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Estimating Vital Rates and Abundance: Indices vs. Estimates in Wildlife Surveys

This document explores the intricacies of estimating vital rates in wildlife populations, differentiating between indices and estimates. It emphasizes the importance of understanding the underlying relationship between the index and the parameter of interest for accurate assessments. The pitfalls of relying on indices, such as reliance on volunteer data collection and maintaining past methods, are discussed. Various sampling techniques, including line transect and distance sampling, are outlined, highlighting common mistakes and providing recommendations for better practice in wildlife assessments.

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Estimating Vital Rates and Abundance: Indices vs. Estimates in Wildlife Surveys

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Presentation Transcript

  1. Estimating Vital Rates

  2. Vital rates • BIDE Equation

  3. Indices vs. Estimates • Index • A value thought to be related to the parameter of interest • Estimate • Estimated value of the parameter of interest

  4. When do indices work? • Relationship between index and parameter is known and 1:1 • Index is collected in a statistically valid, repeatable way • Rarely

  5. Examples of Indices • Lek counts • Roadside counts of ____ (name your favorite species) • Breeding Bird Survey

  6. Bad Reasons to Use an Index • Maintaining past data collection • It’s cheap • Volunteers like to collect them • We don’t know what else to do

  7. Indices vs. Estimates • We, as a profession, can and must do better • Always strive to estimate the parameter of interest • Use an index only as a last resort

  8. Is it an index or estimate? • That depends on the question

  9. Abundance

  10. Abundance: total number of animals • Density: animals per unit area

  11. Census – complete count

  12. Estimate: derived from sampling and an incomplete count • Sampling fraction • Incomplete detection

  13. Sampling Fraction • Proportion of the area selected in a sample • Example: randomly selecting 100 plots out of an area of 10,000 potential plots.

  14. Incomplete Detection • Proportion of animals detected within the area selected to be sampled.

  15. Heuristic Abundance Estimator

  16. Heuristic Abundance Estimator 2 • If a random sample is taken over the “entire” area

  17. Abundance estimate - detection

  18. Abundance Estimate - Detection

  19. Sampling Cows

  20. Environmental Conditions

  21. Does distance matter?

  22. Are all individuals detected equally?

  23. Is p < 1?

  24. Line Transect Sampling • Distribution of animals around a randomly placed line of known width?

  25. Line Transect Sampling

  26. Line transect sampling • How do we estimate p? • A function of frequency of detection by distance

  27. Line Transect Sampling

  28. Line transect sampling

  29. p=blue area/red area

  30. p=blue area/red area Calculus is usefulafter all

  31. Distance Sampling

  32. Distance sampling assumptions • All animals along the line are detected • Detection drops monotonically • Lines placed randomly with respect to the animals • Distances measured accurately • Trigonometry can help • Objects are detected at their initial location

  33. Common pitfalls/recommendations • Animal movement • Measurement error • Heaping • Missing animals on the line • If binning distance smaller bins closer to line • 40 m, 60-80 animals, 15-20 lines

  34. Distance sampling examples • Shipboard whale surveys • Fixed-wing aerial pronghorn surveys • Elephant dung surveys • Helicopter surveys for kangaroos

  35. What if p < 1? • Sightability

  36. Sightability models • Run detection trials on animals in known locations (radio-marked animals) • Fit a model to the trial data • Group size • Cover • Observer • Survey the study area • Apply model results to survey observations

  37. What if p < 1? • Sightability

  38. Sightability Examples • Deer • Elk • Moose

  39. Next: Mark-recapture

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