Butterfly abundance in a warming climate: patterns in space and time are not congruent

We present a model of butterfly abundance on transects in England. The model indicates a significant role for climate, but the direction of association is counter to expectation: butterfly population density is higher on sites with a cooler climate. However, the effect is highly heterogeneous, with one in five species displaying a net positive association. We use this model to project the population-level effects of climate warming for the year 2080, using a medium emissions scenario. The results suggest that most populations and species will decline markedly, but that the total number of butterflies will increase as communities become dominated by a few common species. In particular, Maniola jurtina is predicted to make up nearly half of all butterflies on UK Butterfly Monitoring Scheme (UKBMS) transects by 2080. These results contradict the accepted wisdom that most insect populations will grow as the climate becomes warmer. Indeed, our predictions contrast strongly with those derived from inter-annual variation in abundance, emphasizing that we lack a mechanistic understanding about the factors driving butterfly population dynamics over large spatial and temporal scales. Our study underscores the difficulty of predicting future population trends and reveals the naivety of simple space-for-time substitutions, which our projections share with species distribution modelling

Are neonicotinoid insecticides driving declines of widespread butterflies?

There has been widespread concern that neonicotinoid pesticides may be adversely impacting wild and managed bees for some years, but recently attention has shifted to examining broader effects they may be having on biodiversity. For example in the Netherlands, declines in insectivorous birds are positively associated with levels of neonicotinoid pollution in surface water. In England, the total abundance of widespread butterfly species declined by 58% on farmed land between 2000 and 2009 despite both a doubling in conservation spending in the UK, and predictions that climate change should benefit most species. Here we build models of the UK population indices from 1985 to 2012 for 17 widespread butterfly species that commonly occur at farmland sites. Of the factors we tested, three correlated significantly with butterfly populations. Summer temperature and the index for a species the previous year are both positively associated with butterfly indices. By contrast, the number of hectares of farmland where neonicotinoid pesticides are used is negatively associated with butterfly indices. Indices for 15 of the 17 species show negative associations with neonicotinoid usage. The declines in butterflies have largely occurred in England, where neonicotinoid usage is at its highest. In Scotland, where neonicotinoid usage is comparatively low, butterfly numbers are stable. Further research is needed urgently to show whether there is a causal link between neonicotinoid usage and the decline of widespread butterflies or whether it simply represents a proxy for other environmental factors associated with intensive agriculture

Butterfly abundance is determined by food availability and is mediated by species traits

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

Grizzled skippers stuck in the south: population‐level responses of an early‐successional specialist butterfly to climate across its UK range over 40 years

Aim: Climate change has been predicted to facilitate poleward expansion of many early‐successional specialist invertebrates. The Grizzled Skipper, Pyrgus malvae, is a threatened butterfly in long‐term decline that has not met expectations of northern expansion in Britain, possibly indicating that climate change has not improved northern habitat suitability or that another driver (e.g. land use change) is masking its effects. Here, we explore the effect of climate on population size trends over four decades, and whether any regions show an improving population trend that may be a precursor to northern expansion. Examining detailed spatio‐temporal abundance data can reveal unexpected limitations to population growth that would not be detectable in widely used climate envelope models. Location: Central and southern England. Methods: Mixed models were used to investigate P. malvae population size in relation to time and monthly climate measures across its UK range since 1976, based on repeated transect walks. Results: We found that P. malvae population size declined more over time in the north and west of its UK range than in the south and east, and was negatively related to high December temperature and summer rainfall. However, the effect sizes of temperature and rainfall were minimal. Main Conclusions: The last 40 years of climate change have not ameliorated climate suitability for P. malvae at its range edge, contrary to expectations from spatial‐only climate envelope models. The clear long‐term downward trends in population size are independent of climate change and we propose probably due to habitat deterioration. Our findings highlight potential hazards in predicting species range expansions from spatial models alone. Although some climate variables may be associated with a species’ distribution, other factors may be more dominant drivers of trends and therefore more useful predictors of range changes

