Forecasting seed production in perennial plants: identifying challenges and charting a path forward.
Forecasting seed production in perennial plants: identifying challenges and charting a path forward.
复制标题
预测多年生植物的种子产量：识别挑战并规划前进的道路。
作者：
V. Journé;A. Hacket‐Pain;Iris Oberklammer;M. Pesendorfer;Michał Bogdziewicz
种子生产的年际变化，即所谓的制种，具有深远的生态影响，包括对森林更新和种子消费者的种群动态的影响。由于在由巨型物种主导的生态系统中，管理和养护努力的相对时机往往决定它们最终的成功，因此需要研究巨型物种的机制，并开发种子生产的预测工具。在这里，我们的目标是建立种子产量预测作为一个新的学科分支。我们评估了三个模型--Foremast、ΔT和一个序列模型--的预测能力，这些模型是用一个泛欧洲的水曲柳种子生产数据集来预测树木的种子产量的。这些模型在再现种子生产动态方面取得了一定的成功。关于先前种子产量的高质量数据的可用性提高了序列模型的预测能力，表明有效的种子产量监测方法对于创建预测工具至关重要。就极端事件而言，这些模型在预测作物歉收方面比丰收更好，这可能是因为人们更好地理解了阻止种子生产的因素，而不是导致大规模繁殖事件的过程。我们总结了当前的挑战，并提供了一个路线图，以帮助推进该学科并鼓励MAST预测的进一步发展。
Interannual variability of seed production, known as masting, has far-reaching ecological impacts including effects on forest regeneration and the population dynamics of seed consumers. Because the relative timing of management and conservation efforts in ecosystems dominated by masting species often determines their success, there is a need to study masting mechanisms and develop forecasting tools for seed production. Here, we aim to establish seed production forecasting as a new branch of the discipline. We evaluate the predictive capabilities of three models - foreMast, ΔT, and a sequential model - designed to predict seed production in trees using a pan-European dataset of Fagus sylvatica seed production. The models are moderately successful in recreating seed production dynamics. The availability of high-quality data on prior seed production improved the sequential model's predictive power, suggesting that effective seed production monitoring methods are crucial for creating forecasting tools. In terms of extreme events, the models are better at predicting crop failures than bumper crops, likely because the factors preventing seed production are better understood than the processes leading to large reproductive events. We summarize the current challenges and provide a roadmap to help advance the discipline and encourage the further development of mast forecasting.
登录
查看更多内容
脉冲驱动系统内的数字和行为效应：共享猎物的后果。
影响因子：
4.8
作者：
Schmidt,KennethA;Ostfeld,RichardS
通讯作者：
Ostfeld,RichardS
影响因子：
8.8
作者：
Michał Bogdziewicz;V. Journé;A. Hacket‐Pain;Jakub Szymkowiak
通讯作者：
Michał Bogdziewicz;V. Journé;A. Hacket‐Pain;Jakub Szymkowiak
影响因子：
8.8
作者：
Bogdziewicz, Michal;Ascoli, Davide;Tanentzap, Andrew
通讯作者：
Tanentzap, Andrew
影响因子：
6.6
作者：
Abigail S. L. Lewis;C. Rollinson;A. Allyn;Jaime Ashander;S. Brodie;Cole B. Brookson;Elyssa L. Collins-Elyssa-L.-Coll
通讯作者：
Abigail S. L. Lewis;C. Rollinson;A. Allyn;Jaime Ashander;S. Brodie;Cole B. Brookson;Elyssa L. Collins-Elyssa-L.-Coll
影响因子：
11.6
作者：
Heilman, Kelly A.;Dietze, Michael C.;Evans, Margaret E. K.
通讯作者：
Evans, Margaret E. K.