Modelling wildlife-parasite interactions to help plan and interpret field studies

McCallum H. (1995) Modelling wildlife-parasite interactions to help plan and interpret field studies. Wildlife Research, 22 1: 21-29. doi:10.1071/WR9950021

Author McCallum H.
Title Modelling wildlife-parasite interactions to help plan and interpret field studies
Journal name Wildlife Research   Check publisher's open access policy
ISSN 1035-3712
Publication date 1995-01-01
Sub-type Article (original research)
DOI 10.1071/WR9950021
Open Access Status Not yet assessed
Volume 22
Issue 1
Start page 21
End page 29
Total pages 9
Language eng
Subject 2308 Management, Monitoring, Policy and Law
1105 Dentistry
Abstract Until Anderson and May's models of host-parasite interactions were published in the late 1970s, little attention was given to the effect of parasites and pathogens on host populations, as distinct from host individuals. Since that time, a variety of models of increasing sophistication that investigate theoretically the conditions under which disease may regulate host populations has been developed. It has also clearly been demonstrated that disease can regulate the population density of some animals under laboratory conditions. There are, however, very few studies that demonstrate convincingly that parasites regulate wild mammal populations, nor even that helminth infection has a substantial impact on mammal populations. Krebs has recently suggested that the search for density-dependent regulation has led us astray, and that models have served to obscure, rather than illuminate, understanding of the impact of disease in mammal populations. In this paper, 1 review the positive role that modelling has played in our understanding of wildlife and parasite population dynamics, with particular emphasis on macroparasitic infection, and discuss how modelling can lead to better understanding in the future. Krebs describes models as 'backward looking': seeking to explain the past rather than predict the future. They certainly can be used in this manner. However, perhaps the most important use of models is that they can identify key processes that should be examined experimentally. They can also explore the consequences of particular experimental results for wider population behaviour.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: Scopus Import - Archived
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Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 18 times in Scopus Article | Citations
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