Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Stanaway, Mark; Mengersen, Kerrie; Reeves, Robert W.

doi:10.1007/s10651-010-0152-x

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…This problem has also been noted in studies dealing with biological invasions (Abad-Franch et al 2010;Regan et al 2010;Stanaway et al 2011). Given that amphibian detection often varies over space and time (Mazerolle et al 2007;Curtis and Paton 2010;Gómez-Rodríguez et al 2010), our ability to produce correct inferences from co-occurrence patterns was heavily compromised by hypothetic false-null values.…”

Section: Study Area and Sampling Designmentioning

confidence: 94%

Multi-event models reveal the absence of interaction between an invasive frog and a native endangered amphibian

Cayuela

Besnard

Joly³

2013

Biol Invasions

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International audienceAmong the multiple factors involved in the decline of amphibians, competition with alien species remains understudied. In Western Europe, non-native marsh frogs (Pelophylax ridibundus and other sister species) have been widely imported for gastronomic purposes and have then often escaped from captivity. Apart from closely related species, the impact of the subsequent colonization of non-native species on native amphibians is still unknown. In this study, we analysed the response of a threatened species, the yellow-bellied toad (Bombina variegata), faced with the presence of alien frogs in mountain rivers in France. We studied the co-occurrence pattern of the native toad and the alien frog, while taking into account co-dependence of the detection rates in both species. We tested three main scenarios to explain a non-random pond occur- rence for each species and to establish their respective habitat preferenda: (1) a pond-area-dependent scenario, predicting different responses from each species for a given pond size, (2) a fish-dependent scenario, predict- ing different responses from each species given the presence of fish in ponds, and (3) a floodplain-width- dependent scenario, predicting different responses from each species given the geomorphological characteristics of the floodplain. Taking into account site-specific covariates, we concluded that introduced frogs do not currently have an impact on the native population of the yellow-bellied toad

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Section: Study Area and Sampling Designmentioning

confidence: 94%

Multi-event models reveal the absence of interaction between an invasive frog and a native endangered amphibian

Cayuela

Besnard

Joly³

2013

Biol Invasions

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“…Inferences can be used to disentangle aspects of the invasion dynamics itself from patterns of data collection, develop improved future surveying schemes, and design more efficient invasion management strategies. the invasion, misguiding management efforts, and misjudging potential future impacts (Stanaway et al 2011).…”

Section: Accounting For Imperfect Observation and Estimating True Spementioning

confidence: 99%

“…Imperfect detection introduces two sorts of errors into spatio‐temporal distribution data of range‐expanding species: firstly, sites may appear currently uninvaded although the species is already present, resulting in false absences; and secondly, the documented time of first colonisation of a site may lag considerably behind the actual invasion of the species. Besides potentially biasing parameter estimates of invasive spread models, these errors bear the risk of underestimating the current extent of the invasion, misguiding management efforts, and misjudging potential future impacts (Stanaway et al ).…”

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confidence: 99%

Accounting for imperfect observation and estimating true species distributions in modelling biological invasions

et al. 2017

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The documentation of biological invasions is often incomplete with records lagging behind the species’ actual spread to a spatio‐temporally heterogeneous extent. Such imperfect observation bears the risk of underestimating the already realised distribution of the invading species, misguiding management efforts and misjudging potential future impacts. In this paper, we develop a hierarchical modelling framework which disentangles the determinants of the invasion and observation processes, models spatio‐temporal heterogeneity in detection patterns, and infers the actual, yet partly undocumented distribution of the species at any particular time. We illustrate the model with a case study application to the invasion of common ragweed Ambrosia artemisiifolia in Austria. The invasion part of the model reconstructs the historical spread of this species across a grid of ∼ 6 × 6 km2 cells as driven by spatio‐temporal variation in physical site conditions, propagule production, dispersal, and ‘background’ introductions from unknown sources. The observation part models the detection of the species’ occurrences based on heterogeneous sampling efforts, human population density, and estimated local invasion level. We fitted the hierarchical model using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC). The actual spread of A. artemisiifolia concentrated on the climatically well‐suited lowlands and was mainly driven by spatio‐temporal propagule pressure from source cells with long‐distance dispersal occurring rather frequently. Annual detection probabilities were estimated to vary between about 1 and up to 28%, depending mainly on sampling intensity. The model suggested that by 2005 about half of the actual distribution of the species was not yet documented. Our hierarchical model offers a flexible means to account for imperfect observation and spatio‐temporal variability in detection efficiency. Inferences can be used to disentangle aspects of the invasion dynamics itself from patterns of data collection, develop improved future surveying schemes, and design more efficient invasion management strategies.

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“…However, because long-distance dispersal events have a greater impact on rates of spread than short-distance dispersal events, monitoring outlying populations remains critically important (Liebhold and Tobin 2008). In addition, because species' range expansion is the result of combined local population growth and dispersal (Liebhold and Tobin 2008), the development of tools to predict within-site population growth would provide valuable insights into why and when a newly invaded site becomes a source of emigrants (Stanaway et al 2011), Current work modeling M. schimitscheki population dynamics in relation to within-site biotic factors, such as host plant seed masting cycles and interspecific competition with the conspecific M. pinsapinis, will help refine our ability to focus monitoring and control efforts for this invasive wasp.…”

Section: Implications For Invasion Monitoring and Landscape Managementmentioning

confidence: 99%

Reconstruction of a windborne insect invasion using a particle dispersal model, historical wind data, andBayesian analysis of genetic data

Lander

Klein

Oddou‐Muratorio

et al. 2014

Ecology and Evolution

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Understanding how invasive species establish and spread is vital for developing effective management strategies for invaded areas and identifying new areas where the risk of invasion is highest. We investigated the explanatory power of dispersal histories reconstructed based on local-scale wind data and a regional-scale wind-dispersed particle trajectory model for the invasive seed chalcid wasp Megastigmus schimitscheki (Hymenoptera: Torymidae) in France. The explanatory power was tested by: (1) survival analysis of empirical data on M. schimitscheki presence, absence and year of arrival at 52 stands of the wasp's obligate hosts, Cedrus (true cedar trees); and (2) Approximate Bayesian analysis of M. schimitscheki genetic data using a coalescence model. The Bayesian demographic modeling and traditional population genetic analysis suggested that initial invasion across the range was the result of long-distance dispersal from the longest established sites. The survival analyses of the windborne expansion patterns derived from a particle dispersal model indicated that there was an informative correlation between the M. schimitscheki presence/absence data from the annual surveys and the scenarios based on regional-scale wind data. These three very different analyses produced highly congruent results supporting our proposal that wind is the most probable vector for passive long-distance dispersal of this invasive seed wasp. This result confirms that long-distance dispersal from introduction areas is a likely driver of secondary expansion of alien invasive species. Based on our results, management programs for this and other windborne invasive species may consider (1) focusing effort at the longest established sites and (2) monitoring outlying populations remains critically important due to their influence on rates of spread. We also suggest that there is a distinct need for new analysis methods that have the capacity to combine empirical spatiotemporal field data, genetic data, and environmental data to investigate dispersal and invasion.

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Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Cited by 11 publications

References 38 publications

Multi-event models reveal the absence of interaction between an invasive frog and a native endangered amphibian

Multi-event models reveal the absence of interaction between an invasive frog and a native endangered amphibian

Accounting for imperfect observation and estimating true species distributions in modelling biological invasions

Reconstruction of a windborne insect invasion using a particle dispersal model, historical wind data, andBayesian analysis of genetic data

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