Spatial Analysis in Population Biology

大多数与生态和进化相关的数据都位于地理空间中。这种数据的空间背景，如鸟巢的模式、遗传变异模式或树木分布，导致了生态和进化理论中有趣的问题，同时，要求在涉及这些数据的统计分析中考虑这些空间模式。到目前为止，后者在很大程度上被忽视了，索卡尔博士的工作将有助于生态学家和进化论者认识到它们的重要性。Sokal博士将通过各种分析（数学）和计算机模拟方法来检验近年来流行的一些测试的有效性。这项研究的结果将极大地促进从模式到过程的推断在人口生物学的各种各样的问题。这些程序具有相当大的实际应用，因为几乎所有的环境问题都具有空间维度。因此，在对诸如酸雨等污染物的影响进行测试时，需要在评价这些结果之前考虑到数据的空间关系。迄今为止，在环境或流行病学工作中很少采用这种方法。

Most data of ecological and evolutionary interest are located in geographic space. This spatial context of data such as patterns of birds' nests, genetic variation patterns, or tree distributions leads to interesting problems in ecological and evolutionary theory, and, at the same time, requires allowance for these spatial patterns in statistical analyses that involve such data. The latter have largely been ignored to date, and Dr. Sokal's work will contribute to a recognition of their importance among ecologists and evolutionists. Dr. Sokal will examine, by a variety of analytical (mathematical) and computer simulation approaches, the validity of a number of tests that have become popular in recent years. The results of this research should greatly facilitate inferences from patterns to processes in a wide variety of problems in population biology. These procedures have considerable practical application since virtually all environmental problems have a spatial dimension. Thus, tests of the effects of pollutants, such as acid rain, need to take the spatial relationships of the data into consideration before evaluation of those results. This is an approach that has so far rarely been followed in either environmental or epidemiological work.