Wayne M. Getz

A movement ecology paradigm for unifying organismal movement research.

Movement of individual organisms is fundamental to life, quilting our planet in a rich tapestry of phenomena with diverse implications for ecosystems and humans. Movement research is both plentiful and insightful, and recent methodological advances facilitate obtaining a detailed view of individual movement. Yet, we lack a general unifying paradigm, derived from first principles, which can place movement studies within a common context and advance the development of a mature scientific discipline. This introductory article to the Movement Ecology Special Feature proposes a paradigm that integrates conceptual, theoretical, methodological, and empirical frameworks for studying movement of all organisms, from microbes to trees to elephants. We introduce a conceptual framework depicting the interplay among four basic mechanistic components of organismal movement: the internal state (why move?), motion (how to move?), and navigation (when and where to move?) capacities of the individual and the external factors affecting movement. We demonstrate how the proposed framework aids the study of various taxa and movement types; promotes the formulation of hypotheses about movement; and complements existing biomechanical, cognitive, random, and optimality paradigms of movement. The proposed framework integrates eclectic research on movement into a structured paradigm and aims at providing a basis for hypothesis generation and a vehicle facilitating the understanding of the causes, mechanisms, and spatiotemporal patterns of movement and their role in various ecological and evolutionary processes. ”Now we must consider in general the common reason for moving with any movement whatever.“ (Aristotle, De Motu Animalium, 4th century B.C.)

Superspreading and the effect of individual variation on disease emergence

Population-level analyses often use average quantities to describe heterogeneous systems, particularly when variation does not arise from identifiable groups. A prominent example, central to our current understanding of epidemic spread, is the basic reproductive number, R0, which is defined as the mean number of infections caused by an infected individual in a susceptible population. Population estimates of R0 can obscure considerable individual variation in infectiousness, as highlighted during the global emergence of severe acute respiratory syndrome (SARS) by numerous ‘superspreading events’ in which certain individuals infected unusually large numbers of secondary cases. For diseases transmitted by non-sexual direct contacts, such as SARS or smallpox, individual variation is difficult to measure empirically, and thus its importance for outbreak dynamics has been unclear. Here we present an integrated theoretical and statistical analysis of the influence of individual variation in infectiousness on disease emergence. Using contact tracing data from eight directly transmitted diseases, we show that the distribution of individual infectiousness around R0 is often highly skewed. Model predictions accounting for this variation differ sharply from average-based approaches, with disease extinction more likely and outbreaks rarer but more explosive. Using these models, we explore implications for outbreak control, showing that individual-specific control measures outperform population-wide measures. Moreover, the dramatic improvements achieved through targeted control policies emphasize the need to identify predictive correlates of higher infectiousness. Our findings indicate that superspreading is a normal feature of disease spread, and to frame ongoing discussion we propose a rigorous definition for superspreading events and a method to predict their frequency.

Approaching a state shift in Earth’s biosphere

Localized ecological systems are known to shift abruptly and irreversibly from one state to another when they are forced across critical thresholds. Here we review evidence that the global ecosystem as a whole can react in the same way and is approaching a planetary-scale critical transition as a result of human influence. The plausibility of a planetary-scale ‘tipping point’ highlights the need to improve biological forecasting by detecting early warning signs of critical transitions on global as well as local scales, and by detecting feedbacks that promote such transitions. It is also necessary to address root causes of how humans are forcing biological changes.

The potential impact of male circumcision on HIV in Sub-Saharan Africa.