A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes

1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales

Understanding the Distribution of Marine Megafauna in the English Channel Region: Identifying Key Habitats for Conservation within the Busiest Seaway on Earth

The temperate waters of the North-Eastern Atlantic have a long history of maritime resource richness and, as a result, the European Union is endeavouring to maintain regional productivity and biodiversity. At the intersection of these aims lies potential conflict, signalling the need for integrated, cross-border management approaches. This paper focuses on the marine megafauna of the region. This guild of consumers was formerly abundant, but is now depleted and protected under various national and international legislative structures. We present a meta-analysis of available megafauna datasets using presence-only distribution models to characterise suitable habitat and identify spatially-important regions within the English Channel and southern bight of the North Sea. The integration of studies from dedicated and opportunistic observer programmes in the United Kingdom and France provide a valuable perspective on the spatial and seasonal distribution of various taxonomic groups, including large pelagic fishes and sharks, marine mammals, seabirds and marine turtles. The Western English Channel emerged as a hotspot of biodiversity for megafauna, while species richness was low in the Eastern English Channel. Spatial conservation planning is complicated by the highly mobile nature of marine megafauna, however they are important components of the marine environment and understanding their distribution is a first crucial step toward their inclusion into marine ecosystem management

Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities

1.Biodiversity is changing at unprecedented rates, and it is increasingly important that these changes are quantified through monitoring programmes. Previous recommendations for developing or enhancing these programmes focus either on the end goals, that is the intended use of the data, or on how these goals are achieved, for example through volunteer involvement in citizen science, but not both. These recommendations are rarely prioritized. 2.We used a collaborative approach, involving 52 experts in biodiversity monitoring in the UK, to develop a list of attributes of relevance to any biodiversity monitoring programme and to order these attributes by their priority. We also ranked the attributes according to their importance in monitoring biodiversity in the UK. Experts involved included data users, funders, programme organizers and participants in data collection. They covered expertise in a wide range of taxa. 3.We developed a final list of 25 attributes of biodiversity monitoring schemes, ordered from the most elemental (those essential for monitoring schemes; e.g. articulate the objectives and gain sufficient participants) to the most aspirational (e.g. electronic data capture in the field, reporting change annually). This ordered list is a practical framework which can be used to support the development of monitoring programmes. 4.People's ranking of attributes revealed a difference between those who considered attributes with benefits to end users to be most important (e.g. people from governmental organizations) and those who considered attributes with greatest benefit to participants to be most important (e.g. people involved with volunteer biological recording schemes). This reveals a distinction between focussing on aims and the pragmatism in achieving those aims. 5.Synthesis and applications. The ordered list of attributes developed in this study will assist in prioritizing resources to develop biodiversity monitoring programmes (including citizen science). The potential conflict between end users of data and participants in data collection that we discovered should be addressed by involving the diversity of stakeholders at all stages of programme development. This will maximize the chance of successfully achieving the goals of biodiversity monitoring programmes

The sensitivity of breeding songbirds to changes in seasonal timing is linked to population change but cannot be directly attributed to the effects of trophic asynchrony on productivity

A consequence of climate change has been an advance in the timing of seasonal events. Differences in the rate of advance between trophic levels may result in predators becoming mismatched with prey availability, reducing fitness and potentially driving population declines. Such “trophic asynchrony” is hypothesized to have contributed to recent population declines of long-distance migratory birds in particular. Using spatially extensive survey data from 1983 to 2010 to estimate variation in spring phenology from 280 plant and insect species and the egg-laying phenology of 21 British songbird species, we explored the effects of trophic asynchrony on avian population trends and potential underlying demographic mechanisms. Species which advanced their laying dates least over the last three decades, and were therefore at greatest risk of asynchrony, exhibited the most negative population trends. We expressed asynchrony as the annual variation in bird phenology relative to spring phenology, and related asynchrony to annual avian productivity. In warmer springs, birds were more asynchronous, but productivity was only marginally reduced; long-distance migrants, short-distance migrants and resident bird species all exhibited effects of similar magnitude. Long-term population, but not productivity, declines were greatest among those species whose annual productivity was most greatly reduced by asynchrony. This suggests that population change is not mechanistically driven by the negative effects of asynchrony on productivity. The apparent effects of asynchrony on population trends are therefore either more likely to be strongly expressed via other demographic pathways, or alternatively, are a surrogate for species' sensitivity to other environmental pressures which are the ultimate cause of decline

