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Winter provides many challenges for insects, including direct injury to tissues and energy drain due to low food availability. As a result, the geographic distribution of many species is tightly coupled to their ability to survive winter. In this review, we summarize molecular processes associated with winter survival, with a particular focus on coping with cold injury and energetic challenges. Anticipatory processes such as cold acclimation and diapause cause wholesale transcriptional reorganization that increases cold resistance and promotes cryoprotectant production and energy storage. Molecular responses to low temperature are also dynamic and include signaling events during and after a cold stressor to prevent and repair cold injury. In addition, we highlight mechanisms that are subject to selection as insects evolve to variable winter conditions. Based on current knowledge, despite common threads, molecular mechanisms of winter survival vary considerably across species, and taxonomic biases must be addressed to fully appreciate the mechanistic basis of winter survival across the insect phylogeny. 

Nonnative species are a key agent of global change. However, nonnative invertebrates remain understudied at the community scales where they are most likely to drive local extirpations. We use the North American NEON pitfall trapping network to document the number of nonnative species from 51 invertebrate communities, testing four classes of drivers. We sequenced samples using the eDNA from the sample's storage ethanol. We used AICc informed regression to evaluate how native species richness, productivity, habitat, temperature, and human population density and vehicular traffic account for continent‐wide variation in the number of nonnative species in a local community. The percentage of nonnatives varied 3‐fold among habitat types and over 10‐fold (0%–14%) overall. We found evidence for two types of constraints on nonnative diversity. Consistent with Capacity rules (i.e., how the number of niches and individuals reflect the number of species an ecosystem can support) nonnatives increased with existing native species richness and ecosystem productivity. Consistent with Establishment Rules (i.e., how the dispersal rate of nonnative propagules and the number of open sites limits nonnative species richness) nonnatives increased with automobile traffic—a measure of human‐generated propagule pressure—and were twice as common in pastures than native grasslands. After accounting for drivers associated with a community's ability to support native species (native species richness and productivity), nonnatives are more common in communities that are regularly seasonally disturbed (pastures and, potentially deciduous forests) and those experiencing more vehicular traffic. These baseline values across the US North America will allow NEON's monitoring mission to document how anthropogenic change—from disturbance to propagule transport, from temperature to trends in local extinction—further shape biotic homogenization.

 

Most studies exploring molecular and physiological responses to temperature have focused on constant temperature treatments. To gain a better understanding of the impacts of fluctuating temperatures, we investigated impacts of increased temperature variation on Phanaeus vindex dung beetles across levels of biological organization. Specifically, we hypothesized that increased temperature variation is energetically demanding. We predicted that thermal sensitivity of metabolic rate and energetic reserves would be reduced with increasing fluctuation. To test this, we examined responses of dung beetles exposed to constant (20°C), low fluctuation (20±5°C), or high fluctuation (20±12°C) temperature treatments using respirometry, energetic reserves, and HPLC-MS-based metabolomics. We found no significant differences in metabolic rates or energetic reserves, suggesting increased fluctuations were not energetically demanding. To understand why there was no effect of increased amplitude on energetics, we assembled and annotated a de novo transcriptome, finding non-overlapping transcriptomic and metabolomic responses of beetles exposed to different fluctuations. We found that 58 metabolites increased in abundance in both fluctuation treatments, but 15 only did so in response to high amplitude fluctuations. We found 120 transcripts were significantly upregulated following acclimation to any fluctuation, but 174 were upregulated only in beetles from the high amplitude fluctuation. Several differentially expressed transcripts were associated with post-translational modifications to histones that support a more open chromatin structure. Our results demonstrate that acclimation to different temperature fluctuations is distinct and may be supported by increasing transcriptional plasticity. Our results indicate for the first time that histone modifications may underlie rapid acclimation to temperature variation. 

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.

 

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.

 

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.