Comparing records of exotic insects between citizen science observations and taxonomic collections

Currently Russia doesn't have a national biodiversity information system, and is still not a GBIF (Global Biodiversity Information Facility) member. Nevertheless, GBIF is the largest source of biodiversity data for Russia. As of August 2020, >5M species occurrences were available through the GBIF portal, of which 54% were published by Russian organisations. There are 107 institutions from Russia that have become GBIF publishers and 357 datasets have been published. The important trend of data mobilization in Russia is driven by the considerable contribution of citizen science. The most popular platform is iNaturalist. This year, the related GBIF dataset (Ueda 2020) became the largest one for Russia (793,049 species occurrences as of 2020-08-11). The first observation for Russia was posted in 2011, but iNaturalist started becoming popular in 2017. That year, 88 observers added >4500 observations that represented 1390 new species for Russia, 7- and 2-fold more respectively, than for the previous 6 years. Now we have nearly 12,000 observers, about 15,000 observed species and >1M research-grade observations. The ratio of observations for Tracheophyta, Chordata, and Arthropoda in Russia is different compared to the global scale. There are almost an equal amount of observations in the global iNaturalist GBIF dataset for these groups. At the same time in Russia, vascular plants make up 2/3rds of the observations. That is due to the "Flora of Russia" project, which attracted many professional botanists both as observers and experts. Thanks to their activity, Russia has a high proportion of research-grade observations in iNaturalist, 78% versus 60% globally. Another consequence of wide participation by professional researchers is the high rate of species accumulation. For some taxonomic groups conspicuous species were already revealed. There are about 850 bird species in Russia of which 398 species were observed in 2018, and only 83 new species in 2019. Currently, the number of new species recorded over time is decreasing despite the increase in observers and overall user activity. Russian iNaturalist observers have shared a lot of archive photos (taken during past years). In 2018, it was nearly 1/4 of the total number of observations and about 3/4 of new species for the year, with similar trends observed during 2019. Usually archive photos are posted from December until April, but the 2020 pandemic lockdown spurred a new wave of archive photo mobilisation in April and May. There are many iNaturalist projects for protected areas in Russia: 27 for strict nature reserves and national parks, and about 300 for others. About 100,000 observations (7.5% of all Russian observations) from the umbrella project "Protected areas of Russia" represent >34% of the species diversity observed in Russia. For some regions, e.g., Novosibirsk, Nizhniy Novgorod and Vladimir Oblasts, almost all protected areas are covered by iNaturalist projects, and are often their only source of available biodiversity data. There are also other popular citizen science platforms developed by Russian researchers. The first one is the Russian birdwatching network RU-BIRDS.RU. The related GBIF dataset (Ukolov et al. 2019) is the third largest dataset for Russia (>370,000 species occurrences). Another Russian citizen science system is wildlifemonitoring.ru, which includes thematic resources for different taxonomic groups of vertebrates. This is the crowd-sourced web-GIS maintained by the Siberian Environmental Center NGO in Novosibirsk. It is noteworthy that iNaturalist activities in Russia are developed more as a social network than as a way to attract volunteers to participate in scientific research. Of 746 citations in the iNaturalist dataset, only 18 articles include co-authors from Russia. iNaturalist data are used for the management of regional red lists (in the Republic of Bashkortostan, Novosibirsk Oblast and others), and as an additional information source for regional inventories. RU-BIRDS data were used in the European Russia Breeding Bird Atlas and the new edition of the European Breeding Bird Atlas. In Russia, citizen science activities significantly contribute to filling gaps in the global biodiversity map. However, Russian iNaturalist observations available through GBIF originate from the USA. It is not ideal, because the iNaturalist GBIF dataset is growing rapidly, and in the future it will represent more than all other datasets for Russia combined. In our opinion, iNaturalist data should be repatriated during the process of publishing through GBIF, as it is implemented for the eBird dataset (Levatich and Ligocki 2020).

