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.