@article{fdi:010086852,
  title = {{R}obust identification of potential habitats of a rare demersal species (blackspot seabream) in the {N}ortheast {A}tlantic},
  author = {{D}e {C}ubber, {L}ola and {T}renkel, {V}. {M}. and {D}iez, {G}. and {G}il-{H}errera, {J}. and {P}abon, {A}. {M}. {N}. and {E}me, {D}. and {L}orance, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}pecies distribution models ({SDM}) are commonly used to identify potential habitats. {W}hen fitting them to heterogeneous, opportunistically collated presence/absence data, imbalance in the number of presence and absence observations often occurs, which could influence results. {T}o robustly identify potential habitats for blackspot seabream ({P}agellus bogaraveo) throughout its distribution area in the {N}ortheast {A}tlantic and the western {M}editerranean {S}ea, we used an ensemble species distribution modelling (e{SDM}) approach, modelling gridded presence-absence data with environmental predictors for two types of occurrence data sets. {T}he first data set displayed the observed unbalanced spatially heterogeneous presence/absence ratio and the second a balanced presence/absence ratio. {T}he data covered the full distribution area, including the {E}uropean {A}tlantic shelf, the {A}zorean region and the {W}estern {M}editerranean {S}ea. {A}cross these regions, populations display variable status. {T}he main environmental predictors for potential habitats were bathymetry and annual maximum {SST}. {T}he fitted ensemble compromise (e{SDM}) was projected over the whole grid to create a habitat suitability map. {T}his 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. {U}sing 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. {H}owever, the choice of validation index had an even greater impact (up to 15 000%). {T}his 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.},
  keywords = {{P}agellus bogaraveo ; {S}pecies distribution models ; {E}nsemble modelling ; {H}eterogeneous data set ; {P}resence-absence imbalance ; {ATLANTIQUE} ; {ATLANTIQUE} {NORD}},
  booktitle = {},
  journal = {{E}cological {M}odelling},
  volume = {477},
  numero = {},
  pages = {110255 [13 ]},
  ISSN = {0304-3800},
  year = {2023},
  DOI = {10.1016/j.ecolmodel.2022.110255},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086852},
}