Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, variation in effort, and variation in observer skill.To demonstrate key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate three widely applied metrics for describing species distributions: encounter rate, occupancy probability, and relative abundance. For each method, we outline approaches for data processing and modelling that are suitable for using citizen science data for estimating species distributions.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with two key processes 1) the use of complete checklists rather than presence-only data, and 2) the use of covariates describing variation in effort and detectability for each checklist. Including these covariates accounted for heterogeneity in detectability and reporting, and resulted in substantial differences in predicted distributions. The data processing and analytical steps we outlined led to improved model performance across a range of sample sizes.When using citizen science data it is imperative to carefully consider the appropriate data processing and analytical procedures required to address the bias and variation. Here, we describe the consequences and utility of applying our suggested approach to semi-structured citizen science data to estimate species distributions. The methods we have outlined are also likely to improve other forms of inference and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

Climate warming is driving changes in species distributions, although many species show a so-called climatic debt, where their range shifts lag behind the fast shift in temperature isoclines. Protected areas (PAs) may impact the rate of distribution changes both positively and negatively. At the cold edges of species distributions, PAs can facilitate species distribution changes by increasing the colonization required for distribution change. At the warm edges, PAs can mitigate the loss of species, by reducing the local extinction of vulnerable species. To assess the importance of PAs to affect species distribution change, we evaluated the changes in a non-breeding waterbird community as a response to temperature increase and PA status, using changes of species occurrence in the Western-Palearctic over 25 years (97 species, 7,071 sites, 39 countries, 1993– 2017). We used a community temperature index (CTI) framework based on species thermal affinities to investigate the species turn-over induced by temperature increase. In addition, we measured whether the thermal community adjustment was led by cold-dwelling species extinction and/or warm-dwelling species colonization, by modelling the change in standard deviation of the CTI (CTIsd). Using linear mixed-effects models, we investigated whether communities within PAs had lower climatic debt and different patterns of community change regarding the local PA surface. Thanks to the combined use of the CTI and CTIsd, we found that communities inside PAs had more species, higher colonization, lower extinction and the climatic debt was 16% lower than outside PAs. The results suggest the importance of PAs to facilitate warm-dwelling species colonization and attenuate cold-dwelling species extinction. The community adjustment was however not sufficiently fast to keep pace with the strong temperature increase in central and northeastern Western-Palearctic regions. Our study underlines the potential of the combined CTI and CTIsd metrics to understand the colonization-extinction patterns driven by climate warming.