Improving Transferability of Introduced Species’ Distribution Models: New Tools to Forecast the Spread of a Highly Invasive Seaweed

Verbruggen, Heroen; Tyberghein, L.; Belton, Gareth S.; Mineur, Frédéric; Jueterbock, Alexander; Hoarau, Galice; Gurgel, Carlos Frederico D.; Clerck, Olivier De

doi:10.1371/journal.pone.0068337

Cited by 97 publications

(89 citation statements)

References 77 publications

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“…Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5].…”

Section: Predicting the Unknownmentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

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Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

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Section: Predicting the Unknownmentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

Self Cite

435

456

View full text Add to dashboard Cite

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“…OccurrenceThinner uses probability algorithms to remove occurrence records based on an associated kernel density grid. The probability that an occurrence will be removed is proportional to occurrence density described by the kernel density grid (Verbruggen et al 2013). Due to the extremely high density of occurrences in some regions (e.g.…”

Section: Data Sources and Preparationmentioning

confidence: 99%

Niche overlap of mountain hare subspecies and the vulnerability of their ranges to invasion by the European hare; the (bad) luck of the Irish

et al. 2016

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Niche conservatism is the tendency of related species to retain ancestral tolerances after geographic separation. We used Ecological Niche Modelling and Principal Components Analysis of bioclimatic and habitat variables to describe the extent of the species niche, and degrees of bioclimatichabitat niche conservatism within the mountain hare (L. timidus) clade. Mountain hare niche space was contrasted with that of the European hare (L. europaeus), to shed light on species interactions in contact zones throughout Europe. All five subspecies of mountain hare had quantifiably distinct niches. Fennoscandian (L.t. sylvaticus, L.t. timidus) and highland (L.t. scoticus, L.t. varronis) subspecies, however, were most similar, exhibiting greatest apparent niche conservatism. They inhabit tundra, boreal forest and uplands, and, hence are presumed most similar to the ancestral form. The Irish hare was distinct, being consistently distinguished from other mountain hares in both 2D and nth dimensional (4D) niche space. The ecological distinctiveness of the Irish hare provides further evidence that it is an Evolutionarily Significant Unit, particularly vulnerable to Electronic supplementary material The online version of this article

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“…The first application of SDMs to marine invaders was published in 2006 (Inglis et al, 2006). Since then, other publications presented new tools for forecasting invasion patterns (Johnston and Purkis, 2012) and other research efforts have focused on improving the transferability of SDMs for introduced species (Verbruggen et al, 2013).…”

Section: Study Goalsmentioning

confidence: 99%

A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

et al. 2017

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In the marine environment Species Distribution Models (SDMs) have been used in hundreds of papers for predicting the present and future geographic range and environmental niche of species. We have analyzed ways in which SDMs are being applied to marine species in order to recommend best practice in future studies. This systematic review was registered as a protocol on the Open Science Framework: https:// osf.io/tngs6/. The literature reviewed (236 papers) was published between 1992 and July 2016. The number of papers significantly increased through time (R 2 = 0.92, p < 0.05). The studies were predominantly carried out in the Temperate Northern Atlantic (45%) followed by studies of global scale (11%) and studies in Temperate Australasia (10%). The majority of studies reviewed focused on theoretical ecology (37%) including investigations of biological invasions by non-native organisms, conservation planning (19%), and climate change predictions (17%). Most of the studies were published in ecological, multidisciplinary, or biodiversity conservation journals. Most of the studies (94%) failed to report the amount of uncertainty derived from data deficiencies and model parameters. Best practice recommendations are proposed here to ensure that novice and advanced SDM users can (a) understand the main elements of SDMs, (b) reproduce standard methods and analysis, and (c) identify potential limitations with their data. We suggest that in the future, studies of marine SDMs should report on key features of the approaches employed, data deficiencies, the selection of the best explanatory model, and the approach taken to validate the SDM results. In addition, based on the literature reviewed, we suggest that future marine SDMs should account for uncertainty levels as part of the modeling process.

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Improving Transferability of Introduced Species’ Distribution Models: New Tools to Forecast the Spread of a Highly Invasive Seaweed

Cited by 97 publications

References 77 publications

Outstanding Challenges in the Transferability of Ecological Models

Outstanding Challenges in the Transferability of Ecological Models

Niche overlap of mountain hare subspecies and the vulnerability of their ranges to invasion by the European hare; the (bad) luck of the Irish

A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice

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