Grow wider canopies or thicker stems: Variable response of woody plants to increasing dryness

Ding, Jingyi; Travers, Samantha K.; Eldridge, David J.

doi:10.1111/geb.13212

Cited by 7 publications

(3 citation statements)

References 52 publications

(87 reference statements)

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“…Seidel et al, 2021)-despite the fact that our data are from an environment with significant abiotic variation that may create more intraspecific structural variation. Water limitation may affect tree structure by limiting height (Fajardo et al, 2019) and altering crown dimensions (Ding et al, 2020;Lines et al, 2012), leading to greater intraspecific variability across our plot network. The high topographic variability of our study area also means wind exposure varies between plots, inducing changes to tree structure (Watt et al, 2005), irrespective of drought conditions (Niez et al, 2019) programme (Horn, 1971;Pugnaire & Valladares, 2007), and studies in plantation forests (Zou et al, 2017) and with monospecific plots (Xi et al, 2020) are unlikely to exhibit the same structural convergence due to this lack of extrinsic stresses (MacFarlane & Kane, 2017;Martin-Ducup et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Tree species classification from complex laser scanning data in Mediterranean forests using deep learning

et al. 2022

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1. Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three-dimensional vegetation structure at high resolution, but the scalability of using these data for large-scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer-level graphics processing units, provide new opportunities.2. Here, we apply a deep learning-based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre-processing such as leaf-wood separation or quantitative reconstructions.3. Using modern network architectures and data augmentation techniques, and without extensive pre-processing, we are able to achieve high overall and perspecies accuracy that is comparable or higher than in existing work while using data from a water-limited ecosystem complicated by structural convergence and multi-stem trees. 4. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle-individual species identification-and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.

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Section: Discussionmentioning

confidence: 99%

Tree species classification from complex laser scanning data in Mediterranean forests using deep learning

et al. 2022

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“…Also, consideration of environmental factors is of great importance in understanding the state, stability, and ecological role of forest stands [56][57][58][59][60][61][62][63]. It is especially important for protection and restoration of forest ecosystems [64][65][66] as well as in their management [67][68][69].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Biological and Environmental Factors Influence on Fire Hazard in Pine Forests: A Case Study in Central Forest-Steppe of the East European Plain

Slavskiy

Litovchenko

Матвеев

et al. 2022

Land

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Vast forest areas are spreaded in Russia and perform environment-forming, nature-protective and climate-regulating functions, including carbon sequestration. At the same time, increasing of destructive forest fires scale in recent decades has led to depletion of forest resources. To combat forest fires, it is necessary to develop preventive measures to reduce the number and severity of forest fires and establish reliable evaluation criteria for fire hazard assessment in forestry. However, indices of fire hazard assessment that exist in Russia are not always allowed to determine the degree of fire hazard reliably. The studies were performed in pine forests on the territory of the Central Forest-Steppe. The key forestry factors influencing the fire hazard situation in pine stands are identified: the presence and amount of combustible materials, the state of the stand, as well as the age structure of tree stand. According to burning indices, the highest fire hazard was common for young and middle-aged pine stands, while for ripening, mature and old-growth forests, fire hazard increasing was not observed. A set of parameters that characterize soil moisture and ground cover peculiarities have also a significant impact. Forest growth conditions were shown to be an important indicator for assessment of fire hazard class. Identified factors that have a key impact on the fire hazard in forests will make it possible to improve methodological approach for monitoring and preservation of forests.

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“…The climate variability in arid and semi‐arid regions is apparent, and there are different climate states over a wide range. This method of ‘space for time’ provides a good platform for simulating how plant communities respond to climate change and has been widely used in global studies (Ding et al, 2021). We used different precipitation use efficiencies to indicate the predicted dryness under climate change and the RMSD to represent the trade‐offs between aboveground and belowground biomass.…”

Section: Methodsmentioning

confidence: 99%

Plant biomass allocation is mediated by precipitation use efficiency in arid and semiarid ecosystems

et al. 2022

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The arid and semiarid grassland ecosystems are characterized by limited water resources and are generally vulnerable to climate change. Understanding how plants in arid and semiarid ecosystems respond to global climatic variation is crucial for ecological restoration under a changing climate. Although the effects of climate on aboveground biomass (AGB) and belowground biomass (BGB) have been widely explored, how AGB and BGB respond to climatic variation is seldom disentangled.There is still a need to understand how plant communities respond to global climate change. In this study, we conducted a transect survey across grasslands in Inner Mongolia to capture changes in AGB and BGB in plant communities. Then, we used structural equation modelling (SEM) to explore the relationship between environmental factors and the root:shoot ratio to understand how plant communities respond to ecological drought under global climate change. Our results showed that low precipitation use efficiency (LPUE) results in a high root:shoot (HRS) ratio, and BGB was more sensitive to environmental changes. By contrast, high precipitation use efficiency (HPUE) led to a low root:shoot (LRS) ratio, and environmental factors had a greater impact on AGB. For the LPUE pattern, soil water content (SWC), pH, and soil total nitrogen (STN) mainly affected the HRS ratio. Soil water content and STN influenced the HRS ratio through a positive effect on BGB . For the HPUE pattern, SWC, STN, and plant abundance (PA) predominantly regulate the LRS ratio, while biodiversity (plant abundance) affects the LRS ratio by positively affecting AGB. Our results highlight the differential impact of precipitation use efficiency on aboveground and belowground biomass allocation. This is important for monitoring the impact of drought events on plant biomass, improving productivity assessment models in arid and semi-arid regions, and assessing local carbon storage accurately.

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Grow wider canopies or thicker stems: Variable response of woody plants to increasing dryness

Cited by 7 publications

References 52 publications

Tree species classification from complex laser scanning data in Mediterranean forests using deep learning

Tree species classification from complex laser scanning data in Mediterranean forests using deep learning

Assessment of Biological and Environmental Factors Influence on Fire Hazard in Pine Forests: A Case Study in Central Forest-Steppe of the East European Plain

Plant biomass allocation is mediated by precipitation use efficiency in arid and semiarid ecosystems

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