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Predicting the Distribution of Ancient and Other Noteworthy Trees Across the UK
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Predicting the Distribution of Ancient and Other Noteworthy Trees Across the UK
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Predicting the Distribution of Ancient and Other Noteworthy Trees Across the UK
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Dissertation
Predicting the Distribution of Ancient and Other Noteworthy Trees Across the UK
2021
Overview
Ancient, veteran and notable trees are ecologically important keystone organisms and have tangible connections to folklore, history and sociocultural practices. Although found worldwide, few countries have such a rich history of recording and treasuring these trees as the UK, which has resulted in the formation over the past 15 years of a large, comprehensive database of ancient and other noteworthy trees, the Ancient Tree Inventory (ATI). Although the ATI contains over 200,000 recorded trees, there are still thought to be many more that are undiscovered across the UK, and information about their status, condition and distribution is lacking. The primary aim of this thesis is to use the ATI to gain novel and detailed insights into the true distribution of ancient and veteran trees across the UK, important predictors of their presence, and key habitat types in which they are found. The ATI suffers many of the problems of large species databases, including sampling bias, which is a major focus of this thesis. To address this problem, sampling bias is first identified and quantified, and then established and novel bias correction methods are employed to improve predictions of ancient and veteran tree distributions. By combining mathematical models at various scales, from specific habitats to the whole of England, with additional independent data from desk and field surveys, robust accurate distribution maps of ancient and other noteworthy trees are produced and verified. The models suggest that woodpasture is a particularly important habitat for ancient and veteran trees, and that their distributions are highly influenced by historical features of the environment and human factors. A key result emerging from multiple chapters of this thesis is the potentially large number of undiscovered ancient and veteran trees predicted across England: diverse alternative models produced similar and impressive total estimates of around two million trees. These results can be used to inform the conservation and protection of ancient trees, and highlight the need for more targeted surveying, tree planting and implementation of policy measures to ensure their persistence and survival into the future.
