Predicting the spread of invasive species in an uncertain world: accommodating multiple vectors and gaps in temporal and spatial data for Bythotrephes longimanus

Gertzen, E. L.; Leung, Brian

doi:10.1007/s10530-011-0082-z

Cited by 17 publications

(17 citation statements)

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“…We have also shown that our method can be used to estimate the magnitude of deviation of probabilistic predictions from reality using empirical observations, along with the associated uncertainty in these estimates (Jolliffe ). Moreover, we applied it to a real‐world non‐trivial ecological model (Gertzen & Leung ), comparing a priori model predictions to >100 independently sampled locations for validation. A complete validation of binary prediction models in ecology using data independent of that used to construct a model is rarely carried out in practice (Guisan & Thuiller ).…”

Section: Discussionmentioning

confidence: 99%

“…We then assess the properties of VMAPP theoretically, testing its performance in terms of statistical power to identify miscalibrated predictions, as well as evaluating the accuracy of the estimator of model deviations from reality across the range of model predictions. We then demonstrate the utility of VMAPP via application to a published model in invasion ecology for which validation data have since been collected (Gertzen & Leung ).…”

Section: Methodsmentioning

confidence: 99%

“…The predictive model outlined in the next section was built using the data collected during 2005/2006 as well as the historical record of confirmed invasions dating back to 1989. For validation, an additional 102 lakes were selected to be compared against the predictions from Gertzen & Leung (). The validation lakes were chosen to cover the full extent of the prediction range (low, medium and high predicted probabilities) and were sampled in 2010.…”

Section: Methodsmentioning

confidence: 99%

“…The modelling framework for forecasting the spatial spread of Bythotrephes is described in detail in Gertzen & Leung (). This methodology incorporates several levels of real‐world uncertainty involved in predicting the trajectory of the invasion process.…”

Section: Methodsmentioning

confidence: 99%

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Validation and calibration of probabilistic predictions in ecology

2014

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Summary1. Predictive models in ecology are important for guiding policy and management. However, they are necessarily abstractions of natural systems, making predictive validation imperative. Models, which make predictions about binary outcomes (e.g. Species distribution models, population viability analysis, disease/invasion models), are widespread in the ecological literature. When supporting probability-based management decisions, these predictions need to be assessed with respect to the degree to which predicted probabilities agree with future outcomes. Many predictive models are not validated using external data and are often only assessed in terms of their ability to discriminate between outcomes rather than the degree to which they predicted the correct probabilities. 2. We develop a novel Validation Metric Applied to Probabilistic Predictions (VMAPP), which provides a goodness-of-fit test of calibration for probabilistic prediction models using binary data (e.g. presences and absences in models of species distributions). We analyse the theoretic properties of this test and compare its performance against existing methods, and apply it to a published model in invasion biology, which forecasts the establishment probability of the zooplanktivorous spiny water flea (Bythotrephes longimanus). We selected 102 additional sites to sample four years after the training data were collected and use this independently collected data to assess predictive reliability using VMAPP.3. Theoretic simulation analysis shows that VMAPP outperforms existing metrics (Cox's regression technique and Hosmer & Lemeshow's v 2 test) in terms of statistical power to identify model miscalibration. Further, we find that under realistic conditions where model parameters are estimated (and have associated uncertainty) that VMAPP is more robust, retaining the appropriate type-I error rates (5%) where previous metrics fail (≤17%). Application of VMAPP to a published invasion model using empirical validation data shows that in addition to having high discriminative power, the model's probabilistic predictions agree with the observed outcomes as measured by VMAPP. 4. We argue that quantifying ecological predictions as probabilities with associated uncertainty provides the most useful information to support management decisions. Ecological predictions, while uncertain, should still be rigorously validated. Identifying the circumstances in which our predictions deviate from observation can further inform the next generation of the model, bringing prediction and reality ever closer.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Validation and calibration of probabilistic predictions in ecology

2014

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“…Which is the total boater traffic from all invaded sources to lake j in time step t . For more details, see Gertzen & Leung (). While each human vector model predicts a unique trip distribution matrix, the total number of boater trips taken, or the overall magnitude of traffic flow in the system as a whole, is constant across both models.…”

Section: Methodsmentioning

confidence: 99%

Predicting invasions: alternative models of human‐mediated dispersal and interactions between dispersal network structure and Allee effects

