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Abstract
Higher biodiversity can stabilize the productivity and functioning of grassland communities when subjected to extreme climatic events. The positive biodiversity–stability relationship emerges via increased resistance and/or recovery to these events. However, invader presence might disrupt this diversity–stability relationship by altering biotic interactions. Investigating such disruptions is important given that invasion by non‐native species and extreme climatic events are expected to increase in the future due to anthropogenic pressure. Here we present one of the first multisite invader × biodiversity × drought manipulation experiment to examine combined effects of biodiversity and invasion on drought resistance and recovery at three semi‐natural grassland sites across Europe. The stability of biomass production to an extreme drought manipulation (100% rainfall reduction; BE: 88 days, BG: 85 days, DE: 76 days) was quantified in field mesocosms with a richness gradient of 1, 3, and 6 species and three invasion treatments (no invader, Lupinus polyphyllus, Senecio inaequidens). Our results suggest that biodiversity stabilized community productivity by increasing the ability of native species to recover from extreme drought events. However, invader presence turned the positive and stabilizing effects of diversity on native species recovery into a neutral relationship. This effect was independent of the two invader's own capacity to recover from an extreme drought event. In summary, we found that invader presence may disrupt how native community interactions lead to stability of ecosystems in response to extreme climatic events. Consequently, the interaction of three global change drivers, climate extremes, diversity decline, and invasive species, may exacerbate their effects on ecosystem functioning.
Understanding the effects of temperature and moisture on radial growth is vital for assessing the impacts of climate change on carbon and water cycles. However, studies observing growth at sub-daily temporal scales remain scarce.
We analysed sub-daily growth dynamics and its climatic drivers recorded by point dendrometers for 35 trees of three temperate broadleaved species during the years 2015–2020. We isolated irreversible growth driven by cambial activity from the dendrometer records. Next, we compared the intra-annual growth patterns among species and delimited their climatic optima.
The growth of all species peaked at air temperatures between 12 and 16°C and vapour pressure deficit (VPD) below 0.1 kPa. Acer pseudoplatanus and Fagus sylvatica, both diffuse-porous, sustained growth under suboptimal VPD. Ring-porous Quercus robur experienced a steep decline of growth rates with reduced air humidity. This resulted in multiple irregular growth peaks of Q. robur during the year. By contrast, the growth patterns of the diffuse-porous species were always right-skewed unimodal with a peak in June between day of the year 150–170.
Intra-annual growth patterns are shaped more by VPD than temperature. The different sensitivity of radial growth to VPD is responsible for unimodal growth patterns in both diffuse-porous species and multimodal growth pattern in Q. robur.
Significant alterations of cambial activity might be expected due to climate warming, leading to growing season extension and higher growth rates especially in cold-limited forests. However, assessment of climate-change-driven trends in intra-annual wood formation suffers from the lack of direct observations with a timespan exceeding a few years. We used the Vaganov-Shashkin process-based model to: (i) simulate daily resolved numbers of cambial and differentiating cells; and (ii) develop chronologies of the onset and termination of specific phases of cambial phenology during 1961–2017. We also determined the dominant climatic factor limiting cambial activity for each day. To asses intra-annual model validity, we used 8 years of direct xylogenesis monitoring from the treeline region of the Krkonoše Mts. (Czechia). The model exhibits high validity in case of spring phenological phases and a seasonal dynamics of tracheid production, but its precision declines for estimates of autumn phenological phases and growing season duration. The simulations reveal an increasing trend in the number of tracheids produced by cambium each year by 0.42 cells/year. Spring phenological phases (onset of cambial cell growth and tracheid enlargement) show significant shifts toward earlier occurrence in the year (for 0.28–0.34 days/year). In addition, there is a significant increase in simulated growth rates during entire growing season associated with the intra-annual redistribution of the dominant climatic controls over cambial activity. Results suggest that higher growth rates at treeline are driven by (i) temperature-stimulated intensification of spring cambial kinetics, and (ii) decoupling of summer growth rates from the limiting effect of low summer temperature due to higher frequency of climatically optimal days. Our results highlight that the cambial kinetics stimulation by increasing spring and summer temperatures and shifting spring phenology determine the recent growth trends of treeline ecosystems. Redistribution of individual climatic factors controlling cambial activity during the growing season questions the temporal stability of climatic signal of cold forest chronologies under ongoing climate change.