Background A randomized controlled trial (RCT) has shown that male circumcision (MC) reduces sexual transmission of HIV from women to men by 60% (32%−76%; 95% CI) offering an intervention of proven efficacy for reducing the sexual spread of HIV. We explore the implications of this finding for the promotion of MC as a public health intervention to control HIV in sub-Saharan Africa. Methods and Findings Using dynamical simulation models we consider the impact of MC on the relative prevalence of HIV in men and women and in circumcised and uncircumcised men. Using country level data on HIV prevalence and MC, we estimate the impact of increasing MC coverage on HIV incidence, HIV prevalence, and HIV-related deaths over the next ten, twenty, and thirty years in sub-Saharan Africa. Assuming that full coverage of MC is achieved over the next ten years, we consider three scenarios in which the reduction in transmission is given by the best estimate and the upper and lower 95% confidence limits of the reduction in transmission observed in the RCT. MC could avert 2.0 (1.1−3.8) million new HIV infections and 0.3 (0.1−0.5) million deaths over the next ten years in sub-Saharan Africa. In the ten years after that, it could avert a further 3.7 (1.9−7.5) million new HIV infections and 2.7 (1.5−5.3) million deaths, with about one quarter of all the incident cases prevented and the deaths averted occurring in South Africa. We show that a) MC will increase the proportion of infected people who are women from about 52% to 58%; b) where there is homogenous mixing but not all men are circumcised, the prevalence of infection in circumcised men is likely to be about 80% of that in uncircumcised men; c) MC is equivalent to an intervention, such as a vaccine or increased condom use, that reduces transmission in both directions by 37%. Conclusions This analysis is based on the result of just one RCT, but if the results of that trial are confirmed we suggest that MC could substantially reduce the burden of HIV in Africa, especially in southern Africa where the prevalence of MC is low and the prevalence of HIV is high. While the protective benefit to HIV-negative men will be immediate, the full impact of MC on HIV-related illness and death will only be apparent in ten to twenty years.

LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions

Parametric kernel methods currently dominate the literature regarding the construction of animal home ranges (HRs) and utilization distributions (UDs). These methods frequently fail to capture the kinds of hard boundaries common to many natural systems. Recently a local convex hull (LoCoH) nonparametric kernel method, which generalizes the minimum convex polygon (MCP) method, was shown to be more appropriate than parametric kernel methods for constructing HRs and UDs, because of its ability to identify hard boundaries (e.g., rivers, cliff edges) and convergence to the true distribution as sample size increases. Here we extend the LoCoH in two ways: “fixed sphere-of-influence,” or r-LoCoH (kernels constructed from all points within a fixed radius r of each reference point), and an “adaptive sphere-of-influence,” or a-LoCoH (kernels constructed from all points within a radius a such that the distances of all points within the radius to the reference point sum to a value less than or equal to a), and compare them to the original “fixed-number-of-points,” or k-LoCoH (all kernels constructed from k-1 nearest neighbors of root points). We also compare these nonparametric LoCoH to parametric kernel methods using manufactured data and data collected from GPS collars on African buffalo in the Kruger National Park, South Africa. Our results demonstrate that LoCoH methods are superior to parametric kernel methods in estimating areas used by animals, excluding unused areas (holes) and, generally, in constructing UDs and HRs arising from the movement of animals influenced by hard boundaries and irregular structures (e.g., rocky outcrops). We also demonstrate that a-LoCoH is generally superior to k- and r-LoCoH (with software for all three methods available at http://locoh.cnr.berkeley.edu).

Epidemic Models: Thresholds and Population Regulation

The complexity of epidemic processes is often obscured by the relatively simple way in which transmission is characterized in some epidemic models. A large part of current deterministic epidemic theory extends the S (susceptible host density), I (infective host density), and N (total host population density) differential equation model first proposed by Ross (cf. Ross 1916; Ross and Hudson 1917). Most of these models predict that the density of susceptibles (S) must exceed a threshold (NT) before an infectious disease can become epidemic (for a review of the theory, see Serfling 1952; Bailey 1975). Here we show that a seemingly minor change in the way transmission is modeled can significantly alter conclusions as to whether such a threshold exists and to whether an infectious disease can regulate its host population. In a recent series of papers Anderson and May (Anderson 1978, 1979a, 1979b, 1980, 1982; Anderson and May 1978, 1979, 1980, 1981, 1982; May and Anderson 1978, 1979) focused considerable attention, using Ross-type differential equation models, on the role of infectious diseases in the observed dynamics of host populations including invertebrate hosts. They examined the role of parasitesbroadly defined to include viruses, bacteria, protozoans and helminths-in biological control (Anderson 1979a, 1982; Anderson and May 1980), and they determined measures necessary for the control and eradication of pathogens, especially viral diseases such as rabies, whooping cough, and measles (Anderson et al. 1981; Anderson and May 1982). In most cases, their models predict the existence of a threshold NT> 0. This threshold is a consequence of the assumption that the rate of disease transmission is proportional to the number of random encounters between infectives (of density I) and susceptibles (of density S) in the population, i.e., the transmission rate is P3SI, where I is the transmission parameter. More complex characterizations of transmissions are common and have been discussed at length (Anderson 1979b, 1980, 1982; Bailey 1975; Anderson and May 1981; Yorke et al. 1979). Generally, the rate of disease transmission will depend on the rate at which propagules are produced by the infected individuals within a population, the dynamics of propagule transfer (e.g., vector dynamics), and the rate at which propagules invade susceptible hosts. For our purposes we assume that propagule production is a linear function of the number of infectives and either that the vector density remains sufficiently high so that transmission is essentially independent of their dynamics or that vectors are not required for transmission (e.g., venereal diseases). Then, if we assume that the rate at which propagules invade susceptible hosts is dependent on the proportion of individuals at risk in the host

Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures.

