In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset

Abstract

Aim Ideally, datasets for species distribution modelling (SDM) contain evenly sampled records covering the entire distribution of the species, confirmed absences and auxiliary ecophysiological data allowing informed decisions on relevant predictors. Unfortunately, these criteria are rarely met for marine organisms for which distributions are too often only scantly characterized and absences generally not recorded. Here, we investigate predictor relevance as a function of modelling algorithms and settings for a global dataset of marine species. Location Global marine. Methods We selected well‐studied and identifiable species from all major marine taxonomic groups. Distribution records were compiled from public sources (e.g., OBIS, GBIF, Reef Life Survey) and linked to environmental data from Bio‐ORACLE and MARSPEC. Using this dataset, predictor relevance was analysed under different variations of modelling algorithms, numbers of predictor variables, cross‐validation strategies, sampling bias mitigation methods, evaluation methods and ranking methods. SDMs for all combinations of predictors from eight correlation groups were fitted and ranked, from which the top five predictors were selected as the most relevant. Results We collected two million distribution records from 514 species across 18 phyla. Mean sea surface temperature and calcite are, respectively, the most relevant and irrelevant predictors. A less clear pattern was derived from the other predictors. The biggest differences in predictor relevance were induced by varying the number of predictors, the modelling algorithm and the sample selection bias correction. The distribution data and associated environmental data are made available through the R package marinespeed and at http://marinespeed.org. Main conclusions While temperature is a relevant predictor of global marine species distributions, considerable variation in predictor relevance is linked to the SDM set‐up. We promote the usage of a standardized benchmark dataset (MarineSPEED) for methodological SDM studies.

Ensemble modeling of the potential distribution of the whale shark in the Atlantic Ocean

“…This index measures model accuracy for presence‐only test data against a geographical‐environmental background. We calculated the Boyce index for the modeled presences, and also for an additional set of presences retrieved from the MarineSPEED dataset (Bosch, Tyberghein, Deneudt, Hernandez, & Clerck, ), as an external evaluation of the models.…”

What and where? Predicting invasion hotspots in the Arctic marine realm

“…Depth was an important predictor, as shown by Bosch et al. (2018) and Snickars et al. (2014) who suggested that bathymetry is of high relevance for modelling various taxa.…”

“…Water temperature has been identified as the most relevant predictor of global marine species distribution and land distance of moderate importance for both benthic and planktonic species (Bosch, Tyberghein, Deneudt, Hernandez, & De Clerck, 2018;Bradie & Leung, 2017). Depth was an important predictor, as shown by Bosch et al (2018) and Snickars et al (2014) who suggested that bathymetry is of high relevance for modelling various taxa. Ice thickness was moderately important for phytobenthos and zooplankton, perhaps reflecting limits on distribution of the former due to ice abrasion, changes in light exposure and a preference of ice-free waters for the latter (Clark et al, 2013;Kube, Postel, Honnef, & Augustin, 2007;Pascual et al, 2015;Richardson, 1979).…”

“…Sea surface temperature and land distance were retained in all models, consistent with other studies that found these predictors to contribute significantly to explaining species distributions (Bradie & Leung, 2017; Leidenberger, Obst, et al, 2015; Stelzer, Heyer, Bourlat, & Obst, 2013). Water temperature has been identified as the most relevant predictor of global marine species distribution and land distance of moderate importance for both benthic and planktonic species (Bosch, Tyberghein, Deneudt, Hernandez, & De Clerck, 2018; Bradie & Leung, 2017). Depth was an important predictor, as shown by Bosch et al.…”

Dictyota falklandica sp. nov. (Dictyotales, Phaeophyceae) from the Falkland Islands and southernmost South America

“…Stationarity and convergence of runs was assessed visually using For all specimens, we collected mean sea surface temperature (SST mean) based on locality information and environmental layers present in Bio-Oracle v2.0 (Assis et al 2018). Averaged mean surface temperatures correlate equally well or better with marine species distribution ranges compared to average minimum or maximum temperatures as demonstrated by Bosch et al (2018). Ancestral states of SST affinities were reconstructed and visualised on the phylogeny using the fastAnc and contMap functions of the R package phytools (Revell 2012).…”