Detecting Changes in Population Trends for Cook Inlet Beluga Whales (Delphinapterus Leucas) Using Alternative Schedules for Aerial Surveys

Résumé du livre

Measuring trends in population growth, and detecting a change in the trend, of Cook Inlet beluga whales (CIB) (Delphinapterus leucas) has a specific role in the co-management agreement that determines harvest levels and a more general application in the management of the population. The choice of an annual aerial survey schedule has an impact on both of these considerations. Detecting a change in trend in a population abundance time series which represents a change in the growth rate of the population and its vital rates involves two types of errors: Type 1 in which we conclude that a change in trend has occurred when it hasn't, and Type 2 in which we conclude that no change in the trend has occurred when it has. I examined the risk of each type of error in the context of five alternative survey schedules for the years after 2012: 1) annual surveys, 2) surveys on even years, 3) surveys every 3rd year, 4) surveys in the 4th and 5th years of a 5-year co-management period, and 5) surveys in the 3rd and 5th years of a 5-year co-management period. I also examined the impact of each of these schedules on our ability to identify a change point, the year in which a change in growth rate occurred. A stochastic age- and sex-structured population model was used to project the population from 1994 to 2032 with two modifications: first a change in the birth rate and survival rate occurred in 2012 to increase or decrease the population's intrinsic growth rate by a fixed amount depending on the growth scenario; and second, an additional output was created for each model run to simulate a time series of aerial survey abundance estimates. The time series of simulated estimates were then analyzed to determine the probability of each type of error under each sampling schedule. Twelve growth scenarios were considered: increases of 1%, 2%, 3%, 4%, no change, and decreases of -1%, -2%, -3%, -4%, -5%, -7%, and -10% per year. To test if a change in trend was indicated when none had occurred (Type 1 error), I used a linear regression of the natural logarithm of the estimated abundance on year to measure the trend and change in trend. The trend-change model assumes that the trend changes began in 2012. For each of the proposed schedules, the series of abundance estimates from the last 11 years (2002-2012) was used, then the alternative schedule for the years 2013 and later. For the measurement of the change in trend, I used a one tailed t-test with alpha = 0.05 to determine if the values for the change in trend were significantly different from zero. I also fit a model with no change in trend to the time series of estimated abundance and used Akaike Information Criteria (AICc) to choose between the trend-change model and the no-change model. With no change in the growth rate of the population, there was an 8% to 22% chance that the estimated change would be significantly different from zero. The probability that the AICc would conclude that a change had occurred when there was no change in the growth rate was very low (