FN Archimer Export Format
PT J
TI Can we generate robust species distribution models at the scale of the Southern Ocean?
BT 
AF FABRI-RUIZ, Salome
   Danis, Bruno
   David, Bruno
   Saucède, Thomas
AS 1:1;2:2;3:1,3;4:1;
FF 1:;2:;3:;4:;
C1 Biogéosciences; UMR 6282 CNRS; Université Bourgogne Franche-Comté; Dijon ,France
   Laboratoire de Biologie Marine; Université Libre de Bruxelles (ULB); Brussels ,Belgium
   Muséum national d'Histoire naturelle; Paris ,France
C2 UNIV FRANCHE COMTE, FRANCE
   UNIV LIBRE BRUSSELS, BELGIUM
   MNHN, FRANCE
IN DOAJ
IF 3.993
TC 14
UR https://archimer.ifremer.fr/doc/00458/56990/58881.pdf
   https://archimer.ifremer.fr/doc/00458/56990/58882.pdf
LA English
DT Article
DE ;Antarctic;biogeography;conservation;Echinoidea;ecological niche;random forest;sampling effort;sub-Antarctic
AB Aim Species distribution modelling (SDM) represents a valuable alternative to predict species distribution over vast and remote areas of the ocean. We tested whether reliable SDMs can be generated for benthic marine organisms at the scale of the Southern Ocean. We aimed at identifying the main large‐scale factors that determine the distribution of the selected species. The robustness of SDMs was tested with regards to sampling effort, species niche width and biogeography. Location Southern Ocean. Methods The impact of sampling effort was tested using two sets of data: one set with all presence‐only data available until 2005, and a second set using all data available until 2015 including recent records from campaigns carried out during the Census of Antarctic Marine Life (CAML) and the International Polar Year (IPY) period (2005–2010). The accuracy of SDMs was tested using a ground‐truthing approach by comparing recent presence/absence data collected during the CAML and IPY period to pre‐CAML model predictions. Results Our results show the significance of the SDM approach and the role of abiotic factors as important drivers of species distribution at broad spatial scale. The addition of recent data to the models significantly improved the prediction of SDM and changed the respective contributions of environmental predictors. However, the intensity of change varied between models depending on sampling tools, species ecological niche width and biogeographic barriers to dispersal. Main conclusions We highlight the need for new data and the significance of the ground‐truthing approach to test the accuracy of SDMs. We show the importance of data collected through international initiatives, su ch as the CAML and IPY to the improvement of species distribution modelling at broad spatial scales. Finally, we discussed the relevance of SDM as a relevant marine conservation tool particularly in the context of climate change and the definition of Marine Protected Areas. 
PY 2019
PD JAN
SO Diversity And Distributions
SN 1366-9516
PU Wiley
VL 25
IS 1
UT 000455265600004
BP 21
EP 37
DI 10.1111/ddi.12835
ID 56990
ER

EF