Climate change influences the global and regional distribution of many
species. For thermophilic insects, range expansions towards the north and to
higher elevations are expected in the course of climatic warming across the
Northern Hemisphere. The distribution of the European mantis (Mantis religiosa) has recently
expanded from Mediterranean regions in France to Hesse in central Germany.
This is interpreted as a response to rising mean temperatures, and further
northward expansion is expected to occur with increasing climate warming. In
this study, potential changes in the regional distribution across Hesse were
modeled for Mantis religiosa using the present distribution and climate across Europe as
the baseline. We estimated potential changes in the regional distribution for
two time periods until 2080 based on two climate change scenarios. The
results showed that the current range of M. religiosa in Hesse is smaller than expected
based on its climatic niche, i.e., the distribution is not in equilibrium
with the present climate. With climate warming the model predicts an
expansion of the potential distribution for the period 2041–2060. For the
period 2061–2080, our model predicts, however, a range contraction in
spite of continued warming. This unexpected result warrants further
investigation in order to elucidate whether the ongoing climate change may
have negative consequences for thermophilic species such as M. religiosa.
Introduction
Increasing greenhouse gas emissions have led to changes in the mean and
seasonal variation of the air temperature (Hughes, 2000; Pecl et al.,
2017; Hübener and Schönwiese, 2018). Temperatures are generally
rising, and the warming has been shown to influence a large number of
species, both negatively (Thomas et al., 2004; Descimon et al., 2005;
Bässler et al., 2010; Jantz et al., 2015) and positively (Bale et al.,
2002; Musolin, 2007). Insects, which are the largest taxonomic group of
animals (Deutsch et al., 2008), are susceptible to climate change because
they are ectothermic and thus are particularly sensitive to changes in air
temperature (Beck, 1983). However, insects are also able to adapt rapidly to
changes in their environment. Physiological adaptations allow insects to
respond to an increase in temperature by extending their flight periods and
by shifting events within their life cycles (Robinet and Roques, 2010).
Further ecological responses include higher reproductive rates due to
increasing the number of generations within a year (Menéndez, 2007). For
some species, changing distribution ranges have been linked to changes in
gene expression that confer physiological tolerance, such as cold resistance
(Strachan et al., 2011; Telonis-Scott et al., 2012). For thermophilic
insects in a warming climate, these responses may act in concert to allow an
expansion of their distributional range northwards (Hickling et al., 2006;
Menéndez, 2007; Robinet and Roques, 2010).
The European mantis Mantis religiosa prefers warm, sunny and dry habitats (Berg et al.,
2011). With global warming, climatic conditions in central Europe have
become warmer and drier (Bartholy et al., 2012), allowing M. religiosa to expand the
northern border of its range from Mediterranean regions to southern and
central Germany (Liana, 2007; Walther et al., 2009; Linn and Griebeler,
2016). M. religiosa is thought to have colonized Germany via two distinct routes: the
central lineage has spread from the Czech Republic via Poland to the federal
state of Brandenburg (Landeck et al., 2013; Zielinski et al., 2018), whereas
the western lineage originated from eastern France and used the Burgundy
Gate and/or the Moselle Valley (Berg, 2011) to reach central Europe. The two
lineages show genetic differences (Linn and Griebeler, 2015). These routes
have been used by many species during recolonization of central Europe
after the last ice age; some of them also include distinct genetic lineages
(e.g., the grasshopper Chorthippus parallelus, the two hedgehogs Erinaceus europaeus and E. concolor, and the European oak species;
Hewitt, 1999; Aspöck, 2008). Stable populations of the western lineage
of the European mantis in the German federal states of Rhineland-Palatinate
and Saarland were first found in the 1990s (Petrischak and Ulrich, 2012;
Linn, 2016); they were also found in Baden-Württemberg with high abundances at the
“Kaiserstuhl” in the Upper Rhine valley (Petrischak and Ulrich, 2012). In
the southern part of the German state of Hesse, M. religiosa has had established populations
since 2004. Because the species is considered to be relevant for
conservation (HLNUG, 2017), monitoring and management activities were
then initiated. Distribution models focusing on the regional scale that take
into account the influence of climate factors on native species and
alien species are important for nature conservation planning, which is in the area of competence of the federal states in
Germany. Therefore, we focused on
the federal state of Hesse and addressed two questions: first, is the
present distribution of M. religiosa in equilibrium with current climatic conditions,
i.e., has the species established populations in all suitable areas? This
question follows from an analysis of historical records of the upper
elevational border of insects in the Bavarian forest, where predictions
based on climate warming fell short of the actual distribution (Bässler
et al., 2013). The second question addressed the relative importance of
different climate variables for changes in the distribution of M. religiosa in Hesse: is
the mean annual temperature sufficient to predict the distribution of this
ectothermic species, or do other aspects of climate play a key role?
