Terry Tyson J, Hardegree Stuart P, Adler Peter B
Department of Wildland Resources and the Ecology Center, Utah State University, Logan, Utah, USA.
Department of Disturbance Ecology and Vegetation Dynamics, Bayreuth University, Bayreuth, Bavaria, Germany.
Ecol Appl. 2024 Dec;34(8):e3028. doi: 10.1002/eap.3028. Epub 2024 Sep 16.
Exotic annual grass invasions in water-limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely-sensed imagery. Accuracy metrics from our out-of-sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%-17% from 1989 to 2019 across all aspects at low- to mid-elevation sites, while high- elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.
在水资源有限的地区，外来一年生禾本科植物的入侵会导致本地动植物群落退化，并增加火灾风险。入侵一年生禾本科植物的生活史使其对天气的年际变化高度敏感。然而，目前基于遥感得出的分布和丰度模型，仅能粗略了解物种对天气的反应，难以预测气候变化将如何影响入侵的脆弱性。在此，我们从机理萌发和土壤小气候模型中得出萌发协变量（速率总和），以量化美国西部蒿属植物草原系统中2662个地点30年间土壤小气候对芒麦草（Bromus tectorum L.）建立和生长的适宜性。我们仅使用四个生物气候协变量的方法，预测芒麦草分布的准确性与之前使用多年遥感影像拟合的模型相当。我们样本外测试数据集的准确性指标表明，我们的模型对分布的预测良好（总体准确率72%），但对丰度模式的解释较差（R = 0.22）。芒麦草存在的气候适宜性取决于秋季和春季速率总和的空间（平均值）和时间（年度异常）变化。预计平均秋季土壤温暖湿润且春季土壤温暖湿润（这些时期速率总和高）的地点芒麦草丰度较高。秋季土壤条件的年际变化对芒麦草的存在和丰度的影响大于春季条件。我们的模型预测，气候变化已经影响了芒麦草的分布，在低至中海拔地区，1989年至2019年期间，适宜的小气候条件在各个方面都扩大了10%-17%，而高海拔地区（>2100米）由于春季和秋季土壤寒冷，仍然不利于芒麦草生长。
