the extinction risk of species threatened by poachers, predators, pathogens, and invasive competitors. Dynamic two-species occupancy models provide a flexible framework to decompose complex species interaction patterns while accounting for imperfect detection. In particular, these models can describe poachers-wildlife interactions by considering the occupancy, the extinction and colonisation probabilities of wildlife conditional on the presence or absence of poachers and vice versa. We apply our model to a case study on wildlife poaching in the Eastern plains of Cambodia. We used co-occurrence data extracted from the database of the SMART partnership to study the distribution dynamics between poachers and six ungulate species regarded as main prey of tigers. We used 4 years of survey data reporting the locations of ranger patrols on the detection of snares with visual detections or presence signs of the ungulates. Our results showed that a substantial proportion of the sites occupied by ungulate species went extinct over the years of the study while the proportion of sites colonised by poachers increased. We also showed, for the first time, that spatio-temporal heterogeneity in the patrolling effort explains a great deal of the variation in the detection of poachers and ungulates. Our approach provides practitioners with a flexible and robust tool to assess conservation status of species and extinction risk of wildlife populations. It can assist managers in better evaluating, learning and adapting the patrolling strategies of rangers.

Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically “imperfect”. Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be “undetected” or “detected” at each time step. Detected animals can be assigned an infection state, such as “susceptible” (S), “infected” (I), or “recovered” (R), or an “unknown” (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.

The extent to which the fitness costs of infection are mediated by key life-history traits such as age or social status is still unclear. Within populations, individual heterogeneity in the outcome of infection is the result of two successive processes; the degree of contact with the pathogen (exposure) and the immune response to infection. In social mammals, because individuals holding high social status typically interact more frequently with group members, they should be more often in contact with infected individuals than those of low social status. However, when access to resources is determined by social status, individuals with a high social status are often better nourished, have a greater opportunity to allocate resources to immune processes and therefore should have a smaller chance of succumbing to infection than individuals with low social status.We investigated the risk and fitness costs of infection during a virulent epidemic of canine distemper virus (CDV) in a social carnivore, the spotted hyena, in the Serengeti National Park. We analysed two decades of detailed life-history data from 625 females and 816 males using a multi-event capture-mark-recapture model that accounts for uncertainty in the assignment of individual infection states.Cubs of mothers with a high social status had a lower probability of CDV infection and were more likely to survive infection than those with low social status. Subadult and adult females with high social status had a higher infection probability than those with low social status. Subadult females and pre-breeder males that had recovered from CDV infection had a lower survival than susceptible ones.Our study disentangles the relative importance of individual exposure and resource allocation to immune processes, demonstrates fitness costs of infection for juveniles, particularly for those with low social status, shows that patterns of infection can be driven by different mechanisms among juveniles and adults and establishes a negative relationship between infection and fitness in a free-ranging mammal. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.13059/suppinfo is available for this article.

This excel table contains 4 sheets: 2 with the CMR datasets associated with the manuscript by Marescot et al. 2018 Functional ecology (Social status mediates the fitness costs of infection with canine distemper virus in Serengeti spotted hyenas) and 2 other sheets defining the variables included in the datasets.

Humans are primary drivers of declining abundances and extirpation of large carnivores worldwide. Management interventions to restore biodiversity patterns, however, include carnivore reintroductions, despite the many unresolved ecological consequences associated with such efforts. Using multistate capture-mark-recapture models, we explored age-specific survival and cause-specific mortality rates for 134 pumas (Puma concolor) monitored in the Greater Yellowstone Ecosystem during gray wolf (Canis lupus) recovery. We identified two top models explaining differences in puma survivorship, and our results suggested three management interventions (unsustainable puma hunting, reduction in a primary prey, and reintroduction of a dominant competitor) have unintentionally impacted puma survival. Specifically, puma survival across age classes was lower in the 6-month hunting season than the 6-month nonhunting season; human-caused mortality rates for juveniles and adults, and predation rates on puma kittens, were higher in the hunting season. Predation on puma kittens, and starvation rates for all pumas, also increased as managers reduced elk (Cervus elaphus) abundance in the system, highlighting direct and indirect effects of competition between recovering wolves and pumas over prey. Our results emphasize the importance of understanding the synergistic effects of existing management strategies and the recovery of large, dominant carnivores to effectively conserve subordinate, hunted carnivores in human-dominated landscapes.