Material and methodsSpecies and environmental data
Distribution data for M. religiosa are available from the Global Biodiversity
Information Facility (GBIF.org, 2018) and from the database of the Hessian
State Office of Conservation, Environment and Geology (HLNUG, Abteilung
Naturschutz, 2018). Both datasets only contain presence data. A total of 39
occurrences of the praying mantis were reported from Hesse. As climate
change in Hesse is expected to create climatic conditions that are similar
to those in other parts of the European range of M. religiosa, modeling was based on
records from its entire distribution range in Europe, combining all the
records from GBIF and from the database for Hesse.
An underlying assumption in distribution models is that the distribution of
a species is in equilibrium with environmental conditions (Gallien et al.,
2012). However, an equilibrium cannot be expected for an expanding species
at its distributional border (Václavík and Meentemeyer, 2012). We
assumed that this issue would not unduly affect the results of the study
because most of the records came from the center of the distribution of the
western lineage of mantis, where an equilibrium between the distribution and
environmental conditions is likely.
After duplicate records were removed, the final dataset used in our study
comprised 2799 occurrence points and contains the GBIF dataset and the
reported locations in Hesse. To compare the environmental conditions at
these points with the overall environmental conditions in Europe, 8000
background points were randomly generated (Phillips et al., 2004; Phillips
and Dudík, 2008). The background points were distributed across the
actual entire distribution area of Europe. Bioclimatic data were retrieved
from the CHELSA database (http://chelsa-climate.org/, last access: 24 May 2019), which includes 19
bioclimatic variables (Table 1). The variables have a spatial resolution of
30 arcsec (approx. 600 m) and are based on monthly averages for
temperature and precipitation over the period 1979–2013.
Both current climate data and projections until 2080 (2041–2060 and
2061–2080), each with four different emission scenarios (representative
concentration pathways, RCPs) are available in the CHELSA database. The four
scenarios are denoted as RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, with the
numerical values indicating the magnitude of the mean temperature increase
expected by 2100. The scenarios are driven by predicted levels of greenhouse
gas emissions and are therefore related to environmental policies (IPCC,
2014). RCP 8.5 assumes a “business-as-usual” approach without particular
interventions, leading to a fourfold increase in atmospheric CO2
concentrations from pre-industrial values by 2100. By contrast, the
mitigation strategies assumed in RCP 2.6 would reduce global greenhouse gas
emissions after a decade, with near-zero levels reached ∼60 years from now. In this scenario, the increase in the global mean
temperature since pre-industrial times is unlikely to exceed 2 ∘C
(IPCC, 2014). We used both RCP 2.6 and RCP 8.5 in our study.
Bioclimatic variables of the CHELSA database
(http://chelsa-climate.org/, last access: 24 May 2019), the situation under current conditions, and the
values for Hesse according to scenarios RCP 2.6 and RCP 8.5. Each value was
calculated on the basis of 80 204 grid cells for Hesse. One cell measures
approx. 600m×600m.
