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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 maximum sustainable yield is usually higher than the optimum sustainable yield and maximum economic yield. MSY is extensively used for fisheries management . Unlike the logistic ( Schaefer ) model, [ 1 ] MSY has been refined in most modern fisheries models and occurs at around 30% of the unexploited population size.
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 ...
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]
The Beverton–Holt model is a classic discrete-time population model which gives the expected number n t+1 (or density) of individuals in generation t + 1 as a function of the number of individuals in the previous generation, + = + /.
A contour plot of the hours of daylight as a function of latitude and day of the year, using the most accurate models described in this article. It can be seen that the area of constant day and constant night reach up to the polar circles (here labeled "Anta. c." and "Arct. c."), which is a consequence of the earth's inclination.
Ackermann's formula provides a direct way to calculate the necessary adjustments—specifically, the feedback gains—needed to move the system's poles to the target locations. This method, developed by Jürgen Ackermann , [ 2 ] is particularly useful for systems that don't change over time ( time-invariant systems ), allowing engineers to ...
) + / A detailed proof of this formula can be found here: [14] This identity is similar to some of Ramanujan 's formulas involving π , [ 13 ] and is an example of a Ramanujan–Sato series . The time complexity of the algorithm is O ( n ( log n ) 3 ) {\displaystyle O\left(n(\log n)^{3}\right)} .