Trait correlates of distribution trends in the Odonata of Britain and Ireland

A major challenge in ecology is understanding why certain species persist, while others decline, in response to environmental change. Trait-based comparative analyses are useful in this regard as they can help identify the key drivers of decline, and highlight traits that promote resistance to change. Despite their popularity trait-based comparative analyses tend to focus on explaining variation in range shift and extinction risk, seldom being applied to actual measures of species decline. Furthermore they have tended to be taxonomically restricted to birds, mammals, plants and butterflies. Here we utilise a novel approach to estimate occurrence trends for the Odonata in Britain and Ireland, and examine trait correlates of these trends using a recently available trait dataset. We found the dragonfly fauna in Britain and Ireland has undergone considerable change between 1980 and 2012, with 22 and 53% of species declining and increasing, respectively. Distribution region, habitat specialism and range size were the key traits associated with these trends, where habitat generalists that occupy southern Britain tend to have increased in comparison to the declining narrow-ranged specialist species. In combination with previous evidence, we conclude that the lower trend estimates for the narrow-ranged specialists could be a sign of biotic homogenization with ecological specialists being replaced by warm-adapted generalists

Effects of future agricultural change scenarios on beneficial insects

Insects provide vital ecosystem services to agricultural systems in the form of pollination and natural pest control. However, there are currently widespread declines in the beneficial insects which deliver these services (i.e. pollinators and ‘natural enemies’ such as predators and parasitoids). Two key drivers of these declines have been the expansion of agricultural land and intensification of agricultural production. With an increasing human population requiring additional sources of food, further changes in agricultural land use appear inevitable. Identifying likely trajectories of change and predicting their impacts on beneficial insects provides a scientific basis for making informed decisions on the policies and practices of sustainable agriculture. We created spatially explicit, exploratory scenarios of potential changes in the extent and intensity of agricultural land use across Great Britain (GB). Scenarios covered 52 possible combinations of change in agricultural land cover (i.e. agricultural expansion or grassland restoration) and intensity (i.e. crop type and diversity). We then used these scenarios to predict impacts on beneficial insect species richness and several metrics of functional diversity at a 10km (hectad) resolution. Predictions were based on species distribution models derived from biological records, comprising data on 116 bee species (pollinators) and 81 predatory beetle species (natural enemies). We identified a wide range of possible consequences for beneficial insect species richness and functional diversity as result of future changes in agricultural extent and intensity. Current policies aimed at restoring semi-natural grassland should result in increases in the richness and functional diversity of both pollinators and natural enemies, even if agricultural practices remain intensive on cropped land (i.e. land-sparing). In contrast, any expansion of arable land is likely to be accompanied by widespread declines in richness of beneficial insects, even if cropping practices become less intensive (i.e. land-sharing), although effects of functional diversity are more mixed

Towards a unified approach to formal risk of bias assessments for causal and descriptive inference

Statistics is sometimes described as the science of reasoning under uncertainty. Statistical models provide one view of this uncertainty, but what is frequently neglected is the invisible portion of uncertainty: that assumed not to exist once a model has been fitted to some data. Systematic errors, i.e. bias, in data relative to some model and inferential goal can seriously undermine research conclusions, and qualitative and quantitative techniques have been created across several disciplines to quantify and generally appraise such potential biases. Perhaps best known are so-called risk of bias assessment instruments used to investigate the likely quality of randomised controlled trials in medical research. However, the logic of assessing the risks caused by various types of systematic error to statistical arguments applies far more widely. This logic applies even when statistical adjustment strategies for potential biases are used, as these frequently make assumptions (e.g. data missing at random) that can never be guaranteed in finite samples. Mounting concern about such situations can be seen in the increasing calls for greater consideration of biases caused by nonprobability sampling in descriptive inference (i.e. survey sampling), and the statistical generalisability of in-sample causal effect estimates in causal inference; both of which relate to the consideration of model-based and wider uncertainty when presenting research conclusions from models. Given that model-based adjustments are never perfect, we argue that qualitative risk of bias reporting frameworks for both descriptive and causal inferential arguments should be further developed and made mandatory by journals and funders. It is only through clear statements of the limits to statistical arguments that consumers of research can fully judge their value for any specific application.Comment: 12 page

We need to talk about nonprobability samples

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of ‘big data’, however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts

Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data

Multi-species biodiversity indicators are increasingly used to assess progress towards the 2020 ‘Aichi’ targets of the Convention on Biological Diversity. However, most multi-species indicators are biased towards a few well-studied taxa for which suitable abundance data are available. Consequently, many taxonomic groups are poorly represented in current measures of biodiversity change, particularly invertebrates. Alternative data sources, including opportunistic occurrence data, when analysed appropriately, can provide robust estimates of occurrence over time and increase the taxonomic coverage of such measures of population change. Occupancy modelling has been shown to produce robust estimates of species occurrence and trends through time. So far, this approach has concentrated on well-recorded taxa and performs poorly where recording intensity is low. Here, we show that the use of weakly informative priors in a Bayesian occupancy model framework greatly improves the precision of occurrence estimates associated with current model formulations when analysing low-intensity occurrence data, although estimated trends can be sensitive to the choice of prior when data are extremely sparse at either end of the recording period. Specifically, three variations of a Bayesian occupancy model, each with a different focus on information sharing among years, were compared using British ant data from the Bees, Wasps and Ants Recording Society and tested in a simulation experiment. Overall, the random walk model, which allows the sharing of information between the current and previous year, showed improved precision and low bias when estimating species occurrence and trends. The use of the model formulation described here will enable a greater range of datasets to be analysed, covering more taxa, which will significantly increase taxonomic representation of measures of biodiversity change

