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Offspring size is a key functional trait that can affect subsequent life history stages; in many species, it exhibits both local adaptation and phenotypic plasticity. Variation among populations in offspring size may be explained by various factors, including local climatic conditions. However, geographic variation in climate may be partitioned into long-term and interannual sources of variation, which may differ in their effects on population mean offspring size. To assess environmental correlates of offspring size, we evaluated geographic variation in seed mass among 88 populations representing 6 species of Streptanthus (Brassicaceae) distributed across a broad climatic gradient in California. We examined the effects of temperature-mediated growing season length and precipitation on population mean seed mass to determine whether it is best explained by (1) long-term mean climatic conditions; (2) interannual climate anomalies (i.e., deviations in climate from long-term means) during the year of seed development, or (3) interactions between climate variables. Both long-term mean climate and climate anomalies in the year of collection were associated with population mean seed mass, but their effects differed in direction and magnitude. Relatively large seeds were produced at chronically wet sites but also during drier-than-average years. This contrast indicates that these associations may be generated by different mechanisms (i.e., adaptive evolution vs. phenotypic plasticity) and may be evidence of countergradient plasticity in seed mass. In addition, populations occurring in locations characterized by relatively long growing seasons produced comparatively large seeds, particularly among chronically dry sites. This study highlights the need to consider that the responses of seed mass to long-term versus recent climatic conditions may differ and that climate variables may interact to predict seed mass. Such considerations are especially important when using these patterns to forecast the long- and short-term responses of seed mass to climate change. The results presented here also contribute to our broader understanding of how climate drives long-term (e.g., local adaptation) and short-term (e.g., phenotypic plasticity) variation in functional traits, such as offspring size across landscapes.

PremiseForecasting how species will respond phenologically to future changes in climate is a major challenge. Many studies have focused on estimating species- and community-wide phenological sensitivities to climate to make such predictions, but sensitivities may vary within species, which could result in divergent phenological responses to climate change.MethodsWe used 743 herbarium specimens of the mountain jewelflower (Streptanthus tortuosus, Brassicaceae) collected over 112 years to investigate whether individuals sampled from relatively warm vs. cool regions differ in their sensitivity to climate and whether this difference has resulted in divergent phenological shifts in response to climate warming.ResultsDuring the past century, individuals sampled from warm regions exhibited a 20-day advancement in flowering date; individuals in cool regions showed no evidence of a shift. We evaluated two potential drivers of these divergent responses: differences between regions in (1) the degree of phenological sensitivity to climate and (2) the magnitude of climate change experienced by plants, or (3) both. Plants sampled from warm regions were more sensitive to temperature-related variables and were subjected to a greater degree of climate warming than those from cool regions; thus our results suggest that the greater temporal shift in flowering date in warm regions is driven by both of these factors.ConclusionsOur results are among the first to demonstrate that species exhibited intraspecific variation in sensitivity to climate and that this variation can contribute to divergent responses to climate change. Future studies attempting to forecast temporal shifts in phenology should consider intraspecific variation.

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI's were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.