| Autor: |
White, Matthew D.1 (AUTHOR) matt.white@delwp.vic.gov.au, Hollings, Tracey1 (AUTHOR), Sinclair, Steve J.1 (AUTHOR), Williams, Kristen J.2 (AUTHOR), Dickson, Fiona3 (AUTHOR), Brenton, Peter4 (AUTHOR), Raisbeck‐Brown, Nat5 (AUTHOR), Warnick, Amy6 (AUTHOR), Lyon, Peter3 (AUTHOR), Mokany, Karel2 (AUTHOR), Liu, Canran1 (AUTHOR), Pirzl, Rebecca2 (AUTHOR) |
| Předmět: |
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| Zdroj: |
Ecological Applications. Jan2023, Vol. 33 Issue 1, p1-13. 13p. |
| Abstrakt: |
A cost‐effective way of undertaking comprehensive, continental‐scale, assessments of ecological condition is needed to support large‐scale conservation planning, monitoring, reporting, and decision‐making. Currently, cross‐jurisdictional inconsistency in assessment methods limits the capacity to scale‐up monitoring. Here we present a novel way to build a coherent continent‐wide site‐level ecological condition dataset, using cross‐calibration methods to integrate assessments from many observers. We focus on the use of condition assessments from individual expert observers, a currently untapped resource. Our approach has two components: (1) a simple online tool that captures expert assessments at specific locations; (2) a process of calibrating and rescaling disparate expert evaluations that can be applied to the data to provide a consistent dataset for use in conservation assessments. We describe a pilot study, involving 28 experts, who contributed 314 individual site condition assessments across a wide range of ecosystems and regions throughout continental Australia. A correction factor for each expert was used to rescale the contributed site condition assessment scores, based on a set of 77 photographic images, each scored for their condition by multiple experts, using a linear mixed model. Our approach shows strong promise for delivering the volumes of data required to develop continental‐scale reference libraries of site condition assessments. Although developed from expert elicitation, the approach could also be used to harmonize the collation of existing condition datasets. The process we demonstrate can also facilitate online citizen scientists to make site condition assessments that can be cross‐calibrated using contributed images. [ABSTRACT FROM AUTHOR] |
| Databáze: |
GreenFILE |
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