Professional summary

Dr Louise Barwell’s research aims to understand insect populations and plant pathogens using statistical modelling of spatial data at regional to global scales. Her interests are in the application of comparative trait-based and phylogenetic approaches to understand how ecological traits and socio-environmental factors interact to drive biological invasions. From 2016 to 2020, Louise was a post-doctoral researcher on the THAPBI Phyto-threats project, where she applied cross-species models in the context of horizon scanning and risk assessment to predict future global threats to the UK from Phytophthora.

Louise has actively engaged with UK stakeholders in the forestry and nursery sectors and plant health policy to understand how these data and models could be translated into web-based tools adapted for end-users. Louise is now developing phylogenetic comparative models of plant pests and pathogens’ potential host ranges and introduction through trade pathways, for projects funded under the TREESCAPES (NewLEAF) and Future-Proofing Plant Health programmes. A project funded by Scotland's Plant Health Centre will co-develop model outputs with cross-sectoral stakeholders to support Phytophthora preparedness in Scotland. 

Web tools and apps

Experimental webtools designed to explore how ecological models and databases can support biosecurity awareness and decision-making when choosing and procuring plants-for-planting or managing habitats and ecosystems. The platform was created to facilitate the co-design and tailoring of future decision support tools with stakeholders in multiple sectors.  

Developing tools to support plant health biosecurity decisions in Scotland (loubar.github.io)

Selected publications

Green Sarah et al. , (2021), PHYTO-THREATS: addressing threats to UK forests and woodlands from Phytophthora; identifying risks of spread in trade and methods for mitigation. Forests, 12, http://dx.doi.org/10.3390/f12121617

Barwell Louise J. et al. , (2020), Evolutionary trait‐based approaches for predicting future global impacts of plant pathogens in the genus Phytophthora. Journal of Applied Ecology, http://dx.doi.org/10.1111/1365-2664.13820

Marsh Charles J. et al. , (2018), downscale: an R package for downscaling species occupancy from coarse-grain data to predict occupancy at fine-grain sizes. Journal of Statistical Software, 86, http://dx.doi.org/10.18637/jss.v086.c03

Barwell, Louise J. et al. , (2014), Can coarse-grain patterns in insect atlas data predict local occupancy?. Diversity and Distributions, 20, 895-907, http://onlinelibrary.wiley.com/doi/10.1111/ddi.12203/pdf