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This paper is concerned with a kind of Bobwhite quail population model $$\begin{aligned} x_{n+1}=A+Bx_n+\frac{x_n}{x_{n-1}x_{n-2}},\ \ n=0,1,\cdots , \end{aligned}$$ x n + 1 = A + B x n + x n x n - 1 x n - 2 , n = 0 , 1 , ⋯ , where the parameters and initial values are positive parabolic fuzzy numbers. According to g-division of fuzzy sets and based on the symmetrical parabolic fuzzy numbers, the conditional stability of this model is proved. Besides the existence, boundedness and persistence of its unique positive fuzzy solution. When some fuzzy stability conditions are satisfied, the model evolution exhibits oscillations with return to a fixed fuzzy equilibrium no matter what the initial value is. This phenomena provided a vivid counterexample to Allee effect in density-dependent populations of organisms. As a supplement, two numerical examples with data-table are interspersed to illustrate the effectiveness. Our findings have been verified precise with collected northern bobwhite data in Texas, and will help to form some efficient density estimates for wildlife populations of universal applications.
This paper is concerned with a kind of Bobwhite quail population model $$\begin{aligned} x_{n+1}=A+Bx_n+\frac{x_n}{x_{n-1}x_{n-2}},\ \ n=0,1,\cdots , \end{aligned}$$ x n + 1 = A + B x n + x n x n - 1 x n - 2 , n = 0 , 1 , ⋯ , where the parameters and initial values are positive parabolic fuzzy numbers. According to g-division of fuzzy sets and based on the symmetrical parabolic fuzzy numbers, the conditional stability of this model is proved. Besides the existence, boundedness and persistence of its unique positive fuzzy solution. When some fuzzy stability conditions are satisfied, the model evolution exhibits oscillations with return to a fixed fuzzy equilibrium no matter what the initial value is. This phenomena provided a vivid counterexample to Allee effect in density-dependent populations of organisms. As a supplement, two numerical examples with data-table are interspersed to illustrate the effectiveness. Our findings have been verified precise with collected northern bobwhite data in Texas, and will help to form some efficient density estimates for wildlife populations of universal applications.
The article is concerned with large time behavior of solution to second-order fractal difference equation with positive fuzzy parameters x n + 1 = A + x n B + x n - 1 , n = 1 , 2 , ⋯ , here the initial values x i (i = -1, 0) and the parameters A, B are positive fuzzy numbers. Utilizing a generalization of division (g-division) of fuzzy numbers, one presents large time behaviors of positive fuzzy solution including persistence, boundedness, global convergence. Moreover, two numerical examples verify the effectiveness of the qualitative analysis.
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