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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.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.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.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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.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.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.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.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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current dearth of long‐term insect population trends is a major obstacle to conservation. Occupancy models have been proposed as a solution, but it remains unclear whether they can yield long‐term trends from natural history collections, since specimen records are normally very sparse. A common approach for sparse data is to coarsen its spatial and/or temporal resolution, although coarsening risks violating model assumptions.We (i) test whether occupancy trends of three social wasp (Hymenoptera: Vespidae: Vespinae) species – the common wasp (Vespula vulgaris), the German wasp (Vespula germanica) and the European hornet (Vespa crabro) – have changed in England between 1900 and 2016, and (ii) test the effect of spatiotemporal resolution on the performance of occupancy models using very sparse data. All models are based on an integrated dataset of occurrence records and natural history collection specimen records.We show that occupancy models can yield long‐term species‐specific trends from very sparse natural history collection specimens. We present the first quantitative trends for three Vespinae species in England over 116 years. Vespula vulgaris and V. germanica show stable trends over the time series, whilst V. crabro's occupancy decreased from 1950 to 1970 and increased since 1970. Moreover, we show that spatiotemporal resolution has little effect on model performance, although coarsening the spatial grain is an appropriate method for achieving enough records to estimate long‐term changes.With the increasing availability of biological records, the model formulation used here has the potential to provide novel insights by making use of natural history collections' unique specimen assemblages. [ABSTRACT FROM AUTHOR]

The negative relationship between body size and population density in mammals is often interpreted as resulting from energetic constraints. In a global change scenario, however, this relationship might be expected to change, given the size‐dependent nature of anthropogenic pressures and vulnerability to extinction. Here we test whether the size‐density relationship (SDR) in mammals has changed over the last 50 years. We show that the relationship has shifted down and became shallower, corresponding to a decline in population density of 31–73%, for the largest and smallest mammals, respectively. However, the SDRs became steeper in some groups (e.g. carnivores) and shallower in others (e.g. herbivores). The Anthropocene reorganisation of biotic systems is apparent in macroecological relationships, reinforcing the notion that biodiversity pattens are contingent upon conditions at the time of investigation. We call for an increased attention to the role of global change on macroecological inferences. [ABSTRACT FROM AUTHOR]

Many long‐term wildlife population monitoring programmes rely on citizen scientists for data collection. This can offer several benefits over traditional monitoring practices as it is a cost‐effective, large‐scale approach capable of providing long time series data and raising public environmental awareness. Whilst there is a debate about the quality of citizen science data, a standardised sampling design can allow citizen science data to be of a similar quality to those collected by professionals. However, many programmes use subjective, opportunistic selection of monitoring sites and this introduces several types of bias, which are not well understood.Using bat roost counts as a case study, we took a 'virtual ecologist' approach to simulate the effect of opportunistic site selection and uneven observer retention on our ability to accurately detect abundance trends. We simulated populations with different levels of temporal variability and site fidelity.Our simulations reveal that opportunistic site selection and low observer retention can result in biased trends and that these biases are magnified when monitored populations exhibit high levels of inter‐annual variation and low site fidelity. These results show that the synergistic effects of observer behaviour, site selection, and population dynamics lead to biased abundance trends in monitoring programmes.This study highlights the value of engaging and retaining citizen science observers, a standardised sampling design, and the collection of metadata. We conclude that monitoring programmes need to be aware of their focal species' temporal variability and site fidelity to adequately assess the potential bias caused by opportunistic site selection and low observer retention.Synthesis and applications. Accurate data on population changes are key for conservation success. Therefore, it is important that citizen science monitoring programmes assess and potentially quantify the biases present in their data. We demonstrate the applicability of an established simulation framework to assess the effect of biases on our ability to correctly detect abundance trends. Our findings highlight that monitoring programmes need to be aware of their study species' temporal variability and site fidelity to assess and account for the effects of biased site selection and observer retention. [ABSTRACT FROM AUTHOR]

