• Energy Research

AbstractUrbanization and agricultural intensification are considered the main causes of recent insect decline in temperate Europe, while direct climate warming effects are still ambiguous. Nonetheless, higher temperatures advance spring leaf emergence, which in turn may directly or indirectly affect insects. We therefore investigated how Sentinel-2-derived start of season (SOS) and its spatial variability (SV-SOS) are affected by spring temperature and whether these green-up variables can explain insect biomass and richness across a climate and land-use gradient in southern Germany. We found that the effects of both spring green-up variables on insect biomass and richness differed between land-use types, but were strongest in forests. Here, insect richness and biomass were higher with later green-up (SOS) and higher SV-SOS. In turn, higher spring temperatures advanced SOS, while SV-SOS was lower at warmer sites. We conclude that with a warming climate, insect biomass and richness in forests may be affected negatively due to earlier and more uniform green-up. Promising adaptation strategies should therefore focus on spatial variability in green-up in forests, thus plant species and structural diversity.

Phenology is believed to be a suitable bio-indicator to track climate change. Based on the strong statistical association between phenology and temperature phenological observations provide an inexpensive means for the temporal and spatial analysis of the urban heat island. However, other environmental factors might also weaken this relationship. In addition, the investigation of urban phenology allows an estimation of future phenology from current information since cities with their amplified temperatures may serve as a proxy for future conditions. Nevertheless, the design of spatial compared to long-term studies might be influenced by different factors which should be taken into consideration when interpreting results from a specific study. In general, plants located in urban areas tend to flush and bloom earlier than in the countryside. What are the consequences of these urban-rural differences? This review will document existing findings on urban phenology and will highlight areas in which further research is needed.

AbstractThe future performance of native tree species under climate change conditions is frequently discussed, since increasingly severe and more frequent drought events are expected to become a major risk for forest ecosystems. To improve our understanding of the drought tolerance of the three common European temperate forest tree species Norway spruce, silver fir and common beech, we tested the influence of climate and tree‐specific traits on the inter and intrasite variability in drought responses of these species. Basal area increment data from a large tree‐ring network in Southern Germany and Alpine Austria along a climatic cline from warm‐dry to cool‐wet conditions were used to calculate indices of tolerance to drought events and their variability at the level of individual trees and populations. General patterns of tolerance indicated a high vulnerability of Norway spruce in comparison to fir and beech and a strong influence of bioclimatic conditions on drought response for all species. On the level of individual trees, low‐growth rates prior to drought events, high competitive status and low age favored resilience in growth response to drought. Consequently, drought events led to heterogeneous and variable response patterns in forests stands. These findings may support the idea of deliberately using spontaneous selection and adaption effects as a passive strategy of forest management under climate change conditions, especially a strong directional selection for more tolerant individuals when frequency and intensity of summer droughts will increase in the course of global climate change.

A progressive global increase in the burden of allergic diseases has affected the industrialized world over the last half century and has been reported in the literature. The clinical evidence reveals a general increase in both incidence and prevalence of respiratory diseases, such as allergic rhinitis (common hay fever) and asthma. Such phenomena may be related not only to air pollution and changes in lifestyle, but also to an actual increase in airborne quantities of allergenic pollen. Experimental enhancements of carbon dioxide (CO[Formula: see text]) have demonstrated changes in pollen amount and allergenicity, but this has rarely been shown in the wider environment. The present analysis of a continental-scale pollen data set reveals an increasing trend in the yearly amount of airborne pollen for many taxa in Europe, which is more pronounced in urban than semi-rural/rural areas. Climate change may contribute to these changes, however increased temperatures do not appear to be a major influencing factor. Instead, we suggest the anthropogenic rise of atmospheric CO[Formula: see text] levels may be influential.

