Spatial and Temporal Patterns in Macrofaunal Diversity Components Relative to Sea Floor Landscape Structure

Abstract

We examined temporal changes in macrofaunal α- and β-diversity over several spatial scales (within patches, among patches, across landscapes and across regions) in Long Island Sound on the northeast USA coast. Regional ε-diversity was estimated at 144 taxa, however γ-diversity fluctuated over time as did α- and β-diversity components. Based on additive partitioning, patch- and region-scale β-diversity components generally had the highest contributions to γ-diversity; lower percentages were found at within-patch and landscape scales. Multiplicative diversity partitioning indicated highest species turnover at within- and among patch scales. For all partition results, within-patch and patch-scale β-diversity increased sharply when hypoxia impacted benthic communities. Spatial variation in diversity components can be attributed to the collection of different patch types at varying spatial scales and their associated habitats across the benthic landscapes, as well as gradients in depth and other estuarine-scale characteristics. Temporal variation in diversity components across spatial scales may be related to seasonal changes in habitat heterogeneity, species population dynamics, and seasonal disturbances. Rare species were significant and temporally consistent components of macrofaunal diversity patterns over different spatial scales. Our findings agree with other marine and terrestrial studies that show diversity components vary significantly over different spatial scales and the importance of habitat/landscape heterogeneity in supporting diversity. However, our results indicate that the relative contributions of scale-specific β-diversity components can also change significantly over time. Thus, studies of diversity patterns across patches and landscapes based on data collected at one time, or assembled into a single data set from different times, may not capture the full suite of diversity patterns that occur over varying spatial scales and any time-specific determinants of those patterns. Many factors that shape and maintain sedimentary communities vary temporally, and appear to play an important role in determining and maintaining macrofaunal diversity over different spatial scales.

Common trends in German Bight benthic macrofaunal communities: Assessing temporal variability and the relative importance of environmental variables

“…DFA is a multivariate time-series analysis technique to estimate common trends, to study the interactions between response variables and to determine the effects of explanatory variables in a time series data set (Zuur and Pierce, 2004;Zuur et al, 2003a). The underlying DFA model is given by (Zuur and Pierce, 2004):…”

“…(2) translates into Eq. (3) (Kisekka et al, 2013;Kuo et al, 2011;Ritter and Muñoz-Carpena, 2006;Zuur and Pierce, 2004;Zuur et al, 2007):…”

“…C n is a constant level parameter as in linear regression model which increases or decreases the linear combination of common trends (Kuo and Lin, 2010). If the time series are standardized, the constant parameters are 0 (Zuur and Pierce, 2004).…”

“…Benthic communities may fluctuate over time because of characteristics of the species' life cycles and/or in response to environmental variability (Convey, 1996;Sibly and Calow, 1989). In this context, assessing temporal patterns of benthic community development and their underlying drivers is critical for understanding the ecology of diverse marine ecosystems (Robinson and Sandgren, 1983;Zajac et al, 2013). In fact, understanding patterns of change in benthic fauna through the monitoring of communities (e.g., community structure and composition, species richness) might allow for separating effects of climate variability and anthropogenic disturbance on diversity and the functioning of the marine benthic ecosystem (Munari, 2011).…”

“…It also quantifies the canonical correlation between temporal trends and macrofaunal abundance time series (Nye et al, 2010). DFA is used to identify underlying common trends among multivariate time series while taking the effects of explanatory variables into account (Kuo and Lin, 2010;Zuur and Pierce, 2004). Here, we focus on a 30 year time series of benthic macrofaunal abundance and environmental variables in the German Bight.…”