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    Is the benthic index AMBI impervious to seasonality and data transformations while evaluating the ecological status of an anthropized monsoonal estuary?

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    Ocean_Coas_Manag_186_105080a.pdf (693.9Kb)
    Ocean_Coas_Manag_186_105080_Appendix_A.pdf.doc (58Kb)
    Ocean_Coas_Manag_186_105080_Appendix_B.pdf.xls (37.5Kb)
    Ocean_Coas_Manag_186_105080_Appendix_C.pdf.doc (68Kb)
    Date
    2020
    Author
    Mulik, J.
    Sukumaran, S.
    Dias, H.Q.
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    Abstract
    Benthic indices are a potential measure of a system's ecological quality status. It is vital for an efficient index to distinguish the anthropogenic effects from the natural stress associated with seasonality, especially in complex ecosystems such as the tropical monsoonal estuaries. AZTI's Marine Biotic Index (AMBI), one of the most successful indices, relies on the distribution of soft-bottom macrobenthic communities into five different ecological groups (EGs) according to their sensitivity to stress. The AMBI was employed in the anthropized Ulhas estuary (India) to test its vulnerability to seasonality. Also it was endeavored in this study to test whether data transformations enhanced the efficiency of AMBI. AMBI values obtained during monsoon failed to distinguish between differently impacted stations in the major stretch of the estuary due to the uniformly dominant occurrence of Namalycastis ouanaryensis, an EG IV species with affinity for low salinity. The AMBI successfully evaluated the status of the estuary during the non-monsoonal seasons whereas it failed to perform well during the monsoon as it either underestimated or overestimated the ecological quality status of >55% of the stations. The results obtained by averaging the AMBI scores of the non-monsoonal seasons gave an accurate ecological status of the estuary. A good level of agreement between all the applied transformations implied that the data transformations did not improve the AMBI efficiency. Conversely, AMBI based on untransformed data indicated a stronger correlation with the contamination proxies than those derived from various data transformations.
    URI
    https://doi.org/10.1016/j.ocecoaman.2019.105080
    http://drs.nio.org/drs/handle/2264/8319
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