Adaptive stacked species distribution modelling: Novel approaches to large scale quantification of blue carbon to support marine management

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Abstract

This study introduces a novel concept of ‘Adaptively Stacked’ Species Distribution Models (AS-SDMs) to predict blue carbon habitat distribution, abundance, carbon stocks, and carbon sequestration potential in Orkney. AS-SDMs are built from Weighted Boosted Regression Trees (WBRTs) that adaptively stack blue carbon sediment thickness, sediment carbon content, and sequestration potential to predicted abundance. A novel method to describe substrate types by relative inputs of mud, sand, and gravel is detailed that better characterises the determining factors of seagrass, maerl, and horse mussel abundance. This study also introduces a novel use of indexes to mitigate double counting issues of mixed species distribution models.

Seagrass, maerl, horse mussel, and mixed seagrass and maerl habitats (SGM) are estimated to cover a maximum area of 657 km2 in Orkney, have a total sediment carbon stock of 16 Mt. C, and sequester 6000 t C yr−1. Applying a conservative threshold of 50 % abundance to habitat predictions, six key potential areas of blue carbon offset projects are identified. These areas cover just over 9 km2, have a total carbon stock of 330,000 t C, and sequester 330 t C yr−1. When applied to UK carbon credit value, assuming integration with regulated markets and compliance with accreditation criteria, the habitats in these areas have a potential value of £24.5 million. If applied as annualised values, these areas have carbon stocks with a potential value of £0.93 million yr−1 and a carbon sequestration potential value of £24,000 yr−1.
Original languageEnglish
Article number174993
JournalScience of the Total Environment
Volume949
Early online date22 Jul 2024
DOIs
Publication statusE-pub ahead of print - 22 Jul 2024

Keywords

  • biodiversity
  • blue carbon
  • climate change
  • policy
  • predictive modelling
  • seagrass

ASJC Scopus subject areas

  • Pollution
  • Waste Management and Disposal
  • Environmental Engineering
  • Environmental Chemistry

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