Data from: Machine learning identification of microhabitat features associated with occupancy of artificial nestboxes by hazel dormice (Muscardinus avellanarius) in a UK woodland site

Hazel dormice (Muscardinus avellanarius) have severely declined since 2000
leading to increased legislative protection in the UK and Europe.
Artificial nestboxes are widely used for its conservation and monitoring.
Previous research has focused on how to identify suitable areas for
nestboxes, but where to place individual boxes to promote occupancy is
less well understood. Here, we demonstrate the use of machine learning
Random Forest regression to predict nestbox occupancy from a wide range of
microhabitat variables using a UK woodland as a case study. Random forest
models are powerful predictive tools that allow simultaneous testing of
many predictors with relatively few observations. Field data included
observed nestbox occupancy (2017-2021) and measurements of 76 microhabitat
variables collected in the summer of 2021 from 45 occupied and unused
nestboxes located in a deciduous woodland in Berkshire, UK. We applied
Random Forest regression to identify important variables and predict
nestbox occupancy demonstrating robust approaches to tune model
hyperparameters and evaluate importance metrics. In our study area,
nestboxes were more likely to be occupied in sites with more hazel
(Corylus avellana), greater overall tree abundance but not fully closed
canopies (optimal 80-85%), more honeysuckle (Lolium periclymenum) and
hawthorn (Crataegus monogyna), and when located further from footpaths and
woodland margins. Occupancy over the study period was well predicted using
microhabitat variables (13.3% OOB error) but future occupancy was more
uncertain (33.3% error for 2021-2023 records). Modelling approaches that
allow consideration of numerous variables from few locations or
observations can be help identify relevant features and predict desirable
outcomes of conservation actions. Here we demonstrate this approach
identifying microhabitat variables that influence artificial nestbox
occupancy by hazel dormice in a UK woodland. Findings offer some
recommendations for local management that could promote nestbox occupancy
and improve monitoring and conservation efforts.