Two‐scale dispersal estimation for biological invasions via synthetic likelihood

Barbu, Corentin; Sethuraman, Karthik; Billig, Erica; Levy, Michael Z.

doi:10.1111/ecog.02575

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…We divided the presence data on the invaded region into training and testing data sets for each species based on the date the species were first reported in a given locality. This approach is more robust than randomly selecting training and testing presence data [19, 49, 50]. It also allows the evaluation of each model using a temporally independent validation to recreate the invasion process followed by the species and investigate if the patterns detected by the models at time 1 are similar at time 2[37, 51, 52].…”

Section: Methodsmentioning

confidence: 99%

Improving Species Distribution Modelling of freshwater invasive species for management applications

et al. 2019

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Freshwater ecosystems rank among the most endangered ecosystems in the world and are under increasing threat from aquatic invasive species (AIS). Understanding the range expansion of AIS is key for mitigating their impacts. Most approaches rely on Species Distribution Models (SDMs) to predict the expansion of AIS, using mainly environmental variables, yet ignore the role of human activities in favouring the introduction and range expansion of AIS. In this study, we use five SDM algorithms (independently and in ensemble) and two accuracy measures (TSS, AUC), combined with a null modelling approach, to assess the predictive performance of the models and to quantify which predictors (environmental and anthropogenic from the native and introduced regions) best explain the distribution of nine freshwater invasive species (including fish, arthropods, molluscs, amphibians and reptiles) in a large island (Great Britain), and which species characteristics affect model performance. Our results show that the distribution of invasive species is difficult to predict by SDMs, even in cases when TSS and AUC model accuracy values are high. Our study strongly advocates the use of null models for testing SDMs performance and the inclusion of information from the native area and a variety of both human-related and environmental predictors for a more accurate modelling of the range expansion of AIS. Otherwise, models that only include climatic variables, or rely only on standard accuracy measures or a single algorithm, might result in mismanagement of AIS.

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Section: Methodsmentioning

confidence: 99%

Improving Species Distribution Modelling of freshwater invasive species for management applications

et al. 2019

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“…Since T. infestans migration is heterogeneous, we incorporate this spatial heterogeneity into the hazard function, h ij (t) [15]. It is important to note that any number of spatial kernels can be implemented into this approach.…”

Section: Spatial Dynamicsmentioning

confidence: 99%

A real-time search strategy for finding urban disease vector infestations

Rose

Roy

Castillo-Neyra

et al. 2020

Epidemiologic Methods

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ObjectivesContaining domestic vector infestation requires the ability to swiftly locate and treat infested homes. In urban settings where vectors are heterogeneously distributed throughout a dense housing matrix, the task of locating infestations can be challenging. Here, we present a novel stochastic compartmental model developed to help locate infested homes in urban areas. We designed the model using infestation data for the Chagas disease vector species Triatoma infestans in Arequipa, Peru.MethodsOur approach incorporates disease vector counts at each observed house, and the vector’s complex spatial dispersal dynamics. We used a Bayesian method to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigated the properties of the model through simulation studies, followed by field testing in Arequipa.ResultsSimulation studies showed the model to be accurate in its estimates of two parameters of interest: the growth rate of a domestic triatomine bug colony and the probability of a triatomine bug successfully invading a new home after dispersing from an infested home. When testing the model in the field, data collection using model estimates was hindered by low household participation rates, which severely limited the algorithm and in turn, the model’s predictive power.ConclusionsWhile future optimization efforts must improve the model’s capabilities when household participation is low, our approach is nonetheless an important step toward integrating data with predictive modeling to carry out evidence-based vector surveillance in cities.

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“…Given the wide applicability of BSL (e.g. Karabatsos, 2018;Barbu et al, 2018), it is important that BSL methods are directly accessible to practitioners. In this paper we introduce our BSL R package, which implements the Bayesian version of synthetic likelihood and many of the extensions listed earlier together with additional functionality detailed later in the paper.…”

Section: ∝ P(y|θ)p(θ)mentioning

confidence: 99%

BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood

An¹,

South²,

Drovandi³

2019

Preprint

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Bayesian synthetic likelihood (BSL) (Price et al., 2018) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a computationally intractable likelihood function. Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared to alternative methods such as approximate Bayesian computation (ABC), BSL requires little tuning and requires less model simulations than ABC when the chosen summary statistic is high-dimensional. The original synthetic likelihood relies on a multivariate normal approximation of the intractable likelihood, where the mean and covariance are estimated by simulation. An extension of BSL considers replacing the sample covariance with a penalised covariance estimator to reduce the number of required model simulations. Further, a semi-parametric approach has been developed to relax the normality assumption. In this paper, we present an R package called BSL that amalgamates the aforementioned methods and more into a single, easy-to-use and coherent piece of software. The R package also includes several examples to illustrate how to use the package and demonstrate the utility of the methods.

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Two‐scale dispersal estimation for biological invasions via synthetic likelihood

Cited by 13 publications

References 52 publications

Improving Species Distribution Modelling of freshwater invasive species for management applications

Improving Species Distribution Modelling of freshwater invasive species for management applications

A real-time search strategy for finding urban disease vector infestations

BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood

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