Department of Biology, California State University Northridge, Northridge, California, USA.
U.S. Geological Survey, Western Ecological Research Center, Boulder City, Nevada, USA.
Ecol Lett. 2024 Aug;27(8):e14478. doi: 10.1111/ele.14478.
Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years-but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.
量化全球变化对野生种群的影响仍然具有挑战性，特别是对于那些系统数据集代表性较差的物种。在这里，我们从 15 年的众包观测中推断出约书亚树（Yucca brevifolia 和 Y. jaegeriana）的气候变化对结实的影响，约书亚树是莫哈韦沙漠的关键多年生植物。我们在 iNaturalist 众包平台上对 10212 张约书亚树的地理参考图像进行了物候期注释，并使用这些图像来训练机器学习模型，从年度天气记录中预测开花。使用经过训练的模型对 1900 年进行回溯预测，成功地恢复了独立历史记录中的开花事件，并揭示了自 20 世纪初以来支持开花的条件略有增加的频率。这反映了年降水量的变化增加，这推动了湿润年份的结实事件——但也反映了温度升高和干旱压力增加，这可能对繁殖产生净负面影响。我们的研究结果再次证实了众包在了解气候变化对生物多样性的影响方面的价值。
