Institute of Wildlife Research, School of Biological Sciences, Heydon-Laurence Building, University of Sydney, New South Wales, Australia.
PLoS One. 2013 May 30;8(5):e63931. doi: 10.1371/journal.pone.0063931. Print 2013.
Dingoes (Canis lupus dingo) were introduced to Australia and became feral at least 4,000 years ago. We hypothesized that dingoes, being of domestic origin, would be adaptable to anthropogenic resource subsidies and that their space use would be affected by the dispersion of those resources. We tested this by analyzing Resource Selection Functions (RSFs) developed from GPS fixes (locations) of dingoes in arid central Australia. Using Generalized Linear Mixed-effect Models (GLMMs), we investigated resource relationships for dingoes that had access to abundant food near mine facilities, and for those that did not. From these models, we predicted the probability of dingo occurrence in relation to anthropogenic resource subsidies and other habitat characteristics over ∼ 18,000 km(2). Very small standard errors and subsequent pervasively high P-values of results will become more important as the size of data sets, such as our GPS tracking logs, increases. Therefore, we also investigated methods to minimize the effects of serial and spatio-temporal correlation among samples and unbalanced study designs. Using GLMMs, we accounted for some of the correlation structure of GPS animal tracking data; however, parameter standard errors remained very small and all predictors were highly significant. Consequently, we developed an alternative approach that allowed us to review effect sizes at different spatial scales and determine which predictors were sufficiently ecologically meaningful to include in final RSF models. We determined that the most important predictor for dingo occurrence around mine sites was distance to the refuse facility. Away from mine sites, close proximity to human-provided watering points was predictive of dingo dispersion as were other landscape factors including palaeochannels, rocky rises and elevated drainage depressions. Our models demonstrate that anthropogenically supplemented food and water can alter dingo-resource relationships. The spatial distribution of such resources is therefore critical for the conservation and management of dingoes and other top predators.
澳洲野犬(Canis lupus dingo)于 4000 多年前被引入澳大利亚,并成为了野生动物。我们假设澳洲野犬源自于家养犬种,因此能够适应人类提供的资源补充,并认为其空间利用模式会受到这些资源分布的影响。我们通过分析澳大利亚干旱中心地区的澳洲野犬 GPS 定位(位置)来检验这一假设,研究使用了资源选择函数(RSF)。我们使用广义线性混合效应模型(GLMM)研究了两种情况下的资源关系:一是有大量食物补充的矿区附近的澳洲野犬,二是没有食物补充的矿区附近的澳洲野犬。根据这些模型,我们预测了在人为资源补充和其他生境特征的影响下,澳洲野犬在约 18000 平方公里范围内的出现概率。随着数据集(如我们的 GPS 追踪记录)的增大,非常小的标准误差和随之而来的高 P 值将变得更加重要。因此,我们还研究了一些方法来最小化样本之间的序列和时空相关性以及不平衡研究设计的影响。通过使用 GLMM,我们解释了部分 GPS 动物追踪数据的相关结构;但是,参数标准误差仍然很小,所有预测因子都具有高度显著性。因此,我们开发了一种替代方法,使我们能够在不同的空间尺度上审查效应大小,并确定哪些预测因子在生态学上具有足够的意义,可以纳入最终的 RSF 模型。我们发现,矿区附近最能预测澳洲野犬出现的因素是距离垃圾处理场的距离。在远离矿区的地方,靠近人类提供的水源点与澳洲野犬的分散有关,其他景观因素如古河道、岩石突起和高地排水洼地也是如此。我们的模型表明,人为补充的食物和水可以改变澳洲野犬与资源的关系。因此,这些资源的空间分布对于澳洲野犬和其他顶级捕食者的保护和管理至关重要。