Habitat fragmentation may present a major impediment to species range shifts caused by climate change, but how it affects local community dynamics in a changing climate has so far not been adequately ...investigated empirically. Using long‐term monitoring data of butterfly assemblages, we tested the effects of the amount and distribution of semi‐natural habitat (SNH), moderated by species traits, on climate‐driven species turnover. We found that spatially dispersed SNH favoured the colonisation of warm‐adapted and mobile species. In contrast, extinction risk of cold‐adapted species increased in dispersed (as opposed to aggregated) habitats and when the amount of SNH was low. Strengthening habitat networks by maintaining or creating stepping‐stone patches could thus allow warm‐adapted species to expand their range, while increasing the area of natural habitat and its spatial cohesion may be important to aid the local persistence of species threatened by a warming climate.
We tested the effects of the amount and distribution of semi‐natural habitat, moderated by species traits, on climate‐driven species turnover in butterfly communities. We found that spatially dispersed habitats favoured the colonisation of warm‐adapted and mobile species, while extinction risk of cold‐adapted species increased when their habitats were dispersed and present in low amount.
1. 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. 2. 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. 3. Distribution trends in opportunistic and monitoring data were well-matched. Strong trends observed in monitoring data were rarely missed in opportunistic data. 4. 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.
Analyses of species' population losses typically show a dichotomy between strongly affected, rare, and localized species and apparently unaffected, common, and widespread species. We analyzed 16 ...years (1992-2007) of butterfly transect count data from The Netherlands in a reevaluation of the trends of common, widespread species. Fifty-five percent (11 of 20 species) of these species suffered severe declines in distribution and abundance. Overall, cumulative butterfly abundance declined by around 30%. Some of the species in decline used to be omnipresent in gardens and parks, and 2 of the species were previously considered agricultural pests. Based on their declines over the last 16 years, 2 of the 20 species (Lasiommata megera and Gonepteryx rhamni) reached endangered status in The Netherlands under the IUCN (International Union for Conservation of Nature) population-decline criterion, and 2 species (Inachis io and Thymelicus lineola) met vulnerable criterion. Butterflies in farmland, urban, and particularly woodland areas showed the largest decline in species abundance. The abundance of species associated with vegetation types found mainly in nature reserves (dunes, heathland, and, to a lesser extent, seminatural grassland) increased or remained stable. The decline of widespread species requires additional conservation strategies in the wider landscape.
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.
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.
This paper presents an updated checklist of the butterflies of Europe, together with their original name combinations, and their occurrence status in each European country. According to this ...checklist, 496 species of the superfamily Papilionoidea occur in Europe. Changes in comparison with the last version (2.6.2) of Fauna Europaea are discussed. Compared to that version, 16 species are new additions, either due to cryptic species most of which have been discovered by molecular methods (13 cases) or due to discoveries of Asian species on the eastern border of the European territory in the Ural mountains (three cases). On the other hand, nine species had to be removed from the list, because they either do not occur in Europe or lost their species status due to new evidence. In addition, three species names had to be changed and 30 species changed their combination due to new evidence on phylogenetic relationships. Furthermore, minor corrections were applied to some authors’ names and years of publication. Finally, the name
Polyommatusottomanus
Lefèbvre, 1831, which is threatened by its senior synonym
Lycaenalegeri
Freyer, 1830, is declared a
nomen protectum
, thereby conserving its name in the current combination
Lycaenaottomana
.
Opportunistic butterfly records from 1890 to 2017 were analysed to quantitatively estimate the overall long-term change in occurrence of butterfly species in the Netherlands. For 71 species, we ...assessed trends in the number of occupied 5 km × 5 km sites by applying a modified List Length method, which takes into account changes in observation effort. We summarised the species trends in a Multi-Species Indicator (MSI) by taking the geometric mean of the species indices. Between 1890–1930 and 1981–1990, the MSI decreased by 67%; downward trends were detected for 42 species, many of which have disappeared completely from the Netherlands. Monitoring count data available from 1992 showed a further 50% decline in MSI. Combined, this yields an estimated decline of 84% in 1890–2017. We argue that in reality the loss is likely even higher. We also assessed separate MSIs for three major butterfly habitat types in the Netherlands: grassland, woodland and heathland. Butterflies strongly declined in all three habitats alike. The trend has stabilised over recent decades in grassland and woodland, but the decline continues in heathland.
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. 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). 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. 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.
Global warming may explain the current poleward shift of species distributions. However, paradoxically, climatic warming can lead to microclimatic cooling in spring by advancing plant growth, an ...effect worsened by excess nitrogen. We suggest that spring-developing but thermophilous organisms, such as butterflies hibernating as egg or larva, are particularly sensitive to the cooling of microclimates. Using published data on butterfly trends in distribution, we report a comparatively greater decline in egg-larva hibernators in European countries with oceanic climates and high nitrogen deposition, which supports this explanation. Furthermore, trends in abundance from a nationwide butterfly monitoring scheme reveal a 63% decrease over 13 years (1992-2004) for egg-larva hibernators in the Netherlands, contrasting with a nonsignificant trend in adult-pupa hibernators. This evidence supports the hypothesis that these environmental changes pose new threats to spring-developing, thermophilous species. We underline the threat of climate change to biodiversity, as previously suggested on the basis of mobility, habitat fragmentation and evolutionary adaptation, but we here emphasize a different ecological axis of change in habitat quality.
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.
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