Linking scales of flow variability to lotic ecosystem structure and function

Abstract

Obtaining a better knowledge of how flow variability affects lotic biota is of considerable importance to stream and river management. We contend that processes at different hierarchical levels of organization in lotic ecosystems are sensitive to variation in flow at related hierarchical temporal scales. Ecosystem disturbance caused by large-scale events (i.e. infrequent, but high magnitude flow events with a recurrence interval of years to many days) tend to determine high-level characteristics of ecosystem structure (e.g. determining species pools, periphyton versus macrophyte dominance) and function (e.g. balance between auto-and heterotrophy). The high-level ecosystem characteristics then set the stage for processes that are influenced by flow variation that occurs at smaller temporal scale (i.e. minutes to milliseconds) such as colonization, biotic interactions and mass transfer enhancement of production. We contend that large-scale temporal events predominantly affect lotic ecosystems through physical drag processes ('drag-disturbance'), whereas small-scale flow variations affect ecosystems through mass-transfer processes (including invertebrate and fish food-uptake). Drag-disturbance and mass-transfer related processes mark the opposite ends of a continuum of flow variability controlled processes, with moderate temporal scale flow variability events affecting ecosystems through both drag-disturbance and mass-transfer processes in similar proportions. Flow variability, and associated effects on ecosystems, across these scales is discussed with reference to New Zealand rivers. We suggest that these concepts can be integrated across the full range of temporal scales based on a spectrum of velocity variations. This may provide a unifying conceptual model for how the structure and functioning of lotic ecosystems are linked to flow variability.

Response of algal metrics to nutrients and physical factors and identification of nutrient thresholds in agricultural streams

“…Previous studies that have tried to relate chlorophyll or AFDW with TN or TP have often determined poor relationships (Munn et al 1989;Kjeldsen 1994;Dodds et al 2002;Porter et al 2008; but see Biggs and Close 1989;Lohman et al 1992). Although these two metrics often are examined as part of biomonitoring efforts focusing on nutrient enrichment, a number of studies have determined that these biomass estimates may be more related to hydrologic stability, water clarity, light availability, and the abundance of grazers rather than nutrients (Rosemond et al 1993;Poff and Ward 1995;Riseng et al 2004;Biggs et al 2005). Algal biomass and nutrient relationships may be positive when existing algal biomass is low and negative when biomass is high (Newbold et al 1982;Mulholland 1996).…”

Animal perception in gravel-bed rivers: scales of sensing and environmental controls on sensory information

“…It is also known that turbulent flow characteristics have little relationship to averaged measures, demonstrating that correlations between animal distributions and averaged flow parameters are unlikely to be causal, but more likely to reflect correlation between the flow and other environmental parameters. It is therefore important to relate animal distributions and activity to finer scale flow and roughness measurements in hydraulically complex environments (Hart et al 1996;Biggs et al 2005;Wilkes et al 2013). Although there is increasing research focused on the distribution and activity of animals in turbulent hydrodynamic environments, these studies rarely consider how an animal perceives the flow or what it is specifically capable of perceiving, both of which would improve experimental design and, in turn, understanding of hydraulic habitat selection.…”

“…Whilst this is a daunting challenge, incorporating information from differing scales is a commonly encountered issue in ecological and geomorphic research (e.g. Biggs et al 2005;Rice et al 2010) and there have been many successful attempts in other environments to relate smallscale sensory information to models of entire animal populations (i.e. Madliger 2012 and references therein).…”

Incorporating student-centered approaches into catchment hydrology teaching: a review and synthesis

“…Signatures are outward manifestations of the internal catchment dynamics. The nature of the signatures changes with temporal and spatial scales at which they are observed or analyzed (Atkinson et al, 2003;Biggs et al, 2005;Farmer et al, 2003;Kirchner et al, 2004;Thoms and Parsons, 2003). Examples of signatures include, but are not limited to, those characterizing inter-annual variability (e.g., runoff coefficient, baseflow index), mean within-year variability (regime curve), random variability of daily flows within the year (i.e., the flow duration curve), the recession curve and the flood frequency curve.…”