Alex Zerbini National Marine Mammal Laboratory Alaska Fisheries Science Center, NOAA Fisheries
Introduction Abundance Estimation Methods (Line Transect Sampling) Survey Design Data collection
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
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
Abundance estimation techniques for marine mammals 1.Capture-recapture methods
Abundance estimation techniques for marine mammals 1.Capture-recapture methods 2.Shore/Aerial counts
Abundance estimation techniques for marine mammals 1.Capture-recapture methods 2.Shore/Aerial counts 3.Strip transect 4.Line transect sampling
Abundance estimation techniques for marine mammals 1.Capture-recapture methods 2.Shore/Aerial counts 3.Strip transect 4.Line transect sampling 5.Other (acoustics)
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
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
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
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
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
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
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
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
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
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
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
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
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
Survey Design Uniform coverage probability Parallel trackline design (aerial surveys) Random Systematic with random start
Survey Design Uniform coverage probability Zig-zag/Sawtooth designs (ship surveys) Random start
Survey Design Irregular survey areas Random start Systematic with random start
Data collection 1
Data collection 2
Data collection 2 horizon Marine Mammal Reticles Angle boards 0 θ 270 90
Data collection 2
Data Analysis Program Distance (Research Unit for Wildlife Populations University of St. Andrews - Scotland
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 - http://www.ruwpa.st-and.ac.uk/distance
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