Aim: Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, whereas trait data are often not missing at random (e.g., more data for bigger species). Here, we evaluate the performance of approaches for handling missing values when considering biased datasets. Location: Any. Time period: Any. Major taxa studied: Any. Methods: We simulated continuous traits and separate response variables to test the performance of nine imputation methods and complete‐case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating the error in imputed trait values (deviation from the true value) and inferred trait–response relationships (deviation from the true relationship between a trait and response). Results: Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response–trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete‐case analysis frequently outperforming Mice imputation and, to a lesser degree, BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions: Imputation can handle missing data effectively in some conditions but is not always the best solution. None of the methods we tested could deal effectively with severe biases, which can be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimize errors. [ABSTRACT FROM AUTHOR]

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence‐only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence‐only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high‐quality absences from OR based on questionnaires sent to observers. Location: Both datasets cover the same large area (Sweden) and time period (2000–2013). Methods: Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data was assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR. Results: For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species–environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found. Main conclusions: Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist. [ABSTRACT FROM AUTHOR]

Planning for nature conservation has increasingly emphasised the concepts of resilience and spatial networks. Although the importance of habitat networks for individual species is clear, their significance for long‐term ecological resilience and multi‐species conservation strategies is less established.Referencing spatial network theory, we describe the conceptual basis for defining and assessing a network of wildlife areas that supports species' resilience to multiple forms of perturbations and pressures. We explore actions that could enhance network resilience at a range of scales, based on ecological principles, with reference to four well‐established strategies for intervention in a spatial network ("Better, Bigger, More and Joined") from the influential Making Space for Nature report by Lawton et al. ().Building existing theory into useable and scalable approaches applicable to large numbers of species is challenging but tractable. We illustrate the policy context, describe the elements of a long‐term adaptive management plan and provide example actions, metrics and targets for early implementation using England as a case study, where there is an opportunity to include large‐scale ecological planning in a newly launched 25‐year environment plan.Policy implications. The concept of resilient ecological networks has attracted scientific and political support, but there is no consensus on what a resilient network would look like, or how to assess it. Therefore, it is unclear whether existing targets for action will be sufficient to achieve network resilience. We show that the scientific principles to place resilience and network theory at the heart of large‐scale and long‐term environmental planning are established and ready to implement in practice. Delivering a resilient network to support nature recovery is achievable and can be integrated with ongoing conservation actions and targets, by assessing their effectiveness on properties of the entire network. England's 25 Year Environment Plan promises to deliver a natural environment that is protected and enhanced for the future and so provides the ideal testbed. [ABSTRACT FROM AUTHOR]

Abstract: Aim: Although the effects of life history traits on population density have been investigated widely, how spatial environmental variation influences population density for a large range of organisms and at a broad spatial scale is poorly known. Filling this knowledge gap is crucial for global species management and conservation planning and to understand the potential impact of environmental changes on multiple species. Location: Global. Time period: Present. Major taxa studied: Terrestrial amphibians, reptiles, birds and mammals. Methods: We collected population density estimates for a range of terrestrial vertebrates, including 364 estimates for amphibians, 850 for reptiles, 5,667 for birds and 7,651 for mammals. We contrasted the importance of life history traits and environmental predictors using mixed models and tested different hypotheses to explain the variation in population density for the four groups. We assessed the predictive accuracy of models through cross‐validation and mapped the partial response of vertebrate population density to environmental variables globally. Results: Amphibians were more abundant in wet areas with high productivity levels, whereas reptiles showed relatively higher densities in arid areas with low productivity and stable temperatures. The density of birds and mammals was typically high in temperate wet areas with intermediate levels of productivity. The models showed good predictive abilities, with pseudo‐R2 ranging between 0.68 (birds) and 0.83 (reptiles). Main conclusions: Traits determine most of the variation in population density across species, whereas environmental conditions explain the intraspecific variation across populations. Species traits, resource availability and climatic stability have a different influence on the population density of the four groups. These models can be used to predict the average species population density over large areas and be used to explore macroecological patterns and inform conservation analyses. [ABSTRACT FROM AUTHOR]

