Reliability of Trend Lines Obtained from Variable Counts

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Abstract
Standard errors (SE) of trend lines arising from highly variable counts of animals are drived analytically given the variability of the counts, the number of years of trend monitoring, the number of replicate counts each year, and certain assumptions. Examples of the analytical solution''s use and graphs of expected trend line variability are presented. When counts are highly variable, performing multiple counts each year is shown to be the only way to achieve precision of a population trend estimate within a short (< 12 years) time frame. Computer simulations to test the robustness of the analytical treatment under assumption violations show that it never overestimates and sometimes underestimates the true variability of trend lines. Accordingly, when count variability cannot be reduced, I suggest increasing the number of replicate counts each year above that indicated by the analytical solution to ensure that confidence limits (CL) about the trend line include the population''s true rate of growth.

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