Trends in Ecology & Evolution
Should we expect population thresholds for wildlife disease?
Introduction
Ideas about threshold levels of host abundance for invasion or persistence of infectious diseases are central to the theory and practice of disease ecology 1, 2, 3, but have their roots in human epidemiology. The notion of a threshold population for invasion (NT) (see Glossary) is a founding principle of epidemiological theory 4, 5, 6, and the critical community size (ccs) required for disease persistence dates back to Bartlett's seminal analyses of measles data [7]. Evidence of population thresholds in wildlife disease systems has been described as ‘rare’ [8] and ‘weak’ [9], yet these concepts underpin all efforts to eradicate wildlife diseases by reducing the numbers of susceptible hosts through controversial methods such as culling, sterilization, or vaccination (e.g. 10, 11, 12). Recent empirical studies have sought to identify invasion and persistence thresholds in wildlife with mixed success 8, 9, 12, 13, 14, 15. Here, we consider these findings in the context of theoretical models of disease spread, which reveal that abrupt population thresholds are not expected for many disease-host systems. Moreover, even when thresholds are expected, demographic stochasticity makes them difficult to measure under field conditions. We discuss how conventional theories underlying population thresholds neglect many factors relevant to natural populations such as seasonal births or compensatory reproduction, raising doubts about the general applicability of standard threshold concepts in wildlife disease systems. These findings call into question the wisdom of centering control policies on threshold targets and open important avenues for future research.
Section snippets
Setting the stage
We first introduce some general concepts to frame the discussion.
Population thresholds for disease invasion
We begin by describing the conceptual basis for invasion thresholds, and then use simulations to illustrate the challenges in identifying them.
Stochastic fadeout and thresholds for disease persistence
After a disease has successfully invaded a population, it can still go extinct or ‘fade out’ owing to random fluctuations in the number of infected individuals. Two types of stochastic fadeout are distinguished chiefly by their starting conditions: endemic fadeout refers to the extinction of a disease from a relatively stable endemic state (Figure 2a) [28], whereas epidemic fadeout describes extinction occurring after a major outbreak depletes the available number of susceptibles (Figure 2b)
Detecting thresholds in natural populations: observations and challenges
Several recent studies have investigated population thresholds in wildlife disease systems (Table 1). These studies represent major investments in field research and analysis, but their ability to draw definitive conclusions has been limited by the inherent challenges described above and additional complexities of real disease-host interactions (Box 3). Here, we review their substantial contributions and identify recurring obstacles.
Thresholds in disease control: applications and evidence
Wildlife diseases usually spread unchecked, but are sometimes managed if they pose risks to humans, livestock or sensitive species. Control efforts typically aim to reduce the susceptible host population through culling, sterilization or vaccination 3, 23, 47, 48, 49, 50, 51, 52, 53, 54. These measures represent the most important application of threshold concepts and the best potential source of large-scale experimental data testing those concepts.
When links to theory are stated explicitly,
Conclusions and the way forward
The concept of population thresholds for wildlife disease has the potential to guide or mislead us and should be applied with caution in research and control efforts. Four major points arise from our review:
(1) Population thresholds for disease are not abrupt in most natural systems: there are no ‘magic numbers’ separating dynamical regimes. Invasion thresholds exist if R0 of a disease increases with N and the host population is large and well-mixed, but they are blurred by stochasticity and
Acknowledgements
This paper arose from a graduate seminar led by J.L.-S. and P.C.C. structured around [1]. We are grateful for the input of other members of the seminar group, as well as Hans Heesterbeek, Ingemar Nåsell, and two anonymous reviewers. This research was funded by NSF-NIH Ecology of Infectious Disease Grants DEB-0090323 and NIEHS R01-ES12067, NIH-NIDA grants R01-DA10135 and POHC01000726, USDA CREES grant 2003–35316–13767, and a James S. McDonnell Foundation Grant.
Glossary
- Basic reproductive number (R0):
- the expected number of secondary cases caused by the first infectious individual in a wholly susceptible population. This acts as a threshold criterion because disease invasion can succeed only if R0>1.
- Critical community size (CCS):
- the host population size above which stochastic fadeout of a disease over a given period is less probable than not. Because disease dynamics do not change abruptly with population size, the CCS is traditionally set by subjective
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