Survey data from 2931 Ontario lakes were analyzed to determine how fish species richness was empirically related to a set of 19 physical and chemical limnological variables. Lake area was the dominant factor, explaining 18% of the variation in species number. Total aluminum, latitude, dissolved organic carbon, and elevation together explained an additional 16%. The strong relationship between species number and lake area was quantified using lakes with pH [Formula: see text] and then applied to ail the surveyed lakes to estimate species number. Deviations from the expected values indicated that species number decreased with decreasing pH below 6.0, resulting in significantly fewer species in lakes with pH < 6.0. In the subset of lakes with pH < 6.0, pH alone explained 21% of the variation in species number; elevation, lake area, and dissolved organic carbon together explained an additional 20%. Interactions between pH and lake area were identified; lake size decreased significantly both at low pH (<6.8) and at high pH [Formula: see text]. An understanding of these interactions was essential in explaining the relative roles of pH and lake area in determining species richness.

No abstract

Creel survey and independent assessment data on the lake trout (Salvelinus namaycush) and smallmouth bass (Micropterus dolomieui) populations of Lake Opeongo were evaluated. Annual estimates of total mortality, fishing mortality, and abundance were generated for each population over the period 1936–83. Large variations in survey efficiency, angler efficiency, fishing mortality, and abundance were identified over this period. We argue that a creel survey, which is expected to provide reliable information on fish population dynamics, requires an overall study design which includes collection of data on the number and relative efficiency of different kinds of anglers and periodic assessment studies aimed at providing independent checks on both survey effectiveness and population behaviour.

Regression analyses have been used for over 2 decades to develop useful statistical relationships between estimates of fish catch and various members of a set of abiotic and biotic variables. In this study a number of conceptual and data refinements have been attempted with respect to certain lake data. More complete data are now available than was the case with earlier authors such as D. S. Rawson and R. A. Ryder. Rather than use average long-term catch, approximate estimates of maximum sustainable yield (MSY) were derived. The MSY of all species combined, Ct, was estimated as well as the MSY of a set of preferred taxa, Cs, comprising lake trout (Salvelinus namaycush) plus lake whitefish (Coregonus clupeaformis) plus walleye (Stizostedion vitreum) plus sauger (S. canadense). The most significant predictive relationships included only one independent variable, average dry weight of bottom fauna standing crop. which explained 83% of the variation of Ct per unit water area in a semilog relationship, and 80c• of the variation of Ct per unit area and C• per unit area in log-log relationships. The best relationships incorporating solely abiotic variables included mean depth and total dissolved solids concentration as the only significant independent variables, and explained less than 70% of the variation. Other factors analyzed included the annual cumulative degree days above 5.6 C, the presence or absence of thermal stratification, and the average dry weight of net plankton standing crop. 1966. Reindeer, A•nisk, Wollaston, La Ronge, Cree, lie a la Crosse, Churchill, Little Peter Pond, Big Peter Pond, Athabasca: Rawson 1960; MacKay 1960. Great Slave: Rawson 1950, 1953a, 1953b; Burns 1973. c TWWS refers to the set of taxa lake trout, lake whitefish, walleye, and sanger. a TDSt is the TDS esti•nate relevant to the 15-yr period to which the catch of all species applies. '• TDSs is the TDS esti•nate relevant to the 15-yr period to which the catch of TWWS applies. fDDAYS is the annual cumulative nmnber of degree days above 5.56 C. g STRAT indicates the presence (=2) or absence (-1) of a •noderate or strong sramher stratification. n BFWT is the benthos standing crop dry weight, •nollusc shells re•noved. l NPWT is the net plankton standing crop dry weight. J Data for 1896 are missing so that the year has been omitted froin consideration. k Only 10-yr period could be considered. An intense •nink ranch fishery with very approxi•nate yield esti•nates associated with it limited the periods. 390 TRANS. AM. FISH. SOC., VOL. I07, NO. 3, I978 C•5 z 5 IO o .5 MORPHOEDAPHIC INDEX (ppm?rn) FIGURE 2.--Regression of Cs per unit water area on the morphoedaphic index, TDSt/Z. der's__(1965) morphoedaphic index, MEI = TDS/Z, resulted in equations (3) and (4) which are pictured in Figures 1 and 2, respectively. Since the correlation ooe log (water area) with log (Ct) and log (Cs) was greater than water area with Ct and C.•, log (Ct)Aog (water area) and log (C.•)Aog (water area) were used as dependent variables in order to lessen the amount of...

We used daily air and water temperatures from 14 lakes in Ontario to develop and test a simple method for constructing lake-specific empirical models for predicting daily littoral water temperatures. Data requirements for prediction are modest (average air temperature, day of the year) and the method is robust and practical, requiring only a few (15)(16)(17)(18)(19)(20) well-spaced water temperature observations to construct a single-year model that can generate reasonably accurate predictions for an entire ice-free season. The ability of a singe-lake model to predict several years of temperature data is significantly improved by explicitly including information on ice-out date in the model. Our multiyear model for Lake Opeongo described most (18 of 22) years well. Years that were not well described were usually coincident with El Nifio Southern Oscillation events. Used with caution, the method can be an effective tool for supplementing direct monitoring of littoral water temperatures and for generating historical water temperature estimates when direct estimates are lacking. These capabilities should be of particular use to fisheries biologists studying or managing populations of fish species with critical life stages that are affected by littoral water temperatures.