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Integrating telemetry data at several scales with spatial capture–recapture to improve density estimates

机译:将遥测数据与几个尺度集成,空间捕获重新捕获以提高密度估计

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Accurate population estimates are essential for monitoring and managing wildlife populations. Mark–recapture sampling methods have regularly been used to estimate population parameters for rare and cryptic species, including the federally listed Mojave desert tortoise (Gopherus agassizii ); however, the methods employed are often plagued by violations of statistical assumptions, which have the potential to bias density estimates. By incorporating spatial information into conventional density estimation models, spatial capture–recapture (SCR) models can account for common assumption violations such as spatially heterogeneous detection probabilities and temporary emigration when animals leave plots during a survey. We conducted mark–recapture surveys at 10 1‐km~(2)plots in and adjacent to the Ivanpah Valley of California and Nevada from 2015 to 2019. Locality data were collected concurrently using radio‐telemetry and GPS data loggers. GPS data demonstrated that desert tortoises frequently exhibited temporary emigration outside a plot during the survey periods, thereby complicating standard approaches for closed‐model density estimation. We integrated mark–recapture survey data for subadults and adults at each plot with corresponding spatial capture locations and supplementary spatial data using a modified SCR model fitted in a Bayesian framework. We compared density estimates modeled with conventional non‐spatial methods, as well as three SCR models based on symmetrical usage areas described by various levels and types of supplementary spatial data. The conventional model consistently resulted in inflated estimates of density while the SCR models allowed us to generate spatially corrected estimates for a species where detectability and densities are low. However, we found that if not properly specified, the temporal scale of supplementary data may result in an unintended source of bias in parameter estimates. Integrating spatial data over a larger temporal scale than mark–recapture surveys were conducted resulted in higher detection probabilities and lower density estimates, due to an overestimation of space use. Our results not only demonstrate the importance of accounting for spatial information but also the value of understanding the potential for bias when integrating multiple data sets at different temporal resolutions. The methods presented can be used to enhance monitoring efforts for the Mojave desert tortoise and other species where mark–recapture methods are used.
机译:准确的人口估计对于监测和管理野生动物人口至关重要。 Mark-Recapture采样方法经常用于估计稀有和隐秘物种的人口参数,包括联邦上市的Mojave Desery龟( Gophustagasizii);然而,所采用的方法通常因违规的统计假设而困扰,这具有偏置密度估计的可能性。通过将空间信息纳入传统的密度估计模型,空间捕获 - 重新捕获(SCR)模型可以考虑诸如当动物在调查期间留下地块时的空间异构检测概率和临时移除的常见假设违规。我们在2015年至2015年到2015年到2019年,我们在加利福尼亚州的Ivanpah谷和内华达州伊万帕谷的10毫米〜(2)个地块进行了标记 - 重新捕获调查。使用无线电遥测和GPS数据记录器同时收集地区数据。 GPS数据表明,在调查期间,沙漠龟经常在地块外临时移除,从而复杂化闭合模型密度估计的标准方法。我们在每个绘图中集成了Mark-ReCapture调查数据,每个绘图的子地位和成人使用相应的空间捕获位置和使用修改的SCR模型在贝叶斯框架中安装的补充空间数据。我们比较了用传统的非空间方法建模的密度估计,以及基于各种级别和类型的补充空间数据描述的对称使用区域的三种SCR模型。传统模型一致地导致密度升高,而SCR模型允许我们在可检测性和密度低的物种中产生空间校正的估计。但是,我们发现如果没有正确指定,则补充数据的时间标度可能导致参数估计中的非预期偏置源。由于空间使用的高估导致,对比标记重拍调查相比,在较大的时间刻度上进行了比标记重拍调查相结合的空间数据导致更高的检测概率和更低的密度估计。我们的结果不仅展示了空间信息核对的重要性,而且展示了在不同时间分辨率下集成多个数据集时理解偏置潜力的值。所提出的方法可用于增强Mojave Desert龟和其他物种的监测努力,其中使用标记重复方法。

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