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Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species-level dataset of presence–absence records with a co-located dataset of group-level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM.

Modern agriculture has drastically changed global landscapes and introduced pressures on wildlife populations. Policy and management of agricultural systems has changed over the last 30 years, a period characterized not only by intensive agricultural practices but also by an increasing push towards sustainability. It is crucial that we understand the long-term consequences of agriculture on beneficial invertebrates and assess if policy and management approaches recently introduced are supporting their recovery. In this study, we use large citizen science datasets to derive trends in invertebrate occupancy in Great Britain between 1990 and 2019. We compare these trends between regions of no- (0%), low- (greater than 0–50%) and high-cropland (greater than 50%) cover, which includes arable and horticultural crops. Although we detect general declines, invertebrate groups are declining most strongly in high-cropland cover regions. This suggests that even in the light of improved policy and management over the last 30 years, the way we are managing cropland is failing to conserve and restore invertebrate communities. New policy-based drivers and incentives are required to support the resilience and sustainability of agricultural ecosystems. Post-Brexit changes in UK agricultural policy and reforms under the Environment Act offer opportunities to improve agricultural landscapes for the benefit of biodiversity and society.

Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined ‘regions’ (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the ‘ideal’ and ‘minimal’ criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings.

1. In order to better quantify spatial and temporal patterns in freshwater biodiversity, and potential underlying drivers of change, we must utilise the increasingly broad range of data available on freshwater ecosystems. Statistical advances in the field of integrated modelling provide new opportunities to further our understanding through the combined and simultaneous analysis of these diverse datasets. 2. We briefly introduce integrated modelling in the context of freshwater biodiversity and outline the key steps involved in its implementation, from data collection to analysis. We highlight both opportunities and challenges for the application of integrated approaches. 3. To illustrate the potential for integrated models to improve our understanding of freshwater biodiversity compared to standard approaches, we combine two datasets collected using different methods to model the distribution of Agabus water beetles in England. The integrated model had greater power to detect covariate effects on Agabus distribution, and reduced parameter uncertainty compared with analysis using only a single dataset. 4. We show that integrated methods have the potential to increase our understanding of freshwater systems and enable us to make full use of the diversity of freshwater data available.

Protected areas are a key conservation tool, yet their effectiveness at maintaining biodiversity through time is rarely quantified. Here, we assess protected area effectiveness across sampled portions of Great Britain (primarily England) using regionalized (protected vs unprotected areas) Bayesian occupancy-detection models for 1238 invertebrate species at 1 km resolution, based on ~1 million occurrence records between 1990 and 2018. We quantified species richness, species trends, and compositional change (temporal beta diversity; decomposed into losses and gains). We report results overall, for two functional groups (pollinators and predators), and for rare and common species. Whilst we found that protected areas have 15 % more species on average than unprotected ones, declines in occupancy are of similar magnitude and species composition has changed 27 % across protected and unprotected areas, with losses dominating gains. Pollinators have suffered particularly severe declines. Still, protected areas are colonized by more locally-novel pollinator species than unprotected areas, suggesting that they might act as ‘landing pads’ for range-shifting pollinators. We find almost double the number of rare species in protected areas (although rare species trends are similar in protected and unprotected areas); whereas we uncover disproportionately steep declines for common species within protected areas. Our results highlight strong invertebrate reorganization and loss across both protected and unprotected areas. We therefore call for more effective protected areas, in combination with wider action, to bend the curve of biodiversity loss – where we provide a toolkit to quantify effectiveness. We must grasp the opportunity to effectively conserve biodiversity through time.

•Humanity benefits immensely from nature, including through cultural ecosystem services. Geotagged crowdsourced data provide an opportunity to characterize these services at large scales. Flickr data, for example, have been widely used as an indicator of recreational value, while Wikipedia data are increasingly being used as a measure of public interest, potentially capturing often overlooked and less-tangible aspects of socio-cultural values (such as educational, inspirational, and spiritual values). So far, few studies have explored how various geotagged crowdsourced data complement each other, or how correlated these may be, particularly at national scales. To address this knowledge gap, we compare Flickr and Wikipedia datasets in their ability to help characterize the sociocultural value of designated areas in England and assess how this value relates to species richness. •Our results show that there was at least one Flickr photo in 35% of all designated areas in England, and at least one Wikipedia page in 60% of them. The Wikipedia and Flickr data were shown not to be independent of each other and were significantly correlated. Species richness was positively and significantly associated with the presence of at least one geotagged Wikipedia page; more biodiverse designated areas, however, were not any more likely to have at least one Flickr photo within them. Our results highlight the potential for new, emerging datasets to capture and communicate the socio-cultural value of nature, building on the strengths of more established crowdsourced data.