Summary Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80–90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.

Modelling the biological control of insect pests: a review of host-parasitoid models

Successful biological control results when an introduced natural enemy, very often a parasitoid, is able to suppress the abundance of an insect pest to a level at which it no longer causes economic damage. We review the host-parasitoid models that have been developed to describe the process of biological control by parasitoids or more specifically, the dynamics of interacting host and parasitoid populations. We trace the origins and basic framework for both discrete time (Nicholson-Bailey) models and continuous time (Lotka-Volterra) models, through the search for stabilizing mechanisms in local populations to the more recent focus on spatial and temporal heterogeneity in the distribution of parasitoid attack. In particular, we review the functional response that underlies all consumer-resource models, partial host refuges generated by spatial heterogeneity or temporal asynchrony of parasitoid attack or by host stage structure, the co-existence of competing parasitoid species, and size-dependent host feeding and sex allocation by parasitoids. The mechanistic explanations for biological control derived from these host-parasitoid models are then compared with the few case studies of successful biological control projects that have received sufficient study. We conclude by questioning, and suggesting improvements for, the basic assumptions of discrete-time and continuous-time models for biological pest control.

Gray Wolves as Climate Change Buffers in Yellowstone

Understanding the mechanisms by which climate and predation patterns by top predators co-vary to affect community structure accrues added importance as humans exert growing influence over both climate and regional predator assemblages. In Yellowstone National Park, winter conditions and reintroduced gray wolves (Canis lupus) together determine the availability of winter carrion on which numerous scavenger species depend for survival and reproduction. As climate changes in Yellowstone, therefore, scavenger species may experience a dramatic reshuffling of food resources. As such, we analyzed 55 y of weather data from Yellowstone in order to determine trends in winter conditions. We found that winters are getting shorter, as measured by the number of days with snow on the ground, due to decreased snowfall and increased number of days with temperatures above freezing. To investigate synergistic effects of human and climatic alterations of species interactions, we used an empirically derived model to show that in the absence of wolves, early snow thaw leads to a substantial reduction in late-winter carrion, causing potential food bottlenecks for scavengers. In addition, by narrowing the window of time over which carrion is available and thereby creating a resource pulse, climate change likely favors scavengers that can quickly track food sources over great distances. Wolves, however, largely mitigate late-winter reduction in carrion due to earlier snow thaws. By buffering the effects of climate change on carrion availability, wolves allow scavengers to adapt to a changing environment over a longer time scale more commensurate with natural processes. This study illustrates the importance of restoring and maintaining intact food chains in the face of large-scale environmental perturbations such as climate change.

A framework for generating and analyzing movement paths on ecological landscapes

The movement paths of individuals over landscapes are basically represented by sequences of points (xi, yi) occurring at times ti. Theoretically, these points can be viewed as being generated by stochastic processes that in the simplest cases are Gaussian random walks on featureless landscapes. Generalizations have been made of walks that (i) take place on landscapes with features, (ii) have correlated distributions of velocity and direction of movement in each time interval, (iii) are Lévy processes in which distance or waiting-time (time-between steps) distributions have infinite moments, or (iv) have paths bounded in space and time. We begin by demonstrating that rather mild truncations of fat-tailed step-size distributions have a dramatic effect on dispersion of organisms, where such truncations naturally arise in real walks of organisms bounded by space and, more generally, influenced by the interactions of physiological, behavioral, and ecological factors with landscape features. These generalizations permit not only increased realism and hence greater accuracy in constructing movement pathways, but also provide a biogeographically detailed epistemological framework for interpreting movement patterns in all organisms, whether tossed in the wind or willfully driven. We illustrate the utility of our framework by demonstrating how fission–fusion herding behavior arises among individuals endeavoring to satisfy both nutritional and safety demands in heterogeneous environments. We conclude with a brief discussion of potential methods that can be used to solve the inverse problem of identifying putative causal factors driving movement behavior on known landscapes, leaving details to references in the literature.