Evaluation of Models Predicting Mixing Depth

Micheline Hanna

doi:10.1139/f90-108

Evaluation of Models Predicting Mixing Depth

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f90-108 ◽

1990 ◽

Vol 47 (5) ◽

pp. 940-947 ◽

Cited By ~ 39

Author(s):

Micheline Hanna

Keyword(s):

Predictive Power ◽

Nutrient Recycling ◽

Empirical Models ◽

Effective Length ◽

Lake Area ◽

Lake Productivity ◽

Mixing Depth ◽

Geographical Indices ◽

Improved Model ◽

Single Predictor

Since mixing depth affects many aspects of lake productivity, including nutrient recycling, I evaluated the predictive power of 17 empirical models that relate mixing depth to morphometric variables to identify the best predictor. These models were tested empirically by compiling data from 123 temperate lakes of differing morphometry, geometry, and trophy. Four statistical indices of precision and bias, indicate that the model published by Shuter et al. (1983) using maximum effective length of the lake was the best published model for predicting mixing depth, although it is slightly biased. I then examined the effect of alternate predictors, reflecting lake configuration, basin shape, and geographical indices, to formulate an improved model. The best single predictor of therrnodine depth (THER) was maximum effective length (MEL): Log THER = 0.336 Log MEL − 0.245. No improvement in predictive power was obtained by combining other variables. This model is statistically superior to that of Shuter et al. (1983) beause it is not biased, it represents a greater number of lakes, and it covers a broader range of lake sizes and shapes over a more extensive geographical region. Two other models, using lake area and length of shoreline are proposed as alternate predictive tools, if MEL is not readily available.

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Empirical Models for Zoobenthic Biomass in Lakes

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f87-117 ◽

1987 ◽

Vol 44 (5) ◽

pp. 990-1001 ◽

Cited By ~ 56

Author(s):

Joseph B. Rasmussen ◽

Jacob Kalff

Keyword(s):

Littoral Zone ◽

Chlorophyll Concentration ◽

Empirical Models ◽

Phosphorus Concentration ◽

Lake Area ◽

Total Phosphorus Concentration ◽

Secchi Disk Transparency ◽

Littoral Zones ◽

Explained Variance ◽

Local Variability

Estimates of macrozoobenthos from the literature were regressed against a series of limnological variables to yield empirical models for zoobenthic biomass in the profundal, sublittoral, and littoral zones of lakes. Variables indicative of phytoplankton biomass (chlorophyll concentration, total phosphorus concentration, and Secchi disk transparency) explained between 14 and 57% of the variance of zoobenthic biomass ((g/m2)0.1). Other factors such as humic colour, morphometry (slope, mean depth, ratio of mean to maximum depth, and lake area), and mean annual air temperature substantially increased the amount of explained variance. In the profundal and sublittoral zones, the best models explain 70% of the variance in zoobenthic biomass. Littoral zone models explained less than 50%, and this deficiency was attributed to sampling difficulties and to high local variability of slope and wave exposure in the littoral zone.

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Can growth be used as a surrogate measure of walleye (Sander vitreus) abundance change?

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f05-129 ◽

2005 ◽

Vol 62 (9) ◽

pp. 2159-2168 ◽

Cited By ~ 21

Author(s):

Greg G Sass ◽

James F Kitchell

Keyword(s):

Positive Influence ◽

Growth Patterns ◽

Sander Vitreus ◽

Adult Density ◽

Lake Productivity ◽

Regional Patterns ◽

Density Dependent ◽

Morphoedaphic Index ◽

Improved Model ◽

Surrogate Measures

We examined several models to determine the relative contributions of density-dependent and density-independent factors on walleye (Sander vitreus) growth in the ceded territory of Wisconsin from 1990 to 1999. We then used independent data from 2000 to determine how well each model predicted walleye growth and examined several models to determine if growth could be used to predict density. Adult density best predicted the mean size of age-3 males ( [Formula: see text]3m) and age-5 females ( [Formula: see text]5f). Density-independent measures of pH, the morphoedaphic index, maximum depth, and conductance had a positive influence on growth, while adult density negatively affected growth. The solely density-dependent models predicted [Formula: see text]3m and [Formula: see text]5f poorly in 2000. The addition of density-independent factors improved model predictions of [Formula: see text]3m and [Formula: see text]5f. Walleye growth did not predict adult walleye densities. Regional patterns in walleye growth were correlated with surrogate measures of lake productivity and to a lesser extent adult density. Density dependence had a relatively weak affect on growth patterns, suggesting that growth cannot be used as a surrogate method for monitoring abundance change.

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Exploring the relationship between the optical properties of water and the quality and quantity of dissolved organic carbon in aquatic ecosystems: strong correlations do not always mean strong predictive power

Environmental Science Processes & Impacts ◽

10.1039/c4em00473f ◽

2015 ◽

Vol 17 (3) ◽

pp. 619-630 ◽

Cited By ~ 4

Author(s):

Darren. S. Baldwin ◽

William Valo

Keyword(s):

Optical Properties ◽

Organic Carbon ◽

Dissolved Organic Carbon ◽

Aquatic Ecosystems ◽

Predictive Power ◽

Empirical Models ◽

Strong Correlations ◽

Correlation Studies ◽

The Relationship

The robustness of empirical models derived from correlation studies needs to be independently verified before being relied on.

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Direct and indirect effects of fish on pelagic nitrogen and phosphorus availability in oligotrophic Arctic Alaskan lakes

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f10-085 ◽

2010 ◽

Vol 67 (10) ◽

pp. 1635-1648 ◽

Cited By ~ 11

Author(s):

Cody R. Johnson ◽

Chris Luecke ◽

Stephen C. Whalen ◽

Mary Anne Evans

Keyword(s):

Indirect Effects ◽

Nutrient Recycling ◽

Zooplankton Biomass ◽

Trophic Levels ◽

Arctic Lakes ◽

Nitrogen And Phosphorus ◽

Lake Productivity ◽

Nutrient Excretion ◽

Significant Difference ◽

Excretion Rates

The importance of fish nutrient recycling for lake primary production increases with lake productivity. However, fish in low-productivity lakes may have substantial indirect effects on nutrient recycling from lower trophic levels. We measured nutrient excretion rates from fish and zooplankton in oligotrophic Arctic lakes and investigated direct and indirect fish effects on consumer nutrient recycling. Fish nutrient excretion rates were small relative to phytoplankton nutrient demand. Zooplankton excretion, however, supplied 19%–130% and 37%–200% of phytoplankton nitrogen and phosphorus demand, respectively. Fish had a significant effect on zooplankton biomass; in lakes with fish, this was approximately 80% lower than in lakes without fish. The difference in zooplankton biomass was due primarily to a decrease in zooplankton density; no significant difference in average zooplankton size was observed between fish and fishless lakes. Fish also impacted zooplankton community composition; communities in lakes with fish were dominated by copepods compared with cladoceran dominance in lakes without fish. Because of lower zooplankton biomass, lakes with fish showed lower rates of zooplankton nitrogen and phosphorus excretion relative to lakes without fish. Thus, our results support the hypothesis that fish have major indirect effects on lake nutrient cycles, even when direct excretion from fish is minimal.

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