Large-scale biodiversity monitoring remains a challenge in science and policy. ‘Biodiversity Observation Networks’ provide an integrated infrastructure for monitoring biodiversity through timely discovery, access, and re-use of data, but their establishment relies on an in-depth understanding of existing monitoring effort. We performed a scoping review and network analysis to assess the scope of available data on amphibians and reptiles in the UK and catalogue the mobilisation of information across the data landscape, thereby highlighting existing gaps. The monitoring portfolio has grown rapidly in recent decades, with over three times as many data sources than there are amphibian and reptile species in the UK now available. We identified 45 active sources of ‘FAIR’ (‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’) data. The taxonomic, geographic and temporal coverage of datasets appears largely uneven and no single source is currently suitable for producing robust multispecies assessments on large scales. A dynamic and patchy exchange of data occurs between different recording projects, recording communities and digital data platforms. The National Biodiversity Network Atlas is a highly connected source but the scope of its data (re-)use is potentially limited by insufficient accompanying metadata. The emerging complexity and fragmented nature of this dynamic data landscape is likely to grow without a concerted effort to integrate existing activities. The factors driving this complexity extend beyond the UK and to other facets of biodiversity. We recommend integration and greater stakeholder collaboration behind a coordinated infrastructure for data collection, storage and analysis, capable of delivering comprehensive assessments for large-scale biodiversity monitoring.

•1. Species shifting their ranges under climate change are a conservation dilemma. Range-shifters may be threatened by climate change in their historic range. However, range-shifters are likely to be generalist opportunists, which could mean they could harm aspects of biodiversity in their new ecosystems. Therefore, we need approaches to rapidly assess how range-shifters may integrate into the community of historically resident species. •2. The small red-eyed damselfly (Erythromma viridulum) has shifted into the United Kingdom since 1999 and may affect resident Odonata via intraguild predation. We asked whether the damselfly's arrival is associated with a decline in resident Odonata. •3. We harnessed the British Dragonfly Society's dataset, using records from 49,788 site visits between 2000 and 2015 to construct dynamic species occupancy models for 17 resident UK Odonata. We estimated the potential effect of E. viridulum presence on the probability that each species would persist at a given site, while controlling for potential effects of climate and recording effort. •4. On average, dragonflies (Anisoptera) persisted more frequently at sites where E. viridulum had established, while damselflies (Zygoptera) showed no change in persistence. Nevertheless, two resident damselflies, including E. viridulum's congener, disappeared more frequently when the range-shifter established. •5. We suggest that E. viridulum poses minimal risk to most UK resident Odonata. Rather, E. viridulum may be differentially establishing in areas with good habitat quality, where many species of historically resident Odonata are also found. Therefore, high quality, biodiverse sites may become home to increasing numbers of range-shifters in future. Our approach permits rapid detection of how range-shifters are integrating into resident biota.

The conservation of evolutionary history has been linked to increased benefits for humanity and can be captured by phylogenetic diversity (PD). The Evolutionarily Distinct and Globally Endangered (EDGE) metric has, since 2007, been used to prioritise threatened species for practical conservation that embody large amounts of evolutionary history. While there have been important research advances since 2007, they have not been adopted in practice because of a lack of consensus in the conservation community. Here, building from an interdisciplinary workshop to update the existing EDGE approach, we present an “EDGE2” protocol that draws on a decade of research and innovation to develop an improved, consistent methodology for prioritising species conservation efforts. Key advances include methods for dealing with uncertainty and accounting for the extinction risk of closely related species. We describe EDGE2 in terms of distinct components to facilitate future revisions to its constituent parts without needing to reconsider the whole. We illustrate EDGE2 by applying it to the world’s mammals. As we approach a crossroads for global biodiversity policy, this Consensus View shows how collaboration between academic and applied conservation biologists can guide effective and practical priority-setting to conserve biodiversity.

Biodiversity targets are a key tool, used at a global and national policy level, to align biodiversity goals, promote conservation action, and recover nature. Yet most biodiversity targets are not met. In England, the government has committed to legally-binding targets to halt and recover the decline in species abundance by 2030 and 2042. We present evidence from recent population trends of 670 terrestrial animal species (for which abundance time series are available) as a species abundance indicator, together with a synthesis of case studies on species recovery, to assess the degree to which these targets are achievable. The case studies demonstrate that recovery is possible through a range of approaches. The indicator demonstrates that theoretically targets can be achieved by addressing severe declines in a relatively small number of species, as well as creating smaller benefits for many species through landscape-scale interventions. The fact that multiple pathways exist to achieve the species abundance targets in England presents choices but also raises the possibility that targets might be reached with perverse consequences. We demonstrate that evidence on achievability is a necessary but not sufficient condition for determining what is required to deliver conservation outcomes and restore biodiversity.

