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1. Global change may cause widespread phenological shifts. But knowledge of the extent and generality of these shifts is limited by the availability of phenological records with sufficiently large spatiotemporal extents. Using North American odonates (damselflies and dragonflies) as a model system, we show how a combination of natural history museum and community science collections, beginning in 1901 and extending through 2020, can be leveraged to better understand phenology. 2. We begin with an analysis of odonate functional traits. Principal coordinate analysis is used to place odonate genera within a three-dimensional trait ordination. From this, we identify seven distinct functional groups and select a single odonate genus to represent each group. Next, we pair the odonate records with a list of environmental covariates, including air temperature and degree days, photoperiod, precipitation, latitude and elevation. An iterative subsampling process is then used to mitigate spatiotemporal sampling bias within the odonate dataset. Finally, we use path analysis to quantify the direct effects of degree days, photoperiod and precipitation on odonate emergence timing, while accounting for indirect effects of latitude, elevation and year. 3. Path models showed that degree days, photoperiod and precipitation each have a significant influence on odonate emergence timing, but degree days have the largest overall effect. Notably, the effect that each covariate has on emergence timing varied among functional groups, with positive relationships observed for some group representatives and negative relationships observed for others. For instance, Calopteryx sp. emerged earlier as degree days increased, while Sympetrum sp. emerged later. 4. Previous studies have linked odonate emergence timing to temperature, photoperiod or precipitation. By using natural history museum and community science data to simultaneously examine all three influences, we show that systems-level understanding of odonate phenology may now be possible. 

Abstract A positive relationship between body size and trophic position is often assumed in ecology, but efforts to confirm the generality of this relationship for freshwater vertebrates have produced mixed results. Some authors have tested for among‐species relationships, using species‐level estimates of average size and trophic position. Others have used individual‐level body size and trophic position data to test for within‐species relationships. However, no study has yet estimatedprevalence, defined here as the fraction of total standing stock biomass within a given ecosystem that consists of taxa exhibiting positive size versus trophic position relationships.Individual‐level estimates of body size and relative trophic position (inferred from bulk‐tissue nitrogen stable isotopes) were collected for vertebrates in six temperate streams. In each stream, all locally occurring vertebrate taxa were collected and closed population depletion samples were used to obtain standing stock biomass estimates.Ordinary least squares regression was used with the individual‐level data to test for positive relationships between body size and relative trophic position (r‐STPRs). A separater‐STPR model was tested for each vertebrate species collected at the six study sites. Linear mixed‐effects modelling was then used to test for differingr‐STPRs among species.Prevalence of ther‐STPR was calculated for each site by summing the standing stock biomass of all taxa that exhibited a statistically significant, positive STPR at a given site, then dividing this number by the total standing stock biomass of vertebrates at the site.Significant, positiver‐STPRs were detected in 15 of 22 species × site regression models. Prevalence of species with positiver‐STPRs ranged from 91 to 100% of the total vertebrate biomass in western streams and from 45 to 66% in eastern streams.Results confirm that positiver‐STPRs are characteristic of many of the vertebrate taxa considered in this study. Furthermore, species that exhibit positiver‐STPRs comprise a clear majority of the standing stock biomass in five of six study streams. By using individual‐level data to account for prevalence, a more complete understanding of size‐dependent trophic dynamics should be possible in freshwater ecosystems. 

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological community or food web. Importantly, the size spectrum assumes that individual size, rather than species’ behavioral or life history characteristics, is the primary determinant of abundance within an ecosystem. Thus, unlike traditional allometric relationships that focus on species-level data (e.g., mean species’ body size vs. population density), size spectra analyses are ‘ataxic’ – individual specimens are identified only by their size, without consideration of taxonomic identity. Size spectra models are efficient representations of traditional, complex food webs and can be used in descriptive as well as predictive contexts (e.g., predicting responses of large consumers to changes in basal resources). Empirical studies from diverse aquatic ecosystems have also reported moderate to high levels of similarity in size spectra slopes, suggesting that common processes may regulate the abundances of small and large organisms in very different settings. This is a protocol to model the community-level size spectrum in wadable streams. The protocol consists of three main steps. First, collect quantitative benthic fish and invertebrate samples that can be used to estimate local densities. Second, standardize the fish and invertebrate data by converting all individuals to ataxic units (i.e., individuals identified by size, irrespective of taxonomic identity), and summing individuals within log2 size bins. Third, use linear regression to model the relationship between ataxic M and D estimates. Detailed instructions are provided herein to complete each of these steps, including custom software to facilitate D estimation and size spectra modeling.