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Ecological and evolutionary causes of geographic variation in reproductive phenology and seed mass in the California jewelflowers (Streptanthus, Brassicaceae)
Geographic variation in fitness-related traits among populations and species may be driven by long-term climatic conditions, which may contribute to local adaptation, as well as by inter-annual variation in climate, which can cause both rapid, short-term evolution and plastic responses. Few studies, however, have assessed both factors, limiting our ability to predict how species will respond to future climate change. In my dissertation, I evaluated both long- and short-term climatic drivers of geographic variation in two traits that influence individual fitness in many species: flowering date and seed mass. To detect drivers of geographic variation in flowering time, I analyzed ~750 herbarium specimens of Streptanthus tortuosus, a widespread California jewelflower (Brassicaceae). To detect climatic influences on seed mass â a much more evolutionary conservative trait than flowering time â I examined 88 populations representing six Streptanthus species. In both studies, I used linear models to detect the independent and interacting effects of long-term and inter-annual climate conditions on the focal trait. With respect to flowering time, S. tortuosus is phenologically sensitive to both long-term and inter-annual variation in temperature and precipitation. Additionally, S. tortuosus exhibits high regional variation in the response of flowering time to local climatic conditions. Plants sampled from warm regions are phenologically more sensitive to temperature-related variables and experienced a greater increase in temperature over the last century than plants sampled from cool regions. Together, these differences resulted in significantly greater phenological advancement in warm regions than in cool regions during the past century. To apply these results to the future, I used region-specific phenological models in combination with species distribution models to forecast the effects of upcoming climate change on both the phenology and the geographic distribution of S. tortuosus. These models predict range loss and flowering time divergence between warm and cool regions during the next century. My investigation of seed mass variation similarly detected sensitivity to long-and short-term variation in temperature-mediated growing season length and precipitation. Specifically, both long-term precipitation and inter-annual precipitation anomalies affected 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, suggesting that these associations may be generated by different mechanisms (i.e., adaptive evolution vs. phenotypic plasticity). Collectively, these studies inform our understanding of how traits respond to long-vs. short-term variation in climate both within and among species and will improve our ability to predict speciesâ responses to future climate change
A new fineâgrained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction
International audienceHerbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fineâscale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize.Methods: We trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask RâCNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size.Results: The main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus , the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers.Discussion: This method has great potential for automating the analysis of reproductive structures in highâresolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work