ALBERT

Description: Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of “normal”—expected growth under average conditions—which has not received sufficient scrutiny. We re-examined legacy effect analyses using the International Tree-Ring Data Bank (ITRDB) and then produced synthetic tree-ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto-correlation of the radial growth time series (phi), (ii) depend on climate-growth cross-correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold. Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto-correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity. Synthesis. We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto-correlated residuals of climate-growth regression models a posteriori, thereby retrospectively adjusting expectations for “normal” growth variability. Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process-based representation of vegetation carbon dynamics in mechanistic models.

Description: Ecological research, just as all Earth System Sciences, is becoming increasingly data-rich. Tools for processing of “big data” are continuously developed to meet corresponding technical and logistical challenges. However, even at smaller scales, data sets may be challenging when best practices in data exploration, quality control and reproducibility are to be met. This can occur when conventional methods, such as generating and assessing diagnostic visualizations or tables, become unfeasible due to time and practicality constraints. Interactive processing can alleviate this issue, and is increasingly utilized to ensure that large data sets are diligently handled. However, recent interactive tools rarely enable data manipulation, may not generate reproducible outputs, or are typically data/domain-specific. We developed datacleanr, an interactive tool that facilitates best practices in data exploration, quality control (e.g., outlier assessment) and flexible processing for multiple tabular data types, including time series and georeferenced data. The package is open-source, and based on the R programming language. A key functionality of datacleanr is the “reproducible recipe”—a translation of all interactive actions into R code, which can be integrated into existing analyses pipelines. This enables researchers experienced with script-based workflows to utilize the strengths of interactive processing without sacrificing their usual work style or functionalities from other (R) packages. We demonstrate the package’s utility by addressing two common issues during data analyses, namely 1) identifying problematic structures and artefacts in hierarchically nested data, and 2) preventing excessive loss of data from ‘coarse,’ code-based filtering of time series. Ultimately, with datacleanr we aim to improve researchers’ workflows and increase confidence in and reproducibility of their results.

Description: Tree-specific canopy conductance (Gc) and its adjustment play a critical role in mitigating excess water loss in changing environmental conditions. However, the change of Gc sensitivity to environmental conditions due to drought remains unclear for European tree species. Here we quantified the environmental operational space of Gc, i.e., the water supply (soil moisture, tree water deficit) and demand conditions (vapor pressure deficit) under which Gc ≥ 50% is possible (Gc50OS), at two sites with different soil water availability for three common European tree species. We collected sap flow and dendrometer measurements for co-occurring Pinus sylvestris, Fagus sylvatica and Quercus petraea growing under different soil hydrological conditions (drier/wetter). These measurements were combined with meteorological variables and soil moisture conditions in five depths. Dendrometer measurements were used to confirm soil water availability patterns. For all analyses, the contrasting soil hydrology between sites was the main driver of Gc response. At the drier sites, F. sylvatica and P. sylvestris reduced their water consumption in response to decreasing soil water supply earlier in the growing season than Q. petraea. However, our analysis on the Gc50OS revealed that at the drier sites, F. sylvatica and Q. petraea reduced the extent of their Gc50OS to a higher degree than P. sylvestris. This indicates a higher level of Gc50OS adjustment to the drier site conditions for the two broadleaved species. These differences were more pronounced when using the dendrometer-derived tree internal water status as proxy for tree water supply. Our results provide preliminary evidence for diverging short-term Gc responses when temperate trees are exposed to prolonged reduction in water availability. These findings suggest that Gc50OS can help to constrain species-specific predictions of water use by mature trees, especially when combined with high-resolution water potential measurements.

Description: Excess heat (i.e., Urban Heat Island; UHI) and other urban conditions affect tree physiology with outcomes from enhanced growth to mortality. Resilient urban forests in the face of climate change require species-specific understanding of growth responses. However, previous studies assessing growth dynamics were primarily based on remote sensing of communities rather than individuals, or relied on labor-intensive methods that can limit the spatial coverage necessary to account for highly variable urban growing conditions. Here, we analyze growth dynamics of common urban street tree species over time and across space for Berlin (Germany) combining dendroecological (temporal) and inventory assessments (spatial). First, we show annual increments increased across the 20th century for early (i.e., young) growth. Second, we use an approach relying on open inventory data to identify growth potential in relation to excess heat while accounting for age, potential management effects, and the urban fabric (i.e., planting area; building density, height; available soil nutrients) with generalized additive models for the ten most abundant species. Our analyses showed that younger trees may benefit from increased temperatures, while older individuals feature lower growth at greater UHI magnitudes. Furthermore, planting area as well as building density modulate growth responses to temperature. Lastly, we discuss management implications in the context of climate change mitigation, considering that younger trees are predominantly located at UHI “hot spots” and will undergo the observed age-dependent shift in temperature-growth sensitivity. By relying on increasingly available open data, our approach here is or will be transferable to other urban regions.