Motivation: Population density is a key demographic parameter influencing many ecological processes, and macroecology has described both intra- and interspecific patterns of variation. Population density data are expensive to collect and contain many forms of noise and potential bias; these factors have impeded investigation of macroecological patterns, and many hypotheses remain largely unexplored. Population density also represents fundamental information for conservation, because it underlies population dynamics and, ultimately, extinction risk. Here we present TetraDENSITY, an extensive dataset with > 18,000 records of density estimates for terrestrial vertebrates, in order to facilitate new research on this topic. Main types of variable contained: The dataset includes taxonomic information on species, population density estimate, year of data collection, season, coordinates of the locality, locality name, habitat, sampling method and sampling area. Spatial location and grain: Global. Spatial accuracy varies across studies; conservatively, it can be considered at 18, but for many data it is much finer. Time period and grain: From 1926 to 2017. Temporal accuracy is yearly in most cases, but studies with higher temporal resolution (season, month) are also present. Major taxa and level of measurement: Amphibians in terrestrial phase, reptiles, birds and mammals. Estimates derive from multiple methods, reflecting the study taxon, location and techniques available at the time of density estimation. [ABSTRACT FROM AUTHOR]

ABSTRACT: Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence‐only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi‐source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter‐ or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi‐source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA‐based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals. [ABSTRACT FROM AUTHOR]

Land-use change is one of the primary drivers of species loss, yet little is known about its effect on other components of biodiversity that may be at risk. Here, we ask whether, and to what extent, landscape simplification, measured as the percentage of arable land in the landscape, disrupts the functional and phylogenetic association between primary producers and consumers. Across seven European regions, we inferred the potential associations (functional and phylogenetic) between host plants and butterflies in 561 seminatural grasslands. Local plant diversity showed a strong bottom-up effect on butterfly diversity in the most complex landscapes, but this effect disappeared in simple landscapes. The functional associations between plant and butterflies are, therefore, the results of processes that act not only locally but are also dependent on the surrounding landscape context. Similarly, landscape simplification reduced the phylogenetic congruence among host plants and butterflies indicating that closely related butterflies become more generalist in the resources used. These processes occurred without any detectable change in species richness of plants or butterflies along the gradient of arable land. The structural properties of ecosystems are experiencing substantial erosion, with potentially pervasive effects on ecosystem functions and future evolutionary trajectories. Loss of interacting species might trigger cascading extinction events and reduce the stability of trophic interactions, as well as influence the longer term resilience of ecosystem functions. This underscores a growing realization that species richness is a crude and insensitive metric and that both functional and phylogenetic associations, measured across multiple trophic levels, are likely to provide additional and deeper insights into the resilience of ecosystems and the functions they provide. [ABSTRACT FROM AUTHOR]

Aim There is little consensus about the relative roles of biotic versus abiotic factors in setting limits to species distributions or in generating geographical patterns of species richness. However, despite the probable importance of host availability in governing the distribution and diversity of consumers, few studies have simultaneously tested the effects of resource distribution and diversity on consumer ranges and richness patterns. Location Sierra de Guadarrama, central Spain. Methods We examined the effects of biotic resources, consumer attributes and climate on the ranges and species richness patterns of 43 specialist butterflies at 40 sites over a 1800-m elevational gradient. Evidence for resource use was based on comprehensive field records of oviposition and larval feeding on host plants. Results We show that limitation by either biotic interactions with resources (the distributions and parts eaten of the larval host plants) or intrinsic dispersal ability was stronger at upper than lower elevational range limits for butterflies. Both resource and consumer richness followed a unimodal, humped pattern over the elevational gradient, but host plant richness peaked 300 m lower than butterfly richness. In addition, whereas changes in butterfly species richness were roughly symmetrical around peak richness over the gradient studied, the host plants showed markedly lower species richness at high elevations (> 1750 m). Butterfly species richness increased with host plant resource diversity and relative humidity, with a steeper response to host plant richness in cooler sites (at higher elevations). Main conclusions The results demonstrate the role of bottom-up control by resource availability in limiting consumer distributions and richness. Importantly, resource limitation had increasing relevance towards the coolest parts of environmental gradients and those poorest in resource species, with potential consequences for ecological responses to environmental change. [ABSTRACT FROM AUTHOR]

