scholarly journals Resilience to changes in lake trophic state: Nutrient allocation into Daphnia resting eggs

2019 ◽  
Vol 9 (22) ◽  
pp. 12813-12825
Author(s):  
Jana Isanta Navarro ◽  
Carmen Kowarik ◽  
Martin Wessels ◽  
Dietmar Straile ◽  
Dominik Martin‐Creuzburg
1980 ◽  
Vol 37 (4) ◽  
pp. 640-646 ◽  
Author(s):  
R. P. Reid ◽  
C. H. Pharo ◽  
W. C. Barnes

Apatite is a common accessory mineral in the source rocks for the glacial debris supplying sediments to many Canadian lakes. A method has been developed which uses scanning electron microscopy and energy dispersive X-ray emission spectrometry for direct identification of apatite. This method has been used to examine the apatite content of various size fractions in Kamloops Lake sediments. Apatite concentrations obtained by this direct examination correlate well (r > 0.999) with apatite concentrations determined by chemical analyses and indicate that, in addition to comprising as much as 70% of the total phosphorus load, apatite may comprise as much as one-third of the "dissolved" (< 0.45 μm) inorganic phosphorus load. Consequently the use of classical (e.g. Vollenweider 1968; Vollenweider and Dillon 1974) methods of estimating lake trophic state from inorganic phosphorus concentrations in lake water must be done with care, recognizing that the bulk of inorganic phosphorus in lakes deriving sediment from glaciated igneous or metamorphic terrains may be in the form of apatite.Key words: apatite, lake, trophic state, phosphorus load, scanning electron microscopy


Hydrobiologia ◽  
1996 ◽  
Vol 319 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Ray W. Drenner ◽  
J. Durward Smith ◽  
Stephen T. Threlkeld

1992 ◽  
Vol 1 (3) ◽  
pp. 173-197 ◽  
Author(s):  
Eugene B. Welch ◽  
Richard P. Barbiero ◽  
Debra Bouchard ◽  
Clain A. Jones

Author(s):  
Farnaz Nojavan ◽  
Betty J Kreakie ◽  
Jeffrey W Hollister ◽  
Song Qian

Lake trophic state indices have long been used to provide a measure of the trophic state of lakes. Over time it has been determined that these indices perform better when they utilize multiple metrics and provide a continuous measurement of trophic state. We utilize such a method for trophic state that is based upon a Proportional Odds Logistic Regression (POLR) model and extend this model with a Bayesian multilevel model that predicts nutrient concentrations from universally available GIS data. This Bayesian multilevel model provides relatively accurate measures of trophic state and has an overall accuracy of 60%. The approach illustrates a method for estimating a continuous, mutli-metric trophic state index for any lake in the United States. Future improvements to the model will focus on improving overall accuracy and use variables that are more sensitive to change over time.


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