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Abstract The electrolytes Na and K both function to maintain water balance and membrane potential. However, these elements work differently in plants—where K is the primary electrolyte—than in animals—where ATPases require a balanced supply of Na and K. Here, we use monthly factorial additions of Na and K to simulate bovine urine inputs and explore how these electrolytes ramify through a prairie food web. Against a seasonal trend of increasing grass biomass and decreasing water and elemental tissue concentrations, +K and +Na plots boosted water content and, when added together, plant biomass. Compared to control plots, +Na and +K plots increased element concentrations in above‐ground plant tissue early in summer and decreased them in September. Simultaneously, invertebrate abundance on Na and K additions were sequentially higher and lower than control plots from June to September and were most suppressed when grass was most nutrient rich. K was the more effective plant electrolyte, but Na frequently promoted similar changes in grass ionomes. The soluble/leachable ions of Na and K showed significant ability to shape plant growth, water content, and the 15‐element ionome, with consequences for higher trophic levels. Grasslands with high inputs of Na and K—via large mammal grazers or coastal aerosol deposition—likely enhance the ability of plants to adjust their above‐ground ionomes, with dramatic consequences for the distribution of invertebrate consumers. 

Abstract Activity density (AD), the rate at which animals collectively move through their environment, emerges as the product of a taxon's local abundance and its velocity. We analyze drivers of seasonal AD using 47 localities from the National Ecological Observatory Network (NEON) both to better understand variation in ecosystem rates like pollination and seed dispersal as well as the constraints of using AD to monitor invertebrate populations. AD was measured as volume from biweekly pitfall trap arrays (ml trap⁻¹14 days⁻¹). Pooled samples from 2017 to 2018 revealed AD extrema at most temperatures but with a strongly positive overall slope. However, habitat types varied widely in AD's seasonal temperature sensitivity, from negative in wetlands to positive in mixed forest, grassland, and shrub habitats. The temperature of maximum AD varied threefold across the 47 localities; it tracked the threefold geographic variation in maximum growing season temperature with a consistent gap ofca. 3°C across habitats, a novel macroecological result. AD holds potential as an effective proxy for investigating ecosystem rates driven by activity. However, our results suggest that its use for monitoring insect abundance is complicated by the many ways that both abundance and velocity are constrained by a locality's temperature and plant physiognomy. 

Abstract Activity density (AD), the rate that an individual taxon or its biomass moves through the environment, is used both to monitor communities and quantify the potential for ecosystem work. The Abundance Velocity Hypothesis posited that AD increases with aboveground net primary productivity (ANPP) and is a unimodal function of temperature. Here we show that, at continental extents, increasing ANPP may have nonlinear effects on AD: increasing abundance, but decreasing velocity as accumulating vegetation interferes with movement. We use 5 yr of data from the NEON invertebrate pitfall trap arrays including 43 locations and four habitat types for a total of 77 habitat–site combinations to evaluate continental drivers of invertebrate AD. ANPP and temperature accounted for one‐third to 92% of variation in AD. As predicted, AD was a unimodal function of temperature in forests and grasslands but increased linearly in open scrublands. ANPP yielded further nonlinear effects, generating unimodal AD curves in wetlands, and bimodal curves in forests. While all four habitats showed no AD trends over 5 yr of sampling, these nonlinearities suggest that trends in AD, often used to infer changes in insect abundance, will vary qualitatively across ecoregions. 

Abstract Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa. 

Beyond the better-studied carbohydrates and the macronutrients nitrogen and phosphorus, a remaining 20 or so elements are essential for life and have distinct geographical distributions, making them of keen interest to ecologists. Here, I provide a framework for understanding how shortfalls in micronutrients like iodine, copper, and zinc can regulate individual fitness, abundance, and ecosystem function. With a special focus on sodium, I show how simple experiments manipulating biogeochemistry can reveal why many of the variables that ecologists study vary so dramatically from place to place. I conclude with a discussion of how the Anthropocene's changing temperature, precipitation, and atmospheric CO 2 levels are contributing to nutrient dilution (decreases in the nutrient quality at the base of food webs).