Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data

Schaub, Michael; Giménez, Olivier; Sierro, Antoine; Arlettaz, Raphaël

doi:10.1111/j.1523-1739.2007.00743.x

Cited by 186 publications

(201 citation statements)

References 44 publications

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“…Further approaches, such as radio or satellite tagging of juveniles or integrated population modelling, might be useful to estimate the missing parameters, i.e. juvenile survival and emigration, to provide a complete understanding of the ecological processes relating to population growth and persistence of the Mediterranean monk seal colony (Coulson et al 2001, Schaub et al 2007, Tavecchia et al 2009). …”

Section: Discussionmentioning

confidence: 99%

Population resilience of the Mediterranean monk seal Monachus monachus at Cabo Blanco peninsula

Martínez‐Jauregui¹,

Tavecchia²,

Cedenilla³

et al. 2012

Mar. Ecol. Prog. Ser.

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Environmental and demographic stochasticity alone can push small populations to extinction. However, some small populations can persist for a long time at a low density, maintaining potential for rapid growth. We investigated the demographic mechanisms underlying the response of the population of the Mediterranean monk seal Monachus monachus on the Cabo Blanco peninsula (Mauritania/Morocco). The population was dramatically reduced after a demographic crash in 1997, which was initially thought to have pushed the population toward extinction. Census data, individual-based data on presence−absence and breeding state data were obtained by direct observation, by photo-identification and through the analysis of video images at the only known breeding sites for the species. We found that the monk seal population at Cabo Blanco is recovering, with means of 31 (24.8, 36.8) sub-adults, 46 (42.3, 49.7) adult females and 39 (29.1, 48.4) adult males using the 2 breeding sites in 2007. The population structure is similar to proportions prior to the mass mortality event. Annual survival probabilities for sub-adults, adult females and adult males were 0.81 (0.676 to 0.904), 0.99 (0.926 to 0.998) and 0.87 (0.81 to 0.94), respectively. At present, juvenile survival is unknown, but the high survival of females and the high breeding potential are likely to be responsible for the population recovery.

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Section: Discussionmentioning

confidence: 99%

Population resilience of the Mediterranean monk seal Monachus monachus at Cabo Blanco peninsula

Martínez‐Jauregui¹,

Tavecchia²,

Cedenilla³

et al. 2012

Mar. Ecol. Prog. Ser.

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“…Developments in the estimation of both abundance and demography, and inference about the relationship between them, have developed over the past decade in both classical (Besbeas et al 2002(Besbeas et al , 2003(Besbeas et al , 2005Borysiewicz et al 2009;Tavecchia et al 2009) and Bayesian (Brooks et al 2004Schaub et al 2007;King et al 2008) frameworks. See Schaub and Abadi (this volume) for a comprehensive bibliography regarding the prevalent models.…”

Section: A Brief Historymentioning

confidence: 99%

Quantifying changes in abundance without counting animals: extensions to a method of fitting integrated population models

Freeman

Besbeas

2011

J Ornithol

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Article (refereed) -postprint AbstractIntegrated population modelling techniques combine information from population surveys and independent demographic studies to estimate population size, survival and productivity rates simultaneously. We review the development of the approach, and investigate further the potential to incorporate sources of population survey data other than those currently employed. Generally, the simpler the field protocol, the more data can be gathered; in the simplest case only a list of species encountered when a site is surveyed might be recorded. We extend the integrated approach to the case of presence/absence survey data from species lists. We consider specifically the extent to which high-quality demographic data, used in conjunction with an ecologically sound model, may result in credible estimates of change and the drivers of it in the context of either counts or presence/absence survey data. We propose an approach to practical model fitting, applicable in either context, using standard software and we illustrate its performance in practice. Examples are based on simulated data and records of species with very different trend and ecology, and they are used to compare approaches.

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“…Of particular interest are so-called integrated population models, which combine a model for a time-series of abundance data, such as a state-space model, with models for individual demographic information, such as a model for ringrecovery data (Besbeas et al 2002). In bird population studies, frequentist analysis of integrated population models has been limited to linear, Gaussian approximations using the Kalman filter (Besbeas et al 2002;Gauthier et al 2007;Tavecchia et al 2009), while Bayesian analysis has allowed nonlinear relationships and/or non-Gaussian variation (Brooks et al 2004;Schaub et al 2007;King et al 2008b). Methods for frequentist analysis allowing nonlinear and/or non-Gaussian state-space models have been developed in other areas of statistics and ecology, but they have not been used in integrated population models.…”

Section: Introductionmentioning

confidence: 99%

Frequentist analysis of hierarchical models for population dynamics and demographic data

Valpine

2011

J Ornithol

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Hierarchical models include random effects or latent state variables. This class of models includes statespace models for population dynamics, which incorporate process and sampling variation, and models with random individual or year effects in capture-mark-recapture models, for example. This paper reviews methods for frequentist analysis of hierarchical models and gives an example of a non-Gaussian, potentially nonlinear analysis of Lapwing data using the Monte Carlo kernel likelihood (MCKL) method for maximum-likelihood estimation and bridge sampling for calculation of likelihood values given estimated parameters. The Lapwing example uses the state-space model as part of an integrated population model, which combines survey data with ring-recovery demographic data. The methods reviewed include filtering methods, such as the Kalman filter and sequential Monte Carlo (or particle filtering) methods, Monte Carlo expectation maximization, data cloning, and MCKL. The latter methods estimate the maximum-likelihood parameters but omit a normalizing constant from the likelihood that is needed for model comparisons, such as the Akaike information criterion and likelihood ratio tests. The methods reviewed for normalizing constant calculation include filtering, importance sampling, likelihood ratios from importance sampling, and bridge sampling. For the Lapwing example, a novel combination of MCKL parameter estimation, bridge sampling likelihood calculation, and profile likelihood confidence intervals for an integrated population model is presented to illustrate the feasibility of these methods. A complementary view of Bayesian and frequentist analysis is taken.

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Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data

Cited by 186 publications

References 44 publications

Population resilience of the Mediterranean monk seal Monachus monachus at Cabo Blanco peninsula

Population resilience of the Mediterranean monk seal Monachus monachus at Cabo Blanco peninsula

Quantifying changes in abundance without counting animals: extensions to a method of fitting integrated population models

Frequentist analysis of hierarchical models for population dynamics and demographic data

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