Comprehending and quantifying local variability in plant phenology, alongside its impacts on tree growth, is challenging due to spatially and temporally heterogeneous environmental factors that interact to affect phenological events and periods. Although previous studies have focused on the climatic factors driving phenological events at the stand level, the influences of competition, neighborhood characteristics, species richness, and water availability on plant phenology remain unclear. In the present paper, we used hourly terrestrial LiDAR surveys performed between April 2020 and April 2021 to investigate the influence of local factors (neighborhood and topography) on the phenology of silver birch trees (Betula pendula Roth.). We also examined how phenological events affect growth in tree height and crown area. All response and explanatory variables were estimated using LiDAR time-series data and a field survey campaign. Our findings demonstrate a high within-species variability in plant phenology that is controlled by biotic and abiotic characteristics of the ecosystem. We found that between tree variation in leaf burst, completion of the leaf growth and length of canopy growth period were affected by tree size, neighborhood species richness, level of suppression and competition, which potentially indicate plant responses to light availability. The beginning of senescence, completion of leaf drop, and length of autumn phenology were mostly affected by topographic water index, which reflects water availability and can be linked to nutrient availability and exposure to wind. Furthermore, we demonstrated that an earlier leaf burst, and delayed beginning of senescence were associated with larger absolute growth in canopy area but found no clear relationship between height growth and phenology. Our study highlights the capability of dense, high-quality LiDAR time-series to detect significant phenological variation among individuals of the same species within the same locality, demonstrating the potential of LiDAR time-series as a tool for understanding phenology and its impacts.

中文翻译：

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使用密集 LiDAR 时间序列的银桦树物候个体间变异的因素和影响


理解和量化植物物候的局部变异性及其对树木生长的影响是具有挑战性的，因为空间和时间上的异质环境因素相互作用会影响物候事件和时期。尽管以前的研究集中在林分水平驱动物候事件的气候因素上，但竞争、邻域特征、物种丰富度和水可用性对植物物候的影响仍不清楚。在本文中，我们使用了 2020 年 4 月至 2021 年 4 月期间进行的每小时地面 LiDAR 调查来调查当地因素（邻里和地形）对银桦树 （Betula pendula Roth.） 物候的影响。我们还研究了物候事件如何影响树高和树冠面积的生长。所有响应和解释变量均使用 LiDAR 时间序列数据和实地调查活动进行估计。我们的研究结果表明，植物物候的种内变异性很高，受生态系统的生物和非生物特性控制。我们发现，在树木爆发的叶子变化之间，叶子生长的完成和树冠生长期的长度受到树木大小、邻域物种丰富度、抑制水平和竞争的影响，这可能表明植物对光可用性的反应。衰老的开始、落叶的完成和秋季物候的长度主要受地形水分指数的影响，地形水分指数反映了水的可用性，可能与养分可用性和暴露在风中有关。 此外，我们证明，较早的叶爆裂和延迟衰老的开始与冠层面积较大的绝对生长有关，但发现高度生长与物候之间没有明确的关系。我们的研究强调了密集、高质量的 LiDAR 时间序列能够检测同一地点内同一物种个体之间的显着物候变化，证明了 LiDAR 时间序列作为理解物候及其影响的工具的潜力。