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The maximum sustainable yield (MSY) is the largest amount of biomass that can be collected annually for indefinite periods. MSY assesses the productive capacity of the fishery, rather than demand or economic costs. MSY output may be greater or less than monopolistic or competitive output.
The concept of maximum sustainable yield (MSY) has been used in fisheries science and fisheries management for more than a century. Originally developed and popularized by Fedor Baranov early in the 1900s as the "theory of fishing," it is often credited with laying the foundation for the modern understanding of the population dynamics of fisheries. [1]
In population ecology and economics, the maximum sustainable yield or MSY is, theoretically, the largest catch that can be taken from a fishery stock over an indefinite period. [ 8 ] [ 9 ] Under the assumption of logistic growth, the MSY will be exactly at half the carrying capacity of a species, as this is the stage at when population growth ...
In fisheries terms, maximum sustainable yield (MSY) is the largest average catch that can be captured from a stock under existing environmental conditions. [22] MSY aims at a balance between too much and too little harvest to keep the population at some intermediate abundance with a maximum replacement rate.
5.10 Example of implementation in Python. 6 See also. 7 References. ... C is the Equation of the center value needed to calculate lambda ... For example, for an ...
The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.
The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
Also, the gain factor, +, depends on our confidence in the new data sample, as measured by the noise variance, versus that in the previous data. The initial values of x ^ {\displaystyle {\hat {x}}} and C e {\displaystyle C_{e}} are taken to be the mean and covariance of the aprior probability density function of x {\displaystyle x} .