We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems （GIS） -based approaches： logistic regression and Akaike＇s Information Criterion （AIC）, Mu...We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems （GIS） -based approaches： logistic regression and Akaike＇s Information Criterion （AIC）, Multiple Criteria Evaluation （MCE）, and Bayesian Analysis （specifically Dempster-Shafer theory）. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified （overall accuracy）, the failure to predict a species where it occurred （omission error） and the prediction of presence where there was absence （commission error）. Our overall accuracy showed the logistic regression approach was the most accurate （74.51%）. The multiple criteria evaluation was intermediate （39.22%）, while the Dempster-Shafer （D-S） theory model was the poorest （29.90%）. However, omission and commission error tell us a different story： logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach （e.g. Dempster-Shafer） that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans .展开更多
We applied <em>n</em>-variable conserving nonlinear differential equations (<em>n</em>-CNDEs) to the population data of the 10-year cycles of Canadian lynx (1821-2016) and the snowshoe hare (18...We applied <em>n</em>-variable conserving nonlinear differential equations (<em>n</em>-CNDEs) to the population data of the 10-year cycles of Canadian lynx (1821-2016) and the snowshoe hare (1845-1921). Modeling external effects as perturbations to population dynamics, recovering and restorations from disintegrations (or extinctions), stability and survival strategies are discussed in terms of the conservation law inherent to dynamical interactions among species. The 2-variable conserving nonlinear interaction (2CNIs) is extended to 3, 4, ... <em>n</em>-variable conserving nonlinear interactions (<em>n</em>-CNIs) of species by adjusting minimum unknown parameters. The population cycle of species is a manifestation of conservation laws existing in complicated ecosystems, which is suggested from the CNDE analysis as <em>a standard rhythm</em> of interactions. The ecosystem is a consequence of the long history of nonlinear interactions and evolutions among life-beings and the natural environment, and the population dynamics of an ecosystem are observed as approximate CNIs. Physical analyses of the conserving quantity in nonlinear interactions would help us understand why and how they have developed. The standard rhythm found in nonlinear interactions should be considered as a manifestation of the survival strategy and the survival of the fittest to the balance of biological systems. The CNDEs and nonlinear differential equations with time-dependent coefficients would help find useful physical information on the survival of the fittest and symbiosis in an ecosystem.展开更多
文摘We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems （GIS） -based approaches： logistic regression and Akaike＇s Information Criterion （AIC）, Multiple Criteria Evaluation （MCE）, and Bayesian Analysis （specifically Dempster-Shafer theory）. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified （overall accuracy）, the failure to predict a species where it occurred （omission error） and the prediction of presence where there was absence （commission error）. Our overall accuracy showed the logistic regression approach was the most accurate （74.51%）. The multiple criteria evaluation was intermediate （39.22%）, while the Dempster-Shafer （D-S） theory model was the poorest （29.90%）. However, omission and commission error tell us a different story： logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach （e.g. Dempster-Shafer） that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans .
文摘We applied <em>n</em>-variable conserving nonlinear differential equations (<em>n</em>-CNDEs) to the population data of the 10-year cycles of Canadian lynx (1821-2016) and the snowshoe hare (1845-1921). Modeling external effects as perturbations to population dynamics, recovering and restorations from disintegrations (or extinctions), stability and survival strategies are discussed in terms of the conservation law inherent to dynamical interactions among species. The 2-variable conserving nonlinear interaction (2CNIs) is extended to 3, 4, ... <em>n</em>-variable conserving nonlinear interactions (<em>n</em>-CNIs) of species by adjusting minimum unknown parameters. The population cycle of species is a manifestation of conservation laws existing in complicated ecosystems, which is suggested from the CNDE analysis as <em>a standard rhythm</em> of interactions. The ecosystem is a consequence of the long history of nonlinear interactions and evolutions among life-beings and the natural environment, and the population dynamics of an ecosystem are observed as approximate CNIs. Physical analyses of the conserving quantity in nonlinear interactions would help us understand why and how they have developed. The standard rhythm found in nonlinear interactions should be considered as a manifestation of the survival strategy and the survival of the fittest to the balance of biological systems. The CNDEs and nonlinear differential equations with time-dependent coefficients would help find useful physical information on the survival of the fittest and symbiosis in an ecosystem.
