How well do the spring indices predict phenological activity across plant species?

Uncovering the role of solar radiation and water stress factors in constraining decadal intra-site spring phenology variability in diverse ecosystems across the Northern Hemisphere

The spring phenology has advanced significantly over recent decades with climate change, impacting large-scale biogeochemical cycles, climate feedback, and other essential ecosystem processes. Although numerous prognostic models have been developed for spring phenology, regional analyses of the optimality (OPT) strategy model that incorporate environmental variables beyond temperature and photoperiod remain lacking. We investigated the roles of solar radiation (SR) and three water stress factors (precipitation (P), soil moisture, and vapor pressure deficit (VPD)) on spring phenology from 1982 to 2015 using the OPT model with Global Inventory Modeling and Mapping Studies NDVI3g dataset and environmental data from TerraClimate, CRU_TS, and Global Land Data Assimilation System across the Northern Hemisphere (> 30°N). Our results show that SR and water stress factors significantly impacted intrasite decadal spring phenology variability, with water stress factors dominant in grassland ecosystems while SR dominated in the rest of the ecosystem types. Enhanced models incorporating SR (OPT-S) and VPD (OPT-VPD) outperformed the original OPT model, likely due to improved representation of the adaptive strategy of spring phenology to optimize photosynthetic carbon gain while minimizing frost risk. Our research enhances the understanding of the key environmental drivers influencing decadal spring phenology variation in the Northern Hemisphere and contributes to more accurate forecasts of ecological responses to global environmental change.

Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech

Transcriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33 042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the single nucleotide polymorphism-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-autumn transitions for two consecutive years involved 4197 differentially expressed protein coding genes. Using Populus orthologues we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed gene ontology terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech.

Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus

Regional populations of geographically widespread species may respond to different environmental factors across the species' range, generating divergent effects of climate change on life-history phenology. Using thousands of citizen science observations extracted from iNaturalist and associated with corresponding temperature, precipitation, elevation, and daylength information, we examined the drivers of adult mating and of nymphal phenology, development, and group size for populations of the large milkweed bug, Oncopeltus fasciatus, in different ecoregions. Research-grade iNaturalist images were correctly identified 98.3% of the time and yielded more than 3000 observations of nymphal groups and 1000 observations of mating adults spanning 18 years. Mating phenology showed distinct regional patterns, ranging from year-round mating in California to temporally restricted mating in the Great Lakes Northeastern Coast ecoregion. Relative temperature increases of 1°C for a given daylength expanded the mating season by more than a week in western ecoregions. While increases in relative temperature delayed mating phenology in all ecoregions, greater winter precipitation advanced mating in the California ecoregion. In the eastern ecoregions, nymphal phenology was delayed by increases in summer rainfall but was advanced by relative temperature increases, whereas in western regions, relative temperature increases delayed nymphal phenology. Furthermore, accumulated growing degree days (AGDD) was a poor predictor of developmental progression, as we found a positive but weak correlation between AGDD and age structure only for the Appalachian Southeast North America and the Great Lakes Northern Coast ecoregions. These complex phenological responses of O. fasciatus are just one example of how populations may be differentially susceptible to a diversity of climatic effects; using data across a species' whole distribution is critical for exposing regional variations, especially for species with large, continental-scale ranges. This study demonstrates the potential of photodocumented biodiversity data to aid in the monitoring of life history, host plant-insect interactions, and climate responsiveness.

An empirical method to account for climatic adaptation in plant phenology models

Phenological shifts in plant species are one of the most conspicuous signs of climate change impact on the biosphere. Modeling phenological variations of plant species over broad regions is challenging because of the varied climatic requirements of geographic populations due to local adaptation. In this study, we developed an empirical method to calibrate phenological models of temperate trees using latitude as a predictor to account for local adaptation of populations to a N-S temperature gradient. Fourteen widely distributed tree species in the eastern U.S.A. were investigated using data from the USA-National Phenology Network. We implemented the method in a basic thermal time bud break model to introduce the algorithm of the method and test its effectiveness. For each species, dates of breaking leaf buds were first predicted using a traditional non-spatial model and then with a spatial model that has the critical thermal forcing requirements calibrated for different populations at varied latitudes. As anticipated, non-spatial model predictions that assumed a uniform forcing requirement across latitudes showed consistent and systematic biases at both higher (overestimation-predictions being later) and lower (underestimation-predictions being earlier) latitudes. Spatial models that have been calibrated using our method removed the geographic biases and yielded latitudinal gradients that more closely matched those of the observations. The spatial models also reduced the overall prediction errors from an average root mean square error (RMSE) of 32.2 days to 20.4 days for the training dataset and an average root mean square error for prediction (RMSEP) of 32.2 days to 19.9 days for the testing dataset. This paper is focused on introducing the new calibration method as a preparatory step toward developing operational models that may potentially predict large-scale and range-wide phenological responses of various plant species to climatic changes with improved local accuracy.