@article{fdi:010079141,
  title = {{A}daptive niche-based sampling to improve ability to find rare and elusive species : simulations and field tests},
  author = {{C}hiffard, {J}. and {M}arciau, {C}. and {Y}occoz, {N}. {G}. and {M}ouillot, {F}lorent and {D}uchateau, {S}. and {N}adeau, {I}. and {F}ontanilles, {P}. and {B}esnard, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}ampling efficiency is crucial to overcome the data crisis in biodiversity and to understand what drives the distribution of rare species. {A}daptive niche-based sampling ({ANBS}) is an iterative sampling strategy that relies on the predictions of species distribution models ({SDM}s). {B}y predicting highly suitable areas to guide prospection, {ANBS} could improve the efficiency of sampling effort in terms of finding new locations for rare species. {I}ts iterative quality could potentially mitigate the effect of small and initially biased samples on {SDM}s. {I}n this study, we compared {ANBS} with random sampling by assessing the gain in terms of new locations found per unit of effort. {T}he comparison was based on both simulations and two field surveys of mountain birds. {W}e found an increasing probability of contacting the species through iterations if the focal species showed specialization in the environmental gradients used for modelling. {W}e also identified a gain when using pseudo-absences during first iterations, and a general tendency of {ANBS} to increase the omission rate in the spatial prediction of the species' niche or habitat. {O}verall, {ANBS} is an effective and flexible strategy that can contribute to a better understanding of distribution drivers in rare species.},
  keywords = {adaptive monitoring ; low detectability ; niche-based sampling ; rare species ; sampling efficiency ; species distribution model ; {FRANCE} ; {ESPAGNE} ; {PYRENEES}},
  booktitle = {},
  journal = {{M}ethods in {E}cology and {E}volution},
  volume = {11},
  numero = {8},
  pages = {899--909},
  ISSN = {2041-210{X}},
  year = {2020},
  DOI = {10.1111/2041-210x.13399},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079141},
}