1. Understanding the drivers of population abundance across species and sites is crucial for effective conservation management. At present, we lack a framework for predicting which sites are likely to support abundant butterfly communities. 2. We address this problem by exploring the determinants of abundance among 1111 populations of butterflies in the UK, spanning 27 species on 54 sites. Our general hypothesis is that the availability of food resources is a strong predictor of population abundance both within and between species, but that the relationship varies systematically with species' traits. 3. We found strong positive correlations between butterfly abundance and the availability of food resources. Our indices of host plant and nectar are both significant predictors of butterfly population density, but the relationship is strongest for host plants, which explain up to 36% of the inter-site variance in abundance for some species. 4. Among species, the host plant-abundance relationship is mediated by butterfly species traits. It is strongest among those species with narrow diet breadths, low mobility and habitat specialists. Abundance for species with generalist diet and habitat associations is uncorrelated with our host plant index. 5. The host plant-abundance relationship is more pronounced on sites with predominantly north-facing slopes, suggesting a role for microclimate in mediating resource availability. 6. Synthesis and applications. We have shown that simple measures can be used to help understand patterns in abundance at large spatial scales. For some butterfly species, population carrying capacity on occupied sites is predictable from information about the vegetation composition. These results suggest that targeted management to increase host plant availability will translate into higher carrying capacity. Among UK butterflies, the species that would benefit most from such intervention have recently experienced steep declines in both abundance and distribution. The host plant-abundance relationship we have identified is likely to be transferrable to other systems characterized by strong interspecific interactions across trophic levels. This raises the possibility that the quality of habitat patches for specialist species is estimable from rapid assessment of the host plant resource. [ABSTRACT FROM AUTHOR]

In 2003, 24 presence-absence β-diversity metrics were reviewed and a number of trade-offs and redundancies identified. We present a parallel investigation into the performance of abundance-based metrics of β-diversity., β-diversity is a multi-faceted concept, central to spatial ecology. There are multiple metrics available to quantify it: the choice of metric is an important decision., We test 16 conceptual properties and two sampling properties of a β-diversity metric: metrics should be 1) independent of α -diversity and 2) cumulative along a gradient of species turnover. Similarity should be 3) probabilistic when assemblages are independently and identically distributed. Metrics should have 4) a minimum of zero and increase monotonically with the degree of 5) species turnover, 6) decoupling of species ranks and 7) evenness differences. However, complete species turnover should always generate greater values of β than extreme 8) rank shifts or 9) evenness differences. Metrics should 10) have a fixed upper limit, 11) symmetry (β A, B = β B, A), 12) double-zero asymmetry for double absences and double presences and 13) not decrease in a series of nested assemblages. Additionally, metrics should be independent of 14) species replication 15) the units of abundance and 16) differences in total abundance between sampling units. When samples are used to infer β-diversity, metrics should be 1) independent of sample sizes and 2) independent of unequal sample sizes. We test 29 metrics for these properties and five 'personality' properties., Thirteen metrics were outperformed or equalled across all conceptual and sampling properties. Differences in sensitivity to species' abundance lead to a performance trade-off between sample size bias and the ability to detect turnover among rare species. In general, abundance-based metrics are substantially less biased in the face of undersampling, although the presence-absence metric, βsim, performed well overall. Only βBaselga R turn, βBaselga B-C turn and βsim measured purely species turnover and were independent of nestedness. Among the other metrics, sensitivity to nestedness varied >4-fold., Our results indicate large amounts of redundancy among existing β-diversity metrics, whilst the estimation of unseen shared and unshared species is lacking and should be addressed in the design of new abundance-based metrics. [ABSTRACT FROM AUTHOR]

Policy-makers increasingly demand robust measures of biodiversity change over short time periods. Long-term monitoring schemes provide high-quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present, we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy-relevant time-scales., We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method-scenario combination, as well as the power to detect genuine trends., We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition, all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful., We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably Frescalo and occupancy-detection models., The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection. [ABSTRACT FROM AUTHOR]

