Search

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 [...]

We investigated variation of fish assemblages in response to environmental factors using Long Term Resource Monitoring Program data. Data were collected from 1993 to 2000 from five physical habitats in the unimpounded upper Mississippi River. We captured 89 species composing 18 families. Of these, 26% were fluvial specialists, 25% were fluvial dependent and 49% were generalists. The numerically dominant component of the adult fish assemblage (species accounting for >10% of total catch) accounted for 50% of the assemblage and was comprised of only three species: gizzard shad (Dorosoma cepedianum: 25%), common carp (Cyprinus carpio; 15%) and channel catfish (Ictalurus punctatus; 10%). The dominant component of the YOY fish assemblage was comprised of only two species, which accounted for 76% of the total catch: freshwater drum (Aplodinotus grunniens; 39%) and gizzard shad (37%). We used a cross-validation multivariate approach to explore how adult and young-of-the-year (YOY) assemblages varied with respect to physical habitat and environmental gradients. Furthermore, we were interested how the fish assemblages changed over time. Partial canonical correspondence analyses (pCCA) demonstrated significant effects of physical habitats. Such effects differed between young-of-the-year and adult fishes. The four main environmental gradients influencing overall assemblage structure for both age groups were river elevation, water velocity, conductivity and depth of gear deployment. Morisita's index revealed similar adult assemblage structure over time. However, the YOY assemblage present in 1995 was dissimilar from assemblages present during the other years. We speculate this is a lag effect from the backwater spawning episodes (floodpulse) that occurred with the 500-y flood in 1993. Shannon-Weiner diversity and Camargo's evenness indices were low, but stable across years for the adult assemblage, but wafted across years for the YOY assemblage.