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Abstract Intraspecific trait variation (ITV) is an increasingly important aspect of biodiversity and can provide a more complete perspective on how abiotic and biotic processes affect individuals, species' niches and ultimately community‐level structure than traditional uses of trait means. Body size serves as a proxy for a suite of traits that govern species' niches. Distributions of co‐occurring species body sizes can inform niche overlap, relate to species richness and uncover mechanistic drivers of diversity.We leveraged individual‐level body size (length) in freshwater fishes and environmental data from the National Ecological Observatory Network (NEON) for 17 lakes and streams in the contiguous United States to explore how abiotic and biotic factors influence fish species richness and trait distributions of body size. We calculated key abiotic (climate, productivity, land use) and biotic (phylogenetic diversity, trait diversity, community‐level overlap of trait probability densities) variables for each site to test hypotheses about drivers of ITV in body size and fish diversity.Abiotic variables were consistently important in explaining variation in fish body size and species richness across sites. In particular, productivity (as chlorophyll) was a key variable in explaining variation in body size trait richness, evenness and divergence, as well as species richness.This study yields new insights into continental‐scale patterns of freshwater fishes, possible only by leveraging the paired high frequency, in situ abiotic data and individual‐level traits collected by NEON. 

Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations. 

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. 

Ground beetles are a highly sensitive and speciose biolog- ical indicator, making them vital for monitoring biodiver- sity. However, they are currently an underutilized resource due to the manual effort required by taxonomic experts to perform challenging species differentiations based on sub- tle morphological differences, precluding widespread ap- plications. In this paper, we evaluate 12 vision models on taxonomic classification across four diverse, long-tailed datasets spanning over 230 genera and 1769 species, with images ranging from controlled laboratory settings to chal- lenging field-collected (in-situ) photographs. We further ex- plore taxonomic classification in two important real-world contexts: sample efficiency and domain adaptation. Our re- sults show that the Vision and Language Transformer com- bined with an MLP head is the best performing model, with 97% accuracy at genus and 94% at species level. Sample efficiency analysis shows that we can reduce train data re- quirements by up to 50% with minimal compromise in per- formance. The domain adaptation experiments reveal sig- nificant challenges when transferring models from lab to in-situ images, highlighting a critical domain gap. Overall, our study lays a foundation for large-scale automated tax- onomic classification of beetles, and beyond that, advances sample-efficient learning and cross-domain adaptation for diverse long-tailed ecological datasets. 

This dataset is composed of a collection of 577 images of ethanol-preserved beetles collected at NEON sites in 2018. Each image contains a collection of beetles of the same species from a single plot at the labeled site. In 2022, they were arranged on a lattice and photographed; the elytra length and width were then annotated for each individual in each image using Zooniverse. The individual images were segemented out based on scaling the elytra measurement pixel coordinates to the full-size images (more information on this process is available on the Imageomics/2018-NEON-beetles-processing repository).