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Article

Climatic Niche of an Invasive Mantid Species in Europe: Predicted New Areas for Species Expansion

by
Alexandru-Mihai Pintilioaie
1,
Lucian Sfîcă
2 and
Emanuel Stefan Baltag
1,*
1
Marine Biological Station “Prof. Dr. Ioan Borcea”, Alexandru Ioan Cuza University of Iași, 907015 Agigea, Romania
2
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700506 Iași, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(13), 10295; https://doi.org/10.3390/su151310295
Submission received: 29 April 2023 / Revised: 12 June 2023 / Accepted: 27 June 2023 / Published: 29 June 2023
(This article belongs to the Special Issue Application of Big Data and Geographic Information Systems for Sustainable Ecological Management)
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Abstract

:
While some species naturally expand their range by finding suitable climatic and trophic niches in new areas, others have been transported intentionally or unintentionally by humans since their journey from Africa to other continents. This phenomenon has occurred throughout history, being more prevalent at the end of the Middle Ages and at the start of the Industrial Revolution, with its frequency increasing in recent times due to globalization. Hierodula tenuidentata Saussure, 1869 is a mantis species originally distributed from India to Caucasus, that started to become more and more common in many European countries in the last few years, being considered an alien species. However, there is limited information available regarding its distribution range, habitat preference, and other ecological requirements that can help us understand its movements. We used observation data from its range, along with bioclimatic and elevation variables, to build Species Distribution Models in MaxEnt. This allowed us to analyze the species’ spatial ranges and understand and predict its distribution across Europe. Before selecting the best-fitting models, the occurrence data were spatially filtered, and bioclimatic variables tested for multicollinearity. Based on the present species distribution models, with AUC values of 0.967 for the training data and 0.960 for the test data, Hierodula tenuidentata emphasizes a coastal occurrence in the Black Sea and Mediterranean Sea regions, with local observations in southeastern Europe, an area that is likely to be occupied in the next few years through species expansion. Our data show that the expansion of Hierodula tenuidentata in Europe is influenced by the natural movement of the species westward combined with human introduction in some areas. It is now evident that the species’ presence in Europe is not solely based on human-aided dispersion, as was previously believed. The main variables influencing the distribution of Hierodula tenuidentata across Eurasia are temperature and precipitation, both of which have been significantly modified in recent years due to climate change.

1. Introduction

Accurate species distribution ranges across extensive geographical areas are difficult, or even impossible to obtain, especially in areas with a low number of specialists or observations, or for animal groups which are less studied. To analyze the species distribution, characteristic environmental variables are extracted and used to identify bioclimatic patterns that match the ecological requirements of the target species [1]. These requirements are projected on a larger scale to predict the potential occurrence range for the analyzed species in a specific area [2]. Species distribution models (SDMs) can be used very efficiently as a tool to analyze and understand the ecological characteristics of a species, but also to predict some behavior patterns [3]. These models are particularly important for invasive species because they help calculate the potential distribution range and enable the implementation of conservation measures if necessary.
Alien species can establish viable populations and expand their range in areas with similar abiotic conditions as their native habitats. Bioclimatic factors might be of high importance especially in areas where the alien species do not have competitors or predators [4]. Among the abiotic variables, air temperature and precipitation have been frequently found to drive alien species occurrence, distribution, or richness [5,6]. Additionally, areas with a higher resource availability, such as habitat or land use diversity, with many ecotone areas, have been associated with a higher incidence of alien species flora and fauna [7].
On the other hand, alien species feel more comfortable in highly populated areas where the ecosystem functionality is adulterated, and they can exploit many gaps efficiently to find food and nesting resources [8,9,10]. In areas where the ecosystem structure is weaker, they can more easily find a suitable niche.
In a functional ecosystem, native species use the available resources more efficiently, leaving fewer empty niches for alien species to infiltrate [11,12]. However, even if the ecosystem is functional with a large species diversity, well adapted, and with a healthy and diverse phylogenetic structure, the abundant and diverse available resources could allow some empty space, unoccupied niches which can be explored by alien species [13], making these communities more receptive to alien species [14]. In new areas, invasive species can proliferate without or with a lower number of predators, diseases or competitive species, becoming a problem for ecosystem function. However, some species expand their geographic range, occupying new areas, especially now, when climate change affects the local climates. In order to take action, we must understand the species’ ecology, geographic potential range, and its interactions with local species.
The praying mantis (Ord. Mantodea) constitutes a group of charismatic insects that comprises over 2500 species adapted to many types of habitats, from tropical rainforests to arid deserts. They are exclusively predatory and, being voracious, represent one of the best insect groups when it comes to regulating insects’ pest population using biocontrol methods. They can eat aphids, caterpillars, true bugs, beetles, grasshoppers, and crickets, and even small mammals, birds, and reptiles [15].
Regrettably, most of their ecology remains poorly studied, and we have limited information available regarding their habitat preference, movements, and ecological niche requirements. While the Giant Asian Mantis Hierodula tenuidentata is considered an alien species in Europe, its distribution across Eurasia is largely unknown. Previous studies have suggested that its presence in Europe is due to human transportation, whether intentional or unintentional. However, the species’ climatic requirements and the potential areas for its expansion in the near future remain uncertain. Although the presence of the species has been documented in the Caucasus region, it is unclear whether it could naturally expand its range in Europe. Currently, the available knowledge presents this species as an alien one, likely transported to Europe through commercial routes.
This study uses bioclimatic and elevation variables to build species distribution models, giving the opportunity to identify the bioclimatic constraints which can influence the distribution of Hierodula tenuidentata Saussure, 1869 in Europe, in order to understand the species’ current range and potential new areas for its occurrence in the near future.

