Main methods of fishery resources estimation

The main methods for estimating fishery resources are as follows:

1. Biological methods include calculation based on fishing yield, calculation based on fish biology and calculation based on labeled exile method. Calculation by fishing output refers to the calculation of fishery resources reserves according to fishing output and fishing coefficient. This method is simple, but the error is large, because the fishing coefficient is affected by many factors and it is difficult to estimate accurately.

2. Biological calculation of fish refers to the calculation of fishery resources reserves according to biological parameters such as fish growth rate, mortality rate and reproduction rate. This method has high accuracy, but it needs a lot of field investigation and laboratory research, and its cost is high.

3. Estimation by marking release method refers to releasing some species after marking, fishing after a period of time, and calculating fishery resources reserves according to the number of marked fish caught. This method has high precision, but it needs a lot of manpower and material resources and a long time period.

4. Mathematical modeling methods include static model and dynamic model. Static model refers to estimating fishery resources by establishing mathematical equations. This method is simple, but its accuracy is low. Dynamic model refers to estimating fishery resources as a function of time by establishing differential equations. This method has high accuracy, but it needs a lot of data and complicated calculation.

Advantages and disadvantages of fishery resources estimation are as follows:

First, advantages

1, scientific: based on objective data and scientific principles, the results obtained by biological and mathematical models are more scientific and credible.

2. Accuracy: In some cases, comprehensive data and methods can estimate fishery resources more accurately.

3. Flexibility: Different estimation methods can be selected according to different needs and scenarios to meet diverse needs.

Second, shortcomings.

1, high cost: Many estimation methods need a lot of field investigation, data collection and laboratory analysis, and the cost is high.

2. Large error: Due to the complexity of the environment and ecosystem, many factors are difficult to measure accurately, resulting in a large error in the estimation results.

3. Relying on subjective judgment: In some cases, the estimation result may be influenced by human factors or subjective judgment. However, continuous optimization of estimation methods and improvement of data quality are effective ways to reduce these shortcomings.