A spatial fingerprint of land-water linkage of biodiversity uncovered by remote sensing and environmental DNA

Environmental DNA and remote sensing datasets reveal the spatial distribution of aquatic insects in a disturbed subtropical river system

Biodiversity datasets with high spatial resolution are critical prerequisites for river protection and management decision-making. However, traditional morphological biomonitoring is inefficient and only provides several site estimates, and there is an urgent need for new approaches to predict biodiversity on fine spatial scales throughout the entire river systems. Here, we combined the environmental DNA (eDNA) and remote sensing (RS) technologies to develop a novel approach for predicting the spatial distribution of aquatic insects with high spatial resolution in a disturbed subtropical Dongjiang River system of southeast China. First, we screened thirteen RS-based vegetation indices that significantly correlated with the eDNA-inferred richness of aquatic insects. In particular, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge2 (NDRE2) were closely related to eDNA-inferred richness. Second, using the gradient boosting decision tree, our data showed that the spatial pattern of eDNA-inferred richness could achieve a high spatial resolution to 500 m reach and accurate prediction of more than 80%, and the prediction efficiency of the headwater streams (Strahler stream order = 1) was slightly higher than the downstream (Strahler stream order >1). Third, using the random forest algorithm, the spatial distribution of aquatic insects could reach a prediction rate of over 70% for the presence or absence of specific genera. Overall, this study provides a new approach to achieving high spatial resolution prediction of the distribution of aquatic insects, which supports decision-making on river diversity protection under climate changes and human impacts.

Multi-omics for studying and understanding polar life

Polar ecosystems are experiencing amongst the most rapid rates of regional warming on Earth. Here, we discuss 'omics' approaches to investigate polar biodiversity, including the current state of the art, future perspectives and recommendations. We propose a community road map to generate and more fully exploit multi-omics data from polar organisms. These data are needed for the comprehensive evaluation of polar biodiversity and to reveal how life evolved and adapted to permanently cold environments with extreme seasonality. We argue that concerted action is required to mitigate the impact of warming on polar ecosystems via conservation efforts, to sustainably manage these unique habitats and their ecosystem services, and for the sustainable bioprospecting of novel genes and compounds for societal gain.

A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels

The current advances of environmental DNA (eDNA) bring profound changes to ecological monitoring and provide unique insights on the biological diversity of ecosystems. The very nature of eDNA data is challenging yet also revolutionizing how biological monitoring information is analysed. In particular, new metrics and approaches should take full advantage of the extent and detail of molecular data produced by genetic methods. In this perspective, machine learning algorithms are particularly promising as they can capture complex relationships between the multiple environmental pressures and the diversity of biological communities. We investigated the potential of a new generation of biomonitoring tools that implement machine-learning techniques to fully exploit eDNA datasets. We trained a machine learning model to discriminate between reference and impacted communities of freshwater macroinvertebrates and assessed its performances using a large eDNA dataset collected at 64 standard federal monitoring sites across Switzerland. We show that a model trained on eDNA is significantly better than a naive model and performs similarly to a model trained on traditional data. Our proof-of-concept shows that such a combination of eDNA and machine learning approaches has the potential to complement or even replace traditional environmental monitoring, and could be scaled along temporal or spatial dimensions.

Destabilizing Effects of Environmental Stressors on Aquatic Communities and Interaction Networks across a Major River Basin

Human-driven environmental stressors are increasingly threatening species survival and diversity of river systems worldwide. However, it remains unclear how the stressors affect the stability changes across aquatic multiple communities. Here, we used environmental DNA (eDNA) data sets from a human-dominated river in China over 3 years and analyzed the stability changes in multiple communities under persistent anthropogenic stressors, including land use and pollutants. First, we found that persistent stressors significantly reduced multifaceted species diversity (e.g., species richness, Shannon's diversity, and Simpson's diversity) and species stability but increased species synchrony across multiple communities. Second, the structures of interaction networks inferred from an empirical meta-food web were significantly changed under persistent stressors, for example, resulting in decreased network modularity and negative/positive cohesion. Third, piecewise structural equation modeling proved that the persistent stress-induced decline in the stability of multiple communities mainly depended upon diversity-mediated pathways rather than the direct effects of stress per se; specifically, the increase of species synchrony and the decline of interaction network modularity were the main biotic drivers of stability variation. Overall, our study highlights the destabilizing effects of persistent stressors on multiple communities as well as the mechanistic dependencies, mainly through reducing species diversity, increasing species synchrony, and changing interaction networks.