Dambly, Lea Irene; (2023) Improving our understanding and use of citizen science data to model spatio-temporal trends of British bats. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Successful biodiversity conservation relies on large quantities of data, which can be used to evaluate and track the extent of biodiversity change. In the United Kingdom (UK), bats are one of the most important contributors to mammalian biodiversity. They are systematically monitored through a citizen science monitoring programme run by the Bat Conservation Trust (BCT), the National Bat Monitoring Programme (NBMP), which produces abundance trends for nine native bat species. The NBMP analysis is complicated by the use of multiple survey methods, which raises several methodological challenges. This is expected to be further complicated by newly emerging data collection streams. In this thesis, I address issues arising from the developing data landscape of bat monitoring in the UK using a variety of modelling techniques. The overall aim is to improve the understanding of the available data and explore the best approaches to modelling trends in British bat populations. I introduce the concept of biodiversity monitoring in Chapter 1, review modelling methods and present information about British bats. In Chapter 2, I use a simulation approach to assess the effect of biases on the ability to correctly detect abundance trends arising from NBMP roost count data. I find that synergistic effects of site selection, population dynamics, and observer behaviour can lead to substantially biased trend estimates. In Chapter 3, I tackle issues associated with model specification in a Bayesian method known as Integrated Nested Laplace Approximation (INLA). Using two datasets to model the spatial distribution of the serotine bat, Eptesicus serotinus, I show how spatial predictions and covariate effects change depending on the user-defined parameterisation of terms designed to address spatial autocorrelation. In Chapter 4, I use a Bayesian occupancy-detection framework to assess the statistical properties of models integrating multiple datasets with different survey protocols, including a previously unexploited dataset originating from car-mounted passive acoustic sensors. My results highlight where individual datasets can benefit from data integration to improve occupancy trend estimates. In Chapter 5, I apply one of the models from Chapter 4 to explore how Artificial Light at Night (ALAN) influences bat occupancy trends of light opportunistic bats (i.e., bats that utilise artificial light for foraging opportunities) from the same guild. I find that trends differ between species and areas of ALAN concentration, which shows that these species do not respond uniformly to ALAN. Finally, in Chapter 6 I summarise the overall findings of this thesis and discuss their implications for the future of data integration and citizen science.

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