Chris van Swaay

Differences in the climatic debts of birds and butterflies at a continental scale

Climate changes have profound effects on the distribution of numerous plant and animal species(1-3). However, whether and how different taxonomic groups are able to track climate changes at large spatial scales is still unclear. Here, we measure and compare the climatic debt accumulated by bird and butterfly communities at a European scale over two decades (1990-2008). We quantified the yearly change in community composition in response to climate change for 9,490 bird and 2,130 butterfly communities distributed across Europe(4). We show that changes in community composition are rapid but different between birds and butterflies and equivalent to a 37 and 114 km northward shift in bird and butterfly communities, respectively. We further found that, during the same period, the northward shift in temperature in Europe was even faster, so that the climatic debts of birds and butterflies correspond to a 212 and 135 km lag behind climate. Our results indicate both that birds and butterflies do not keep up with temperature increase and the accumulation of different climatic debts for these groups at national and continental scales.

Advantages of Volunteer-Based Biodiversity Monitoring in Europe

Without robust and unbiased systems for monitoring, changes in natural systems will remain enigmatic for policy makers, leaving them without a clear idea of the consequences of any environmental policies they might adopt. Generally, biodiversity-monitoring activities are not integrated or evaluated across any large geographic region. The EuMon project conducted the first large-scale evaluation of monitoring practices in Europe through an on-line questionnaire and is reporting on the results of this survey. In September 2007 the EuMon project had documented 395 monitoring schemes for species, which represents a total annual cost of about 4 million euro, involving more than 46,000 persons devoting over 148,000 person-days/year to biodiversity-monitoring activities. Here we focused on the analysis of variations of monitoring practices across a set of taxonomic groups (birds, amphibians and reptiles, mammals, butterflies, plants, and other insects) and across 5 European countries (France, Germany, Hungary, Lithuania, and Poland). Our results suggest that the overall sampling effort of a scheme is linked with the proportion of volunteers involved in that scheme. Because precision is a function of the number of monitored sites and the number of sites is maximized by volunteer involvement, our results do not support the common belief that volunteer-based schemes are too noisy to be informative. Just the opposite, we believe volunteer-based schemes provide relatively reliable data, with state-of-the-art survey designs or data-analysis methods, and consequently can yield unbiased results. Quality of data collected by volunteers is more likely determined by survey design, analytical methodology, and communication skills within the schemes rather than by volunteer involvement per se.

Butterfly monitoring in Europe: methods, applications and perspectives

Since the first Butterfly Monitoring Scheme in the UK started in the mid-1970s, butterfly monitoring in Europe has developed in more than ten European countries. These schemes are aimed to assess regional and national trends in butterfly abundance per species. We discuss strengths and weaknesses of methods used in these schemes and give examples of applications of the data. A new development is to establish supra-national trends per species and multispecies indicators. Such indicators enable to report against the target to halt biodiversity loss by 2010. Our preliminary European Grassland Butterfly Indicator shows a decline of 50% between 1990 and 2005. We expect to develop a Grassland Butterfly Indicator with an improved coverage across European countries. We see also good perspectives to develop a supra-national indicator for climate change as well as an indicator for woodland butterflies.

Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models

Many publications documenting large-scale trends in the distribution of species make use of opportunistic citizen data, that is, observations of species collected without standardized field protocol and without explicit sampling design. It is a challenge to achieve reliable estimates of distribution trends from them, because opportunistic citizen science data may suffer from changes in field efforts over time (observation bias), from incomplete and selective recording by observers (reporting bias) and from geographical bias. These, in addition to detection bias, may lead to spurious trends. We investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data. Occupancy models use detection/nondetection data and yield estimates of the percentage of occupied sites (occupancy) per year. These models take the imperfect detection of species into account. By correcting for detection bias, they may simultaneously correct for observation and reporting bias as well. We compared trends in occupancy (or distribution) of butterfly and dragonfly species derived from opportunistic data with those derived from standardized monitoring data. All data came from the same grid squares and years, in order to avoid any geographical bias in this comparison. Distribution trends in opportunistic and monitoring data were well-matched. Strong trends observed in monitoring data were rarely missed in opportunistic data. Synthesis and applications. Opportunistic data can be used for monitoring purposes if occupancy models are used for analysis. Occupancy models are able to control for the common biases encountered with opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale. Opportunistic data may thus become an important source of information to track distribution trends in many groups of species.

Bias in phenology assessments based on first appearance data of butterflies

Data on the first appearance of species in the field season are widely used in phenological studies. However, there are probabilistic arguments for bias in estimates of phenological change if sampling methods or population abundances change. We examined the importance of bias in three measures of phenological change: (1) the date of the first X appearances, (2) the date of the first Y% of all first appearances and (3) the date of the first Z% of the individuals observed during the entire flight period. These measures were tested by resampling the data of the Dutch Butterfly Monitoring Scheme and by simulations using artificial data. We compared datasets differing in the number of sampling sites, population abundance and the start of the observation period. The date of the first X appearances proved to be sensitive to the number of sampling sites. Both the date of the first X appearances and the date of the first Y% of all first appearances were sensitive to population trend. No such biases were found for estimates of the first Z% of the flight period, but all three measures were sensitive to changes in the start of the observation period. The conclusions were similar for both the study on butterfly data and the simulation study. Bias in phenology assessments based on first appearance data may be considerable and should no longer be ignored in phenological research.

