Emerging technologies in citizen science and potential for insect monitoring.

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Population abundance estimates in conservation and biodiversity research.

Measuring and tracking biodiversity from local to global scales is challenging due to its multifaceted nature and the range of metrics used to describe spatial and temporal patterns. Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. We differentiate between relative and absolute abundance, and discuss the advantages and disadvantages of each for biodiversity monitoring, conservation, and ecological research. We highlight when absolute abundance can be advantageous and should be prioritized in biodiversity monitoring and research, and conclude by providing avenues for future research directions to better assess the necessity of absolute abundance in biodiversity monitoring.

A protocol for harvesting biodiversity data from Facebook.

The expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity-a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority.

Un protocolo para recolectar datos sobre biodiversidad en Facebook Resumen El uso creciente de plataformas de ciencia comunitaria ha causado un incremento exponencial de los datos sobre biodiversidad en los repositorios mundiales. Sin embargo, el conocimiento sobre la distribución de las especies todavía está incompleto. Los datos sobre biodiversidad obtenidos de las redes sociales tienen el potencial para disminuir el vacío de conocimiento sobre la biodiversidad mundial. No obstante, no existe una guía práctica o un método estandarizado para recolectar dichos datos. Seguimos los protocolos de privacidad y protección de datos para diseñar un método estandarizado para extraer registros de la distribución de especies de grupos en Facebook que permiten el acceso a sus datos. El método consta de tres pasos: selección del grupo, extracción de datos y georreferenciación de la localidad registrada. También planteamos cómo estructurar las palabras clave, buscar fotografías de especies y georreferenciar las localidades de dichos registros. Además, resaltamos algunos retos que los usuarios pueden enfrentar al extraer los datos de distribución de Facebook y sugerimos algunas soluciones. Aplicamos nuestro marco de trabajo propuesto a un estudio de caso de la biodiversidad en Bangladesh, un país tropical megadiverso en el sureste de Asia. Reunimos casi 45,000 registros georreferenciados únicos para 967 especies y encontramos una media de 27 registros por especie. Casi el 12% de los datos de distribución correspondió a especies amenazadas, que representaban el 27% de todas las especies. También obtuvimos datos para 56 especies deficientes de datos en Bangladesh. Si los datos de las redes sociales se recolectan con cuidado, éstos pueden reducir de forma significativa el vacío de conocimiento para la biodiversidad mundial. Como consecuencia, es una prioridad para la investigación el desarrollo de una herramienta automatizada para extraer e interpretar los datos sobre biodiversidad de las redes sociales.

Assessing adequacy of citizen science datasets for biodiversity monitoring.

Tracking the state of biodiversity over time is critical to successful conservation, but conventional monitoring schemes tend to be insufficient to adequately quantify how species' abundances and distributions are changing. One solution to this issue is to leverage data generated by citizen scientists, who collect vast quantities of data at temporal and spatial scales that cannot be matched by most traditional monitoring methods. However, the quality of citizen science data can vary greatly. In this paper, we develop three metrics (inventory completeness, range completeness, spatial bias) to assess the adequacy of spatial observation data. We explore the adequacy of citizen science data at the species level for Australia's terrestrial native birds and then model these metrics against a suite of seven species traits (threat status, taxonomic uniqueness, body mass, average count, range size, species density, and human population density) to identify predictors of data adequacy. We find that citizen science data adequacy for Australian birds is increasing across two of our metrics (inventory completeness and range completeness), but not spatial bias, which has worsened over time. Relationships between the three metrics and seven traits we modelled were variable, with only two traits having consistently significant relationships across the three metrics. Our results suggest that although citizen science data adequacy has generally increased over time, there are still gaps in the spatial adequacy of citizen science for monitoring many Australian birds. Despite these gaps, citizen science can play an important role in biodiversity monitoring by providing valuable baseline data that may be supplemented by information collected through other methods. We believe the metrics presented here constitute an easily applied approach to assessing the utility of citizen science datasets for biodiversity analyses, allowing researchers to identify and prioritise regions or species with lower data adequacy that will benefit most from targeted monitoring efforts.

Using social media records to inform conservation planning.

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega-populated country, we examined the importance of social media records in conservation decision-making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF-only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000 km2 . Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.

