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Abstract Climatic and soil features influence resources and mate availability for plants. Because of different resource/mating demands of the male and female reproductive pathways, environmental variation can drive geographic patterns of sex‐specific factors in sexually polymorphic species. Yet, the relationship between environment and sex, sexual dimorphism or sex chromosomes at the range‐wide scale is underexamined.Using ~7000 herbarium and iNaturalist specimens we generate a landscape‐scale understanding of how sex ratio and sexual dimorphism vary with geographic, climatic and soil gradients in the sexually polymorphic wild strawberry (Fragaria virginiana) and test whether these conform to predictions from theory. Then, for ~300 specimens we use genotyping of the sex‐determining region (SDR haplotypes) to reveal geographic and phenotypic patterns in sex chromosome types.Across North America, the sex ratio was hermaphrodite/male‐biased and was associated more with soil attributes than climate. Sex ratio‐environment associations matched predictions for subdioecy in the West but for gynodioecy in the East. Climatic factors correlated with sexual dimorphism in traits related to carbon acquisition (leaf size and runnering while flowering) but not mate access (petal size, flowering time). Variation in sexual dimorphism was due to one sex being more responsive to the environmental variation than the other. Specifically, leaf length in females was more responsive to variation in precipitation than in hermaphrodite/males, but the probability of runnering while flowering in hermaphrodite/males was more responsive to variation in temperature than in females. The ancestral sex chromosome type was most common overall. But the frequency of the more derived sex chromosomes varied with environmental factors that differed between East–West regions.Synthesis. A landscape‐level perspective revealed that variation in soil and climate factors can explain geospatial variation in sex ratio and sexual dimorphism in a wild strawberry. Variation in sex ratio was associated more with soil resources than climate, while variation in sexual dimorphism was the result of sex‐differential responses to climate for vegetative traits but a similar response to abiotic factors in mate access traits. Finally, sex chromosome types were associated with soil moisture and precipitation in ways that could contribute to the evolution of sex determination. 

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans ( p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.