Of power and despair in cetacean conservation: estimation and detection of trend in abundance with noisy and short time-series

Abstract

Many conservation instruments rely on detecting and estimating a population decline in a target species to take action. Trend estimation is difficult because of small sample size and relatively large uncertainty in abundance/density estimates of many wild populations of animals. Focusing on cetaceans, we performed a prospective analysis to estimate power, type-I, sign (type-S) and magnitude (type-M) error rates of detecting a decline in short time-series of abundance estimates with different signal-to-noise ratio. We contrasted results from both unregularized (classical) and regularized approaches. The latter allows to incorporate prior information when estimating a trend. Power to detect a statistically significant estimates was in general lower than 80%, except for large declines. The unregularized approach (status quo) had inflated type-I error rates and gave biased (either over- or under-) estimates of a trend. The regularized approach with a weakly-informative prior offered the best trade-off in terms of bias, statistical power, type-I, type-S and type-M error rates and confidence interval coverage. To facilitate timely conservation decisions, we recommend to use the regularized approach with a weakly-informative prior in the detection and estimation of trend with short and noisy time-series of abundance estimates.

Small Cetacean in a Human High-Use Area: Trends in Harbor Porpoise Abundance in the North Sea Over Two Decades

“…Although the North Sea-wide assessments found abundance to be similar, a notable southward shift in the distribution was detected with now highest porpoise densities in the south-western North Sea (Hammond et al, 2013(Hammond et al, , 2017. However, trend estimation is difficult when comparing only a few point estimates of abundance over a large area with their large uncertainty (Authier et al, 2020). While there are ways to derive trends between short and noisy time series from sporadic large scale survey estimates (see for example, Hammond et al, 2017), these rarely consider all sources of uncertainty and variability within the underlying data and more sophisticated approaches are needed (see for example, Authier et al, 2020).…”

“…However, trend estimation is difficult when comparing only a few point estimates of abundance over a large area with their large uncertainty (Authier et al, 2020). While there are ways to derive trends between short and noisy time series from sporadic large scale survey estimates (see for example, Hammond et al, 2017), these rarely consider all sources of uncertainty and variability within the underlying data and more sophisticated approaches are needed (see for example, Authier et al, 2020). Statistical power to detect change in marine mammal population is crucial for policy making, but achieving statistical power is challenging and has long been debated in conservation practice (Taylor and Gerrodette, 1993;Wade, 2000;Taylor et al, 2007).…”

Detecting population trends for US marine mammals

“…However, there is a lack of highquality, long-term monitoring for many marine mammals (Avila et al, 2018;Jewell et al, 2012;Kaschner et al, 2012). While long-term time series data are valuable, they are effort-intensive, expensive, and often unable to achieve the appropriate precision and accuracy needed for trend analysis (Authier et al, 2020;Katsanevakis et al, 2012). In addition, it is not always clear how many years of sampling are needed to achieve appropriate statistical power to detect trends.…”

Estimating cetacean population trends from static acoustic monitoring data using Paired Year Ratio Assessment (PYRA)

“…Here we address the problem of estimating population trends from data obtained by static acoustic monitoring of cetaceans. Such data may have been recorded at different times in different years [ 7 , 9 – 11 ] and may have other limitations. We do not address the question of site representativeness, but rather focus on methods for determining trends in data from one or more fixed sites even when there may be:…”