Biological processes in running waters and their implications for the assessment of ecological integrity

Abstract

Although biomonitoring approaches are being increasingly used in the measurement of stream and river health, critical assumptions about the nature of biological populations and communities that underpin them are often ignored. Many approaches based on pattern detection in plant and animal communities assume high temporal persistence in the absence of anthropogenic disturbances. However, this has been rarely tested with long-term data sets and there is evidence that this assumption is not true in some river systems. Biological processes, such as predation and recruitment, can account for considerable spatial and temporal variation in the structure of some stream communities. These processes may prevent the development of robust predictive models or indices based on pattern detection. Measurements of population or community attributes also are often used to infer ecosystem processes, yet the link between pattern and process has rarely been demonstrated. Many goals of river management relate to the maintenance of natural ecological processes and ecosystem function: direct measurement of these processes is, however, often neglected in assessment programs. Such measures are often sensitive to causal faetors that are known to affect river health and it is possible to develop simple but powerful predictive models. Perhaps more importantly, should an impact to be detected, strategies for remediation are more obvious as the causal processes are generally better known. The ultimate success of biomonitoring approaches depends on how well we understand the biophysical processes that influence the structure and dynamics of stream and river systems, and the way they function.

Flow variability and macroinvertebrate community response within riverine systems

“…In addition, the potential ecological importance of climate variability and large scale climatic diagnostic indices, such as the North Atlantic Oscillation (NAO), have been demonstrated in previous studies (Bradley and Ormerod, 2001). However, caution should be exercised when developing models of benthic community variability since the changes observed in abundance, structure and composition do not necessarily imply causality (Bunn and Davies, 2000). The influence of flow variability can be masked by other factors, such as anthropogenic disturbances (for example Englund and Malmqvist, 1996;Bunn and Arthington, 2002;Lytle and Poff, 2004) and the natural heterogeneity of the local-scale physical and biotic environment (for example Karr, 1991;Weigel, et al, 2003).…”

“…Baseline data collected as part of biomonitoring programmes for water quality provides an opportunity to develop methodologies for evaluating the ecological integrity of riverine systems over a range of time scales (Davies, 2000;Wright, 2000). However, attempts to integrate hydrological variability with baseline ecological data have been relatively limited due to the absence of appropriate medium-and long-term hydrological and, to a greater extent, biological data sets for analysis (notable exceptions include: Wood, et al, 2000;Woodward, et al, 2002;Wright, et al, 2004).…”

Effects of dilution stress on the functioning of a saline Mediterranean stream

“…Recently, functional indicators of ecosystem processes (metabolic rates, organic matter decomposition, nutrient processes, etc.) have received more attention and are now considered fundamental to determine the health of stream and river ecosystems (Bunn et al, 1999;Bunn & Davies, 2000;Gessner & Chauvet, 2002;Young et al, 2004;Udy et al, 2006) Ecosystem metabolism, the combination of primary production (gross primary production, GPP) and ecosystem respiration (ER), provides a measure of the amount of organic carbon produced and consumed within the system, respectively. Both rates often show seasonal patterns as a consequence of their dependence on environmental factors (light, temperature, nutrients) but are also highly sensitive to many human-induced environmental stressors making them good stream health indicators (Gessner & Chauvet, 2002;Young et al, 2004).…”

Combining Logistic Models with Multivariate Methods for the Rapid Biological Assessment of Rivers Using Macroinvertebrates

“…But, the fluctuation of the flow regime of the Iberian river systems and environmental harshness is responsible for poorly predictable macroinvertebrate assemblages in this region (Gasith and Resh, 1999;Aguiar et al, 2002). Such a high temporal variability in community structure has also the potential to limit the sensitivity of other biomonitoring approaches, such as AUSRIVAS and RIVPACS systems, as is recognized by Bunn and Davies (2000), besides the mentioned huge effort to build those systems. For these authors, low persistence in benthic community structure makes it extremely difficult to construct robust predictive models, since temporal changes in community composition may be more stochastic and unrelated to inter-annual variation in environmental parameters.…”