|Spatial and temporal variation in benthic macrofauna and relationships with environmental variables in an estuarine, intertidal soft-sediment environment|Ysebaert, T.; Herman, P.M.J. (2002). Spatial and temporal variation in benthic macrofauna and relationships with environmental variables in an estuarine, intertidal soft-sediment environment. Mar. Ecol. Prog. Ser. 244: 105-124. hdl.handle.net/10.3354/meps244105
In: Marine Ecology Progress Series. Inter-Research: Oldendorf/Luhe. ISSN 0171-8630, more
Abundance; Benthos; Biomass; Chlorophylls; Community composition; Ecological distribution; Salinity data; Sediment analysis; Spatial variations; Temporal variations; ANE, Netherlands, Westerschelde [Marine Regions]; Marine
Spatial and temporal scale · Hierarchical ANOVA · Variance components · Multivariate analysis · Variation partitioning · Intertidal soft-sediment communities · Schelde estuary
We quantified the distribution, abundance, biomass and assemblage structure of benthic macrofauna at different spatio-temporal scales in a temperate estuarine, intertidal soft-sediment environment in the Schelde estuary, The Netherlands. Hierarchically scaled surveys were conducted yearly between 1994 and 2000, covering 4 spatial scales: region (104 m), transect (103 m), station (102 m), and replicate samples (10-1 m). Our approach provided a powerful framework for quantifying the proportion of the variation among samples that was attributable to each spatial or temporal scale, and we explicitly aimed at identifying the role of environmental variables in explaining the observed variability.Variance components calculated for 11 dominant macrobenthic species revealed that variations at the scale of stations and year × station interactions were the most important components of variability. Regional and transect differences were only apparent for a few species, although multivariate analysis revealed clear inter-regional differences for the macrobenthic assemblage structure. Only a few species displayed significant variability associated with the factor year solely. Both spatial and temporal components explained >70% of the total variance. A substantial part of the total variation in abundance of individual species was explained by the observed environmental variables (27 to 56%). Multiple regression with subdivision of the environmental variables into a long-term average and a temporal component showed that the long-term averages were much more important than the short-term deviations from this average, with local environmental variables (mud content, chlorophyll a, bed level height) explaining the largest part of the observed variation. For a few species and total biomass, salinity also explained a large part of the observed variation. Canonical correspondence analysis (CCA) with forward selection revealed that salinity and mud content, and to a lesser extent chlorophyll a and bed-level height, accounted for most of the variance in the macrobenthic species data. Partial CCA indicated that of the variation in the species data a large part was spatially structured (56.7%), with about half of this variation being explained by the environmental variables used. Only 4.1% of the species variation was temporally structured. Our results have important consequences for both the interpretation of monitoring programmes and the enhancement of sampling designs.