The acceleration of global environmental change underscores the pressing need for a comprehensive understanding of how the biosphere interacts with its environment. To reliably examine these connections across diverse ecosystems, having extensive and spatially comprehensive data on plant functional traits is imperative. The TRY database boasts an extensive repository of plant trait measurements for thousands of species, and, while previous approaches have attempted to spatially extrapolate these traits using environmental predictors and remote sensing data, the scarcity of the original data leads to significant uncertainty in the extrapolations. Meanwhile, citizen scientists have been actively gathering dense observations of species occurrences worldwide, and when matched with trait data, can adequately represent global trait patterns. Here, we explore the use of citizen science and Earth observation data to generate global maps of 31 ecologically relevant plant functional traits. The study utilizes sparse spatial grids created by linking species occurrences from the Global Biodiversity Information Facility (GBIF) with the TRY gap-filled database to generate continuous global trait maps as a function of climate, soil, and remote sensing data. We first evaluated model performance using spatial cross-validation, and they demonstrated up to R2 = 0.53 with a normalized RMSE = 0.21. We then compared mean trait values from the GBIF-based extrapolations to community-weighted mean traits from sPlotOpen, a global, environmentally balanced dataset of vegetation plot data. Our results show correlations between the two datasets of up to r = 0.73 with particular resilience to decreasing map resolution. When compared to similar extrapolations based on sPlotOpen alone, we found that GBIF-based extrapolations increased global spatial applicability for all maps by up to 12%. Additionally, we show that GBIF-based extrapolations have higher correlations to sPlotOpen-derived maps than the majority of previously published trait maps. Despite the inherent noise and biases of their crowd-sourced origins, GBIF-based models are remarkably capable of producing even closer approximations of the trait distributions of scientifically controlled vegetation plots than their own sparse reference data. Considering the rapid growth and availability of crowd-sourced data, the capacity of models to overcome their noisy and opportunistic nature further affirms the potential of databases such as GBIF to complement more scientifically rigorous data collections.

The study of nocturnal bird migration brings observational challenges because of reduced visibility and observability of birds at night. Remote sensing tools, especially radars, have long been the preferred choice of scientists to study nocturnal migrations. A major downside of these remote sensing tools is the lack of species-level information. With technological advances in recent decades and with improved accessibility and affordability of acoustic tools, sound recordings have steeply increased in popularity. In Europe, there is no exhaustive qualitative and quantitative evaluation of the content of such acoustic databases and therefore the value for migration science and migration-related applications, such as bird collision hazard assessments, is mostly unknown. In the present work we compared migration schedules estimated from citizen science data with quantitative temporal occurrence of species in four years of acoustic recordings. Furthermore, we contrasted acoustic recordings with citizen science observations and weather radar data from one spring and one autumn season to assess the qualitative and quantitative yield of acoustic recordings for migration-related research and applications. Migration intensity estimated from weather radar data correlated best at low levels with acoustic records including all species in spring while in autumn passerine species showed stronger correlation than the entire species composition. Our findings identify a minor number of species whose call records may be eligible for applications derived from acoustics. Especially the highly vocal species Song thrush and Redwing showed relatively good correlations with radar and citizen science migration schedules. Most long-distance passerine migrants and many other migrants were not captured by acoustics and an estimated seasonal average of about 50% of nocturnally migrating passerine populations remained undetected. Overall, the ability of acoustic records to act as a proxy of overall migration dynamics is highly dependent on the migration period and species involved.

GBIF (Global Biodiversity Information Facility) is an international research data infrastructure that mediates data from various sources such as museum collections, citizen science observations and machine generated data such as camera trap and environmental DNA. Data shared with GBIF comes with a taxonomic identification—normally a Linnaean binomial. Large data flows are now coming to GBIF without formal names but are identified by informal species hypotheses, usually based on DNA sequence similarity to a curated reference library. GBIF’s task is to integrate all this data in a repository that is accessible via a single taxonomic framework that integrates the various individual taxonomic practices. This made more challenging by idiosyncratic names that appear in GBIF-mediated datasets, which are not found in existing taxonomies. This taxonomy is known as the GBIF taxonomic backbone. GBIF is transitioning its infrastructure to build the backbone in the new ChecklistBank infrastructure so that GBIF can take advantage of the new tools the Catalogue of Life-GBIF partnership has built. This taxonomy will be more responsive to community input and will be able to integrate new knowledge at a much faster rate.

AimDespite the unprecedented rate of species redistribution during the Anthropocene, there are few monitoring programmes at the appropriate spatial and temporal scale to detect distributional change of marine species and to infer climate‐ versus human‐mediated drivers of change. Here, we present an approach that combines citizen science with expert knowledge to classify out‐of‐range occurrences for marine fishes as potential range extensions or human‐mediated dispersal events.InnovationOur stepwise approach includes decision trees, scoring and matrices to classify citizen science observations of species occurrences and to provide a measure of confidence and validation using expert knowledge. Our method draws on peer‐reviewed literature, knowledge of the species (e.g. contributing to its detectability, and potential to raft with, or foul, man‐made structures or debris) and information obtained from citizen science observations (e.g. life stage, number of individuals). Using a case study of suspected out‐of‐range marine fishes in Aotearoa New Zealand, we demonstrate our approach to defining species’ ranges, assigning confidence to these definitions and considering the species detectability to overcome the data deficiencies that currently hinder monitoring the range dynamics of these species. Our classification of citizen science observations revealed that six of ten species had out‐of‐range occurrences; one of these was classified as an extralimital vagrant, four species had potentially extended their ranges and one species occurrence was likely due to human‐mediated dispersal.ConclusionThe case study of marine fishes in New Zealand validates our approach combining citizen science observations with expert knowledge to infer species range dynamics in real time. Our stepwise approach helps to identify data deficiencies important in informing scientific inferences and management actions and can be refined to suit other data sources, taxonomic groups, geographic settings or extended with new steps and existing tools.

