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Tree growth at northern boreal treelines is generally limited by summer temperature, hence tree rings serve as natural archives of past climatic conditions. However, there is increasing evidence that a changing summer climate as well as certain micro-site conditions can lead to a weakening or loss of the summer temperature signal in trees growing in treeline environments. This phenomenon poses a challenge to all applications relying on stable temperature-growth relationships such as temperature reconstructions and dynamic vegetation models. We tested the effect of differing ecological and climatological conditions on the summer temperature signal of Scots pine at its northern distribution limits by analyzing twelve sites distributed along a 2200 km gradient from Finland to Western Siberia (Russia). Two frequently used proxies in dendroclimatology, ring width and maximum latewood density, were correlated with summer temperature for the period 1901–2013 separately for (i) dry vs. wet micro-sites and (ii) years with dry/warm vs. wet/cold climate regimes prevailing during the growing season. Differing climate regimes significantly affected the temperature signal of Scots pine at about half of our sites: While correlations were stronger in wet/cold than in dry/warm years at most sites located in Russia, differing climate regimes had only little effect at Finnish sites. Both tree-ring proxies were affected in a similar way. Interestingly, micro-site differences significantly affected absolute tree growth, but had only minor effects on the climatic signal at our sites. We conclude that, despite the treeline-proximal location, growth-limiting conditions seem to be exceeded in dry/warm years at most Russian sites, leading to a weakening or loss of the summer temperature signal in Scots pine here. With projected temperature increase, unstable summer temperature signals in Scots pine tree rings might become more frequent, possibly affecting dendroclimatological applications and related fields.
Species of the genus Wolffia are traditionally used as human food in some of the Asian countries. Therefore, all 11 species of this genus, identified by molecular barcoding, were investigated for ingredients relevant to human nutrition. The total protein content varied between 20 and 30% of the freeze-dry weight, the starch content between 10 and 20%, the fat content between 1 and 5%, and the fiber content was ~25%. The essential amino acid content was higher or close to the requirements of preschool-aged children according to standards of the World Health Organization. The fat content was low, but the fraction of polyunsaturated fatty acids was above 60% of total fat and the content of n-3 polyunsaturated fatty acids was higher than that of n-6 polyunsaturated fatty acids in most species. The content of macro- and microelements (minerals) not only depended on the cultivation conditions but also on the genetic background of the species. This holds true also for the content of tocopherols, several carotenoids and phytosterols in different species and even intraspecific, clonal differences were detected in Wolffia globosa and Wolffia arrhiza. Thus, the selection of suitable clones for further applications is important. Due to the very fast growth and the highest yield in most of the nutrients, Wolffia microscopica has a high potential for practical applications in human nutrition.
Quantitative reconstructions of past vegetation cover commonly require pollen productivity estimates (PPEs). PPEs are calibrated in extensive and rather cumbersome surface-sample studies, and are so far only available for selected regions. Moreover, it may be questioned whether present-day pollen-landcover relationships are valid for palaeo-situations. We here introduce the ROPES approach that simultaneously derives PPEs and mean plant abundances from single pollen records. ROPES requires pollen counts and pollen accumulation rates (PARs, grains cm−2 year−1). Pollen counts are used to reconstruct plant abundances following the REVEALS approach. The principle of ROPES is that changes in plant abundance are linearly represented in observed PAR values. For example, if the PAR of pine doubles, so should the REVEALS reconstructed abundance of pine. Consequently, if a REVEALS reconstruction is “correct” (i.e., “correct” PPEs are used) the ratio “PAR over REVEALS” is constant for each taxon along all samples of a record. With incorrect PPEs, the ratio will instead vary. ROPES starts from random (likely incorrect) PPEs, but then adjusts them using an optimization algorithm with the aim to minimize variation in the “PAR over REVEALS” ratio across the record. ROPES thus simultaneously calculates mean plant abundances and PPEs. We illustrate the approach with test applications on nine synthetic pollen records. The results show that good performance of ROPES requires data sets with high underlying variation, many samples and low noise in the PAR data. ROPES can deliver first landcover reconstructions in regions for which PPEs are not yet available. The PPEs provided by ROPES may then allow for further REVEALS-based reconstructions. Similarly, ROPES can provide insight in pollen productivity during distinct periods of the past such as the Lateglacial. We see a potential to study spatial and temporal variation in pollen productivity for example in relation to site parameters, climate and land use. It may even be possible to detect expansion of non-pollen producing areas in a landscape. Overall, ROPES will help produce more accurate landcover reconstructions and expand reconstructions into new study regions and non-analog situations of the past. ROPES is available within the R package DISQOVER.
Climate Change-Induced Shift of Tree Growth Sensitivity at a Central Himalayan Treeline Ecotone
(2018)
Production-Integrated Compensation in Environmental Offsets—A Review of a German Offset Practice
(2018)