Introduction
In the past decades, environmental DNA (eDNA) analysis has been developed and remarkably applied in multiple fields of 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 an extra-organismal DNA released from macro-organisms as a form of feces, skin, mucus, and gamete (Barnes & Turner, 2016; Rodriguez-Ezpeleta et al., in press). 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 a potential to improve the monitoring of biodiversity and ecosystem, allowing for more effective conservation and management of biodiversity and resources.
In addition to species presence/absence, eDNA analysis can be used to predict species abundance from target eDNA concentrations. Several studies have reported positive correlations between eDNA concentrations and species abundance for various taxa and environments (Takahara et al., 2012; Pilliod et al., 2013; Klymus et al., 2015; Salter et al., 2019). However, a recent meta-analysis demonstrated that the correlation 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 81 % and 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/or 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 an effective conservation management of biodiversity and precise stock assessment via eDNA analysis, it is important to value the factors affecting such variabilities with regard to the estimation accuracy, and improve the accuracy of eDNA-based abundance estimation.
The amount of eDNA in a field is determined by a function of its production, transport, and degradation (Strickler et al., 2015; Barnes & Turner, 2016). Thus, in addition to processes of eDNA transport and degradation, the relationships between eDNA concentration 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 abundancevia 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 substantially associate the difference in eDNA production sources and processes among taxa.
Cellular and molecular states of eDNA can also associate with its transport and degradation processes closely, consequently influencing the spatiotemporal range of target eDNA signals 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 larger-sized and intra-cellular eDNA 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 (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 more 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.
As far as we know, there is no study to directly value the importance of eDNA production source and state for the accuracy of eDNA-based abundance estimation. However, meta-analyses, synthesizing previous findings and statistically re-analyzing them, may shed a light on the relationship between eDNA-based estimation of species abundance and such eDNA characteristics. In this study, we investigated how different eDNA production sources and states 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 production sources and states. 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-analyses will enable us to elucidate the relationships between species abundance estimation and eDNA characteristics, which would not be recognized in individual eDNA studies.