The use of Global Biodiversity Information Facility (GBIF)-mediated data in publications written in Chinese

Desolation comes from the sky: Invasive Hymenoptera species as prey of Chilean giant robber flies (Diptera: Asilidae) through field observations and citizen science

Abstract. The 10 May 2024 geomagnetic storm, referred to as the Gannon Storm in this paper, was one of the most extreme to have occurred in over 20 years. In the era of smartphones and social media, millions of people from all around the world were alerted to the possibility of exceptional auroral displays. Hence, many people not only witnessed but also photographed the aurora during this event. These citizen science observations, although not from scientific instruments operated by observatories or research groups, can prove to be invaluable in obtaining data to characterise this extraordinary event. In particular, many observers saw and photographed the aurora at mid-latitudes, where ground-based instruments targeting auroral studies are sparse or absent. Moreover, the proximity of the event to the Northern Hemisphere summer solstice meant that many optical instruments were not in operation due to the lack of suitably dark conditions. We created an online survey and circulated it within networks of aurora photographers to collect observations of the aurora and of disruptions in technological systems that were experienced during this superstorm. We obtained 696 citizen science reports from over 30 countries, containing information such as the time and location of aurora sightings and the observed colours and auroral forms, as well as geolocalisation, network, and power disruptions noticed during the geomagnetic storm. We supplemented the obtained dataset with 186 auroral observations logged in the Skywarden catalogue (https://taivaanvahti.fi, last access: 19 December 2024) by citizen scientists. The main findings enabled by the data collected through these reports are that the aurora was widely seen from locations at geomagnetic latitudes ranging between 30 and 60°, with a few reports from even lower latitudes. This was significantly further equatorward than predicted by auroral oval models. The reported auroral emission colours, predominantly red and pink and intense enough to reach naked-eye visibility, suggest that the auroral electron precipitation contained large fluxes of low-energy (< 1 keV) particles. This study also reveals the limitations of citizen science data collection via a rudimentary online form. We discuss possible solutions to enable more detailed and quantitative studies of extreme geomagnetic events with citizen science in the future.

I will cover how the Global Biodiversity Information Facility (GBIF) handles data quality issues, with specific focus on coordinate location issues, such as gridded datasets (Fig. 1) and country centroids. I will highlight the challenges GBIF faces identifying potential data quality problems and what we and others (Zizka et al. 2019) are doing to discover and address them. GBIF is the largest open-data portal of biodiversity data, which is a large network of individual datasets (> 40k) from various sources and publishers. Since these datasets are variable both within themselves and dataset-to-dataset, this creates a challenge for users wanting to use data collected from museums, smartphones, atlases, satellite tracking, DNA sequencing, and various other sources for research or analysis. Data quality at GBIF will always be a moving target (Chapman 2005), and GBIF already handles many obvious errors such as zero/impossible coordinates, empty or invalid data fields, and fuzzy taxon matching. Since GBIF primarily (but not exclusively) serves lat-lon location information, there is an expectation that occurrences fall somewhat close to where the species actually occurs. This is not always the case. Occurrence data can be hundereds of kilometers away from where the species naturally occur, and there can be multiple reasons for why this can happen, which might not be entirely obvious to users. One reasons is that many GBIF datasets are gridded. Gridded datasets are datasets that have low resolution due to equally-spaced sampling. This can be a data quality issue because a user might assume an occurrence record was recorded exactly at its coordinates. Country centroids are another reason why a species occurrence record might be far from where it occurs naturally. GBIF does not yet flag country centroids, which are records where the dataset publishers has entered the lat-long center of a country instead of leaving the field blank. I will discuss the challenges surrounding locating these issues and the current solutions (such as the CoordinateCleaner R package). I will touch on how existing DWCA terms like coordinateUncertaintyInMeters and footprintWKT are being utilized to highlight low coordinate resolution. Finally, I will highlight some other emerging data quality issues and how GBIF is beginning to experiment with dataset-level flagging. Currently we have flagged around 500 datasets as gridded and around 400 datasets as citizen science, but there are many more potential dataset flags.

