A generalized abundance index for seasonal invertebrates

Wildlife-friendly garden practices increase butterfly abundance and species richness in urban and arable landscapes

Insect declines are being reported worldwide and private, residential gardens could provide refugia for these species under increasing land use change. Interest in wildlife-friendly gardening has increased, but many management recommendations lack a scientific evidence-base. We used a large citizen science scheme, the Garden Butterfly Survey (GBS), with data from over 600 gardens across Great Britain (2016-2021) to determine how the surrounding landscape influences the abundance and species richness of butterflies in gardens and whether wildlife-friendly gardening practices, such as having long grass and providing nectar plants, benefit butterflies. First, we show that GBS provides reliable estimates of species abundances by comparing with results from standardised, long-term monitoring data. Garden size and surrounding land use had significant effects on butterfly abundance and richness in gardens, including positive relationships with garden size, woodland and arable farmland and negative relationships with urbanisation. Both the presence and area of long grass in gardens were positively related to higher butterfly richness and abundance, with the latter being driven by butterflies that use grasses as larval host plants. These effects differed depending on the surrounding landscape, such that long grass resulted in higher garden butterfly abundance in landscapes dominated by arable farming, and higher abundance and richness in highly urbanised areas. The presence of flowering ivy (Hedera spp.) in gardens resulted in higher abundance of Celastrina argiolus holly blue which uses ivy as a larval host, and of Vanessa atalanta red admiral and Polygonia c-album comma, which favour it as a nectar source. Our work provides evidence that undertaking simple wildlife-friendly garden practices can be beneficial for attracting butterflies, particularly in heavily modified areas. With over 728,000 ha of gardens in Great Britain, the cumulative effect of leaving areas of lawn uncut and providing nectar and larval host plants could be key for helping biodiversity.

Population links between an insectivorous bird and moths disentangled through national-scale monitoring data

Insects are key components of food chains, and monitoring data provides new opportunities to identify trophic relationships at broad spatial and temporal scales. Here, combining two monitoring datasets from Great Britain, we reveal how the population dynamics of the blue tit Cyanistes caeruleus are influenced by the abundance of moths - a core component of their breeding diet. We find that years with increased population growth for blue tits correlate strongly with high moth abundance, but population growth in moths and birds is less well correlated; suggesting moth abundance directly affects bird population change. Next, we identify moths that are important components of blue tit diet, recovering associations to species previously identified as key food sources such as the winter moth Operoptera brumata. Our work provides new evidence that insect abundance impacts bird population dynamics in natural communities and provides insight into spatial diet turnover at a national-scale.

rGAI: An R package for fitting the generalized abundance index to seasonal count data

The generalized abundance index (GAI) provides a useful tool for estimating relative population sizes and trends of seasonal invertebrates from species' count data and offers potential for inferring which external factors may influence phenology and demography through parametric descriptions of seasonal variation. We provide an R package that extends previous software with the ability to include covariates when fitting parametric GAI models, where seasonal variation is described by either a mixture of Normal distributions or a stopover model which provides estimates of life span. The package also generalizes the models to allow any number of broods/generations in the target population within a defined season. The option to perform bootstrapping, either parametrically or nonparametrically, is also provided. The new package allows models to be far more flexible when describing seasonal variation, which may be dependent on site‐specific environmental factors or consist of many broods/generations which may overlap, as demonstrated by two case studies. Our open‐source software, available at https://github.com/calliste‐fagard‐jenkin/rGAI, makes these extensions widely and freely available, allowing the complexity of GAI models used by ecologists and applied statisticians to increase accordingly.

Combining range and phenology shifts offers a winning strategy for boreal Lepidoptera

Species can adapt to climate change by adjusting in situ or by dispersing to new areas, and these strategies may complement or enhance each other. Here, we investigate temporal shifts in phenology and spatial shifts in northern range boundaries for 289 Lepidoptera species by using long-term data sampled over two decades. While 40% of the species neither advanced phenology nor moved northward, nearly half (45%) used one of the two strategies. The strongest positive population trends were observed for the minority of species (15%) that both advanced flight phenology and shifted their northern range boundaries northward. We show that, for boreal Lepidoptera, a combination of phenology and range shifts is the most viable strategy under a changing climate. Effectively, this may divide species into winners and losers based on their propensity to capitalize on this combination, with potentially large consequences on future community composition.

