In arid and semiarid systems of western North America, the most damaging invasive plants are winter annuals. These plants are destroying wildlife habitat, reducing livestock production, and increasing wildfires. Monitoring these plants for lasting population changes is challenging because their abundances vary widely from year to year. Some of this variation is due to weather, and quantifying effects of weather is important for distinguishing transcient from lasting population changes and understanding effects of climate change. Fall and spring weather affect germination and seed production of the current generation of plants and, therefore, impact population sizes of subsequent generations of plants. Extensive data are required to estimate effects of fall and spring weather on multiple generations of plants. We used Bayesian statistics to integrate experimental and long‐term (31 years) monitoring data and quantify invasive annual grass [downy brome (Bromus tectorum L.) and Japanese brome (Bromus japonicus Thunb.)] responses to weather. Bromes ranged from nearly absent to comprising half of total biomass depending on three previous years of weather. Brome biomass increased with precipitation one, two, and three falls prior to measurement. Fall precipitation is projected to increase, and a mere 6.5 mm increase, which is just 2% of mean annual precipitation, would increase brome biomass 40% (28%, 54%) (mean [95% CI]) according to our model. Increased fall precipitation could favor many invasive winter annual grasses and forbs. Dry spring conditions reduced brome biomass the current year but increased brome biomass one and likely two (p = 0.08) years later, perhaps because dry conditions weakened perennial competitors. This finding casts doubt on several one‐year precipitation experiments that concluded drier spring weather would reduce brome abundances. Integrating short‐term experiments and long‐term monitoring is useful for estimating invasive plant responses to the weather and characterizing their responses to climate change. Our research provides predictions of brome abundances that could improve monitoring efforts by helping land managers interpret population dynamics in the context of seasonal precipitation patterns.

中文翻译：

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整合实验和监测揭示了入侵冬季一年生植物对降水的极度敏感性


在北美西部的干旱和半干旱系统中，最具破坏性的入侵植物是冬季一年生植物。这些植物正在破坏野生动物栖息地，减少畜牧业生产，并增加野火。监测这些植物的持久种群变化是具有挑战性的，因为它们的丰度每年都有很大差异。其中一些变化是由于天气造成的，量化天气的影响对于区分短暂的和持久的人口变化以及理解气候变化的影响非常重要。秋季和春季的天气会影响当前一代植物的发芽和种子生产，因此会影响后续几代植物的种群规模。需要大量数据来估计秋季和春季天气对多代植物的影响。我们使用贝叶斯统计来整合实验和长期 （31 年） 监测数据，并量化入侵性一年生草 [霜霉 （Bromus tectorum L.） 和日本 brome （Bromus japonicus Thunb.）] 对天气的反应。根据前三年的天气情况，溴从几乎不存在到占总生物量的一半不等。Brome 生物量在测量前 1 、 2 和 3 次降水增加。根据我们的模型，预计秋季降水量将增加，仅增加 6.5 毫米，仅占年平均降水量的 2%，就会使菠萝生物量增加 40%（28%，54%）（平均值 [95% CI]）。秋季降水增加可能有利于许多入侵性冬季一年生草和杂草。干燥的春季条件降低了当年的溴生物量，但在一年后和可能两年后增加了溴生物量 （p = 0.08），这可能是因为干燥条件削弱了多年生竞争者。 这一发现对几个为期一年的降水实验提出了怀疑，这些实验得出的结论是，春季较干燥的天气会减少菠萝的丰度。将短期实验和长期监测相结合有助于估计入侵植物对天气的反应并描述它们对气候变化的反应。我们的研究提供了对溴丰度的预测，这些预测可以通过帮助土地管理者在季节性降水模式的背景下解释种群动态来改善监测工作。