Anthropogenic environmental changes, or ‘stressors’, increasingly threaten biodiversity and ecosystem functioning worldwide. Multiple-stressor research is a rapidly expanding field of science that seeks to understand and ultimately predict the interactions between stressors. Reviews and meta-analyses of the primary scientific literature have largely been specific to either freshwater, marine or terrestrial ecology, or ecotoxicology. In this cross-disciplinary study, we review the state of knowledge within and among these disciplines to highlight commonality and division in multiple-stressor research. Our review goes beyond a description of previous research by using quantitative bibliometric analysis to identify the division between disciplines and link previously disconnected research communities. Towards a unified research framework, we discuss the shared goal of increased realism through both ecological and temporal complexity, with the overarching aim of improving predictive power. In a rapidly changing world, advancing our understanding of the cumulative ecological impacts of multiple stressors is critical for biodiversity conservation and ecosystem management. Identifying and overcoming the barriers to interdisciplinary knowledge exchange is necessary in rising to this challenge. Division between ecosystem types and disciplines is largely a human creation. Species and stressors cross these borders and so should the scientists who study them.

• This is the pre-peer reviewed version of the article, which has been published in final form at: http://dx.doi.org/10.1002/rra.933

Biodiversity metrics are critical for assessment and monitoring of ecosystems threatened by anthropogenic stressors. Existing sorting and identification methods are too expensive and labour-intensive to be scaled up to meet management needs. Alternately, a high-throughput DNA sequencing approach could be used to determine biodiversity metrics from bulk environmental samples collected as part of a large-scale biomonitoring program. Here we show that both morphological and DNA sequence-based analyses are suitable for recovery of individual taxonomic richness, estimation of proportional abundance, and calculation of biodiversity metrics using a set of 24 benthic samples collected in the Peace-Athabasca Delta region of Canada. The high-throughput sequencing approach was able to recover all metrics with a higher degree of taxonomic resolution than morphological analysis. The reduced cost and increased capacity of DNA sequence-based approaches will finally allow environmental monitoring programs to operate at the geographical and temporal scale required by industrial and regulatory end-users.

Spatio‐temporal variability in river flow is a fundamental control on instream habitat structure and riverine ecosystem biodiversity and integrity. However, long‐term riverine ecological time‐series to test hypotheses about hydrology–ecology interactions in a broader temporal context are rare, and studies spanning multiple rivers are often limited in their temporal coverage to less than five years. To address this research gap, a unique spatio‐temporal hydroecological analysis was conducted of long‐term instream ecological responses (1990–2000) to river flow regime variability at 83 sites across England and Wales. The results demonstrate clear hydroecological associations at the national scale (all data). In addition, significant differences in ecological response are recorded between three ‘regions’ identified (RM1–3*) associated with characteristics of the flow regime. The effect of two major supra‐seasonal droughts (1990–1992 and 1996–1997) on inter‐annual (IA) variability of the LIFE scores is evident with both events showing a gradual decline before and recovery of LIFE scores after the low flow period. The instream community response to high magnitude flow regimes (1994 and 1995) is also apparent, although these associations are less striking. The results demonstrate classification of rivers into flow regime regions offers a way to help unravel complex hydroecological associations. The approach adopted herein could easily be adapted for other geographical locations, where datasets are available. Such work is imperative to understand flow regime–ecology interactions in a longer term, wider spatial context and so assess future hydroecological responses to climate change and anthropogenic modification of riverine ecosystems. Copyright © 2008 John Wiley & Sons, Ltd.