Abstract
The observed insect decline, which threatens agricultural productivity and ecosystem stability, calls for comprehensive international insect monitoring. Monitoring implementation demands standardisation and the integration of new and innovative methods. Therefore, we compared two quantitative butterfly survey methods – the commonly applied transect counts (or ‘Pollard walks’) and more extensive area-time counts. We evaluated the influence of the two methods on the estimation of biodiversity variables such as species richness and species abundance to examine whether they could be applied alternatively for the calculation of butterfly trend indicators. During 576 surveys we conducted 5-minute transect counts and 25-minute area-time counts simultaneously at 144 different sites in Western Austria. The estimated relative butterfly abundance of the two methods for 119 observed species showed a strong linear relationship. While we found 2.4 times more species per site with the more extensive area-time counts than with the transect counts, we also observed a strong correlation between estimates of local abundance (Pearson’s r = 0.85) and observed species richness (Pearson’s r = 0.81) based on the two methods. Area-time counts provide thorough assessments on a given location, enabling a close connection to specific habitat types and facilitating comparability with other plot-based biodiversity assessments. They are more suitable than transect counts when aiming to analyse the drivers of temporal and spatial variability in butterfly occurrence. Furthermore, area-time counts can be used synergistically for the calculation of international butterfly abundance trends (e.g., European butterfly indicators), as we found strong linear relationships for all applied indicators with both methods.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Insects are a pre-requisite for many ecosystem functions (Yang and Gratton 2014) and related ecosystem services that are essential for human welfare (Losey and Vaughan 2006; Macadam and Stockan 2015; Rader et al. 2016). Somewhat surprisingly, recent reports on insect decline (e.g., Hallmann et al. 2017; Sánchez-Bayo and Wyckhuys 2019; van Klink et al. 2020) have attracted broader public attention to the global biodiversity crisis and its consequences (Cardoso et al. 2020). A remarkable decline in insect abundance and diversity is observed in many different regions and terrestrial habitats (Lister and Garcia 2018; Wepprich et al. 2019; Wagner 2020), including protected areas (Rada et al. 2019). This is of special concern as it threatens the functioning of ecosystems and the supply of important ecosystem services, such as pollination, food supply, biological control, and soil fertility regulation, and of diverse cultural ecosystem services (Noriega et al. 2018). The documented decline is severe and relevant enough to demand deliberate and consequential political action (Donkersley et al. 2022). Nevertheless, data on most insect populations and their development are still inadequate (Eggleton 2020; Goulson 2019).
Long-term data about population trends are missing for most insect species (IPBES 2019). To date, only two out of 26 European biodiversity indicators are directly based on the abundance and distribution of select taxa: the common bird index and the grassland butterfly index (EEA - European Environment Agency 2012). Butterfly monitoring, with divers methodological approaches, has a long history, with the first schemes starting in Europe and North America in the 1970s (Brereton et al. 2010; Taron and Ries 2015). Due to extensive and constantly growing monitoring networks such as the European butterfly monitoring scheme (eBMS) (Sevilleja et al. 2020), butterflies are one of the best-studied insect groups. Similar schemes for other insects are lacking even though the importance of such programs is widely acknowledged by science (e.g., Magurran et al. 2010; Dicks et al. 2016; Breeze et al. 2021). Recent developments such as the EU pollinator initiative, which proposed a monitoring mechanism as part of the EU Biodiversity Strategy (Potts et al. 2021) and the development of the European soil monitoring (Montanarella and Panagos 2021) show the growing political interest in the monitoring of insect biodiversity.
For the international mapping and monitoring of species populations, the integration of regional and national observation data is needed (Jetz et al. 2019). The combination of data from different monitoring programs can provide benefits such as a broader database with extended spatial and temporal coverage and the potential to draw more robust and general conclusions (Henry et al. 2008). However, data integration can also be challenging due to variations in methods, spatiotemporal and taxonomic scope, collection purposes, and terminologies (Guralnick et al. 2018; König et al. 2019). Due to the high value of long-term monitoring data (Lindenmayer et al. 2012), keeping methods constant over time is often considered more important than uniform methods in the monitoring network when integrating existing schemes. Even if monitoring schemes are newly established, other objectives of the regional programs or differences in local conditions can oppose the use of standardised methods in a monitoring network (Parr et al. 2002). When integrating monitoring schemes, the applied methods should be evaluated regarding their compatibility to ensure that reliable conclusions can be drawn from the data. This is also of importance when studies on different species or taxonomic groups combine different methods depending on the appropriate survey design for the targeted taxa (Montgomery et al. 2021).
Butterflies are one of the few taxa for which a standardized quantitative sampling method is commonly applied in many different countries – Pollard walks, a transect count method (Pollard and Yates 1993). Pollard walks have long been established and used in the eBMS (Schmucki et al. 2015), North American butterfly monitoring networks (Taron and Ries 2015) and many other studies on butterflies with different objectives (e.g., Bruppacher et al. 2016; Dániel-Ferreira et al. 2020; Kolkman et al. 2021). The observation protocols in general follow the original recommendation of Pollard and Yates (1993) regarding weather conditions and the distance from the observer at which butterflies are counted. The length of transects differs substantially and ranges from short transects of 50 m (Loos et al. 2014) to a few kilometres (Herrnando et al. 2016).
Apart from transect counts, area and point counts are widely applied visual survey methods in the monitoring of butterflies and other insects that can be easily identified in the field (Montgomery et al. 2021). In contrast to the mostly standardised transect counts, there are varying applications of the area and point counts. For point counts, observers count all butterflies around them during a defined period of time. Point counts are now commonly applied in citizen science projects such as the Big Butterfly Count (Dennis et al. 2017) or can be applied when sites are difficult to cross, e.g., due to dense vegetation or sensitive habitats (Montgomery et al. 2021). Implementations of the method differ substantially in the time spent surveying, the distance from the surveyor at which butterflies are counted and the shape of the surveyed area (e.g., full or semi-circle) (Henry et al. 2015; Dennis et al. 2017; Lang et al. 2019).
Counts in a defined area also show varying sampling designs. The main difference in area counts is that while transect counts have a fixed path that is walked by the observers, area counts have no such fixed path. In some studies, large areas are surveyed while recording the survey effort (time) but are not covered systematically (e.g., the 4th of July butterfly count in the US on survey circles with a 7.5-mile radius; Swengel 1990). Area counts as defined by Hardersen and Corezzola (2014) as plot-based surveys are a more standardized approach, as a defined amount of time is spent on each survey site. From here on, we use the term area-time count for such standardized approaches where a patch of a defined size is surveyed for a defined amount of time. Area-time counts were used for butterfly counts in studies that considered different taxonomic groups on the same survey sites (Su et al. 2004; Grill et al. 2005; Marini et al. 2009) or assessed the impact of environmental factors on butterfly communities (Debinski et al. 2001; Fiedler et al. 2017). Area-time counts provide a thorough assessment of species richness at a specific site and are therefore advantageous in heterogeneous landscapes with small habitat patches. Furthermore, they provide better coverage of rare species, facilitating the analysis of drivers of spatial and temporal patterns in butterfly abundance and richness. However, transect counts can be implemented more easily in a citizen science context, as has been done for many decades in different countries (van Swaay et al. 2020), as transects can very often follow existing trails, and hence, disturbance of grassland sites can be minimized.
