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Distribution models calibrated with independent field data predict two million ancient and veteran trees in England

Nolan, Victoria; Gilbert, Francis; Reed, Tom; Reader, Tom

Authors

Victoria Nolan

Tom Reed

TOM READER tom.reader@nottingham.ac.uk
Associate Professor



Abstract

Large, citizen-science species databases are powerful resources for predictive species distribution modeling (SDM), yet they are often subject to sampling bias. Many methods have been proposed to correct for this, but there exists little consensus as to which is most effective, not least because the true value of model predictions is hard to evaluate without extensive independent field sampling. We present here a nationwide, independent field validation of distribution models of ancient and veteran trees, a group of organisms of high conservation importance, built using a large and internationally unique citizen-science database: the Ancient Tree Inventory (ATI). This validation exercise presents an opportunity to test the performance of different methods of correcting for sampling bias, in the search for the best possible prediction of ancient and veteran tree distributions in England. We fitted a variety of distribution models of ancient and veteran tree records in England in relation to environmental predictors and applied different bias correction methods, including spatial filtering, background manipulation, the use of bias files, and, finally, zero-inflated (ZI) regression models, a new method with great potential to investigate and remove sampling bias in species data. We then collected new independent field data through systematic surveys of 52 randomly selected 1-km2 grid squares across England to obtain abundance estimates of ancient and veteran trees. Calibration of the distribution models against the field data suggests that there are around eight to 10 times as many ancient and veteran trees present in England than the records currently suggest, with estimates ranging from 1.7 to 2.1 million trees compared to the 200,000 currently recorded in the ATI. The most successful bias correction method was systematic sampling of occurrence records, although the ZI models also performed well, significantly predicting field observations and highlighting both likely causes of undersampling and areas of the country in which many unrecorded trees are likely to be found. Our findings provide the first robust nationwide estimate of ancient and veteran tree abundance and demonstrate the enormous potential for distribution modeling based on citizen-science data combined with independent field validation to inform conservation planning.

Citation

Nolan, V., Gilbert, F., Reed, T., & Reader, T. (2022). Distribution models calibrated with independent field data predict two million ancient and veteran trees in England. Ecological Applications, 32(8), Article e2695. https://doi.org/10.1002/eap.2695

Journal Article Type Article
Acceptance Date Mar 31, 2022
Online Publication Date Aug 9, 2022
Publication Date 2022-12
Deposit Date Jul 22, 2022
Publicly Available Date Aug 9, 2022
Journal Ecological Applications
Print ISSN 1051-0761
Electronic ISSN 1939-5582
Publisher Ecological Society of America
Peer Reviewed Peer Reviewed
Volume 32
Issue 8
Article Number e2695
DOI https://doi.org/10.1002/eap.2695
Keywords ancient trees, bias correction, conservation, sampling bias, species distribution modeling, veteran trees, zeroÔÇÉinflated
Public URL https://nottingham-repository.worktribe.com/output/9092493
Publisher URL https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2695

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