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首页> 外文期刊>Journal of Forest Planning >Spatial Estimation of Sika Deer Population Density Distribution
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Spatial Estimation of Sika Deer Population Density Distribution

机译：梅花鹿种群密度分布的空间估计

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We estimated the spatial distribution of the sika deer (Cervus nippon) population density in areas around Mt. Hiko in Fukuoka Prefecture, Japan. The data comprised pellet counts in 86 plots from February to April 1999 and from November 1999 to March 2000. Taking this to correspond to the population density in the plots, the sika deer population density was mapped using a kriging interpolator. Five semivariograms were examined in a geographic information system (GIS): spherical, circular, exponential,Gaussian, and linear with sill models. We used the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) to select the appropriate semivariogram model. The spherical model produced the smallest values in both the AIC and BIC. The ordinary kriging interpolator with the spherical model provided the deer population density at intervals of 50m. Four distinct high-density areas, in which the deer density exceeded 30/km2, were found around Mt. Kosho and Mt. Hiko. Using independent data for the damage caused by deer and the map of the sika deer density, we estimated the deer densities in all plots where the damage by deer had been checked, and determined the relation between population density and the degree of browsing damage, which is most serious in Fukuoka Prefecture. As the sika deer population density increases, the proportion of damaged plots increases. Of plots with the density exceeding 40 deer/km~2, browsing damage occurred in 83%. This method provides insight into the density of deer over a broad region and, in combination with the information about deer damage, can be applied to give the probability of deer damage.
机译：我们估算了富士山周围地区梅花鹿（日本鹿）种群密度的空间分布。日本福冈县的Hiko。数据包括1999年2月至1999年4月以及1999年11月至2000年3月的86个样地中的颗粒计数。使用该数据对应样地中的种群密度，使用克里格插值器绘制了梅花鹿种群密度。在地理信息系统（GIS）中检查了五个半变异函数：球形，圆形，指数型，高斯型和带有窗台的线性模型。我们使用了Akaike信息准则（AIC）和贝叶斯信息准则（BIC）来选择适当的半变异函数模型。球形模型在AIC和BIC中均产生最小值。具有球形模型的普通克里格插值器以50m的间隔提供了鹿的种群密度。在山周围发现了四个不同的高密度区域，其中鹿的密度超过了30 / km2。 Kosho和山彦子使用独立数据得出鹿群造成的破坏以及梅花鹿密度图，我们在检查了鹿群破坏的所有地块中估计了鹿的密度，并确定了种群密度与浏览破坏程度之间的关系，在福冈县最严重。随着梅花鹿种群密度的增加，受损地块的比例也会增加。在密度超过40鹿/ km〜2的地块中，浏览损坏发生率为83％。该方法可洞察大范围内的鹿密度，并与有关鹿的伤害信息结合使用，可得出鹿受到伤害的可能性。

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