Fish survival in response to operational changes in the Pee Dee River

1 Methods

Simulations using the Quantus-subdaily (Quantus-SD) model were run for nine operational scenarios to evaluate the effects of environmental ramping restrictions now and under future demands.The Quantus-subdaily model is described elsewhere.

To evaluate results, we plotted the effects of operational scenarios by simulating survival of early lifestages of smallmough bass nesting in tailwaters. We plotted results and fitted regression models to simulated survival estimates to assess the effect of restricting nighttime flow fluctuations (Is.Env=TRUE) for three different demand scenarios (2024, 2036, and 2050), and of other ramping restrictions (2036). We evaluated the effects of spawning cohort, brood year (a wet and dry year), and temperature. Separate regressions were fitted for three life stages, egg, larvae, and juveniles (young-of-the-year, i.e., post-swim-up to age 1).

2 Results

2.1 Nighttime ramping restrictions increased YOY survival

Regression results showed that scenarios restricting nighttime flow scenarios were consistently associated with higher survival than scenarios that did not restrict nighttime flow fluctuations (Table 1.1). However, this effect was large-enough to be significant only for young-of-the-year (YOY) juveniles. Survival of flow regimes with restricted nighttime flows had the greatest survival benefit for YOY bass in the 2024 scenarios (Figure 2.1). Temperature had significant linear effects on all early life stages and significant quadratic term for eggs and larvae (Table 1.1). The wetter hydrologic year (2013) had a positive effect on swimup larvae and a negative effect on eggs, and no significant effect on YOY (Table 1.1).

In addition to nighttime restrictions, there were significant differences in YOY survival for flows shaped to meet demands in future years 2036 and 2050 (Table 1.1). Generally, YOY survival was higher in 2024 than in future years (Figure 2.1).

Figure 2.1: Early survival under flows resulting from operational scenarios that do and do not include nighttime ramping restrictions.

(#tab:table-1.1) Table 1.1 Regression modeling of QUANTUS-SD results for a) eggs, b) larvae, and c) YOY juveniles: effects of nighttime flow restrictions.

	Eggs	Larvae	YOY
(Intercept)	0.61 ***	0.19 ***	0.01 ***
	(0.05)	(0.03)	(0.00)
Future.year2036	-0.01	0.00	-0.01 ***
	(0.05)	(0.03)	(0.00)
Future.year2050	0.00	0.01	-0.01 ***
	(0.06)	(0.03)	(0.00)
Is.envTRUE	0.02	0.02	0.00 ***
	(0.04)	(0.02)	(0.00)
poly(tQile, 2)1	-1.53 ***	-3.53 ***	-0.00 **
	(0.30)	(0.15)	(0.00)
poly(tQile, 2)2	-2.08 ***	1.01 ***	-0.00
	(0.30)	(0.15)	(0.00)
brood.yr2013	-0.17 ***	0.10 ***	-0.00
	(0.04)	(0.02)	(0.00)
N	198	198	198
R2	0.32	0.76	0.86
*** p < 0.001; ** p < 0.01; * p < 0.05.

2.2 Unrestricted ramping decreased YOY survival

For future year 2036, we compared operation scenarios with unrestricted ramping (Ahigh) and load-restricted ramping (Alow) (2.1). The unrestricted ramping was simulated with (Env) and without (NoEnv) nightime restrictions. Effects on survival of eggs and larvae were not statistically significant, but unrestricted ramping had a strong negative effect on simulated YOY bass survival (Table 1.2).

(#tab:table-1.2) Table 1.2 Regression modeling to compare effects of unrestricted ramping (Ahigh) and load-restricted ramping (Alow) on a) eggs, b) larvae, and c) YOY

	Eggs	Larvae	YOY
(Intercept)	0.54 ***	0.16 ***	0.00 ***
	(0.07)	(0.04)	(0.00)
poly(tQile, 2)1	-1.11 **	-2.61 ***	-0.00 ***
	(0.39)	(0.20)	(0.00)
poly(tQile, 2)2	-1.56 ***	0.73 ***	0.00
	(0.39)	(0.20)	(0.00)
brood.yr2013	-0.16 *	0.11 **	0.00
	(0.07)	(0.04)	(0.00)
Is.envTRUE	0.01	0.02	0.00 ***
	(0.09)	(0.05)	(0.00)
Is.highTRUE	0.07	0.04	0.00
	(0.09)	(0.05)	(0.00)
N	66	66	66
R2	0.33	0.76	0.67
*** p < 0.001; ** p < 0.01; * p < 0.05.

2.3 Hydrologic conditions and spawning time strongly influenced cohort survival

Spawning time and brood year strongly influenced overall age-0 survival. The most significant influence on egg and larval survival was spawning date. Survival was highest for eggs spawned at intermediate dates, whereas survival of larvae was highest for those from the earliest nests. Overall survival was higher for earlier nests (Figures 2.1, 2.2).

Results also highlighted the differences between two hydrologic years. Flows were much higher in 2012 than in 2013. However, simulated survival rates were not consistently higher in 2012 (dash-dot lines in Figure 2.1). For both years, nest spawned earlier experienced higher survival. However, later cohorts experienced better survival in the wetter year (2013) than in the drier year (2012) (Figure 2.2). This can be important because a longer nesting season spreads risk and can increase resilience under future climate.

Figure 2.2: Comparison of age-0 survival for different brood years and spawning times.

2.4 Survival was higher in the drier year (2012) and affected different life stages differently by operation scenarios

2.5 Attribution to mechanistic mortality risks aids in interpretation

We compared survival due to five simulated causes (cold shock, dewatering, high flow, stranding and high temperature). Several of these (cold shock high flow, and high temperature) apply to all life stages, whereas dewatering of nests is the functional equivalent of stranding for juveniles. The horizontal dashed lines in Figure ?? show the product of survival of all causes across scenarios.

For YOY juveniles, which generally had the lowest survival, results show higher survival due to cold shock in scenarios with no nighttime ramping (Figure ??). Mortality due to increasing flows also varied quite a bit among scenarios (Figure ??).

Figure 2.3: Survival under different operational scenarios for each of the three early life stages. Lines connect median of spawning cohorts and brood years.