Global change may cause widespread phenological shifts. But knowledge of the extent and generality of these shifts is limited by the availability of phenological records with sufficiently large spatiotemporal extents. Using North American odonates (damselflies and dragonflies) as a model system, we show how a combination of natural history museum and community science collections, beginning in 1901 and extending through 2020, can be leveraged to better understand phenology.

We begin with an analysis of odonate functional traits. Principal coordinate analysis is used to place odonate genera within a three-dimensional trait ordination. From this, we identify seven distinct functional groups and select a single odonate genus to represent each group. Next, we pair the odonate records with a list of environmental covariates, including air temperature and degree days, photoperiod, precipitation, latitude, and elevation. An iterative subsampling process is then used to mitigate spatiotemporal sampling bias within the odonate dataset. Finally, we use path analysis to quantify the direct effects of degree days, photoperiod, and precipitation on odonate emergence timing, while accounting for indirect effects of latitude, elevation, and year.

Path models showed that degree days, photoperiod, and precipitation each have a significant influence on odonate emergence timing, but degree days have the largest overall effect. Notably, the effect that each covariate has on emergence timing varied among functional groups, with positive relationships observed for some group representatives and negative relationships observed for others. For instance, Calopteryx sp. emerged earlier as degree days increased, while Sympetrum sp. emerged later.

Previous studies have linked odonate emergence timing to temperature, photoperiod, or precipitation. By using natural history museum and community science data to simultaneously examine all three influences, we show that systems-level understanding of odonate phenology may now be possible.