Staff, Students, and Associates

Dr Nick Schurch
Principal Statistician for Ecology & Environmental Science


Biomathematics and Statistics Scotland
The James Hutton Institute,
AB15 8QH,
Scotland, UK

Tel: +44 (0)1224 395126

I joined BioSS as the Principal Statistician for Ecology & Environmental Science in 2019 and lead the Ecology and Environmental Science team at BioSS. In this role, I line manage four statisticians (who are primarily embedded with the James Hutton Institute at Craigiebuckler in Aberdeen), oversee and lead the teams consultancy collaborations (both external and as part of the current RESAS Strategic Research Programme), oversee the wider BioSS consultancy programmes with NatureScot and the UK Centre for Ecology & Hydrology, lead BioSS consultancy work on forensic science work, and supervise three PhD students working on applied environmental science & ecology problems, alongside working on my own personal research interests.

Recent consultancy work:


Since joining BioSS in 2019 I have explored a range of ecology and environmental science research areas with a focus on bringing my quantitative and computational science experience to bear in an environmental context. My primary current research interests are:

  • eDNA, citizen science, and JSDMs:

Joint Species Distribution Models are analytical frameworks for analysing both the environmental drivers of biodiversity and community ecology data and the strength of the impact (direct or indirect) that different species exert on each other. Currently, however, few implementations have the computational efficiency to work effectively with the very large numbers of species that are routinely identified in eDNA experiments, or work efficiently with large numbers of environmental covariates, or incorporate the very different bias characteristics of  datasets when integrating data from multiple disparate sources (e.g., surveys, camera trap records, eDNA measurements and citizen science records). I am interested in developing extensions to Joint Species Distribution Modelling frameworks to enable these models to tackle these issues, and to use them to get a more nuanced picture of patterns of diversity at local and national scales.

  • AI in ecology:

AI is now a widely used tool across society, but it's use in ecological and environmental contexts is still limited. I am interested in developing accessible and usable AI tools for ecological and environmental scientists. Current projects in this area include exploring the efficacy of using transfer learning methods with Convolutional Neural Networks to facilitate species, sex, individual and behaviour characterization from small-scale camera and video trap surveys, and using Long Short-Term Memory to model, and predict, environmental time-series from other environmental measurements (for example, predicting river temperature or flow, from weather, geological and location data).


I did my undergraduate MSci in physics at the University of Bristol, before completing my Astrophysics Ph.D. thesis at the University of Leicester in 2002. Continuing my PhD work, I completed post-docs/fellowships across three continents studying the X-ray spectra of obscured Seyfert galaxies (Carnegie Mellon University, Durham University and a fellowship at the Institute for High Energy Physics at the Chinese Academy of Sciences). In 2009 I changed fields, joining the University of Dundee as a bioinformatician with Prof. Geoff Barton's Lab, studying transcription and gene expression. I completed several post-docs with the Barton group, culminating in a Senior Researcher position leading a small team of experienced bioinformaticians working on data from a wide range of cell biology data and developing several novel open-source bioinformatics tools, before leaving to join BioSS in 2019.

Open Science:

I am strongly committed to ideals of transparency, replicability, and reproducibility in scientific research (both my own and others). Alongside our BioSS Open Science Champion, Dr Helen Kettle, I am leading the adoption of Open Science best practice within BioSS and across our collaborator institutions, including developing and delivering Open Science training courses and providing advice and guidance on aspects of Open Science and best practice Data Management.

Current & Previous PhD Students:

External Links: