Review of The Biology of Freshwater Wetlands: Biology of Habitats by Arnold G. van der Valko

Wetland ecology is a relatively new field that developed from an initial interest in a few direct benefits that wetlands provide to society. Consequently, much early scientific work was stimulated by economic returns from specific wetland services, such as production of peat and provision of habitat for economically valuable wildlife (e.g., waterfowl and furbearers). Over time, societal interest in wetlands broadened, and these unique habitats are now valued for many additional services, including some that bear non market value. Common examples include carbon sequestration, flood reduction, water purification, and aesthetics. The increased recognition of the importance of wetlands has generated a diversity of job opportunities in wetland ecology and management. Despite the increased knowledge base and enhanced job market, I am not aware of any institutions that offer specialty degrees in this new discipline. Indeed, relatively few institutions offer specific wetland ecology classes, with Arnold G. van der Valk and a few of his peers at other universities being notable exceptions.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.