Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering

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 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.