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Quantifying the abundance of species is essential to ecology, evolution, and conservation. The distribution of species abundances is fundamental to numerous longstanding questions in ecology, yet the empirical pattern at the global scale remains unresolved, with a few species’ abundance well known but most poorly characterized. In large part because of heterogeneous data, few methods exist that can scale up to all species across the globe. Here, we integrate data from a suite of well-studied species with a global dataset of bird occurrences throughout the world—for 9,700 species (∼92% of all extant species)—and use missing data theory to estimate species-specific abundances with associated uncertainty. We find strong evidence that the distribution of species abundances is log left skewed: there are many rare species and comparatively few common species. By aggregating the species-level estimates, we find that there are ∼50 billion individual birds in the world at present. The global-scale abundance estimates that we provide will allow for a line of inquiry into the structure of abundance across biogeographic realms and feeding guilds as well as the consequences of life history (e.g., body size, range size) on population dynamics. Importantly, our method is repeatable and scalable: as data quantity and quality increase, our accuracy in tracking temporal changes in global biodiversity will increase. Moreover, we provide the methodological blueprint for quantifying species-specific abundance, along with uncertainty, for any organism in the world.

Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e. time between original survey and re‐survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility) and spatial sampling biases (i.e. site‐selection biases). We use PREDICTS model‐based estimates as a proxy for the real‐world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30‐year period, the number of sites for detecting trends doubled for a decade, increased 10‐fold within three years and yearly trends were undetectable. Measurement errors had a non‐linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.

Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a \"leaderboard\" framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally \"valuable?\" Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.

Citizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that have unstructured protocols and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the unstructured iNaturalist platform with that from a semi-structured citizen science platform—eBird—for the continental United States. We tested whether four traits of species (body size, commonness, flock size, and color) predicted if a species was under- or over-represented in the unstructured dataset compared with the semi-structured dataset. We found strong evidence that large-bodied birds were over-represented in the unstructured citizen science dataset; moderate evidence that common species were over-represented in the unstructured dataset; strong evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in unstructured citizen science data. Our results suggest that biases exist in unstructured citizen science data when compared with semi-structured data, likely as a result of the detectability of a species and the inherent recording process. Importantly, in programs like iNaturalist the detectability process is two-fold—first, an individual organism needs to be detected, and second, it needs to be photographed, which is likely easier for many large-bodied species. Our results indicate that caution is warranted when using unstructured citizen science data in ecological modelling, and highlight body size as a fundamental trait that can be used as a covariate for modelling opportunistic species occurrence records, representing the detectability or identifiability in unstructured citizen science datasets. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and expand our research by including structured citizen science data to understand how biases differ among unstructured, semi-structured, and structured citizen science platforms.

As the number of observations submitted to the citizen science platform iNaturalist continues to grow, it is increasingly important that these observations can be identified to the finest taxonomic level, maximizing their value for biodiversity research. Here, we explore the benefits of acting as an identifier on iNaturalist.

Citizen scientists play an increasingly important role in biodiversity monitoring. Most of the data, however, are unstructured—collected by diverse methods that are not documented with the data. Insufficient understanding of the data collection processes presents a major barrier to the use of citizen science data in biodiversity research. We developed a questionnaire to ask citizen scientists about their decision-making before, during and after collecting and reporting species observations, using Germany as a case study. We quantified the greatest sources of variability among respondents and assessed whether motivations and experience related to any aspect of data collection. Our questionnaire was answered by almost 900 people, with varying taxonomic foci and expertise. Respondents were most often motivated by improving species knowledge and supporting conservation, but there were no linkages between motivations and data collection methods. By contrast, variables related to experience and knowledge, such as membership of a natural history society, were linked with a greater propensity to conduct planned searches, during which typically all species were reported. Our findings have implications for how citizen science data are analysed in statistical models; highlight the importance of natural history societies and provide pointers to where citizen science projects might be further developed.

Color research continuously demands better methods and larger sample sizes. Citizen science (CS) projects are producing an ever‐growing geo‐ and time‐referenced set of photographs of organisms. These datasets have the potential to make a huge contribution to color research, but the reliability of these data need to be tested before widespread implementation. We compared the difference between color extracted from CS photographs with that of color extracted from controlled lighting conditions (i.e., the current gold standard in spectrometry) for both birds and plants. First, we tested the ability of CS photographs to quantify interspecific variability by assessing > 9,000 CS photographs of 537 Australian bird species with controlled museum spectrometry data. Second, we tested the ability of CS photographs to quantify intraspecific variability by measuring petal color data for two plant species using seven methods/sources with varying levels of control. For interspecific questions, we found that by averaging out variability through a large sample size, CS photographs capture a large proportion of across species variation in plumage color within the visual part of the spectrum (R2 = 0.68–0.71 for RGB space and 0.72–0.77 for CIE‐LAB space). Between 12 and 14 photographs per species are necessary to achieve this averaging effect for interspecific studies. Unsurprisingly, the CS photographs taken with commercial cameras failed to capture information in the UV part of the spectrum. For intraspecific questions, decreasing levels of control increase the color variation but averaging larger sample sizes can partially mitigate this, aside from particular issues related to saturation and irregularities in light capture. CS photographs offer a very large sample size across space and time which offers statistical power for many color research questions. This study shows that CS photographs contain data that lines up closely with controlled measurements within the visual spectrum if the sample size is large enough, highlighting the potential of CS photographs for both interspecific and intraspecific ecological or biological questions. With regard to analyzing color in CS photographs, we suggest, as a starting point, to measure multiple random points within the ROI of each photograph for both patterned and unpatterned patches and approach the recommended sample size of 12–14 photographs per species for interspecific studies. Overall, this study provides groundwork in analyzing the reliability of a novel method, which can propel the field of studying color forward. Citizen science photographic data are a massive and continuously growing resource, with a largely untapped potential for breaking logistical barriers in color research. Using over 9,000 photographs of 537 species of Australian birds and two plant species, we assessed color accuracy and precision inter‐ and intraspecifically in comparison with spectrometry, the current gold standard.

