Projecting Tree Species Composition Changes of European Forests for 2061–2090 Under RCP 4.5 and RCP 8.5 Scenarios

Climate change poses certain threats to the World’s forests. That is, tree performance declines if species-specific, climatic thresholds are surpassed. Prominent climatic changes negatively affecting tree performance are mainly associated with so-called hotter droughts. In combination with biotic pathogens, hotter droughts cause a higher tree vulnerability and thus mortality. As a consequence, global forests are expected to undergo vast changes in the course of climate change. Changed climatic conditions may on the one hand locally result in more frequent dieback of a particular tree species but on the other hand allow other—locally yet absent species—to establish themselves, thereby potentially changing local tree-species diversity. Although several studies provide valuable insights into potential risks of prominent European tree species, we yet lack a comprehensive assessment on how and to which extent the composition of European forests may change. To overcome this research gap, we here project future tree-species compositions of European forests. We combine the concept of climate analogs with national forest inventory data to project the tree-species composition for the 26 most important European tree species at any given location in Europe for the period 2061–2090 and the two most relevant CMIP5 scenarios RCP 4.5 and RCP 8.5. Our results indicate significant changes in European forests species compositions. Species richness generally declined in the Mediterranean and Central European lowlands, while Scandinavian and Central European high-elevation forests were projected an increasing diversity. Moreover, 76% (RCP 4.5) and 80% (RCP 8.5) of the investigated locations indicated a decreasing abundance of the locally yet most abundant tree species while 74 and 68% were projected an increasing tree-species diversity. Altogether, our study confirms the expectation of European forests undergoing remarkable changes until the end of the 21st century (i.e., 2061–2090) and provides a scientific basement for climate change adaptation with important implications for forestry and nature conservation

A positive Living-in-History effect: the case of the fall of the Berlin Wall

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Research has shown that individuals use a combination of cultural life script events and historical events when dating personal memories, providing evidence for a cultural life script effect and Living-in-History (LiH) effect on the temporal organisation of autobiographical memory. Yet, in contrast to life script events, the LiH effect has only been found for negative events such as war or natural disasters. Therefore, this study tested whether a positive historical event, here the fall of the Berlin Fall, also elicits a LiH effect and whether this effect would differ due to the subsequent changes in life. Comparing West and East Germans, we found a moderate LiH effect for the fall of the Berlin Wall in East Germans but not in West Germans. Yet, the LiH effect in East Germans did not relate to the perceived change in life or the valence of the historical event. Additionally, this study replicated the finding that life script events serve as temporal landmarks when navigating through one’s autobiographical timeline

Compensatory Growth of Scots Pine Seedlings Mitigates Impacts of Multiple Droughts Within and Across Years

Tree seedling resistance to and recovery from abiotic stressors such as drought and warming are crucial for forest regeneration and persistence. Selection of more resilient provenances and their use in forest management programs might alleviate pressures of climate change on forest ecosystems. Scots pine forests in particular have suffered frequent drought-induced mortality, suggesting high vulnerability to extreme events. Here, we conducted an experiment using potted Scots pine seedlings from ten provenances of its south-western distribution range to investigate provenance-specific impacts of multiple drought events. Seedlings were grown under ambient and elevated temperatures for 1.5 years and were subjected to consecutive droughts during spring and summer. Growth (height, diameter, and needle) and spring phenology were monitored during the whole study period and complemented by biomass assessments (bud, needle, wood, and needle/wood ratio) as well as measurements of chlorophyll fluorescence and of needle stable carbon isotope ratio. Phenology, growth and biomass parameters as well as carbon isotope ratio and their (direct) responses to reoccurring droughts differed between provenances, indicating genotypic adaptation. Seedling growth was plastic during drought with intra- and inter-annual compensatory growth after drought stress release (carryover effects), however, not fully compensating the initial impact. For (smaller) seedlings from southern/drier origins, sometimes greater drought resistance was observed which diminished under warmer conditions in the greenhouse. Warming increased diameter growth and advanced phenological development, which was (partly) delayed by drought in 2013, but advanced in 2014. Earlier phenology was linked to higher growth in 2013, but interestingly later phenology had positive effects on wood and needle biomass when subjected to drought. Lastly, stable carbon isotope ratios indicated a clear drought response of carbon assimilation. Drought-induced reduction of the photosystem II efficiency was only observed under warmer conditions but showed compensation under ambient temperatures. Besides these direct drought impacts, also interactive effects of previous drought events were shown, either reinforcing or sometimes attenuating the actual impact. Thus, depending on amount and timing of events, Scots pine seedlings, particularly from southern origins, might be well adapted and resilient to drought stress and should be considered when discussing assisted migration under changing climatic conditions

