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      The Ancient Tree Inventory: a summary of the results of a 15 year citizen science project recording ancient, veteran and notable trees across the UK

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          Abstract

          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, with its extensive Royal and aristocratic land ownership, unique land management methods and long-standing interest in natural history and species record collecting. As a result, the UK has collated an extensive database of ancient, veteran and notable trees called the Ancient Tree Inventory (ATI). The ATI is the result of a successful, long-term citizen science recording project and is the most comprehensive database of ancient and other noteworthy trees to date. We present here the first review of the ATI in its entirety since its initiation in 2004, including summaries of the UK ancient, veteran and notable tree distributions, the status and condition of the trees, and key information about the recording process and maintenance of the database. Statistical analysis of components of the dataset, comprising 169,967 tree records, suggest there are significant differences in the threats, size, form and location of different types of trees, especially in relation to taxonomic identity and tree age. Our goal is to highlight the value of the ATI in the UK, to encourage the development of similar ancient tree recording projects in other countries, and to emphasise the importance to conservation of continued efforts to maintain and expand databases of this kind.

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          Modern Applied Statistics with S

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            Spatial filtering to reduce sampling bias can improve the performance of ecological niche models

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              Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

              MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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                Author and article information

                Contributors
                Journal
                Biodiversity and Conservation
                Biodivers Conserv
                Springer Science and Business Media LLC
                0960-3115
                1572-9710
                October 2020
                August 18 2020
                October 2020
                : 29
                : 11-12
                : 3103-3129
                Article
                10.1007/s10531-020-02033-2
                57a9cdf2-e555-48ce-91eb-118d05592706
                © 2020

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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