The NERC-funded project, led by Dr Eleni Matechou, with Dr Alex Bush, University of Lancaster, Professor Jim Griffin, Statistical Science, UCL, Professor Richard Griffiths, Durrell Institute of Conservation and Ecology, University of Kent, and Professor Doug Yu, UEA as co-Investigators, is part of NERC’s Strategic Priorities Fund on Landscape Decisions: Towards a new framework for using land assets programme. <\/p>\n
The RA on the project will be in place until 31st January 2022 and will work on meeting the four model development and two implementation and knowledge exchange objectives of the project.<\/p>\n
Model Development (MD) objectives:<\/em> MD2 Identify important landscape predictors for site-specific species composition and metacommunity structure<\/strong> MD3 Perform simulations to identify the optimal study design for DNA-based surveys under different scenarios<\/strong> MD4 Identify the effects of errors in survey data or suboptimal study design on the selection of conservation priorities (e.g. Implementation and Knowledge Exchange (IKE) objectives:<\/em><\/p>\n IKE1 Develop R-Shiny apps to implement MD1-4, making them accessible to users<\/strong> IKE2 Disseminate project outputs and software to users via training workshops and project partners<\/strong>
\nMD1 Account for sampling and analysis errors and taxonomic uncertainty in DNA-based data<\/strong>
\nSingle-species (barcoding) and multi-species (metabarcoding) DNA data sets have more complicated properties than data
\ncollected using traditional techniques using visual, aural or physical observations. For example, eDNA surveys are prone to
\nfalse-positive and false-negative errors at both the sampling and analysis stages as well as to taxonomic mismatches. We
\nwill develop new statistical models that account for the probabilistic nature of eDNA data, reliably quantifying all levels of
\nuncertainty in eDNA surveys.<\/p>\n
\nThe probability of species presences, species interactions and metacommunity structures is affected by landscape
\ncharacteristics. Accounting for these effects is necessary to obtain an understanding of the system and how to maintain it
\nor improve it. Failing to account for important effects can lead to inaccurate inference and biased results. However, the
\nstrength and direction of these effects are typically unknown and we will develop a novel Bayesian modelling framework
\nand sophisticated algorithms for inferring landscape effects efficiently at the species and metacommunity levels using
\neDNA data.<\/p>\n
\nThe power to correctly infer the metacommunity structure and the landscape effects that shape it using eDNA data
\ndepends on the number of sites sampled, the number of samples collected from each site, the number of PCR replicates
\nperformed for each sample, as well as on landscape characteristics. We will perform extensive simulations to identify the
\noptimal study design under different scenarios and levels of error to provide practitioners with informed guidelines on how
\nto design eDNA surveys.<\/p>\n
\nhigh-value habitat or restoration priorities)<\/strong>
\nSeveral conservation planning software exist that aim to infer the most efficient allocation of resources. Our Bayesian
\nmodel defined by MD1-2 as well as the power analysis resulting from MD3 provide a valuable predictive tool for species
\npresences and metacommunity structures, with corresponding measures of uncertainty, across the landscape. Using the
\npredictions generated by the fitted model in our case study system of UK ponds, we will obtain and compare different
\nsolutions provided by planning software that are frequently employed in decision-making processes and assess how and if
\ndecision-making is informed when uncertainty in inference is explicitly accounted for.<\/p>\n
\nThe new statistical framework for eDNA data (MD1-4) will be implemented into R-Shiny apps and be accompanied by
\nexamples of data analyses and corresponding interpretation to enable practitioners with limited understanding of statistics
\nand no prior knowledge of programming to employ our methods when analysing eDNA data.<\/p>\n
\nWe will organise two training workshops to take place in the last quarter of the project and after MD1-4 and IKE1 are
\ncomplete to disseminate the new methods to research users. These will complement ongoing – and fully subscribed –
\ntraining workshops that the research team have been running for practitioners in the general area of eDNA and statistical
\nmodelling.<\/p>\n