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首页> 外文期刊>Wildlife Research >Modelling the abundance of wildlife using field surveys and GIS: non-native sambar deer (Cervus unicolor) in the Yarra Ranges, south-eastern Australia
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Modelling the abundance of wildlife using field surveys and GIS: non-native sambar deer (Cervus unicolor) in the Yarra Ranges, south-eastern Australia

机译:使用野外调查和GIS对野生生物的数量进行建模:澳大利亚东南部Yarra山脉的非本地水鹿(鹿)

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摘要

Combining abundance data collected in designed field surveys with biophysical data derived from geographic information systems is a powerful way to investigate predictors of spatial variation in the abundance of wildlife. We used such an approach to evaluate hypotheses about factors influencing the abundance of sambar deer (Cervus unicolour Kerr, 1792), a large non-native herbivore, in south-eastern Australia. We developed a spatial model for the abundance of sambar deer faecal pellets in a 3650-ha area in the Upper Yarra Ranges, Victoria. We counted the number of sambar deer faecal pellets along 100 randomly located transects and used a geographic information system to estimate biophysical variables around each transect. We formulated our hypotheses about how those variables might affect the abundance of sambar deer pellets into 22 candidate models and used the deviance information criterion to identify the ‘best’ model(s). Because five models had strong support we used model averaging to generate a predictive model. The three variables included in the predictive model were aspect (abundance of pellets declined with increasing ‘northerliness’ and increased with increasing ‘easterliness’), distance to water and elevation; the latter two variables were positively correlated and had a negative effect on the abundance of pellets. In contrast to previous models of sambar deer abundance in south-eastern Australia, our spatial predictions of the abundance of faecal pellets can be easily tested and updated. Our approach would be useful for modelling the abundances of other wildlife species at a range of spatial scales.
机译:将在设计现场调查中收集的丰度数据与从地理信息系统获得的生物物理数据相结合,是研究野生动植物丰度空间变化预测因子的有效方法。我们使用这种方法来评估有关影响东南澳大利亚大型非本地食草动物水鹿鹿数量的因素的假说(鹿(Cervus unicolour Kerr,1792年))。我们为维多利亚州上亚拉山脉3650公顷的地区中的水鹿鹿粪便颗粒量开发了空间模型。我们计算了沿100个随机分布的样带的鹿鹿粪便颗粒的数量,并使用了地理信息系统来估算每个样带周围的生物物理变量。我们将关于这些变量如何影响水鹿鹿丸数量的假设表述为22个候选模型,并使用偏差信息标准来确定“最佳”模型。由于五个模型具有强大的支持力,因此我们使用模型平均来生成预测模型。预测模型中包含的三个变量是纵横比(随着“北方”的增加,颗粒数量减少;随着“东方”的增加,颗粒数量增加),距水的距离和海拔;后两个变量是正相关的,并且对颗粒的丰度有负面影响。与澳大利亚东南部以往的鹿鹿丰度模型相比,我们对粪便颗粒丰度的空间预测可以轻松进行测试和更新。我们的方法对于在一系列空间尺度上模拟其他野生动植物物种的丰富度将很有用。

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    《Wildlife Research 》 |2009年第3期| p.231-241| 共11页
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    A Arthur Rylah Institute for Environmental Research, Department of Sustainability and Environment, 123 Brown Street, Heidelberg, Vic. 3084, Australia. B Vertebrate Pest Research Unit, NSW Department of Primary Industries, Forest Road, Orange, NSW 2800, Australia. C Corresponding author. Email: dave.forsyth@dse.vic.gov.au;

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