Data and code from : Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
Date of Archiving
2020Archive
Zenodo
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Open access
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Organization
Experimental Plant Ecology
Ecology
Audience(s)
Biology
Key words
biodiversity loss; insect decline; temporal scalingAbstract
To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.
Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.
To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic
data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).
Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.
Descriptions of the deposited files:
Groups.csv
MF_NR = identifier of each of the six malaise trap locations
yrf = year of sampling
pot = sample identifier
dt = number of sampling days
from.dnr = day-of-the-year on which a pot was attached to a malaise trap
to.dnr = day-of-the-year on which a pot was collected from a malaise trap
mean.daynr = mean day-of-the-year of the sampling period
Nspec = number of different hoverfly species found in a pot
Nind = number of hoverfly individuals found in a pot
Counts.csv
A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.
PairedData.csv
pot = sample identifier
JAHR = year of sampling
MF_NR = identifier of each of the six malaise trap locations
dt = number of sampling days
from.dnr = day-of-the-year on which a pot was attached to a malaise trap
to.dnr = day-of-the-year on which a pot was collected from a malaise trap
NI = number of hoverfly individuals found in a potbiomass.daily
NSP = number of different hoverfly species found in a pot
biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.
ModelFrame.csv
MF_NR = identifier of each of the six malaise trap locations
yrf = year of sampling
pot = sample identifier
dt = number of sampling days
from.dnr = day-of-the-year on which a pot was attached to a malaise trap
to.dnr = day-of-the-year on which a pot was collected from a malaise trap
mean.daynr = mean day-of-the-year of the sampling period
plot = identifier of each of the six malaise trap locations
date = date for which the weather variables are interpolated
daynr = day-of-the-year for which the weather variables are interpolated
altitude = altitude [m] of the malaise trap locations
year = year of sampling
temperature = interpolated temperature [degrees Celsius]
precipitation = interpolated precipitation [mm per day]
wind.speed = interpolated wind speed [m/s]
Data_Rcode.pdf
This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.
syrphidModel.jag
This BUGS-code is required for running the daily-activity model in JAGS.
We greatly acknowledge members of the Entomological Society Krefeld and cooperating botanists and entomologists that were involved in the investigations: K. Coelln, B. Franzen, M. Grigo, M. Hellenthal, J. Hembach, A. Hemmersbach, T. Hoerren, J. Illmer, N. Mohr, S. Risch, O. and W. Schmitz, H. Schwan, R. Seliger, W. Stenmans, H. Sumser, and H. Wolf. For funding, C.A.H. and E.J. were supported by NWO Grants 840.11.001 and 841.11.007. The investigations of the Entomological Society Krefeld and its members are spread over numerous individual projects at different locations and in different years. Grants and permits that have made this work possible are as follows: F + E Biodiversitaetsverluste in FFH-LRT des Offenlandes, gefoerdert durch das Bundesamt fuer Naturschutz mit Mitteln des Bundesministerium fuer Umwelt, Naturschutz und nukleare Sicherheit (BMU), Bezirksregierung Koeln, Bergischer Naturschutzverein, Rhein-Sieg Kreis, and Land Nordrhein-Westfalen–Europaeische Gemeinschaft ELER.
References
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 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12:e0185809
Hallmann CA, Ssymank A, Sorg M, de Kroon H, Jongejans E (2020) Insect biomass decline scaled to species diversity: general patterns derived from a hoverfly community. Proceedings of the National Academy of Sciences of the USA. doi: 10.1073/pnas.2002554117
Schwan H, Sorg M, Stenmans W (1993) Naturkundliche Untersuchungen zum Naturschutzgebiet 'Die Spey' (Stadt Krefeld, Kreis Neuss) - I. Untersuchungsstandorte und Methoden. Natur. Am. Niederrh. 8, 1–13
Sorg M (1990) Entomophage Insekten des Versuchsgutes Höfchen (BRD, Burscheid).- Teil 1. Aphidiinae (Hymenoptera, Braconidae). Pflanzenschutz-Nachrichten Bayer 43, 29–45
Sorg M, Schwan H, Stenmans W, Müller A (2013) Ermittlung der Biomassen flugaktiver Insekten im Orbroicher Bruch mit Malaise Fallen in den Jahren 1989 und 2013. Mitteilungen Entomologischen Verein Krefeld 2013, 1–5
Ssymank A, Sorg M, Doczkal D, Ruhig B, Merkel-Wallner G, Vischer-Leopold M (2018) Praktische Hinweise und Empfehlungen zur Anwendung von Malaisefallen für Insekten in der Biodiversitätserfassung und im Monitoring. Series Naturalis 1, 1–12
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