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As anthropogenic pressures continue to increase, generalist species tend to be more resilient than specialised species. Specialisation can take various forms, among else dependence on other species through biotic interactions. Some Lycaenid butterflies (gossamer‐winged butterflies: blues, coppers and hairstreaks; Lycaenidae Leach, 1815)) rely on host ants for larval care and survival (myrmecophily). This dependence may pose an additional threat. To investigate whether myrmecophily is associated with the long‐term trends of Lycaenids, we compared 40‐year occupancy trends derived from occupancy‐detection models of ant‐independent, facultative and obligate myrmecophile Lycaenidae in a central European model region. Contrary to our expectations, obligate myrmecophile butterflies did not show more declines compared to ant‐independent ones. Five out of seven obligate myrmecophile butterflies increased, while five out of eight ant‐independent Lycaenids decreased. Trends among facultative butterflies were highly ambiguous. The differences between the groups were not significant. Although obligate myrmecophile butterflies are protected significantly more often under stricter rules, the degree of protection did not affect Lycaenid long‐term trends. European obligate myrmecophile butterflies interact with several ant species within widespread genera (primarily Myrmica Latreille, 1804, also Formica Linnaeus, 1758 and Lasius Fabricius, 1804) potentially protecting the larvae against environmental impacts and thus mitigating the effects of changing conditions on the butterflies. Incomplete understanding of the varying degrees of ant affiliation hinders the identification of specific interactions that may require increased conservation efforts. In our rapidly changing world, monitoring changes in the opportunities and strengths of species interactions is needed to prevent coupled species' extinctions and improve conservation outcomes. [ABSTRACT FROM AUTHOR]

ABSTRACT Big biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large‐scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long‐term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow. [ABSTRACT FROM AUTHOR]

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'. [ABSTRACT FROM AUTHOR]

Strong biodiversity declines have been reported across the European Union, especially in insects, despite conservation policy such as the Habitats Directive that aims to halt biodiversity loss. Using 50 years of observational data, we examined indicators for the goals of the Directive in terms of improving monitoring efforts and occupancy trends of butterfly and dragonfly annex species in a central European region. We quantified annual monitoring effort and used occupancy‐detection models to compare species trends for 18 years before and after legal implementation of the Directive. Monitoring efforts increased after implementation, while occupancy trends both improved and deteriorated. Contrary to its main goal, the European Habitats Directive did not prevent a worsening of all annex species' occupancy trends in the studied region. While the increased monitoring efforts aid biodiversity assessments, more serious broad‐scale conservation measures are needed to halt biodiversity loss across Europe. [ABSTRACT FROM AUTHOR]

One way to improve the value of citizen science data for a specific aim is through promoting adaptive sampling, where the marginal value of a citizen science observation is dependent on existing data collected to address a specific question. Adaptive sampling could increase sampling at places or times—using a dynamic and updateable framework—where data are expected to be most informative for a given ecological question or conservation goal. We used an experimental approach to test whether the participants in a popular Australian citizen science project—FrogID—would follow an adaptive sampling protocol aiming to maximize understanding of frog diversity. After a year, our results demonstrated that these citizen science participants were willing to adopt an adaptive sampling protocol, improving the sampling of biodiversity consistent with a specific aim. Such adaptive sampling can increase the value of citizen science data for biodiversity research and open up new avenues for citizen science project design. [ABSTRACT FROM AUTHOR]

Large‐scale biodiversity databases have great potential for quantifying long‐term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias – how spatial bias has changed over time – has been largely overlooked. We examined the spatial bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well‐known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long‐term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land‐use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long‐term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the spatial bias through time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Geographic biases cause error when estimating species occupancy. • Using theory and simulations, we show that error is larger at finer resolutions. • Estimates of occupancy are also more "useful" at finer scales. • So, there is a trade-off between "usefulness" and accuracy. Species occupancy is often defined as the proportion of areal units (sites) in a landscape that the focal species occupies, but it is usually estimated from the subset of sites that have been sampled. Assuming no measurement error, we show that three quantities–the degree of sampling bias (in terms of site selection), the proportion of sites that have been sampled and the variability of true occupancy across sites–determine the extent to which a sample-based estimate of occupancy differs from its true value across the wider landscape. That these are the only three quantities (measurement error notwithstanding) to affect the accuracy of estimates of species occupancy is the fundamental insight of the "Meng equation", an algebraic re-expression of statistical error. We use simulations to show how each of the three quantities vary with the spatial resolution of the analysis and that absolute estimation error is lower at coarser resolutions. Absolute error scales similarly with resolution regardless of the size and clustering of the virtual species' distribution. Finely resolved estimates of species occupancy have the potential to be more useful than coarse ones, but this potential is only realised if the estimates are at least reasonably accurate. Consequently, wherever there is the potential for sampling bias, there is a trade-off between spatial resolution and accuracy, and the Meng equation provides a theoretical framework in which analysts can consider the balance between the two. An obvious next step is to consider the implications of the Meng equation for estimating a time trend in species occupancy, where it is the confounding of error and true change that is of most interest. [ABSTRACT FROM AUTHOR]

