Evaluation of spatial sampling designs for redd surveys

Transcription

1 Evaluation of spatial sampling designs for redd surveys Martin Liermann 1, Dan Rawding 2, George Pess 1, Bryce Glaser 2 1 Northwest Fisheries Science Center, 2 Washington Department of Fish and Wildlife.

2 Acknowledgements Survey teams GIS and data support: Steve VanderPloeg Funding: Pacific Coast Salmon Recovery Fund, Pacific Salmon Commission s Letter of Agreement (Chinook Technical Committee) and Southern Boundary Fund, NOAA-Fisheries Mitchell Act, the Washington State General Fund, and the Bonneville Power Administration through the Pacific Northwest Aquatic Partnership. Prior theoretical and applied salmon sample design research.

3 Question What is the best way to choose a sub sample of reaches?

4 Question What is the best way to choose a sub sample of reaches?

5 Question What is the best way to choose a sub sample of reaches?

6 Main points Redd locations are geographically clumpy This produces much more variable estimates (from sample to sample). There are effective strategies for reducing this variability. Spatially balanced designs Stratified designs Regression estimators

7 Main points Redd locations are geographically clumpy This produces much more variable estimates (from sample to sample). There are effective strategies for reducing this variability. Spatially balanced designs Stratified designs Regression estimators

8 Main points Redd locations are geographically clumpy This produces much more variable estimates (from sample to sample). There are effective strategies for reducing this variability. Spatially balanced designs Stratified designs Regression estimators

9

10

11

12

13 Approach Bin redds into 1km reaches. Select a sample from the reaches based on a design (SRS, GRTS, stratified). Repeat many times for each sampling design. Summarize results.

14 Germany, Abernathy, and Mill, Steelhead 2007

15 Redds Abernathy Germany Mill Other River Km 20

16

17

18 Approach Bin redds into 1km reaches. Select a random sample from the reaches. Repeat many times for each sampling design. Summarize results.

19 Redds Abernathy Germany Mill Other River Km 20

20 Approach Bin redds into 1km reaches. Select a random sample from the reaches. Repeat many times for each sampling design. Summarize results.

21 Estimated total redds = 310 Redds Abernathy Germany Mill Other River Km 20

22 Estimated total redds = 420 Redds Abernathy Germany Mill Other River Km 20

23 Estimated total redds = 240 Redds Abernathy Germany Mill Other River Km 20

24 Approach Bin redds into 1km reaches. Select a random sample from the reaches. Repeat many times for each sampling design. Summarize results.

25

26 420

27 210

28

29 Sampling approaches Simple random sampling (SRS) Generalized Random Tesselation Stratified (GRTS) Stratified GRTS Peak count census + regression estimator

30 Sampling approaches Redds SRS GRTS (Generalized Random Tesselation Stratified) Abernathy Stratified GRTS Germany Peak count census + regression estimator Mill Other River Km 20

31 Sampling approaches Redds SRS GRTS (Generalized Random Tesselation Stratified) Abernathy Stratified GRTS Germany Peak count census + regression estimator Mill Other River Km 20

32 Sampling approaches SRS GRTS (Generalized Random Tesselation Stratified) Stratified GRTS Peak count census + regression estimator

33 Sampling approaches Redds SRS GRTS (Generalized Random Tesselation Stratified) Abernathy Stratified GRTS Germany Mill Other Peak count census + regression estimator River Km 20

34 Sampling approaches SRS GRTS (Generalized Random Tesselation Stratified) Stratified GRTS Peak count census + regression estimator

35 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

36 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

37 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

38 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

39 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

40 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

41 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

42 Stratified GRTS Redds Abernathy Germany Mill Other River Km 20

43 Sampling approaches SRS GRTS (Generalized Random Tesselation Stratified) Stratified GRTS Peak count census + regression estimator

44 Peak count census + regression estimator

45 Results

46 Redds Abernathy Germany Mill Other River Km 20

47 Redds Abernathy Germany Mill Other River Km 20

48 Redds Abernathy Germany Mill Other River Km 20

49 Redds Abernathy Germany Mill Other River Km 20

50 Redds Abernathy Germany Mill Other River Km 20

51 Redd distribution Mill, Abernathy & Germany Cr Coweeman River East Fork Lewis River Variance / mean AutoCorr r r

52 Redd distribution Mill, Abernathy & Germany Cr Coweeman River East Fork Lewis River Variance / mean AutoCorr r r

53 Peak count census + regression estimator

54 Redd distribution Mill, Abernathy & Germany Cr Coweeman River East Fork Lewis River Variance / mean AutoCorr r r

55

56

57

58

59

60

61

62

63

64

65

66 Conclusions Redd locations are geographically clumpy This produces much more variable estimates (from sample to sample). There are effective strategies for reducing this variability (GRTS, stratified, regression).

67 Implications Great data set that is likely representative of many other systems. Spatially balanced GRTS is a no-brainer. Stratified design effectiveness depends on the strata. Regression estimator depends on the auxiliary variable. Peak count census works well.

68 Further work Panel designs, etc Other aux. vars. for regression estimators Redds to spawners expansion.

69

70 % Reduction in CV Correlation

71 Steelhead redd distribution (IMW complex) Abernathy Germany Mill N Columbia River

72 Sampling approaches SRS GRTS (Generalized Random Tesselation Stratified) Stratified GRTS Peak count census + regression estimator Strata 1 Strata 2

73 Sampling approaches SRS GRTS (Generalized Random Tesselation Stratified) Stratified GRTS Peak count census + regression estimator

74 Describe spatial distribution