The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this study, we used an emerging, photogrammetry-based technique to simulate a pilot study and test the efficiency of different sampling schemes (Simple Random—SRS-, Systematic—SyS- and Strip—SS-) for estimating the abundances of megabenthic taxa. For taxa showing an aggregated distribution, we also applied an adaptive cluster sampling (ACS) design. In almost the totality of cases, the best accuracy of estimates was achieved with SyS combined with plots of 0.0625 m2. ACS design gave better performances but required a calibration of both the initial sample size and the threshold value to increase efficiency. The ‘one-size-fits-all’ 1 m2 plot size never emerged as the best in any sampling schemes, hence the previously published literature data can be biased. This study represents a fine-scale reference baseline for the study area and the simulations performed will be pivotal in establishing sound-monitoring programmes with sufficient statistical power to detect significative changes in the Antarctic benthos.

Towards a robust baseline for long-term monitoring of Antarctic coastal benthos

Paola Piazza;Alice Guzzi;Stefano Schiaparelli
2020

Abstract

The Southern Ocean represents one of the world regions most sensitive to warming and there is an urgent need for quantitative data to understand changes in coastal communities. This goal can be achieved through the establishment of permanent monitoring sites and robust sampling designs. In this study, we used an emerging, photogrammetry-based technique to simulate a pilot study and test the efficiency of different sampling schemes (Simple Random—SRS-, Systematic—SyS- and Strip—SS-) for estimating the abundances of megabenthic taxa. For taxa showing an aggregated distribution, we also applied an adaptive cluster sampling (ACS) design. In almost the totality of cases, the best accuracy of estimates was achieved with SyS combined with plots of 0.0625 m2. ACS design gave better performances but required a calibration of both the initial sample size and the threshold value to increase efficiency. The ‘one-size-fits-all’ 1 m2 plot size never emerged as the best in any sampling schemes, hence the previously published literature data can be biased. This study represents a fine-scale reference baseline for the study area and the simulations performed will be pivotal in establishing sound-monitoring programmes with sufficient statistical power to detect significative changes in the Antarctic benthos.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1031343
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