International biodiversity monitoring approaches such as the envisaged EU Pollinator Monitoring Scheme (Potts et al.2021) call for a standardization of methods as well as an integration of different methods. Ensuring data comparability is crucial when methodologies differ (Parr et al. 2002). We therefore compared two commonly applied butterfly survey methods: the traditional Pollard and Yates (1993) transect counts and area-time counts. For this purpose, we analysed data from 144 sites from a newly established butterfly monitoring scheme in western Austria where both methods were applied simultaneously.
To evaluate whether transect counts and area-time counts could be applied alternatively for the calculation of trend indicators in the context of long-term butterfly abundance monitoring, we analysed whether and how the two methods influence the estimation of different biodiversity variables. At the site level, we used variables such as species richness, species abundance and butterfly habitat quality. At the regional level, we used variables that describe the regional population state (and trend) of individual species, such as relative abundance (summed over all sites) and site occupancy (the number of sites on which a species was detected).
Methods
Study area and survey sites
The study area comprises the Austrian federal states of Vorarlberg and Tyrol in the Eastern Alps, with an area of 15.241 km2. In each state, 100 survey sites were designated in grassland habitats using a stratified random and spatially balanced sampling design. The sampling design aims to representatively survey all grassland habitats of the study region and to select homogenous habitats at individual sites. This approach allows for the implementation of probability-based designs – in our case, a stratified random sampling – while maintaining a spatial balance among the selected locations (Theobald et al. 2007). With a spatially balanced sampling design, the sampling locations are evenly spread over the entire survey area (or strata). A spatially balanced sampling hence supports efficient surveys when response variables have a spatial trend, even if the spatial pattern of this trend is not known before the sample is drawn (Kermorvant et al. 2019). The additional use of strata enables the application of variable sampling density if the area covered by the different strata is of substantially different size, shape or habitat quality. The applied strata should ideally cover more or less homogenous habitat types or homogenous response units delimited by basic landscape (e.g., relief) and land use or land cover (LULC) characteristics (Havlík et al. 2011; Schirpke et al. 2020). This enables us to better account for LULC changes when calculating butterfly trend indicators, supports a space-for-time substitution to model past or future trajectories of butterfly communities where historic baseline data are missing (Blois et al. 2013; Montgomery et al. 2020) and facilitates future habitat analysis and modelling.
Therefore, we divided all grassland habitats of the federal states of Tyrol und Vorarlberg into four strata: (1) flat valley meadows with a slope gradient of less than 15%, (2) hillside meadows with a slope gradient of 15% or more, (3) high mountain to alpine meadows - basically all meadows above the current timberline, and (4) all grasslands in protected Natura 2000 sites (European Commission 2020). Slope gradient (to differentiate (1) and (2)) was used as a proxy for management intensity, as the intensity of management practices generally decreases with increasing slope gradient (Rüdisser et al. 2015). In each federal state, 25 sites (covering all strata) are surveyed per year. Therefore, four years are needed to survey all sites, and repetition starts in the fifth year. In Tyrol, data are already available from all 100 sites surveyed in the years 2018–2021. In Vorarlberg, monitoring started in 2020 and hence covered 50 sites from 2020 to 2021.
Butterfly surveys
The butterfly survey took place from 2018 to 2021. To optimize the sampling effort in relation to the additional information gain, we opted to survey each site four times in one year (as described above) between mid-May and early September (Barkmann 2020; Hardersen and Corezzola 2014; Lang et al. 2016; Roy et al. 2007). Observers selected survey dates flexibly to optimize weather conditions (see next paragraph) and to achieve on average three to four weeks and a minimum of at least one week between two consecutive surveys. This approach aims to broadly capture the butterfly species composition of the entire season (Stewart et al. 2020; Hardersen and Corezzola 2014).
At six of the study sites, only three surveys took place due to unfavourable weather conditions at high elevations. These six sites were excluded from the analysis, leaving 144 sites (Fig. 1).
The butterfly surveys were carried out between 10:00 and 17:00 in warm (13 C or more), dry and calm windy conditions (Beaufort scale: 0–3) following Pollard and Yates (1993). At temperatures below 17 °C, surveys were only conducted in full sunshine. At temperatures over 17 °C, cloud cover of up to 40% was permitted. To allow a comparison of the two methods, all surveys were carried out in two phases:
First, a 50-m transect was sampled as described by Pollard and Yates (1993) by walking slowly at a speed of approximately 10 m min− 1, sampling all sitting and passing butterflies in an imaginary rectangle of 2.5 m on the sides and 5 m in front. In exceptional cases, the transect was unilateral, i.e., 5 m on one side. This variant was chosen, for example, if there was no suitable path through the area and the transect was only possible at the edge of the area. The resulting walking time per 50-m transect was approx. 5 min.
Second, an area-time count was carried out for an additional 25 min. For the area-time count, the area around the original transect was extended to 1000 m², i.e., the area 10 m to the left and right of the transect. During the area-time count, the area was walked slowly in winding lines, avoiding multiple counting of the same individuals.
Double-counting during the survey cannot be completely ruled out but was avoided as best as possible. Individuals that were already detected during the transect count were therefore not recorded again during the following 25-min area-time count. Although the complete area-time count of the survey sites comprises both phases of the survey, the two phases were separated for the data analysis presented herein to obtain two independent datasets.
All butterflies were identified either in-flight or caught and identified in-hand before release. Animals were caught only if necessary to ensure species identification. If this was the case, the observer interrupted the survey time to either identify the butterfly, take a picture or collect for later identification and then resumed the survey where they had interrupted it. Only in exceptional cases were individuals collected for later identification. The cryptic species pairs Aricia agestis/artaxerxes, Colias hyale/alfacariensis, Leptides sinapis/realis and Pyrgus malvae/malvoides are difficult to differentiate in the field and were therefore treated as species complexes.
