Alex Zerbini. National Marine Mammal Laboratory Alaska Fisheries Science Center, NOAA Fisheries

Transcription

1 Alex Zerbini National Marine Mammal Laboratory Alaska Fisheries Science Center, NOAA Fisheries

2 Introduction Abundance Estimation Methods (Line Transect Sampling) Survey Design Data collection

3 Why do we want to estimate abundance? 1.Management and Conservation a) determine population status b) determine population trends c) establish mortality limits/removal quotas 2.Ecological studies a) address predator-prey relationships

4 How do we compute abundance? 1.Census: complete enumeration of individuals in the population 2.Sampling: use of statistical techniques to collect/measure a sample and then make inferences about the unknown population

5 Abundance estimation techniques for marine mammals 1.Capture-recapture methods

6 Abundance estimation techniques for marine mammals 1.Capture-recapture methods 2.Shore/Aerial counts

7 Abundance estimation techniques for marine mammals 1.Capture-recapture methods 2.Shore/Aerial counts 3.Strip transect 4.Line transect sampling

8 Abundance estimation techniques for marine mammals 1.Capture-recapture methods 2.Shore/Aerial counts 3.Strip transect 4.Line transect sampling 5.Other (acoustics)

9 The early years: Strip Transect Sampling A Study Area A Survey lines l 1, l 2, l 3 = L l 1 l 2 l 3

10 The early years: Strip Transect Sampling A Study Area A Survey lines l 1, l 2, l 3 = L Surveyed area a a = 2 W L l 1 l 2 l 3 w w w w w w

11 The early years: Strip Transect Sampling A Study Area A Survey lines l 1, l 2, l 3 = L Surveyed area a a = 2 W L Detected n objects l 1 l 2 l 3 w w w w w w Density Estimator n D ˆn n Dˆ = = a a2 WL

12 The early years: Strip Transect Sampling A Density Estimator n Dˆ = = a n 2WL l 1 l 2 l 3 ^ N Abundance Estimator = A Dˆ = A n 2WL w w w w w w

13 A Example A = 50 ft x 10 ft = 500 ft 2 l 1 = l 2 = l 3 = 10 ft, L = 30ft W = 2 ft a = 2WL = 2*2*30 = 120 ft 2 n 1 = n 2 = n 3 = 20, n = 60 a = 2 W L l 1 l 2 l 3 w w w w w w ^ N = n Dˆ = = a A Dˆ = n 2WL A n 2WL Density (D) n/a = 60/120 = 0.5 obj / ft 2 Abundance (N) A*D = 500 * 0.5 = 250 obj

14 Strip Transect Sampling A Major assumption: all objects are detected Visibility bias: objects are missed. Why are objects missed? Perception bias: observer fatigue, distance, glare, sea state Availability bias: animals are under the water (or land, vegetation in terrestrial animals) l 1 w w

15 Line Transect Sampling Does not make the assumption that all objects within the sampling strip are detected Proportion of animals missed is estimated by measuring distance from the objects/animals to the survey line

16 Sampling perpendicular distances A x x x x x x Measure perpendicular distance x from each object to the survey line x x x l 1 l 2 l 3 w w w w w w

17 Estimating detection probability (or proportion seen, P) Frequency (n) μ Sightability usually drops as distance from survey line increases Fit a model g(x) through perpendicular distance data 0 Perpendicular distance (x) W μ = w o g ( x) dx Computes the proportion seen Transform μ into a probability (P) (assuming all objects in the line are seen [g(0) = 1]) P μ = W

18 Strip Transect vs. Line Transect Sampling 1 STRIP TRANSECT P 0 Perpendicular distance (x) W LINE TRANSECT Strip Transect Estimator ^ N = A Dˆ = A n 2WL Line Transect Estimator ^ N = A Dˆ = A n P 1 2WL

19 Assumptions 1)All objects/animals in the trackline are seen g(0) = 1 2)No movement prior to detection Avoidance = underestimation of abundance w w Attraction = overestimation of abundance

20 Assumptions 1)All objects/animals in the trackline are seen g(0) = 1 2)No movement prior to detection 3)No measurement error w Negative bias is distance estimation = overestimation of abundance w Positive bias in distance estimation = underestimation of abundance

21 Assumptions 4)Detections are independent w Lack of independence underestimation of standard errors w If objects/animals occur in cluster, estimate number of clusters and cluster sizes ^ N = A n P 1 2 WL _ gs

22 Survey Design Uniform coverage probability Parallel trackline design (aerial surveys) Random Systematic with random start

23 Survey Design Uniform coverage probability Zig-zag/Sawtooth designs (ship surveys) Random start

24 Survey Design Irregular survey areas Random start Systematic with random start

25 Data collection 1

26 Data collection 2

27 Data collection 2 horizon Marine Mammal Reticles Angle boards 0 θ

28 Data collection 2

29 Data Analysis Program Distance (Research Unit for Wildlife Populations University of St. Andrews - Scotland

30 Advanced Topics Use of covariates in estimates of P (MCDS) Methods do estimate g(0) (MRDS) Spatial models Literature Buckland et al. (2001). Introduction to Distance Sampling, Estimating Abundance of Wildlife Populations. Oxford University Press. Various journal papers Software Distance -

31 Thank you!