State-space models for the dynamics of wild animal populations

Buckland, Stephen T.; Newman, Ken; Thomas, Len; Koesters, Nils

doi:10.1016/j.ecolmodel.2003.08.002

Cited by 261 publications

(254 citation statements)

References 23 publications

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“…If biological process models are required that have the potential to explain ecosystem composition and structure, then it seems necessary to model the biological processes that give rise to this composition and structure: that is, birth, survival and movement, and the effects of species interactions and the environment on these processes. Buckland et al (2004) provide a state-space framework for modelling ecosystems in this way. Long time series of data from well-designed monitoring surveys allow such models to be fitted using computer-intensive techniques such as Markov chain Monte Carlo and sequential importance sampling (Newman et al in preparation).…”

Section: Discussionmentioning

confidence: 99%

Monitoring change in biodiversity through composite indices

Buckland

Magurran

Green

et al. 2005

Phil. Trans. R. Soc. B

325

306

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The need to monitor trends in biodiversity raises many technical issues. What are the features of a good biodiversity index? How should trends in abundance of individual species be estimated? How should composite indices, possibly spanning very diverse taxa, be formed? At what spatial scale should composite indices be applied? How might change-points-points at which the underlying trend changes-be identified? We address some of the technical issues underlying composite indices, including survey design, weighting of the constituent indices, identification of change-points and estimation of spatially varying time trends. We suggest some criteria that biodiversity measures for use in monitoring surveys should satisfy, and we discuss the problems of implementing rigorous methods. We illustrate the properties of different composite indices using UK farmland bird data. We conclude that no single index can capture all aspects of biodiversity change, but that a modified Shannon index and the geometric mean of relative abundance have useful properties.

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Section: Discussionmentioning

confidence: 99%

Monitoring change in biodiversity through composite indices

Buckland

Magurran

Green

et al. 2005

Phil. Trans. R. Soc. B

325

306

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“…Likelihood for the population-survey (PS) data.-We used a state-space model to describe the populationsurvey data (Besbeas et al 2002, de Valpine and Hastings 2002, Buckland et al 2004). The state process models the true, but unknown population trajectory under the population matrix model defined by Eq.…”

Section: Discussionmentioning

confidence: 99%

“…A promising, recently developed tool to analyze different sources of data simultaneously is integrated population modeling (Besbeas et al 2002, 2003, Buckland et al 2004, Thomas et al 2005. The core of this model is a projection matrix parameterized with demographic parameters, which maps population sizes of different age or stage classes from one year to the next.…”

Section: Introductionmentioning

confidence: 99%

An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence

Abadi

Giménez

Arlettaz

et al. 2010

Ecology

171

182

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Abstract. Understanding population dynamics requires accurate estimates of demographic rates. Integrated population models combine demographic and survey data into a single, comprehensive analysis and provide more coherent estimates of vital rates. Integrated population models rely on the assumption that different data sets are independent, which is frequently violated in practice. Moreover, the precision that can be gained using integrated modeling compared to conventional modeling is only known from empirical studies. The present study used simulation methods to assess how the violation of the assumption of independence affects the statistical properties of the parameter estimators. Further, the gains in precision and accuracy from the model were explored under varying sample sizes. For capture-recapture, population survey, and reproductive success, we generated independent and dependent data that were analyzed with integrated and conventional models. We found only a minimal impact of the violation of the assumption of independence on the parameter estimates. Furthermore, we observed an overall gain in precision and accuracy when all three data sets were analyzed simultaneously. This was particularly pronounced when the sample size was small. These findings contribute to clearing the way for the application of integrated population models in practice.

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“…Recently, a growing number of analytical methods have been developed to deal with one or both of these issues (Fauchald & Tveraa 2003, Buckland et al 2004, Jonsen et al 2005, Patterson et al 2010. To identify intensively used areas along predators' tracks 2 types of approaches can be distinguished: descriptive methods and process-based models.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data

Dragon¹,

Bar-Hen

Monestiez³

et al. 2012

Mar. Ecol. Prog. Ser.

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Identifying animals' successful foraging areas is a major challenge, but such comprehensive knowledge is needed for the management and conservation of wild populations. In recent decades, numerous specific analytic methods have been developed to handle tracking data and to identify preferred foraging areas. In this study, we assessed the efficiency of different track-based methods on Argos and GPS predators' tracks. We investigated (1) the consistency in the detection of foraging areas between track-based methods applied to 2 tracking data resolutions and (2) the similarity of foraging behaviour identification between track-based methods and an independent index of foraging success. We focused on methods that are commonly used in the literature: empirical descriptors of foraging effort, Hidden Markov Models (HMMs) and first passage time analysis. We applied these methods to satellite tracking data collected on 6 long-ranging elephant seals equipped with both Argos and GPS tags. Seals were also equipped with time depth recorder loggers from which we estimated an independent index, based on the drift rate and the changes in the seals' body condition, as a proxy for foraging success along the tracks. Favourable foraging zones identified by track-based methods were compared to locations where the body condition of the seals significantly increased. With or without an environmental covariate, HMMs were the most reliable for identifying successful foraging areas on both high (GPS) and low (Argos) resolution data. Areas identified by HMMs as intensively used were congruent with the locations where seals significantly increased their body condition given a 4 d metabolisation lag.

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State-space models for the dynamics of wild animal populations

Cited by 261 publications

References 23 publications

Monitoring change in biodiversity through composite indices

Monitoring change in biodiversity through composite indices

An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence

Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data

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