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ABSTRACT In order to better predict climate change effects on plants and their communities, we need to improve our understanding of how various plant traits and community properties respond to warming, as well as what contexts contribute to variation in these responses. To address this knowledge gap, we compiled data from 126 in situ passive experimental warming studies on 13 different plant trait and community property responses. We then collected metadata from these studies to define 9 different study contexts spanning environmental, experimental, and plant‐level scales. We find that, globally, some traits decrease when warmed (e.g., aboveground N content), while others increase (e.g., plant biomass). We also identify contexts that contribute to variation in plant responses to warming, such as latitude, distance from northern range edge, and plant functional group, but the importance of these contexts varies based on the trait or community property measured. For example, as latitude increases, the effect of warming on reproductive traits becomes stronger, but this latitude‐trait relationship did not hold for all traits. Our study highlights how multiple plant traits and community properties respond to warming across the globe, the importance of carefully designing and interpreting the outcomes of climate change experiments, and the need for coordinated warming experiments across varying environmental contexts in order to mechanistically understand and predict plant community responses to climate warming. 

Abstract Anthropogenic climate warming affects plant communities by changing community structure and function. Studies on climate warming have primarily focused on individual effects of warming, but the interactive effects of warming with biotic factors could be at least as important in community responses to climate change. In addition, climate change experiments spanning multiple years are necessary to capture interannual variability and detect the influence of these effects within ecological communities. Our study explores the individual and interactive effects of warming and insect herbivory on plant traits and community responses within a 7‐year warming and herbivory manipulation experiment in two early successional plant communities in Michigan, USA. We find stronger support for the individual effects of both warming and herbivory on multiple plant morphological and phenological traits; only the timing of plant green‐up and seed set demonstrated an interactive effect between warming and herbivory. With herbivory, warming advanced green‐up, but with reduced herbivory, there was no significant effect of warming. In contrast, warming increased plant biomass, but the effect of warming on biomass did not depend upon the level of insect herbivores. We found that these treatments had stronger effects in some years than others, highlighting the need for multiyear experiments. This study demonstrates that warming and herbivory can have strong direct effects on plant communities, but that their interactive effects are limited in these early successional systems. Because the strength and direction of these effects can vary by ecological context, it is still advisable to include levels of biotic interactions, multiple traits and years, and community type when studying climate change effects on plants and their communities. 

Abstract PremiseThe selection ofArabidopsisas a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled research. MethodsHere, we examine the ability of models based onArabidopsisgene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested onArabidopsisdata achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained onArabidopsisdata, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. ResultsThe identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance fromArabidopsis.k‐nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. DiscussionOur data‐driven results highlight that the assertion that knowledge fromArabidopsisis translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis onArabidopsisand prioritize plant diversity.