OPS 1-8
Deriving phenological metrics from the National Phenology Database: quantifying uncertainty across species and scales
Phenology is an important indicator of ecological response to climate change. Yet, phenological responses are highly variable among species and biogeographic regions. Recent monitoring initiatives have generated large phenological databases comprised of both professional and volunteer observations from across the US. Observers have typically employed “event monitoring,” recording only the single date when a phenophase is first observed, with no information about the frequency of their observations. In contrast, “status monitoring” is an approach that focuses on recording observations throughout the full development of life cycle phases (phenophases) rather than only first dates; thus this approach incorporates observations of both presence and absence of activity. In this way, status monitoring allows for quantification of uncertainty in the timing of the transition from an inactive to an active phenophases, allowing more robust estimation of onset, magnitude, and duration of phenophases across species, populations, and geographies. However, such estimation remains challenging given temporal and spatial gaps in many phenological datasets. To address this issue, we used status-based monitoring data curated by the USA National Phenology Network and generated three datasets for estimating the mean onset of springtime leaf-out using increasingly conservative data selection criteria for 11 deciduous tree species in the eastern US. We examined (1) how the timing of breaking leaf buds relates to minimum spring temperatures and latitude in the eastern United States, and (2) how onset date estimates are influenced by the variable frequency of data collection.
Results/Conclusions
We detected significant spatiotemporal patterns with minimum springtime temperatures across a large latitudinal gradient. Onset of breaking leaf buds in deciduous trees significantly advanced with higher temperatures and showed significant delays at progressively northern latitudes. This trend was consistent across species and years. In addition, the patterns held regardless of the data selection criteria, suggesting there is low risk in maximizing sample size by being liberal with data selection. However, to answer questions designed to inform natural resource management at the local to landscape scale, we recommend the use of more conservative data selection criteria to reduce uncertainty in calculating the onset of phenological events.