Beyond species detection<b>—</b>leveraging environmental DNA and environmental RNA to push beyond presence/absence applications

Environmental DNA (eDNA) has unequivocally revolutionized the science of species detection and identification. Ficetola et al. (2008) first demonstrated the potential application of eDNA for the detection of aquatic macroorganisms. The application of genetic tools to detect (and quantify) macroorganismal eDNA has seen subsequent widespread use by researchers and burgeoning adoption by conservation managers (Bruce et al., 2021; Jerde et al., 2013; Jerde, 2019; Sepulveda et al., 2020). For aquatic macroorganisms, the capture and analysis of eDNA represents a cost-effective means to detect rare and/or invasive species and quantify community composition (Cristescu & Hebert, 2018; Jerde, 2019; Jerde et al., 2013; Taberlet et al., 2012), often outperforming traditional approaches (Boivin-Delisle et al., 2021; Sard et al., 2019; Sigsgaard et al., 2015; Spear et al., 2015). More recently, the toolbox of molecular methods to identify organisms from environmental samples has been expanded to include the analysis of environmental RNA (eRNA; Pochon et al., 2017; von Ammon et al., 2019). However, novel research has demonstrated that eDNA and eRNA could provide additional genetic and ecological information beyond species detection inferences. The articles within this Special Issue focus on five important areas of emergent research that illustrate the potential utility of eDNA/eRNA beyond presence/absence applications. First, an improved understanding of "eDNA dynamics" (production, transportation, degradation, etc.) is crucial for the accurate interpretation of quantitative eDNA signals. Such considerations are particularly important for the application of eDNA to infer metrics of organism abundance, a second key theme tackled by several studies within this Special Issue. Environmental DNA has also been identified as a potential source of population-level genetic information, highlighting its potential utility to quantify genetic diversity in natural populations. The distinct properties of eRNA enable its application to differentiate conspecific organisms (e.g., living or dead organisms, life-history stages, and physiology); an improved understanding of eRNA dynamics is thus critical for advancing its application in ecology and the study of biodiversity. Finally, several studies demonstrate the application of eDNA and eRNA to test broad ecological or biological inferences beyond characterizing community composition, including quantifying functional diversity, assessing species' life-history events, and its potential application for ecological impact assessments. Collectively, the articles herein highlight the rapid pace of development of eDNA science in the last 15 years and illustrate the potential breadth and flexibility of environmental nucleic acids (eNAs) as tools to study ecology and biodiversity. The quantitative interpretation of eDNA signals in natural ecosystems requires the consideration of the processes responsible for the release of eDNA by organisms, its state in the environment, transport, deposition, and degradation processes, and its spatial distribution. These factors were originally summarized as the "ecology of eDNA" (Barnes & Turner, 2016) and more recently as the "nature of eDNA" (Beentjes et al., 2019) or "eDNA dynamics" (Lacoursière-Roussel & Deiner, 2021; Mauvisseau et al., 2022). To this date, there is no overarching framework that can account for the effects of all these processes on the distribution of eDNA in nature. The development of such a model is, nevertheless, critical for correctly interpreting quantitative eDNA data, particularly as it applies to relating eDNA signals to spatial distribution, metrics of organism abundance, and life-history events (e.g., spawning). However, the patchwork of case studies delivering data points for the eventual calibration of such a model is constantly growing, with several relevant publications in this Special Issue making notable contributions. Suter et al. (2023) quantify Antarctic krill (Euphausia superba) eDNA via qPCR and measure its degradation for different fragment lengths of the 16S gene. Based on the comparably faster degradation of the longest fragment, they can thus discern signals obtained from "recent" versus "older" eDNA. This approach is successfully applied to samples collected in the Southern Ocean along a 4800 km long transect. Additionally, the authors show that Antarctic krill eDNA is unlikely to be detected via metabarcoding if a sample was classified as containing "older" eDNA with the qPCR assays. Scriver et al. (2023) review eDNA and eRNA persistence with a focus on potential applications of these processes to coastal marine biosecurity monitoring. They provide a standardized summary of previously measured decay rates and the factors influencing them in marine environments as a basis for accurately modeling eDNA and eRNA persistence in the future. Albeit