The BBCH scale is a two-digit key of growth stages in plants that is based on standardised definitions of plant development stages. The extended BBCH scale, used in this paper, enables the coding of the entire development cycle of all mono- and dicotyledonous plants. Using this key, the frequency distribution of phenological stages was recorded which required a less intense sampling frequency. The onset dates of single events were later estimated from the frequency distribution of BBCH codes. The purpose of this study was to present four different methods from which those onset dates can be estimated. Furthermore, the effects of (1) a less detailed observation key and (2) changes in the sampling frequency on estimates of onset dates were assessed. For all analyses, phenological data from the entire development cycle of four grass species were used. Estimates of onset dates determined by Weighted Plant Development (WPD), Pooled pre-/post-Stage Development (PSD), Cumulative Stage Development (CSD) and Ordinal Logistic Regression (OLR) methods can all be used to determine the phenological progression of plants. Moreover, results show that a less detailed observation key still resulted in similar onset dates, unless more than two consecutive stages were omitted. Further results reveal that the simulation of a less intense sampling frequency had only small impacts on estimates of onset dates. Thus, especially in remote areas where an observation interval of a week is not feasible, estimates derived from the frequency distribution of BBCH codes appear to be appropriate.

AbstractClimate change may reduce forest growth and increase forest mortality, which is connected to high carbon costs through reductions in gross primary production and net ecosystem exchange. Yet, the spatiotemporal patterns of vulnerability to both short‐term extreme events and gradual environmental changes are quite uncertain across the species’ limits of tolerance to dryness. Such information is fundamental for defining ecologically relevant upper limits of species tolerance to drought and, hence, to predict the risk of increased forest mortality and shifts in species composition. We investigate here to what extent the impact of short‐ and long‐term environmental changes determines vulnerability to climate change of three evergreen conifers (Scots pine, silver fir, Norway spruce) and two deciduous hardwoods (European beech, sessile oak) tree species at their southernmost limits of distribution in the Mediterranean Basin. Finally, we simulated future forest growth under RCP 2.6 and 8.5 emission scenarios using a multispecies generalized linear mixed model. Our analysis provides four key insights into the patterns of species’ vulnerability to climate change. First, site climatic marginality was significantly linked to the growth trends: increasing growth was related to less climatically limited sites. Second, estimated species‐specific vulnerability did not match their a priori rank in drought tolerance: Scots pine and beech seem to be the most vulnerable species among those studied despite their contrasting physiologies. Third, adaptation to site conditions prevails over species‐specific determinism in forest response to climate change. And fourth, regional differences in forests vulnerability to climate change across the Mediterranean Basin are linked to the influence of summer atmospheric circulation patterns, which are not correctly represented in global climate models. Thus, projections of forest performance should reconsider the traditional classification of tree species in functional types and critically evaluate the fine‐scale limitations of the climate data generated by global climate models.

Over a large temperature range, the statistical association between spring phenology and temperature is often regarded and treated as a linear function. There are suggestions that a sigmoidal relationship with definite upper and lower limits to leaf unfolding and flowering onset dates might be more realistic. We utilised European plant phenological records provided by the European phenology database PEP725 and gridded monthly mean temperature data for 1951-2012 calculated from the ENSEMBLES data set E-OBS (version 7.0). We analysed 568,456 observations of ten spring flowering or leafing phenophases derived from 3657 stations in 22 European countries in order to detect possible nonlinear responses to temperature. Linear response rates averaged for all stations ranged between -7.7 (flowering of hazel) and -2.7 days °C-1 (leaf unfolding of beech and oak). A lower sensitivity at the cooler end of the temperature range was detected for most phenophases. However, a similar lower sensitivity at the warmer end was not that evident. For only ∼14 % of the station time series (where a comparison between linear and nonlinear model was possible), nonlinear models described the relationship significantly better than linear models. Although in most cases simple linear models might be still sufficient to predict future changes, this linear relationship between phenology and temperature might not be appropriate when incorporating phenological data of very cold (and possibly very warm) environments. For these cases, extrapolations on the basis of linear models would introduce uncertainty in expected ecosystem changes.