Traditional biodiversity assessments are limited by being taxa-/ecosystem-specific. Advances in molecular and remote sensing (RS) techniques create novel opportunities for unified biodiversity assessments. Individually, eDNA and RS already offer unprecedented temporal and spatial resolutions to track state and change of biodiversity due to major global drives. We want to integrate both approaches and to assess and link biodiversity and ecosystem processes in whole catchments, with a focus on diversity of dominant vegetation cover and decomposers, carbon stocks and fluxes. This integration has never been aimed for before. Ultimately, we want to use our results to upscale and predict diversity within and among-sites at the landscape level.
The key variable of biodiversity sciences, biodiversity itself, is usually measured only for a restricted set of taxa, and commonly used sampling techniques differ greatly between ecosystems, sampling is time-intense and difficult to be automated. This subsequently challenges comparisons of biodiversity and its change across ecosystems at an adequate resolution and comparable variables (Pereira et al., 2015).
We propose integrating two novel and highly innovative biodiversity measures by combining eDNA (Kelly et al. 2014, Deiner et al. 2016) and RS techniques (Skidmore et al. 2016), allowing an upscaling and unification of biodiversity assessments at large scales. Our ultimate goal is to assess biodiversity and carbon fluxes across the land-water interface across selected test sites of the URPP GCB. Individually, eDNA and RS offer unprecedented temporal and spatial resolutions of biodiversity information and ecosystem descriptors (e.g., carbon stocks/fluxes), and are therefore most suitable to track biodiversity change due to the major global drives or to be used as an early warning tool of the drivers themselves (e.g. invasive species, global change triggers). Their integration has never been aimed for before. This biodiversity influences different levels of ecosystem’s functioning, from biomass production to decomposers re-organizing or mineralizing organic matter. These functions describe different aspects of the carbon cycle that could be used as indicators for the system’s efficiency.
We will apply eDNA and RS approaches to measure biodiversity in river catchments associated to selected URPP GCB sites. The ultimate application is for example at sites Lägern, Borneo, or Siberia. In a first step, due to the challenges involved, we will do proximate method development at various small catchments in NE Switzerland („sandbox-approach“). From these catchments we have already preliminary land-use and community variables, and their replicated nature make them suitable for testing also the variability associated with the methods. We will measure biodiversity across the land-water interface by hierarchically sampling eDNA in dendritic river networks (Altermatt 2013), and process eDNA with high-throughput sequencing tools. We will assess biodiversity across the domain of life, including bacteria, plants and animals, in a unifying way, using 16S and COI genes (Deiner et al. 2016). This will provide fundamental and comparable knowledge of biodiversity across the sampling sites. In parallel, we will assess biodiversity (focus on dominant vegetation cover) using RS and automated classification of plant species/functional types. Based on this, we will build a reference database of plant species occurring in our sites, their genetic fingerprint as well as a spatially explicit map of individual vegetation-forming plants. We will link these spatially specific plant-diversity patterns to our eDNA based biodiversity measures (-/-diversity), in order to find the spatial resolution of congruence with respect to distances to the river line and subsequent signaling therein. Observed spatial fluxes of eDNA based on biodiversity signals can be linked to carbon stocks/fluxes derived from ground-based and remote sensing based vegetation assays. Ultimately, our goal is to upscale and predict biodiversity within and among-sites at the landscape level.
The milestones are:
i) Measure diversity in a set of replicated small tributaries (each 2–20 km2) based on eDNA.
ii) Assess the stock/identity of dominant vegetation cover using RS in a spatially explicit way (i.e., identity based on individual plants).
iii) Develop a model describing fluxes from the terrestrial compartment into the river, allowing the identification of spatial scales at which terrestrial signals show up in the aquatic realm.
iv) Quantify carbon stocks and fluxes.
Altermatt F. 2013. Diversity in riverine metacommunities: a network perspective. Aquatic Ecology, 47, 365-377.
Deiner, K., E. A. Fronhofer, E. Mächler, J.-C. Walser, and F. Altermatt. 2016. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nature Communications 7:12544.
Kelly, R.P., Port, J.A., Yamahara, K.M., Martone, R.G., et al. 2014. Harnessing DNA to improve environmental management. Science, 344, 1455-1456.
Pereira, H. M., S. Ferrier, M. Walters, G. N. Geller, et al. 2013. Essential Biodiversity Variables. Science 339:277.
Skidmore, A. K., N. Pettorelli, N. C. Coops, et al. 2016. Environmental science: Agree on biodiversity metrics to track from space. Nature 523:403-405.