Reconstructing shifts in vital rates driven by long‐term environmental change: a new demographic method based on readily available data

González, Edgar J.; Martorell, II Carlos

doi:10.1002/ece3.549

Cited by 7 publications

(17 citation statements)

References 48 publications

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“…We fit inverse models to infer demographic transitions without direct observation of those transitions (Wood ). For example, population size is the outcome of survival, growth and reproduction, and it can be used to infer parameter values for each of these vital rates in stage structured models (González & Martorell ) or in unstructured models (Besbeas, Lebreton & Morgan ). Using the survival and growth parameters inferred above, which is derived from extensive data, along with initial estimates of fecundity parameters, we inferred a fecundity value for all species groups that would (i) reflect approximate long‐term recruitment, so that the species had the potential to contribute to forest structure and (ii) demonstrate low overall sensitivity of dynamics to this estimated recruitment component.…”

Section: Methodsmentioning

confidence: 99%

Forest community response to invasive pathogens: the case of ash dieback in a British woodland

et al. 2016

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Summary Large‐scale mortality events in forests are increasing in frequency and intensity and can lead to both intermediate‐ and long‐term changes in these systems. Specialist pests and pathogens are unique disturbances, as they commonly target individual species that are relatively prevalent in the community. Understanding the consequences of pathogen‐caused mortality requires using sometimes limited available data to create statistical models that can forecast future community states. In the last two decades, ash dieback disease has swept through Europe causing widespread mortality of Fraxinus excelsior L. (European ash) across much of its distribution. In the UK, F. excelsior is an abundant and ecologically important species. Using demographic data from an 18 ha plot in Wytham Woods, Oxfordshire, we built models that forecast the response of this forest plot to the loss of F. excelsior. We combine integral projection models and individual‐based models to link models of growth, survival and fecundity to population dynamics. We demonstrate likely responses in Wytham by comparing projections under different levels of F. excelsior mortality. To extrapolate results to other systems, we test hypotheses regarding the role of abundance, spatial structure and demographic differences between species in determining community response to disease disturbance. We show that the outcome of succession is determined largely by the differing demographic strategies and starting abundances of competing species. Spatial associations between species were shown to have little effect on community dynamics at the spatial scale of this plot. Synthesis. Host‐specific pests and pathogens are an increasingly important type of disturbance. We have developed a framework that makes use of forest inventory data to forecast changes in the population dynamics of remaining species and the consequences for community structure. We use our framework to predict how a typical British woodland will respond to ash dieback disease and show how vital rates, spatial structure and abundance impact the community response to the loss of a key species.

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Section: Methodsmentioning

confidence: 99%

Forest community response to invasive pathogens: the case of ash dieback in a British woodland

et al. 2016

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“…By imposing bounds (as done by González & Martorell ) or priors on the parameters (or combinations of them), these scenarios can be avoided. However, we did not impose informative priors in our implementation of the inverse procedure, as we wanted to evaluate its ability to correctly reconstruct the vital rates in the absence of any information.…”

Section: Discussionmentioning

confidence: 99%

“…Ghosh, Gelfand & Clark (2012a) used an IPM to reconstruct the vital rates using population structures as input to a similar problem. González & Martorell () attempted an even harder problem: to reconstruct structured vital rates that change over time. These studies have shown that under different assumptions, using inverse modelling is a viable approach to estimate vital rates when only population‐level data are available.…”

Section: Introductionmentioning

confidence: 99%

Inverse estimation of integral projection model parameters using time series of population‐level data

2016

Self Cite

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Summary Integral projection models (IPMs) allow us to describe quantitatively the dynamics of a population structured by a continuous variable. They rely on information gathered at the individual level by recording survival, reproduction and changes in some structuring variable over time. This requires the ability to track individuals over the course of their entire life cycle. When this is not feasible, we would like to use alternative information to infer a population's dynamics. Time series of population‐level data are an option. An inverse modelling approach allows inferring the vital rates of a population when only population‐level data, in the form of a time series of the size of a population and the distribution of its individuals along a structuring variable, are available. The approach also allows incorporating estimates obtained through individual‐level data. Here, we explore how inverse modelling performs with simulated data and a relatively simple demographic model. We explore scenarios of data availability in terms of time‐series length, per‐year sample size and availability of independent vital‐rate estimates. We also test model performance in a real system using a 15‐year long data set from a chamaephyte plant, Cryptantha flava. We show that an inverse model can provide accurate reconstructions of the vital rates in a scenario where no individual‐level information is available. Better results can be obtained if independent estimates on any vital rate are provided, as was the case for C. flava where high interannual variation is present. Parameter estimation becomes more difficult with shorter time series, but per‐year sample size can be greatly reduced without significantly affecting parameter accuracy. Inverse modelling of IPMs allow for the estimation of unobserved vital rates, which is important for systems where any or all of the vital rates are hard to quantify. It also helps to determine whether a forward IPM is capturing the population dynamics: if the inverse version produces incorrect reconstructions of the vital rates, the forward IPM can be considered as inadequately describing the system.

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“…). For example, González and Martorell () obtained maximum‐likelihood estimates of kernel parameters for an IPM of a long‐lived cactus by fitting model simulations to a 12‐year time series of field observations (see also González et al. for a similar approach).…”

Section: Introductionmentioning

confidence: 99%

“…Our approach permits the estimation of uncertain local demographic parameters from a time series of population observations, while accounting for both process and measurement error in those data, and overcomes some limitations faced by prior efforts (Ghosh et al. , González and Martorell , González et al. ).…”

Section: Introductionmentioning

confidence: 99%

Fitting state‐space integral projection models to size‐structured time series data to estimate unknown parameters

White

Nickols

Malone

et al. 2016

Ecological Applications

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TitleFitting state-space integral projection models to size-structured time series data to estimate unknown parameters Abstract. Integral projection models (IPMs) have a number of advantages over matrixmodel approaches for analyzing size-structured population dynamics, because the latter require parameter estimates for each age or stage transition. However, IPMs still require appropriate data. Typically they are parameterized using individual-scale relationships between body size and demographic rates, but these are not always available. We present an alternative approach for estimating demographic parameters from time series of size-structured survey data using a Bayesian state-space IPM (SSIPM). By fitting an IPM in a state-space framework, we estimate unknown parameters and explicitly account for process and measurement error in a dataset to estimate the underlying process model dynamics. We tested our method by fitting SSIPMs to simulated data; the model fit the simulated size distributions well and estimated unknown demographic parameters accurately. We then illustrated our method using nine years of annual surveys of the density and size distribution of two fish species (blue rockfish, Sebastes mystinus, and gopher rockfish, S. carnatus) at seven kelp forest sites in California. The SSIPM produced reasonable fits to the data, and estimated fishing rates for both species that were higher than our Bayesian prior estimates based on coast-wide stock assessment estimates of harvest. That improvement reinforces the value of being able to estimate demographic parameters from local-scale monitoring data. We highlight a number of key decision points in SSIPM development (e.g., open vs. closed demography, number of particles in the state-space filter) so that users can apply the method to their own datasets.

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Reconstructing shifts in vital rates driven by long‐term environmental change: a new demographic method based on readily available data

Cited by 7 publications

References 48 publications

Forest community response to invasive pathogens: the case of ash dieback in a British woodland

Forest community response to invasive pathogens: the case of ash dieback in a British woodland

Inverse estimation of integral projection model parameters using time series of population‐level data

Fitting state‐space integral projection models to size‐structured time series data to estimate unknown parameters

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