Downscaling species occupancy from coarse spatial scales
Downscaling species occupancy from coarse spatial scales
复制标题
DOI：
10.1890/11-0536.1
复制
发表时间：
2012-04-01
影响因子：
5
通讯作者：
Kunin, William E.
中科院分区：
文献类型：
--
作者：
Azaele, Sandro;Cornell, Stephen J.;Kunin, William E.
不同空间尺度上物种种群的测量和预测对于空间生态学和保护生物学至关重要。实现此类种群估计的一个有效但具有挑战性的目标包括记录特定区域范围内经验物种的存在和不存在，然后尝试预测更精细范围内的占用情况。到目前为止，大多数方法都是基于特定物种的分布特征，这些特征被认为对于缩小占用率至关重要。然而，其中只有少数明确地处理了特定的空间特征。在这里，我们采用了一类广泛的空间点过程，即散粒噪声 Cox 过程 (SNCP)，来模拟不同空间尺度的物种占用情况，并表明物种的空间聚集对于从较粗略的尺度开始预测精细尺度的种群估计至关重要。这些模型是在连续空间中制定的，并且无论用于研究空间模式的任意分辨率如何，都可以定位点。我们比较了在区域尺度上校准的九个模型的性能，并证明了一个非常简单的 SNCP 类（托马斯过程）能够在预测占用率方面优于其他已发布的模型，其占用率比用于参数化的占用率小四个数量级。最后，我们解释了该方法从空间隐式测量推断空间显式信息的能力、该框架结合利基模型和空间模型的潜力，以及扭转该方法以允许升级的可能性。
The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling.