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Direct and Indirect Effects of Environmental Limitations on White Spruce Xylem Anatomy at Treeline
(2021)
Treeline ecosystems are of great scientific interest to study the effects of limiting environmental conditions on tree growth. However, tree growth is multidimensional, with complex interactions between height and radial growth. In this study, we aimed to disentangle effects of height and climate on xylem anatomy of white spruce [Picea glauca (Moench) Voss] at three treeline sites in Alaska; i.e., one warm and drought-limited, and two cold, temperature-limited. To analyze general growth differences between trees from different sites, we used data on annual ring width, diameter at breast height (DBH), and tree height. A representative subset of the samples was used to investigate xylem anatomical traits. We then used linear mixed-effects models to estimate the effects of height and climatic variables on our study traits. Our study showed that xylem anatomical traits in white spruce can be directly and indirectly controlled by environmental conditions: hydraulic-related traits seem to be mainly influenced by tree height, especially in the earlywood. Thus, they are indirectly driven by environmental conditions, through the environment’s effects on tree height. Traits related to mechanical support show a direct response to environmental conditions, mainly temperature, especially in the latewood. These results highlight the importance of assessing tree growth in a multidimensional way by considering both direct and indirect effects of environmental forcing to better understand the complexity of tree growth responses to the environment.
Coastal sand dunes near the Baltic Sea are a dynamic environment marking the boundary between land and sea and oftentimes covered by Scots pine (Pinus sylvestris L.) forests. Complex climate-environmental interactions characterize these ecosystems and largely determine the productivity and state of these coastal forests. In the face of future climate change, understanding interactions between coastal tree growth and climate variability is important to promote sustainable coastal forests. In this study, we assessed the effect of microsite conditions on tree growth and the temporal and spatial variability of the relationship between climate and Scots pine growth at nine coastal sand dune sites located around the south Baltic Sea. At each site, we studied the growth of Scots pine growing at microsites located at the ridge and bottom of a dune and built a network of 18 ring-width and 18 latewood blue intensity chronologies. Across this network, we found that microsite has a minor influence on ring-width variability, basal area increment, latewood blue intensity, and climate sensitivity. However, at the local scale, microsite effects turned out to be important for growth and climate sensitivity at some sites. Correlation analysis indicated that the strength and direction of climate-growth responses for the ring-width and blue intensity chronologies were similar for climate variables over the 1903–2016 period. A strong and positive relationship between ring-width and latewood blue intensity chronologies with winter-spring temperature was detected at local and regional scales. We identified a relatively strong, positive influence of winter-spring/summer moisture availability on both tree-ring proxies. When climate-growth responses between two intervals (1903–1959, 1960–2016) were compared, the strength of growth responses to temperature and moisture availability for both proxies varied. More specifically, for the ring-width network, we identified decreasing temperature-growth responses, which is in contrast to the latewood blue intensity network, where we documented decreasing and increasing temperature-growth relationships in the north and south respectively. We conclude that coastal Scots pine forests are primarily limited by winter-spring temperature and winter-spring/summer drought despite differing microsite conditions. We detected some spatial and temporal variability in climate-growth relationships that warrant further investigation.
The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the “learning process” defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms’ performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.
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.