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

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.ZusammenfassungIntegrierte 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.

[1]  Jon T. Schnute,et al.  A General Framework for Developing Sequential Fisheries Models , 1994 .

[2]  Stephen R. Baillie,et al.  Population processes in European Blackbirds Turdus merula: a state–space approach , 2010, Journal of Ornithology.

[3]  Bruce C. Lubow,et al.  Fitting population models to multiple sources of observed data , 2002 .

[4]  Russell B. Millar,et al.  Bayesian state-space modeling of age-structured data: fitting a model is just the beginning , 2000 .

[5]  Stephen N. Freeman,et al.  Changing demography and population decline in the Common Starling Sturnus vulgaris: a multisite approach to Integrated Population Monitoring , 2007 .

[6]  David L. Thomson,et al.  Demographic mechanisms of the population decline of the song thrush Turdus philomelos in Britain , 2004 .

[7]  Marc Kery,et al.  Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses , 2010 .

[8]  Stephen R. Baillie,et al.  Temporal variation in the annual survival rates of six granivorous birds with contrasting population trends , 2008 .

[9]  Joseph Hilbe,et al.  Bayesian Analysis for Population Ecology , 2009 .

[10]  Stephen T. Buckland,et al.  Embedding Population Dynamics Models in Inference , 2007, 0708.3796.

[11]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[12]  R. Lande Risks of Population Extinction from Demographic and Environmental Stochasticity and Random Catastrophes , 1993, The American Naturalist.

[13]  Byron J. T. Morgan,et al.  THE POTENTIAL OF INTEGRATED POPULATION MODELLING † , 2005 .

[14]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[15]  P Besbeas,et al.  Integrating Mark–Recapture–Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters , 2002, Biometrics.

[16]  Mark N. Maunder,et al.  A Bayesian integrated population dynamics model to analyze data for protected species , 2004, Animal Biodiversity and Conservation.

[17]  Panagiotis Besbeas,et al.  Population growth in snow geese: a modeling approach integrating demographic and survey information. , 2007, Ecology.

[18]  Stephen T. Buckland,et al.  A UNIFIED FRAMEWORK FOR MODELLING WILDLIFE POPULATION DYNAMICS † , 2005 .

[19]  Panagiotis Besbeas,et al.  Methods for joint inference from panel survey and demographic data. , 2006, Ecology.

[20]  H. Caswell Matrix population models : construction, analysis, and interpretation , 2001 .

[21]  Olivier Gimenez,et al.  Estimation of immigration rate using integrated population models , 2010 .

[22]  Richard A. Parker,et al.  WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods , 2009 .

[23]  Byron J. T. Morgan,et al.  Detecting parameter redundancy , 1997 .

[24]  I. Newton,et al.  Population Limitation in Birds , 1998 .

[25]  O. Gimenez,et al.  Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[26]  Daniel Goodman,et al.  METHODS FOR JOINT INFERENCE FROM MULTIPLE DATA SOURCES FOR IMPROVED ESTIMATES OF POPULATION SIZE AND SURVIVAL RATES , 2004 .

[27]  Jean-Dominique Lebreton,et al.  The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes) , 2008 .

[28]  Ruth King,et al.  An Integrated Population Model From Constant Effort Bird-Ringing Data , 2010 .

[29]  Stephen P Brooks,et al.  Bayesian computation: a statistical revolution , 2003, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[30]  S. Brooks,et al.  A Bayesian approach to combining animal abundance and demographic data , 2004 .

[31]  Byron J. T. Morgan,et al.  The efficient integration of abundance and demographic data , 2003 .

[32]  S. T. Bucklanda,et al.  State-space models for the dynamics of wild animal populations , 2003 .

[33]  James S. Clark,et al.  POPULATION TIME SERIES: PROCESS VARIABILITY, OBSERVATION ERRORS, MISSING VALUES, LAGS, AND HIDDEN STATES , 2004 .

[34]  P. M. Kelly,et al.  Effects on climate , 1980, Nature.

[35]  Stephen P. Brooks,et al.  Using a State-Space Model of the British Song Thrush Turdus philomelos Population to Diagnose the Causes of a Population Decline , 2009 .

[36]  S. Ellner,et al.  Integral Projection Models for Species with Complex Demography , 2006, The American Naturalist.

