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The European Union's Natura 2000 (N2000) is among the largest international networks of protected areas. One of its aims is to secure the status of a predetermined set of (targeted) bird and butterfly species. However, nontarget species may also benefit from N2000. We evaluated how the terrestrial component of this network affects the abundance of nontargeted, more common bird and butterfly species based on data from long-term volunteer-based monitoring programs in 9602 sites for birds and 2001 sites for butterflies. In almost half of the 155 bird species assessed, and particularly among woodland specialists, abundance increased (slope estimates ranged from 0.101 [SD 0.042] to 3.51 [SD 1.30]) as the proportion of landscape covered by N2000 sites increased. This positive relationship existed for 27 of the 104 butterfly species (estimates ranged from 0.382 [SD 0.163] to 4.28 [SD 0.768]), although most butterflies were generalists. For most species, when land-cover covariates were accounted for these positive relationships were not evident, meaning land cover may be a determinant of positive effects of the N2000 network. The increase in abundance as N2000 coverage increased correlated with the specialization index for birds, but not for butterflies. Although the N2000 network supports high abundance of a large spectrum of species, the low number of specialist butterflies with a positive association with the N2000 network shows the need to improve the habitat quality of N2000 sites that could harbor open-land butterfly specialists. For a better understanding of the processes involved, we advocate for standardized collection of data at N2000 sites., (© 2019 Society for Conservation Biology.)

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., (© 2019 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

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., (© 2017 John Wiley & Sons Ltd.)

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade-offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011-2014) of a short-duration citizen science project (Big Butterfly Count [BBC]) with those from long-running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3-week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3-week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short-duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species' flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass-participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass-participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land-use types), as well as serving to reconnect an increasingly urban human population with nature., (© 2017 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.)

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

There is growing recognition as to the importance of extreme climatic events (ECEs) in determining changes in species populations. In fact, it is often the extent of climate variability that determines a population's ability to persist at a given site. This study examined the impact of ECEs on the resident UK butterfly species (n = 41) over a 37-year period. The study investigated the sensitivity of butterflies to four extremes (drought, extreme precipitation, extreme heat and extreme cold), identified at the site level, across each species' life stages. Variations in the vulnerability of butterflies at the site level were also compared based on three life-history traits (voltinism, habitat requirement and range). This is the first study to examine the effects of ECEs at the site level across all life stages of a butterfly, identifying sensitive life stages and unravelling the role life-history traits play in species sensitivity to ECEs. Butterfly population changes were found to be primarily driven by temperature extremes. Extreme heat was detrimental during overwintering periods and beneficial during adult periods and extreme cold had opposite impacts on both of these life stages. Previously undocumented detrimental effects were identified for extreme precipitation during the pupal life stage for univoltine species. Generalists were found to have significantly more negative associations with ECEs than specialists. With future projections of warmer, wetter winters and more severe weather events, UK butterflies could come under severe pressure given the findings of this study., (© 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.)

At a time of climate change and major loss of biodiversity, it is important to have efficient tools for monitoring populations. In this context, animal abundance indices play an important rôle. In producing indices for invertebrates, it is important to account for variation in counts within seasons. Two new methods for describing seasonal variation in invertebrate counts have recently been proposed; one is nonparametric, using generalized additive models, and the other is parametric, based on stopover models. We present a novel generalized abundance index which encompasses both parametric and nonparametric approaches. It is extremely efficient to compute this index due to the use of concentrated likelihood techniques. This has particular relevance for the analysis of data from long-term extensive monitoring schemes with records for many species and sites, for which existing modeling techniques can be prohibitively time consuming. Performance of the index is demonstrated by several applications to UK Butterfly Monitoring Scheme data. We demonstrate the potential for new insights into both phenology and spatial variation in seasonal patterns from parametric modeling and the incorporation of covariate dependence, which is relevant for both monitoring and conservation. Associated R code is available on the journal website., (© 2016 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

Differences in phenological responses to climate change among species can desynchronise ecological interactions and thereby threaten ecosystem function. To assess these threats, we must quantify the relative impact of climate change on species at different trophic levels. Here, we apply a Climate Sensitivity Profile approach to 10,003 terrestrial and aquatic phenological data sets, spatially matched to temperature and precipitation data, to quantify variation in climate sensitivity. The direction, magnitude and timing of climate sensitivity varied markedly among organisms within taxonomic and trophic groups. Despite this variability, we detected systematic variation in the direction and magnitude of phenological climate sensitivity. Secondary consumers showed consistently lower climate sensitivity than other groups. We used mid-century climate change projections to estimate that the timing of phenological events could change more for primary consumers than for species in other trophic levels (6.2 versus 2.5-2.9 days earlier on average), with substantial taxonomic variation (1.1-14.8 days earlier on average).

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.

The responses of animals and plants to recent climate change vary greatly from species to species, but attempts to understand this variation have met with limited success. This has led to concerns that predictions of responses are inherently uncertain because of the complexity of interacting drivers and biotic interactions. However, we show for an exemplar group of 155 Lepidoptera species that about 60% of the variation among species in their abundance trends over the past four decades can be explained by species-specific exposure and sensitivity to climate change. Distribution changes were less well predicted, but nonetheless, up to 53% of the variation was explained. We found that species vary in their overall sensitivity to climate and respond to different components of the climate despite ostensibly experiencing the same climate changes. Hence, species have undergone different levels of population "forcing" (exposure), driving variation among species in their national-scale abundance and distribution trends. We conclude that variation in species' responses to recent climate change may be more predictable than previously recognized.