VariableNameUnitSituation in Hesse Actual Scenario RCP 2.6 Scenario RCP 8.5 MinMaxMeanMinMaxMeanMinMaxMeanAnnual mean temperaturebio01∘C5.3118.96.2129.97.31311Mean diurnal rangebio02∘C6.67.26.98.99.69.310.911.911.3Isothermality (BIO2/BIO7)bio03∘C2.72.92.83.33.53.33.73.83.7Temperature seasonality (standard deviation)bio04∘C5.86.66.36.17.06.66.37.36.8Max temperature of warmest monthbio05∘C18.324.822.320.22724.222.729.526.9Min temperature of coldest monthbio06∘C-6.1-0.4-2.2-7-1.4-3.2-7.3-1.6-3.4Temperature annual range (BIO5-BIO6)bio07∘C23.125.824.525.829.227.528.531.930.3Mean temperature of wettest quarterbio08∘C-2.119.512.2-1.420.913.2-1.42312.1Mean temperature of driest quarterbio09∘C-2.712.24.2-2.420.14.6-1.315.611.2Mean temperature of warmest quarterbio10∘C13.82017.615.221.519.116.723.220.7Mean temperature of coldest quarterbio11∘C-32.50.7-2.43.21.303.61.7Annual precipitationbio12mm466148579345014307684631497794Precipitation of wettest monthbio13mm481638450146844716682Precipitation of driest monthbio14mm299752288948328551Precipitation seasonality (coefficient of variation)bio15mm824149241562313Precipitation of wettest quarterbio16mm144465242134428231137490235Precipitation of driest quarterbio17mm9130316487283155100268160Precipitation of warmest quarterbio18mm139367218123332196137371216Precipitation of coldest quarterbio19mm9737118187350164100398190Statistical analyses and mapping
To predict the potential current and future distributions of M. religiosa, we used the
maximum entropy approach implemented in Maxent. This method is available in the
dismo package for R (version 3.5.3). Maxent is a popular method for species
distribution modeling and is able to work with presence-only data (Elith et
al., 2011; Merow et al., 2013; Amici et al., 2015). To select the
explanatory variables for the baseline model of the current distribution of
M. religiosa, we applied the function MaxentVariableSelection (Jueterbock et al., 2016; Gottwald et al., 2017;
Steger et al., 2020) with the following settings: (1) a contribution
threshold for inclusion in the model of 2 %, a value that reflects the
importance of environmental variables in limiting the distribution of the
species; (2) a correlation threshold of 0.7 to exclude correlated predictor
variables; and (3) the use of a beta multiplier from 0.5 to 3.0 to regulate the
fitting of the projected distribution to the training data. All combinations
of the different feature classes of Maxent (linear, quadratic, product, threshold and
automatic feature selection) were included, and forward stepwise selection
was carried out to obtain the best combination of variables. In total, 1815
models of 64 different feature class combinations were calculated. The area
under the receiving operator curve (AUC) and the corrected Akaike
information criterion (AICc) were used to evaluate the models and to select
the most parsimonious one. The AUC value can be interpreted as the
probability of a randomly selected occurrence point being rated higher than
a randomly selected background point (Fieldling and Bell, 1997). The AICc
takes the number of parameters into account and selects the model with the
lowest number of predictive variables (Warren et al., 2014). The relative
importance of each climate variable included in the model was evaluated
using the Jackknife method implemented in the Maxent program (Phillips et al.,
2006). The chosen output format was “logistic”, which yields an indicator
for the relative probability of occurrence, a value that is easy to
interpret and visualize (Phillips et al., 2009; Merow et al., 2013). The
distribution model obtained using the data on current climatic conditions
was subsequently used to predict the potential future distribution under the
two climate change scenarios (RCP 2.6, RCP 8.5) for the time periods
2041–2060 and 2061–2080, respectively. The baseline model used ca. 36 million grid cells representing all of Europe, whereas the projected future
distribution was calculated using the 80 240 grid cells of the German
federal state of Hesse. For the predictions of the future distribution, the
model of the current distribution was used, with the predicted climatic
variables entered into the model for the two emission scenarios and the two
time spans. The occurrence probability in each grid cell was displayed on a
scale from 0 to 1. The suitability of potential distribution areas was
classified as described in Yang et al. (2013) with low (<0.2),
moderate (0.2–0.4), good (0.4–0.6) and high (>0.6) occurrence
probability. The resulting distribution maps were edited in QGIS Desktop
(version 3.4.2.).
Results
The best model for the current climatic situation had an AUC of 0.87. For
this model, the variables with the highest predictive power selected using
the jackknife method were bio04, bio05, bio11, bio15 and bio18 (Table 2).
The variable with the largest contribution was temperature seasonality
(bio04, 54 %) and that with the lowest contribution was the seasonality of precipitation (bio15, 2 %).
Range and mean values of climatic variables included in the
baseline model for Hesse as well as the range of values for these variables
from locations with records of Mantis religiosa. Bio04 is temperature seasonality (standard
deviation), bio05 is max temperature of warmest month (∘C]),
bio11 is mean temperature of the coldest quarter (∘C), bio15 is
precipitation seasonality (coefficient of variation), and bio18 is precipitation
of the warmest quarter (mm per quarter).