Indicators and data analysis
Data analysis aimed to understand and describe the influence of the two different survey methods on butterfly abundance and diversity estimates. For this purpose, we compared the results of the 50 m transect counts, which took 5 min, with the results of the 25-min area-time counts for species richness, butterfly abundance, and butterfly habitat quality (BHQ) at the site level and regarding individual species abundance and site occupancy at the regional level. BHQ is an indicator that combines min-max normalized abundance and species richness and is a suitable metric to compare different sites regarding their general habitat quality for butterflies (Rüdisser et al. 2017). BHQ was calculated as
where \({S}_{i}\) is the species richness at site i and \({A}_{i}\) is the abundance at the corresponding site. \({S}_{Min}\) and \({S}_{Max}\) are the minimal and maximal species richness of all sites, and \({A}_{Min}\) and \({A}_{Max}\)are the minimal and maximal species abundance of all sites.
For all analyses, data from the four visits per site were summed. For individual species abundance at the regional level, all counts were summed, and site occupancy was determined by identifying the number of sites where the specific species occurred at least once.
For each of the abovementioned variables, the correlation between the data from the transect count and from the area-time count was calculated using the Pearson correlation coefficient. For analysis of BHQ, the square root of the indicator was used for both methods for a less skewed distribution of the data. There were some outliers with a high number of individuals for the abundance data for both the individual species and survey sites. Thus, the data from both methods were square-root transformed, and the correlation was calculated for both the original and the transformed data.
Additionally, we compared the individual-based species accumulation curves of both survey methods for all four strata. The species abundance data from all sites within a stratum were summed, and species richness was interpolated and extrapolated as described in Chao et al. (2014) with 50 bootstrap replications. The data analysis was conducted for all sites and additionally for the different strata individually.
Data analysis was conducted in R (Core Team 2021). The iNEXT package (Hsieh et al. 2016) was used to calculate rarefaction and extrapolation curves, and the ggplot2 package (Wickham 2019) was used for data visualisation.
Results
During the 576 surveys, we made 9817 butterfly detections and observed 119 species. A total of 7499 butterflies were counted during the area-time counts and 2318 during the transect counts. The time between two consecutive surveys at the same site ranged from 7 to 63 days, with a mean of 23.9 days (SD: 8.9 days). For 95% of two consecutive surveys, the time between the visits ranged from 11 to 42 days.
The butterfly abundances detected per site with the area-time counts ranged from two to 211 (mean: 54.2, SD: 41.9) and with the transect counts from one to 103 (mean: 16.6, SD: 15.7). With the area-time counts one to 30 (mean: 12.8, SD: 6.2) and with the transect counts one to 18 (mean: 6.4, SD: 3.8) species were detected per site. Individual species were detected on average at 2.3 times more sites with the area-time counts than with the transect counts. On average, 2.4 times more species per site were detected with the area-time counts than with the transect counts (mean of the proportion calculated for individual sites).
Individual species abundances at the regional level, and hence over all survey sites, ranged from zero to 905 (mean: 63.0, SD: 119.4) individuals detected with the area-time counts and from zero to 292 (mean: 19.5, SD: 37.1) individuals detected with the transect counts. With the area-time counts, individual species were detected at zero to 106 (mean: 15.5, SD: 19.7) sites, and with the transect counts, they were detected at zero to 64 (mean: 7.8, SD: 11.6) sites (Supplementary information Table S1). Three species were detected during transect counts only, and 20 species were detected during area-time counts only.
The correlation analysis for the data at the regional level showed a strong linear relationship between the area-time counts and the transect counts, with a Pearson correlation coefficient of r = 0.96 for the species site occupancy, r = 0.96 for the untransformed species abundance and r = 0.96 for the square-root transformed species abundance (Fig. 2). The correlation coefficients for the individual strata ranged from r = 0.77 to r = 0.94 for site occupancy, from r = 0.70 to r = 0.98 for untransformed abundance and from r = 0.84 to r = 0.96 for the square-root transformed abundance. All correlations were significant with p < 0.001. The abundances of two species showed a noticeable deviation: Pieris rapae was counted more often and Cupido minimus less often on the area-time counts compared to the transect counts than would be expected from the linear correlation between the two variables.
The rarefied species accumulation curves based on the data of the two survey methods consistently overlapped for all strata except ‘high mountain to alpine meadows’. Species accumulation curves based on the area-time count data were largely saturated across all strata, while those based on the transect data were not saturated (Fig. 3).
At the site level, the correlation analysis also showed a clear linear relationship between the data from the transect counts and the area time counts, with Pearson correlation coefficients of r = 0.80 for butterfly abundance per site, r = 0.85 for square-root-transformed abundance, r = 0.81 for species richness, and r = 0.86 for the square root of BHQ (Fig. 4a-c). The correlations at site level within the individual strata ranged from r = 0.72 to r = 0.87 for untransformed abundance, from r = 0.79 to r = 0.85 for the square root transformed abundance, from r = 0.73 to r = 0.81 for species richness and from r = 0.80 to r = 0.88 for the square root of the BHQ. All correlations were significant at p < 0.001.
Discussion
In the face of the observed insect decline, the importance of sound insect monitoring is being increasingly emphasized. The envisaged establishment of EU-wide pollinator monitoring (Potts et al. 2021) could be a major and important step towards better insect conservation (Samways et al. 2020; Warren et al. 2021) and the establishment of comprehensive European biodiversity monitoring. Although there already exists broad experience in butterfly monitoring, the development and establishment of transcontinental pollinator monitoring is still in its infancy. Standardization of methods as well as the development and integration of new innovative approaches are essential for successful and sustainable implementation. For this purpose, butterfly survey methods must be compared to assess their efficiency and mutual compatibility. We added to this evaluation with an extensive comparison between transect and area-time counts.
Not surprisingly, we observed more species per site with the more extensive area-time counts than with the transect counts. The 2.4-times higher species richness per site for the area-time counts also resulted in 2.3-times the number of sites where a species was detected. This led to a more complete coverage of the butterfly community composition in the different strata. The species accumulation curves were saturated across all strata when based on area-time count data. The species accumulation curves for transect count data were not yet saturated due to the lower sampling effort, but were very similar to the curves for the area-time counts for all but one stratum. For the alpine meadows the curves diverge slightly. This might be caused by the high habitat diversity combined with naturally lower butterfly abundance in this stratum. However, we assume that species accumulation curves for transect counts would saturate too with increased sampling effort (more sites or higher sampling frequency).
Both methods – transect counts and area-time counts – led to surprisingly consistent proportional results regarding relative butterfly abundance (r = 0.96) and species occupancy (r = 0.96) on the regional scale. The observed strong linear relationship between the local diversity estimates of abundance (r = 0.80), species richness (r = 0.81), and BHQ (r = 0.86) based on the transect counts and on the more comprehensive area-time counts is very good news. This confirms both the robustness of the transect counts and their compatibility with the more comprehensive area-time counts.
