Water Temperature Regime
6.2 Methods for evaluating Chiloglanid biological indices
Standard electrofishing techniques (Chapter 4) were used to sample riffle habitats within the Sabie River during October 2000 and 2001, and May 2000, 2001 and 2002 (Figure 6.2). Site descriptions are provided in Table 6.1. Lengths (mm) and masses (g) for C.
anoterus adult individuals were recorded. Unadjusted abundance numbers were used to plot abundance curves ofC.anoterusandC.paratus.
25°00'
• October 2000 ... May 2001 iIiI October 2001
N
Main rivers 1;~,;,t::!'1Sabie catchment31°00' 31 °30'
N
10 0
A
10 20 Kilometres~
Figure 6.2May and Octoberelectrofishing survey sites forChiloglanissampling within the Sabie catchment.
di t . f tion fi hi d
Table 6.1 Chiloelanis electro IS mz site names an eo-or ma e m orma I
Altitude Longitude Latitude Site no. Site name
(OE) (OS)
(m.asl)
Ca 1 Madras 421.15 31.173 25.022
Ca2 Hoxani 401.69 31.205 25.019
Ca3 Mkuhlu 395.3 31.250 24.998
Ca4 Cork 342.04 31.312 24.978
Ca 5 Lisbon 309.98 31.394 24.971
Ca6 Sabie Park 285.25 31.471 24.987
Ca 7 Visvang 1 234.84 31.621 24.985
Ca8 Visvang 2 252.83 31.653 24.976
Ca9 Post confluence Sabie 216.87 31.742 24.963
Ca 10 Nkuhlu 187.97 31.798 25.032
Ca 11 Lubyelubye 157.26 31.886 25.099
Ca12 Lower Sabie weir 165.91 31.925 25.121
Ca 13 Molondosi 131.67 31.999 25.160
Stuckenberg (1969) recognised the biological relevance of a cumulative heating parameter (effective temperature - analogous to cumulative degree days above some threshold value).
A temperature threshold used extensively in studies between water temperature and trout is a measure known as the maximum weekly allowable temperature (MWAT) (Armour 1991). This upper temperature threshold can be related to maximum daily water temperatures using Equation 6.1.
MWAT= OT+ (UU/LT- 01)/3 [6.1]
where OTis the optimal temperature for that species, and UU/LT is the upper temperature tolerance for the species of fish being considered. OTwas calculated as the midpoint in the temperature range ofC. anoterus, which was estimated using the absolute minimum and maximum water temperatures recorded at the upper and lower sites that C. anoterus was recorded from during May 2001, respectively. While it is recognised that cool season water temperature values are not necessarily an ideal measure of optimal temperatures, May temperatures were chosen since these values could be directly applied to the relative
abundance curves obtained from the May 2001 electrofishing survey. UUILT was estimated by adding two degrees to the upper limit temperature.
This approach was used to calculate a critical upper threshold temperature for C. anoterus.
The duration (number of hours) that this temperature was exceeded annually was calculated using hourly temperature data from eight water temperature sites for the period 1 June 2001 to 31 May 2002 (Chapter 2). In addition, the frequency that this threshold was exceeded during the same time period was calculated, based on a seven-day moving average of daily maximum water temperatures. This is a similar approach to that used by Robison et al. (1999), who characterized water temperatures using a seven-day moving average of maximum water temperatures, as a measure representing prolonged exposure by aquatic life to high temperatures. Logistic regressions were calculated using the presence (1) or absence (0) ofC.anoterus at 16 sites, to calculate the probability of occurrence ofC.
anoterus as a function of MWAT.
6.2.1 Chiloglanis ratio of relative abundances index
Two approaches were followed to determine the degree of species affinity that may exist between C.anoterus and C. paratus. For the first approach, a 2x2 contingency table was constructed, based on presence (1) and absence data (0) for all sample sites surveyed throughout the study period, to test for species association, i.e. did both species of Chiloglanis tend to occur together more often than expected? Expected values for each cell within the contingency table were calculated by dividing the product of row and column totals by the grand total. The null hypothesis was that both species were independent (no association). A chi-square
(I)
test statistic was calculated to test for significance of this association (Ludwig and Reynolds 1988).A second qualitative approach was used to test for the degree of significance of species covariation, and in particular to determine whether there was a significant negative association between C.anoterus and C.paratus. The Spearman's Rank correlation is a robust non-parametric measure of species association (Ludwig and Reynolds 1988). The relative abundances ofChiloglanis anoterus and C. paratus were ranked from largest to smallest according to sites arranged on a downstream gradient. This exercise was performed using the abundance values obtained from the May 2000, October 2000, May 2001, October 2001 and May 2002 electrofishing surveys. These data were kept separate since the October sites were sampled at riffle sites only, while the May sites were chosen as representative sites along the longitudinal axis of the Sabie River, and did not necessarily include riffle habitat. Spearman's Rank Correlations were also performed using each of the survey dates individually. The null hypothesis was that the species ranked abundances were uncorrelated. The Spearman's Rank Correlation coefficient was calculated using Equation 6.2 (Ludwig and Reynolds 1988).
[6.2]
wherey is the relative abundance (a ranked vector) for speciesiandk.
These values range from +1 to-1. In all cases, sample size (N) was less than 10,and the coefficient(r) was compared against significance values in Ludwig and Reynolds (1988) (p. 149) for P < 0.05 (degrees of freedom = N-2). For a small sample (N < 10) the assumption of normality is not valid (Ludwig and Reynolds 1988).
Relative abundances for each species were used to calculate ratios ofC. anoterus and C.
paratus. Ratios were calculated using two different approaches (Equations 6.3a-b). Point values for October 2000, October 2001 and May 2001 were plotted against downstream distance (chainage), and simple linear regressions fitted to each dataset.
R = (_A_)
*100A+B [6.3a]
[6.3b]
where R is the calculated ratio, and A and B are two different species. Itwas necessary to log-transform the calculated ratio in Equation 6.3b to adjust for cases of extreme low values (abundances of 1).
Results from these analyses are presented in Section 6.3.1.
6.2.2 C. anoterus condition factor index
Determining a condition factor involved defining the relationship between the lengths (mm) and masses (g) ofC. anoterus. Thisrelationship is used for calculating condition factors in fish (Ricker (1968), due to allometric scaling between body mass and body length in fish (Equation 6.4a) (Ricker 1968; Peters 1983; McEwan and Hecht 1984;
Schmidt-Nielsen 1984). Condition factors were calculated by dividing mass by length, according to Equation 6.4b. A subsequent step in using the condition factor as an index of thermal change was to determine whether a trend existed between the condition factor, and downstream distance (km) along the longitudinal axis of the Sabie River. Box-and- whisker plots were used to illustrate the relationship between condition factor variability and downstream distance.
[6.4a]
[6.4b]
where M is body mass (g), L is body length (mm), a is the proportionality coefficient or intercept; b is the exponent or slope; and CF is the condition factor.
The influences of sampling date and sex, on the condition factor were tested for using one- way analyses of variance (no blocking). ANOV As of length, mass and condition versus sampling dates (May 2000, October 2000, May 200 I and May 2002); and length,mass and condition versus sex (male and female), were performed, with the hypothesis (p <0.01) that sampling dates and sex had no effect on body length, mass or condition. Results from these analyses are presented in Section 6.3.2.