Registros de las redes sociales para guiar la planeación de la conservación Resumen La ciencia ciudadana es importante para monitorear la biodiversidad e informar la conservación. Con el creciente uso de los teléfonos inteligentes, muchas personas comparten información de la biodiversidad en redes sociales, pero todavía no se usa ampliamente en la conservación. Analizamos la importancia de los registros de las redes sociales para las decisiones de conservación enfocados en Bangladesh, un país tropical megadiverso y mega poblado. Cotejamos los registros de distribución de especies de aves y mariposas en Facebook y Global Biodiversity Information Facility (GBIF), las agrupamos en datos sólo de GBIF o datos combinados de Facebook y GBIF e investigamos las diferencias en la identificación de las áreas de conservación críticas. La combinación de los datos de Facebook con los de GBIF mejoró la precisión de las evaluaciones de la planeación de la conservación sistemática al identificar otras áreas importantes de conservación en el noroeste, sureste y centro de Bangladesh, extendiendo así las áreas prioritarias de conservación en unos 4,000-10,000 km2 . Se requieren esfuerzos comunitarios para impulsar la implementación de los objetivos ambiciosos del Marco Global de Biodiversidad Kunming-Montreal, especialmente en países tropicales que carecen de datos confiables y actuales sobre la distribución de las especies. Destacamos que la planeación de la conservación puede mejorarse si se incluye información tomada de las redes sociales.

Multi-taxon biodiversity responses to the 2019-2020 Australian megafires.

Conditions conducive to fires are becoming increasingly common and widespread under climate change. Recent fire events across the globe have occurred over unprecedented scales, affecting a diverse array of species and habitats. Understanding biodiversity responses to such fires is critical for conservation. Quantifying post-fire recovery is problematic across taxa, from insects to plants to vertebrates, especially at large geographic scales. Novel datasets can address this challenge. We use presence-only citizen science data from iNaturalist, collected before and after the 2019-2020 megafires in burnt and unburnt regions of eastern Australia, to quantify the effect of post-fire diversity responses, up to 18 months post-fire. The geographic, temporal, and taxonomic sampling of this dataset was large, but sampling effort and species discoverability were unevenly spread. We used rarefaction and prediction (iNEXT) with which we controlled sampling completeness among treatments, to estimate diversity indices (Hill numbers: q = 0-2) among nine broad taxon groupings and seven habitats, including 3885 species. We estimated an increase in species diversity up to 18 months after the 2019-2020 Australian megafires in regions which were burnt, compared to before the fires in burnt and unburnt regions. Diversity estimates in dry sclerophyll forest matched and likely drove this overall increase post-fire, while no taxon groupings showed clear increases inconsistent with both control treatments post-fire. Compared to unburnt regions, overall diversity across all taxon groupings and habitats greatly decreased in areas exposed to extreme fire severity. Post-fire life histories are complex and species detectability is an important consideration in all post-fire sampling. We demonstrate how fire characteristics, distinct taxa, and habitat influence biodiversity, as seen in local-scale datasets. Further integration of large-scale datasets with small-scale studies will lead to a more robust understanding of fire recovery.

Unveiling global species abundance distributions.

Whether most species are rare or have some intermediate abundance is a long-standing question in ecology. Here, we use more than one billion observations from the Global Biodiversity Information Facility to assess global species abundance distributions (gSADs) of 39 taxonomic classes of eukaryotic organisms from 1900 to 2019. We show that, as sampling effort increases through time, the shape of the gSAD is unveiled; that is, the shape of the sampled gSAD changes, revealing the underlying gSAD. The fraction of species unveiled for each class decreases with the total number of species in that class and increases with the number of individuals sampled, with some groups, such as birds, being fully unveiled. The best statistical fit for almost all classes was the Poisson log-normal distribution. This strong evidence for a universal pattern of gSADs across classes suggests that there may be general ecological or evolutionary mechanisms governing the commonness and rarity of life on Earth.

Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh.

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

Smaller Australian raptors have greater urban tolerance.

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.

Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions.

Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale - whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry - whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.

EVI and NDVI as proxies for multifaceted avian diversity in urban areas.

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.

Large-scale spatial variability in urban tolerance of birds.

Quantifying intraspecific and interspecific trait variability is critical to our understanding of biogeography, ecology and conservation. But quantifying such variability and understanding the importance of intraspecific and interspecific variability remain challenging. This is especially true of large geographic scales as this is where the differences between intraspecific and interspecific variability are likely to be greatest. Our goal is to address this research gap using broad-scale citizen science data to quantify intraspecific variability and compare it with interspecific variability, using the example of bird responses to urbanization across the continental United States. Using more than 100 million observations, we quantified urban tolerance for 338 species within randomly sampled spatial regions and then calculated the standard deviation of each species' urban tolerance. We found that species' spatial variability in urban tolerance (i.e. standard deviation) was largely explained by the variability of urban cover throughout a species' range (R2  = 0.70). Variability in urban tolerance was greater in species that were more tolerant of urban cover (i.e. the average urban tolerance throughout their range), suggesting that generalist life histories are better suited to adapt to novel anthropogenic environments. Overall, species differences explained most of the variability in urban tolerance across spatial regions. Together, our results indicate that (1) intraspecific variability is largely predicted by local environmental variability in urban cover at a large spatial scale and (2) interspecific variability is greater than intraspecific variability, supporting the common use of mean values (i.e. collapsing observations across a species' range) when assessing species-environment relationships. Further studies, across different taxa, traits and species-environment relationships are needed to test the role of intraspecific variability, but nevertheless, we recommend that when possible, ecologists should avoid using discrete categories to classify species in how they respond to the environment.