Pollinator monitoring more than pays for itself

1. Resilient pollination services depend on sufficient abundance of pollinating insects over time. Currently, however, most knowledge about the status and trends of pollinators is based on changes in pollinator species richness and distribution only. 2. Systematic, long‐term monitoring of pollinators is urgently needed to provide baseline information on their status, to identify the drivers of declines and to inform suitable response measures. 3. Power analysis was used to determine the number of sites required to detect a 30% change in pollinator populations over 10 years. We then evaluated the full economic costs of implementing four national monitoring schemes in the UK: (a) professional pollinator monitoring, (b) professional pollination service monitoring, (c) volunteer collected pan traps and (d) volunteer focal floral observations. These costs were compared to (a) the costs of implementing separate, expert‐designed research and monitoring networks and (b) the economic benefits of pollination services threatened by pollinator loss. 4. Estimated scheme costs ranged from £6,159/year for a 75‐site volunteer focal flower observation scheme to £2.7 M/year for an 800‐site professional pollination service monitoring network. The estimated research costs saved using the site network as research infrastructure range from £1.46–4.17 M/year. The economic value of UK crop yield lost following a 30% decline in pollinators was estimated at ~£188 M/year. 5. Synthesis and applications. We evaluated the full costs of running pollinator monitoring schemes against the economic benefits to research and society they provide. The annual costs of monitoring are <0.02% of the economic value of pollination services that would be lost after a 30% decline in pollination services. Furthermore, by providing high‐quality scientific data, monitoring schemes would save at least £1.5 on data collection per £1 spent. Our findings demonstrate that long‐term systematic monitoring can be a cost‐effective tool for both answering key research questions and setting action points for policymakers. Careful consideration must be given to scheme design, the logistics of national‐scale implementation and resulting data quality when selecting the most appropriate combination of surveyors, methods and site networks to deliver a successful scheme

Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century

Global warming has advanced the timing of biological events, potentially leading to disruption across trophic levels. The potential importance of phenological change as a driver of population trends has been suggested. To fully understand the possible impacts, there is a need to quantify the scale of these changes spatially and according to habitat type. We studied the relationship between phenological trends, space and habitat type between 1965 and 2012 using an extensive UK dataset comprising 269 aphid, bird, butterfly and moth species. We modelled phenologies using generalized additive mixed models that included covariates for geographical (latitude, longitude, altitude), temporal (year, season) and habitat terms (woodland, scrub, grassland). Model selection showed that a baseline model with geographical and temporal components explained the variation in phenologies better than either a model in which space and time interacted or a habitat model without spatial terms. This baseline model showed strongly that phenologies shifted progressively earlier over time, that increasing altitude produced later phenologies and that a strong spatial component determined phenological timings, particularly latitude. The seasonal timing of a phenological event, in terms of whether it fell in the first or second half of the year, did not result in substantially different trends for butterflies. For moths, early season phenologies advanced more rapidly than those recorded later. Whilst temporal trends across all habitats resulted in earlier phenologies over time, agricultural habitats produced significantly later phenologies than most other habitats studied, probably because of nonclimatic drivers. A model with a significant habitat‐time interaction was the best‐fitting model for birds, moths and butterflies, emphasizing that the rates of phenological advance also differ among habitats for these groups. Our results suggest the presence of strong spatial gradients in mean seasonal timing and nonlinear trends towards earlier seasonal timing that varies in form and rate among habitat types