Contribution [%]Range and mean of current Conditions at locations withclimatic data in Hesse records M. religiosa in HesseMinMaxMeanbio0453.95.86.66.36.3–6.4bio1120.3-32.50.71.8–2.4bio0518.318.324.822.324.3–24.9bio185.5139367218134–155bio15282414-3.6–5.6
The predicted current distribution range with a good occurrence probability
exceeded the area with actual records of M. religiosa (Fig. 1). Only in the region
around Frankfurt do records of species occurrences largely match the areas with
good (0.4–0.6) and high (>0.6) predicted occurrence
probabilities. Nevertheless, the high potential areas are exceeded, i.e., the
species has already spread northwards into the good potential areas (Fig. 1). In the north of Hesse, almost no areas with good or high potential for
the occurrence of the European mantis were detected. A large area with a
good potential for the occurrence but without actual records was found
around the city of Gießen. All of the low mountain ranges in Hesse (e.g.,
Vogelsberg, between Gießen and Fulda; Rhön, west of Fulda; and Taunus,
northeast of Frankfurt) showed only low probabilities (<0.2) for
the occurrence of M. religiosa under the current climate.
Modeling the distribution of M. religiosa using RCP 2.6 (Fig. 2) yielded a spatial
pattern of predicted occurrence probabilities until 2060 that was quite
similar to the pattern predicted for the current climate. Overall, Hesse was
divided into suitable southern areas and mostly unsuitable northern areas. Under
scenario RCP 8.5, the area of good and moderate potential habitat was
predicted to be larger than under RCP 2.6 between 2041 and 2060, and almost
no low-potential habitat remained (Fig. 2). According to both scenarios,
the low mountain ranges of Taunus and Vogelsberg, as well as the Rhön, will
not have suitable areas for M. religiosa in the future either. During the period 2061 to
2080, however, areas with good and high potential were predicted to decrease
under the RCP 8.5 scenario (Fig. 2). Overall, only moderate changes in the
distribution of M. religiosa in Hesse were predicted.
Map of the occurrences as collated by the database of the Hessian
State Office for Nature Conservation, Environment and Geology and current
potential areas of Mantis religiosa in Hesse, predicted using the Maxent method. The model
is based on the distribution of the mantis across Europe with actual
climatic conditions (see also Tables 1 and 2).
Maps of the potential distribution areas of M. religiosa in Hesse, Germany.
The potential future distribution during two time periods was predicted
using the identified variables under actual climate conditions and the
scenarios RCP 2.6 and RCP 8.5.
Discussion
Climate has been shown to influence the life history of many insect species
(Menéndez, 2007). Insects are ectothermic and rapidly react to changes
in their physical environment, especially to changes in temperature (Beck,
1983; Bale et al., 2002; Zeuss et al., 2017). Our baseline model identified
temperature seasonality (bio04) as the most influential variable for the
potential future distribution of M. religiosa in Hesse. It is defined as the standard
deviation of the monthly temperature averages throughout the year (Xu et
al., 2013; Grünig et al., 2017), and larger values indicate greater
temperature differences between the months.
The response curve of our model showed that the occurrence probability of
M. religiosa decreased abruptly between temperature seasonality values between 7 and 8
(Fig. 3), suggesting that this represents a critical value that limits the
occurrence probability of M. religiosa. Therefore, a high variability between the monthly
temperatures is not beneficial for the Mantis religiosa. The curve shows that the
smaller the temperature seasonality is, the higher the occurrence probability will be.
Response curves of the predictor variables of temperature
seasonality, mean temperature of coldest quarter, max temperature of warmest
month, precipitation of warmest quarter and precipitation seasonality, as
functions of the occurrence probability in the model that predicts the
actual potential distribution (Fig. 1).
The second most influential variable in our model was the mean temperature
of the coldest quarter (bio11). Consistent with our initial observations,
the probability of M. religiosa occurrence increased if the temperature during the
coldest quarter rose (Fig. 3). Thus, not only warm summers but also warm
winters are clearly beneficial to the mantis (Parent, 1976, as cited in Berg
et al., 2011). Females of the European mantis deposit their eggs in up to
three egg cases in autumn, with the eggs then undergoing a diapause in
winter (Berg et al., 2011). As the temperatures increases in spring, the
eggs continue to develop and ∼125 larvae per clutch hatch in
May–June (Linn and Griebeler, 2016). The life cycle of the European mantis
therefore depends on the winter temperatures and on an early temperature
increase in spring. A longer vegetation period may lead to a growing
population and thus to a larger number of dispersing individuals (Liana,
2007). This could be mediated by female mantises producing more than one egg
case during longer summers (Harz, 1983). Favorable temperatures in
spring are important for larval development (Linn, 2016). Cold springs with
a lot of precipitation can delay ootheca hatching and sometimes even drive
populations to extinction (Ehrmann, 2011). As the environment becomes warmer
in response to climate change, new habitats with suitable conditions for the
hatching and early development of M. religiosa may become increasingly available. Warm,
dry conditions also could ensure a better food supply for this carnivorous
insect, based on the assumption that climate change will have positive
effects on its food resources, i.e., other insects (Berg et al., 2011;
Ehrmann, 2011; Linn, 2016).
The predicted current distribution area of M. religiosa in Hesse was larger than the
area from which occurrences have actually been reported, which suggests that
the distribution of the European mantis may not have reached equilibrium
with respect to climatic variables at this distributional border. The model
predicting the current distribution was calculated using records from across
Europe. These data roughly cover the range where the species has established
populations for a longer time period and might have reached equilibrium with
climatic conditions; thus, one can assume that our model predicted the area
that would have been occupied by the insect if its distribution was at an
equilibrium with the climate. The northern boundary of the potential range
in Hesse was located in the area of the cities of Gießen and Marburg,
whereas the current northernmost observations are located south of and
around Frankfurt. Several reasons might explain why distribution lags behind
climate. First, the large metropolitan region of Frankfurt and/or natural
boundaries such as the Taunus, a low mountain range west of Frankfurt
consisting of unsuitable areas, may have hindered the spread of M. religiosa. Second,
intensive land use and habitat fragmentation, together with the low dispersal
ability of the European mantis, may have slowed expansion.
Prediction of the future distribution of M. religiosa must take unpredictable climatic
events such as heat waves, extreme droughts with no precipitation or storm
surges into account. The largest expansion of the distribution area might be
expected in the case of climate change scenario RCP 8.5, a worst-case
scenario of no greenhouse gas mitigation strategies, that would lead to the
largest increase in temperature and a significant decrease in precipitation.
While these environmental changes might be expected to be favorable for the
European mantis, only a slight northward expansion of the distribution of
M. religiosa was predicted to occur by 2041. Why the benefit provided by the predicted
climate warming does not translate into a larger expansion of the
thermophilic M. religiosa is unclear.
In contrast to the increase in suitable areas predicted by RCP 8.5 until
2041, by 2080 the amount of suitable area was predicted to decrease to a
range similar to that predicted by scenario RCP 2.6. Thus, while in the next
20 years species such as M. religiosa might profit from global warming, in the 20 years
thereafter losses in areas of high potential might be expected. This
observation might be explained due to the increase of diseases and
pathogenes which profit from shorter and milder winters in the ongoing
climate warming (Harvell et al., 2002). Under the scenario of RCP 2.6,
according to which the rise in temperatures will be relatively small, the
distribution area of M. religiosa until 2080 will not expand much compared with the
current predicted area. However, regardless of future scenarios, an expansion
of the range of M. religiosa to the predicted current area might be expected, although
the speed of this expansion remains speculative. Nevertheless, our model
shows that, at least on a regional scale, climate warming may lead to
fluctuating distributional borders of thermophilic species despite a
continued warming of the climate.
While generalist species of insects can readily establish themselves in new
areas, changing conditions pose a challenge to the survival of specialists
(Pampus, 2004). For M. religiosa, reproduction occurs under specific environmental
conditions, and a spread is potentially limited by the insect's sedentary
lifestyle. Nonetheless, M. religiosa has benefitted from the recent warming of
temperatures in central Europe, expanding its range boundary to Hesse. A
study by Linn and Griebeler (2016) showed that the distribution of M. religiosa is more
strongly limited by the influence of environmental conditions on adults
rather than on eggs, which especially during the maturation period could be
spread passively by wind. Humans might also be a driver of dispersal
processes, inadvertently transporting M. religiosa to regions conducive to its
establishment. Either mechanism would allow the European mantis and other
sedentary species to overcome large distances.
In summary, this study showed that species such as M. religiosa can be expected to
benefit from a decreasing temperature seasonality during the next 20 years.
The finding that continued climate warming as projected for the
business-as-usual scenario of RCP 8.5 may not translate into further
northward or upward shifts of the range boundary calls for further
investigation of the causal links between climate variables and determinants
of the distribution range of thermophilic insects.
Data availability
The occurrence data can be downloaded from 10.15468/dl.s0fpzo (GBIF.org, 2018) in their current form. The dataset from Hesse is not publicly accessible. Current climate data and the future projections of the respective climate change scenarios can be downloaded at http://chelsa-climate.org (Karger et al., 2017).
Author contributions
JS designed the modeling processes and developed the modeling code, and AS
carried them out. JS prepared the manuscript with contributions from all
co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Review statement
This paper was edited by Daniel Montesinos and reviewed by two anonymous referees.
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