Cost-effective variable selection in habitat surveys
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| Authors: | Braun, DC; Reynolds, JD |
| Year: | 2012 |
| Journal: | Methods in Ecology and Evolution 3: 388-396 Article Link (DOI) |
| Title: | Cost-effective variable selection in habitat surveys |
| Abstract: | 1. Researchers usually expect to understand the ecological systems better when they examine more variables. However, we cannot measure everything because time and money are limited, so we need to make difficult choices. Decisions are complicated by the fact that variables are often either uninformative or highly correlated, leading to diminishing returns on information with new variables. Correlated variables and diminishing returns on information per variable can be explicitly incorporated with costs of data collection to design cost-effective survey programmes. 2. We develop a step-by-step quantitative protocol to evaluate the cost-effectiveness of survey designs under different cost scenarios to help scientists and managers design cost-effective surveys. We illustrate this protocol using a case study that relates physical stream habitat variables to variation in sockeye salmon spawning populations. 3. We present our protocol by comparing linear regression models containing different combinations of variables representing different survey designs. The steps of the protocol are to (i) eliminate redundant variables, (ii) calculate costs scenarios, (iii) calculate survey performance metrics and (iv) identify and compare a subset of survey designs that maximize effectiveness at a given cost. Survey designs are compared by their ranked performance using R 2, AICc, average cost-effectiveness ratio and incremental cost-effectiveness ratio. 4. Our case study shows diminishing returns on the information provided by the addition of more variables as survey costs increase. The protocol supports the design of cost-effective monitoring programmes and leads to a general discussion relating changing environmental conditions to survey costs, including the need for clear and measurable objectives, which allow scientific information to be translated into management options. |
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