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Abstract Plants must respond to changing climatic conditions while continuing to defend against herbivores. While numerous studies have investigated how one type of stress affects plants, the effects of multiple abiotic and biotic stressors and their interactions are less understood. We used the Rainfall Exclusion eXperiment (REX) at the Kellogg Biological Station Long-Term Ecological Research site (KBS LTER) to quantify the individual and interactive effects of drought and warming treatments (abiotic stressors), and galling byRhopalomyia solidaginis(biotic stress) on tall goldenrod (Solidago altissima), a common native plant species in Michigan, USA. At the end of the 2021 and 2022 growing seasons, we measured stem height and biomass as a proxy for plant productivity, and seed mass per stem as a proxy for reproductive fitness. We also measured gall biomass, larval chamber number, and larval chamber volume to reflect the effects of drought and warming on the gallmaker. We found that warming mitigated some negative galling effects; galled plants were 7.1 cm shorter than non-galled plants in ambient conditions, but under warming, there was no reduction in height for galled plants. Furthermore, drought exacerbated some galling effects: galled plants experiencing drought conditions had the lowest probability of producing seeds (0.47) compared to plants from all other treatments. Understanding how plants respond to individual abiotic and biotic stressors as well as their interactions will enhance our ability to predict plant fitness and community dynamics under new climate regimes. 

These data and R scripts are from a study of climate change impacts on galling in goldenrod (Solidago altissima), at Kellogg Biological Station Long-Term Ecological Research (KBS LTER) site, Hickory Corners, Michigan, USA during the summers of 2021-2022 (REX 2024). This study set is part of the KBS LTER Rainfall Exclusion eXperiment (REX). Goldenrod plants with and without galls caused by Rhopalomyia solidaginis were exposed to warmed, drought, and warmed x drought treatments, and ambient (no treatment) and irrigated control conditions. Warming was achieved by use of open-top chambers for tall-stature plant communities (Welshofer et al. 2018 MEE) and a 6-week drought was implemented by use of rain-out shelters (Kahmark et al. 2024 Zenodo). L0 data are available upon request; they include the raw data from the KBS LTER REX project. The scripts that are used to clean L0 data and produce L1 data are also available upon request. The L1 data are the result of merged L0 data and are cleaned for typos and use standardized names. L1 data contain plant and gall traits from all treatments. The L2 scripts use the L1 data for statistical analyses and to create figures. Literature cited: Kahmark, K., Jones, M., Bohm, S., Baker, N., & Robertson, G. P. (2024). Rainfall manipulation shelters for agricultural research. Zenodo. https://doi.org/10.5281/zenodo.10607631. Rain Exclusion eXperiment (REX). (2024). https://lter.kbs.msu.edu/research/rainfall-exclusion-experiment/. https://lter.kbs.msu.edu/research/rainfall-exclusion-experiment/. Welshofer KB, Zarnetske PL, Lany NK, Thompson LAE (2018) Open-top chambers for temperature manipulation in taller-stature plant communities. Methods Ecol Evol 9:254–259. https://doi.org/10.1111/2041-210X.12863. 

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 Plant volatile organic compound (VOC) emissions are important mediators for plant interactions with biotic and abiotic factors in the environment. Changes in VOC emissions can be caused by factors associated with climate change, such as warming and drought. However, we currently lack an understanding of how warming and drought affect plants' emissions in their natural environment, let alone how these climate factors may interact to synergistically affect emissions. To fill these knowledge gaps, we measured VOC emissions from tall goldenrod (Solidago altissima) in an early successional plant community under four climate treatments: ambient control, warmed, drought, and warmed + drought. Treatments were applied in situ using open‐top chambers for warming and rainout shelters for drought. Drought treatments (drought and warmed + drought) have a stronger effect on VOC emissions compared to nondrought treatments (ambient and warmed). Furthermore, while the overall abundance of VOCs did not differ between treatments, there were specific compounds associated with one or more climate treatments. For example, diisopropyl adipate was more abundant in the drought and warmed + drought treatments. Our study shows that in goldenrod, drought may have a stronger effect than warming on VOC emissions, but moreover, that specific compounds are especially sensitive to certain climate treatments. However, additional experimentation is necessary to validate the functions associated with the affected compounds. These findings demonstrate that climate change alters chemical emissions, which in turn could have implications for ecosystem functioning via changes in plant–plant communication, plant–insect interactions, and overall plant fitness. 

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. 

Vegetation plays a crucial role in coastal dune building. Species‐specific plant characteristics can modulate sediment transport and dune shape, but this factor is absent in most dune building numerical models. Here, we develop a new approach to implement species‐specific vegetation characteristics into a process‐based aeolian sediment transport model. Using a three‐step approach, we incorporated the morphological differences of three dune grass species dominant in the US Pacific Northwest coast (European beachgrassAmmophila arenaria, American beachgrassA. breviligulata, and American dune grassLeymus mollis) into the model AeoLiS. First, we projected the tiller frontal area of each grass species onto a high resolution grid and then re‐scaled the grid to account for the associated vegetation cover for each species. Next, we calibrated the bed shear stress in the numerical model to replicate the actual sand capture efficiency of each species, as measured in a previously published wind tunnel experiment. Simulations were then performed to model sand bedform development within the grass canopies with the same shoot densities for all species and with more realistic average field densities. The species‐specific model shows a significant improvement over the standard model by (a) accurately simulating the sand capture efficiency from the wind tunnel experiment for the grass species and (b) simulating bedform morphology representative of each species' characteristic bedform morphology using realistic field vegetation density. This novel approach to dune modeling will improve spatial and temporal predictions of dune morphologic development and coastal vulnerability under local vegetation conditions and variations in sand delivery. 