Urbanization rapidly changes landscape structure worldwide, thereby enlarging the human-wildlife interface. The emerging urban structures should have a key influence on the spread and distribution of wildlife diseases such as canine distemper, by shaping density, distribution and movements of wildlife. However, little is known about the role of urban structures as proxies for disease prevalence. To guide management, especially in densely populated cities, assessing the role of landscape structures in hampering or promoting disease prevalence is thus of paramount importance. Between 2008 and 2013, two epidemic waves of canine distemper hit the urban red fox (Vulpes vulpes) population of Berlin, Germany. The directly transmitted canine distemper virus (CDV) causes a virulent disease infecting a range of mammals with high host mortality, particularly in juveniles. We extracted information about CDV serological state (seropositive or seronegative), sex and age for 778 urban fox carcasses collected by the state laboratory Berlin Brandenburg. To assess the impact of urban landscape structure heterogeneity (e.g., richness) and shares of green and gray infrastructures at different spatial resolutions (areal of 28 ha, 78 ha, 314 ha) on seroprevalence we used Generalized Linear Mixed-Effects Models with binomial distributions. Our results indicated that predictors derived at a 28 ha resolution were most informative for describing landscape structure effects (AUC = 0.92). The probability to be seropositive decreased by 66% (0.6 to 0.2) with an increasing share of gray infrastructure (40 to 80%), suggesting that urbanization might hamper CDV spread in urban areas, owing to a decrease in host density (e.g., less foxes or raccoons) or an absence of wildlife movement corridors in strongly urbanized areas. However, less strongly transformed patches such as close-to-nature areas in direct proximity to water bodies were identified as high risk areas for CDV transmission. Therefore, surveillance and disease control actions targeting urban wildlife or human-wildlife interactions should focus on such areas. The possible underlying mechanisms explaining the prevalence distribution may be increased isolation, the absence of alternative hosts or an abiotic environment, all impairing the ability of CDV to persist without a host.

Predicting the impact of disease epidemics on wildlife populations is one of the twenty-first century's main conservation challenges. The long-term demographic responses of wildlife populations to epidemics and the life history and social traits modulating these responses are generally unknown, particularly for K-selected social species. Here we develop a stage-structured matrix population model to provide a long-term projection of demographic responses by a keystone social predator, the spotted hyena, to a virulent epidemic of canine distemper virus (CDV) in the Serengeti ecosystem in 1993/1994 and predict the recovery time for the population following the epidemic. Using two decades of longitudinal data from 625 known hyenas, we demonstrate that although the reduction in population size was moderate, i.e., the population showed high ecological 'resistance' to the novel CDV genotype present, recovery was slow. Interestingly, high-ranking females accelerated the population's recovery, thereby lessening the impact of the epidemic on the population.

Effective species management and conservation relies on accurate estimates of vital rates and an understanding of their link to environmental variables. We used multistate capture–mark–recapture models to directly quantify effects of predation on age-specific survival of black-tailed deer Odocoileus hemionus columbianus in California, USA. Survival probabilities were derived from individual encounter histories of 136 fawns and 57 adults monitored over 4 years. Based on results from our survival analysis we parameterized a Lefkovitch matrix and used elasticity analyses to investigate contributions of mortality due to predation to changes in population growth. We found strong evidence for age-specific survival including senescence. Survival of females >1 year old was consistently low (0.56 ± 0.18 for yearlings, 0.77 ± 0.13 for prime-aged females, and 0.55 ± 0.08 for senescent individuals), primarily due to high puma Puma concolor predation during summer. Predation from black bears Ursus americanus and coyotes Canis latrans was the primary cause for low annual survival of fawns (0.24 ± 0.16). Resulting estimates of population growth rates were indicative of a strongly declining population (λ = 0.82 ± 0.13). Despite high sensitivity to changes in adult survival, results from a lower-level elasticity analysis suggested that predation on fawns was the most significant individual mortality component affecting population decline. Our results provide a rare, direct link between predation, age-specific survival and the predicted population decline of a common ungulate species. The magnitude of predation was unexpected and suggests that ungulates in multi-predator systems struggle to cope with simultaneous reductions in survival probabilities from predators targeting different age classes.