Developing a Bayesian species occupancy/abundance indicator for the UK National Plant Monitoring Scheme

Pescott O. L.
Powney G. P.
Walker K. J.

The National Plant Monitoring Scheme (NPMS) is a volunteer-based structured plant recording scheme. This report focuses on the development of a new statistical model for the species-level data generated by the NPMS. The aim is ultimately for this to contribute to a new indicator of UK habitat quality. NPMS surveyors collect data on plant abundance (percentage covers) from small plots targeted at specific habitats. They can participate at different levels, with the level of participation influencing the list of species sought in the field. Typically, surveyors record around 5 small plots in a 1 km square, with each plot being visited twice a year. NPMS data must be processed in order to accurately represent the information content of the plot surveys. Because surveyors use different lists of species depending on their level, in some cases we need to distinguish between true absences (species on a surveyors target list but not reported) and unknown cases (species not on a surveyors target list, meaning that absence from a list is not informative). We present a novel hierarchical statistical model for NPMS species-level data. This model seeks to make maximum use of the data collected, and integrates a standard occupancy modelling approach for plot detections with a Beta distribution model for a species non-zero cover data. We evaluate the proposed model using a variety of different simulated datasets. The performance of the model is assessed in relation to the bias and variance shown relative to the actual parameters used in the data simulations. The simulations indicate that the model performs as expected under a perfect scenario. Smaller datasets induce various biases, many of which can be traced to the fact that, in our simulations, abundance and detectability are closely related. This biases the estimated mean of the underlying cover distribution upwards, and also impacts estimates of the intercept and regression coefficient in the detection sub-model. In real datasets this relationship would likely be less clear-cut, and we do not expect these biases to affect species relative annual trend estimates. Finally, we apply the model to NPMS data collected between 2015 and 2018 for 86 grassland species. The model estimates ecologically sensible mean cover values for the species analysed. However, mean plot occupancies tended to centre on 0.5, suggesting that many species may not yet have sufficient data for mean occupancy to be well estimated. A novel combined abundance/occupancy indicator has been developed for NPMS data in a Bayesian framework. The simulation tests and applications to real data explored in this report indicate that the model performs well in ideal scenarios; biases in less data-rich scenarios can largely be explained by relationships between abundance and detectability. These are likely to be less clear-cut in real datasets, and future work will explore how additional covariates describing a species detectability could be incorporated. Extending the model to create annual indices, and considering how these may be aggregated, will also be required for the future creation of a habitat quality indicator using NPMS data.

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