Changes in the environment will alter the growth rate of trees and forests. Different disciplines assess such growth rates differently, for example, with tree-ring width data, forest inventories or with carbon-flux data from eddy covariance towers. Such data is used to quantify forests biomass increment, forest’s carbon sequestration or to reconstruct environmental variables before instrumental records. However, raw measurement data is typically not considered to be representative for the average growth rate of trees or forests. Depending on the research question, the effects of certain environmental variables or effects of tree and forest structure have to be removed first. It can be challenging to define and quantify a growth trend that can answer a specific research question because trees and forests grow and respond to environmental change in multiple ways simultaneously, for example, with altered radial increment, height growth, and stand density. Further challenges pose time-lagged feedback loops, for example, between height and radial increment or between stand density and radial increment. Generally, different environments will lead to different tree and forest structures, but because of tree’s longevity this adaptation to the new environment will take decades or even centuries. Consequently, there can be an offset between the present forest structure and what we term the potential natural forest (PNF): Similar to the potential natural vegetation (PNV), the PNF represents that forest that would develop under the current environmental conditions in the absence of human intervention. Because growth rates are affected by the tree and forest structure, growth-trend estimates will differ between the present and the potential forest. Consequently, if the legacy effects of the past are not of interest, the PNF is the theoretical baseline to correct and estimate growth trends.
Individual white spruce (Picea glauca (Moench) Voss) growth limitations at treelines in Alaska
(2018)
White spruce (Picea glauca (Moench) Voss) is one of the most common conifers in Alaska and various treelines mark the species distribution range. Because treelines positions are driven by climate and because climate change is estimated to be strongest in northern latitudes, treeline shifts appear likely. However, species range shifts depend on various species parameters, probably most importantly on phenotypic plasticity, genetic adaptation
and dispersal. Due to their long generation cycles and their immobility, trees evolved to endure a wide variety of climatic conditions. In most locations, interannual climate variability is larger than the expected climate change until 2100. Thus treeline position is typically thought of as the integrated effect of multiple years and to lag behind gradual climate change by several decades. Past dendrochronological studies revealed that growth of white spruce in Alaska can be limited by several climatic variables, in particular water stress and low temperatures. Depending on how the intensity of climate warming, this could result in a leading range edge at treelines limited by low temperatures and trailing treelines where soil moisture is or becomes most limiting. Climate-growth correlations are the dendrochronological version of reaction norms and describe the relationship between an environmental variable and traits like tree-ring parameters (e.g. ring width, wood density, wood anatomy). These correlations can be used to explore potential effects of climate change on a target species. However, it is known that individuals differ with respect to multiple variables like size, age, microsite conditions, competition status or their genome. Such individual differences could be important because they can modulate climate-growth relationships and consequently also range shifts and growth trends. Removing individual differences by averaging tree-ring parameters of many individuals into site chronologies could be an oversimplification that might bias estimates of future white spruce performance. Population dynamics that emerge from the interactions of individuals (e.g. competition) and the range of reactions to the same environmental drivers can only be studied via individual tree analyses. Consequently, this thesis focuses on factors that might alter individual white spruce’ climate sensitivity and methods to assess such effects. In particular, the research articles included explore three topics:
1. First, clones were identified via microsatellites and high-frequency climate signals of clones were compared to that of non-clonal individuals. Clonal and non-clonal individuals showed similar high-frequency climate signals which allows to use clonal and non-clonal individuals to construct mean site chronologies. However, clones were more frequently found under the harsher environmental conditions at the treelines which could be of interest for the species survival strategy at alpine treelines and is further explored in the associated RESPONSE project A5 by David Würth.
2. In the second article, methods for the exploration and visualization of individual-tree differences in climate sensitivity are described. These methods represent a toolbox to explore causes for the variety of different climate sensitivities found in individual
trees at the same site. Though, overlaying gradients of multiple factors like temperature, tree density and/or tree height can make it difficult to attribute a single cause to the range of reaction norms (climate growth correlations).