While the transect counts with 50-m-long transects and four surveys resulted in a cumulative survey time of approximately 20 min per transect and year – covering an area of 250 m² – the area-time counts resulted in a cumulative sampling time of 100 min per site and year – covering an area of 1000 m². However, the strong linear correlation between the data from the transect counts and from the far more comprehensive area-time counts indicate that even a few surveys with relatively low effort can be highly informative. This is true for both population monitoring at the regional scale and for local site comparison regarding diversity and overall habitat quality.
While documented differences in sampling effort can be accounted for when combining data from multiple monitoring schemes, differences in the detectability of distinct species can lead to severe biases (Kellner and Swihart 2014). It is well known that visual survey methods can underestimate the presence of cryptic or sedentary species (Dennis et al. 2006). Distance sampling and mark-recapture studies revealed that butterfly detectability during transect counts varies substantially among species and is influenced by colour, wingspan, and behaviour (Isaac et al. 2011; Pellet et al. 2012). Therefore, the data obtained from active visual counting methods do not reflect true butterfly abundance but rather species-specific relative abundances (van Swaay et al. 2008). In contrast to the transect counts, the area-time counts do not follow a fixed path and hence could lead to a changed detectability of specific species via disturbance or better capturing of the spatiotemporal variation of resources (e.g., nectar availability, microclimate, water) (Dennis et al. 2006). We showed that this was not the case. This is in line with Kadlec et al. (2012), who compared area-time counts and transect counts at ten sites in the Czech Republic and found no difference between the detectability of mobile and imperceptible species between the two methods.
There was a strong linear relationship between the species abundance counts from the two methods. Only two species showed a considerable deviation from the linear correlation (Fig. 2a). For Pieris rapae, more individuals were detected with the area-time counts than would have been expected from the abundance counted on the transects. In contrast, Cupido minimus was counted in proportionally higher abundance on the transect counts. This deviation is likely due to mud-puddling, a behaviour that is often observed in adult butterflies and can lead to aggregations of individuals (Downes 1973; Boggs and Dau 2004). Such aggregations can cause high local densities of adult butterflies on open spots such as paths. This was observed on one transect in early July, during which 70 individuals of Cupido minimus were counted on a transect. On the subsequent area-time count, only two individuals were detected. Consequently, the respective site also deviates from the linear correlation for on-site butterfly abundance (Fig. 4a). In this case, the transect count likely overestimates abundance, while the area-time count provides a more realistic assessment. In this context, note that we separated the data from the transect counts and the consecutive area-time counts to obtain independent variables for the presented methodological analysis. This might have contributed to an underestimation of the butterfly abundance and species richness estimates based on area-time counts in relation to the estimates based on transect counts.
The similar detectability of butterfly species with both methods supports the integration of data from area-time counts and transect counts for the analysis of trends in butterfly abundance. Differences in the time spent surveying and the area of survey sites do not hinder a comparison and combination of abundance data because, unlike species richness (Gotelli and Colwell 2001), species abundance is scale-independent. Due to this and other favourable mathematical characteristics, such as monotonicity and proportionality to species’ declines and increases (van Strien et al. 2012), the geometric mean of species abundance is often used for the calculation of indicators. These indicators, such as the living planet index (Loh et al. 2005) and the UK wild bird indicator (Gregory et al. 2008), generally assess and combine population trends of multiple species. The EU butterfly indicator for grassland species (van Swaay et al. 2019) and additional indicators developed in the eBMS framework (Schmucki et al. 2015; van Swaay et al. 2020) are also based on changes in the geometric mean of species abundance.
For the calculation of international butterfly indicators, density estimates of the contributing national (or regional) butterfly monitoring schemes are combined (van Swaay et al. 2020). A comparison of methods – as was done here – allows us to assess the factor by which the detected species densities differ between methods and hence enables the conversion of density estimates and facilitates the calculation of collated indicators.
Depending on the aims and requirements of a monitoring scheme, area-time counts can be a reasonable alternative to the commonly applied transect counts. Transect counts can be implemented much easier in a citizen science context, as has been done for many decades in different countries (van Swaay et al. 2020), because transects can follow existing trails and hence disturbance of grassland sites can be minimized. Shorter transects that stay within the same LULC class can give a good impression of butterfly habitat quality, as shown in this study, but more detailed information on butterfly fauna, such as data on rare species, requires more extensive surveys. Area-time counts provide a more complete assessment of species richness and community composition at a specific site and are therefore especially useful in small-structured anthropogenic-influenced landscapes. Next to the information gain, area-time counts are also more efficient regarding the time spent travelling and surveying. Much effort in systematic monitoring does not result solely from the mere survey time but rather from time travelling to sites and documenting site and weather conditions. Area-time counts allow more thorough assessments of a site when focusing surveys on a given location. They enable a closer connection to specific habitat types and better comparability with other plot-based monitoring schemes (Levanoni et al. 2011; Hardersen and Corezzola 2014). Especially in heterogeneous landscapes, this approach facilitates the selection of sites that cover a homogenous LULC class and are influenced by the same local environmental factors. In contrast, transects with a comparable survey effort often lead through diverse habitats (Caritg et al. 2011; Taron and Ries 2015), which makes it more challenging to relate monitoring data to environmental variables. In some monitoring schemes, transects are divided into shorter sections that lead through homogenous habitats to overcome this obstacle (e.g., Luppi et al. 2018). However, sections of uniform length do not often match changes in habitat, while sections with variable length impede the comparison of data from different habitat types due to differences in survey effort. Area-time counts are therefore better suited than transect counts when aiming to analyse the drivers of temporal and spatial variations in butterfly species richness, abundance and community composition, particularly on small scales (Guariento et al. 2022). Going beyond the detection of population trends and investigating the causes of declines in butterfly abundance is crucial for taking adequate conservation measures.
Conclusion
Area-time counts and transect counts resulted in similar assessments of relative abundance and site occupancy on regional level. Both methods can therefore be used alternatively or synergistically for the calculation of abundance-based trends. Area-time counts have many benefits for the analysis of drivers of spatial and temporal patterns in butterfly abundance and richness because they provide a better coverage of small habitat patches. However, transect counts can be a valuable complementation to area-time counts in order to enhance spatial or temporal coverage e.g., in a citizen science context.
Data availability
The published data are available via eBMS data access (https://butterfly-monitoring.net/ebms-data%20access) or directly upon request with the corresponding author (Johannes.Ruedisser@uibk.ac.at).
