Investigating the effect of survey designs on urban forest population estimates: A simulation based approach using Bayesian spatial models

Abstract

Field-based survey methods are a commonly used ecological approach for developing a greater understanding of the benefits of urban forests. Such surveys often aim to collect information on trees contained in several pre-specified plot locations. However, uncertainty exists on the total minimum number of survey plots required to effectively quantify urban forest benefits. By optimising the number of survey plots we ensure that surveys of urban forests in UK towns and cities can be carried out as quickly and cheaply as possible. In this thesis we propose a simulation-based approach for exploring the optimal number of survey plots required for urban forest surveys. Our approach uses state of the art Bayesian spatial modelling to account for the spatial nature of the survey data and characteristics of the city such as many features of the prevailing landscape and their spatial properties. We illustrate our models using bespoke code written in the STAN software language, which allows for modelling of spatially dependent data. Simulations from our models are then used to explore a variety of different survey plot designs, by considering the efficacy of the survey plot design in estimating total tree populations. We illustrate our methods using both survey data and tree locations derived from areal photography. Using the proposed simulation methodology, we obtain robust results and compare those with similar results reported by other authors using non-model based methods. Assessment of population errors from the simulations, highlighted the need for more survey plots in areas with higher variation in the rate of trees. Relative population errors simulated under a range of different conditions have been produced, with conclusions on the minimum number of survey plots required dependent on which simulation conditions are deemed most appropriate. Generally, the accuracy in tree population estimates increased at a lower rate after 200 survey plots, suggesting further plots may not provide much additional information, however this is subject to personal interpretation of an acceptable level of estimation accuracy. Stratified survey designs are found to have little impact on the accuracy of urban forest population estimates in our research, however are likely to result in more representative surveys.

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Identifiers

ORCID for Philip Wells: orcid.org/0000-0002-0584-0899

ORCID for Sujit Sahu: orcid.org/0000-0003-2315-3598

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