Rebalancing 133 Simulate the model in Task 1 1,000 times, and finally output the water quality in steady state that is consistent with the actually observed value We set out the criteria for judging water qualit · chlorophyll a≡(000x-1.2785)/0.7568 Total concentration of organics x2×02438×69×0.2,115]+x1×[242,493] Percentage of different in theexcrement: C10%, N0.4%, P0.6% Meets c(1)-c1(1)|s100 and c(2)-c1(2)≤100 Meets/n(1)-n1(1)l< 10 and In(2)-n1(2) <10 · Chlorophyll meets ca-45≤0.15 We sort out results meeting the above requirements, thatis, the numbers of three species when the water quality obtained through simulation similar to the observed one, and show the result in Table 5.2 Table 5 Simuation results. Simulation results Initial number x103 70. Number in steady state x103 46.1 1540L-900) Estimated from data Number in steady state x10457 93 09 Tomake thenumbers of the species close to those predicted in the model, we compare the numbers of existing species with those observed in Bolinao area. Here we take into account that the added feedstuff for milkfish can revise the model in Task 1, that is we can add a constant a to the the third equation of the model in Task 1 to express the influence of feedstuff on the numbers of the species. The revised model is ()=n(1-M1-0m () =T2x2 3(t)=r33(-1 +λ N3""N2 setinitial values(70000, [8008, 89951, 1100), and calculate the steady-state numbers of all the species: (46062, 8989, 1051), as shown in Figure 6 2EDITOR'S NOTE: The accompanying Matlab code does not impose the constraints indicated above on n and CRebalancing 133 * Simulate the m6del in Task 1 1,000 times, and finally output the water quality in steady state that is consistent with the actually observed value. We set out the criteria for judging water quality: "* Chlorophyll a -- (0.00O0x, - 1.2785)/0.7568. "* Total concentration of organics = X2 x 0.2438 x 6.9 x [0.2, 11.5] + xl x [242,4931. "* Percentage of different elements in the excrement: C 10%, N 0.4%, P 0.6%. "* C meets Ic(1) - cl(1)1 < 100 and Ic(2) - cl(2)1 : 100. "* N meets In(l)- nl(1)I • 10 and In(2)- nl(2)1:5 10. "* Chlorophyll meets Ica - 4.51 < 0.15. We sort out results meeting the above requirements, that is, the numbers of three species when the water quality obtained through simulation similar to the observed one, and show the result in Table 5.2 Table 5. Simulation results. Pop.1 Pop.2 Pop.3 Simulation results Initial number x 103 70.0 [8.01,9.00] 1.10 Number in steady state x 103 46.1 9.0 1.04 Estimated from data Number in steady state x 103 45.7 9.3 0.9 To make the numbers of the species close to those predicted in the model, we compare the numbers of existing species with those observed in Bolinao area. Here we take into account that the added feedstuff for milkfish can revise the model in Task 1, that is, we can add a constant A to the the third equation of the model in Task 1 to express the influence of feedstuff on the numbers of the species. The revised model is: *t(t) = xr, 1 - + - 73 , Lý3(t) = r3X - X- + a4 X2 ,. We set initial values (70000, [8008,8995], 1100), and calculate the steady-state numbers of all the species: (46062, 8989, 1051), as shown in Figure 6. 2EDITOR'S NOTE: The accompanying Matlab code does not impose the constraints indicated above on N and C