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.