Simone Ciuti

Human activities can induce significant behavioural changes in wildlife. Often explored through extractive interactions (e.g. hunting) that can favour certain behavioural traits, the implications of non-extractive ones, such as wildlife feeding, remain understudied. Research shows that people tend to favour bolder individuals within populations despite their dynamics and consequences being unclear. Using fallow deer in a peri-urban environment, we studied whether mothers that show reduced fear of humans and consistently approach them for food adopt weaker anti-predator strategies by selecting less concealed fawning bedsites closer to human hotspots. This would provide the advantage of additional feeding opportunities in comparison with shyer mothers while keeping their fawns close. Our dataset encompassed 281 capture events of 172 fawns from 110 mothers across 4 years. Surprisingly, mothers that regularly accepted food from humans selected more concealed bedsites farther from human hotspots, giving their offspring better protection while also benefitting from additional food during lactation. Our results show behavioural adaptations by a subset of females and, for the first time, link the tendency to approach humans and strategies to protect offspring. Given previous findings that these begging females also deliver heavier fawns at birth, our research further investigates human–wildlife feeding interactions and their behavioural implications.

Classification systems are useful tools for aggregating diseases to study them in more detail. The most widespread system for the classification of human diseases is the International Classification of Diseases (ICD); however, the ICD was designed for use in health care and fails to capture many details of zoonotic diseases as a result. We propose a framework for zoonotic disease classification which combines four known classification types (pathogen type, life cycle, transmission direction and ecosystem) into one systematic method. We chose to focus on four specific aspects to provide as broad an overview as possible of zoonoses, in order to inform the risk of transmission to humans. This framework can be applied to the zoonoses occurring in any non‐human animal species and supports the generation of systematically collected empirical data that are useful for monitoring the spatio‐temporal dynamics of zoonoses. To demonstrate the utility of our framework, we focus on six deer species that are widespread in Eurasia: red deer Cervus elaphus , roe deer Capreolus capreolus , fallow deer Dama dama , sika deer Cervus nippon , Reeves's muntjac Muntiacus reevesi and Chinese water deer Hydropotes inermis . We examined the worldwide occurrence and prevalence of zoonoses in these species through a scoping review of the literature. Altogether, 996 records of zoonotic diseases in the six wild deer species, from 431 peer‐reviewed papers, and 120 zoonotic pathogens were found. Within deer populations, the most prevalent zoonoses were bacterial, while the most prevalent life cycles were metazoonoses. We also found that the most prevalent pathogens in deer were those with hosts capable of occupying sylvatic, peridomestic and domestic ecosystems. Baseline data generated using our framework method can provide important starting points for making projections of human disease risk, assisting wildlife managers and policy‐makers in making wildlife management decisions, and assisting experts in the development of disease‐monitoring and management plans.

For species of conservation concern and human–wildlife conflict, it is imperative that spatial population data be available to design adaptive‐management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low‐resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high‐resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high‐resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low‐resolution data into a high‐resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence‐only and presence–absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine‐scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.