Pollination by hoverflies in the Anthropocene

Pollinator declines, changes in land use and climate-induced shifts in phenology have the potential to seriously affect ecosystem function and food security by disrupting pollination services provided by insects. Much of the current research focuses on bees, or groups other insects together as ‘non-bee pollinators’, obscuring the relative contribution of this diverse group of organisms. Prominent among the ‘non-bee pollinators’ are the hoverflies, known to visit at least 72% of global food crops, which we estimate to be worth around US$300 billion per year, together with over 70% of animal pollinated wildflowers. In addition, hoverflies provide ecosystem functions not seen in bees, such as crop protection from pests, recycling of organic matter and long-distance pollen transfer. Migratory species, in particular, can be hugely abundant and unlike many insect pollinators, do not yet appear to be in serious decline. In this review, we contrast the roles of hoverflies and bees as pollinators, discuss the need for research and monitoring of different pollinator responses to anthropogenic change and examine emerging research into large populations of migratory hoverflies, the threats they face and how they might be used to improve sustainable agriculture

Recent trends in UK insects that inhabit early successional stages of ecosystems

Improved recording of less popular groups, combined with new statistical approaches that compensate for datasets that were hitherto too patchy for quantitative analysis, now make it possible to compare recent trends in the status of UK invertebrates other than butterflies. Using BRC datasets, we analysed changes in status between 1992 and 2012 for those invertebrates whose young stages exploit early seral stages within woodland, lowland heath and semi-natural grassland ecosystems, a habitat type that had declined during the 3 decades previous to 1990 alongside a disproportionally high number of Red Data Book species that were dependent on it. Two clear patterns emerged from a meta-analysis involving 299 classifiable species belonging to ten invertebrate taxa: (i) during the past 2 decades, most early seral species that are living near their northern climatic limits in the UK have increased relative to the more widespread members of these guilds whose distributions were not governed by a need for a warm micro-climate; and (ii) independent of climatic constraints, species that are restricted to the early stages of woodland regeneration have fared considerably less well than those breeding in the early seral stages of grasslands or, especially, heathland. The first trend is consistent with predicted benefits for northern edge-of-range species as a result of climate warming in recent decades. The second is consistent with our new assessment of the availability of early successional stages in these three ecosystems since c. 1990. Whereas the proportion and continuity of early seral patches has greatly increased within most semi-natural grasslands and lowland heaths, thanks respectively to agri-environmental schemes and conservation management, the representation of fresh clearings has continued to dwindle within UK woodlands, whose floors are increasingly shaded and ill-suited for this important guild of invertebrates

occAssess: an R package for assessing potential biases in species occurrence data

Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf-nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities

Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups

Range shifting is vital for species persistence, but there is little consensus on why individual species vary so greatly in the rates at which their ranges have shifted in response to recent climate warming. Here, using 40 years of distribution data for 291 species from 13 invertebrate taxa in Britain, we show that interactions between habitat availability and exposure to climate change at the range margins explain up to half of the variation in rates of range shift. Habitat generalists expanded faster than more specialised species, but this intrinsic trait explains less of the variation in range shifts than habitat availability, which additionally depends on extrinsic factors that may be rare or widespread at the range margin. Similarly, while climate change likely underlies polewards expansions, we find that more of the between-species variation is explained by differences in habitat availability than by changes in climatic suitability. A model that includes both habitat and climate, and their statistical interaction, explains the most variation in range shifts. We conclude that climate-change vulnerability assessments should focus as much on future habitat availability as on climate sensitivity and exposure, with the expectation that habitat restoration and protection will substantially improve species' abilities to respond to uncertain future climates

Enriching the Shared Socioeconomic Pathways to co-create consistent multi-sector scenarios for the UK

As the pressure to take action against global warming is growing in urgency, scenarios that incorporate multiple social, economic and environmental drivers become increasingly critical to support governments and other stakeholders in planning climate change mitigation or adaptation actions. This has led to the recent explosion of future scenario analyses at multiple scales, further accelerated since the development of the Intergovernmental Panel on Climate Change (IPCC) research community Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). While RCPs have been widely applied to climate models to produce climate scenarios at multiple scales for investigating climate change impacts, adaptation and vulnerabilities (CCIAV), SSPs are only recently being scaled for different geographical and sectoral applications. This is seen in the UK where significant investment has produced the RCP-based UK Climate Projections (UKCP18), but no equivalent UK version of the SSPs exists. We address this need by developing a set of multi-driver qualitative and quantitative UK-SSPs, following a state-of-the-art scenario methodology that integrates national stakeholder knowledge on locally-relevant drivers and indicators with higher level information from European and global SSPs. This was achieved through an intensive participatory process that facilitated the combination of bottom-up and top-down approaches to develop a set of UK-specific SSPs that are locally comprehensive, yet consistent with the global and European SSPs. The resulting scenarios balance the importance of consistency and legitimacy, demonstrating that divergence is not necessarily the result of inconsistency, nor comes as a choice to contextualise narratives at the appropriate scale