Weather Types Affect Rain Microstructure: Implications for Estimating Rain Rate

Quantitative precipitation estimation (QPE) through remote sensing has to take rain microstructure into consideration, because it influences the relationship between radar reflectivity Z and rain intensity R. For this reason, separate equations are used to estimate rain intensity of convective and stratiform rain types. Here, we investigate whether incorporating synoptic scale meteorology could yield further QPE improvements. Depending on large-scale weather types, variability in cloud condensation nuclei and the humidity content may lead to variation in rain microstructure. In a case study for Bavaria, we measured rain microstructure at ten locations with laser-based disdrometers, covering a combined 18,600 h of rain in a period of 36 months. Rain was classified on a temporal scale of one minute into convective and stratiform based on a machine learning model. Large-scale wind direction classes were on a daily scale to represent the synoptic weather types. Significant variations in rain microstructure parameters were evident not only for rain types, but also for wind direction classes. The main contrast was observed between westerly and easterly circulations, with the latter characterized by smaller average size of drops and a higher average concentration. This led to substantial variation in the parameters of the radar rain intensity retrieval equation Z-R. The e ect of wind direction on Z-R parameters was more pronounced for stratiform than convective rain types. We conclude that building separate Z-R retrieval equations for regional wind direction classes should improve radar-based QPE, especially for stratiform rain events

Automated Processing of Webcam Images for Phenological Classification

Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels’ time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun. Executable example code is provided as supplementary material

Bayesian analysis of the species-specific lengthening of the growing season in two European countries and the influence of an insect pest

A recent lengthening of the growing season in mid and higher latitudes of the northern hemisphere is reported as a clear indicator for climate change impacts. Using data from Germany (1951–2003) and Slovenia (1961–2004), we study whether changes in the start, end, and length of the growing season differ among four deciduous broad-leaved tree species and countries, how the changes are related to temperature changes, and what might be the confounding effects of an insect attack. The functional behaviour of the phenological and climatological time series and their trends are not analysed by linear regression, but by a new Bayesian approach taking into account different models for the functional description (one change-point, linear, constant models). We find advanced leaf unfolding in both countries with the same species order (oak > horse chestnut, beech, and birch). However, this advance is non linear over time and more apparent in Germany with clear change-points in the late 1970s, followed by marked advances (on average 3.67 days decade−1 in the 2000s). In Slovenia, we find a more gradual advance of onset dates (on average 0.8 days decade−1 in the 2000s). Leaf colouring of birch, beech, and oak has been slightly delayed in the last 3 decades, especially in Germany, however with no clear functional behaviour. Abrupt changes in leaf colouring dates of horse chestnut with recent advancing onset dates can be linked across countries to damage by a newly emerging pest, the horse chestnut leaf-miner (Cameraria ohridella). The lengthening of the growing season, more distinct in Germany than in Slovenia (on average 4.2 and 1.0 days decade−1 in the 2000s, respectively), exhibits the same species order in both countries (oak > birch > beech). Damage by horse chestnut leaf-miner leads to reduced lengthening (Germany) and drastic shortening (Slovenia) of the horse chestnut growing season (-12 days decade−1 in the 2000s). Advanced spring leaf unfolding and lengthening of the growing season of oak, beech and birch are highly significantly related to increasing March temperatures in both countries. Only beech and oak leaf unfolding in Germany, which is generally observed later in the year than that of the other two species, is more closely correlated with April temperatures, which comparably exhibit marked change-points at the end of the 1970s

Contrasting Hydraulic Architectures of Scots Pine and Sessile Oak at Their Southernmost Distribution Limits

Many temperate European tree species have their southernmost distribution limits in the Mediterranean Basin. The projected climatic conditions, particularly an increase in dryness, might induce an altitudinal and latitudinal retreat at their southernmost distribution limit. Therefore, characterizing the morphological and physiological variability of temperate tree species under dry conditions is essential to understand species’ responses to expected climate change. In this study, we compared branch-level hydraulic traits of four Scots pine and four sessile oak natural stands located at the western and central Mediterranean Basin to assess their adjustment to water limiting conditions. Hydraulic traits such as xylem- and leaf-specific maximum hydraulic conductivity (KS-MAX and KL-MAX), leaf-to-xylem area ratio (AL:AX) and functional xylem fraction (FX) were measured in July 2015 during a long and exceptionally dry summer. Additionally, xylem-specific native hydraulic conductivity (KS-N) and native percentage of loss of hydraulic conductivity (PLC) were measured for Scots pine. Interspecific differences in these hydraulic traits as well as intraspecific variability between sites were assessed. The influence of annual, summer and growing season site climatic aridity (P/PET) on intraspecific variability was investigated. Sessile oak displayed higher values of KS-MAX, KL-MAX, AL:AX but a smaller percentage of FX than Scots pines. Scots pine did not vary in any of the measured hydraulic traits across the sites, and PLC values were low for all sites, even during one of the warmest summers in the region. In contrast, sessile oak showed significant differences in KS-MAX, KL-MAX, and FX across sites, which were significantly related to site aridity. The striking similarity in the hydraulic traits across Scots pine sites suggests that no adjustment in hydraulic architecture was needed, likely as a consequence of a drought-avoidance strategy. In contrast, sessile oak displayed adjustments in the hydraulic architecture along an aridity gradient, pointing to a drought-tolerance strategy

Automated wildlife image classification: An active learning tool for ecological applications

Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses. Our methodological proposal is two-fold: (1) We improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. (2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images. We supply a software package that enables researchers to use these methods directly and thereby ensure the broad applicability of the proposed framework in ecological practice. We show that our tuning strategy improves predictive performance. We demonstrate how the AL pipeline reduces the amount of pre-labeled data needed to achieve a specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance. We conclude that the combination of tuning and AL increases predictive performance substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America