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1. Experimental warming differentially affects vegetative and reproductive phenology of tundra plants

   https://doi.org/10.1038/s41467-021-23841-2  

   Collins, Courtney G.; Elmendorf, Sarah C.; Hollister, Robert D.; Henry, Greg H.; Clark, Karin; Bjorkman, Anne D.; Myers-Smith, Isla H.; Prevéy, Janet S.; Ashton, Isabel W.; Assmann, Jakob J.; et al (December 2021, Nature Communications)

   null (Ed.)

   Abstract Rapid climate warming is altering Arctic and alpine tundra ecosystem structure and function, including shifts in plant phenology. While the advancement of green up and flowering are well-documented, it remains unclear whether all phenophases, particularly those later in the season, will shift in unison or respond divergently to warming. Here, we present the largest synthesis to our knowledge of experimental warming effects on tundra plant phenology from the International Tundra Experiment. We examine the effect of warming on a suite of season-wide plant phenophases. Results challenge the expectation that all phenophases will advance in unison to warming. Instead, we find that experimental warming caused: (1) larger phenological shifts in reproductive versus vegetative phenophases and (2) advanced reproductive phenophases and green up but delayed leaf senescence which translated to a lengthening of the growing season by approximately 3%. Patterns were consistent across sites, plant species and over time. The advancement of reproductive seasons and lengthening of growing seasons may have significant consequences for trophic interactions and ecosystem function across the tundra. 

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2. Extirpated prairie species demonstrate more variable phenological responses to warming than extant congeners

   https://doi.org/10.1002/ajb2.1684  

   Zettlemoyer, Meredith_A; Renaldi, Katarina; Muzyka, Michael_D; Lau, Jennifer_A (June 2021, American Journal of Botany)

   PREMISEShifting phenology in response to climate is one mechanism that can promote population persistence and geographic spread; therefore, species with limited ability to phenologically track changing environmental conditions may be more susceptible to population declines. Alternatively, apparently nonresponding species may demonstrate divergent responses to multiple environmental conditions experienced across seasons. METHODSCapitalizing on herbarium records from across the midwestern United States and on detailed botanical surveys documenting local extinctions over the past century, we investigated whether extirpated and extant taxa differ in their phenological responses to temperature and precipitation during winter and spring (during flowering and the growing season before flowering) or in the magnitude of their flowering time shift over the past century. RESULTSAlthough warmer temperatures across seasons advanced flowering, extirpated and extant species differed in the magnitude of their phenological responses to winter and spring warming. Extirpated species demonstrated inconsistent phenological responses to warmer spring temperatures, whereas extant species consistently advanced flowering in response to warmer spring temperatures. In contrast, extirpated species advanced flowering more than extant species in response to warmer winter temperatures. Greater spring precipitation tended to delay flowering for both extirpated and extant taxa. Finally, both extirpated and extant taxa delayed flowering over time. CONCLUSIONSThis study highlights the importance of understanding phenological responses to seasonal warming and indicates that extirpated species may demonstrate more variable phenological responses to temperature than extant congeners, a finding consistent with the hypothesis that appropriate phenological responses may reduce species’ likelihood of extinction. 

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3. The growing and vital role of botanical gardens in climate change research

   https://doi.org/10.1111/nph.17410  

   Primack, Richard_B; Ellwood, Elizabeth_R; Gallinat, Amanda_S; Miller‐Rushing, Abraham_J (May 2021, New Phytologist)

   Summary Botanical gardens make unique contributions to climate change research, conservation, and public engagement. They host unique resources, including diverse collections of plant species growing in natural conditions, historical records, and expert staff, and attract large numbers of visitors and volunteers. Networks of botanical gardens spanning biomes and continents can expand the value of these resources. Over the past decade, research at botanical gardens has advanced our understanding of climate change impacts on plant phenology, physiology, anatomy, and conservation. For example, researchers have utilized botanical garden networks to assess anatomical and functional traits associated with phenological responses to climate change. New methods have enhanced the pace and impact of this research, including phylogenetic and comparative methods, and online databases of herbarium specimens and photographs that allow studies to expand geographically, temporally, and taxonomically in scope. Botanical gardens have grown their community and citizen science programs, informing the public about climate change and monitoring plants more intensively than is possible with garden staff alone. Despite these advances, botanical gardens are still underutilized in climate change research. To address this, we review recent progress and describe promising future directions for research and public engagement at botanical gardens. 

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4. Disorder or a new order: How climate change affects phenological variability

   https://doi.org/10.1002/ecy.3846  

   Stemkovski, Michael; Bell, James R.; Ellwood, Elizabeth R.; Inouye, Brian D.; Kobori, Hiromi; Lee, Sang Don; Lloyd‐Evans, Trevor; Primack, Richard B.; Templ, Barbara; Pearse, William D. (October 2022, Ecology)

   Abstract Advancing spring phenology is a well documented consequence of anthropogenic climate change, but it is not well understood how climate change will affect the variability of phenology year to year. Species' phenological timings reflect the adaptation to a broad suite of abiotic needs (e.g., thermal energy) and biotic interactions (e.g., predation and pollination), and changes in patterns of variability may disrupt those adaptations and interactions. Here, we present a geographically and taxonomically broad analysis of phenological shifts, temperature sensitivity, and changes in interannual variability encompassing nearly 10,000 long‐term phenology time series representing more than 1000 species across much of the Northern Hemisphere. We show that the timings of leaf‐out, flowering, insect first‐occurrence, and bird arrival were the most sensitive to temperature variation and have advanced at the fastest pace for early‐season species in colder and less seasonal regions. We did not find evidence for changing variability in warmer years in any phenophase groups, although leaf‐out and flower phenology have become moderately but significantly less variable over time. Our findings suggest that climate change has not to this point fundamentally altered the patterns of interannual phenological variability. 

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5. Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images

   https://doi.org/10.3389/fpls.2021.787407  

   Reeb, Rachel A.; Aziz, Naeem; Lapp, Samuel M.; Kitzes, Justin; Heberling, J. Mason; Kuebbing, Sara E. (January 2022, Frontiers in Plant Science)

   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. 

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