Abstract Understanding the relationship between intraspecific trait variability (ITV) and its biotic and abiotic drivers is crucial for advancing population and community ecology. Despite its importance, there is a lack of guidance on how to effectively sample ITV and reduce bias in the resulting inferences. In this study, we explored how sample size affects the estimation of population‐level ITV, and how the distribution of sample sizes along an environmental gradient (i.e., sampling design) impacts the probabilities of committing Type I and II errors. We investigated Type I and II error probabilities using four simulated scenarios which varied sampling design and the strength of the ITV‐environment relationships. We also applied simulation scenarios to empirical data on populations of the small mammal,Peromyscus maniculatusacross gradients of latitude and temperature at sites in the National Ecological Observatory Network (NEON) in the continental United States. We found that larger sample sizes reduce error rates in the estimation of population‐level ITV for both in silico andPeromyscus maniculatuspopulations. Furthermore, the influence of sample size on detecting ITV‐environment relationships depends on how sample sizes and population‐level ITV are distributed along environmental gradients. High correlations between sample size and the environment result in greater Type I error, while weak ITV–environmental gradient relationships showed high Type II error probabilities. Therefore, having large sample sizes that are even across populations is the most robust sampling design for studying ITV‐environment relationships. These findings shed light on the complex interplay among sample size, sampling design, ITV, and environmental gradients. 

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’. 

Abstract MotivationBiodiversity in many areas is rapidly declining because of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor and conserve biodiversity hotspots. This is especially true for frugivores, species consuming fruit, because of their important role in seed dispersal and maintenance of forest structure and health. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the functional trait information required for these studies can be sparse for certain taxa and specific traits and difficult to harmonize across disparate data sources, especially in biodiversity hotspots. To help fill this need, we compiled Frugivoria, a trait database containing ecological, life‐history, morphological and geographical traits for mammals and birds exhibiting frugivory. Frugivoria encompasses species in contiguous moist montane forests and adjacent moist lowland forests of Central and South America—the latter specifically focusing on the Andean states. Compared with existing trait databases, Frugivoria harmonizes existing trait databases, adds new traits, extends traits originally only available for mammals to birds also and fills gaps in trait categories from other databases. Furthermore, we create a cross‐taxa subset of shared traits to aid in analysis of mammals and birds. In total, Frugivoria adds 8662 new trait values for mammals and 14,999 for birds and includes a total of 45,216 trait entries with only 11.37% being imputed. Frugivoria also contains an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyse traits in space. As such, this open‐access database, which aligns with FAIR data principles, fills a major knowledge gap, enabling more comprehensive trait‐based studies of species in this ecologically important region. Main Types of Variable ContainedEcological, life‐history, morphological and geographical traits. Spatial Location and GrainNeotropical countries (Mexico, Guatemala, Costa Rica, Panama, El Salvador, Belize, Nicaragua, Ecuador, Colombia, Peru, Bolivia, Argentina, Venezuela and Chile) with contiguous montane regions. Time Period and GrainIUCN spatial data: obtained February 2023, spanning range maps collated from 1998 to 2022. IUCN species data: obtained June 2019–September 2022. Newly included traits: span 1924 to 2023. Major Taxa and Level of MeasurementClasses Mammalia and Aves; 40,074 species‐level traits; 5142 imputed traits for 1733 species (mammals: 582; birds: 1147) and 16 sub‐species (mammals). Software Format.csv; R. 

Climate warming is altering life cycles of ectotherms by advancing phenology and decreasing generation times. Theoretical models provide powerful tools to investigate these effects of climate warming on consumer–resource population dynamics. Yet, existing theory primarily considers organisms with simplified life histories in constant temperature environments, making it difficult to predict how warming will affect organisms with complex life cycles in seasonal environments. We develop a size-structured consumer–resource model with seasonal temperature dependence, parameterized for a freshwater insect consuming zooplankton. We simulate how climate warming in a seasonal environment could alter a key life-history trait of the consumer, number of generations per year, mediating responses of consumer–resource population sizes and consumer persistence. We find that, with warming, consumer population sizes increase through multiple mechanisms. First, warming decreases generation times by increasing rates of resource ingestion and growth and/or lengthening the growing season. Second, these life-history changes shorten the juvenile stage, increasing the number of emerging adults and population-level reproduction. Unstructured models with similar assumptions found that warming destabilized consumer–resource dynamics. By contrast, our size-structured model predicts stability and consumer persistence. Our study suggests that, in seasonal environments experiencing climate warming, life-history changes that lead to shorter generation times could delay population extinctions.