Summary1. The analysis of large heterogeneous data sets of avian point-count surveys compiled across studies is hindered by a lack of analytical approaches that can deal with detectability and variation in survey protocols. 2. We reformulated removal models of avian singing rates and distance sampling models of the effective detection radius (EDR) to control for the effects of survey protocol and temporal and environmental covariates on detection probabilities. 3. We estimated singing rates and EDR for 75 boreal forest songbird species and found that survey protocol, especially point-count radius, explained most of the variation in detectability. However, environmental and temporal covariates (date, time, vegetation) affected singing rates and EDR for 73% and 59% of species, respectively. 4. Unadjusted survey counts increased by an average of 201% from a 5-min, 50-m radius survey to a 10-min, 100-m radius survey (n = 75 species). This variability was decreased to 8Á5% using detection probabilities estimated from a combination of removal and distance sampling models. 5. Our modelling approach reduced computation when fitting complex models to large data sets and can be used with a wide range of statistical techniques for inference and prediction of avian densities.

We used binomial distance-sampling models to estimate the effective detection radius (EDR) of point-count surveys across boreal Canada. We evaluated binomial models based on 0-50 m and >50 m distance categories for goodness-of-fit and sensitivities to variation in survey effort and habitats sampled. We also compared binomial EDRs to Partners in Flight's maximum detection distances (MDD) to determine differences in landbird population sizes derived from each. Binomial EDRs had a small positive bias (4%) averaged across 86 species and a large positive bias (30-82%) for two species when compared with EDRs estimated using multinomial distance sampling. Patterns in binomial EDRs were consistent with how bird songs attenuate in relation to their frequencies and transmission through different habitats. EDR varied 12% among habitats and increased 17% when birds were counted to an unlimited distance, compared with a limited distance of 100 m. The EDR did not vary with the duration of surveys, and densities did not differ when using unlimited-distance versus truncated data. Estimated densities, however, increased 19% from 3-to 5-min counts and 25% from 5-to 10-min counts, possibly from increases in the availability, movement, or double counting of birds with longer counts. Thus, investigators should be cautious when comparing distance-sampling results among studies if methods vary. Population sizes estimated using EDR averaged 5 times (0.8-15 times) those estimated with MDD. Survey data from which to estimate binomial EDRs are widely available across North America and could be used as an alternative to MDD when estimating landbird population sizes. Utilisation de modèles binomiaux d'échantillonnages basés sur la distance pour estimer le rayon de détection effectif lors d'inventaires par points d'écoute en région boréale canadienneRésumé.-Nous avons utilisé des modèles binomiaux d'échantillonnages basés sur la distance afin d'estimer le rayon de détection effectif (RDE) des inventaires par points d'écoute en région boréale canadienne. Nous avons évalué des modèles binomiaux basés sur des catégories de distance de 0-50 m et >50 m pour en déterminer l'adéquation et les sensibilités aux variations de l'effort d'inventaire et les habitats échantillonnés. Nous avons aussi comparé les RDE binomiaux aux distances de détection maximales de Partenaires d'envol (DDM) afin de déterminer les différences dans les tailles de populations des oiseaux terrestres estimées. Les RDE binomiaux avaient un léger biais positif (4 %) en moyenne pour 86 espèces et un biais positif élevé (30-82 %) pour deux espèces lorsque comparés aux RDE estimés à l'aide d'un échantillonnage multinomial selon la distance. Les patrons des RDE binomiaux étaient consistants avec la façon dont les chants d'oiseaux s'atténuent en fonction de leur fréquence et de leur transmission à travers différents habitats. Les RDE variaient de 12 % entre les habitats et augmentaient de 17 % lorsque les oiseaux étaient dénombrés à une distance illimitée, comparativement à une distanc...

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To accomplish the objectives of a long-term ecological monitoring program (LTEM), repurposing research data collected by other researchers is an alternative to original data collection. The Boreal Avian Modelling (BAM) Project is a 10-year-old project that has integrated the data from >100 avian point-count studies encompassing thousands of point-count surveys, and harmonized across data sets to account for heterogeneity induced by methodological and other differences. The BAM project faced the classic data-management challenges any LTEM must deal with, as well as special challenges involved with harmonizing so many disparate data sources. We created a data system consisting of 4 components: Archive (to preserve each contributor's data), Avian Database (harmonized point-count data), Biophysical Database (spatially explicit environmental covariates), and Software Tools library (linking the other components and providing analysis capability). This system has allowed the project to answer many questions about boreal birds; we believe it to be successful enough to merit consideration for use in monitoring other taxa. We have learned a number of lessons that will guide the project as it moves forward. These include the importance of creating a data protocol, the critical importance of high-quality metadata, and the need for a flexible design that accommodates changes in field techniques. One of the challenges the BAM team faced-gaining access to relevant data sets-may become easier with the increased expectation by journals and funding agencies that documenting and preserving research data be a standard part of scientific research. Ó 2015 The Wildlife Society.

We used conventional and finite mixture removal models with and without time-varying covariates to evaluate availability given presence for 152 bird species using data from point counts in boreal North America. We found that the choice of model had an impact on the estimability of unknown model parameters and affected the bias and variance of corrected counts. Finite mixture models provided better fit than conventional removal models and better adjusted for count duration. However, reliably estimating parameters and minimizing variance using mixture models required at least 200–1,000 detections. Mixture models with time-varying proportions of infrequent singers were best supported across species, indicating that accounting for date- and time-related heterogeneity is important when combining data across studies over large spatial scales, multiple sampling time frames, or variable survey protocols. Our flexible and continuous time-removal modeling framework can be used to account for such heterogeneity through the incorporation of easily obtainable covariates, such as methods, date, time, and location. Accounting for availability bias in bird surveys allows for better integration of disparate studies at large spatial scales and better adjustment of local, regional, and continental population size estimates.