The benefits of contributing to the citizen science platform iNaturalist as an identifier.

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.

Improving quantitative synthesis to achieve generality in ecology.

Synthesis of primary ecological data is often assumed to achieve a notion of 'generality', through the quantification of overall effect sizes and consistency among studies, and has become a dominant research approach in ecology. Unfortunately, ecologists rarely define either the generality of their findings, their estimand (the target of estimation) or the population of interest. Given that generality is fundamental to science, and the urgent need for scientific understanding to curb global scale ecological breakdown, loose usage of the term 'generality' is problematic. In other disciplines, generality is defined as comprising both generalizability-extending an inference about an estimand from the sample to the population-and transferability-the validity of estimand predictions in a different sampling unit or population. We review current practice in ecological synthesis and demonstrate that, when researchers fail to define the assumptions underpinning generalizations and transfers of effect sizes, generality often misses its target. We provide guidance for communicating nuanced inferences and maximizing the impact of syntheses both within and beyond academia. We propose pathways to generality applicable to ecological syntheses, including the development of quantitative and qualitative criteria with which to license the transfer of estimands from both primary and synthetic studies.

Anthropogenic habitat modification alters calling phenology of frogs.

Anthropogenic habitat modification significantly challenges biodiversity. With its intensification, understanding species' capacity to adapt is critical for conservation planning. However, little is known about whether and how different species are responding, particularly among frogs. We used a continental-scale citizen science dataset of >226,000 audio recordings of 42 Australian frog species to investigate how calling-a proxy for breeding-phenology varied along an anthropogenic modification gradient. Calling started earlier and breeding seasons lengthened with increasing modification intensity. Breeding seasons averaged 22.9 ± 8.25 days (standard error) longer in the most modified compared to the least modified regions, suggesting that frog breeding activity was sensitive to habitat modification. We also examined whether calls varied along a modification gradient by analysing the temporal and spectral properties of advertisement calls from a subset of 441 audio recordings of three broadly distributed frog species. There was no appreciable effect of anthropogenic habitat modification on any of the measured call variables, although there was high variability. With continued habitat modification, species may shift towards earlier and longer breeding seasons, with largely unknown ecological consequences in terms of proximate and ultimate fitness.

Decision-making of citizen scientists when recording species observations.

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.

A response to estimating hybridization in the wild using community science data: A path forward.

When working with a citizen science database like eBird, there are many possible ways to filter or subsample observations. Here, we discuss the potential biases and assumptions that surround different subsampling approaches or filtering that can be done to the eBird database. Restricting observations to species that are known to frequently hybridize, a specific time of the year, or a specific location, has the potential to greatly inflate the calculated per-individual rate of hybridization. Such filtering also assumes that researchers know a birds' capacity to hybridize with all other species in its range, which we argue is an unfounded assumption. We ultimately conclude that a limited filtering approach is ideal when using a citizen science database to attempt to address a broad question such as: what is the per individual rate of hybridization across all of the bird species in the United States?

Large-bodied birds are over-represented in unstructured citizen science data.

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.

Thermal flexibility and a generalist life history promote urban affinity in butterflies.

Urban expansion poses a serious threat to biodiversity. Given that the expected area of urban land cover is predicted to increase by 2-3 million km2 by 2050, urban environments are one of the most widespread human-dominated land-uses affecting biodiversity. Responses to urbanization differ greatly among species. Some species are unable to tolerate urban environments (i.e., urban avoiders), others are able to adapt and use areas with moderate levels of urbanization (i.e., urban adapters), and yet others are able to colonize and even thrive in urban environments (i.e., urban exploiters). Quantifying species-specific responses to urbanization remains an important goal, but our current understanding of urban tolerance is heavily biased toward traditionally well-studied taxa (e.g., mammals and birds). We integrated a continuous measure of urbanization-night-time lights-with over 900,000 species' observations from the Global Biodiversity Information Facility to derive a comprehensive analysis of species-specific (N = 158 species) responses of butterflies to urbanization across Europe. The majority of butterfly species included in our analysis avoided urban areas, regardless of whether species' urban affinities were quantified as a mean score of urban affinity across all occurrences (79%) or as a species' response curve to the whole urbanization gradient (55%). We then used species-specific responses to urbanization to assess which life history strategies promote urban affinity in butterflies. These trait-based analyses found strong evidence that the average number of flight months, likely associated with thermal niche breath, and number of adult food types were positively associated with urban affinity, while hostplant specialism was negatively associated with urban affinity. Overall, our results demonstrate that specialist butterflies, both in terms of thermal and diet preferences, are most at risk from increasing urbanization, and should thus be considered in urban planning and prioritized for conservation.