Phenological patterns of flowering across biogeographical regions of Europe

Abstract

Long-term changes of plant phenological phases determined by complex interactions of environmental factors are in the focus of recent climate impact research. There is a lack of studies on the comparison of biogeographical regions in Europe in terms of plant responses to climate. We examined the flowering phenology of plant species to identify the spatio-temporal patterns in their responses to environmental variables over the period 1970-2010. Data were collected from 12 countries along a 3000-km-long, North-South transect from northern to eastern Central Europe.Biogeographical regions of Europe were covered from Finland to Macedonia. Robust statistical methods were used to determine the most influential factors driving the changes of the beginning of flowering dates. Significant species-specific advancements in plant flowering onsets within the Continental (3 to 8.3 days), Alpine (2 to 3.8 days) and by highest magnitude in the Boreal biogeographical regions (2.2 to 9.6 days per decades) were found, while less pronounced responses were detected in the Pannonian and Mediterranean regions. While most of the other studies only use mean temperature in the models, we show that also the distribution of minimum and maximum temperatures are reasonable to consider as explanatory variable. Not just local (e.g. temperature) but large scale (e.g. North Atlantic Oscillation) climate factors, as well as altitude and latitude play significant role in the timing of flowering across biogeographical regions of Europe. Our analysis gave evidences that species show a delay in the timing of flowering with an increase in latitude (between the geographical coordinates of 40.9 and 67.9), and an advance with changing climate. The woody species (black locust and small-leaved lime) showed stronger advancements in their timing of flowering than the herbaceous species (dandelion, lily of the valley). In later decades (1991-2010), more pronounced phenological change was detected than during the earlier years (1970-1990), which indicates the increased influence of human induced higher spring temperatures in the late twentieth century.

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

“…2). In general, the characteristics of phenological phases presented in this study stay in agreement with very detailed (i.e., based on larger number of stations) nationwide phenological patterns observed in the past decades (Tomaszewska and Rutkowski 1999), although the acceleration of particular phenological phases is clearly visible and follows trends described in other research studies for this part of Europe (Menzel et al 2006; Czernecki and Jabłońska 2016; Templ et al 2017). …”

“…The subjective nature of ground-based phenological observations has always been an issue in contemporary phenological research (Schaber and Badeck 2002; Fisher et al 2007; Scheifinger and Templ 2016; Templ et al 2017). Numerous attempts to cover the gaps by means of airborne sensors and empirical-statistical models that take into account plant sensitivity to temperature, precipitation, and photoperiod indices showed that this problem has yet to be entirely solved (Studer et al 2007; Fisher and Mustard 2007; Fisher et al 2007; Almeida et al 2012).…”

Bulbous perennials precisely detect the length of winter and adjust flowering dates

“…Most explanatory studies concluded that monthly average temperatures of preceding seasons exhibit the best correlation with flowering data (White, 1979; Fitter et al ., 1995; Amano et al ., 2010). Nevertheless, snowpack, frost events, growing degree days, soil temperature and mean sunshine strength and duration (White, 1979; Inouye & McGuire, 1991; Tooke & Battey, 2010; Carbognani et al ., 2016; Wadgymar et al ., 2018), or complex meteorological patterns like La Niña episodes (Inouye et al ., 2002) and North Atlantic Oscillations (Templ et al ., 2017) were also considered in some studies. In order to make statistical analyses feasible by traditional methods, all of these studies evaluated a restricted number of aggregated environmental variables (typically, monthly or seasonal mean values) to explain phenological changes.…”

The Interactive Effects of Chilling, Photoperiod, and Forcing Temperature on Flowering Phenology of Temperate Woody Plants

“…The changed timing of recurring biological events becomes a global concern against the background of climate warming. The earlier spring phenophases (e.g., budburst date, leaf-out date) and later autumn phenophases (e.g., leaf coloring date) of woody plants were observed over the past several decades in middle and high latitudes of the Northern Hemisphere (Chmielewski and Rötzer, 2001;Menzel et al, 2006;Gonsamo et al, 2013;Ge et al, 2015;Templ et al, 2017). Such climate-associated phenological change could influence carbon assimilation by modifying the length of the growing season (Keenan et al, 2014;Xia et al, 2015).…”