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Drivers of black grouse trends in the French Alps

Cite this dataset

Coline, Canonne (2022). Drivers of black grouse trends in the French Alps [Dataset]. Dryad.


This dataset is composed of three R files: One entitled "BGcounts" that contains an array named "count", composed of counts of black grouse males during lek season conducted on reference sites monitored by a french organisation called "OGM" ( The network is composed of 47 counting sites (raws of the dataset), and counts started on various dates on each site (from 1980's to 1990's). Columns (40) are years from 1979 to 2018. Finally,  during the same lek season, up to 3 replicates per site have been done, they are contained by the third dimension of the array. Details of the method and sites can be found in the related article accepted in Diversity and Distribution. Note that because black grouse is a sensitive species in France, data have been randomized, hence site numbers on this dataset do not reflect OGM's original site numbers. A second entitled "elevation_surface" containing scaled (x=x-mean/sd) values of sites elevation (column altitude) and surface. The last one named "covariates" with covariates values, harvested number of birds on reference sites, cable density, snow cover (50% and 100% of bare ground), length of vegetative season, starting date of vegetative season and harvest density. Refer to the Readme file to find details about scaling and years provided.


Counts have been conducted annually or every two years, following a standardized protocol established for black grouse monitoring in France (described in Leonard, 1989; Montadert, 2016). As many observers as required to cover the studied area patrol the sector walking or staying in a predetermined fixed position (depending on the site structure) and count, from a certain distance, displaying males on or around leks (Ellison & Magnani, 1985). Counts are performed from early morning, before sunrise, until no later than two hours after sunrise between the last week of April and the end of May, as close as possible to the activity peak(Montadert, 2016). Intra-annual replicates (2 or 3) have also been performed on most sites since 1995. Counting is always performed when weather condi­tions are good, i.e. avoiding heavy rain, strong winds or dense fog (Leonard, 1989; Baines, 1996; Montadert, 2016).

We assembled variables related to environmental conditions and human recreative activities that we postulated might affect black grouse population trends. All variables were scaled (x=x-mean/sd), either on the full matrix or site by site when data were not comparable between sites.

The surface area and mean elevation of each site were calculated based on the contour and centroids of the monitoring site.

We extracted snowmelt dates from the moderate resolution imaging spectroradiometer (MODIS) on board the Terra platform’s Earth Observing System. MODIS data was available from 2000 onwards. We defined the date of snowmelt as the end of the period of continuous snow cover. Date_100 corresponds to total snowmelt at the site (100% bare ground), and date_50 to 50% bare ground (see Novoa et al., 2016 for the use of such covariates on another Galliformes). In parallel, we used MODIS normalized difference vegetation index (NDVI) data with the “greenbrown” package in R (Forkel & Wutzler, 2015; R Core Team, 2018) to estimate two metrics that summarize vegetation phenology: start of growing season (SOS) and length of growing season (LOS). Both the start and end of the growing season were defined from the derivative of the seasonal curve, as the mid-points of spring green up and autumn senescence, respectively (Forkel et al., 2015).

We based our weather indices on meteorological parameters estimated by SAFRAN (Système d’analyse fournissant des renseignements atmosphériques à la neige), a mesoscale atmospheric analysis system for surface variables using ground data observations from Météo France (Durand et al., 1993). While the temporal resolution of SAFRAN is very good, with data available from 1958 onwards, its spatial resolution of 8 km was not well suited to our small mountainous sites. Thus, we chose to define only two synthetic variables, the first representing the summer temperature (mean temperature in June, July and August) and the second summarizing precipitation, calculated as the sum of monthly precipitation in June, July and August divided by the number of days with available data.

We extracted yearly ski-related cable density as a proxy of winter sports activity (public data from OGM: We used a 2-km buffer zone around monitoring sites, defined to correspond to the black grouse’s altitudinal range (1400–2300 m).

We gathered hunting bag data from departmental hunting organizations. Data collection of hunting bags started on different dates depending on the site (between 1994 and 2006). For 17 sites, hunting bag data matched monitoring sites exactly (in terms of geographical area). For these sites we created a variable for harvest density (number of birds killed per ha) and tested its effect on both median trends and the inter-annual variation in growth rates. For the remaining 19 sites for which we had hunting data, the surface area on which hunting bags were collected could differ to a certain extent from that of the monitoring site, but remained constant over the study period. We then built a second variable for the 36 sites (for which we had data on the number of harvested birds).