FN Archimer Export Format
PT J
TI Robust identification of potential habitats of a rare demersal species (blackspot seabream) in the Northeast Atlantic
BT 
AF De Cubber, Lola
   TRENKEL, Verena
   Diez, Guzman
   Gil-Herrera, Juan
   Novoa Pabon, Ana Maria
   EME, David
   LORANCE, Pascal
AS 1:1,2;2:1;3:3;4:4;5:5;6:1,6;7:1;
FF 1:;2:PDG-RBE-HALGO;3:;4:;5:;6:PDG-RBE-EMH;7:PDG-RBE-HALGO-EMH;
C1 DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAe, Institut-Agro - Agrocampus Ouest, Nantes, France
   Institut de Recherche pour le Développement (IRD), MARBEC (IRD, Ifremer, CNRS, Univ Montpellier), Av. Jean Monnet - Sète, France
   AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Txatxarramendi ugartea z/g, 48395 Sukarrieta Bizkaia, Spain
   Centro Oceanográfico de Cádiz (IEO-CSIC), Muelle de Levante s/n, 11006, Cádiz, Spain
   Departamento de Oceanografia e Pescas, Universidade dos Açores, 9901-862 Horta, Portugal
   RiverLY Research Unit, National Research Institute for Agriculture Food and Environment (INRAE), Villeurbanne, France
C2 IFREMER, FRANCE
   IRD, FRANCE
   AZTI, SPAIN
   IEO CSIC, SPAIN
   UNIV ACORES, PORTUGAL
   INRAE, FRANCE
SI NANTES
SE PDG-RBE-HALGO
   PDG-RBE-EMH
   PDG-RBE-HALGO-EMH
UM MARBEC
   DECOD
IN WOS Ifremer UMR
   WOS Cotutelle UMR
   copubli-france
   copubli-p187
   copubli-europe
IF 3.1
TC 2
UR https://archimer.ifremer.fr/doc/00814/92582/99076.pdf
LA English
DT Article
CR EVHOE EVALUATION HALIEUTIQUE OUEST DE L'EUROPE
   MEDITS
DE ;Pagellus bogaraveo;Species distribution models;Ensemble modelling;Heterogeneous data set;Presence-absence imbalance
AB Species distribution models (SDM) are commonly used to identify potential habitats. When fitting them to heterogeneous, opportunistically collated presence/absence data, imbalance in the number of presence and absence observations often occurs, which could influence results. To robustly identify potential habitats for blackspot seabream (Pagellus bogaraveo) throughout its distribution area in the Northeast Atlantic and the western Mediterranean Sea, we used an ensemble species distribution modelling (eSDM) approach, modelling gridded presence–absence data with environmental predictors for two types of occurrence data sets. The first data set displayed the observed unbalanced spatially heterogeneous presence/absence ratio and the second a balanced presence/absence ratio. The data covered the full distribution area, including the European Atlantic shelf, the Azorean region and the Western Mediterranean Sea. Across these regions, populations display variable status. The main environmental predictors for potential habitats were bathymetry and annual maximum SST. The fitted ensemble compromise (eSDM) was projected over the whole grid to create a habitat suitability map. This map exhibited higher probabilities of presence for the balanced-ratio data set. A binary presence–absence map was then generated using optimized presence probability thresholds for four validation indices. Using the true skill statistic to optimize the threshold, the surface areas of the binary presence–absence map was 53% smaller for the balanced data set than for the observed unbalanced data set. However, the choice of validation index had an even greater impact (up to 15 000%). This indicates that studies using opportunistic data for SDM fitting need to pay attention to the effects of presence/absence data imbalance and the choice of validation index to fully evaluate uncertainty. 
PY 2023
PD MAR
SO Ecological Modelling
SN 0304-3800
PU Elsevier
VL 477
UT 000918025700001
DI 10.1016/j.ecolmodel.2022.110255
ID 92582
ER

EF