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                HyperNiche (生态位模型预〇测)

                HyperNiche   (生态位模型预测)
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                • HyperNiche   (生态位模型预测)
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                产品报价: 12047
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                HyperNiche (生态位模型预测)
                高教
                北京
                详细说明

                HyperNiche   (生态位模型预测)

                如需本款软件的最终价格,请留言咨询 ,我们将尽快给您报价。

                HyperNiche是用户友好的软件,为非参数回归。我们的主要目的是提供一个灵活的工具,为增加的生态模型——生态模型,预测器结合乘法而不是叠加性。这是一个灵∑活和强大的方法来栖息地建模。
                 

                HyperNiche有许多潜在的用途:

                为物种存在或者不存在构建生态模型(估计』发生的可能性与多个栖息地参数或其他预测)。

                为物种丰度构建生态模型 (估计丰度有关预测)。

                估计生理响应表面有关1、2或更多的环境参数。打开你的生理变量々作为响应矩阵和你的环境变量作为预测矩阵。

                建立实证模型的物种多样性与多个预测因子。把你的多样性措ㄨ施(计算与PC-ORD或电子表格)放在响应矩阵。

                HyperNiche is user-friendly software for nonparametric regression. Our primary purpose is to provide a flexible tool for multiplicative habitat modeling – habitat models where the predictors are combined multiplicatively rather than additively. This is a flexible and powerful approach to habitat modeling.

                HyperNiche has many potential uses:

                Build habitat models for species presence-absence (estimate likelihood of occurrence in relation to multiple habitat parameters or other predictors).


                Build habitat models for species abundance (estimate abundance in relation to predictors).


                Estimate physiological response surfaces in relation to 1, 2, or more environmental parameters. Open your physiological variables as the response matrix and your environmental variables as the predictor matrix.


                Build empirical models of species diversity in relation to multiple predictors. Place your diversity measures (calculated with PC-ORD or a spreadsheet) in the response matrix.


                Relate community ordination scores to multiple environmental variables. Save your ordination scores in a spreadsheet, then open this as the response matrix and your environmental variables as the predictors.


                Optimize sample stratification – choose combinations of variable to maximize differences in a response variable among strata. Place potential stratifying variables in the predictor matrix, then conduct a free search to find the best combination of variables for predicting the response.


                Build multiple regression models for any nonlinear response to multiple interacting factors.

                Simple, ecologically reasonable response surfaces pose difficult challenges to traditional habitat modeling tools. HyperNiche provides tools that easily find complex response surfaces, such as this hump that combines sigmoid and Gaussian curves.
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