magnifications (x 6.5 40) depending on the of the larvae (Figure – 4). Since golden perch flexion occurs between 4.9–7.3 mm TL (Serafini and Humphries 2004), the mean 6.1 mm TL was used as a cut-off for the classification of larvae as pre-flexion in this study. Figure 4. Developmental stages of golden larvae: a) and b) pre-flexion larvae; c) and d) post-flexion larvae. Scale bar 1 mm. Environmental variables and data analysis Data for flow (discharge in ML d-1), level (metres AHD, level relative to Australian Height Datum), water temperature (°C), and conductivity (μS.cm-1 @ 25 °C) were obtained for both sites, throughout the study, from the DEWNR Surface Water Archive (www.waterconnect.sa.gov.au; accessed on 10/08/2012). Water level data were transformed to relative water levels by measuring their deviation from pool height (Site 1: 0.75 m, Site 2: 16.3 m). ‘normal’ The volume of filtered through each plankton net was used to calculate the standardised abundance of fish per m3. Six plankton tows generated six replicates for each site during each sampling trip. To account for the dispersion or concentration of larval fish under the extremely variable flow conditions, the standardised abundance of fish per m3 was multiplied by the discharge volume at the time of sampling day to provide the relative abundance, as a flow corrected abundance indicator which was used for all analyses. Fish assemblage structure was characterised by the species composition and the relative abundance of each species. Statistical analysis was conducted on the relative abundance of golden perch larvae to detect the difference between years (2011-12 vs previous years) and two sampling sites. Only samples from October to mid January were included in the statistical analysis because larval sampling was not conducted from late January to
magnifications (x 6.5 – 40) depending on the of the larvae (Figure 4). Since golden perch flexion occurs between 4.9–7.3 mm TL (Serafini and Humphries 2004), the mean 6.1 mm TL was used as a cut-off for the classification of larvae as pre-flexion in this study. Figure 4. Developmental stages of golden larvae: a) and b) pre-flexion larvae; c) and d) post-flexion larvae. Scale bar 1 mm. Environmental variables and data analysis Data for flow (discharge in ML d-1), level (metres AHD, level relative to Australian Height Datum), water temperature (°C), and conductivity (μS.cm-1 @ 25 °C) were obtained for both sites, throughout the study, from the DEWNR Surface Water Archive (www.waterconnect.sa.gov.au; accessed on 10/08/2012). Water level data were transformed to relative water levels by measuring their deviation from ‘normal’ pool height (Site 1: 0.75 m, Site 2: 16.3 m). The volume of filtered through each plankton net was used to calculate the standardised abundance of fish per m3. Six plankton tows generated six replicates for each site during each sampling trip. To account for the dispersion or concentration of larval fish under the extremely variable flow conditions, the standardised abundance of fish per m3 was multiplied by the discharge volume at the time of sampling day to provide the relative abundance, as a flow corrected abundance indicator which was used for all analyses. Fish assemblage structure was characterised by the species composition and the relative abundance of each species. Statistical analysis was conducted on the relative abundance of golden perch larvae to detect the difference between years (2011-12 vs previous years) and two sampling sites. Only samples from October to mid January were included in the statistical analysis because larval sampling was not conducted from late January to