References
Barkmann F (2020): Optimization of butterfly surveys in the context of long-term monitoring. Master’s Thesis. University of Innsbruck, 55pp
Blois JL, Williams JW, Fitzpatrick MC, Jackson ST, Ferrier S (2013) Space can substitute for time in predicting climate-change effects on biodiversity. PNAS 110:9374–9379. https://doi.org/10.1073/pnas.1220228110
Boggs CL, Dau B (2004) Resource specialization in puddling Lepidoptera. Environ Entomol 33:1020–1024. https://doi.org/10.1603/0046-225X-33.4.1020
Breeze TD, Bailey AP, Balcombe KG, Brereton T, Comont R, Edwards M, Garratt MP, Harvey M, Hawes C, Isaac N, Jitlal M, Jones CM, Kunin WE, Lee P, Morris RKA, Musgrove A, O’Connor RS, Peyton J, Potts SG, Roberts SPM, Roy DB, Roy HE, Tang CQ, Vanbergen AJ, Carvell C (2021) Pollinator monitoring more than pays for itself. J Appl Ecol 58:44–57. https://doi.org/10.1111/1365-2664.13755
Brereton T, Roy DB, Middlebrook I, Botham M, Warren M (2010) The development of butterfly indicators in the United Kingdom and assessments in 2010. J Insect Conserv 15:139–151. https://doi.org/10.1007/s10841-010-9333-z
Bruppacher L, Pellet J, Arlettaz R, Humbert J-Y (2016) Simple modifications of mowing regime promote butterflies in extensively managed meadows: evidence from field-scale experiments. Biol Conserv 196:196–202. https://doi.org/10.1016/j.biocon.2016.02.018
Cardoso P, Barton PS, Birkhofer K, Chichorro F, Deacon C, Fartmann T, Fukushima CS, Gaigher R, Habel JC, Hallmann CA, Hill MJ, Hochkirch A, Kwak ML, Mammola S, Ari Noriega J, Orfinger AB, Pedraza F, Pryke JS, Roque FO, Settele J, Simaika JP, Stork NE, Suhling F, Vorster C, Samways MJ (2020) Scientists’ warning to humanity on insect extinctions. Biol Conserv 242:108426. https://doi.org/10.1016/j.biocon.2020.108426
Caritg R, Domènech M, Dantart J, Jubany J (2011) Andorran butterfly monitoring scheme. J Insect Conserv 15:341–344. https://doi.org/10.1007/s10841-010-9352-9
Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84:45–67. https://doi.org/10.1890/13-0133.1
Core Team R(2021) R: A Language and Environment for Statistical Computing. https://www.R-project.org/
Dániel-Ferreira J, Bommarco R, Wissman J, Öckinger E (2020) Linear infrastructure habitats increase landscape-scale diversity of plants but not of flower-visiting insects. Sci Rep 10:21374. https://doi.org/10.1038/s41598-020-78090-y
Debinski DM, Ray C, Saveraid EH (2001) Species diversity and the scale of the landscape mosaic: do scales of movement and patch size affect diversity? Biol Conserv 98:179–190. https://doi.org/10.1016/S0006-3207(00)00153-1
Dennis R, Shreeve TG, Isaac N, Roy DB, Hardy PB, Fox R, Asher J (2006) The effects of visual apparency on bias in butterfly recording and monitoring. Biol Conserv 128:486–492. https://doi.org/10.1016/j.biocon.2005.10.015
Dennis EB, Morgan BJT, Brereton TM, Roy DB, Fox R (2017) Using citizen science butterfly counts to predict species population trends. Conserv Biol 31:1350–1361. https://doi.org/10.1111/cobi.12956
Dicks LV, Viana B, Bommarco R, Brosi B, Del Arizmendi MC, Cunningham SA, Galetto L, Hill R, Lopes AV, Pires C, Taki H, Potts SG (2016) Ten policies for pollinators. Science 354:975–976. https://doi.org/10.1126/science.aai9226
Donkersley P, Ashton L, Lamarre GPA, Segar S (2022) Global insect decline is the result of wilful political failure: a battle plan for entomology. Ecol Evol 12:e9417. https://doi.org/10.1002/ece3.9417
Downes JA (1973) Lepidoptera feeding at puddle-margins, dung, and carrion. J Lepidopterists’ Soc 27:89–99
EEA - European Environment Agency (2012): Streamlining European biodiversity indicators 2020: Building a future on lessons learnt from the SEBI 2010 process. Technical report 11. European Environment Agency. Copenhagen
Eggleton P (2020) The state of the World’s insects. Annu Rev Environ Resour 45:61–82. https://doi.org/10.1146/annurev-environ-012420-050035
European Commission (2020) Natura 2000. https://ec.europa.eu/environment/nature/natura2000/index_en.htm
Fiedler K, Wrbka T, Dullinger S (2017) Pluralism in grassland management promotes butterfly diversity in a large central european conservation area. J Insect Conserv 21:277–285. https://doi.org/10.1007/s10841-017-9974-2
Gotelli NJ, Colwell RK (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett 4:379–391. https://doi.org/10.1046/j.1461-0248.2001.00230.x
Goulson D (2019) The insect apocalypse, and why it matters. Curr Biol 29:R967–R971. https://doi.org/10.1016/j.cub.2019.06.069
Gregory RD, Vořišek P, Noble DG, van Strien A, Klvaňová A, Eaton M, Gmelig Meyling AW, Joys A, Foppen RPB, Burfield IJ (2008) The generation and use of bird population indicators in Europe. Bird Conserv Int 18:S223–S244. https://doi.org/10.1017/S0959270908000312
Grill A, Knoflach B, Cleary DFR, Kati V (2005) Butterfly, spider, and plant communities in different land-use types in Sardinia, Italy. Biodivers Conserv 14:1281–1300. https://doi.org/10.1007/s10531-004-1661-4
Guariento E, Rüdisser J, Fiedler K, Paniccia C, Stifter S, Tappeiner U, Seeber J, Hilpold A (eds) (2022) From diverse to simple: butterfly communities erode from extensive grasslands to intensively used farmland and urban areas. Biodivers Conserv. https://doi.org/10.1007/s10531-022-02498-3
Guralnick R, Walls R, Jetz W (2018) Humboldt Core - toward a standardized capture of biological inventories for biodiversity monitoring, modeling and assessment. Ecography 41:713–725. https://doi.org/10.1111/ecog.02942
Hallmann CA, Sorg M, Jongejans E, Siepel H, Hofland N, Schwan H, Stenmans W, Müller A, Sumser H, Hörren T, Goulson D, de Kroon H (2017) More than 75% decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12:e0185809. https://doi.org/10.1371/journal.pone.0185809
