@article{fdi:010075272,
  title = {{M}odelling built infrastructure heights to evaluate common assumptions in aquatic conservation},
  author = {{J}anuchowski-{H}artley, {S}. {R}. and {J}{\'e}z{\'e}quel, {C}{\'e}line and {T}edesco, {P}ablo},
  editor = {},
  language = {{ENG}},
  abstract = {{B}uilt infrastructure, such as dams and weirs, are some of the most impactful stressors affecting aquatic ecosystems. {H}owever, data on the distribution and characteristics of small built infrastructure that often restrict fish movement, impede flows, and retain sediments and materials, remain limited. {C}ollection of this necessary information is challenged by the large number of built infrastructure with unknown dimensions (e.g., height), which means scientists and practitioners need to make assumptions about these characteristics in research and decision-making. {E}valuating these common assumptions is essential for advancing conservation that is more effective. {W}e use a statistical modelling approach to double the number of small (<= 5 m high) built infrastructure with height values in {F}rance. {U}sing two scenarios depicting common assumptions (all infrastructure without height data are impassable, or all are passable for all species) and one based on our modelled heights, we demonstrate how assumptions can influence our understanding of river fragmentation. {A}ssuming all built infrastructure without height data are passable results in a 5-fold reduction in estimated river fragmentation for fish species that cannot pass built infrastructure >= 1.0 m. {T}he opposite is true for fish species that cannot pass >= 2.0 m, where assuming all built infrastructure without height data are impassable results in a 7-fold increase in fragmentation compared to the scenario with modelled heights to attribute built infrastructure passability. {O}ur findings suggest that modelled height data leads to better understanding of river fragmentation, and that knowledge of different fish species' abilities to pass a variety of built infrastructure is essential to guide more effective management strategies. {O}ur modelling approach, and results, are of particular relevance to regions where efforts to both remediate and remove built infrastructure is occurring, but where gaps in data on characteristics of built infrastructure remain, and limit effective decision making.},
  keywords = {{B}oosted regression trees ; {D}ams ; {F}ish ; {F}ragmentation ; {P}assability ; {R}ivers ; {FRANCE}},
  booktitle = {},
  journal = {{J}ournal of {E}nvironmental {M}anagement},
  volume = {232},
  numero = {},
  pages = {131--137},
  ISSN = {0301-4797},
  year = {2019},
  DOI = {10.1016/j.jenvman.2018.11.040},
  URL = {https://www.documentation.ird.fr/hor/fdi:010075272},
}