Analysis of Effects of Multiple Environmental Factors on Early Life-history for Growth and Stress Accumulation Using a Dynamic-state-dependent Model

동적상태의존모델을 이용하여 복합적 환경영향이 어류의 초기 생활사에 미치는 영향 분석

Abstract

Environmental changes can affect life-history traits, such as growth rate and reproduction, and organisms adapt on a given environmental condition to maximize ecological fitness. This study shows the effects of water temperature and dissolved oxygen level on early growth and accumulated damage in fish using a dynamic-state-dependent model. I have hypothesized that the level of foraging activity is related to growth and stress and so the optimal level can maximize reproductive success - ultimately, fitness. The critical temperature and dissolved oxygen (DO) is also defined as inducing the maximum growth rate at the level. So, the model predicts the highest growth rate at oxygen saturation and lower growth rate at lower or higher level of DO in water. Lower DO (i.e., hypoxia) causes slower growth rate through higher amount of accumulated stress whereas higher DO (i.e., hyperoxia) induces faster growth rate, but smaller body size. In addition, I show that there is lower impact when considering simple or independent environmental factors on environmental assessment. My findings suggest that multiple environmental factors as physiological ecology approach should be considered to improve impact assessment in environmental changes and a further study is needed to develop advanced assessment tools considering multiple environmental factors.

환경변화는 생물의 성장과 번식 등의 생활사에 영향을 주며, 생물은 살아가는 서식환경에서 생태적 적합도를 가장 극대화 시킬 수 있도록 적응되어 왔다. 본 연구에서는 동적상태의존모델을 이용하여 수온과 용존산소량의 변화가 어류의 초기 성장과 체내 스트레스 누적 과정에 미치는 영향을 분석하였다. 본 연구에서 제시한 어류의 생활사 모델은 취식 행동이 성장과 체내 스트레스 누적에 영향을 받는다고 가정하였다. 또한 수온과 용존산소량의 임계점은 가장 빠른 성장속도를 유도하는 수온과 용존산소량으로 가정하였다. 이에 모델은 수온과 용존산소량의 임계점에서 성장속도가 가장 빨랐으며 임계점보다 크거나 낮은 경우 성장속도는 느렸다. 용존산소량의 저산소 상태는 체내에 누적된 스트레스양의 증가로 성장속도는 느렸고, 고산소 상태는 성장속도를 향상 시켰으나 누적된 스트레스로 신체 크기를 감소시켰다. 본 연구를 통해 환경변화가 생물에 미치는 영향을 예측하는데 있어 단일 또는 독립적인 환경요인 보다 복합적 요인들의 영향이 높음을 보였다. 따라서 환경영향평가에서 환경변화에 대한 영향 예측을 향상시키기 위하여 생리생태학적 측면에서의 복합적 환경 요인을 평가에 도입이 필요하고 또한 관련된 기법 개발에 대한 연구가 후속 되어야 한다.

Figure 2. Predicted somatic growth rate (mean±s.d.; SGR) in related to (a) temperature and (b) dissolved oxygen (T_C, critical temperature; O_C, oxygen saturation)

Figure 3. Predicted (a) SGR and (b) accumulated damage in related to interaction between temperature and dissolvedoxygen (DO; white tringle-0.5, black circle-1, white square-1.5)

Figure 4. Predicted body size at maturation in related to temperature and dissolved oxygen (white tringle- 0.5, black circle-1, white square-1.5)

Figure 6. Relation between body size at maturation and accumulated damage. Symbols represent mean value in each treatment group

Figure 1. (a) The four classes of growth trajectories produced by our modeling framework. (b) Frequencies of each growth trajectory. (c) Linear discriminant function analysis of the parameters and first-order parameter interactions associated with each type of growth trajectory. Trajectory types are indicated by the different marker types I-IV are indicated by the light grey circles, black circles, white circles, and dark grey circles, respectively.

Figure 5. (a) Proportion of critical temperature in accumulated damage in related to dissolved oxygen and (b) proportion of normoxia in accumulated damage in related to temperature. Grey bar in (b) represents mean proportion among temperature treatment groups

Table 1. Summary of variables and parameters definitions and the range of values used in the simulation. Note that for the state variables and control, the range indicates the set of achievable values within the optimization routine. For the parameters, the range indicates the support over which values were drawn at random. Parentheses represents number of categories