Marine Biogeography

Molinos, Jorge García; Alabia, Irene D.

doi:10.1002/9781119882381.ch10

Cited by 1 publication

(1 citation statement)

References 119 publications

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“…Causal hypotheses for a LDG and contrarian patterns are plentiful ( Rivadeneira & Poore, 2020 ; García Molinos & Alabia, 2021 ). Such hypotheses can sometimes be tested in groups with rich and relatively unbiased spatial data from both extant and extinct taxa ( Jablonski, Roy & Valentine, 2006 ; Krug, Jablonski & Valentine, 2007 ; Jablonski et al, 2013 ) or those with independent molecular phylogenetic evidence ( Rabosky et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps

Kopperud

Lidgard

Liow

2022

PeerJ

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We use natural language processing (NLP) to retrieve location data for cheilostome bryozoan species (text-mined occurrences (TMO)) in an automated procedure. We compare these results with data combined from two major public databases (DB): the Ocean Biodiversity Information System (OBIS), and the Global Biodiversity Information Facility (GBIF). Using DB and TMO data separately and in combination, we present latitudinal species richness curves using standard estimators (Chao2 and the Jackknife) and range-through approaches. Our combined DB and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for extant cheilostomes for the first time, with peaks in the temperate zones. A total of 79% of the georeferenced species we retrieved from TMO (N = 1,408) and DB (N = 4,549) are non-overlapping. Despite clear indications that global location data compiled for cheilostomes should be improved with concerted effort, our study supports the view that many marine latitudinal species richness patterns deviate from the canonical latitudinal diversity gradient (LDG). Moreover, combining online biodiversity databases with automated information retrieval from the published literature is a promising avenue for expanding taxon-location datasets.

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