Prime Butterfly Areas of Europe: An Initial Selection of Priority Sitesfor Conservation

The Red Data Book of European Butterflies, published in 1999, showed that butterflies have declined seriously across Europe and that 71 of the 576 species are threatened (12% of the total) either because of their extreme rarity or rapid decline. Many more species were shown to be declining in substantial parts of their range. A follow up project was conducted in 2002–3 to identify Prime Butterfly Areas (PBAs) in Europe where conservation should be targeted as a priority. Due to constraints of time and resources, this concentrated on identifying the most important (prime) areas for 34 target species, using a network of national compilers. The book gives details of 431 areas covering 1.8% of the land surface of Europe, and shows that target butterflies are declining in one quarter of PBAs, indicating that breeding habitats are continuing to deteriorate even though many are protected by national designation. Chief threats are from agricultural intensification, afforestation, abandonment of traditional practices, and isolation. We make nine recommendations: (1) Produce detailed descriptions of the PBAs within each country and protect all PBAs under national law; (2) Protect PBAs under relevant international EU law (e.g. EU Habitats and Species Directive); (3) Provide adequate protection of PBAs in non EU countries; (4) Ensure sound habitat management within PBAs and sympathetic management in surrounding areas; (5) Take measures to conserve the wider environment and whole landscapes within and surrounding PBAs in order to sustain viable metapopulations; (6) Monitor populations of target species and conduct research to identify appropriate habitat management techniques. (7) Revise pan-European legislation to take account of the new information provided in the Red Data Book of European butterflies (e.g. Bern Convention and the EU Habitats and Species Directive); (8) Conduct a more comprehensive review of Important Butterfly Areas in Europe as soon as possible; (9) Keep the list of Prime Butterfly Areas up-to-date (e.g. via the internet).

Monitoring butterflies in the Netherlands and Flanders: the first results

Butterfly monitoring started in the Netherlands in 1990 and in Flanders in 1991. During the last few years butterflies have been counted at nearly 300 sites. This high number of transects makes it possible to calculate not only national, but also regional and habitat-indices for many species. Special attention is paid to Red list species in the Netherlands. In the near future the number of sites per species on this list is to be increased to at least 20. This can be achieved by a ‘Red list monitoring scheme‘, in which monitoring is restricted to the flight period of the species.

The European Grassland Butterfly Indicator: 1990–2011

This report presents the European Grassland Butterfly Indicator, based on national Butterfly Monitoring Schemes (BMS) in 19 countries across Europe, most of them in the European Union. The indicator shows that since 1990 till 2011 butterfly populations have declined by almost 50 %, indicating a dramatic loss of grassland biodiversity. This also means the situation has not improved since the first version of the indicator published in 2005. Of the 17 species, 8 have declined in Europe, 2 have remained stable and 1 increased. For six species the trend is uncertain. The main driver behind the decline of grassland butterflies is the change in rural land use: agricultural intensification where the land is relatively flat and easy to cultivate, and abandonment in mountains and wet areas, mainly in eastern and southern Europe. Agricultural intensification leads to uniform, almost sterile grasslands for biodiversity. Grassland butterflies thus mainly survive in traditionally farmed low‑input systems (High Nature Value (HNV) Farmland) as well as nature reserves, and on marginal land such as road verges and amenity areas. (Less)

Ecological determinants of butterfly vulnerability across the European continent

In drawing up Red Lists, the extinction risks of butterflies and other insects are currently assessed mainly by using information on trends in distribution and abundance. Incorporating information on species traits may increase our ability to predict species responses to environmental change and, hence, their vulnerability. We summarized ecologically relevant life-history and climatic niche traits in principal components, and used these to explain the variation in five vulnerability indicators (Red List status, Endemicity, Range size, Habitat specialisation index, Affinity for natural habitats) for 397 European butterfly species out of 482 species present in Europe. We also evaluated a selection of 238 species to test whether phylogenetic correction affected these relationships. For all but the affinity for natural habitats, climatic niche traits predicted more variation in vulnerability than life-history traits; phylogenetic correction had no relevant influence on the findings. The life-history trait component reflecting mobility, development rate, and overwintering stage, proved the major non-climatic determinant of species vulnerability. We propose that this trait component offers a preferable alternative to the frequently used, but ecologically confusing generalist-specialist continuum. Our analysis contributes to the development of trait-based approaches to prioritise vulnerable species for conservation at a European scale. Further regional scale analyses are recommended to improve our understanding of the biological basis of species vulnerability.

Monitoring Essential Biodiversity Variables at the Species Level

The Group on Earth Observations Biodiversity Observation Network (GEO BON) is developing a monitoring framework around a set of Essential Biodiversity Variables (EBVs) which aims at facilitating data integration, spatial scaling and contributing to the filling of gaps. Here we build on this framework to explore the monitoring of EBV classes at the species level: species populations, species traits and community composition. We start by discussing cross-cutting issues on species monitoring such as the identification of the question to be addressed, the choice of variables, taxa and spatial sampling scheme. Next, we discuss how to monitor EBVs for specific taxa, including mammals, amphibians, butterflies and plants. We show how the monitoring of species EBVs allows monitoring changes in the supply of ecosystem services. We conclude with a discussion of challenges in upscaling local observations to global EBVs and how indicator and model development can help address this challenge.