Another Consideration of the Morphoedaphic Index

Youngs, William D.; Heimbuch, Douglas G.

doi:10.1577/1548-8659(1982)111<151:acotmi>2.0.co;2

Cited by 46 publications

(29 citation statements)

References 6 publications

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“…To limit the probability of falsely rejecting null hypotheses, we used the sequential Bonferroni adjustment (Rice 1989) with a tablewide significance level (␣) of 0.05 to evaluate correlations. We expected to observe positive correlations for all species between lake-record weights and log surface area (Jenkins and Morais 1971;Hanson and Leggett 1982;Youngs and Heimbuch 1982) and growing-season length (Jenkins and Morais 1971;Schlesinger and Regier 1982). We used one-tailed binomial tests (Siegel 1956) to determine, regardless of the statistical significance of individual tests, whether there was an excess proportion of positive correlations between the lake-record weight of fish and log surface area and growing-season length.…”

Section: Methodsmentioning

confidence: 99%

Relationship between Lake‐Record Weights of Fishes and Reservoir Area and Growing Season

Wilde

Pope

2004

N American J Fish Manag

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We used information from an angler recognition program to assess the relationship between lake‐record weights of freshwater fishes captured by Texas anglers and two predictors of fish production and growth: reservoir surface area and growing‐season length. The lake‐record weights of two species, largemouth bass Micropterus salmoides and flathead catfish Pylodictis olivaris, were directly related to the logarithm of surface area. The record weights of all species studied were unrelated to growing‐season length. Regardless of the statistical significance of individual correlations, there was a significant excess of positive correlations across all species between lake‐record weights of fish and log surface area. This indicates the presence of a general relationship between the record weights of fish and reservoir area. Our results suggest that record weights of fish may be constrained by reservoir surface area.

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Section: Methodsmentioning

confidence: 99%

Relationship between Lake‐Record Weights of Fishes and Reservoir Area and Growing Season

Wilde

Pope

2004

N American J Fish Manag

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“…Reasonable estimates of yield for Lakes Michigan, Huron, and Winnipeg were obtained from the surface area model of Youngs and Heimbuch (1982). Lake Superior and Lake Ontario yields are overestimated probably because of the large deep areas of unproductive water in those lakes.…”

Section: Tdsmentioning

confidence: 99%

A Review of Methods for Prediction of Potential Fish Production with Application to the Great Lakes and Lake Winnipeg

Leach¹,

Dickie²,

Shuter³

et al. 1987

Can. J. Fish. Aquat. Sci.

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Can. J. Fish. Aquat. Sci. M(Suppl. 2 ) : 471485.Methods for estimating fish production in aquatic ecosystems range from simple empirically derived estimators, such as morphoedaphic indices, to complex ecosystem simulation models. As first-order estimators, the former are attractive to managers because they are simple and relatively inexpensive to apply and interpret. Application of the latter group has been limited because many of the data inputs are difficuit and expensive to obtain. Between these extremes are several models, such as the biomasssize spectrum model, that provide useful information for moderate expenditures of time and effort. Existing and new methods are reviewed in the light of production theory and several are applied to Great Lakes and Lake Winnipeg data. Eight empirical models derived from limnological variables were selected from the literature and used to estimate potential fish yield for the Great Lakes and Lake Winnipeg. The models predicted a fairly narrow range of potential yields, but when compared with historic yields, none was consistent for all lakes. The best overall empirically derived estimator of potential yield in the Great Lakes was the morphoedaphic index. Potential fish production estimated from invertebrate production with Borgmann's biomasssize spectrum mode! was considerably greater than historic yields or the yield theorie de la productivite et on applique piusieurs de ces m6thodes B des donn6es recueillies dans \es Grands Lacs et E e lac Winnipeg. On a choisi dans les ouvrages publies huit modeles empiriques tires de variables limnologiques; ils sewent a determiner le rendement potentiel en poissons des Grands Lacs et du lac Winnipeg. Les modeles predisent une gamme assez petite de rendements potentiels; toutefois, une '~oncribution No. 86-03 sf the Ontario Ministry of Natural Resources, Research Section, Fisheries Branch. Box 50, Maple, Ont. LOJ fE0. Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by Duke University on 10/14/12 For personal use only.Since Ryder's (1965) landmark paper there has k e n extensive Base of ME1 in addition to other physical, chemical, and biological indices. In general, such indices are designed for simplicity of application. They treat yield of fish as a "shadow" statistic representing fish production. This embodies the implicit assumption that the yield for a given lake represents the same fraction of total production as the yields for the other lakes with which it is to be compared. Where reliable and comparable

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“…Since Rawson (1951) established the first predictive ratio between an environmental variable (total dissolved solids) and the potential fish yield for a group of lakes, different ratios have appeared in the literature of recognised value in predicting the production of fish biomass in both lakes and reservoirs (Jenkins 1976(Jenkins , 1982. Some of these predictive ratios are simple, like the relation to the average depth (Rawson 1952(Rawson , 1955Henderson & Welcomme 1974;Quiro´s 1990;Moreau & De Silva 1991), surface area, volume or shoreline development (Youngs & Heimbuch 1982;Marshall 1984;Crul 1992;Jayasinghe, Amarasinghe & De Silva 2006), quantity of nutrients (Hanson & Legget 1982), conductivity (Henderson & Welcomme 1974), biomass of phytoplankton or primary chlorophyll production (McConnell, Lewis & Olsen 1977;Oglesby 1977;Jones & Hoyer 1982;Quiro´s, Cuch & Baigu´n 1986; Quiro´s power of prediction (Bajdik & Schneider 1991;Crul 1992;Lae¨1997;Matthews 1998;Nissanka & Amarasingue 2000;Wiff & Quin˜ones 2004).…”

Section: Introductionmentioning

confidence: 99%

Trophometric index: a predictor for fish density, biomass and production in Mediterranean reservoirs in Spain

Lara

Encina

–Ruiz

2009

Fisheries Management Eco

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A trophometric index (TMI) is proposed to estimate fish density, biomass and production in Mediterranean reservoirs. It consists of simple and integrated environmental variables such as the form index (IF = S/V ), the volume percentage of water with sufficient oxygen to sustain fish life, conductivity, chlorophyll a concentration and perimeter, the synergic effect of which explains the fish structure and accounts for the functioning of the ecosystems. This index was tested in eight reservoirs in the Guadalquivir River basin, showing a good fit with a potential model for fish density and a linear model for fish biomass and production (P < 0.01; r 2 > 0.8). The TMI is a valuable tool for evaluating fishery production in Mediterranean reservoirs and allows these to be classified by type for better management and evaluation in the framework of the European Water Directive.biomass, density, fish production, fishery evaluation, reservoir, trophometric index.

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Another Consideration of the Morphoedaphic Index

Cited by 46 publications

References 6 publications

Relationship between Lake‐Record Weights of Fishes and Reservoir Area and Growing Season

Relationship between Lake‐Record Weights of Fishes and Reservoir Area and Growing Season

A Review of Methods for Prediction of Potential Fish Production with Application to the Great Lakes and Lake Winnipeg

Trophometric index: a predictor for fish density, biomass and production in Mediterranean reservoirs in Spain

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