Journal of Ornithology

, Volume 152, Supplement 1, pp 227–237 | Cite as

Integrated population models: a novel analysis framework for deeper insights into population dynamics

Review

Abstract

Integrated population models (IPMs) represent the single, unified analysis of population count data and demographic data. This modelling framework is quite novel and can be implemented within the classical or the Bayesian mode of statistical inference. Here, we briefly show the basic steps that need to be taken when an integrated population model is adopted, and review existing integrated population models for birds and mammals. There are important advantages of integrated compared to conventional analyses that analyse each dataset separately and then try to make an inference about population dynamics. First, integrated population models allow the estimating of more demographic quantities, because there is information about all demographic processes operating in a population, and this information is exploited. Second, parameter estimates become more precise, and this enhances statistical power. Finally, all sources of uncertainty due to process variability and the sampling process(es) are adequately included. Core of the integrated models is the link of changes in the population size and the demographic rates via a demographic model (usually a Leslie matrix model) and the likelihoods of all existing datasets. We discuss some critical assumptions that are typically made in integrated population models and highlight fruitful areas of future research. Currently, we have found 25 studies that used integrated population models. Central to most studies was statistical development rather than their application to address an ecological question, which is not surprising given that integrated population models are still a new development. We predict that integrated population models will become a common and important tool in studies of population dynamics, both in ecology and its applications, such as conservation biology or wildlife management.

Keywords

Bayesian Demography Leslie matrix Population dynamics Population growth rate State-space model 

Zusammenfassung

Integrierte Populationmodelle (IPM) sind universelle Auswertungsmodelle mit denen jährliche Populationszählungen und demographische Daten simultan ausgewertet werden können. Diese Auswertungsmodelle sind relativ neu und können sowohl Bayesianisch wie auch frequentistisch analysiert werden. In diesem Artikel zeigen wir die wichtigsten Schritte, die es braucht, um ein IPM aufzustellen und geben eine Übersicht über die bisher auf Vögel- und Säugerdaten angewendeten IPM. Die Anwendung integrierter Populationsmodellen hat wichtige Vorteile gegenüber einer klassischen Auswertung, die die einzelnen Datensätze separat auswertet. Erstens, erlauben die IPM die Schätzung von demographischen Parametern, von denen keine spezifischen Daten vorliegen. Dies ist möglich, weil in den Populationszählungen Information über alle demographischen Prozesse vorhanden ist, und diese Information wird in IPM explizit extrahiert. Zweitens werden alle Parameter präziser geschätzt, was Rückschlüsse und die weitere Modellierung erleichtern kann. Und drittens werden die gesamten Unsicherheiten, die auf Grund der Datensammlung bestehen, adequat berücksichtigt. Zentral für ein IPM ist eine Beziehung zwischen den demographischen Parametern und der Populationsgrösse (meist via einer Leslie Matrix) und Wahrscheinlichkeitsmodelle aller Datensätze. Wir diskutieren die kritischen Annahmen der IPM und zeigen mögliche zukünftige Forschungsfelder auf. Wir fanden 25 Studien, die IPM verwendet haben. Ein zentraler Punkt bei fast allen war die statistische Weiterentwicklung. Wir sind überzeugt, dass sich die IPM für viele Studien im Bereich der Populationsdynamik, aber auch von Naturschutz-und Wildbiologie, zu einem wichtigen Auswertungsinstrument entwickeln werden.

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Copyright information

© Dt. Ornithologen-Gesellschaft e.V. 2010

Authors and Affiliations

  1. 1.Swiss Ornithological InstituteSempachSwitzerland
  2. 2.Institute of Ecology and Evolution, Division of Conservation BiologyUniversity of BernBernSwitzerland
  3. 3.South African National Biodiversity InstituteKirstenbosch Research CentreClaremontSouth Africa
  4. 4.Animal Demography Unit, Department of ZoologyUniversity of Cape TownRondeboschSouth Africa

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