Detection probability of Aquatic Warbler males by a single observer along a 1-km transect at different vegetation heights in different years in the Biebrza valley, Poland, estimated with a binomial mixture model.

<p>Error bars represent 95% confidence intervals.</p

Improving Aquatic Warbler Population Assessments by Accounting for Imperfect Detection

<div><p>Monitoring programs designed to assess changes in population size over time need to account for imperfect detection and provide estimates of precision around annual abundance estimates. Especially for species dependent on conservation management, robust monitoring is essential to evaluate the effectiveness of management. Many bird species of temperate grasslands depend on specific conservation management to maintain suitable breeding habitat. One such species is the Aquatic Warbler (<i>Acrocephalus paludicola</i>), which breeds in open fen mires in Central Europe. Aquatic Warbler populations have so far been assessed using a complete survey that aims to enumerate all singing males over a large area. Because this approach provides no estimate of precision and does not account for observation error, detecting moderate population changes is challenging. From 2011 to 2013 we trialled a new line transect sampling monitoring design in the Biebrza valley, Poland, to estimate abundance of singing male Aquatic Warblers. We surveyed Aquatic Warblers repeatedly along 50 randomly placed 1-km transects, and used binomial mixture models to estimate abundances per transect. The repeated line transect sampling required 150 observer days, and thus less effort than the traditional ‘full count’ approach (175 observer days). Aquatic Warbler abundance was highest at intermediate water levels, and detection probability varied between years and was influenced by vegetation height. A power analysis indicated that our line transect sampling design had a power of 68% to detect a 20% population change over 10 years, whereas raw count data had a 9% power to detect the same trend. Thus, by accounting for imperfect detection we increased the power to detect population changes. We recommend to adopt the repeated line transect sampling approach for monitoring Aquatic Warblers in Poland and in other important breeding areas to monitor changes in population size and the effects of habitat management.</p></div

Outline of the study area in the Biebrza valley, Poland, indicating the location of 50 1-km transects along which singing male Aquatic Warblers were surveyed in 2011 and 2012.

<p>The outline represents the border of the Biebrza National Park, the grey shaded areas depict suitable Aquatic Warbler habitat that is surveyed during the ‘full count’ approach.</p

Estimated abundance of singing Aquatic Warbler males on 1-km transects at different water levels in the Biebrza valley, Poland, based on a binomial mixture model with survey data from 2011 – 2013.

<p>Error bars represent 95% confidence intervals. Water level was recorded in four categories: dry, wet after trampling, standing water<15 cm above ground, standing water > 15 cm above ground.</p

Parameter estimates (mean, standard deviation, lower and upper 95% credible intervals) of the most parsimonious binomial mixture model to estimate abundance of singing male Aquatic Warblers in the Biebrza valley, Poland, in 2011 – 2013.

<p>Parameter estimates (mean, standard deviation, lower and upper 95% credible intervals) of the most parsimonious binomial mixture model to estimate abundance of singing male Aquatic Warblers in the Biebrza valley, Poland, in 2011 – 2013.</p

Model selection table showing binomial mixture models of Aquatic Warbler abundance in Biebrza valley, Poland, from 2011 to 2013.

<p>Models have two components accounting for variation in abundance and detection probability, and all models included a temporal trend parameter on abundance. <i>k</i>  =  number of estimable parameters, AIC  =  Akaike's information criterion, ΔAIC  =  difference in AIC units to the most parsimonious model, ωAIC  =  relative weight of evidence for each model. Only the best 20 of a total of 45 fitted models are shown, remaining models were not supported by the available data (ΔAIC > 45).</p