Climate change is strongly impacting agriculture, reducing crop production and shifting the geographic distribution of suitable areas for crop cultivation. To safeguard future global yield and feed a growing world population, the migration of crop production areas to new suitable sites represents a way to adapt to a changing climate. Here, we aim to identify the ecological niche of agricultural Prunus species, namely peach, plum, almond, apricot and sweet cherry, and examine their expected future shifts under climate change. For each of the five species, we selected from the literature processed-based phenological models for dormancy break, blooming and fruit ripening, whose fulfillment determines whether an area is suitable for crop cultivation. We simulated the current pheno-suitability across Europe and validated the estimated niches with occurrence data from the Global Biodiversity Information Facility. We then implemented the phenological models to predict potential shifts in the suitablity niches under future climate change scenarios. Historically, the ecological niche of Prunus species spans mid-low European latitudes, while higher latitudes fail to satify the forcing requirements for blooming and fruit ripening. In the future, this constraint is expected to become less restrictive with a northwards expansion of the suitable areas. However, this will be contrasted by a contraction of the niche at low latitudes due to dormancy break failures. While bridging established mechanistic knowledge on the climatic effects of plant phenological traits with citizen science observations, our work brings new insights into how fruit crops will respond to global warming.

Ladybirds (Coleoptera: Coccinellidae) provide services that are critical to food production, and they fulfill an ecological role as a food source for predators. The richness, abundance, and distribution of ladybirds, however, are compromised by many anthropogenic threats. Meanwhile, a lack of knowledge of the conservation status of most species and the factors driving their population dynamics hinders the development and implementation of conservation strategies for ladybirds. We conducted a review of the literature on the ecology, diversity, and conservation of ladybirds to identify their key ecological threats. Ladybird populations are most affected by climate factors, landscape composition, and biological invasions. We suggest mitigating actions for ladybird conservation and recovery. Short-term actions include citizen science programs and education, protective measures for habitat recovery and threatened species, prevention of the introduction of non-native species, and the maintenance and restoration of natural areas and landscape heterogeneity. Mid-term actions involve the analysis of data from monitoring programs and insect collections to disentangle the effect of different threats to ladybird populations, understand habitat use by taxa on which there is limited knowledge, and quantify temporal trends of abundance, diversity, and biomass along a management-intensity gradient. Long-term actions include the development of a worldwide monitoring program based on standardized sampling to fill data gaps, increase explanatory power, streamline analyses, and facilitate global collaborations.

Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science's unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.

Due to their potential role in pathogen transmission, invasive mosquitoes pose considerable threats to human and animal health. Several studies have identified the most important ecological drivers mediating the establishment and spread of key mosquito species (e.g., Aedes aegypti, and Ae. albopictus), and made predictions for future distribution. We evaluated the effect of an exhaustive list of environmental predictors on the distribution of three invasive species in Hungary (Ae. albopictus, Ae. japonicus, and Ae. koreicus) by using the same standards for data collection based on citizen science observations. Current distribution maps of these species were generated from a 5-year survey, then were compared with various predictor maps reflecting climate, habitat type, food supply, traffic, and interspecific competition by using a boosted regression trees approach that resulted in a subset of variables with the strongest impact. The best predictor sets were used to predict the probability of occurrence of the focal species for the whole country, and these predictions based on citizen science were evaluated against the results of an independent recent field surveillance. We uncovered species-specific patterns and found that different predictor sets were selected for the three different species, and only predictions for Ae. albopictus could be validated with direct trapping data. Therefore, citizen science informed distribution maps can be used to identify ecological predictors that determine the spread of invasive mosquitoes, and to estimate risk based on the predicted distribution in the case of Ae. albopictus.

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.

In a world of rapid environmental change, effective biodiversity conservation and management relies on our ability to detect changes in species occurrence. While long-term, standardized monitoring is ideal for detecting change, such monitoring is costly and rare. An alternative approach is to use historical records from natural history collections as a baseline to compare with recent observations. Here, we combine natural history collection data with citizen science observations within a hierarchical Bayesian occupancy modeling framework to identify changes in the occupancy of Californian dragonflies and damselflies (Odonata) over the past century. We model changes in the probability of occupancy of 34 odonate species across years and as a function of climate, after correcting for likely variation in detection probability using proxies for recorder effort and seasonal variation. We then examine whether biological traits can help explain variation in temporal trends. Models built using only opportunistic records identify significant changes in occupancy across years for 14 species, with eight of those showing significant declines and six showing significant increases in occupancy in the period 1900–2013. These changes are consistent with estimates obtained using more standardized resurvey data, regardless of whether resurvey data are used individually or in conjunction with the opportunistic dataset. We find that species increasing in occupancy over time are also those whose occupancy tends to increase with higher minimum temperatures, which suggests that these species may be benefiting from increasing temperatures across California. Furthermore, these species are also mostly habitat generalists, whilst a number of habitat specialists display some of the largest declines in occupancy across years. Our approach enables more robust estimates of temporal trends from opportunistic specimen and observation data, thus facilitating the use of these data in biodiversity conservation and management.