Accurate and robust population monitoring is essential to effective biodiversity conservation, and is at the heart of BTO’s mission. While all BTO-led surveys and schemes are volunteer driven, observation protocols differ among them, spanning the range from the highly structured BTO/JNCC/RSPB Breeding Bird Survey (BBS) to BirdTrack, which allows for the submission of casual observations by any interested birdwatcher. A new BTO study now compares data from these two schemes to see whether trends in BirdTrack reporting rates are comparable to population trends derived from BBS data. Citizen scientists are collecting opportunistic biodiversity records on unprecedented temporal and spatial scales, vastly outnumbering the records achievable from structured surveys. For example, BBS is one of the world’s most comprehensive national monitoring schemes, covering over 1.5% of the UK's land surface. To achieve this remarkable coverage, BBS volunteers conducted approximately 7,600 visits to just under 4,000 sites in 2017. Yet, this impressive number is dwarfed by the almost 80,000 complete lists that were collected by BirdTrack participants at over 16,000 locations during the same time. However, unlike BBS volunteers who visit randomly selected sites, and systematically survey them, BirdTrack participants are free to go when and where they choose. The bounty of opportunistic records, such as BirdTrack lists, therefore hides potential biases in the selection of sites and visit times, as well as variation in observer effort and skill. Such quantity-quality trade-offs between surveys, like BBS, and less structured schemes, such as BirdTrack, remain poorly understood. Recent work has advocated the use of simple statistical trend models as a quick way to leverage opportunistic citizen science data to produce trends, and as a means to fill knowledge gaps on species or regions that are poorly covered by structured surveys. In this study, BTO scientists examined the robustness of population trends of common UK birds derived from BirdTrack and BBS results. They derived trends in BirdTrack reporting rates from BirdTrack lists, using simple statistical models which accounted for variation in observer effort, such as the time spent compiling each BirdTrack list, but not for the non-random nature of the ways sites in BirdTrack are selected. They compared these trends to population trends derived from BBS survey data. For 90 of 141 species, year-to-year changes in BirdTrack reporting rates were positively correlated with BBS trends. However, such correlations were higher for widespread species and those exhibiting marked population change. Less agreement among trends was found for rarer species and those with small or uncertain population trajectories. This study's findings suggest that simple statistical models for estimating population trends from complete BirdTrack lists are robust only for widespread and common species, and do not provide a silver bullet for the monitoring of rarer species, even in a scheme with many observers and extensive coverage. Notwithstanding, all BirdTrack records are valuable. They help to map changing distributions and assess variation in migrant arrival and departure dates and BTO scientists are actively developing novel statistical methods to integrate BirdTrack data into improved distribution and trend estimates.

A review by an international team of statisticians and ecologists, including BTO’s Ecological Statistician, has highlighted novel analytical approaches to better understand species distributions, by integrating data from a wide variety of surveys and other citizen science projects. Every year more and more people contribute records of the animals and plants they observe around them to citizen science schemes. The rapid growth of this form of biodiversity recording means that ecological data are being collected at an unprecedented rate and on vast spatial and temporal scales. For example, BTO’s BirdTrack scheme has received over 5 million records in the current year alone. These massive data sets have the potential to radically improve our understanding of species distributions in time and space, and provide critical information for species monitoring and conservation planning. However, citizen science schemes differ in their format and scope, and consequently the type and quality of data collected. They span a broad spectrum from unstructured schemes, which allow the recording of single species observations at a time and place selected by the participant, to highly structured schemes which follow rigorous observation protocols at predefined survey locations, such as BTO/JNCC/RSPB Breeding Bird Survey. Combining data sets from different schemes can maximize the available information about species distributions and trends, but it is important to account for the properties of different data sources. For example, structured surveys tend to deliver very accurate and unbiased data, but are often limited in their coverage, whereas unstructured schemes may provide very large sample sizes but can suffer from numerous forms of bias, such as preferential sampling at locations with convenient access. Traditional analytical methods tend to be tailored to a single data source, requiring analysts to choose among data sets. The published review highlights a novel analytical framework which enables the integration of different data sources into a single statistical model, retaining the strengths of each input. The modelling approach explicitly separates the biological and data generation processes and can be applied to a wide spectrum of data types, spanning haphazard observations and systematic population counts. It is based on so-called point processes, which are statistical descriptions of the way animals or their home range centres are distributed in space. The review features case studies demonstrating the application of this integrated modelling approach to citizen science data sets of trees, butterflies, frogs, and birds. While some questions remain open, in particular about how much structured data is required to overcome possible biases in unstructured data sets, the integrative approach opens a promising avenue of research, and holds the potential to gain a better understanding of the distribution of rare and uncommon species that are currently poorly covered by schemes such as the Breeding Bird Survey.