Phenology forecasting models for detection and management of invasive annual grasses

IF 2.7 3区 环境科学与生态学 Q2 ECOLOGY
Ecosphere Pub Date : 2024-10-10 DOI:10.1002/ecs2.70023
J. S. Prevéy, I. S. Pearse, D. M. Blumenthal, A. J. Howell, J. A. Kray, S. C. Reed, M. B. Stephenson, C. S. Jarnevich
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Abstract

Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass (Bromus tectorum), and red brome (Bromus rubens). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within-season or within-year decision support.

Abstract Image

用于检测和管理入侵一年生草本植物的物候预测模型
非本地一年生草会极大地改变火灾频率,并降低所入侵生态系统的饲料质量和生物多样性。需要有效的管理技术来减少这些不受欢迎的入侵物种并维持生态系统服务。适时的管理策略(如放牧)可以控制入侵物种的蔓延并减少其影响,在入侵草比本地植物更活跃的时候放牧。然而,在广阔的土地上预测易受管理的关键物候期的时间是很困难的,因为这些物种的物候在时间和空间上会有很大差异。为了应对这一挑战,我们对两种有问题的入侵一年生草类:欺骗草(Bromus tectorum)和红锦葵(Bromus rubens)进行了全域物候预测。我们利用从监测实验、志愿科学、标本馆记录、延时摄影机图像和降尺度网格气候数据中获得的观测数据,测试了一套 18 个机理物候模型,以确定最能预测这两种入侵草种开花和衰老日期的模型。我们发现,光热时间模型能最好地预测骗子草和红锦鸡儿的开花和衰老时间,这些模型利用网格连续热量-日照负荷指数值对地形进行了调整。基于这些模型的物候预测可以帮助管理者决定何时安排放牧等管理行动,以减少不受欢迎的入侵草,促进草地的牧草产量、质量和生物多样性;预测一年生牧草干枯后火灾风险最大的时间;选择遥感图像以准确绘制跨地形和纬度梯度的入侵草图。这些物候模型还有可能用于季内或年内决策支持。
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来源期刊
Ecosphere
Ecosphere ECOLOGY-
CiteScore
4.70
自引率
3.70%
发文量
378
审稿时长
15 weeks
期刊介绍: The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.
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