3. Lastly, the third article attempts to disentangle the effect of age and size on climate-growth correlations. Multiple past studies found that trees of different Ages responded differently to climatic drivers. In contrast, other studies found that trees do not age like many other organisms. Age and size of a trees are roughly correlated, though there are large differences in the growth rate of trees, which can lead to smaller trees that are older than taller trees. Consequently, age is an imperfect Proxy for size and in contrast to age, size has been shown to affect wood anatomy and thus tree physiology. The article compares two tree-age methods and one tree-size method based on cumulative ring width. In line with previous research on aging and Wood anatomy, tree size appeared to be the best predictor to explain ontogenetic changes in white spruce’ climate sensitivity. In particular, tallest trees exhibited strongest correlations with water stress in previous year July. In conclusion, this thesis is about factors that can alter climate-growth relationships (reaction norms) of white spruce. The results emphasize that interactions between climate variables and other factors like tree size or competition status are important for estimates of future tree growth and potential treeline shifts. In line with previous studies on white spruce in Alaska, the results of this thesis underline the importance of water stress for white spruce.
Individuals that are taller and that have more competitors for water appear to be most susceptible to the potentially drier future climate in Alaska. While tree ring based growth trends estimates of white spruce are difficult to derive due to multiple overlaying low frequency (>10 years) signals, all investigated treeline sites showed highest growth at the treeline edge. This could indicate expanding range edges. However, a potential bottleneck for treeline advances and retreats could be seedling establishment, which should be explored in more detail in the future.
A massive shift in agricultural practices over the past decades, to support exceptionally high yields and productivities involving intensive agriculture, have led to unsustainable agriculture practices across the globe. Sustenance of such high yields and productivities demand high use of organic and industrial fertilizers. This acts as a negative pressure on the environment. Excessive use of fertilizers leads to nutrient surplus in the fields, which, as a part of catchment runoff, flows into the water bodies as diffuse pollution. These nutrients through rivers are eventually passed into seas. High nutrients ending up into water bodies cause eutrophication. The situation is worsened when such unsustainable agricultural activities are carried out on drained peatlands. As a result, the nutrients that were not part of the nutrient cycle in the landscape for years begin to leach out due to mineralization of peatlands, thereby putting an additional load of nutrients on the environment, that was already under the negative impact of nutrient surplus. In view of the above, a small lowland catchment of the Ryck river in northeast Germany was assessed for its nitrogen losses from agricultural lands through empirical modelling. Initial empirical modelling resulted in an average annual total nitrogen loss of 14.7 kg ha−1 year−1. After a comparative analysis of these results with procured data, the empirical equation was modified to suit the catchment, yielding more accurate results. The study showed that 75.6% of peatlands in the catchment are under agricultural use. Subsequently, a proposal was made for potential wetland buffer zones in the Ryck catchment. Altogether, 13 peatland sites across 8 sub-catchments were recommended for mitigation of high nutrient runoff. In the end, nutrient efficiency of proposed WBZs in one of the sub-catchments of Ryck has been discussed. The results show that (i) the modified empirical equation can act as a key tool in application-based future strategies for nitrogen reduction in the Ryck catchment, (ii) restoration of peatlands and introduction of WBZs can help in mitigating the nutrient runoff for improved water quality of Ryck, and subsequently (ii) contribute to efficient reduction of riverine loads of nutrients into the Baltic Sea.
Water Consumption of Agriculture and Natural Ecosystems along the Ili River in China and Kazakhstan
(2017)
Pollen productivity estimates (PPEs) are a key parameter for quantitative land-cover reconstructions from pollen data. PPEs are commonly estimated using modern pollen-vegetation data sets and the extended R-value (ERV) model. Prominent discrepancies in the existing studies question the reliability of the approach. We here propose an implementation of the ERV model in the R environment for statistical computing, which allows for simplified application and testing. Using simulated pollen-vegetation data sets, we explore sensitivity of ERV application to (1) number of sites, (2) vegetation structure, (3) basin size, (4) noise in the data, and (5) dispersal model selection. The simulations show that noise in the (pollen) data and dispersal model selection are critical factors in ERV application. Pollen count errors imply prominent PPE errors mainly for taxa with low counts, usually low pollen producers. Applied with an unsuited dispersal model, ERV tends to produce wrong PPEs for additional taxa. In a comparison of the still widely applied Prentice model and a Lagrangian stochastic model (LSM), errors are highest for taxa with high and low fall speed of pollen. The errors reflect the too high influence of fall speed in the Prentice model. ERV studies often use local scale pollen data from for example, moss polsters. Describing pollen dispersal on his local scale is particularly complex due to a range of disturbing factors, including differential release height. Considering the importance of the dispersal model in the approach, and the very large uncertainties in dispersal on short distance, we advise to carry out ERV studies with pollen data from open areas or basins that lack local pollen deposition of the taxa of interest.