Broad‐scale biodiversity monitoring relies, at least in part, on the efforts of citizen, or community, scientists. To ensure robust inferences from citizen science data, it is important to understand the spatial pattern of sampling effort by citizen scientists and how it deviates from an optimal pattern. Here, we develop a generalized workflow to estimate the optimal distribution of sampling effort for inference of species diversity (e.g., species richness, Shannon diversity, and Simpson's diversity) patterns using the relationship between species diversity and land cover. We used data from the eBird citizen science project that was collected across heterogeneous landscapes in Florida (USA) to illustrate this workflow across different grain sizes. We found that a relatively small number of samples are needed to meet 95% sampling completeness when diversity estimation is focused on dominant species: 43, 64, 96, 123, 172, and 176 for 5 × 5, 10 × 10, 15 × 15, 20 × 20, 25 × 25, and 30 × 30‐km2 grain sizes, respectively. In contrast, three to five times more samples are necessary to infer species diversity when estimation is focused on rare species. However, in both cases, the optimal distribution of effort was spatially heterogeneous, with more effort needed in regions of higher diversity. Our results highlight the potential of citizen science data to make informed comparisons of species diversity in space and time, as well as how sampling effort inherently depends on monitoring goals, such as whether dominant or rare species are targeted. Our general workflow allows for the quantification of sampling effort needed to estimate species diversity with citizen science data and can guide future adaptive sampling by citizen science participants. [ABSTRACT FROM AUTHOR]

Aim: Recent studies suggest insect declines in parts of Europe; however, the generality of these trends across different taxa and regions remains unclear. Standardized data are not available to assess large‐scale, long‐term changes for most insect groups but opportunistic citizen science data are widespread for some. Here, we took advantage of citizen science data to investigate distributional changes of Odonata. Location: Germany. Methods: We compiled over 1 million occurrence records from different regional databases. We used occupancy‐detection models to account for imperfect detection and estimate annual distributions for each species during 1980–2016 within 5 × 5 km quadrants. We also compiled data on species attributes that were hypothesized to affect species' sensitivity to different drivers and related them to the changes in species' distributions. We further developed a novel approach to cluster groups of species with similar patterns of distributional change to represent multispecies indicators. Results: More species increased (45%) than decreased (29%) or remained stable (26%) in their distribution (i.e. number of occupied quadrants). Species showing increases were generally warm‐adapted species and/or running water species, while species showing decreases were cold‐adapted species using standing water habitats such as bogs. Time series clustering defined five main patterns of change—each associated with a specific combination of species attributes, and confirming the key roles of species' temperature and habitat preferences. Overall, our analysis predicted that mean quadrant‐level species richness has increased over most of the time period. Main conclusions: Trends in Odonata provide mixed news—improved water quality, coupled with positive impacts of climate change, could explain the positive trends of many species. At the same time, declining species point to conservation challenges associated with habitat loss and degradation. Our study demonstrates the great value of citizen science and the work of natural history societies for assessing large‐scale distributional change. [ABSTRACT FROM AUTHOR]

Large amounts of species occurrence data are compiled by platforms such as the Global Biodiversity Information Facility (GBIF) but these data are collected by a diversity of methods and people. Statistical tools, such as occupancy-detection models, have been developed and tested as a way to analyze these heterogeneous data and extract information on species' population trends. However, these models make many assumptions that might not always be met. More detailed metadata associated with occurrence records would help better describe the observation/detection submodel within occupancy models and improve the accuracy/precision of species' trend estimates. Here, we present examples of occupancy-detection models applied to citizen science datasets, including dragonfly data in Germany, and typical approaches to account for variation in sampling effort and species detectability, including visit covariates, such as list length. Using results from a recent questionnaire in Germany asking citizen scientists about why and how they collect species occurrence data, we also characterize the different approaches that citizen scientists take to sample and report species observations. We use our findings to highlight examples of key metadata that are often missing (e.g., length of time spent searching, complete checklist or not) in data sharing platforms but would greatly aid modelling attempts of heterogeneous species occurrence data. [ABSTRACT FROM AUTHOR]

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. There are many reasons why absolute abundance can benefit biodiversity research, including monitoring, conservation, and ecology. Protocols to measure absolute and relative abundance can differ in data collection and/or analysis, with the key difference being if, and to what extent, the probability of detection is accounted for. As we attempt to 'bend the curve' of biodiversity loss in the Anthropocene, it is important to continuously (re)consider how biodiversity is measured. 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. [ABSTRACT FROM AUTHOR]