Hardersen S, Corezzola S (2014) Plot-based butterfly surveys: statistical and methodological aspects. J Insect Conserv 18:1171–1183. https://doi.org/10.1007/s10841-014-9728-3
Havlík P, Schneider UA, Schmid E, Böttcher H, Fritz S, Skalský R, Aoki K, de Cara S, Kindermann G, Kraxner F, Leduc S, McCallum I, Mosnier A, Sauer T, Obersteiner M (2011) Global land-use implications of first and second generation biofuel targets. Energy Policy 39:5690–5702. https://doi.org/10.1016/j.enpol.2010.03.030
Henry P-Y, Lengyel S, Nowicki P, Julliard R, Clobert J, Čelik T, Gruber B, Schmeller DS, Babij V, Henle K (2008) Integrating ongoing biodiversity monitoring: potential benefits and methods. Biodivers Conserv 17:3357–3382. https://doi.org/10.1007/s10531-008-9417-1
Henry EH, Haddad NM, Wilson J, Hughes P, Gardner B (2015) Point-count methods to monitor butterfly populations when traditional methods fail: a case study with Miami blue butterfly. J Insect Conserv 19:519–529. https://doi.org/10.1007/s10841-015-9773-6
Herrnando S, Brotons L, Anton M, Páramo F, Villero D, Titeux N, Quesada J, Stefanescu C (2016) Assessing impacts of land abandonment on Mediterranean biodiversity using indicators based on bird and butterfly monitoring data. Envir Conserv 43:69–78. https://doi.org/10.1017/S0376892915000260
Hsieh TC, Ma KH, Chao A (2016) iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol Evol 7:1451–1456. https://doi.org/10.1111/2041-210X.12613
IPBES (2019) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science. -Policy Platform on Biodiversity and Ecosystem Services
Isaac NJB, Cruickshanks KL, Weddle AM, Marcus Rowcliffe J, Brereton TM, Dennis RLH, Shuker DM, Thomas CD (2011) Distance sampling and the challenge of monitoring butterfly populations. Methods Ecol Evol 2:585–594. https://doi.org/10.1111/j.2041-210X.2011.00109.x
Jetz W, McGeoch MA, Guralnick R, Ferrier S, Beck J, Costello MJ, Fernandez M, Geller GN, Keil P, Merow C, Meyer C, Muller-Karger FE, Pereira HM, Regan EC, Schmeller DS, Turak E (2019) Essential biodiversity variables for mapping and monitoring species populations. Nat Ecol Evol 3:539–551. https://doi.org/10.1038/s41559-019-0826-1
Kadlec T, Tropek R, Konvicka M (2012) Timed surveys and transect walks as comparable methods for monitoring butterflies in small plots. J Insect Conserv 16:275–280. https://doi.org/10.1007/s10841-011-9414-7
Kellner KF, Swihart RK (2014) Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9:e111436. https://doi.org/10.1371/journal.pone.0111436
Kermorvant C, D’Amico F, Bru N, Caill-Milly N, Robertson B (2019) Spatially balanced sampling designs for environmental surveys. Environ Monit Assess 191:524. https://doi.org/10.1007/s10661-019-7666-y
Kolkman A, Dopagne C, Piqueray J (2021) Sown wildflower strips offer promising long term results for butterfly conservation. J Insect Conserv. https://doi.org/10.1007/s10841-021-00347-2
König C, Weigelt P, Schrader J, Taylor A, Kattge J, Kreft H (2019) Biodiversity data integration-the significance of data resolution and domain. PLoS Biol 17:e3000183. https://doi.org/10.1371/journal.pbio.3000183
Lang A, Bühler C, Dolek M, Roth T, Züghart W (2016) Estimating sampling efficiency of diurnal Lepidoptera in farmland. J Insect Conserv 20:35–48. https://doi.org/10.1007/s10841-015-9837-7
Lang BJ, Dixon PM, Klaver RW, Thompson JR, Widrlechner MP (2019) Characterizing urban butterfly populations: the case for purposive point-count surveys. Urban Ecosyst 22:1083–1096. https://doi.org/10.1007/s11252-019-00880-8
Levanoni O, Levin N, Pe’er G, Turbé A, Kark S (2011) Can we predict butterfly diversity along an elevation gradient from space? Ecography 34:372–383. https://doi.org/10.1111/j.1600-0587.2010.06460.x
Lindenmayer DB, Likens GE, Andersen A, Bowman D, Bull CM, Burns E, Dickman CR, Hoffmann AA, Keith DA, Liddell MJ, Lowe AJ, Metcalfe DJ, Phinn SR, Russell-Smith J, THURGATE N, WARDLE GM (2012) Value of long-term ecological studies. Austral Ecol 37:745–757. https://doi.org/10.1111/j.1442-9993.2011.02351.x
Lister BC, Garcia A (2018) Climate-driven declines in arthropod abundance restructure a rainforest food web. PNAS 115:E10397–E10406. https://doi.org/10.1073/pnas.1722477115
Loh J, Green RE, Ricketts T, Lamoreux J, Jenkins M, Kapos V, Randers J (2005) The Living Planet Index: using species population time series to track trends in biodiversity. Philos Trans R Soc Lond B Biol Sci 360:289–295. https://doi.org/10.1098/rstb.2004.1584
Loos J, Dorresteijn I, Hanspach J, Fust P, Rakosy L, Fischer J (2014) Low-intensity agricultural landscapes in Transylvania support high butterfly diversity: implications for conservation. PLoS ONE 9:e103256. https://doi.org/10.1371/journal.pone.0103256
Losey JE, Vaughan M (2006) The Economic Value of Ecological Services provided by insects. Bioscience 56:311. https://doi.org/10.1641/0006-3568(2006)56[311:TEVOES]2.0.CO;2
Luppi M, Dondina O, Orioli V, Bani L (2018) Local and landscape drivers of butterfly richness and abundance in a human-dominated area. Agric Ecosyst Environ 254:138–148. https://doi.org/10.1016/j.agee.2017.11.020
Macadam CR, Stockan JA (2015) More than just fish food: ecosystem services provided by freshwater insects. Ecol Entomol 40:113–123. https://doi.org/10.1111/een.12245
Magurran AE, Baillie SR, Buckland ST, Dick JM, Elston DA, Scott EM, Smith RI, Somerfield PJ, Watt AD (2010) Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol Evol 25:574–582. https://doi.org/10.1016/j.tree.2010.06.016
Marini L, Fontana P, Battisti A, Gaston KJ (2009) Agricultural management, vegetation traits and landscape drive orthopteran and butterfly diversity in a grassland-forest mosaic: a multi-scale approach. Insect Conserv Divers 2:213–220. https://doi.org/10.1111/j.1752-4598.2009.00053.x
