Xylem Anatomical Variability in White Spruce at Treeline Is Largely Driven by Spatial Clustering

Pampuch, Timo; Anadon‐Rosell, Alba; Zacharias, Melanie; Arx, Georg von; Wilmking, Martin

doi:10.3389/fpls.2020.581378

Cited by 7 publications

(6 citation statements)

References 69 publications

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“…DEN showed similar but overall relatively weaker reactions to climate conditions than CWT. This pattern might be explained by a stronger genetic control on DEN than on CWT, as found in previous studies ( Lenz et al, 2011 ; Hassegawa et al, 2019 ; Pampuch et al, 2020 ). Therefore, it seems possible that especially the relation between total cell area and CWA (i.e., the anatomical allometry) is under a strong genetic control.…”

Section: Discussionsupporting

confidence: 75%

“…These differences must be interpreted carefully, as in both cases we had a high standard error and high p-values, indicating a weak confidence (Supplementary Table 2). Still, they showed that differences might also occur on yet another aspect that was not investigated in this study (e.g., adaptation; Lenz et al, 2011;Pampuch et al, 2020). Stronger differences between the sites regarding LA might have been mitigated by the beneficial effect of smaller lumen, which can also potentially reduce the risk of freeze-thaw embolisms .…”

Section: Discussionmentioning

confidence: 75%

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Direct and Indirect Effects of Environmental Limitations on White Spruce Xylem Anatomy at Treeline

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 75%

Direct and Indirect Effects of Environmental Limitations on White Spruce Xylem Anatomy at Treeline

et al. 2021

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“…Conifers, beech, and oak are some of the most used species for tree ring studies in general ( Scharnweber et al, 2013 ; Príncipe et al, 2017 ; Lange et al, 2018 ; Janecka et al, 2020 ) and specifically for wood anatomical analyses ( Björklund et al, 2020 ; Pampuch et al, 2020 ; Peters et al, 2020 ). Images were collected from several sources, to ensure variability in the image quality and within the sample processing ( Supplementary Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

et al. 2021

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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.

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“…Considered to be one of the hardiest coniferous species, white spruce has a suite of structural and functional traits that may help explain its persistence in the harsh FTE environment where tree growth can be limited by low winter temperatures, short growing seasons, extreme light environments and often limiting rooting volumes due to the occurrence of permafrost. These potentially adaptive traits include strong apical dominance (Nienstaedt & Zasada, 1990), prolific seed production beginning at an early age (Sutton, 1969), xylem morphology capable of desiccation tolerance (Pampuch et al, 2020), strong photoperiodicity of growth (Eitel et al, 2019), early rehydration and initiation of growth despite experiencing extremely cold winters (Eitel et al, 2020) and rapid photoprotection mechanisms (Maguire et al, 2020), among many others. The physiology of white spruce is less well characterized, but studies of both photosynthesis and respiration (e.g., Weger & Guy, 1991a;Man & Lieffers, 1997;McNown & Sullivan, 2013;Stinziano & Way, 2017;Benomar et al, 2018;Prud'homme et al, 2018) indicate the physiology of leaf carbon balance in white spruce is under strong environmental control that could contribute to the establishment and persistence of the FTE.…”

Section: Introductionmentioning

confidence: 99%

High Leaf Respiration Rates May Limit the Success of White Spruce Saplings growing in The Kampfzone at the Arctic Treeline

Griffin

Schmeige

Bruner

et al. 2021

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Arctic Treeline is the transition from the boreal forest to the treeless tundra and may be determined by growing season temperatures. The physiological mechanisms involved in determining the relationship between the physical and biological environment and the location of treeline are not fully understood. In Northern Alaska we studied the relationship between temperature and leaf respiration in 36 white spruce (Picea glauca) trees, sampling both the upper and lower canopy, to test two research hypotheses (H0). The first H01 is that canopy position will not influence leaf respiration. The associated alternative hypothesis (HA) is that the upper canopy leaves which are more directly coupled to the atmosphere will experience more challenging environmental conditions and thus have higher respiration rates to facilitate metabolic function. The second H02 is that tree size will not influence leaf respiration. The associated HA is that saplings (stems that are 5-10 cm DBH (diameter at breast height)) will have higher respiration rates than trees (stems ≥ 10 cm DBH) since saplings represent the transition from seedlings growing in the more favorable aerodynamic boundary layer, to trees which are fully coupled to the atmosphere but of sufficient size to persist. Respiration did not change with canopy position, however respiration at 25°C was 42% higher in saplings compared to trees (3.43 ± 0.19 vs. 2.41 ± 0.14 μmol m-2s-1). Furthermore, there were significant differences in the temperature response of respiration, and seedlings reached their maximum respiration rates at 59°C, more than two degrees higher than trees. Our results demonstrate that the respiratory characteristics of white spruce saplings at treeline are extreme, imposing a significant carbon cost that may contribute to their lack of perseverance beyond treeline. In the absence of thermal acclimation, the rate of leaf respiration could increase by 57% by the end of the century, posing further challenges to the ecology of this massive ecotone.

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Xylem Anatomical Variability in White Spruce at Treeline Is Largely Driven by Spatial Clustering

Cited by 7 publications

References 69 publications

Direct and Indirect Effects of Environmental Limitations on White Spruce Xylem Anatomy at Treeline

Direct and Indirect Effects of Environmental Limitations on White Spruce Xylem Anatomy at Treeline

Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

High Leaf Respiration Rates May Limit the Success of White Spruce Saplings growing in The Kampfzone at the Arctic Treeline

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