Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-plzwj Total loading time: 0.284 Render date: 2022-05-18T21:18:42.404Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

4 - Estimating density dependence in time-series of age-structured populations

Published online by Cambridge University Press:  20 May 2010

R. M. Sibly
Affiliation:
University of Reading
J. Hone
Affiliation:
University of Canberra
T. H. Clutton-Brock
Affiliation:
University of Cambridge
Get access

Summary

Introduction

Detection and estimation of density dependence is complicated because it usually operates with a time-lag due to intrinsic factors in individual development and life history (May 1973, 1981b; MacDonald 1978; Renshaw 1991; Nisbet 1997; Jensen 1999; Claessen et al. 2000) and extrinsic factors in an autocorrelated environment (Williams & Liebhold 1995; Berryman & Turchin 1997), including interspecific ecological interactions (Turchin 1990, 1995; Royama 1992; Turchin & Taylor 1992; Kaitala et al. 1997; Ripa et al. 1998; Hansen et al. 1999). The life history of a species may largely determine the relative importance of intrinsic and extrinsic factors in contributing to time-lags in population dynamics. For short-lived species with high population growth rates, such as some insects, ecological interactions may best explain time-lags longer than a generation (Turchin 1990, 1995; Royama 1992). For long-lived species with low population growth rates, such as many vertebrates, most time-lags may occur within a generation because of life history (Jensen 1999; Coulson et al. 2001; Thompson & Ollason 2001). Understanding density dependence has been impeded by the lack of a general quantitative definition that would allow comparisons among species with different life histories and forms of density dependence (Murdoch 1994).

Time-lags in population dynamics caused by life history have not, to our knowledge, previously been incorporated into methods for detecting and estimating density dependence from population time-series (Bulmer 1975; Pollard et al. 1987; Turchin 1990, 1995; Royama 1992; Turchin & Taylor 1992; Hanski et al. 1993; Dennis & Taper 1994; Zeng et al. 1998).

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)
2
Cited by

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×