1. Survey sampling Outline (1 hr) Survey sampling (sources of variation) Sampling design features Replication Randomization Control of variation Some designs Simple random sampling Stratified random sampling Other (cluster, systematic, double, dual frame, adaptive)

2. Define study objectives Why, What and How? Quantities that are being estimated (abundance, occ.) Spatial (size) and temporal scope (duration) Establish criteria of reliability: precision Constraints: costs, logistical limitations Estimate abundance of manatee in Florida with CV < 20% and for less than 50K.

3. Design issue Define target and sample population Replication: multiple samples from sampled population Randomization: selection of random samples. Guards against the systematic influence of unrecognized sources of variation Estimate reliability: bias/precision/accuracy

4. Detection probability & SV Detection probability Fraction of total area (A) 03/2203/22

5. Sampling designs Sample Random Stratified Cluster double Adaptive

6. Simple Random sampling –n sampling units selected from list of N units –Each unit has probability n/N of being selected –With or without replacement. –Population mean –Unbiased estimate is sample mean –With replacement, the variance –Estimated by

7. Sample Random Sampling Sampling without replacement, need to accommodate for finite population: Notation Estimate:

8. Simple Random Sampling Population Total: Variance: Estimate: Williams et al. (2002), p. 63

9. Sample Size Total area: N plots (N=910), the goal to estimate the mean number of plants per plot within 10% of true value (5% of the time [i.e., alpha=0.05]) CV based on pilot~0.486

10. Stratification Strata 1: Aggregation Strata 2: medium density Strata 3: low density

11. Design: spatial variation 300 sites are sampled to estimate N (3003) 1000 sites have on average 1.5 animals 5040 sites have no animals 260 sites have on average 5.78 animals Frequency 02468 0 500 1500 2500 Frequency 051015 0 500 1000 1500 04/2204/22

12. Stratified random sampling Abundance: 3006 (2079 to 4053)Abundance: 3003 (2735 to 3274) Simple Random Samp. Frequency 10002000300040005000 0 10000 15000 Stratified Rand.Samp. Frequency 10002000300040005000 0 4000 8000 12000

13. Stratified Random Sampling Y_bar_st: mean number of organisms Stratum (i) Ni: number of plots in stratum i N: total # of plots yi-bar: mean number of organisms per plot

14. Stratified Random Sampling Population variance Sample variance (without replacement) is the variance of sampling units in stratum i.

15. Stratified Random Sampling with Bayesian methods More flexible method Not based on asymptotic approximation

16. Sample size stratified random sampling Proportional allocation: sample units are allocated according to relative sizes of strata Optimal allocation allocate samples to strata in a manner that minimizes variance given cost constraint

17. Unequal areas If plots are unequal in size

18. Systematic Sampling Very popular among ecologists because simple –Sample at regular time intervals Treat as simple random samples Potential flaw: variances underestimated –Measurements along environmental gradients –Correlated measures among adjacent individuals. Variation on the theme: random sampling from grids –Random starts (still systematic)

19. Double Sampling Often, have extensive (good) sample, and extensive (poorer) sample method –Auxiliary variable (x i ) on a sample of n’ units (black) –Primary variable (y i ) on a subsample of size n (red) Ground counts vs aerial surveys The n units (overlap) are used to build stat model to predict y from x with linear model (Williams et al. 2002, p. 70) Underutilized method –Cost efficient –Can help account for detection probability

20. Sampling schemes 1 2 3 4 Conroy and Carroll (2010)

21. Cluster sampling (p.68) M :number of clusters in a population (M=50 ponds) –m : size of a sample of clusters (e.g., sample of 5ponds) – N i : secondary units within cluster i, i=1,..., M (# nests in pond[i]) yi is # eggs in pond i:

22. Cluster Sampling “Clusters” of dissimilar objects (ponds) Secondary units may not be visible, but list (frame) exists for primary units: Nests in ponds (maps exist of ponds) Efficient clustering yields relatively large values of within-cluster variance and only a relatively small component of variance associated with the differences.

23. Adaptive sampling For organisms with a patchy distribution Random sampling may be inefficient

24. Adaptive sampling Shaded: cluster with y >= 1 Dark shading: networks

25. Adaptive sampling (p. 72) Estimator of the per unit population mean: y*_k: counts N_k: cluster size of network k z_k inclusion parameter (0 or 1 if initial sample) p_k inclusion probability

26. Take home points Design issues (objectives, random, replication, reliability.) Spatial variation, detection Stratification to improve precision Optimization Unequal areas Cluster, double and adaptive sampling

27. References Cochran, W. G. 1977. Sampling techniques, 3rd edition. Wiley, New York. 428pp. Conroy, M. J. and Carroll, J. P. 2010. Quantitative conservation of vertebrates, Wiley-Blackwell Skalski, J. R. 1994. Estimating wildlife populations based on incomplete areas surveys. Wildl. Soc. Bull 22:192-203. Thompson, W. L., G. C. White, and C. Gowan. 1998. Monitoring Vertebrate Populations. Academic Press, New York. Thompson, S. K. 2002. Sampling. Wiley Press, NY. Williams et al. 2002, Analysis and management of animal populations. Academic Press.