Introduction
In the past decades, environmental DNA (eDNA) analysis has been developed and successfully applied in multiple fields in ecology, fisheries, and environmental science (Ficetola et al., 2008; Bálint et al., 2018; Ruppert et al., 2019; Spear et al., 2021). Environmental DNA is defined as a total pool of DNA isolated from environmental samples such as water and sediment (Pawlowski et al., 2020); in a narrower sense, it is generally defined as extra-organismal DNA released from macro-organisms in the form of feces, skin, mucus, and gamete (Barnes & Turner, 2016; Rodriguez-Ezpeleta et al., 2021). Contrary to traditional methods, PCR-based detection of target eDNA does not require capturing nor observing individuals, and thus eDNA analysis is a feasible approach for non-disruptive, highly-sensitive, and cost-effective biomonitoring (Takahara et al., 2013; Yamanaka & Minamoto, 2016; Deiner et al., 2017; Djurhuus et al., 2020). Therefore, eDNA analysis has potential to improve the monitoring of biodiversity and ecosystems, allowing for more effective conservation and management of biodiversity and resources.
In addition to species presence/absence, eDNA analysis can be used to estimate species abundance from target eDNA quantity. Several studies have reported positive relationships between eDNA concentrations and species abundance for various taxa and environments (e.g., Takahara et al., 2012; Pilliod et al., 2013; Klymus et al., 2015; Salter et al., 2019). However, a recent meta-analysis demonstrated that the relationships between eDNA concentration and species abundance was weaker in natural environments than in controlled laboratory conditions (i.e., aquaria, tanks, or mesocosms) (Yates et al., 2019). According to the study, the mean R2 values were 0.81 and 0.57 in laboratory conditions and natural environments, respectively. This finding is intuitively unsurprising given that abundance can be precisely set in laboratory experiments, but we cannot know ‘true’ species abundance in natural environments where some individuals are not analyzable depending on their developmental stage and the survey method (Yates et al., 2019). In addition, the effects of diffusion and degradation on eDNA detection/quantification would be more substantial in natural environments due to compounding and complicated environmental conditions, including temperature, water chemistry, flow rate, and substrate (Strickler et al., 2015; Jane et al., 2015; Shogren et al., 2018; Jo et al., 2019a). Such factors could hamper the practical application of eDNA-based abundance estimation in natural environments (Hansen et al., 2018). Therefore, toward effective conservation management of biodiversity and precise stock assessment via eDNA analysis, it is critical to assess the factors affecting such variabilities concerning the estimation accuracy and improve the accuracy of eDNA-based abundance estimation.
Given that the amount of eDNA is determined by a function of its production, transport, and degradation (Strickler et al., 2015; Barnes & Turner, 2016; Jo & Minamoto, 2021), the relationships between eDNA quantity and species abundance may also be affected by target eDNA characteristics, including its production source and cellular/molecular state. For example, eDNA production sources and processes may differ among taxa, which could accordingly influence the estimation accuracy of species abundance via eDNA analysis, as well as detection sensitivity of target eDNA. Andruszkiewicz et al. (2021) estimated eDNA shedding rates (pg/hour) of multiple taxa under similar experimental conditions and found that crustaceans (Palaeomenes spp.) had lower shedding rates than fish (Fundulus heteroclitus ) and scyphomedusae (Aurelia aurita and Chrysaora spp.). These findings suggest that external morphology and/or physiology could explain the difference in eDNA production sources and processes among taxa.
Cellular and molecular states of eDNA can also be closely associated with its transport and degradation processes, consequently influencing the spatiotemporal range of target eDNA signals (Barnes & Turner, 2016) and even eDNA-based estimation accuracy of species abundance. Although studies linking eDNA state to its spatiotemporal dynamics are scarce, it has been reported that intra-cellular eDNA collected from larger size fraction (i.e., larger eDNA particles) contained longer DNA fragments more frequently (Jo et al., 2020a), and eDNA decay rates could be determined by eDNA states, such as target gene (mitochondrial/nuclear) and particle size, as well as abiotic factors, including temperature and water chemistry (Strickler et al., 2015; Jo & Minamoto, 2021). In the context of abundance estimation, given the rapid degradation of longer eDNA fragments (Jo et al., 2017) and persistence of smaller-sized eDNA particles (i.e., eDNA from smaller size fractions) in water due to the inflow of degraded eDNA from larger to smaller fractions (Jo et al., 2019b), biological signals from longer eDNA fragments and larger eDNA particles (i.e., eDNA from larger size fractions) could be fresher and spatiotemporally finer in the field, which may consequently improve the accuracy of eDNA-based abundance estimation. Nevertheless, aside from Stewart (2019), who reviewed how biotic factors, such as developmental stage, life history, and species interaction might influence eDNA production and eDNA-based abundance estimation performance, exploration of the effects of eDNA production sources and states on estimation accuracy has been limited.
Furthermore, the estimation accuracy of species abundance can rely on some technical aspects in eDNA analysis. First, although the earlier studies collected eDNA in water samples by centrifugation and precipitation (e.g., Ficetola et al., 2008; Takahara et al., 2012), filtration of water samples is now the most common method for collecting aqueous eDNA (Kumar et al., 2020). Between these collection methods, the volume of water samples (typically 15 mL in the former while hundreds to thousands of milliliters in the latter) and the state of eDNA collected (both membranous and dissolved DNA in the former while only membranous DNA in the latter) could be different. Second, compared to the eDNA concentration estimated by real-time and digital PCR, the eDNA read number estimated by metabarcoding is expected to reflect species abundance less precisely, given the biases introduced during PCR, sequencing, and bioinformatics steps required high-throughput sequencing (Lamb et al., 2019). Nevertheless, some studies have reported a positive relationship between the relative abundance of eDNA read detectedvia high-throughput sequencing and species abundance (e.g., Evans et al., 2016; Li et al., 2021). Third, it is unclear which metrics of species abundance, biomass or number of individuals, exhibit a stronger relationship with eDNA quantity. Although a precedent meta-analytic study found no evidence that there was no significant difference in these metrics for the relationship with eDNA quantity (Yates et al., 2019), the study also acknowledged the need of accumulating future research to specify the difference. As well as the production source and state of eDNA, these technical aspects in eDNA analysis have potentials affecting the estimation accuracy of species abundance via eDNA, whereas these points had not been assessed so far.
As far as we know, there is no study to directly assess the importance of eDNA production source and state, as well as technical aspects in eDNA analysis, for the accuracy of eDNA-based abundance estimation. However, meta-analyses, synthesizing previous findings and statistically re-analyzing them, may shed light on the relationship between eDNA-based estimation of species abundance and eDNA characteristics and methodology. In this study, we investigated how different eDNA production sources, states, and methodology influenced eDNA-based species abundance estimation accuracy by performing meta-analyses of eDNA studies targeting macro-organisms. We conducted a literature search and extracted data on factors influencing eDNA characteristics and methodology. Moreover, since it is unclear how the relationship between species abundance and eDNA concentration differs among various natural environments (e.g., freshwater/marine, lentic/lotic), we also assessed the effect of target environments on eDNA-based abundance estimation accuracy. Integrating and collating previous findings viameta-analysis will enable us to find generalizable patterns in these relationships and to elucidate the hitherto unknown findings in the estimation accuracy of species abundance via eDNA.