Aims  Biogeographical evidence suggests a strong link between climate and patterns of species diversity, and climate change is known to cause range shifts. However, there is little understanding of how shifts affect community composition and we lack empirical evidence of recent impacts of climate change on the diversity of vertebrates. Using a long‐term comprehensive dataset on bird abundance, we explore recent patterns of change in different components of species diversity and avian communities, and postulate a process to explain the observed changes in diversity and specialization. Location  Britain. Methods  We used Breeding Bird Survey data for Britain from 1994 to 2006 to calculate site‐specific diversity and community specialization indices. We modelled these indices using generalized additive models to examine the relationship between local climate and spatial and temporal trends in community metrics and the relationship between changes in diversity and specialization. Results  Local temperature was positively associated with alpha diversity, which increased over the study period, supporting empirical and theoretical predictions of the effect of climate warming. Diversity increased in all habitats, but the rate of increase was greatest in upland areas. However, temperature was negatively associated with community specialization indices, which declined over the same period. Our modelling revealed a nonlinear relationship between community specialization and species diversity. Main conclusions  Our models of diversity and specialization provide stark empirical evidence for a link between warming climate and community homogenization. Over a 13‐year period of warming temperatures, diversity indices increased while average community specialization decreased. We suggest that the observed diversity increases were most likely driven by range expansion of generalist species and that future warming is likely to increase homogenization of community structure. When assessed in combination, diversity and specialization measures provide a powerful index for monitoring the impacts of climate change.

Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias. Innovation We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size. Main conclusions Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non‐detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research.

Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10-58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.

Biodiversity is being lost at an unprecedented rate, and monitoring is crucial for understanding the causal drivers and assessing solutions. Most biodiversity monitoring data are collected by volunteers through citizen science projects, and often crucial information is lacking to account for the inevitable biases that observers introduce during data collection. We contend that citizen science projects intended to support biodiversity monitoring must gather information about the observation process as well as species occurrence. We illustrate this using eBird, a global citizen science project that collects information on bird occurrences as well as vital contextual information on the observation process while maintaining broad participation. Our fundamental argument is that regardless of what species are being monitored, when citizen science projects collect a small set of basic information about how participants make their observations, the scientific value of the data collected will be dramatically improved.