Summary 1.Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme. 2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1-10 (mean 3) replicate surveys during each spring 1994-2005. 3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35-151). 5. Synthesis and applications. Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.

Data on the first appearance of species in the field season are widely used in phenological studies. However, there are probabilistic arguments for bias in estimates of phenological change if sampling methods or population abundances change. We examined the importance of bias in three measures of phenological change: (1) the date of the first X appearances, (2) the date of the first Y% of all first appearances and (3) the date of the first Z% of the individuals observed during the entire flight period. These measures were tested by resampling the data of the Dutch Butterfly Monitoring Scheme and by simulations using artificial data. We compared datasets differing in the number of sampling sites, population abundance and the start of the observation period. The date of the first X appearances proved to be sensitive to the number of sampling sites. Both the date of the first X appearances and the date of the first Y% of all first appearances were sensitive to population trend. No such biases were found for estimates of the first Z% of the flight period, but all three measures were sensitive to changes in the start of the observation period. The conclusions were similar for both the study on butterfly data and the simulation study. Bias in phenology assessments based on first appearance data may be considerable and should no longer be ignored in phenological research.

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Around fifteen thousand fieldworkers annually count breeding birds using standardized protocols in 28 European countries. The observations are collected by using country-specific and standardized protocols, validated, summarized and finally used for the production of continent-wide annual and long-term indices of population size changes of 170 species. Here, we present the database and provide a detailed summary of the methodology used for fieldwork and calculation of the relative population size change estimates. We also provide a brief overview of how the data are used in research, conservation and policy. We believe this unique database, based on decades of bird monitoring alongside the comprehensive summary of its methodology, will facilitate and encourage further use of the Pan-European Common Bird Monitoring Scheme results.