@article{fdi:010054097,
  title = {{A} comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system},
  author = {{L}eclere, {J}. and {O}berdorff, {T}hierry and {B}elliard, {J}. and {L}eprieur, {F}abien},
  editor = {},
  language = {{ENG}},
  abstract = {{E}ven if {E}uropean river management and restoration are largely supported by the use of reliable tools, these tools are most often "generalist" and provide only initial leads of alteration sources. {A}cknowledging that young-of-the-year ({YOY}) fish assemblages are highly dependent on riverine habitat conditions, the development of a {YOY}-based tool might be very useful or even essential in the design and implementation of conservation or restoration plan of large rivers, in measuring more straight-forward the losses and gains of hydro-ecological functionalities. {I}n the past 20 years, new modeling techniques have emerged from a growing sophistication of statistical model applied to ecology. "{M}achine learning methods" ({ML}) are now recognized as holding great promise for the advancement of understanding and prediction of ecological phenomena. {T}he aim of this work was to select the appropriate statistical technique to model {YOY} assemblages according to different meso-scale habitat variables that are meaningful to planners. {T}o do this, two "{M}achine {L}earning" methods, {C}lassification and {R}egression {T}rees ({CART}) and {B}oosted {R}egression {T}rees ({BRT}), were compared to {G}eneralized {L}inear {M}odels ({GLM}). {W}e modeled the occurrence of 9 species from the {S}eine {R}iver basin ({F}rance) in order to compare models abilities to accurately predict the presence and absence of each species. {BRT} appeared to be the best technique for modeling 0+ fish occurrences in our dataset.},
  keywords = {{M}achine learning ; {Y}oung-of-the-year fishes ({YOY}) ; {P}redictive models ; {H}abitat variables ; {L}arge rivers ; {F}rance ; {R}estoration tools},
  booktitle = {},
  journal = {{E}cological {I}nformatics},
  volume = {6},
  numero = {5},
  pages = {276--285},
  ISSN = {1574-9541},
  year = {2011},
  DOI = {10.1016/j.ecoinf.2011.05.001},
  URL = {https://www.documentation.ird.fr/hor/fdi:010054097},
}