Assessing plausible rates of population growth in humpback whales from life-history data
Zerbini, A.N.; Clapham, P.J.; Wade, P.R. (2010). Assessing plausible rates of population growth in humpback whales from life-history data. Mar. Biol. (Berl.) 157(6): 1225-1236. http://dx.doi.org/10.1007/s00227-010-1403-y
In: Marine Biology: International Journal on Life in Oceans and Coastal Waters. Springer: Heidelberg; Berlin. ISSN 0025-3162; e-ISSN 1432-1793, more
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| Authors | | Top |
- Zerbini, A.N.
- Clapham, P.J.
- Wade, P.R.
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| Abstract |
The rate of growth of any population is a quantity of interest in conservation and management and is constrained by biological factors. In this study, recent data on life-history parameters influencing rates of population growth in humpback whales, including survival, age at first parturition and calving rate are reviewed. Monte Carlo simulations are used to compute a distribution of rates of increase (ROIs) taking into account uncertainty in biological parameter estimates. Two approaches for computing juvenile survival are proposed, which taken into account along with other life-history data, resulted in the following estimates of the rate of population growth: Approach A: mean of 7.3%/year (95% CI = 3.5–10.5%/year) and Approach B: mean of 8.6%/year (95% CI = 5.0–11.4%/year). It is proposed that the upper 99% quantile of the resulting distribution of the ROI for Approach B (11.8%/year) be established as the maximum plausible ROI for humpback whales and be used in population assessment of the species. Possible sources of positive and negative biases in the present estimates are presented and include measurement error in estimation of life-history parameters, changes in the environment within the period these quantities are measured, density dependence or other natural factors. However, it is difficult to evaluate potential biases without additional data. The methods presented in this study can be applied to other species for which life-history parameters are available and are useful in assessing plausibility in the estimation of population growth rates from time series of abundance estimates. |
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