@article{fdi:010086478,
  title = {{A} diatom-based predictive model for inferring past conductivity in {C}hadian {S}ahara lakes},
  author = {{R}irongarti, {R}. and {S}ylvestre, {F}lorence and {C}halie, {F}. and {P}ailles, {C}. and {M}azur, {J}. {C}. and {N}our, {A}. {M}. and {B}arthelemy, {W}. and {M}ariot, {H}. and van der {M}eeren, {V}. and {P}oulin, {C}. and {D}eschamps, {P}ierre and {A}bderamane, {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{F}or decades, diatoms have been recognized as powerful bio-indicators of modern water quality. {T}hey have also been utilized in the design of transfer functions, which can be applied to diatom assemblages in lake sediment cores to infer aspects of past lake hydrochemistry and estimate variables that can be incorporated into paleohydrology models. {T}he {O}unianga lakes, in the heart of the {C}hadian {S}ahara, possess unique and well-preserved sediment records that extend back beyond the middle {H}olocene. {T}oday, the lakes display a range of hydrochemical conditions, from fresh to hypersaline. {M}ainly fed by fossil groundwater that originates in the {N}ubian {S}andstone {A}quifer {S}ystem, measured conductivity across these lakes varies from 217 to 352,000 mu {S} cm(-1), values that are influenced by factors such as hydrology, local geomorphology (e.g., depth and area), and aquatic vegetation. {A}lthough these lakes have been on the {UNESCO} {W}orld {H}eritage {L}ist since 2012, they have never been studied in detail because they are located on the fringes of the {C}hadian {S}ahara. {T}he distribution of diatom taxa in the lakes today is closely linked to water-column physical and chemical conditions, especially conductivity. {W}hereas each lake has particular features that influence its diatom flora, diatoms across a conductivity gradient enabled identification of three distinct waterbody types, freshwater lakes, meso-saline to hyper-saline lakes, and freshwater springs. {R}elationships between diatom species distributions and environmental variables were examined using multivariate analysis, which revealed that conductivity is the variable that explains most of the variance in the diatom flora. {W}e used modern diatom assemblages from the lakes to develop a predictive model (transfer function) for conductivity, using the weighted averaging method. {O}ur conductivity prediction model is strong, with a coefficient of determination ({R}-2) of 0.89 between estimated and measured values, and a value of 0.78 using jackknife estimates of prediction. {T}his study better constrained conductivity optima and tolerance values for diatom species found in the {O}unianga lakes, thereby enabling development of a model that will yield better inferences for past conductivity, using diatoms from lake sediment records in the region.},
  keywords = {{S}ahara ; {C}had ; {L}ake ; {D}iatoms ; {C}onductivity ; {T}ransfer functions ; {TCHAD} ; {SAHARA}},
  booktitle = {},
  journal = {{J}ournal of {P}aleolimnology},
  volume = {69},
  numero = {3},
  pages = {231--248},
  ISSN = {0921-2728},
  year = {2023},
  DOI = {10.1007/s10933-022-00270-9},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086478},
}