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Fish community assessments are often based on sampling with multiple gear types. However, multivariate methods used to assess fish community structure and composition are sensitive to differences in the relative scale of indices or measures of abundance produced by different sampling methods. This makes combining data from different sampling gears and methods a serious challenge. We developed a method of combining catch per unit effort data that standardizes catch per unit effort data across gear types, which we call multigear mean standardization (MGMS). We evaluated how well MGMS and other types of standardization reflect underlying community structure through a computer simulation that generated model riverine-fish communities and simulated sampling data for two gears. In these simulations, combining sampling observations from two gears with MGMS produced community structure estimates that were highly correlated with true community structure under a variety of conditions that are common in large rivers. Our simulation results indicate that the use of MGMS to combine data from different sampling gears is an effective data manipulation method for the analysis of fish community structure. Les evaluations de communautes de poissons reposent souvent sur un echantillonnage fait avec differents types d'engins. Les methodes multivariees utilisees pour evaluer la structure et la composition de communautes de poissons sont toutefois sensibles aux differences d'echelle relative des indices ou mesures d'abondance produits par differentes methodes d'echantillonnage, de sorte que le groupement de donnees issues de differents engins et methodes d'echantillonnage constitue un important defi. Nous avons mis au point une methode pour combiner des donnees de prises par unite d'effort qui normalise les donnees de prises par unite d'effort issues de differents types d'engins, methode que nous appelons la normalisation moyenne multiengins (NMME). Nous avons evalue la mesure dans laquelle la NMME et d'autres approches de normalisation refletent la structure sous-jacente des communautes en utilisant des simulations par ordinateur qui ont genere des communautes modeles de poissons de riviere et simule des donnees d'echantillonnage pour deux engins. Dans ces simulations, le groupement d'observations d'echantillonnage issues de deux engins a l'aide de la NMME a produit des estimations de la structure des communautes fortement correlees a la structure reelle des communautes dans differentes conditions couramment observees dans les grandes rivieres. Les resultats des simulations indiquent que l'utilisation de la NMME pour combiner des donnees issues de differents engins est une methode de manipulation de donnees efficace pour l'analyse de la structure des communautes de poissons. [Traduit par la Redaction], Introduction For the vast majority of aquatic ecosystems, no single sampling method is adequate for assessing all species and life stages of fishes (Weaver et al. 1993; Willis and Murphy [...]

Large rivers are susceptible to anthropogenic alteration, which can result in drastic changes to their functional ecology. We evaluated spatial–temporal changes in the functional fish communities of the Upper Mississippi River System (UMRS) using data from six study reaches. Species were classified into one of 14 feeding guilds and mass per unit effort (MPUE) was then calculated for each feeding guild annually per gear type. MPUE was standardized using the multigear mean standardization method (MGMS) and log‐transformed. Both ANOSIM and Chi‐square tests were used to determine differences in MPUE among reaches. We then estimated functional diversity by calculating the number of functional groups (N), Margalef's d, Pielou's J′, Shannon's Diversity, and Simpson's Diversity Index. An AR(1) time series model was used to investigate proportional changes in each guild over 25 years. To evaluate the effect of invasive Carp species in invaded reaches, a Chow test was applied to observations between 2000 and 2005. Analyses revealed differences in the functional fish community among reaches. We found differences in functional diversity metrics among study reaches, but there was little evidence that this differed between invaded and non‐invaded reaches. Results determined that invertivore/detritivores have been consistently declining system‐wide, with few groups showing a net change. There was also little evidence that invasion altered the proportion of any functional guild. Evaluating the spatial–temporal patterns of functional communities is beneficial to understanding the resilience of a system and can provide further insight into its trophic needs when considering future restoration initiatives. [ABSTRACT FROM AUTHOR]

Understanding trends in the diverse resources provided by large rivers will help balance tradeoffs among stakeholders and inform strategies to mitigate the effects of landscape scale stressors such as climate change and invasive species. Absent a cohesive coordinated effort to assess trends in important large river resources, a logical starting point is to assess our ability to draw inferences from existing efforts. In this paper, we use a common analytical framework to analyze data from five disparate fish monitoring programs to better understand the nature of spatial and temporal trends in large river fish assemblages. We evaluated data from programs that monitor fishes in the Colorado, Columbia, Illinois, Mississippi, and Tallapoosa rivers using non-metric dimensional scaling ordinations and associated tests to evaluate trends in fish assemblage structure and native fish biodiversity. Our results indicate that fish assemblages exhibited significant spatial and temporal trends in all five of the rivers. We also document native species diversity trends that were variable within and between rivers and generally more evident in rivers with higher species richness and programs of longer duration. We discuss shared and basin-specific landscape level stressors. Having a basic understanding of the nature and extent of trends in fish assemblages is a necessary first step towards understanding factors affecting biodiversity and fisheries in large rivers. [ABSTRACT FROM AUTHOR]