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The Brownian bridge synoptic model of habitat selection and space use for animals using GPS telemetry data

机译:利用GPS遥测数据对动物进行栖息地选择和空间利用的布朗桥天气模型

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The growing application of GPS telemetry in wildlife studies created need for analytical methods to meet both practical and theoretical concerns when conducting analyses of habitat or resource selection. We devised a new analysis approach of individual-based movement models for estimation of resource selection based on probability of use. We merged the Brownian bridge model of space use with the synoptic model of habitat selection to describe and estimate patterns of habitat selection from GPS telemetry data. In doing so, our approach implicitly defines availability based on movement data when conducting analysis of GPS telemetry data. To do so, we employed a step-by-step approach, based on sequential triplets of observations of the animals’ movements. Availability was portrayed as a circular normal distribution at every middle GPS location, based on the existing Brownian bridge model of space use. This middle observation within the sequential triplet also reflected habitat selection, estimated by maximum likelihoods, based on the deviation from otherwise random movement between the first and third observations. This approach allowed each triplet across time to be treated as independent, identically distributed observations when estimating habitat selection. To demonstrate the utility of the model, we analyzed GPS location data collected from free-ranging mountain goats (Oreamnos americanus) in the Cascade Mountains of the western United States to evaluate patterns of habitat selection while foraging during late spring and early summer. Slope of the terrain was the primary factor influencing resource selection by mountain goats in our study, with females selecting steeper areas closer to escape terrain than males. Finally, we derived a resource selection function applicable over a broad geographic extent to evaluate sites for potential release of mountain goats to augment the population in Washington, which has declined over the last 50 years.
机译:GPS遥测技术在野生生物研究中的日益增长的应用产生了在进行生境或资源选择分析时需要满足实际和理论问题的分析方法的需求。我们设计了一种新的基于个体的运动模型的分析方法,用于根据使用概率估算资源选择。我们将空间使用的布朗桥模型与栖息地选择的概要模型合并在一起,以从GPS遥测数据描述和估算栖息地选择的模式。这样做时,我们的方法在进行GPS遥测数据分析时会根据运动数据隐式定义可用性。为此,我们基于对动物运动的连续三重观察,采用了分步进行的方法。根据现有的空间使用布朗桥模型,可用性被描绘为每个GPS中间位置的圆形正态分布。顺序三联体中的这一中间观测值还反映了栖息地的选择,该估算值是根据与第一观测值和第三观测值之间其他情况下随机移动的偏差,通过最大似然估计的。这种方法可以在估计栖息地选择时将整个时间上的每个三元组视为独立的,分布均匀的观测值。为了证明该模型的实用性,我们分析了从美国西部喀斯喀特山脉的自由放养的野山羊(Oreamnos americanus)收集的GPS位置数据,以评估在春季和夏季初觅食时栖息地选择的模式。在我们的研究中,地形坡度是影响山羊选择资源的主要因素,雌性选择的陡峭区域比雄性更接近逃生地形。最后,我们推导了一种资源选择函数,该函数在广泛的地理范围内适用,以评估可能释放石山羊的地点,以增加华盛顿的人口,在过去的50年中,华盛顿的山羊数量有所下降。

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