Aim: Species atlases provide an economical way to collect data with national coverage, but are typically too coarse-grained to monitor fine-grain patterns in rarity, distribution and abundance. We test the performance of ten downscaling models in extrapolating occupancy across two orders of magnitude. To provide a greater challenge to downscaling models, we extend previous downscaling tests with plants to highly mobile insect taxa (Odonata) with a life history that is tied to freshwater bodies for reproduction. We investigate the species-level correlates of predictive accuracy for the best performing model to understand whether traits driving spatial structure can cause interspecific variation in downscaling success. Location: Mainland Britain. Methods: Occupancy data for 38 British Odonata species were extracted from the Dragonfly Recording Network (DRN). Occupancy at grains ≥ 100 km² was used as training data to parameterize ten downscaling models. Predicted occupancy at the 25, 4 and 1 km² grains was compared to observed data at corresponding grains. Model predictive error was evaluated across species and grains. Main conclusions: The Hui model gave the most accurate downscaling predictions across 114 species:grain combinations and the best predictions for 14 of the 38 species, despite being the only model using information at a single spatial grain. The occupancy-area relationship was sigmoidal in shape for most species. Species' distribution type and dispersal ability explained over half of the variation in downscaling predictive error at the species level. Species with a climatic range limit in Britain were poorly predicted compared with other distribution types, and high dispersal ability was associated with relatively poor downscaling predictions. Our results suggest that downscaling models, using widely available coarse-grain atlas data, provide reasonable estimates of finegrain occupancy, even for insect taxa with strong spatial structure. Linking species- level traits with predictive accuracy reveals general principles about when downscaling will be successful. [ABSTRACT FROM AUTHOR]

The power-law dependence of metabolic rate on body mass has major implications at every level of ecological organization. However, the overwhelming majority of studies examining this relationship have used basal or resting metabolic rates, and/or have used data consisting of species-averaged masses and metabolic rates. Field metabolic rates are more ecologically relevant and are probably more directly subject to natural selection than basal rates. Individual rates might be more important than species-average rates in determining the outcome of ecological interactions, and hence selection., We here provide the first comprehensive database of published field metabolic rates and body masses of individual birds and mammals, containing measurements of 1498 animals of 133 species in 28 orders. We used linear mixed-effects models to answer questions about the body mass scaling of metabolic rate and its taxonomic universality/heterogeneity that have become classic areas of controversy. Our statistical approach allows mean scaling exponents and taxonomic heterogeneity in scaling to be analysed in a unified way while simultaneously accounting for nonindependence in the data due to shared evolutionary history of related species., The mean power-law scaling exponents of metabolic rate vs. body mass relationships were 0·71 [95% confidence intervals ( CI) 0·625-0·795] for birds and 0·64 (95% CI 0·564-0·716) for mammals. However, these central tendencies obscured meaningful taxonomic heterogeneity in scaling exponents. The primary taxonomic level at which heterogeneity occurred was the order level. Substantial heterogeneity also occurred at the species level, a fact that cannot be revealed by species-averaged data sets used in prior work. Variability in scaling exponents at both order and species levels was comparable to or exceeded the differences 3/4−2/3 = 1/12 and 0·71−0·64., Results are interpreted in the light of a variety of existing theories. In particular, results are consistent with the heat dissipation theory of Speakman & Król (2010) and provided some support for the metabolic levels boundary hypothesis of Glazier (2010)., Our analysis provides the first comprehensive empirical analysis of the scaling relationship between field metabolic rate and body mass in individual birds and mammals. Our data set is a valuable contribution to those interested in theories of the allometry of metabolic rates. [ABSTRACT FROM AUTHOR]

Energy equivalence, the notion that population energy flux is independent of body mass, has become a key concept in ecology. We argue that energy equivalence is not an ecological 'rule', as claimed, but a flawed concept beset by circular reasoning. In fact, the independence of mass and energy flux is a null hypothesis. We show that our mechanistic understanding of size-density relationships ( SDRs) follows directly from this null model and the assumption that energy limits abundance. Paradoxically, without this assumption energy equivalence has no meaning and we lack a mechanistic understanding for SDRs. We derive an expression for the strength ( r2) of SDRs under the null model, which provides a framework within which to compare published SDRs. This confirms that tight correlations between mass and abundance are a trivial consequence of the span of body masses considered. Our model implies that energy flux varies by five to six orders of magnitude among similarly sized mammals and to a far greater extent in birds. We conclude that the energetic paradigm can be strengthened by considering alternative, non-energetic, hypotheses. [ABSTRACT FROM AUTHOR]

