An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order to estimate the actual probability of occupancy (i.e. proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. Because the majority of eBird’s data are not collected in the form of repeated observations at individual locations, we explore 2 ways in which the single-visit records could be used in occupancy models. First, we assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. On average, eBird’s observers did not make their observations at locations that were representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Thus, the use of space-for-time substitution is not always appropriate. Second, we explored the utility of including data from single-visit records to supplement sets of repeated-visit data. In a simulation study we found that inclusion of single-visit records increased the precision of occupancy estimates, but only when detection probabilities are high. When detection probability was low, the addition of single-visit records exacerbated biases in estimates of occupancy probability. We conclude that subsets of data from eBird, and likely from similar projects, can be used for occupancy modeling either using space-for-time substitution or supplementing repeated-visit data with data from single-visit records. The appropriateness of either alternative will depend on the goals of a study and on the probabilities of detection and occupancy of the species of interest.
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Abstract Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process.
Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores.
To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change.
The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data.
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Abstract Global climate change is driving species' distributions towards the poles and mountain tops during both non‐breeding and breeding seasons, leading to changes in the composition of natural communities. However, the degree of season differences in climate‐driven community shifts has not been thoroughly investigated at large spatial scales.
We compared the rates of change in the community composition during both winter (non‐breeding season) and summer (breeding) and their relation to temperature changes.
Based on continental‐scale data from Europe and North America, we examined changes in bird community composition using the community temperature index (CTI) approach and compared the changes with observed regional temperature changes during 1980–2016.
CTI increased faster in winter than in summer. This seasonal discrepancy is probably because individuals are less site‐faithful in winter, and can more readily shift their wintering sites in response to weather in comparison to the breeding season. Regional long‐term changes in community composition were positively associated with regional temperature changes during both seasons, but the pattern was only significant during summer due to high annual variability in winter communities. Annual changes in community composition were positively associated with the annual temperature changes during both seasons.
Our results were broadly consistent across continents, suggesting some climate‐driven restructuring in both European and North American avian communities. Because community composition has changed much faster during the winter than during the breeding season, it is important to increase our knowledge about climate‐driven impacts during the less‐studied non‐breeding season.
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Abstract Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas and climate and land-use data to predict distributional changes over a century (the 1970s–2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas. Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km yr −1 towards the north (5% northeast, 45% north, and 40% northwest), compared to a slower observed average shift of ca. 3.9 km yr −1 . Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to ‘climate debt’. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds’ resilience to the expected environmental changes in the future.more » « less
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Protected area networks help species respond to climate warming. However, the contribution of a site's environmental and conservation-relevant characteristics to these responses is not well understood. We investigated how composition of nonbreeding waterbird communities (97 species) in the European Union Natura 2000 (N2K) network (3018 sites) changed in response to increases in temperature over 25 years in 26 European countries. We measured community reshuffling based on abundance time series collected under the International Waterbird Census relative to N2K sites’ conservation targets, funding, designation period, and management plan status. Waterbird community composition in sites explicitly designated to protect them and with management plans changed more quickly in response to climate warming than in other N2K sites. Temporal community changes were not affected by the designation period despite greater exposure to temperature increase inside late-designated N2K sites. Sites funded under the LIFE program had lower climate-driven community changes than sites that did not received LIFE funding. Our findings imply that efficient conservation policy that helps waterbird communities respond to climate warming is associated with sites specifically managed for waterbirds.more » « less
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1. There is increasing availability and use of unstructured and semi-structured citizen science data in biodiversity research and conservation. This expansion of a rich source of ‘big data’ has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets. 2. We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects. 3. We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models. 4. While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity.more » « less
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null (Ed.)Climate warming is driving changes in species distributions and community composition. Many species have a so-called climatic debt, that is, shifts in range lag behind shifts in temperature isoclines. Inside protected areas (PAs), community changes in response to climate warming can be facilitated by greater colonization rates by warm-dwelling species, but also mitigated by lowering extirpation rates of cold-dwelling species. An evaluation of the relative importance of colonization-extirpation processes is important to inform conservation strategies that aim for both climate debt reduction and species conservation. We assessed the colonization-extirpation dynamics involved in community changes in response to climate inside and outside PAs. To do so, we used 25 years of occurrence data of nonbreeding waterbirds in the western Palearctic (97 species, 7071 sites, 39 countries, 1993–2017). We used a community temperature index (CTI) framework based on species thermal affinities to investigate species turnover induced by temperature increase. We determined whether thermal community adjustment was associated with colonization by warm-dwelling species or extirpation of cold-dwelling species by modeling change in standard deviation of the CTI (CTISD). Using linear mixed-effects models, we investigated whether communities in PAs had lower climatic debt and different patterns of community change than communities outside PAs. For CTI and CTISD combined, communities inside PAs had more species, higher colonization, lower extirpation, and lower climatic debt (16%) than communities outside PAs. Thus, our results suggest that PAs facilitate 2 independent processes that shape community dynamics and maintain biodiversity. The community adjustment was, however, not sufficiently fast to keep pace with the large temperature increases in the central and northeastern western Palearctic. Our results underline the potential of combining CTI and CTISD metrics to improve understanding of the colonization-extirpation patterns driven by climate warming.more » « less