Chivers

Leung

2012

Journal of Applied Ecology

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Summary1. Human-mediated dispersal has been shown to be the most important vector for the spread of invasive species, yet there has been little evaluation of alternative models of dispersal in terms of differences in their predictions of invasion patterns. Moreover, no analyses have been attempted to elucidate the potential interaction between alternative models of humanmediated dispersal and population dynamical characteristics, such as Allee effects, which are central to the probability of an invasion. 2. Two prominent models in the literature which have previously been employed to predict human movement patterns are explored: (i) gravity models, which use the attractiveness of and distance to a location to predict travel patterns, and (ii) random utility models, which assume that individuals decide where to travel by maximizing the benefits that they receive according to some partially observable function of individual and site characteristics. 3. While distinction is often drawn between them in the literature, we demonstrate that these two approaches can be reduced to alternative functional forms describing the trip-taking decisions of individuals. 4. Each model was empirically parameterized using a survey of recreational boaters in Ontario, Canada. Within each model, both boater-and site-specific characteristics were important and the functional form provided by the gravity model was significantly better at capturing the behaviour of recreationalboaters. 5. Synthesis and applications. The dispersal and establishment of species into novel habitats are central components of the invasion process and of quantitative risk assessments. However, predictions are dependent on the estimated spatial structure of the dispersal network and its potential interactions with species characteristics. This study demonstrates that Allee effects can interact with dispersal network structure to significantly alter predicted spread rates and that the consequences of these interactions manifest differently at the system and site levels. This modelling framework can be used to inform management interventions aimed at modifying human-mediated dispersal to reduce the spread of invasive species.

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A new baseline for countrywide α‐diversity and species distributions: illustration using >6,000 plant species in Panama

Leung

Hudgins

Potapova

et al. 2019

Ecological Applications

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Estimating α‐diversity and species distributions provides baseline information to understand factors such as biodiversity loss and erosion of ecosystem services. Yet, species surveys typically cover a small portion of any country's landmass. Public, global databases could help, but contain biases. Thus, the magnitude of bias should be identified and ameliorated, the value of integration determined, and application to current policy issues illustrated. The ideal integrative approach should be powerful, flexible, efficient, and conceptually straightforward. We estimated distributions for >6,000 species, integrating species sightings (S) from the Global Biodiversity Information Facility (GBIF), systematic survey data (S2), and “bias‐adjustment kernels” (BaK) using spatial and species trait databases (S2BaK). We validated our approach using both locational and species holdout sets, and then applied our predictive model to Panama. Using sightings alone (the most common approach) discriminated relative probabilities of occurrences well (area under the curve [AUC] = 0.88), but underestimated actual probabilities by ~4,000%, while using survey data alone omitted over three‐quarters of the >6,000 species. Comparatively, S2BaK had no systematic underestimation, and substantially stronger discrimination (AUC = 0.96) and predictive power (deviance explained = 47%). Our model suggested high diversity (~200% countrywide mean) where urban development is projected to occur (the Panama Canal watershed) and also suggested this is not due to higher sampling intensity. However, portions of the Caribbean coast and eastern Panama (the Darién Gap) were even higher, both for total plant biodiversity (~250% countrywide mean), and CITES listed species. Finally, indigenous territories appeared half as diverse as other regions, based on survey observations. However, our model suggested this was largely due to site selection, and that richness in and out of indigenous territories was roughly equal. In brief, we provide arguably the best estimate of countrywide plant α‐diversity and species distributions in the Neotropics, and make >6,000 species distributions available. We identify regions of overlap between development and high biodiversity, and improve interpretation of biodiversity patterns, including for policy‐relevant CITES species, and locations with limited access (i.e., indigenous territories). We derive a powerful, flexible, efficient and simple estimation approach for biodiversity science.

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Predicting the spread of invasive species in an uncertain world: accommodating multiple vectors and gaps in temporal and spatial data for Bythotrephes longimanus

Cited by 17 publications

References 42 publications

Validation and calibration of probabilistic predictions in ecology

Validation and calibration of probabilistic predictions in ecology

Predicting invasions: alternative models of human‐mediated dispersal and interactions between dispersal network structure and Allee effects

A new baseline for countrywide α‐diversity and species distributions: illustration using >6,000 plant species in Panama

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