Population viability analysis for several populations using multivariate state-space models

Hinrichsen, Richard A.

doi:10.1016/j.ecolmodel.2009.02.014

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Many studies of extinction risk have shown that accounting for the pattern and level of temporal correlation is critical in metapopulation forecasting (e.g. the recent study by Hinrichsen 2009). In our forecasts of the population as a whole, we found that the risks of decline are low, essentially because of the South subpopulation which is both robust and acting independently of the other subpopulations.…”

Section: Discussionmentioning

confidence: 99%

Inferring spatial structure from time‐series data: using multivariate state‐space models to detect metapopulation structure of California sea lions in the Gulf of California, Mexico

Ward

Chirakkal

González‐Suárez

et al. 2010

Journal of Applied Ecology

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Summary 1.Understanding spatial structure and identifying subpopulations are critical for estimating population growth rates and extinction risk, and as such essential for effective conservation planning. However, movement and spatiotemporal environmental data are often unavailable, limiting our ability to directly define subpopulations and their level of asynchrony. 2. This study applies a recently developed statistical technique using time-series analysis of abundance data to identify subpopulations. The approach uses multivariate state-space models and Akaike's Information Criterion-based model selection to quantify the data support for different subpopulation numbers and configurations. This technique is applied to the population of California sea lions Zalophus californianus in the Gulf of California, Mexico, distributed across 13 breeding sites. 3. The abundance of California sea lions in the Gulf of California has declined over the last decade, though not all areas have been equally affected. In light of this variation, it is important to understand the population structure to ensure accurate viability assessments and effective management. 4. Our data support the hypothesis that the Gulf of California sea lion population has four subpopulations, each with 2-5 breeding sites. The dynamics between several adjacent subpopulations were correlated, suggesting that they experience similar environmental variation. For each subpopulation, we estimated long-term growth rates, as well as the environmental and observation variation. 5. For most of the subpopulations, our estimates of growth rates were considerably lower than those previously reported. In addition, we found considerable variability across subpopulations in their projected risk of severe decline over the next 50 years. 6. Synthesis and applications. We illustrate a new multivariate state-space modelling technique that uses time series of abundance to quantify the data support for different subpopulation configurations. Our analysis of the California sea lion population in the Gulf of California indicates that the population is spatially structured into four subpopulations, each exhibiting distinct risks of extinction. Based on our results, we recommend that conservation and management efforts in the Gulf of California focus on the two subpopulations with high probabilities of extinction within the next 50 years (Northern Midriff, Southern Midriff). Multivariate state-space models provide a practical approach to determine the spatial structure of virtually any species; they may be particularly useful for species of conservation concern for which data on dispersal and environmental drivers are likely to be scarce.

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Section: Discussionmentioning

confidence: 99%

Inferring spatial structure from time‐series data: using multivariate state‐space models to detect metapopulation structure of California sea lions in the Gulf of California, Mexico

Ward

Chirakkal

González‐Suárez

et al. 2010

Journal of Applied Ecology

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“…Generating model. This was also a state-space model, given that we made inferences on unknown states based on observations, states were interlinked and dependent on one another, and states were modelled as parameters (Hinrichsen, 2009). We used a simple logistic growth model incorporating both environmental and demographic stochasticity (Ovaskainen & Meerson, 2010).…”

Section: Ecological Modelsmentioning

confidence: 99%

“…We estimated population rates using time-series of simulated population density data, which were modelled with observation error. This was also a state-space model, given that we made inferences on unknown states based on observations, states were interlinked and dependent on one another, and states were modelled as parameters (Hinrichsen, 2009). In notation,…”

Section: Ecological Modelsmentioning

confidence: 99%

The value of a datum – how little data do we need for a quantitative risk analysis?

