Detecting negative PAI interactions through eDNA has also become
possible recently. Derocles et al. (2015) for example, successfully
amplified trace DNA from plants- leaf miners-parasitoid interactions and
Thomsen & Sigsgaard (2019) detected large numbers of phytophagous
species, parasitoids, gall inducers, and predator insects through the
metabarcoding of flowers. Cumulatively, these studies provide a
foundation for detecting negative and cryptic plant-arthropod
interactions with applications for disease monitoring and pest
management.
Current limitations
There remains a need to understand eDNA’s current limitations,
especially when it pertains to PAI detection and interpretation.
Limitations are spread out among each step of the
collection-analysis-interpretation process (Figure 2). It is therefore
imperative to identify the necessary strategies before establishment of
eDNA as one newer branch of PAI analysis. The existing limitations of
this method are:
(I) The complex, and often idiosyncratic, ecology of eDNA. In effect,
practitioners may sample different sources of eDNA (cellular,
extracellular, extra-organismal, etc.) (Stewart, 2019;
Rodriguez‐Ezpeleta et al., 2021), which may lead to different PAI
interpretations. For example, pollen and spores (extra organismal DNA)
are more or less ubiquitous in the atmosphere, travel long distances
(through wind or water), and contain adaptations to remain in dormant
stages for long periods of time. These, when settled on non-targeted and
non-interacting organisms, lead to misinterpretation. Alternatively,
extracellular DNA and cellular DNA are generally specific to places
where organisms recently moved and are subject to easy degradation.
Thus, clear differentiation of their behavior may help to draw more
precise conclusions. The production and release of eDNA into the
environment can also occur at different rates, where eDNA concentration
can depend on many variables such as life stage, metabolic activity, or
breeding season (Stewart, 2019). What’s more, production rate of eDNA is
most likely influenced by species interactions themselves (e.g.,
competition between/among species) (Stewart, 2019). In fact,
mixed-species fish populations have been shown to increase eDNA
production rates when housed together compared to single species
populations (Sassoubre et al., 2016). Beside the aforementioned
characteristics, the persistence of eDNA (Barnes & Turner, 2016; Deiner
et al., 2017; Kudoh et al., 2020), and its transport in and between
environmental mediums (air, water, soil) should also be considered
(Barnes & Turner, 2016; Lacoursière‐Roussel & Deiner, 2021),
especially given these parameters have yet to be standardized for many
taxa (Barnes & Turner, 2016).
(II) Translating eDNA quantification metrics to organismal abundance has
been controversial (Marshall et al., 2021), although recent research
have advanced the possibility of absolute quantification (Tillotson et
al. 2018; Hoshino et al., 2021) and even predicting dispersion time of
eDNA within the environment (Marshall et al., 2021).
(III) A universal limitation to any genetic-based species identification
reliant on databases, is certainly missing species sequences, sequencing
error, cloning vector contamination, and the redundancy of data (Singh,
2015). These issues may cause species misidentification which also lead
to the failure in decrypting accurate PAI (Sheppard et al., 2005; Roslin
& Majaneva, 2016).
(IV) Comparative validations between the detection efficiency of eDNA to
that of conventional surveys (e.g., camera, malaise traps), are
necessary to justify the consistency of eDNA methods.
(V) The detection of niche partitioning using eDNA-based methods is only
just beginning (ter Schure et al., 2021) and fine-scale partitioning
(e.g, different herbivory behaviour on the same plant) is difficult with
current eDNA analysis.