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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]

Research Highlight: Sæther, B. E., Engen, S., & Solbu, E. B. (2023a). Assessing the sensitivity and resistance of communities in a changing environment. Journal of Animal Ecology. https://doi.org/10.1111/1365‐2656.14003. In the face of global change, conservation strategies can be informed by understanding which biological communities are most at risk. Metrics that reflect the 'resilience' of communities to change could have great utility, but there is still no consensus on the most useful way to measure it. Sæther et al. introduce an intuitive approach to thinking about and measuring resilience based on how variation in the total number of individuals within a community affects the number of species. By using dynamic species abundance distribution models, they also quantify the different sources of population‐level variation that contribute to community resilience. Evenness emerges as an important predictor of resilience, with more even communities predicted to be more sensitive to abundance loss. An attractive feature of their approach is the ability to estimate the key parameters using commonly used generalized linear mixed effects models, which they illustrate with a case study on forest bird communities. The approach is ripe for comparison across different systems to explore how these proposed metrics complement existing biodiversity metrics and how they help understand the risk of communities from environmental change. [ABSTRACT FROM AUTHOR]

Evaluating the impact of future changes in land-use and climate on species communities, especially species richness, is one of the most important challenges of current research in ecology and conservation. The impact of environmental changes on species richness depends on its sensitivity (i.e., how strongly a given level of change influences the ecological community) and its exposure (i.e., the amount of change that occurs). To examine the sensitivity, exposure, and potential impact of future environmental conditions on bird communities, we compiled data on bird species richness for Pakistan--a neglected region in macro- or country-scale studies. Since bird species richness strongly varies across seasons due to the seasonal occurrence of migratory species in winter, we compared both wintering (migratory plus resident species) and breeding (resident species only) bird richness. We found breeding and wintering species richness to be sensitive to temperature, precipitation and rainfed cropland by being positively related to these factors. Exposure varied regionally, with projected temperature changes being most profound in northern regions while the strongest projected precipitation changes occurred in central and southern regions. The projected impact of future environmental change were highly heterogeneous across the country and differed between the wintering and breeding communities. Overall, the most negatively impacted region was projected to be the Khyber Pakhtunkha province in the North of Pakistan, due to reductions in precipitation and rainfed cropland, resulting in a projected negative impact, especially on wintering species richness. By highlighting the regional and seasonal bird communities most at risk, our findings provide useful information for policy makers to help devise new policies for mitigating negative impacts of future environmental changes on birds within Pakistan. [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]

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. [ABSTRACT FROM AUTHOR]