Variance estimation for integrated population models

Besbeas, Panagiotis; Morgan, Byron J. T.

doi:10.1007/s10182-017-0304-5

Cited by 11 publications

(10 citation statements)

References 36 publications

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“…Literature surveys of IPM are provided by Schaub and Abadi (2011), and in fisheries science, by Maunder and Punt (2013). For recent research in IPM see for example Besbeas and Morgan (2017), Finke et al (2019) and Lahoz-Monfort et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Exact Inference for Integrated Population Modelling

Besbeas

Morgan

2019

Biometrics

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Integrated population modelling is widely used in statistical ecology. It allows data from population time series and independent surveys to be analysed simultaneously. In classical analysis the time‐series likelihood component can be conveniently approximated using Kalman filter methodology. However, the natural way to model systems which have a discrete state space is to use hidden Markov models (HMMs). The proposed method avoids the Kalman filter approximations and Monte Carlo simulations. Subject to possible numerical sensitivity analysis, it is exact, flexible, and allows the use of standard techniques of classical inference. We apply the approach to data on Little owls, where the model is shown to require a one‐dimensional state space, and Northern lapwings, with a two‐dimensional state space. In the former example the method identifies a parameter redundancy which changes the perception of the data needed to estimate immigration in integrated population modelling. The latter example may be analysed using either first‐ or second‐order HMMs, describing numbers of one‐year olds and adults or adults only, respectively. The use of first‐order chains is found to be more efficient, mainly due to the smaller number of one‐year olds than adults in this application. For the lapwing modelling it is necessary to group the states in order to reduce the large dimension of the state space. Results check with Bayesian and Kalman filter analyses, and avenues for future research are identified.

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Section: Introductionmentioning

confidence: 99%

Exact Inference for Integrated Population Modelling

Besbeas

Morgan

2019

Biometrics

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“…The interpretation of σ differs between normal and log-normal observation models, as in Knape et al (2011). There are two HMM solutions for the model with normal observation error; the second solution effectively ignores observation error, a possibility noticed by Dennis et al (2006) and Besbeas and Morgan (2017). We can see that there is very good agreement between the results of Knape et al (2011) and those from the HMM.…”

Section: Kangaroosmentioning

confidence: 68%

“…There are two HMM solutions for the model with normal observation error; the second solution effectively ignores observation error, a possibility noticed by Dennis et al . () and Besbeas and Morgan (). We can see that there is very good agreement between the results of Knape et al .…”

Section: Resultsmentioning

confidence: 99%

A general framework for modeling population abundance data

Besbeas

Morgan

2019

Biometrics

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Time‐series data resulting from surveying wild animals are often described using state‐space population dynamics models, in particular with Gompertz, Beverton‐Holt, or Moran‐Ricker latent processes. We show how hidden Markov model methodology provides a flexible framework for fitting a wide range of models to such data. This general approach makes it possible to model abundance on the natural or log scale, include multiple observations at each sampling occasion and compare alternative models using information criteria. It also easily accommodates unequal sampling time intervals, should that possibility occur, and allows testing for density dependence using the bootstrap. The paper is illustrated by replicated time series of red kangaroo abundances, and a univariate time series of ibex counts which are an order of magnitude larger. In the analyses carried out, we fit different latent process and observation models using the hidden Markov framework. Results are robust with regard to the necessary discretization of the state variable. We find no effective difference between the three latent models of the paper in terms of maximized likelihood value for the two applications presented, and also others analyzed. Simulations suggest that ecological time series are not sufficiently informative to distinguish between alternative latent processes for modeling population survey data when data do not indicate strong density dependence.

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“…The rationale for doing so is to improve the precision of estimates of species detectability, essentially a nuisance parameter, which, however, turns out to be a crucial model component especially for species that are hard to detect. Addressing uncertainty in species detectability is a common theme in many ecological contexts (see, for example, Borchers and Marques 2017;Besbeas and Morgan 2017) and is often addressed within a state-space modelling framework. Patterson et al (2017) provide a review of statistical models of individual animal movement, including approaches based on hidden Markov models, state-space models and diffusion models.…”

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confidence: 99%

“…A comprehensive discussion of practical challenges aims at helping to bridge the gap between the sophisticated statistical methods commonly applied and the actual biological problems that are being addressed using movement data. Besbeas and Morgan (2017) discuss the use of state-space models for integrated population analyses. Such analyses comprise multiple data sets related to population dynamics, the models for which have some parameters in common (e.g.…”

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confidence: 99%

Guest editors’ introduction to the special issue on “Ecological Statistics”

Langrock

Borchers

2017

AStA Adv Stat Anal

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This special issue is a product of discussions between us and the journal editors after observing that only relatively few statisticians in Germany work on ecological data, in contrast (i) to other countries such as the UK, the US or Australia, where there are several large interdisciplinary research groups working almost exclusively on statistical ecology, and (ii) to other disciplines such as economics and medicine.In response to technological and general scientific developments-e.g. in the areas of remote sensing, satellite imaging, camera trapping and genotyping-the discipline of ecology is experiencing a small revolution driven by the new types of data generated by the technology. Many of these new data types, e.g. animal location data collected using global positioning systems (GPS), clearly contain important information on ecological processes and their drivers. Moreover, technology now enables biologists to study species such as marine mammals at much higher spatial and temporal resolution than was possible using more conventional data collection methods.These new kinds of data often generate new challenges for statisticians, such as dealing with complex dependence structures in space and time, missing data and measurement error, to name but a few. As a consequence, there is a rapidly growing demand within the field of ecology for statistical expertise and innovation. Here we argue, perhaps somewhat provocatively, that this demand is not currently being met within Germany and call for a greater exchange of ideas between ecologists and statisticians. B Roland Langrock

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Variance estimation for integrated population models

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 11 publications

References 36 publications

Exact Inference for Integrated Population Modelling

Exact Inference for Integrated Population Modelling

A general framework for modeling population abundance data

Guest editors’ introduction to the special issue on “Ecological Statistics”

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