4.7 Article

Optimising a widely-used coastal health index through quantitative ecological group classifications and associated thresholds

期刊

ECOLOGICAL INDICATORS
卷 69, 期 -, 页码 595-605

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ecolind.2016.04.003

关键词

Biotic index; Macroinvertebrate; AMBI; Shallow estuary; Organic enrichment; Eutrophication; Sedimentation

资金

  1. Wriggle Coastal Management (Nelson, New Zealand)
  2. Waikato Regional Council
  3. Greater Wellington Regional Council
  4. Environment Southland
  5. Todd Foundation (Award for Excellence (Universities), New Zealand)
  6. University of Otago (Doctoral scholarship)

向作者/读者索取更多资源

Many globally applied biotic indices, including the AMBI benthic index, are based on species' sensitivity/tolerance to anthropogenic disturbances. The AMBI scoring primarily relies on the correct assignment of both taxon stressor-sensitivities and the disturbance thresholds or bands. Using an extensive, long-term monitoring dataset from New Zealand (NZ) estuaries, we describe how the AMBI has been strengthened through quantitative derivation of taxon-specific sensitivities and condition thresholds for two key estuarine stressors [mud and total organic carbon (TOC)], and the integration of taxon richness. The results support the use of the existing AMBI condition bands but improve the ability to identify cause; 2-30% mud reflected a 'normal' to 'impoverished' macrofaunal community; 30-95% mud and 1.2-3% TOC 'unbalanced' to 'transitional'; and >3-4% TOC 'transitional' to 'polluted'. The (refined) AMBI was also successfully validated (R-2 values >0.5 for mud, and >0.4 for TOC) for use in shallow, intertidal dominated estuaries NZ-wide. Most biotic indices lack the ability to differentiate between anthropogenic disturbances, which in turn undermine their effectiveness for applied purposes. By integrating key quantitative information to an existing benthic index, these results enable more robust identification of coastal stressors and facilitate defensible management decisions. (C) 2016 Elsevier Ltd. All rights reserved.

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