The decline of butterflies in Europe: Problems, significance, and possible solutions

We review changes in the status of butterflies in Europe, focusing on long-running population data available for the United Kingdom, the Netherlands, and Belgium, based on standardized monitoring transects. In the United Kingdom, 8% of resident species have become extinct, and since 1976 overall numbers declined by around 50%. In the Netherlands, 20% of species have become extinct, and since 1990 overall numbers in the country declined by 50%. Distribution trends showed that butterfly distributions began decreasing long ago, and between 1890 and 1940, distributions declined by 80%. In Flanders (Belgium), 20 butterflies have become extinct (29%), and between 1992 and 2007 overall numbers declined by around 30%. A European Grassland Butterfly Indicator from 16 European countries shows there has been a 39% decline of grassland butterflies since 1990. The 2010 Red List of European butterflies listed 38 of the 482 European species (8%) as threatened and 44 species (10%) as near threatened (note that 47 species were not assessed). A country level analysis indicates that the average Red List rating is highest in central and mid-Western Europe and lowest in the far north of Europe and around the Mediterranean. The causes of the decline of butterflies are thought to be similar in most countries, mainly habitat loss and degradation and chemical pollution. Climate change is allowing many species to spread northward while bringing new threats to susceptible species. We describe examples of possible conservation solutions and a summary of policy changes needed to conserve butterflies and other insects.

Analysis of monitoring data where butterflies fly year-round

Butterfly Monitoring Schemes (BMSs) engage the public in conservation and provide data sets that cover broad geographical areas over long timescales. Most existing BMSs are in temperate climates; however, the Israeli Butterfly Monitoring Scheme (BMS-IL), established in 2009, is a notable exception as it encompasses a large climatic gradient from Euro-Siberian through Mediterranean to hyper-arid regions. Israel's climate poses challenges in analyzing data from year-round butterfly activity, as in other tropical or arid countries. The Regional Generalized Additive Model (Regional GAM) is a butterfly phenology and abundance model based on repeat visits throughout species' flight season. We tested the applicability of Regional GAM for species with complex flight seasonality (e.g., multivoltine) by comparing estimated abundance and seasonal indices for the full data set and rarefied subsets. We assessed the reliability of modeled flight seasons and compared abundance estimates per site resulting from biologically plausible and unreliable seasonal models. The reliability of Regional GAM rises with the number of observations, and the model tends to produce more biologically plausible models for species with simple phenologies (e.g., univoltine with a single peak in activity). Abundance estimates based on unreliable models produce values with inter-quartile ranges of 90%-153% compared with biologically plausible models, while peak time changes with an interquartile range of 0-22.5 d when comparing all rarefied models with the full data set. Regional GAM should be applied with great caution for rare species and those with a complex flight season, and the date of year start needs to be carefully chosen for species that are active year-round. We identified the key sources of error and propose an operational workflow to address them. With few adaptations, Regional GAM can support new BMSs in analyzing data where butterflies are active year-round, including tropical climates. We propose guidelines for analyzing BMS data for species or regions with long activity periods and complex phenologies.

A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species’ indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided.

Supplementary materials accompanying this paper appear online.

Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA

Severe insect declines make headlines, but they are rarely based on systematic monitoring outside of Europe. We estimate the rate of change in total butterfly abundance and the population trends for 81 species using 21 years of systematic monitoring in Ohio, USA. Total abundance is declining at 2% per year, resulting in a cumulative 33% reduction in butterfly abundance. Three times as many species have negative population trends compared to positive trends. The rate of total decline and the proportion of species in decline mirror those documented in three comparable long-term European monitoring programs. Multiple environmental changes such as climate change, habitat degradation, and agricultural practices may contribute to these declines in Ohio and shift the makeup of the butterfly community by benefiting some species over others. Our analysis of life-history traits associated with population trends shows an impact of climate change, as species with northern distributions and fewer annual generations declined more rapidly. However, even common and invasive species associated with human-dominated landscapes are declining, suggesting widespread environmental causes for these trends. Declines in common species, although they may not be close to extinction, will have an outsized impact on the ecosystem services provided by insects. These results from the most extensive, systematic insect monitoring program in North America demonstrate an ongoing defaunation in butterflies that on an annual scale might be imperceptible, but cumulatively has reduced butterfly numbers by a third over 20 years.

Using citizen science butterfly counts to predict species population trends

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.