Conclusions, limitations, and perspectives
To our knowledge, the present meta-analysis is the first to report
variations in eDNA-based estimation accuracy of species abundance among
different target taxa and filter pore sizes (reflecting eDNA particle
size distribution). The significance is that the relationship between
eDNA quantity and species abundance can be driven not only by eDNA
transport and degradation processes but also by the characteristics of
eDNA (its production source and state). Some recent studies have
suggested the possibility of improving the accuracy of eDNA-based
abundance estimation by statistically accounting for the processes of
eDNA production, transport, and degradation (Carraro et al., 2018; Cerco
et al., 2018; Fukaya et al., 2021). In contrast, our meta-analyses shed
a new light on the importance of what characteristics of eDNA should be
targeted for more accurate estimation of species abundance. In
particular, our findings on the effects of eDNA state imply that ‘more
recently released’ eDNA, existing as larger eDNA particles and
potentially longer eDNA fragments, more precisely reflect species
abundance in the field. This knowledge will complement abundance
estimation approaches that consider eDNA spatiotemporal dynamics; that
is, understanding eDNA characteristics, including production source,
particle size, and fragment length, as well as eDNA production,
transport, and degradation processes, will enable us to further enhance
the potential of eDNA analysis as a non-disruptive and cost-efficient
tool for species abundance estimation. Therefore, accumulating knowledge
of eDNA states and their interactions with the spatiotemporal dynamics
(e.g., the processes of production, transport, and degradation) is
crucial (Jo & Minamoto, 2021).
It should be noted that there are some potential biases and limitations
in our meta-analyses. First, regardless of accounting for random effects
and multiple factors, we observed the high degree of heterogeneity
across studies and datasets in our meta-analysis. This infers that there
remain a number of variables that our study could not consider. For
example, environmental parameter such as water qualities and temperature
has not considered here, although this point was barely considered as
the categorical factor (laboratory/natural, lentic/lotic,
freshwater/marine). Degradation of eDNA, which is accelerated by higher
temperature (Strickler et al., 2015; Jo et al., 2019a), could affect its
persistence time in water and possibly the goodness of relationships
between eDNA quantity and species abundance. In addition, the difference
in other technical steps, including eDNA storage and extraction, in the
analysis should be considered. Some studies filtered water samples on
site and transferred the filter samples to the laboratory, while others
transported water samples and filtered them in the laboratory (Kumar et
al., 2020). There are also substantial variations of DNA extraction
protocols across studies (commercial kits or in-house formulations;
Kumar et al., 2020). These methodologies might accordingly influence the
estimation accuracy of species abundance via eDNA, as well as
eDNA detectability and quantification.
Second, we also acknowledge the publication bias in this study. The
asymmetry funnel plot infers that there may be some ‘hidden’ studies
that both the estimation accuracy and its variance are small. This may
partly be attributed to the studies that were not included in this
meta-analysis because the indices of abundance estimation accuracy
(Pearson’s correlation coefficients or R2 values) were
not estimated or presented in the manuscript. Besides, our collected
dataset was concentrated toward studies targeting fish species, which
might cause biased and over-dispersed estimation for other taxa.
Accumulating additional empirical studies for various taxa, assay
strategy, and environmental conditions is necessary to validate the
findings of our meta-analyses and further elucidate the influence of
eDNA characteristics on eDNA-based estimation of species abundances.
Furthermore, although not considered in the present study, nuclear eDNA,
particularly targeting multiple copies of ribosomal RNA genes, may be
applicable for more accurate eDNA-based species abundance estimations.
Relative to mitochondrial eDNA, targeting multi-copy nuclear eDNA can
improve detectability and yield (Minamoto et al., 2017; Jo et al.,
2020b) and nuclear eDNA may degrade more rapidly due to potential
differences in membrane and DNA structures (Bylemans et al., 2018; Jo et
al., 2020b). In addition, nuclear eDNA production may also be less
biased by individual growth and developmental stages, whereas
mitochondrial eDNA production is expected to be suppressed with maturity
and aging (Jo et al., 2020b). Understanding both the characteristics and
dynamics of eDNA will fill a gap between eDNA concentration and species
abundance in the field, and update current eDNA analysis as a more
refined tool for biodiversity and ecosystem monitoring and stock
assessment.