Leung¹,

Steele

2013

Diversity and Distributions

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Aim Conservation managers are typically faced with limited resources, time and information. The philosophy underlying risk assessment should be robust to these limitations. While there is a broad support for the concept of risk assessments, there is a tendency to rely on expert opinion and exclude formal data analysis, possibly because available information is often scarce. When data analyses are conducted, often much simplified models are advocated, even though this means excluding processes believed by experts to be important. In this manuscript, we ask: should statistical analyses be conducted and decisions modified based on a single datum? How many data points are needed before predictions are meaningful? Given limited data, how complex should models be?Location World-wide.Methods We use simulation approaches with known 'true' values to assess which inferences are possible, given different amounts of information. We use two metrics of performance: the magnitude of uncertainty (using posterior mean squared error) and bias (using P-P plots). We assess six models of relevance to conservation ecologists. Results We show that the greatest reduction in uncertainty occurred at the smallest sample sizes for models examined, and much of parameter space could be excluded. Thus, analyses based on even a single datum potentially can be useful. Further, with only a few observations, the predicted distribution of outcomes matched the probabilities of actual occurrences, even for relatively complex state-space models with multiple sources of stochasticity.Main conclusions We highlight the utility of quantitative analyses even with severely limited data, given existing practices and arguments in the conservation literature. The purpose of our manuscript is in part a philosophical discourse, as modifications are needed to how conservation ecologists are often trained to think about problems and data, and in part a demonstration via simulation analysis.

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“…Statespace models have outperformed process-error-only and observation-error-only models in a variety of nonconservation situations (De Valpine 2002, De Valpine and Hastings 2002, Lele et al 2007. They have been applied in a variety of ecological contexts, including fisheries (Meyer and Millar 1999, Millar and Meyer 2000, De Valpine and Hilborn 2005, Hinrichsen 2009, Russell et al 2012, bird populations (Williams et al 2003, J. W. Connelly et al 2004Jamieson and Brooks 2004, Dennis et al 2006, Hefley et al 2013b), animal movement (Newman 2000, Buckland et al 2004, Newman et al 2006, Buckland et al 2007, and plankton communities .…”

Section: Introductionmentioning

confidence: 99%

“…Dennis et al (2010) used a form of the CSEG model that includes density dependence to demonstrate how one additional observation per time step leads to steeper likelihood profiles for each parameter, meaning that parameters are more identifiable, even if the time series were half as long. Hinrichsen (2009) examined the effect of incorporating data from nearby populations and assuming correlated process errors to improve the estimates of mean trend and process error variance. Knape et al (2013) explored the effect of a range of spatial replicates (2-10) on parameter estimates from a model similar to the model employed by Dennis et al (2010), using several different maximum likelihood techniques.…”

Section: Introductionmentioning

confidence: 99%

Reducing bias and improving precision in species extinction forecasts

See

Holmes

2015

Ecological Applications

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Forecasting the risk of population decline is crucial in the realm of biological conservation and figures prominently in population viability analyses (PVA). A common form of available data for a PVA is population counts through time. Previous research has suggested that improving estimates of population trends and risk from count data depends on longer observation periods, but that is often impractical or undesirable. Making multiple observations within a single time step is an alternative way to gather more data without extending the observation period. In this paper, we examine the trade-off between the length of the time period over which observations of the population have been taken and the total number of observations or samples that have been recorded through an analysis of simulated data. We found that when the ratio of process error to measurement error variance is high, more precise estimates of quasi-extinction risks can be obtained if replicated observations are taken at each time step, but when the ratio is low, replicated observations add little benefit in improving precision. These results can be used to efficiently design effective monitoring schemes for species of conservation concern.

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Population viability analysis for several populations using multivariate state-space models

Cited by 12 publications

References 15 publications

Inferring spatial structure from time‐series data: using multivariate state‐space models to detect metapopulation structure of California sea lions in the Gulf of California, Mexico

Inferring spatial structure from time‐series data: using multivariate state‐space models to detect metapopulation structure of California sea lions in the Gulf of California, Mexico

The value of a datum – how little data do we need for a quantitative risk analysis?

Reducing bias and improving precision in species extinction forecasts

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