the core focus of Brys et al. (2023) is on the improved detection and quantification of eDNA signals in duplex reactions, the ddPCR amplitude data generated in their study potentially indicate that relatively small, unlinked DNA fragments might be the basis for the majority of amplifications, thus supporting the negative relation between fragment length and abundance detected by Suter et al. (2023). Temperature has also consistently emerged as a critical parameter related to both the production and decay of eDNA (Jo et al., 2019). A number of studies in this Special Issue add to the growing consensus that temperature is a particularly important variable to consider when interpreting quantitative eDNA signals. Bourque et al. (2023), Morrison et al. (2023), and Gaudet-Boulay et al. (2023) demonstrate the importance of the effect of temperature when relating quantitative eDNA signals to metrics of organism abundance (see discussion below), and Jo et al. (2023) similarly highlight the effect of temperature on eDNA and eRNA degradation, particularly when comparing the ratio of eDNA to eRNA. Estimating abundance from quantitative eDNA signals remains a major goal at the forefront of eDNA research. While studies in natural ecosystems have consistently demonstrated positive correlations between quantitative eDNA data and metrics of organism abundance (Lamb et al., 2019; Rourke et al., 2021; Yates et al., 2019), controlled laboratory experiments are critical to understanding the functional links between organism abundance and eDNA concentrations. Bourque et al. (2023) demonstrate that extra-organismal eDNA concentrations in large-scale experimental mesocosms track Daphnia abundance and biomass across space and time, albeit with variable time lag. They further illustrate the importance of integrating eDNA dynamics when interpreting quantitative eDNA signals, as eDNA concentrations across mesocosms were negatively correlated with temperature and algal density. Their study is additionally notable for utilizing an invertebrate as a focal study species, given the strong representation of fish in the eDNA/abundance literature (Rourke et al., 2021). Moving to natural ecosystems, Morrison et al. (2023) demonstrate the importance of seasonal environmental conditions on Atlantic salmon (Salmo salar) eDNA concentrations and their relationship with organism abundance in a riverine system in New Brunswick (Canada). As discussed above, they also find an important role of temperature on trends in eDNA concentrations across time, although Spring eDNA concentrations near the outflow of their riverine study system were most strongly impacted by fluctuations in the abundance of migratory life-story stages of Atlantic salmon. Similarly, Gaudet-Boulay et al. (2023) compare brook trout eDNA concentrations to Brook Trout (Salvelinus fontinalis) fisheries data collected from 30 lakes in Quebec. Employing a systematic design to estimate mean brook trout eDNA concentrations per lake, they observe that eDNA concentrations were generally positively correlated with several fisheries abundance indicators (e.g., fish harvested/ha and fish harvest per unit effort), although the strength of the correlation varied across years. As with Morrison et al. (2023) and Bourque et al. (2023), the authors similarly find an effect of temperature on lake eDNA concentrations, although its effect again exhibited variability across years likely associated with the timing of sample collection. While the previous studies focused on eDNA/abundance relationships among populations within a single species, Skelton et al. (2023) and Yates et al. (2023) examine relationships between quantitative metabarcoding data and interspecific abundance. Skelton et al. (2023) examine relationships between metabarcoding read count and fish species abundance in a garden pond treated with the piscicide rotenone. Although the authors observe that read count data were positively correlated with species abundance, they note that variance in read count among spatial replicates may exhibit a more reliable relationship with organism abundance because biases in detection/amplification of eDNA from dominant taxa can strongly affect correlations with total read numbers. Skelton et al. (2023) also find that read count data was more closely related to allometrically corrected biomass data (i.e., reflecting total surface area) compared with the numerical abundance of species. Yates et al. (2023) extend metabolic theory to model eDNA production, reanalyzing data from Stoeckle et al. (2021) to investigate allometric relationships between eDNA metabarcoding read count data and traditional metrics of abundance in a northwest Atlantic coastal ecosystem. They estimate that the value of the allometric scaling coefficient in this system was 0.77 (0.64–0.92) for bony fishes, following hypothesized relationships based on metabolic/physiological allometric scaling rates. To facilitate the examina

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