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All animals exhibit some combination of spatial and social behaviours. A diversity of interactions occurs between such behaviours, producing emergent phenomena atthe spatial–social interface. Untangling and interrogating these complex, intertwined processes can be vital for identifying the mechanisms, causes and consequences of behavioural variation in animal ecology. Nevertheless, the integrated study of the interactions between spatial and social phenotypes and environments (at the spatial–social interface) is in its relative infancy. In this theme issue, we present a collection of papers chosen to expand the spatial–social interface along several theoretical, methodological and empirical dimensions. They detail new perspectives, methods, study systems and more, as well as offering roadmaps for applied outputs and detailing exciting new directions for the field to move in the future. In this Introduction, we outline the contents of these papers, placing them in the context of what comes before, and we synthesize a number of takeaways and future directions for the spatial–social interface. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’. 

Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of ‘permeability’ from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’. 

Abstract Controlling persistent infectious disease in wildlife populations is an ongoing challenge for wildlife managers and conservationists worldwide, and chronic diseases in particular remain a pernicious problem.Here, we develop a dynamic pathogen transmission model capturing key features ofMycoplasma ovipneumoniaeinfection, a major cause of population declines in North American bighorn sheepOvis canadensis. We explore the effects of model assumptions and parameter values on disease dynamics, including density‐ versus frequency‐dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease‐induced mortality and recovery rates and the epidemic growth rate. Along the way, we estimate the basic reproductive ratio,R₀, forM. ovipneumoniaein bighorn sheep to fall between approximately 1.36 and 1.74.We apply the model to compare efficacies across a suite of management actions following an epidemic, including test‐and‐remove, depopulation‐and‐reintroduction, range expansion, herd augmentation and density reduction.Our results suggest that test‐and‐remove, depopulation‐and‐reintroduction and range expansion could help persistently infected bighorn sheep herds recovery following an epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Other management actions that improve host survival or reduce disease‐induced mortality are also likely to improve population size and persistence of chronically infected herds.Synthesis and applications. Dynamic transmission models like the one employed here offer a structured, logical approach for exploring hypotheses, planning field experiments and designing adaptive management. We find that management strategies that removed infected animals or isolated them within a structured metapopulation were most successful at facilitating herd recovery from a low‐prevalence, chronic pathogen. Ideally, models like ours should operate iteratively with field experiments to triangulate on better approaches for managing wildlife diseases.