Cost‐effective variable selection in habitat surveys
Bibliographic record
Abstract
Summary 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".