Monitoring wildlife populations is essential if global targets to reverse biodiversity declines are to be met. Recent analysis of data from the UK’s long-term National Bat Monitoring Programme (NBMP) suggests stable or increasing population trends for many bat species, and these statistics help inform progress towards national biodiversity targets. However, although based on robust citizen science survey designs, it is unknown how sensitive these trends are to spatial and environmental biases. Here we use Bayesian hierarchical modelling with integrated nested Laplace approximation (INLA), to examine the impact of these types of biases on the population trends using relative occupancy of four species monitored by the NBMP Field Survey in Great Britain (GB): Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula and Eptesicus serotinus. Where possible, we also disaggregated trends to national levels using the best model per species to determine if national differences in trends remain once sampling biases are accounted for. Although we found evidence of spatial clustering in the NBMP Field Survey locations, the previously reported GB-wide population trends are broadly robust to spatial autocorrelation. In most species, accounting for spatial autocorrelation and species-environment relationships improved model fit. The nationally disaggregated models highlighted that GB-wide trends mask differences between England and Scotland, consistent with previous analysis of these data, as well as illustrating large gaps in survey effort, especially in Wales. We suggest that although bat population trends were found to be broadly robust to sampling biases present in these data, small differences could propagate over time and this impact is likely to be more severe in less structured citizen science data. Therefore, ensuring trends are robust to sampling biases present in citizen science datasets is critical to effective monitoring of progress towards biodiversity targets, manag

Multi-species indices (MSI) are widely used as ecological indicators and as instruments to inform environmental policies. Many of these indices combine species-specific estimates of relative population sizes using the geometric mean. Because the geometric mean is not defined when values of zero occur, usually only commoner species are included in MSIs and zero values are replaced by a small non-zero value. The latter can exhibit an arbitrary influence on the geometric mean MSI. Here, we show how the compound Poisson and the negative binomial model can be used in such cases to obtain an MSI that has similar features to the geometric mean, including weighting halving and doubling of a species’ population equally. In contrast to the geometric mean, these two statistical models can handle zero values in population sizes and thus accommodate newly occurring and temporarily or permanently disappearing species in the MSI. We compare the MSIs obtained by the two statistical models with the geometric mean MSI and measure sensitivity to changes in evenness and to population trends in rare and abundant species. Additionally, we outline sources of uncertainty and discuss how to measure them. We found that, in contrast to the geometric mean and the negative binomial MSI, the compound Poisson MSI is less sensitive to changes in evenness when total abundance is constant. Further, we found that the compound Poisson model can be influenced more than the other two methods by trends of species showing a low interannual variance. The negative binomial MSI is less sensitive to trends in rare species compared with the other two methods, and similarly sensitive to trends in abundant species as the geometric mean. While the two new MSIs have the advantage that they are not arbitrarily influenced by rare, newly appearing and disappearing species, both do not weight all species equally. We recommend replacing the geometric mean MSI with either compound Poisson or negative binomial when there

Motivation: Population trend information is an ‘essential biodiversity variable’ for monitoring change in biodiversity over time. Here, we present a database of 1,122 population trends from around the world, describing changes in abundance over time in large mammal species (n = 50) from four families in the order Carnivora. For this subset of taxa, we provide approximately 21 times more trends than BioTIME and three times more trends than the Living Planet database. Main types of variables included: Key data fields for each trend: species, coordinates, trend time-frame, methods of data collection and analysis, and population time series or summarized trend value. Population trend values are reported using quantitative metrics in 75% of records that collectively represent more than 6,500 population estimates. The remaining records qualitatively describe population change (e.g., increase). Spatial location and grain: Trends represent 621 unique locations across the globe (latitude: −51.0 to 80.0; longitude: −166.0 to 166.0). Most trends (86%) are found within the Northern Hemisphere. Time period and grain: On average (mean), trends are derived from 6.5 abundance observations, and span in time from 1726 to 2017, with 92% of trends starting after 1950. Major taxa and level of measurement: We conducted a semi-systematic search for population trend data in 87 species from four families in the order Carnivora: Canidae, Felidae, Hyaenidae and Ursidae. We compiled data for 50 of the 87 species. Software format:.csv.