4.7 Article

Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea

期刊

ECOLOGICAL INDICATORS
卷 73, 期 -, 页码 574-588

出版社

ELSEVIER
DOI: 10.1016/j.ecolind.2016.10.025

关键词

Benthic macrofauna; Bioturbation; Seasonal variability; Random forest; Species distribution modelling; Mapping

资金

  1. German Federal Ministry for Education and Research (KUNO) [03F0666A]
  2. KUNO-Project MOSSCO [03 F0740B]

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This study aims to estimate, model and map the spatial differences of ecosystem functioning expressed by community bioturbation potential (BPc), an indicator of benthic faunal function based on bioturbation, in the German part of the Baltic Sea. The usefulness of bioturbation potential calculations was justified by its moderate but significant correlation with estimates of bioturbation rates derived from diagenetic models fitted to the in situ measured sediment depth profiles of naturally-occurring chlorophyll-a tracer. Seasonal and interannual variations of BPc were assessed and key species contributing to bioturbation in the study area were identified. To generate the most accurate map of BPc and to investigate its predictability based. on abiotic parameters, we have tested 3 different methodological approaches: i) benthic macrofauna community bioturbation potential was initially calculated per station and treated as response variable for species distribution modelling technique (RandomForest, RF) with relevant available environmental layers used as predictors, ii) 35 key species were selected as the most contributing to BPc (responsible. for 90% of total BPc), their population bioturbation potential (BPp) estimates were used as response variables and RF models were fitted on each of them to predict their full coverage distributions, that were subsequently summed up to the BPc, iii) BPc values at stations were interpolated to a raster surface using a natural neighbour technique. The comparison with observed values of BPc indicated that map derived by natural neighbour interpolation was the most accurate one given the considered resolution of 1 x 1 km. (C) 2016 Elsevier Ltd. All rights reserved.

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