Urbanisation is occurring around the world at a rapid rate and is generally associated with negative impacts on biodiversity at local, regional, and global scales. Examining the behavioural response profiles of wildlife to urbanisation helps differentiate between species that do or do not show adaptive responses to changing landscapes and hence are more or less likely to persist in such environments. Species-specific responses to urbanisation are poorly understood in the Southern Hemisphere compared to the Northern Hemisphere, where most of the published literature is focussed. This is also true for raptors, despite their high diversity and comparably high conservation concern in the Southern Hemisphere, and their critical role within ecosystems as bioindicators of environmental health. Here, we explore this knowledge gap using community science data sourced from eBird to investigate the urban tolerance of 24 Australian raptor species at a continental scale. We integrated eBird data with a global continuous measure of urbanisation, artificial light at night (ALAN), to derive an urban tolerance index, ranking species from positive to negative responses according to their tolerance of urban environments. We then gathered trait data from the published literature to assess whether certain traits (body mass, nest substrate, habitat type, feeding guild, and migratory status) were associated with urban tolerance. Body size was negatively associated with urban tolerance, as smaller raptors had greater urban tolerance than larger raptors. Out of the 24 species analysed, 13 species showed tolerance profiles for urban environments (positive response), and 11 species showed avoidance profiles for urban environments (negative response). The results of this study provide impetus to conserve native habitat and improve urban conditions for larger-bodied raptor species to conserve Australian raptor diversity in an increasingly urbanised world.

Contrasting trajectories of biodiversity loss and urban expansion make it imperative to understand biodiversity persistence in cities. Size‐, local‐, and landscape‐level habitat factors of greenspaces in cities may be critical for future design and management of urban greenspaces in conserving bird biodiversity. Most current understanding of bird communities in cities has come from disparate analyses of single cities, over relatively short time periods, producing limited understanding of processes and characteristics of bird patterns for improved biodiversity management of the world's cities. We analyzed bird biodiversity in 112 urban greenspaces from 51 cities across eight countries, using eBird, a broadscale citizen science project. Species richness and Shannon diversity were used as response variables, while percent tree cover, percent water cover, and vegetation index were used as habitat predictor variables at both a landscape (5 and 25 km radius) and local‐scale level (specific to an individual greenspace) in the modeling process, retrieved using Google Earth Engine. Area of a greenspace was the most important predictor of bird biodiversity, underlining the critical importance of habitat area as the most important factor for increasing bird biodiversity and mitigating loss from urbanization. Surprisingly, distance from the city center and distance from the coast were not significantly related to bird biodiversity. Landscape‐scale habitat predictors were less related to bird biodiversity than local‐scale habitat predictors. Ultimately, bird biodiversity loss could be mitigated by protecting and developing large greenspaces with varied habitat in the world's cities.

Most citizen science research inherently separates the observer (citizen science participant) from the observation (e.g. data point), placing artificial boundaries around what matters and how it comes to matter. We apply three elements of the philosophical framework of agential realism to reveal a more complex picture of how data arise within citizen science programmes, and its meaning to both the practice of science and the citizen science participant: ‘intra‐action’ (all entities have agency and are entangled with one another); ‘material becoming’ (what comes to matter); and ‘responsibility’ (accountability for what comes to matter and what is excluded from mattering). We draw on a case study of FrogID—an Australia‐wide citizen science program focused on calling frogs, with over 42,000 participants and over 1 million frog records. We conducted semi‐structured interviews with 30 FrogID users, completing two rounds of thematic and relational coding. Our findings reveal that as a consequence of their recording behaviours, FrogID participants become increasingly entangled with the nocturnal environment, with sound and with their own self. Expanding and reciprocal relationships and experiences shape the nature and frequency of their recordings. Second, meaning influences what comes to matter (i.e. what is recorded and submitted) for FrogID participants. We reveal meaning related to feedback (recognition and thus reciprocity), others (social networks and participation with family and friends) and the self (physical and mental well‐being and identity formation/becoming). These different forms of meaning influenced engagement with app use. Third, participants communicated responsibilities related to their involvement in citizen science, including responsibilities to create knowledge (e.g. longitudinal data collection), to conserve (e.g. actively conserving frog, formally committing areas to conservation) and to educate self and others (e.g. skills and competencies required for environmental action). Synthesis and applications: By recognizing a more comprehensive set of intra‐actions, beyond the observer and the observation, agential realism can reveal when, why and how citizen science observations are made; what observations come to matter and why; and how people can create a more just world. Agential realism can shape how citizen science participation, retention and biodiversity data generation are founded. We propose three opportunities for citizen science programs based on these findings. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog.