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More than 75 percent decline over 27 years in total flying insect biomass in protected areas

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More than 75 percent decline over 27 years in total flying insect biomass in protected areas

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Global declines in insects have sparked wide interest among scientists, politicians, and the general public. Loss of insect diversity and abundance is expected to provoke cascading effects on food webs and to jeopardize ecosystem services. Our understanding of the extent and underlying causes of this decline is based on the abundance of single species or taxo-nomic groups only, rather than changes in insect biomass which is more relevant for ecological functioning. Here, we used a standardized protocol to measure total insect biomass using Malaise traps, deployed over 27 years in 63 nature protection areas in Germany (96 unique location-year combinations) to infer on the status and trend of local entomofauna. Our analysis estimates a seasonal decline of 76%, and midsummer decline of 82% in flying insect biomass over the 27 years of study. We show that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline. This yet unrecognized loss of insect biomass must be taken into account in evaluating declines in abundance of species depending on insects as a food source, and ecosystem functioning in the European landscape.
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RESEARCH ARTICLE
More than 75 percent decline over 27 years in
total flying insect biomass in protected areas
Caspar A. Hallmann
1
*, Martin Sorg
2
, Eelke Jongejans
1
, Henk Siepel
1
, Nick Hofland
1
,
Heinz Schwan
2
, Werner Stenmans
2
, Andreas Mu
¨ller
2
, Hubert Sumser
2
, Thomas Ho
¨rren
2
,
Dave Goulson
3
, Hans de Kroon
1
1Radboud University, Institute for Water and Wetland Research, Animal Ecology and Physiology &
Experimental Plant Ecology, PO Box 9100, 6500 GL Nijmegen, The Netherlands, 2Entomological Society
Krefeld e.V., Entomological Collections Krefeld, Marktstrasse 159, 47798 Krefeld, Germany, 3University of
Sussex, School of Life Sciences, Falmer, Brighton BN1 9QG, United Kingdom
*c.hallmann@science.ru.nl
Abstract
Global declines in insects have sparked wide interest among scientists, politicians, and the
general public. Loss of insect diversity and abundance is expected to provoke cascading
effects on food webs and to jeopardize ecosystem services. Our understanding of the extent
and underlying causes of this decline is based on the abundance of single species or taxo-
nomic groups only, rather than changes in insect biomass which is more relevant for ecologi-
cal functioning. Here, we used a standardized protocol to measure total insect biomass
using Malaise traps, deployed over 27 years in 63 nature protection areas in Germany (96
unique location-year combinations) to infer on the status and trend of local entomofauna.
Our analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying
insect biomass over the 27 years of study. We show that this decline is apparent regardless
of habitat type, while changes in weather, land use, and habitat characteristics cannot
explain this overall decline. This yet unrecognized loss of insect biomass must be taken into
account in evaluating declines in abundance of species depending on insects as a food
source, and ecosystem functioning in the European landscape.
Introduction
Loss of insects is certain to have adverse effects on ecosystem functioning, as insects play a cen-
tral role in a variety of processes, including pollination [1,2], herbivory and detrivory [3,4],
nutrient cycling [4] and providing a food source for higher trophic levels such as birds, mam-
mals and amphibians. For example, 80% of wild plants are estimated to depend on insects for
pollination [2], while 60% of birds rely on insects as a food source [5]. The ecosystem services
provided by wild insects have been estimated at $57 billion annually in the USA [6]. Clearly,
preserving insect abundance and diversity should constitute a prime conservation priority.
Current data suggest an overall pattern of decline in insect diversity and abundance. For
example, populations of European grassland butterflies are estimated to have declined by 50%
in abundance between 1990 and 2011 [7]. Data for other well-studied taxa such as bees [814]
PLOS ONE | https://doi.org/10.1371/journal.pone.0185809 October 18, 2017 1 / 21
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OPEN ACCESS
Citation: Hallmann CA, Sorg M, Jongejans E,
Siepel H, Hofland N, Schwan H, et al. (2017) More
than 75 percent decline over 27 years in total flying
insect biomass in protected areas. PLoS ONE 12
(10): e0185809. https://doi.org/10.1371/journal.
pone.0185809
Editor: Eric Gordon Lamb, University of
Saskatchewan, CANADA
Received: July 28, 2017
Accepted: September 19, 2017
Published: October 18, 2017
Copyright: ©2017 Hallmann et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: CH and EJ were supported by the
Netherlands Organization for Scientific Research
(NWO grants 840.11.001 and 841.11.007), and NH
by the Triodos Foundation. 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
and moths [1518] suggest the same trend. Climate change, habitat loss and fragmentation,
and deterioration of habitat quality have been proposed as some of the prime suspects respon-
sible for the decline [911,13,1822]. However, the number of studies on insect trends with
sufficient replication and spatial coverage are limited [10,2325] and restricted to certain well-
studied taxa. Declines of individual species or taxa (e.g. [7,26]) may not reflect the general
state of local entomofauna [27]. The total insect biomass would then be a better metric for the
status of insects as a group and its contribution to ecosystem functioning, but very few studies
have monitored insect biomass over an extensive period of time [28]. Hence, to what extent
total insect biomass has declined, and the relative contribution of each proposed factor to the
decline, remain unresolved yet highly relevant questions for ecosystem ecology and
conservation.
Here, we investigate total aerial insect biomass between 1989 and 2016 across 96 unique
location-year combinations in Germany, representative of Western European low-altitude
nature protection areas embedded in a human-dominated landscape (S1 Fig). In all years we
sampled insects throughout the season (March through October), based on a standardized
sampling scheme using Malaise traps. We investigated rate of decline in insect biomass, and
examined how factors such as weather, habitat and land use variables influenced the declines.
Knowledge on the state of insect biomass, and it’s direction over time, are of broad importance
to ecology and conservation, but historical data on insect biomass have been lacking. Our
study makes a first step into filling this gap, and provides information that is vital for the
assessment of biodiversity conservation and ecosystem health in agricultural landscapes.
Materials and methods
Data
Biomass data. Biomass data were collected and archived using a standardized protocol
across 63 unique locations between 1989 and 2016 (resulting in 96 unique location-year com-
binations) by the Entomological Society Krefeld. The standardized protocol of collection has
been originally designed with the idea of integrating quantitative aspects of insects in the status
assessment of the protected areas, and to construct a long-term archive in order to preserve
(identified and not-identified) specimens of local diversity for future studies. In the present
study, we consider the total biomass of flying insects to assess the state of local entomofauna as
a group.
All trap locations were situated in protected areas, but with varying protection status: 37
locations are within Natura2000 sites, seven locations within designated Nature reserves, nine
locations within Protected Landscape Areas (with funded conservation measures), six loca-
tions within Water Protection Zones, and four locations of protected habitat managed by
Regional Associations. For all location permits have been obtained by the relevant authorities,
as listed in the S1 Appendix. In our data, traps located in nutrient-poor heathlands, sandy
grasslands, and dune habitats provide lower quantities of biomass as compared to nutrient
nutrient-rich grasslands, margins and wastelands. As we were interested in whether the
declines interact with local productivity, traps locations were pooled into 3 distinct habitat
clusters, namely: nutrient-poor heathlands, sandy grassland, and dunes (habitat cluster 1,
n = 19 locations, Fig 1A), nutrient-rich grasslands, margins and wasteland (habitat cluster 2,
n = 41 locations, Fig 1B) and a third habitat cluster that included pioneer and shrub communi-
ties (n = 3 locations).
Most locations (59%, n = 37) were sampled in only one year, 20 locations in two years, five
locations in three years, and one in four years, yielding in total 96 unique location-year combi-
nations of measurements of seasonal total flying insect biomass. Our data do not represent
Severe flying insect biomass decline in protected areas
PLOS ONE | https://doi.org/10.1371/journal.pone.0185809 October 18, 2017 2 / 21
listed below: Bezirksregierungen Du¨sseldorf &
Ko¨ln, BfN - Bundesamt fu¨r Naturschutz, Land
Nordrhein-Westfalen - Europa¨ische Gemeinschaft
ELER, Landesamt fu¨r Agrarordnung Nordrhein-
Westfalen, Landesamt fu¨r Natur, Umwelt und
Verbraucherschutz Nordrhein-Westfalen,
Landesamt fu¨r Umwelt Brandenburg, Landesamt
fu¨r Umwelt Rheinland-Pfalz, LVR -
Landschaftsverband Rheinland, Naturschutzbund
Deutschland, Nordrhein-Westfalen Stiftung, RBN
Bergischer Naturschutzverein, RVR -
Regionalverband Ruhr, SGD Nord Rheinland-Pfalz,
Universita¨ten Bonn, Duisburg-Essen & Ko¨ln,
Untere Landschaftsbeho¨rden: Kreis Du¨ren, Kreis
Heinsberg, Kreis Kleve, Kreis Viersen, Kreis Wesel
& AGLW, Stadt Du¨sseldorf, Stadt Ko¨ln, Stadt
Krefeld, Rheinisch Bergischer Kreis, Rhein Kreis
Neuss & Rhein-Sieg-Kreis. Members of the
Entomological Society Krefeld and cooperating
botanists and entomologists that were involved in
the empirical investigations are greatly
acknowledged: U.W. Abts, F. Bahr, A. Ba¨umler, D.
& H. Beutler, P. Birnbrich, U. Bosch, J. Buchner, F.
Cassese, K. Co¨lln, A.W. Ebmer, R. Eckelboom, B.
Franzen, M. Grigo, J. Gu¨nneberg, J. Gusenleitner,
K. Hamacher, F. Hartfeld, M. Hellenthal, J.
Hembach, A. Hemmersbach, W. Hock, V.
Huisman-Fiegen, J. Illmer, E. Jansen, U. Ja¨ckel, F.
Koch, M. Kreuels, P. Leideritz, I. Loksa, F. B.
Ludescher, F. J. Mehring, G. Milbert, N. Mohr, P.
Randazzo, K. Reissmann, S. Risch, B. Robert, J. de
Rond, U. Sandmann, S. Scharf, P. Scherz, J.
Schiffer, C. Schmidt, O. & W. Schmitz, B. P. & W.
Schnell, J. L. Scho¨nfeld, E. Schraetz, M. Schwarz,
R. Seliger, H. W. Siebeneicher, F. & H. Sonnenburg
W. J. S. & P. Sorg, A. Ssymank, H. Sticht, M.
Weithmann, W. Wichard and H. Wolf.
Competing interests: The authors have declared
that no competing interests exist.
Fig 1. Examples of operating malaise traps in protected areas in western Germany, in habitat cluster 1 (A) and cluster 2 (B) (see
Materials and methods).
https://doi.org/10.1371/journal.pone.0185809.g001
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longitudinal records at single sites, suitable to derive location specific trends (e.g. [28]). Pro-
longed trapping across years is in the present context (protected areas) deemed undesirable, as
the sampling process itself can negatively impact local insect stocks. However, the data do per-
mit an analysis at a higher spatial level, i.e. by treating seasonal insect biomass profiles as ran-
dom samples of the state of entomofauna in protected areas in western Germany.
Malaise traps were deployed through the spring, summer and early autumn. They operated
continuously (day and night), and catches were emptied at regular intervals, on average every
11.2 days (sd = 6.3). We collected in total 1503 trap samples, with an average of 16 (4–35) suc-
cessive catches per location-year combination (Table 1). Between 1989 and 2016, a total of
53.54kg of invertebrates have been collected and stored, over a total trap exposure period of
16908 days, within an average of 176 exposure days per location-year combination. Malaise
traps are known to collect a much wider diversity of insect species (e.g. [2931]) as compared
to suction traps (e.g. [28]) and are therefore considered superior as a method of collecting fly-
ing insects. On the basis of partial assessments, we can assume that the total number of insects
included in 53.54 kg biomass represents millions of individuals.
The sampling was standardized in terms of trap construction, size and design (identical
parts), colors, type of netting and ground sealing, trap orientation in the field as well as slope at
the trap location. Hence none of the traps differed in any of these field aspects. Our trap model
was similar to the bi-colored malaise trap model by Henry Townes [32,33]. The traps,
Table 1. Overview of malaise-trap samples sizes. For each year, the number of locations sampled, the number of location re-sampled, total number of sam-
ples, as well as mean and standard deviation of exposure time at the trap locations (in days) are presented.
Year Number of locations Number of locations sampled previously Number of Samples Mean exposure time St. Dev exposure time
1989 8 0 162 146.62 12.81
1990 2 0 62 228.50 34.65
1991 1 0 10 146.00
1992 4 0 54 118.75 15.50
1993 4 0 39 109.50 59.74
1994 4 0 60 170.75 72.83
1995 2 0 41 144.00 93.34
1997 1 0 20 162.00
1999 2 0 56 196.00 0.00
2000 2 1 47 174.00 11.31
2001 3 2 81 190.00 0.00
2003 3 1 80 201.00 7.81
2004 2 0 48 200.00 5.66
2005 4 0 70 198.75 30.53
2006 2 0 26 188.00 0.00
2007 2 0 15 192.00 0.00
2008 2 0 24 162.00 0.00
2009 4 0 23 120.50 2.89
2010 2 0 12 85.00 0.00
2011 1 0 4 68.00
2012 2 0 23 158.50 4.95
2013 8 2 126 175.50 21.71
2014 23 19 348 212.74 11.21
2015 1 1 10 224.00
2016 7 7 62 190.86 12.56
https://doi.org/10.1371/journal.pone.0185809.t001
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collecting design, and accompanying methods of biomass measurement as designed and
applied by the Entomological Society Krefeld are described elsewhere [3436] and in S2
Appendix.
Trap catches were stored in 80% ethanol solution, prior to weighing, and total insect bio-
mass of each catch (bottle) was obtained based on a standardized measurement protocol by
first subtracting fluid content. In order to optimally preserve samples for future species deter-
mination, the insects were weighed in an alcohol-wet state. First, the alcohol concentration in
the vessels was stabilized to 80%, while this concentration was controlled with an areometer
over a period of at least two days. In order to obtain biomass per sample with sufficient accu-
racy and comparability, the measuring process was fixed using a standardized protocol [34].
For this purpose the insects of a sample were poured onto a stainless steel sieve (10cm diame-
ter) of 0.8 mm mesh width. This sieve is placed slightly obliquely (30 degrees) over a glass ves-
sel. The skew position accelerates the first runoff of alcohol and thus the whole measuring
procedure. The drop sequence is observed with a stopwatch. When the time between two
drops has reached 10 seconds for the first time, the weighing process is performed with a labo-
ratory scale. For the determination of the biomass, precision scales and analytical scales from
Mettler company were used with an accuracy of at least 0.1g and controlled with calibrated test
weights at the beginning of a new weighing series. In a series of 84 weightings of four different
samples repeating this measurement procedure, an average deviation from the mean value of
the measurement results of 0.4 percent was observed (unpublished results).
Weather data. Climate change is a well-known factor responsible for insect declines [15,
18,21,37]. To test if weather variation could explain the observed decline, we included mean
daily temperature, precipitation and wind speed in our analysis, integrating data from 169
weather stations [38] located within 100km to the trap locations. We examined temporal
trends in each weather variable over the course of the study period to assess changes in climatic
conditions, as a plausible explanation for insect decline. Estimates of each weather variable at
the trap locations were obtained by interpolation of each variable from the 169 climate
stations.
We initially considered mean daily air temperature, precipitation, cloud cover, relative air
moisture content, wind speed, and sunshine duration. However, only temperature, precipita-
tion and wind speed were retained for analysis, as the other variables were significantly corre-
lated with the selected variables [R(temperature, cover) = 43.2%, R(temperature, sunshine) =
53.4%, R(precipitation, moisture) = 47.3%] and because we wanted to keep the number of
covariates as low as possible. Additionally, we calculated the number of frost days and the sum
of precipitation in the months November- February preceding a sampling season. We used
spatio-temporal geostatistical models [39,40] to predict daily values for each weather variable
to each trap location. Amongst other methods, the geostatistical approach is considered a
superior interpolation method in order to derive weather variables to trap locations [41].
Uncertainty in interpolated variables such as wind speed is usually associated with altitude dif-
ferences. However, as our trap locations are all situated in lowland areas with little altitude var-
iation, we do not expect a large error in our interpolations at trap locations.
We decomposed the daily values of each weather variable into a long-term average trend
(between years), a mean seasonal trend, and a yearly seasonal anomaly component (S2 Fig),
modeled using regression splines [42] while controlling for altitude of weather stations. The
remaining residual daily values of each station were further modeled using a spatio-temporal
covariance structure. For example, temperature T, on given day t, of a given year kat a given
trap location sis modeled as:
Tðt;s;kÞ ¼ fkðkÞ þ ftðtÞ þ rðk;tÞ þ ahþCs;tð1Þ
Severe flying insect biomass decline in protected areas
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where f
k
(k) is the long-term trend over the years (a thin plate regression spline), f
t
(t) the mean
seasonal trend within years (a penalized cyclic cubic regression spline), r(k,t) the mean resid-
ual seasonal component, which measures annual anomaly in mean daily values across selected
stations, and ais the linear coefficient for the altitude heffect. The spatio-temporal covariance
structure C
s,t
, fitted independently to the residuals of each weather variable model, allowed us
to deal with lack of independence between daily weather data within and between stations, as
well as to interpolate to trap locations using kriging. Altitude of trap locations was extracted
from a digital elevation models at 90m resolution [43].
Land use data. Land use variables (and changes therein) were derived from aerial photo-
graphs [44] taken within two distinct time periods (between 1989–1994, and between 2012–
2015), and allowed us to characterize land use composition at surroundings of the traps, as
well as changes over time. We distinguished cover of forests, agricultural areas, natural grass-
land, and surface water. For each trap location, aerial photographs were manually processed,
polygons extracted and categorized, and their surface area calculated with a radius of 200
meter. Preliminary analysis of the relationship between log biomass and landuse variables, on
a subset of the trap locations, indicated that land use elements at 200m radius better predicted
insect biomass than elements at 500 and 1000m radius, similar to findings elsewhere for wild
bees [45]. Land use variables were measured at a coarse temporal resolution, but fortunately
cover the temporal span of insect sampling. To link the cover of a given land use variable to
the insect biomass samples in a particular year, we interpolated coverage between the two time
points to the year of insect sampling using generalized linear models with a binomial error dis-
tribution, a logit link, and an estimated dispersion parameter. Mean distributions of land use
at each of the two time points are depicted in S3A & S3B Fig.
Habitat data. Plant inventories were conducted in the immediate surroundings (within
50m) of the trap, in the same season of insect sampling. These data permitted the assessment
of plant species richness (numbers of herbs, shrubs and trees) and environmental conditions
based on average Ellenberg values [4648], as well as changes therein over time. Each Ellen-
berg indicator (we considered nitrogen, pH, light, temperature and moisture) was averaged
over all species for each location-year combination. We examined annual trends in each of the
above-mentioned variables in order to uncover potential structural changes in habitat charac-
teristics over time. Species richness was analyzed using mixed-effects generalized linear models
[49] with a random intercept for trap location and assuming a Poisson distribution for species
richness, and a normal distribution for mean Ellenberg indicator values. Although a Poisson
distribution fitted tree and shrub species adequately, (residual deviance/ degree of free-
dom = 0.94 and 1.04 respectively), severe overdispersion was found for herb species richness
(residual deviance/ degree of freedom = 2.16). Trend coefficients of richness over time
between a Poisson mixed effects model and a negative binomial model were comparable but
differed in magnitude (Poisson GLMM: 0.034 (se = 0.003), vs NB GLMM 0.027
(se = 0.006)). Although the fit is not perfect in the case of herb richness, we believe our trend
adequately describes direction of change over time. Mean changes in plant species richness are
depicted in S3C Fig.
Insect biomass model
The temporal resolution of the trap samples (accumulated over several days) is not directly
compatible with the temporal distribution of the weather data (daily values). Additionally, var-
iable exposure intervals between trap samples is expected to induce variation in trapped bio-
mass between samples, and hence induce heteroscedasticity. Furthermore, biomass data can
numerically only be positive on the real line, and we require a model to reflect this property of
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the data. Because of the unequal exposure intervals however, log-transforming the response
would be inappropriate, because we require the sum of daily values after exponentiation,
rather that the exponent of the sum of log-daily biomass values. In order to indirectly relate
biomass to daily weather variables, to account for the variation in time exposure intervals over
which biomass was accumulated in the samples, and to respect the non-negative nature of our
data, we modeled the biomass of each catch as the sum of the expected (but unobserved) latent
daily biomass. The mass mof each sample j, at site sin year k, is assumed to be distributed nor-
mally about the sum of the latent expected daily mass (z
t,s,k
), with variance s2
j:
mj;s;kNðmj;s;k;s2
jÞð2Þ
subject to mj;s;k¼Pt2ðjÞ
t¼t1ðjÞzt;s;kwhere τ
1
and τ
2
mark the exposure interval (in days) of biomass
collection of each sample j. The latent daily biomass itself is represented by a log normal distri-
bution, in which coefficients for covariates, random effects and residual variance are all repre-
sented on the log scale. In turn, daily biomass is modeled as
zt;s;k¼eyt;s;kð3Þ
yt;s;k¼cþlogðlÞkþXbxþusð4Þ
where cis a global intercept, Xa design matrix of dimensions n×p (number of
samples ×number of covariates; see Model analysis below), β
x
the corresponding vector of
coefficients that measure the weather, habitat and land use effects, and log(λ) a mean annual
population growth rate parameter. The random term (u
s
) denotes the location-specific ran-
dom effect assumed to be distributed normally about zero usNð0;s2
siteÞ. The exponentiation
of the right hand side of Eq (3) ensures expected values to be positive.
The expected residual variance of each sample s2
j, is expressed as the sum of variances of
daily biomass values (s2
t;s;k).
s2
j¼X
t2ðjÞ
t¼t1ðjÞ
s2
t;s;kð5Þ
The variances of daily biomass should respect the non-negative nature of the data as well.
Additionally, we are interested in being able to compare the residual variance with the random
effects variance, and this requires them to be on the same scale. Therefore, we expressed the
variance of the daily biomass as a function of the variance of the logarithm of the daily bio-
mass. Using the method of moments:
s2
t;s;k¼e2yt;s;kþvðev1Þð6Þ
where vrepresents the residual variance of daily log-biomass.
Analysis
We developed a series of models each consisting of a set of explanatory variables that measure
aspects of climate, land use and local habitat characteristics. Significant explanatory variables
in these models were combined into a final model, which was then reduced to exclude insignif-
icant effects. An overview of which covariates were included in each model is given in Table 2.
Weather effects explored were daily temperature, precipitation and wind speed, as well as
the number of frost days and sum of precipitation in the preceding winter. Habitat effects
explored tree and herb species richness, as well as average Ellenberg values for nitrogen, pH,
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light, temperature and moisture, per location-year combination. Land use effects explored the
fractions of agricultural area, forest, grass, and surface water in a radius of 200m around the
plot location.
Parameter values are obtained by the use of Markov chain Monte Carlo (MCMC) methods
by the aid of JAGS (Just Another Gibbs Sampler [50]) invoked through R [51] and the R2Jags
package [52]. JAGS model scripts are given in S1 Code, while data are given in S1 and S2
Dataset. For each model, we ran 3 parallel chains each consisting of 24000 iterations (first 4000
discarded), and kept every 10
th
value as a way to reduce within chain autocorrelation. We used
vague priors for all parameters, with uniform distributions for the residual and random effect
variance components, and flat normal distributions (with very high variance) for all other
parameters. Covariates in Xwere standardized prior to model fitting, with the exception of
year (values 1–26), and land use variables (proportions within 0–1 range).
For all models, we computed the Deviance Information Criterion [53] (DIC) as well as the
squared correlation coefficient (R
2
) between observed and mean posterior estimates of bio-
mass on the log scale. Results are given in Table 3. Parameter convergence was assessed by the
Table 2. Overview of covariates included in each of the seven models. The year covariate yields the annual trend coefficient.
Covariate class Covariate name Null model Basic Weather Habitat Land use Interactions Land use+ Final model
Temporal Intercept ✔ ✔
Day number ✔ ✔
Day number
2
✔ ✔
Year ✔ ✔
Climate Temperature ✔ ✔
Precipitation ✔ ✔
Wind Speed
Frost days ✔ ✔
Winter Precipitation
Habitat Herb Species ✔ ✔
Tree Species ✔ ✔
Nitrogen
pH
Moisture
Light ✔ ✔
Ellen. Temperature ✔ ✔
Habitat cluster 2 ✔ ✔
Habitat cluster 3 ✔ ✔
Landscape Arable land ✔ ✔
Grassland ✔ ✔
Forest ✔ ✔
Water ✔ ✔
Interactions Year ×Day number ✔ ✔
Year ×Day number
2
✔ ✔
Year ×Agriculture ✔ ✔
Year ×Forest ✔ ✔
Year ×Water
Year ×Grassland ✔ ✔
Variance σ
site
✔ ✔
v✔ ✔
https://doi.org/10.1371/journal.pone.0185809.t002
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potential scale reduction factor [54] (commonly ^
R), that measures the ratio of posterior distri-
butions between independent MCM chains (in all models, all parameters attained values
below 1.02). For all models, we confirmed that the posterior distribution of the trend coeffi-
cient did not confound any other variable by plotting the relevant posterior samples and com-
puting pairwise correlation coefficients.
Our basic model included habitat cluster (3 levels), a quadratic effect for day number, an
annual trend coefficient measuring the rate of biomass change, and the interactions between
the annual trend coefficient and the day number variables. Next we developed 3 models each
consisting of either weather variables (S1 Table), land use variables (S2 Table), or habitat vari-
ables. Because interactions between the annual rate of change and land use variables seemed
plausible, a fourth model was developed to include these interactions (S3 Table). Finally, all
significant variables were combined into our final model (Table 4), which included effects of
an annual trend coefficient, season (linear and quadratic effect of day number), weather (tem-
perature, precipitation, number of frost days), land use (cover of grassland and water, as well
as interaction between grassland cover and trend), and habitat (number of herb and tree spe-
cies as well as Ellenberg temperature).
Our estimate of decline is based on our basic model, from which we can derive seasonal
estimates of daily biomass for any given year. The basic model includes only a temporal
(annual and seasonal effects, as well as interactions) and a basic habitat cluster distinction
(additive effects only) as well as a random trap location effect. We here report the annual trend
coefficient, as well as a weighted estimate of decline that accounts for the within season differ-
ences in biomass decline. The weighted insect biomass decline was estimated by projecting the
seasonal biomass (1-April to 30-October) for years 1989 and 2016 using coefficients our basic
model, and then dividing the summed (over the season) biomass of 2016 by the summed bio-
mass over 1989.
Using our final model, we assessed the relative contribution (i.e. net effect) of the explana-
tory variables to the observed decline, both combined and independently. To this aim we pro-
jected the seasonal daily biomass for the years 1989 and 2016 twice: first we kept covariates at
their mean values during the early stages of the study period, and second we allowed covariate
values to change according to the observed mean changes (see S2 and S3 Figs). Difference in
the total biomass decline between these two projections are interpreted as the relative contri-
bution of the explanatory variables to the decline. The marginal (i.e. independent) effects of
each covariate were calculated by projecting biomass increase/decline as result of the observed
temporal developments in each variable separately, and expressing it as percentual change.
Our data provide repetitions across years for only a subset of locations (n = 26 out of 63).
As such, spatial variation in insect biomass may confound the estimated trend. To verify that
Table 3. Results for 7 models ranked by Deviance Information Criterion (DIC). For each model, the number of parameters, the Deviance Information Cri-
terion, the effective number of parameters (pD), calculated R
2
and difference in DIC units between each model and the model with lowest ΔDIC. See Table 2
for covariates included in each model.
model npar Deviance DIC pD R
2
ΔDIC
Final 23 12082.48 12177.07 94.59 0.67 0.00
Weather 13 12178.84 12261.52 82.68 0.65 84.45
Land use+ Interactions 16 12336.22 12427.16 90.95 0.62 250.09
Habitat 15 12354.95 12445.93 90.98 0.62 268.86
Land use 12 12377.27 12453.23 75.97 0.61 276.16
Basic 8 12390.26 12465.08 74.82 0.61 288.00
Null 5 13230.65 13307.59 76.94 0.39 1130.52
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this is not the case, we fitted our basic model (but excluding the day number and year interac-
tion to avoid overparameterization) to the subset of our data that includes only locations that
were sampled in more than one year. Seasonal profiles of daily biomass values are depicted in
S4 Fig. Finally, we reran our basic model for the two (of the three) habitat clusters (for which
sufficient data existed; see Biomass Data) separately in order to compare the rate of decline
between them (S5 Fig).
Results
Following corrections for seasonal variation and habitat cluster (basic model, see Materials
and methods), the annual trend coefficient of our basic model was significantly negative
(annual trend coefficient = 0.063, sd = 0.002, i.e. 6.1% annual decline). Based on this result,
we estimate that a major (up to 81.6% [79.7–83.4%]) decline in mid-summer aerial insect bio-
mass has taken place since 1989 (Fig 2A). However, biomass loss was more prominent in mid-
summer as compared to the start and end of the season (Fig 3A), indicating that the highest
losses occur when biomass is highest during the season (Fig 2B). As such, a seasonally weighted
estimate (covering the period 1-April to 30-October; see methods) results in an overall 76.7%
[74.8–78.5%] decline over a 27 year period. The pattern of decline is very similar across loca-
tions that were sampled more than once (Fig 4), suggesting that the estimated temporal decline
based on the entire dataset is not confounded by the sampling procedure. Re-estimation of the
Table 4. Posterior parameter estimates of the final mixed effects model of daily insect biomass. For each included variable, the corresponding coeffi-
cient mean, standard deviation and 95% credible intervals are given. P-values were calculated empirically based on posterior distributions of coefficients.
Class Variable mean sd 2.50% 97.50% P
Temporal Intercept 2.450 0.233 1.983 2.891 0.000 ***
log(λ) -0.080 0.007 -0.094 -0.067 0.000 ***
Day number -0.100 0.028 -0.155 -0.045 0.001 ***
Day number
2
-0.447 0.029 -0.504 -0.392 0.000 ***
Weather Temperature 0.304 0.022 0.263 0.347 0.000 ***
Precipitation -0.071 0.034 -0.143 -0.009 0.014 *
Frost days -0.021 0.024 -0.067 0.025 0.194
Land use Habitat Cluster 2 0.420 0.162 0.080 0.729 0.007 **
Habitat Cluster 3 0.332 0.237 -0.133 0.806 0.078 .
Arable land -1.063 0.184 -1.420 -0.709 0.000 ***
Forest -0.522 0.216 -0.947 -0.121 0.007 **
Grassland 0.819 0.233 0.367 1.265 0.000 ***
Water -0.327 0.170 -0.659 0.005 0.027 *
Habitat Herb species -0.054 0.045 -0.137 0.037 0.119
Tree Species 0.104 0.032 0.041 0.167 0.000 ***
Ell. Nitrogen 0.181 0.065 0.051 0.311 0.003 **
Ell. Light 0.162 0.039 0.088 0.236 0.000 ***
Ell. Temperature -0.071 0.031 -0.131 -0.011 0.010 **
Intercations Year ×Day number -0.003 0.001 -0.006 -0.000 0.017 *
Year ×Day number
2
0.010 0.001 0.007 0.013 0.000 ***
Year ×Arable land 0.047 0.008 0.031 0.064 0.000 ***
Year ×Forest 0.035 0.010 0.016 0.055 0.000 ***
Year ×Grassland -0.059 0.014 -0.086 -0.033 0.000 ***
Random effects σ
site
0.334 0.037 0.270 0.412
Residual variation v0.870 0.009 0.852 0.889
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Fig 2. Temporal distribution of insect biomass. (A) Boxplots depict the distribution of insect biomass
(gram per day) pooled over all traps and catches in each year (n = 1503). Based on our final model, the grey
line depicts the fitted mean (+95% posterior credible intervals) taking into account weather, landscape and
habitat effects. The black line depicts the mean estimated trend as estimated with our basic model. (B)
Seasonal distribution of insect biomass showing that highest insect biomass catches in mid summer show
most severe declines. Color gradient in both panels range from 1989 (blue) to 2016 (orange).
https://doi.org/10.1371/journal.pone.0185809.g002
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annual decline based on 26 locations that have been sampled in more than one year (S4 Fig),
revealed a similar rate of decline (76.2%[73.9–78.3%]).
Insect biomass was positively related to temperature and negatively to precipitation (S1
Table). Including lagged effects of weather revealed no effect of either number of frost days, or
winter precipitation, on the biomass in the next season (S1 Table). The overall model fit
improved as compared to our basic model (R
2
= 65.4%, Table 3), explaining within and between
year variation in insect biomass, but not the overall decline (log(λ) = 0.058, sd = 0.002). Over
the course of the study period, some temporal changes occurred in the means of the weather
variables (S2 Fig), most notably an increase by 0.5˚C in mean temperature and a decline 0.2 m/
sec in mean wind speed. Yet, these changes either do not have an effect on insect biomass (e.g.
wind speed) either are expected to positively affected insect biomass (e.g. increased tempera-
ture). Furthermore, a phenological shift with peak biomass earlier in the season could have
resulted in lower biomass in the mid-season (Fig 3A), but this does not appear to be the case as
none of the seasonal distribution quantiles in biomass showed any temporal trend (Fig 3B).
There was substantial variation in trapped insect biomass between habitat clusters (see
Materials and methods), with nutrient-rich grasslands, margins and wasteland containing 43%
more insect biomass than nutrient-poor heathland, sandy grassland, and dunes. Yet, the
annual rate of decline was similar, suggesting that the decline is not specific to certain habitat
types (S5 Fig). To further characterize trap locations, we used past (1989–1994) and present
(2012–2015) aerial photographs and quantified land use cover within 200m around the trap
locations. On average, cover of arable land decreased, coverage of forests increased, while
grassland and surface water did not change much in extent over the last three decades (S3 Fig).
Overall, adding land use variables alone did not lead to a substantial improvement of the
model fit (R
2
= 61.3%, Table 3), nor did it affect the annual trend coefficient (log(λ) = 0.064,
sd = 0.002). While presence of surface water appeared to significantly lower insect biomass,
none of the other variables were significantly related to biomass. However, including interac-
tions between the annual trend coefficient and land use variables increased the model fit
Fig 3. Seasonal decline and phenology. (A) Seasonal decline of mean daily insect biomass as estimated by independent month specific
log-linear regressions (black bars), and our basic mixed effects model with interaction between annual rate of change and a quadratic trend
for day number in season. (B), Seasonal phenology of insect biomass (seasonal quantiles of biomass at 5% intervals) across all locations
revealing substantial annual variation in peak biomass (solid line) but no direction trend, suggesting no phenological changes have occurred
with respect to temporal distribution of insect biomass.
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slightly (Table 3), and revealed significant interactions for all variables except coverage of sur-
face water (S2 Table). These interactions, which were retained in our final model (Table 4),
revealed higher rates of decline where coverage of grassland was higher, while lower declines
where forest and arable land coverage was higher.
We hypothesized that successional changes in plant community [55] or changes in environ-
mental conditions [9,18], could have affected the local insect biomass, and hence explain the
decline. Plant species inventories that were carried out in the immediate vicinity of the traps
and in the same season of trapping, revealed that species richness of trees, shrubs and herbs
declined significantly over the course of the study period (S3 Fig). Including species richness
in our basic model, i.e. number of tree species and log number of herb species, revealed signifi-
cant positive and negative effects respectively on insect biomass, but did not affect the annual
trend coefficient (S3 Table), explaining some variation between locations rather than the
annual trend coefficient. Moreover, and contrary to expectation, trends in herb species rich-
ness were weakly negatively correlated with trends in insect biomass, when compared on per
plot basis for plots sampled more than once. Ellenberg values of plant species provide a reliable
indicator for the environmental conditions such as pH, nitrogen, and moisture [46,47].
Around trap locations, mean indicators (across all locations) were stable over time, with
changes in the order of less than 2% over the course of the study period. Adding these variables
to our basic model revealed a significant positive effect of nitrogen and light, and a significant
Fig 4. Temporal distribution of insect biomass at selected locations. (A) Daily biomass (mean ±1 se)
across 26 locations sampled in multiple years (see S4 Fig for seasonal distributions). (B) Distribution of mean
annual rate of decline as estimated based on plot specific log-linear models (annual trend coefficient = 0.053,
sd = 0.002, i.e. 5.2% annual decline).
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Severe flying insect biomass decline in protected areas
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negative effect of Ellenberg temperature on insect biomass, explaining a major part of the vari-
ation between the habitat types. However, Ellenberg values did not affect the insect biomass
trend coefficient (log(λ) = 0.059, sd = 0.003, S3 Table) and only marginally improved the
model fit (R
2
= 61.9%, Table 3). All habitat variables were considered in our final model
(Table 4), with the exception of of pH and moisture.
Our final model, based on including all significant variables from previous models, revealed
a higher trend coefficient as compared to our basic model (log(λ) = 0.081, sd = 0.006,
Table 4), suggesting that temporal developments in the considered explanatory variables coun-
teracted biomass decline to some degree, leading to an even more negative coefficient for the
annual trend. The marginal net effect of changes in each covariate over time (see Analysis),
showed a positive contribution to biomass growth rate of temporal developments in arable
land, herb species richness, and Ellenberg Nitrogen, while negative effects of developments of
tree species richness and forest coverage (Fig 5). For example, the negative effect of arable land
on biomass (Table 4), in combination with a decrease in coverage of arable land (S3 Fig), have
resulted in a net positive effect for biomass (Fig 5). Projections of our final model, while fixing
the coefficient for the temporal annual trend log(λ) to zero, suggest insect biomass would have
remained stable, or even increased by approximately 8% (mean rate = 1.075, 0.849–1.381) over
the course of the study period.
Discussion
Our results document a dramatic decline in average airborne insect biomass of 76% (up to
82% in midsummer) in just 27 years for protected nature areas in Germany. This considerably
exceeds the estimated decline of 58% in global abundance of wild vertebrates over a 42-year
period to 2012 [56,57]. Our results demonstrate that recently reported declines in several taxa
such as butterflies [7,2527,58], wild bees [814] and moths [1518], are in parallel with a
severe loss of total aerial insect biomass, suggesting that it is not only the vulnerable species,
but the flying insect community as a whole, that has been decimated over the last few decades.
The estimated decline is considerably more severe than the only comparable long term study
on flying insect biomass elsewhere [28]. In that study, 12.2m high suction traps were deployed
Fig 5. Marginal effects of temporal changes in considered covariates on insect biomass. Each bar
represents the rate of change in total insect biomass, as the combined effect of the relevant coefficient
(Table 4) and the temporal development of each covariate independently (S2 and S3 Figs).
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at four locations in the UK over the time period 1973–2002, and showed a biomass decline at
one of the four sites only. However, the sampling designs differ considerably between the two
studies. Suction traps mainly target high-flying insects, and in that study the catches were
largely comprised of flies belonging to the Bibionidae family. Contrary, malaise traps as used
in the present study target insects flying close to the ground surface (up to 1 meter), with a
much wider diversity of taxa. Future investigations should look into how biomass is distrib-
uted among insect species, and how species trends contribute to the biomass decline.
Although the present dataset spans a relatively large number of years (27) and sites (63), the
number of repetitions (i.e. multiple years of seasonal distributions at the same locations) was
lower (n = 26). We are however confident that our estimated rate of decline in total biomass
resembles the true rate of decline, and is not an artifact of site selection. Firstly, our basic
model (including an annual rate of decline) outperformed the null-model (without an annual
rate of decline; ΔDIC = 822.62 units; Table 3), while at the same time, between-plot variation
(i.s. σ
site
) and residual variation (v) decreased by 44.3 and 9.7% respectively, after incorporating
an annual rate of decline into the models. Secondly, using only data from sites at which malaise
traps were operating in at least two years, we estimated a rate of decline similar to using the
full dataset (Fig 4), with the pattern of decline being congruent across locations (S4 Fig).
Taken together, there does not seem to be evidence that spatial variation (between sites) in this
dataset forms a confounding factor to the estimated temporal trend, and conclude that our
estimated biomass decline is representative for lowland protected areas in west Germany.
In light of previously suggested driving mechanisms, our analysis renders two of the prime
suspects, i.e. landscape [9,18,20] and climate change [15,18,21,37], as unlikely explanatory
factors for this major decline in aerial insect biomass in the investigated protected areas. Habitat
change was evaluated in terms of changes in plant species composition surrounding the stan-
dardized trap locations, and in plant species characteristics (Ellenberg values). Land use changes
was evaluated in terms of proportional surface changes in aerial photographs, and not for exam-
ple changes in management regimes. Given the major decline in insect biomass of about 80%,
much stronger relationships would have been expected if changes in habitat and land use were
the driving forces, even with the somewhat crude parameters that were at our disposal.
The decline in insect biomass, being evident throughout the growing season, and irrespec-
tive of habitat type or landscape configuration, suggests large-scale factors must be involved.
While some temporal changes in climatic variables in our study area have taken place, these
either were not of influence (e.g. wind speed), or changed in a manner that should have
increased insect biomass (e.g temperature). However, we have not exhaustively analysed the
full range of climatic variables that could potentially impact insect biomass. For example pro-
longed droughts, or lack of sunshine especially in low temperatures might have had an effect
on insect biomass [5962]. Agricultural intensification [17,20] (e.g. pesticide usage, year-
round tillage, increased use of fertilizers and frequency of agronomic measures) that we could
not incorporate in our analyses, may form a plausible cause. The reserves in which the traps
were placed are of limited size in this typical fragmented West-European landscape, and
almost all locations (94%) are enclosed by agricultural fields. Part of the explanation could
therefore be that the protected areas (serving as insect sources) are affected and drained by the
agricultural fields in the broader surroundings (serving as sinks or even as ecological traps) [1,
6365]. Increased agricultural intensification may have aggravated this reduction in insect
abundance in the protected areas over the last few decades. Whatever the causal factors
responsible for the decline, they have a far more devastating effect on total insect biomass than
has been appreciated previously.
The widespread insect biomass decline is alarming, ever more so as all traps were placed in
protected areas that are meant to preserve ecosystem functions and biodiversity. While the
Severe flying insect biomass decline in protected areas
PLOS ONE | https://doi.org/10.1371/journal.pone.0185809 October 18, 2017 15 / 21
gradual decline of rare insect species has been known for quite some time (e.g. specialized but-
terflies [9,66]), our results illustrate an ongoing and rapid decline in total amount of airborne
insects active in space and time. Agricultural intensification, including the disappearance of
field margins and new crop protection methods has been associated with an overall decline of
biodiversity in plants, insects, birds and other species in the current landscape [20,27,67]. The
major and hitherto unrecognized loss of insect biomass that we report here for protected
areas, adds a new dimension to this discussion, because it must have cascading effects across
trophic levels and numerous other ecosystem effects. There is an urgent need to uncover the
causes of this decline, its geographical extent, and to understand the ramifications of the
decline for ecosystems and ecosystem services.
Supporting information
S1 Appendix. Malaise trap permissions.
(PDF)
S2 Appendix. Malaise traps.
(PDF)
S1 Code.
(PDF)
S1 Dataset.
(CSV)
S2 Dataset.
(CSV)
S1 Fig. Map of study area. Insect trap locations (yellow points) in Nordrhein-Westfalen
(n = 57), Rheinland-Pfalz (n = 1) and Brandenburg (n = 5), as well as weather stations (crosses)
used in the present analysis.
(TIFF)
S2 Fig. Temporal variation in weather variables. Annual means (A-C), daily means (D-F),
and mean daily residual values (G-I) of temperature, precipitation and wind speed respec-
tively. In all panels, black lines depict data while blue and red lines represent long term and
seasonal fitted means of the variables, respectively.
(PDF)
S3 Fig. Land use and plant species richness changes. Mean land use in 1989–1994 (A) and
2012–2014 (B), based on aerial photograph analysis at 63 protected areas show a decrease of
arable land and an increase in forested area over the past 25 years. (C) Changes in plants spe-
cies richness for herbs (black) shrubs (red) and trees (blue). Annual means as well as mean
trends are depicted in the corresponding colors. Linear trends are based on generalized linear
mixed effects models with a Poisson error distribution and a random intercept effect for loca-
tion. Note, zero values for tree and shrub species not depicted.
(PDF)
S4 Fig. Seasonal profiles of daily biomass across 26 locations. For each location, different
colors represent different years, with time color-coded from green (1989) to red (2016). X-axis
represents day number (January 1 = 0).
(PDF)
Severe flying insect biomass decline in protected areas
PLOS ONE | https://doi.org/10.1371/journal.pone.0185809 October 18, 2017 16 / 21
S5 Fig. Daily biomass of insects over time for two habitat clusters. Boxplots depict the distri-
bution of insect biomass pooled over all traps and catches in each year at trap locations in
nutrient-poor heathland, sandy grassland, and dunes (A), and in nutrient-rich grasslands,
margins and wasteland (B). Grey lines depict the fitted mean (+95% posterior credible inter-
vals), while the black lines the mean estimated trend. Estimated annual decline amounts to
7.5%(6.6–8.4) for habitat cluster 1, as compared to 5.2% (4.8–5.5) habitat cluster 2. Models fit-
ted independently for each habitat location. Color gradient in all panels range from 1989
(blue) to 2016 (orange).
(PDF)
S1 Table. Posterior parameter estimates of the mixed effects model including weather vari-
ables. For each included variable, the corresponding coefficient posterior mean, standard devi-
ation and 95% credible intervals are given. P-values are calculated empirically based on
posterior distributions of coefficients.
(PDF)
S2 Table. Posterior parameter estimates of the mixed effects model including land use var-
iables and interactions. For each included variable, the corresponding coefficient posterior
mean, standard deviation and 95% credible intervals are given. P-values are calculated empiri-
cally based on posterior distributions of coefficients.
(PDF)
S3 Table. Posterior parameter estimates of the mixed effects model including habitat vari-
ables. For each included variable, the corresponding coefficient posterior mean, standard devi-
ation and 95% credible intervals are given. P-values are calculated empirically based on
posterior distributions of coefficients.
(PDF)
Acknowledgments
CH and EJ were supported by the Netherlands Organization for Scientific Research (NWO
grants 840.11.001 and 841.11.007), and NH by the Triodos Foundation. The investigations of
the Entomological Society Krefeld and its members are spread over numerous individual proj-
ects at different locations and in different years. Grants and permits that have made this work
possible are listed below:
Bezirksregierungen Du¨sseldorf & Ko¨ln, BfN—Bundesamt fu¨r Naturschutz, Land Nord-
rhein-Westfalen—Europa¨ische Gemeinschaft ELER, Landesamt fu¨r Agrarordnung Nord-
rhein-Westfalen, Landesamt fu¨r Natur, Umwelt und Verbraucherschutz Nordrhein-
Westfalen, Landesamt fu¨r Umwelt Brandenburg, Landesamt fu¨r Umwelt Rheinland-Pfalz,
LVR—Landschaftsverband Rheinland, Naturschutzbund Deutschland, Nordrhein-Westfalen
Stiftung, RBN—Bergischer Naturschutzverein, RVR—Regionalverband Ruhr, SGD Nord
Rheinland-Pfalz, Universita¨ten Bonn, Duisburg-Essen & Ko¨ln, Untere Landschaftsbeho¨rden:
Kreis Du¨ren, Kreis Heinsberg, Kreis Kleve, Kreis Viersen, Kreis Wesel & AGLW, Stadt Du¨ssel-
dorf, Stadt Ko¨ln, Stadt Krefeld, Rheinisch Bergischer Kreis, Rhein Kreis Neuss & Rhein-Sieg-
Kreis. Members of the Entomological Society Krefeld and cooperating botanists and entomol-
ogists that were involved in the empirical investigations are greatly acknowledged: U.W. Abts,
F. Bahr, A. Ba¨umler, D. & H. Beutler, P. Birnbrich, U. Bosch, J. Buchner, F. Cassese, K. Co¨lln,
A.W. Ebmer, R. Eckelboom, B. Franzen, M. Grigo, J. Gu¨nneberg, J. Gusenleitner, K. Hama-
cher, F. Hartfeld, M. Hellenthal, J. Hembach, A. Hemmersbach, W. Hock, V. Huisman-Fiegen,
J. Illmer, E. Jansen, U. Ja¨ckel, F. Koch, M. Kreuels, P. Leideritz, I. Loksa, F. B. Ludescher, F. J.
Severe flying insect biomass decline in protected areas
PLOS ONE | https://doi.org/10.1371/journal.pone.0185809 October 18, 2017 17 / 21
Mehring, G. Milbert, N. Mohr, P. Randazzo, K. Reissmann, S. Risch, B. Robert, J. de Rond, U.
Sandmann, S. Scharf, P. Scherz, J. Schiffer, C. Schmidt, O. & W. Schmitz, B. P. & W. Schnell, J.
L. Scho¨nfeld, E. Schraetz, M. Schwarz, R. Seliger, H. W. Siebeneicher, F. & H. Sonnenburg W.
J. S. & P. Sorg, A. Ssymank, H. Sticht, M. Weithmann, W. Wichard and H. Wolf.
Author Contributions
Conceptualization: Caspar A. Hallmann, Martin Sorg, Eelke Jongejans, Henk Siepel, Dave
Goulson, Hans de Kroon.
Data curation: Martin Sorg, Heinz Schwan, Werner Stenmans, Andreas Mu¨ller, Hubert Sum-
ser, Thomas Ho¨rren.
Formal analysis: Caspar A. Hallmann, Nick Hofland.
Funding acquisition: Martin Sorg, Eelke Jongejans, Heinz Schwan, Werner Stenmans, Hans
de Kroon.
Investigation: Caspar A. Hallmann, Martin Sorg, Eelke Jongejans, Henk Siepel, Heinz
Schwan, Hubert Sumser, Thomas Ho¨rren, Dave Goulson, Hans de Kroon.
Methodology: Caspar A. Hallmann, Martin Sorg, Heinz Schwan, Werner Stenmans, Hubert
Sumser, Thomas Ho¨rren, Hans de Kroon.
Project administration: Heinz Schwan, Werner Stenmans, Andreas Mu¨ller.
Resources: Martin Sorg, Nick Hofland, Andreas Mu¨ller, Hubert Sumser, Thomas Ho¨rren,
Hans de Kroon.
Software: Caspar A. Hallmann, Nick Hofland.
Supervision: Eelke Jongejans, Henk Siepel, Hans de Kroon.
Validation: Caspar A. Hallmann, Nick Hofland.
Visualization: Caspar A. Hallmann.
Writing – original draft: Caspar A. Hallmann.
Writing – review & editing: Caspar A. Hallmann, Martin Sorg, Eelke Jongejans, Henk Siepel,
Heinz Schwan, Werner Stenmans, Andreas Mu¨ller, Hubert Sumser, Thomas Ho¨rren, Dave
Goulson, Hans de Kroon.
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... However, research on these practices as a major contributor to global change has until recently been overlooked (Bernhardt et al. 2017). It is now clear that pesticide use can result in negative ecological effects such as declines in biodiversity and a reduction of biological pest control (Geiger et al. 2010;Hallmann et al. 2017;Møller et al. 2021). There is evidence that biodiversity declines due to habitat loss or conversion to agriculture are exacerbated by agricultural pesticide use (Gibbs et al. 2009;Tsiafouli et al. 2015). ...
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... Furthermore, these natal effects could weaken and erode populations for years in a cryptic manner, resulting in sudden crashes that appear difficult to explain. This could contribute to the mysterious large-scale declines recently documented for many taxa [41][42][43][44] . Third, the protection of long-lived species often focuses on adult survival as the main target of management action, given its disproportionate role in driving population growth [33][34][35] . ...
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... Invertebrates are being lost from many ecosystems; as illustrations, molluscs (Régnier et al. 2009), insects (Fonseca 2009;Hallmann et al. 2017;Forister et al. 2019) and decapods (De Grave et al. 2015) are all in rapid decline (Eisenhauer et al. 2019). Yet the proportion of invertebrate species that is actually listed by IUCN is far lower than for vertebrates. ...
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... About 41 % of insect species are declining, a third of all insect species are threatened (Sánchez-Bayo and Wyckhuys, 2019). For protected nature areas in Germany an even higher decline of 76% in just 27 years for airborne insect biomass was estimated (Hallmann et al., 2017). Publications on the decline in biodiversity urgently recommend a diversification of agriculture and a reduction of pesticides in agriculture as changes in land-use due to agriculture are the main drivers for species loss (Sánchez-Bayo and Wyckhuys, 2019). ...
Thesis
Modern agriculture faces a conflict between sustainability and the demand for a higher food production. This conflict is exacerbated by climate change and its influence on vegetation, ecology and human society. To reduce land use, the reduction of yield losses and food waste is crucial. Moreover a sustainable intensification is necessary to increase yields, while at the same time input of limited resources such as drinking water or fertilizer should be kept as low as possible. This might be achieved by improving nutrient recycling and plant resistance to abiotic or biotic stress. Bioeffectors (BE) comprise seaweed or plant extracts and microbial inoculums that may stimulate plant growth by phytohormonal changes and increase plant tolerance to abiotic stress (biostimulants), solubilize or mobilize phosphorus from sparingly soluble sources such as Al/Fe or Ca-phosphates in the soil, rock phosphates, recycling fertilizer or organic phosphorus sources like phytate (biofertilizer), or improve plant resistance against pathogens by induced-systemic resistance (ISR) or antibiosis (biocontrol). For this study, in total 18 BE products were tested in germination, pot and field experiments for their potential to improve plant growth, cold stress tolerance, nutrient acquisition and yield in maize and tomato. Additionally, a gene expression analysis in maize was performed using whole transcriptome sequencing (RNA-Seq) after the application of two potential plant growth promoting rhizobacteria (PGPR), the Pseudomonas sp. strain DSMZ 13134 “Proradix” and the Bacillus amyloliquefaciens strain FZB42. Seaweed products supplemented with high amounts of the micronutrients Zn and Mn were effective in reducing detrimental cold stress reactions in maize whereas microbial products and seaweed extracts without micronutrient supplementation failed under the experimental conditions. At optimal temperature the product containing the Pseudomonas sp. strain was repeatedly able to stimulate root and shoot growth of maize plants whereas in tomato only in heat-treated soil substrate significant effects were observed. Results indicate that the efficacy of the product was mainly attributed to stimulation or shifts in the soil microbial community. Additionally, the FZB42 strain was able to stimulate root and plant growth in some experiments whereas the effects were less reproducible and more sensitive to environmental conditions. Fungal BE products were less effective in plant growth stimulation and showed detrimental effects in some experiments. Under the applied experimental conditions BE-derived plant growth stimulation mainly was attributed to biostimulation but aspects of biofertilization or biocontrol cannot be excluded, as all experiments were conducted in non-sterile soil substrates. Root and shoot growth are stimulated in response to hormonal shifts. In the gene expression analysis only weak responses to BE treatments were observed, as previously reported from other studies conducted under non-sterile conditions. Nevertheless, some plant stress responses were observed that resembled in some aspects those reported for phosphorus (P) deficiency in others those reported for ISR/SAR. Especially the activation of plant defence mechanisms, such as the production of secondary metabolites, ethylene production and reception and the expression of several classes of stress-related transcription factors, including JA-responsive JAZ genes, was observed. It also seems probable that in plants growing in PGPR-drenched soils, especially at high application rates, a sink stimulation for assimilates triggers changes in photosynthetic activity and root growth leading to an improved nutrient acquisition. Nevertheless, due to the complexity of interactions in natural soil environments as well as under practice conditions, a designation of a distinct mode of action for plant growth stimulation by microbial BEs is not realistic. A comparison of the overall results with those reported in literature or other working groups in a common research project (“Biofector”) supported the often-reported low reproducibility of plant growth promotion effects by BE products under applied conditions. Factors that influenced BE efficacy were application time and rates, temperature, soil buffer capacity, phosphorus sources and nitrogen fertilization, light conditions and the soil microbial community. Results indicate that in maize cultivation seed treatment is the most economic application technique for microbial products whereas for vegetable or high-value crops with good economic benefit soil drenching is recommended. For seaweed extracts foliar application seems to be the most economic and efficient choice. Furthermore, results emphasize the importance of a balanced natural soil microflora for plant health and yield stability.
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Populations of avian aerial insectivores have declined across North America. A leading factor hypothesized to be driving these trends is a decline in prey populations, although a loss of suitable habitat on the landscape or other factors may also play a role. The Eastern Whip-poor-will (Antrostomus vociferus; hereafter: whip-poor-will) is an aerial insectivorous nightjar that has disappeared from many of its historic breeding locations. We investigated the role that food availability and land cover at multiple scales play in whip-poor-will distribution by estimating their abundance at 23 sites across central Illinois. To do this, we conducted nocturnal point counts to estimate whip-poor-will abundance and collected nocturnal insects using UV-light traps at these sites to quantify potential food abundance. Additionally, we described whip-poor-will diet using DNA metabarcoding of fecal samples. We found that the number of large moths at a site had a positive effect on the abundance of whip-poor-wills, aligning with our diet analysis which identified moths as the primary prey item for this species (present in 92% of samples). Whip-poor-wills also showed an affinity for forest edges, but only when edges were associated with high moth abundances. Conversely, developed land-cover in landscapes surrounding sites led to decreased whip-poor-will abundance. Given the continued expansion of developed areas, coupled with concerning trends in moth populations, declines in the abundance of this species may continue. Efforts should be made to protect and sustain moth populations and the impacts of development should be scrutinized in the pursuit of conserving whip-poor-wills.
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The population extinction pulse we describe here shows, from a quantitative viewpoint, that Earth’s sixth mass extinction is more severe than perceived when looking exclusively at species extinctions. Therefore, humanity needs to address anthropogenic population extirpation and decimation immediately. That conclusion is based on analyses of the numbers and degrees of range contraction (indicative of population shrinkage and/or population extinctions according to the International Union for Conservation of Nature) using a sample of 27,600 vertebrate species, and on a more detailed analysis documenting the population extinctions between 1900 and 2015 in 177 mammal species. We find that the rate of population loss in terrestrial vertebrates is extremely high—even in “species of low concern.” In our sample, comprising nearly half of known vertebrate species, 32% (8,851/27,600) are decreasing; that is, they have decreased in population size and range. In the 177 mammals for which we have detailed data, all have lost 30% or more of their geographic ranges and more than 40% of the species have experienced severe population declines (>80% range shrinkage). Our data indicate that beyond global species extinctions Earth is experiencing a huge episode of population declines and extirpations, which will have negative cascading consequences on ecosystem functioning and services vital to sustaining civilization. We describe this as a “biological annihilation” to highlight the current magnitude of Earth’s ongoing sixth major extinction event.
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There is growing recognition as to the importance of extreme climatic events (ECEs) in determining changes in species populations. In fact, it is often the extent of climate variability that determines a population's ability to persist at a given site. This study examined the impact of ECEs on the resident UK butterfly species (n = 41) over a 37-year period. The study investigated the sensitivity of butterflies to four extremes (drought, extreme precipitation, extreme heat and extreme cold), identified at the site level, across each species' life stages. Variations in the vulnerability of butterflies at the site level were also compared based on three life-history traits (voltinism, habitat requirement and range). This is the first study to examine the effects of ECEs at the site level across all life stages of a butterfly, identifying sensitive life stages and unravelling the role life-history traits play in species sensitivity to ECEs. Butterfly population changes were found to be primarily driven by temperature extremes. Extreme heat was detrimental during overwintering periods and beneficial during adult periods and extreme cold had opposite impacts on both of these life stages. Previously undocumented detrimental effects were identified for extreme precipitation during the pupal life stage for univoltine species. Generalists were found to have significantly more negative associations with ECEs than specialists. With future projections of warmer, wetter winters and more severe weather events, UK butterflies could come under severe pressure given the findings of this study.
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Wild bee declines have been ascribed in part to neonicotinoid insecticides. While short-term laboratory studies on commercially bred species (principally honeybees and bumblebees) have identified sub-lethal effects, there is no strong evidence linking these insecticides to losses of the majority of wild bee species. We relate 18 years of UK national wild bee distribution data for 62 species to amounts of neonicotinoid use in oilseed rape. Using a multi-species dynamic Bayesian occupancy analysis, we find evidence of increased population extinction rates in response to neonicotinoid seed treatment use on oilseed rape. Species foraging on oilseed rape benefit from the cover of this crop, but were on average three times more negatively affected by exposure to neonicotinoids than non-crop foragers. Our results suggest that sub-lethal effects of neonicotinoids could scale up to cause losses of bee biodiversity. Restrictions on neonicotinoid use may reduce population declines.
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