In-situ Temperature Stations Elucidate Species’ Phenological Responses to Climate in the Alps, but Meteorological and Snow Reanalysis Facilitates Broad Scale and Long-Term Studies - Laigle, Idaline; Carlson, Bradley Z.; Delestrade, Anne; Bison, Marjorie; Van Reeth, Colin; Yoccoz, Nigel Gilles (Frontiers in Earth Science, 2022)
Linking climate variability and change to the phenological response of species is particularly challenging in the context of mountainous terrain. In these environments, elevation and topography lead to a diversity of bioclimatic conditions at fine scales affecting species distribution and phenology. In order to quantify in situ climate conditions for mountain plants, the CREA installed 82 temperature stations throughout the southwestern Alps, at different elevations and aspects. Dataloggers at each station provide local measurements of temperature at four heights (5 cm below the soil surface, at the soil surface, 30 cm above the soil surface, and 2 m above ground). Given the significant amount of effort required for station installation and maintenance, we tested whether meteorological data based on the S2M reanalysis could be used instead of station data. Comparison of the two datasets showed that some climate indices, including snow melt-out date and a heat wave index, can vary significantly according to data origin. More general indices such as daily temperature averages were more consistent across datasets, while threshold-based temperature indices showed somewhat lower agreement. Over a twelve year period, the phenological responses of four mountain tree species (ash (Fraxinus excelsior), spruce (Picea abies), hazel (Corylus avellana), birch (Betula pendula)), coal tits (Periparus ater) and common frogs (Rana temporaria) to climate variability were better explained, from both a statistical and ecological standpoint, by indices derived from field stations. Reanalysis data out-performed station data, however, for predicting larch (Larix decidua) budburst date. Overall, our study indicates that the choice of dataset for phenological monitoring ultimately depends on target bioclimatic variables and species, and also on the spatial and temporal scale of the study.