Aim Invasive alien species (IAS) are recognized as major drivers of biodiversity loss, but few causal relationships between IAS and species declines have been documented. In this study, we compare the distribution (Belgium and Britain) and abundance (Belgium, Britain and Switzerland) of formerly common and widespread native ladybirds before and after the arrival of Harmonia axyridis, a globally rapidly expanding IAS. Location Europe Methods We used generalized linear mixed-effects models (GLMMs) to assess the distribution trends of eight conspicuous and historically widespread and common species of ladybird within Belgium and Britain before and after the arrival of H. axyridis. The distribution data were collated largely through public participatory surveys but verified by a recognized expert. We also used GLMMs to model trends in the abundance of ladybirds using data collated through systematic surveys of deciduous trees in Belgium, Britain and Switzerland. Results Five (Belgium) and seven (Britain) of eight species studied show substantial declines attributable to the arrival of H. axyridis. Indeed, the two-spot ladybird, Adalia bipunctata, declined by 30% (Belgium) and 44% (Britain) over 5 years after the arrival of H. axyridis. Trends in ladybird abundance revealed similar patterns of declines across three countries. Main conclusion Together, these analyses show H. axyridis to be displacing native ladybirds with high niche overlap, probably through predation and competition. This finding provides strong evidence of a causal link between the arrival of an IAS and decline in native biodiversity. Rapid biotic homogenization at the continental scale could impact on the resilience of ecosystems and severely diminish the services they deliver. [ABSTRACT FROM AUTHOR]

Ecology Letters (2010) 13: 728–735 The metabolic theory of ecology links physiology with ecology, and successfully predicts many allometric scaling relationships. In recent years, proponents and critics of metabolic theory have debated vigorously about the scaling of metabolic rate. We show that the controversy arose, in part, because researchers examined the mean exponent separately from the variance. We estimate both quantities simultaneously using linear mixed-effects models and data from 1242 animal species. Metabolic rate scaling converges on the predicted value of 3/4 but is highly heterogeneous: 50% of orders lie outside the range 0.68–0.82. These findings are robust to several forms of statistical uncertainty. We then test competing hypotheses about the variation. Metabolic theory is currently unable to explain differences in scaling among orders, but the patterns are not consistent with competing explanations either. We conclude that current theories are inadequate to explain the full range of metabolic scaling patterns observed in nature. [ABSTRACT FROM AUTHOR]

1. We present a statistical analysis of the scaling of resting (basal) metabolic rate, BMR, with body mass, Bm and body temperature, Tb, in mammals. 2. Whilst the majority of the variance in ln BMR is explained by ln Bm, the Tb term is statistically significant. The best fit model was quadratic, indicating that the scaling of ln BMR with ln Bm varies with body size; the value of any scaling exponent estimated for a sample of mammals will therefore depend on the size distribution of species in the study. This effect can account for much of the variation in scaling exponents reported in the literature for mammals. 3. In all models, inclusion of Tb reduced the strength of scaling with ln Bm. The model including Tb suggests that birds and mammals have a similar underlying thermal dependence of BMR, equivalent to a Q10 of 2·9 across the range of Tb values 32–42 °C. 4. There was significant heterogeneity in both the mass scaling exponent and mean BMR across mammalian orders, with a tendency for orders dominated by larger taxa to have steeper scaling exponents. This heterogeneity was particularly marked across orders with smaller mean Bm and the taxonomic composition of the sample will thus also affect the observed scaling exponent. After correcting for the effects of ln Bm and Tb, Soricomorpha, Didelphimorphia and Artiodactyla had the highest BMR of those orders represented by more than 10 species in the data set. 5. Inclusion of Tb in the model removed the effect of diet category evident from a model in ln Bm alone and widely reported in the literature; this was caused by a strong interaction between diet category and Tb in mammals. 6. Inclusion of mean ambient temperature, Ta, in the model indicated a significant inverse relationship between ln BMR and Ta, complicated by an interaction between Ta and Tb. All other things being equal, a polar mammal living at −10 °C has a body temperature ∼2·7 °C warmer and a BMR higher by ∼40% than a tropical mammal of similar size living at 25 °C. [ABSTRACT FROM AUTHOR]

Abstract.Studies of species richness have been hampered by the use of methods that fail to account for phylogenetic non-independence of character states. MacroCAIC is a computer program that extends the method of phylogenetically independent contrasts to encompass species-richness data. It examines user-selected characters for correlation with species richness, thus allowing clearer identification of the factors driving large-scale patterns of diversity. [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]