This paper describes the development of an automated knot selection method (selecting number and location of knots) for bivariate splines in a pure regression framework (SALSA2D). To demonstrate this approach we use carcass location data from Etosha National Park (ENP), Namibia to assess the spatial distribution of elephant deaths. Elephant mortality is an important component of understanding population dynamics, the overall increase or decline in populations and for disease monitoring. The presence only carcass location data were modelled using a downweighted Poisson regression (equivalent to a point-process model) and using developed method, SALSA2D, for knot selection. The result was a more realistic local/clustered intensity surface compared with an existing model averaging approach. Using the new algorithm, the carcass location data were modelled using additional environmental covariates (annual rainfall, distance to water and roads). The results showed high carcass intensity close to water holes ($<$3km) and roads ($<$2km) and in areas of the park with average rainfall ($\sim$450mm annually). Some high risk areas were identified particularly in the north east of the park and the risk of death does not always coincide with elephant distribution across the park. These findings are an important component in understanding population dynamics and drivers for population and park management. Particularly for controlling elephant numbers and/or mitigation of anthrax or other disease outbreaks.

翻译：本文描述了在纯回归框架（SALSA2D）中开发的双变量样条自动化节点选择方法（选取节点数量与位置）。为展示该方法，我们使用纳米比亚埃托沙国家公园（ENP）的尸体定位数据评估大象死亡的空间分布。大象死亡率是理解种群动态、种群总体增减趋势及疾病监测的关键组成部分。仅含尸体存在的定位数据通过降权泊松回归（等价于点过程模型）及所开发的SALSA2D节点选择方法进行建模。与现有模型平均方法相比，该方法生成了更符合现实情况的局部/聚类强度表面。应用新算法时，尸体定位数据进一步结合环境协变量（年降水量、距水源与道路距离）进行建模。结果显示，尸体高密度区域集中于水坑附近（<3公里）、道路周边（<2公里）及公园内年均降水量约450毫米的区域。部分高风险区域被识别，尤其位于公园东北部，且死亡风险并不总与公园内大象分布区域一致。这些发现对于理解种群动态、种群管理及公园管理的关键驱动因素具有重要意义，尤其在控制大象数量及/或减轻炭疽或其他疾病暴发方面。