Implications of survey effort on estimating demographic parameters of a long‐lived marine top predator

Effective management of wildlife populations rely on knowledge of their abundance, survival, and reproductive rates. Maintaining long‐term studies capable of estimating demographic parameters for long‐lived, slow‐reproducing species is challenging. Insights into the effects of research intensity on the statistical power to estimate demographic parameters are limited. Here, we investigate implications of survey effort on estimating abundance, home range sizes, and reproductive output of Indo‐Pacific bottlenose dolphins (Tursiops aduncus), using a 3‐year subsample of a long‐term, capture–recapture study off Bunbury, Western Australia. Photo‐identification on individual dolphins was collected following Pollock's Robust Design, where seasons were defined as “primary periods”, each consisting of multiple “secondary periods.” The full dataset consisted of 12 primary periods and 72 secondary periods, resulting in the study area being surveyed 24 times/year. We simulated reduced survey effort by randomly removing one, two, or three secondary periods per primary period. Capture–recapture models were used to assess the effect of survey intensity on the power to detect trends in population abundance, while individual dolphin sighting histories were used to assess the ability to conduct home range analyses. We used sighting records of adult females and their calving histories to assess survey effort on quantifying reproductive output. A 50% reduction in survey effort resulted in (a) up to a 36% decline in population abundance at the time of detection; (b) a reduced ability to estimate home range sizes, by increasing the time for individuals to be sighted on ≥30 occasions (an often‐used metric for home range analyses) from 7.74 to 14.32 years; and (c) 33%, 24%, and 33% of annual calving events across three years going undocumented, respectively. Results clearly illustrate the importance of survey effort on the ability to assess demographic parameters with clear implications for population viability analyses, population forecasting, and conservation efforts to manage human–wildlife interactions.