Montanarella L, Panagos P (2021) The relevance of sustainable soil management within the european Green Deal. Land use policy 100:104950. https://doi.org/10.1016/j.landusepol.2020.104950
Montgomery GA, Belitz MW, Guralnick RP, Tingley MW (2021) Standards and Best Practices for Monitoring and Benchmarking Insects. Front Ecol Evol 8:513. https://doi.org/10.3389/fevo.2020.579193
Montgomery GA, Dunn RR, Fox R, Jongejans E, Leather SR, Saunders ME, Shortall CR, Tingley MW, Wagner DL (2020) Is the insect apocalypse upon us? How to find out. Biol Conserv 241:108327. https://doi.org/10.1016/j.biocon.2019.108327
Noriega JA, Hortal J, Azcárate FM, Berg MP, Bonada N, Briones MJ, Del Toro I, Goulson D, Ibanez S, Landis DA, Moretti M, Potts SG, Slade EM, Stout JC, Ulyshen MD, Wackers FL, Woodcock BA, Santos AM (2018) Research trends in ecosystem services provided by insects. Basic Appl Ecol 26:8–23. https://doi.org/10.1016/j.baae.2017.09.006
Parr TW, Ferretti M, Simpson IC, Forsius M, Kovács-Láng E (2002) Towards a long-term integrated monitoring programme in Europe: network design in theory and practice. Environ Monit Assess 78:253–290. https://doi.org/10.1023/A:1019934919140
Pellet J, Bried JT, Parietti D, Gander A, Heer PO, Cherix D, Arlettaz R (2012) Monitoring butterfly abundance: beyond Pollard walks. PLoS ONE 7:e41396. https://doi.org/10.1371/journal.pone.0041396
Pollard E, Yates TJ (1993) Monitoring butterflies for ecology and conservation: the british butterfly monitoring scheme. Conservation biology series, vol 1, 1993rd edn. Chapman & Hall, London
Potts S, Dauber J, Hochkirch A, Oteman B, Roy DB, Ahrné K, Biesmeijer K, Breeze TD, Carvell C, Ferreira C, FitzPatrick Ú, Isaac NJ, Kuussaari M, Ljubomirov T, Maes J, Ngo H, Pardo A, Polce C, Quaranta M, Settele J, Sorg M, Stefanescu C, Vujić A (2021) Proposal for an EU pollinator monitoring scheme. EUR, vol 30416. Publications Office of the European Union, Luxembourg
Rada S, Schweiger O, Harpke A, Kühn E, Kuras T, Settele J, Musche M (2019) Protected areas do not mitigate biodiversity declines: a case study on butterflies. Divers Distrib 25:217–224. https://doi.org/10.1111/ddi.12854
Rader R, Bartomeus I, Garibaldi LA, Garratt MPD, Howlett BG, Winfree R, Cunningham SA, Mayfield MM, Arthur AD, Andersson GKS, Bommarco R, Brittain C, Carvalheiro LG, Chacoff NP, Entling MH, Foully B, Freitas BM, Gemmill-Herren B, Ghazoul J, Griffin SR, Gross CL, Herbertsson L, Herzog F, Hipólito J, Jaggar S, Jauker F, Klein A-M, Kleijn D, Krishnan S, Lemos CQ, Lindström SAM, Mandelik Y, Monteiro VM, Nelson W, Nilsson L, Pattemore DE, Pereira NdO, Pisanty G, Potts SG, Reemer M, Rundlöf M, Sheffield CS, Scheper J, Schüepp C, Smith HG, Stanley DA, Stout JC, Szentgyörgyi H, Taki H, Vergara CH, Viana BF, Woyciechowski M (2016) Non-bee insects are important contributors to global crop pollination. PNAS 113:146–151. https://doi.org/10.1073/pnas.1517092112
Roy DB, Rothery P, Brereton T (2007) Reduced-effort schemes for monitoring butterfly populations. J Appl Ecol 44:993–1000. https://doi.org/10.1111/j.1365-2664.2007.01340.x
Rüdisser J, Tasser E, Peham T, Meyer E, Tappeiner U (2015) The dark side of biodiversity: spatial application of the biological soil quality indicator (BSQ). Ecol Ind 53:240–246. https://doi.org/10.1016/j.ecolind.2015.02.006
Rüdisser J, Tasser E, Walde J, Huemer P, Lechner K, Ortner A, Tappeiner U (2017) Simplified and still meaningful: assessing butterfly habitat quality in grasslands with data collected by pupils. J Insect Conserv 21:677–688. https://doi.org/10.1007/s10841-017-0010-3
Samways MJ, Barton PS, Birkhofer K, Chichorro F, Deacon C, Fartmann T, Fukushima CS, Gaigher R, Habel JC, Hallmann CA, Hill MJ, Hochkirch A, Kaila L, Kwak ML, Maes D, Mammola S, Noriega JA, Orfinger AB, Pedraza F, Pryke JS, Roque FO, Settele J, Simaika JP, Stork NE, Suhling F, Vorster C, Cardoso P (2020) Solutions for humanity on how to conserve insects. Biol Conserv 242:108427. https://doi.org/10.1016/j.biocon.2020.108427
Sánchez-Bayo F, Wyckhuys KA (2019) Worldwide decline of the entomofauna: a review of its drivers. Biol Conserv 232:8–27. https://doi.org/10.1016/j.biocon.2019.01.020
Schirpke U, Tscholl S, Tasser E (2020) Spatio-temporal changes in ecosystem service values: Effects of land-use changes from past to future (1860–2100). J Environ Manage 272:111068. https://doi.org/10.1016/j.jenvman.2020.111068
Schmucki R, Pe’er G, Roy DB, Stefanescu C, van Swaay CA, Oliver TH, Kuussaari M, van Strien AJ, Ries L, Settele J, Musche M, Carnicer J, Schweiger O, Brereton TM, Harpke A, Heliölä J, Kühn E, Julliard R (2015) A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes. J Appl Ecol 53:501–510. https://doi.org/10.1111/1365-2664.12561
Sevilleja CG, Collins S, Warren MS, Wynhoff I, van Swaay C, Dennis EB, Schmucki R, Barea Azcon JM, Bonelli S, Bourn N, Cassar LF, Arce Crespo JI de, Dziekanska I, Faltynek Fric Z, Kolev Z, Krenn H, Lehner D, Monteiro E, Munguira ML, Ozden O, Pavlicko A, Pendl M, Rudisser J, Sasic M, Sielezniew M, Settele J, Szabadfalvi A, Teixeira SM, Tzirkalli E, Roy DB (2020) European Butterfly Monitoring Scheme (eBMS): network development. Technical report, Wageningen, The Netherlands. http://nora.nerc.ac.uk/id/eprint/529534/
Stewart JE, Illán JG, Richards SA, Gutiérrez D, Wilson RJ (2020) Linking inter-annual variation in environment, phenology, and abundance for a montane butterfly community. Ecology 101:e02906. https://doi.org/10.1002/ecy.2906
Su JC, Debinski DM, Jakubauskas ME, Kindscher K (2004) Beyond species richness: community similarity as a measure of Cross-Taxon Congruence for Coarse-Filter Conservation. Conserv Biol 18:167–173. https://doi.org/10.1111/j.1523-1739.2004.00337.x
