Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.

The enemy release hypothesis predicts that invasive species will receive less damage from enemies, compared to co-occurring native and noninvasive exotic species in their introduced range. However, release operating early in invasion could be lost over time and with increased range size as introduced species acquire new enemies. We used three years of data, from 61 plant species planted into common gardens, to determine whether (1) invasive, noninvasive exotic, and native species experience differential damage from insect herbivores. and mammalian browsers, and (2) enemy release is lost with increased residence time and geographic spread in the introduced range. We find no evidence suggesting enemy release is a general mechanism contributing to invasiveness in this region. Invasive species received the most insect herbivory, and damage increased with longer residence times and larger range sizes at three spatial scales. Our results show that invasive and exotic species fail to escape enemies, particularly over longer temporal and larger spatial scales.

Phenology is a harbinger of climate change, with many species advancing flowering in response to rising temperatures. However, there is tremendous variation among species in phenological response to warming, and any phenological differences between native and non‐native species may influence invasion outcomes under global warming. We simulated global warming in the field and found that non‐native species flowered earlier and were more phenologically plastic to temperature than natives, which did not accelerate flowering in response to warming. Non‐native species' flowering also became more synchronous with other community members under warming. Earlier flowering was associated with greater geographic spread of non‐native species, implicating phenology as a potential trait associated with the successful establishment of non‐native species across large geographic regions. Such phenological differences in both timing and plasticity between native and non‐natives are hypothesised to promote invasion success and population persistence, potentially benefiting non‐native over native species under climate change.

Abstract. In natural biological communities, species interact with many other species. Multiple species interactions can lead to indirect ecological effects that have important fitness consequences and can cause nonadditive patterns of natural selection. Given that indirect ecological effects are common in nature, nonadditive selection may also be quite common. As a result, quantifying nonadditive selection resulting from indirect ecological effects may be critical for understanding adaptation in natural communities composed of many interacting species. We describe how to quantify the relative strength of nonadditive selection resulting from indirect ecological effects compared to the strength of pairwise selection. We develop a clear method for testing for nonadditive selection caused by indirect ecological effects and consider how it might affect adaptation in multispecies communities. We use two case studies to illustrate how our method can be applied to empirical data sets. Our results suggest that nonadditive selection caused by indirect ecological effects may be common in nature. Our hope is that trait-based approaches, combined with multifactorial experiments, will result in more estimates of nonadditive selection that reveal the relative importance of indirect ecological effects for evolution in a community context.

When two invasion hypotheses are better than one Dozens of hypotheses attempt to explain the success of invasive species (Catford et al., 2009). Though no single hypothesis explains invasions in general (Gurevitch et al., 2011), the temptation to search for a universal mechanism shared by most invasive species has motivated much of the invasion research to date. Instead of studying mechanisms in isolation from one another, perhaps investigating synergies between disparate invasion hypotheses, as Zheng et al. have in this issue of New Phytologist (pp. 1350-1359), will do more to explain the success of invasive species.

Some introduced species become invasive by releasing novel allelochemicals into the soil, directly harming nearby plants and soil microbes. Alliaria petiolata (garlic mustard) is an invasive plant, well known to excrete a suite of phytotoxic and antimicrobial allelochemicals, including allyl isothiocyanate (AITC) and benzyl isothiocyanate (BITC). While the effects of these chemicals on plant-mycorrhizae mutualisms are well documented, the effects on other plant-soil microbe interactions, such as the legume-rhizobia mutualism, have not yet been tested. Here, we performed laboratory and greenhouse experiments with both synthetic chemicals and leaf extracts to investigate the effects of allelochemicals in A.petiolata on a native leguminous plant, Amphicarpaea bracteata, and its rhizobia mutualists. We found that BITC reduced rhizobia growth rate in the lab, but had no effect on nodulation in the greenhouse when rhizobia were grown in the presence of plants. AITC did not directly harm either plants or rhizobia, though plants and rhizobia grown in the presence of AITC showed reduced nodulation, indicating that it disrupted the formation of the mutualism itself. We found no effects of A. petiolata allelochemical leaf extracts on plant performance or nodulation. Our data suggest that AITC causes mutualism disruption in this system by preventing the formation of nodules, which reduces plant growth and could threaten the long-term performance of rhizobia. Our study shows that the allelochemicals in A. petiolata disrupt the legume-rhizobia resource mutualism, adding another impact of these novel weapons in addition to their well-documented role in disrupting plant-mycorrhizae symbioses.

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Authentic, “messy data” contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science itself. While the value of bringing contemporary research and messy data into the classroom is recognized, implementation can seem overwhelming. We discuss the importance of frequent interactions with messy data throughout K–16 science education as a mechanism for students to engage in the practices of science, such as visualizing, analyzing, and interpreting data. Next, we describe strategies to help facilitate the use of messy data in the classroom while building complexity over time. Finally, we outline one potential sequence of activities, with specific examples, to highlight how various activity types can be used to scaffold students' interactions with messy data.