Antarctic and Sub-Antarctic Asteroidea database

Moreau, Camille; Mah, Christopher L.; Agüera, Antonio; Améziane, Nadia; Barnes, David K. A.; Crokaert, Guillaume; Eléaume, Marc; Griffiths, Huw J.; Guillaumot, Charlène; Hemery, Lenaïg G.; Jażdżewska, Anna; Jossart, Quentin; Laptikhovsky, Vladimir; Linse, Katrin; Neill, Kate F.; Sands, Chester J.; Saucède, Thomas; Schiaparelli, Stefano; Siciński, Jacek; Vasset, Noémie; Danis, Bruno

doi:10.3897/zookeys.747.22751

Cited by 14 publications

(16 citation statements)

References 17 publications

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“…The biology, ecology and distribution of these species have been extensively studied and are relatively well documented (McClintock et al 2008, Mah and Blake 2012, Lawrence 2013. Presence-only records were compiled from a recently updated database, thoroughly scrutinised with the World Register of Marine Species (WoRMS Editorial Board 2016), to delete potential discrepancies, update taxonomy and correct for georeferencing errors (Moreau et al 2018).…”

Section: Studied Species and Environmental Descriptorsmentioning

confidence: 99%

Extrapolation in species distribution modelling. Application to Southern Ocean marine species

Guillaumot

Moreau

Danis

et al. 2020

Progress in Oceanography

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Species distribution modelling (SDM) has been increasingly applied to Southern Ocean case studies over the past decades, to map the distribution of species and highlight environmental settings driving species distribution. Predictive models have been commonly used for conservation purposes and supporting the delineation of marine protected areas, but model predictions are rarely associated with extrapolation uncertainty maps.In this study, we used the Multivariate Environmental Similarity Surface (MESS) index to quantify model uncertainty associated to extrapolation. Considering the reference dataset of environmental conditions for which species presence-only records are modelled, extrapolation corresponds to the part of the projection area for which one environmental value at least falls outside of the reference dataset.Six abundant and common sea star species of marine benthic communities of the Southern Ocean were used as case studies. Results show that up to 78% of the projection area is extrapolation, i.e. beyond conditions used for model calibration. Restricting the projection space by the known species ecological requirements (e.g. maximal depth, upper temperature tolerance) and increasing the size of presence datasets were proved efficient to reduce the proportion of extrapolation areas. We estimate that multiplying sampling effort by 2 or 3-fold should help reduce the proportion of extrapolation areas down to 10% in the six studied species.Considering the unexpectedly high levels of extrapolation uncertainty measured in SDM predictions, we strongly recommend that studies report information related to the level of extrapolation. Waiting for improved datasets, adapting modelling methods and providing such uncertainy information in distribution modelling studies are a necessity to accurately interpret model outputs and their reliability.

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Section: Studied Species and Environmental Descriptorsmentioning

confidence: 99%

Extrapolation in species distribution modelling. Application to Southern Ocean marine species

Guillaumot

Moreau

Danis

et al. 2020

Progress in Oceanography

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“…SDMs were generated using presence-only data made available for the sea star O. validus by Moreau et al (2018). Presence-only records of O. validus are strongly aggregated in space (i.e.…”

Section: Occurrence Datasetmentioning

confidence: 99%

Broad-scale species distribution models applied to data-poor areas

Guillaumot

Artois

Saucède

et al. 2019

Progress in Oceanography

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Species distribution models (SDMs) have been increasingly used over the past decades to characterise the spatial distribution and the ecological niche of various taxa. Validating predicted species distribution is important, especially when producing broad-scale models (i.e. at continental or oceanic scale) based on limited and spatially aggregated presence-only records. In the present study, several model calibration methods are compared and guidelines are provided to perform relevant SDMs using a Southern Ocean marine species, the starfish Odontaster validus Koehler, 1906, as a case study. The effect of the spatial aggregation of presence-only records on modelling performance is evaluated and the relevance of a target-background sampling procedure to correct for this effect is assessed. The accuracy of model validation is estimated using k-fold random and spatial cross-validation procedures. Finally, we evaluate the relevance of the Multivariate Environmental Similarity Surface (MESS) index to identify areas in which SDMs accurately interpolate and conversely, areas in which models extrapolate outside the environmental range of occurrence records. Results show that the random cross-validation procedure (i.e. a widely applied method, for which training and test records are randomly selected in space) tends to overestimate model performance when applied to spatially aggregated datasets. Spatial cross-validation procedures can compensate for this over-estimation effect but different spatial cross-validation procedures must be tested for their ability to reduce over-fitting while providing relevant validation scores. Model predictions show that SDM generalisation is limited when working with aggregated datasets at broad spatial scale. The MESS index calculated in our case study show that over half of the predicted area is highly uncertain due to extrapolation. Our work provides methodological guidelines to generate accurate model assessments at broad spatial scale when using limited and aggregated presence-only datasets. We highlight the importance of taking into account the presence of spatial aggregation in species records and using non-random cross-validation procedures. Evaluating the best calibration procedures and correcting for spatial biases should be considered ahead the modelling exercise to improve modelling relevance.

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“…This long-lasting and irregular effort in biogeographical (occurrence) data collection has had an impact on data compilation and has resulted in heterogeneous datasets, as observed in several data papers and associated Integrated Publishing Toolkit (IPT) databases such as Guillaumot et al (2016), Fabri-Ruiz et al (2017) or Moreau et al (2018), or in the general platform biodiversity.aq web portal.…”

Section: Quality Of Datasetsmentioning

confidence: 99%

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

2021

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Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms and the spatial scale and limits of the projection area. We stress the importance of evaluating and communicating model uncertainties, and the most common evaluation metrics are reviewed and discussed accordingly. Based on a selection of case studies on SO benthic invertebrates, we highlight important cautions to take and pitfalls to avoid when modelling the distribution of SO species, and we provide some guidelines along with potential methods and original solutions that can be used for improving model performance.

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Antarctic and Sub-Antarctic Asteroidea database

Cited by 14 publications

References 17 publications

Extrapolation in species distribution modelling. Application to Southern Ocean marine species

Extrapolation in species distribution modelling. Application to Southern Ocean marine species

Broad-scale species distribution models applied to data-poor areas

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

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