Swengel AB (1990) Monitoring butterfly populations using the Fourth of July Butterfly Count. Am Midl Nat 124:395. https://doi.org/10.2307/2426190
Taron D, Ries L (2015) Butterfly Monitoring for Conservation. In: Daniels JC (ed) Butterfly conservation in North America: efforts to help save our charismatic microfauna. Springer, Dordrecht, pp 35–57
Theobald DM, Stevens DL, White D, Urquhart NS, Olsen AR, Norman JB (2007) Using GIS to generate spatially balanced random survey designs for natural resource applications. Environ Manage 40:134–146. https://doi.org/10.1007/s00267-005-0199-x
van Klink R, Bowler DE, Gongalsky KB, Swengel AB, Gentile A, Chase JM (2020) Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368:417–420. https://doi.org/10.1126/science.aax9931
van Strien AJ, Soldaat LL, Gregory RD (2012) Desirable mathematical properties of indicators for biodiversity change. Ecol Ind 14:202–208. https://doi.org/10.1016/j.ecolind.2011.07.007
van Swaay C, Dennis EB, Schmucki R, Sevilleja CG, Balalaikins M, Botham M, Bourn N, Brereton T, Cancela JP, Carlisle B, Chambers PC, Dopagne S, Escobés C, Feldmann R, Fernández-García R, Fontaine JM, Gracianteparaluceta B, Harrower A, Harpke C, Heliölä A, Komac J, Kühn B, Lang E, Maes A, Mestdagh D, Middlebrook X, Monasterio I, Munguira Y, Murray ML, Musche T, Õunap M, Paramo E, Pettersson F, Piqueray LB, Settele J, Stefanescu J, Švitra C, Tiitsaar G, Verovnik A, Warren R, Wynhoff MS, Roy I (2019) DB The EU Butterfly Indicator for Grassland species: 1990–2017: Technical Report
van Swaay C, Dennis EB, Schmucki R, Sevilleja CG, Aghababyan K, Åström S, Balalaikins M, Bonelli S, Botham M, Bourn N, Brereton T, Cancela JP, Carlisle B, Chambers P, Collins S, Dopagne C, Dziekanska I, Escobés R, Faltynek Fric Z, Feldmann R, Fernández-García JM, Fontaine B, Goloshchapova S, Gracianteparaluceta A, Harpke A, Harrower C, Heliölä J, Khanamirian G, Kolev Z, Komac B, Krenn H, Kühn E, Lang A, Leopold P, Lysaght L, Maes D, McGowan D, Mestdagh X, Middlebrook I, Monasterio Y, Monteiro E, Munguira ML, Musche M, Õunap E, Ozden O, Paramo F, Pavlíčko A, Pettersson LB, Piqueray J, Prokofev I, Rákosy L, Roth T, Rüdisser J, Šašić M, Settele J, Sielezniew M, Stefanescu C, Švitra G, Szabadfalvi A, Teixeira SM, Tiitsaar A, Tzirkalli E, Verovnik R, Warren MS, Wynhoff I, Roy D (2020) Assessing Butterflies in Europe -Butterfly Indicators 1990-2018: Technical report. https://doi.org/10.13140/RG.2.2.35763.91688
van Swaay CAM, Nowicki P, Settele J, van Strien AJ (2008) Butterfly monitoring in Europe: methods, applications and perspectives. Biodivers Conserv 17:3455–3469. https://doi.org/10.1007/s10531-008-9491-4
Wagner DL (2020) Insect declines in the Anthropocene. Annu Rev Entomol 65:457–480. https://doi.org/10.1146/annurev-ento-011019-025151
Warren MS, Maes D, van Swaay CAM, Goffart P, van Dyck H, Bourn NAD, Wynhoff I, Hoare D, Ellis S (2021) The decline of butterflies in Europe: problems, significance, and possible solutions. Proc Natl Acad Sci U S A 118. https://doi.org/10.1073/pnas.2002551117
Wepprich T, Adrion JR, Ries L, Wiedmann J, Haddad NM (2019) Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS ONE 14:e0216270. https://doi.org/10.1371/journal.pone.0216270
Wickham H (2019) Create Elegant Data Visualisations Using the Grammar of Graphics [R package ggplot2 version 3.2.1]
Yang LH, Gratton C (2014) Insects as drivers of ecosystem processes. Curr Opin Insect Sci 2:26–32. https://doi.org/10.1016/j.cois.2014.06.004
Acknowledgements
The Viel-Falter Butterfly Monitoring Scheme is coordinated by the Department of Ecology at the University of Innsbruck and implemented together with the Natural History Collections of the Tiroler Landesmuseen Betriebsgesellschaft m.b. H, inatura - Erlebnis Naturschau GmbH and EURAC research. It is financially supported by the states of Tyrol and Vorarlberg as well as by the private Foundation Blühendes Österreich. Our special thanks go to very committed co-workers and partners: Eva Benedikt, Ingrid Felipe, Georg Friebe, Valérian Goueset, Eva Hengsberger, Anette Herburger, Kurt Lechner, Walter Michaeler, Bernd Öggl, Alois Ortner, Constantin Pöll, Johannes Rauch, Petra Schattanek, Daniel Schmid, Barbara Stoinschek, Ruth Swoboda, Florian Westreicher, Benjamin Wiesmair, Ronald Würflinger, and 73 anonymous volunteers. F.B., J.R., and U.T. are members of the ‘Mountain Regions’ research area at the University of Innsbruck.
Funding
This work was financially supported by the states of Tyrol and Vorarlberg as well as the Foundation Blühendes Österreich.
Open access funding provided by University of Innsbruck and Medical University of Innsbruck.
Author information
Authors and Affiliations
Contributions
Friederike Barkmann: Formal analysis, Investigation, Data Curation, Writing- Original Draft, Visualization; Erich Tasser: Conceptualization, Methodology, Writing - Review & Editing; Peter Huemer: Conceptualization, Methodology, Supervision; Ulrike Tappeiner: Conceptualization, Methodology, Supervision. Johannes Rüdisser: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Project administration, Funding acquisition, Writing- Reviewing and Editing, Supervision.
Corresponding author
Ethics declarations
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Compliance with ethical standards
All samples were collected according to the local environmental law. Ethical review and approval were waived for this study, as no vertebrate animals were involved.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Barkmann, F., Huemer, P., Tappeiner, U. et al. Standardized butterfly surveys: comparing transect counts and area-time counts in insect monitoring. Biodivers Conserv 32, 987–1004 (2023). https://doi.org/10.1007/s10531-022-02534-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10531-022-02534-2