@article{fdi:010066883,
  title = {{M}icroclimate data improve predictions of insect abundance models based on calibrated spatiotemporal temperatures},
  author = {{R}ebaudo, {F}ran{\c{c}}ois and {F}aye, {E}. and {D}angles, {O}livier},
  editor = {},
  language = {{ENG}},
  abstract = {{A} large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. {U}sing a 6-year monitoring of three potato moth species, major crop pests in the tropical {A}ndes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. {F}or this, we used three different climatic data sets: (i) the {W}orld{C}lim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). {W}e developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km(2) for the {W}orld{C}lim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). {T}hen, we computed pest performances based on these three datasets. {R}esults for temperature ranging from 9 to 11 degrees {C} revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. {T}emperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. {W}e used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. {R}esults showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. {O}ur simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. {I}n conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.},
  keywords = {insects ; scale ; temperature ; microclimate ; models ; agriculture ; landscape ; {EQUATEUR} ; {ANDES}},
  booktitle = {},
  journal = {{F}rontiers in {P}hysiology},
  volume = {7},
  numero = {},
  pages = {art. 139 [12 p.]},
  ISSN = {1664-042{X}},
  year = {2016},
  DOI = {10.3389/fphys.2016.00139},
  URL = {https://www.documentation.ird.fr/hor/fdi:010066883},
}