Using environmental DNA to investigate avian interactions with flowering plants

Abstract

Animal pollination is an important and highly valued ecosystem function and the role of birds as pollinators is increasingly acknowledged. However, such interactions can be challenging to document and often require extensive field programs. Over the last decade, environmental DNA (eDNA) has been analyzed from several different contemporary sample types, such as water, soil, flowers, and air. The applications of these studies include biodiversity monitoring, detection of endangered species, community compositions, and more recently, flower–arthropod interactions. However, it remains unknown whether flower eDNA is applicable to other taxonomic groups interacting with plants, as well as the deposition and degradation of eDNA on flowers. Here, we test whether eDNA from flowers can be used for detecting bird pollinators. In a controlled environment (an aviary with great tits [Parus major]), we show that birds leave significant traces of DNA on the flowers without observed visits (airborne eDNA). We further show that when birds had been in contact with the flowers, DNA concentrations increased to levels significantly higher than airborne background DNA. Subsequently, we sampled five clusters of wild flowers in Papua New Guinea and detected four species of birds, two of which are nectar‐feeders, and one that is an insectivorous species known to visit flowers. These four bird species were regularly seen in the area and caught in mist‐nets in the days prior to sampling of the flowers. In total, 29 bird species were recorded (18 mist‐netted) in the area and of these, eight are nectarivorous. Our quantitative approach suggests that it is possible to distinguish airborne background bird DNA deposited on flowers from actual flower visits of birds in the wild, although this might be highly context‐specific. Our findings are of broad interest within research on ecosystem functioning, biotic interactions, and plant–animal mutualism.

Environmental DNA metabarcoding from flowers reveals arthropod pollinators, plant pests, parasites, and potential predator–prey interactions while revealing more arthropod diversity than camera traps

“…This disparity among species‐specific detections of Lepidoptera as a whole, the Papilionoidea superfamily, and pollinating bee species (Hymenoptera) most likely is the result of primer limitations or sample collection methods. Future research should take into account recent research that indicates it is possible for small portions of residual arthropod eDNA to be depositied on flowers via airborne contamination (Johnson et al, 2023; Jonsson et al, 2023; Klepke et al, 2022; Roger et al, 2022). Lastly, our eDNA metabarcoding survey did not amplify eDNA across all flower samples (see explanations below).…”

“…For example, it is unclear why the arthropod communities detected via eDNA versus camera trapping differed so substantially and how external factors, such as rain and flower type, impacted eDNA collection. Although our work was focused on arthropod pollinators, this work is applicable for the detection of vertebrate species (e.g., birds and bats; Jonsson et al, 2023; Newton et al, 2023; Walker et al, 2022) and should be explored further. In combination, our results suggest that eDNA represents a useful tool for monitoring arthropod communities associated with specific flowers and assessing their functional diversity, while future refinement should only improve its efficacy.…”

Monitoring the birds and the bees: Environmental DNA metabarcoding of flowers detects plant–animal interactions

“…While the ability to detect insect visits to flowers using eDNA shows promise (Evans & Kitson, 2020; Gamonal Gomez et al, 2022; Harper et al, 2022; Thomsen & Sigsgaard, 2019), the technique has only recently been used to identify vertebrate flower visitors (Jønsson et al, 2023; Walker et al, 2022). Furthermore, while eDNA metabarcoding surveys have been shown to outperform the use of both visual (Barata et al, 2020) and camera trap (Leempoel et al, 2020) techniques in other contexts, there are limitations of the method, including limited knowledge on the deposition and persistence of eDNA on floral surfaces (Barnes & Turner, 2016; Harrison et al, 2019; Valentin et al, 2021).…”

Time is of the essence: A general framework for uncovering temporal structures of communities

“…For instance, deploying cameras and machine learning to identify flowers and even pollinators at very fine temporal scales (Besson et al 2022) can significantly help reduce the workforce needed to collect this type of data. Similarly, DNA approaches can quantify phenologies and even interactions at similar or even finer temporal scales than direct observation (e.g., metabarcoding of airborne pollen DNA, pollinator DNA in flowers, or eDNA analysis in freshwater and marine systems) (Besson et al 2022;Jensen et al 2022;Jønsson et al 2023). Importantly, phenology data provide essential information about the temporal structure of communities by documenting when species are present and active in a given community.…”