[37]  Byron J. T. Morgan,et al.  Weak Identifiability in Models for Mark-Recapture-Recovery Data , 2009 .

[38]  J. Andrew Royle,et al.  Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities , 2008 .

[39]  R. Pradel,et al.  Is the reintroduced white stork (Ciconia ciconia) population in Switzerland self-sustainable? , 2004 .

[40]  Panagiotis Besbeas,et al.  Completing the Ecological Jigsaw , 2009 .

[41]  Byron J. T. Morgan,et al.  An Integrated Analysis of Multisite Recruitment, Mark-Recapture-Recovery and Multisite Census Data , 2009 .

[42]  Panagiotis Besbeas,et al.  Estimating Population Size and Hidden Demographic Parameters with State‐Space Modeling , 2009, The American Naturalist.

[43]  David L. Garshelis,et al.  Integrated Population Modeling of Black Bears in Minnesota: Implications for Monitoring and Management , 2010, PloS one.

[44]  Graeme Caughley,et al.  Directions in conservation biology , 1994 .

[45]  Olivier Gimenez,et al.  An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence. , 2010, Ecology.

[46]  Byron J. T. Morgan,et al.  A simultaneous survival rate analysis of dead recovery and live recapture data , 1995 .

[47]  David R. Anderson,et al.  AIC MODEL SELECTION IN OVERDISPERSED CAPTURE-RECAPTURE DATA' , 1994 .

[48]  A. Jansen Bayesian Methods for Ecology , 2009 .

[49]  Stephen R. Baillie,et al.  Understanding changes in bird populations – the role of bird marking , 2009 .

[50]  Stephen N. Freeman,et al.  The decline of the Spotted Flycatcher Muscicapa striata in the UK: an integrated population model , 2003 .

[51]  Olivier Gimenez,et al.  Massive immigration balances high anthropogenic mortality in a stable eagle owl population: Lessons for conservation , 2010 .

[52]  Henri Weimerskirch,et al.  Effects of climate variability on the temporal population dynamics of southern fulmars. , 2003, The Journal of animal ecology.

[53]  David R. Anderson,et al.  Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies , 1992 .

[54]  David L. Thomson,et al.  The demography and age-specific annual survival of song thrushes during periods of population stability and decline , 1997 .

[55]  M. Conroy,et al.  Modeling demographic processes in marked populations , 2009 .

[56]  Stephen T. Buckland,et al.  Fitting Population Dynamics Models to Count and Cull Data Using Sequential Importance Sampling , 2000 .

[57]  Christopher Fonnesbeck,et al.  Application of integrated Bayesian modeling and Markov chain Monte Carlo methods to the conservation of a harvested species , 2004 .

[58]  Alan Hastings,et al.  FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR , 2002 .

[59]  K. Norris,et al.  Managing threatened species: the ecological toolbox, evolutionary theory and declining-population paradigm , 2004 .

[60]  Ruth King,et al.  Integrated data analysis in the presence of emigration and mark loss , 2009 .

[61]  Guillaume Péron,et al.  Studying dispersal at the landscape scale: efficient combination of population surveys and capture-recapture data. , 2010, Ecology.

[62]  M. Conroy,et al.  Analysis and Management of Animal Populations , 2002 .

[63]  J. Lebreton,et al.  Marked Individuals in the Study of Bird Population , 1993 .

[64]  Byron J. T. Morgan,et al.  Identifying and diagnosing population declines: a Bayesian assessment of lapwings in the UK , 2008 .

[65]  Heather E. Johnson,et al.  Combining ground count, telemetry, and mark–resight data to infer population dynamics in an endangered species , 2010 .

[66]  Byron J. T. Morgan,et al.  Multi-Site Integrated Population Modelling , 2010 .

[67]  Jim Hone,et al.  Population growth rate and its determinants: an overview. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[68]  Richard D. Gregory,et al.  Long‐term changes in over‐winter survival rates explain the decline of reed buntings Emberiza schoeniclus in Britain , 1999 .

[69]  P. Holgate,et al.  Matrix Population Models. , 1990 .

[70]  Mark N. Maunder Population viability analysis based on combining Bayesian, integrated, and hierarchical analyses , 2004 .