Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Biological data have been expressed in abundance (number of individuals per 20 m3).

Modelised abundance of species or prediction uncertainty.

Modelised adundance of several species eggs or prediction uncertainty.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Modelised abundance of species or prediction uncertainty.

Abundances were recoded in term of presence-absence. Geostatistical interpolation : the spatial variation of biological data were analysed using GENSTAT (GENSTAT 7 Committee, 2004), which is a GENeral STATistics package including the main geostatistical tools. It computes experimentala variograms, fits these with various authorised mathematical models and uses them to calculate kriged estimates on a fine regular grid (of latitudes and longitudes). The grid of points was imported into ArcMap and interpolated with the Spatial Analyst extension in order to create a continuous raster of 1 km² resolution. The resulting maps illustrate the spatial distributions and the variations over time for biological data studied in CHARM's area. For legends of maps, approximates of the 5th and the 95th quantiles were used for the minimales and maximales values respectively.

Modelised abundance of species or prediction uncertainty.

Biologic data have been expressed in abundance (numbers or density values (nbr/km²)) and always required to be log-transformed using a log10(x+1) transformation.

Species habitat occupation has been expressed as rare, occasional, frequent, recurrent depending on density levels and variability over time.