Shifting Plant Phenology and Annotations

[keirmorse]

@rupertclayton

The data on these graphs are also vulnerable to fluctuations based on hard-to-predict factors. For M. nutallii we might look at the graph above and conclude “Wow, there was some kind of superbloom in June–December 2021, and again in April and May 2022. But this species has really had trouble flowering since then.” What we’d be missing is that @keirmorse used iNat data to inform a bunch of his research on Malacothamnus, and as part of that made a major effort to add IDs and annotations to observations of the genus several years back. The supposed lack of flowering observations in earlier or later years probably just reflects a change in Keir’s research priorities!

Yep. I annotated everything at the time for my treatment to best represent what the phenology of each taxon was and compare where useful. I probably haven’t annotated any Malacothamnus phenology since. With this data, I also was not looking for variation between years. The weather in the years since iNat started have been quite variable, at least in California. So, I just looked at the full dataset to show what to expect whatever the weather does. I can also note that many Malacothamnus can bloom most of the year but it may only be a random branch or a few flowers off season, which can lead to misleading phenology data due to observer bias. While a population of 1000s may have only a few flowers on one plant outside of the peak season, an iNat observer may be much more likely to photograph that one plant out of context of what the majority of the population is doing. And, if that plant is right on the side of a popular trail, 20 people may photograph it, which can lead to strange peaks in the phenology data.