@article{fdi:010080969,
  title = {{P}redicting species richness and abundance of tropical post-larval fish using machine learning},
  author = {{J}aonalison, {H}. and {D}urand, {J}ean-{D}ominique and {M}ahafina, {J}. and {D}emarcq, {H}erv{\'e} and {T}eichert, {N}. and {P}onton, {D}ominique},
  editor = {},
  language = {{ENG}},
  abstract = {{P}ost-larval prediction is important, as post-larval supply allows us to understand juvenile fish populations. {N}o previous studies have predicted post-larval fish species richness and abundance combining molecular tools, machine learning, and past-days remotely sensed oceanic conditions ({RSOC}s) obtained in the days just prior to sampling at different scales. {P}revious studies aimed at modeling species richness and abundance of marine fishes have mainly used environmental variables recorded locally during sampling and have merely focused on juvenile and adult fishes due to the difficulty of obtaining accurate species richness estimates for post-larvae. {T}he present work predicted post-larval species richness (identified using {DNA} barcoding) and abundance at 2 coastal sites in {SW} {M}adagascar using random forest ({RF}) models. {RF}s were fitted using combinations of local variables and {RSOC}s at a small-scale (8 d prior to fish sampling in a 50 x 120 km(2) area), meso-scale (16 d prior; 100 x 200 km(2)), and large-scale (24 d prior; 200 x 300 km(2)). {RF} models combining local and small-scale {RSOC} variables predicted species richness and abundance best, with accuracy around 70 and 60%, respectively. {W}e observed a small variation of {RF} model performance in predicting species richness and abundance among all sites, highlighting the consistency of the predictive {RF} model. {M}oreover, partial dependence plots showed that high species richness and abundance were predicted for sea surface temperatures <27.0 degrees {C} and chlorophyll a concentrations <0.22 mg m(-3). {W}ith respect to temporal changes, these thresholds were solely observed from {N}ovember to {D}ecember. {O}ur results suggest that, in {SW} {M}adagascar, species richness and abundance of post-larval fish may only be predicted prior to the ecological impacts of tropical storms on larval settlement success.},
  keywords = {{F}ish post-larvae ; {DNA} barcoding ; {S}urface water masses ; {R}emote sensing ; {R}andom {F}orests ; {M}odeling ; {MADAGASCAR} ; {OCEAN} {INDIEN} ; {MOZAMBIQUE} {CANAL} ; {NOSY} {VE} ; {ANAKO} ; {TULEAR}},
  booktitle = {},
  journal = {{M}arine {E}cology {P}rogress {S}eries},
  volume = {645},
  numero = {},
  pages = {125--139},
  ISSN = {0171-8630},
  year = {2020},
  DOI = {10.3354/meps13385},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080969},
}