2. Material and Methods

2.1. Mantis Occurrence Data

For these analyses we used 3498 mantis observation points which are available at Global Biodiversity Information Facility database (GBIF) for Hierodula transcaucasica Brunner von Wattenwyl, 1878 and Hierodula tenuidentata [16,17].
Here we consider Hierodula transcaucasica to be a synonym of Hierodula tenuidentata, based on a study that showed individuals with intermediate characters between these 2 species, but also specimens that could be attributed either to Hierodula transcaucasica or Hierodula tenuidentata, all of them coming probably from a single wild population [18]. Thus, we used the data of both species from the GBIF database for this study, both under the name of Hierodula tenuidentata.
We removed duplicated occurrence points, records without a precise location (with less than 2 decimals), and records with uncertain identifications. For each observation we checked the available photo, to confirm the species, but there were many cases of wrong identification. After all the verification procedure, 3304 geo-referenced observations remained for our analyses.
Considering that these data were recorded by multiple teams or specialists, it was necessary to reduce spatial auto-correlation. To remove the clustered occurrence points a spatial filtering procedure was used, reducing model overfitting [19]. The effects of over-sampling in highly surveyed areas were reduced through a spatial distance filter of 10 km. After these steps, 532 locations for Hierodula tenuidentata occurrences were obtained. To measure the spatial auto-correlation we used Global Moran’s I index in ArcGIS PRO v.3.0.3. If the dataset is clustered, the z-score will be positive and the p-values statistically significant. For a dispersed dataset the Z-score will be negative, and the p-values will be statistically significant. The Global Moran’s I index confirmed a random distribution for our dataset.

2.2. Environmental Variables

For the present study, the bioclimatic data from the WorldClim database were downloaded, version 2.1 [20]. The available bioclimatic variables were generated using weather data from 1970 to 2000 from all climate data from weather stations around the world. For a high resolution all the bioclimatic raster layers were downloaded at 30 arc-seconds, which represents approximately 1 km2 at the equator (Table 1).
Multi-collinearity between environmental variables for species distribution models can influence the results, leading to biased predictions through overrepresentation of the ecological relevance of those correlated variables [21]. Therefore, using Variance Inflation Factor (VIF) analysis, all 19 bioclimatic and 7 climatic variables were tested for multi-collinearity to avoid any bias in our distribution models [22] using the R package cat [23] in R Statistical Software v.4.1.3. This allowed us to detect hidden correlations between predictors that are not apparent in pair-wise correlations. To avoid the variables collinearity, we used a stepwise procedure, retaining only those predictors with a more stringent VIF threshold of <5, which is suitable for multi-collinearity [21].

2.3. Species Distribution Models

To predict the areas suitable for Hierodula tenuidentata expansion, a correlative SDM was developed using MaxEnt (version 3.4.4) which is a maximum entropy machine-learning algorithm. MaxEnt is a robust algorithm which uses only presence data to build distribution models [24]. MaxEnt is mathematical software which uses algorithms to compare the climatic and environmental variables where species occurs against the predictors’ data from a specific area [25]. The model result is a raster map which presents a relative environmental suitability distribution for our species.

2.4. Model Evaluation

To determine the most parsimonious models, we used optimal-model selection based on the Area Under the Curve (AUC). For this analysis we built models of varying complexity using a regularization multiplier (RM), ranging from 0.5 to 4.00 in 0.5 increments and five feature classes (FC): threshold (T), quadratic (Q), hinge (H), linear (L), and product (P), using all possible combinations.
The final distribution maps were generated using all the presence locations for Hierodula tenuidentata, after checking and removing clustered observation points within a 10 km radius to achieve the highest predictive accuracy for distribution estimation [26].
Projected distribution maps were modeled for two important ranges: the entire present distribution range and the native distribution areas. Our main focus was on Europe, particularly the southern regions which are invaded by Hierodula tenuidentata.

3. Results

Using all available observations from GBIF and 48 candidate models, we selected the best MaxEnt using the test AUC. The selected model, based on 532 distribution points for Hierodula tenuidentata and 19 bioclimatic variables, in addition to elevation, has the AUC values of the training data and test data of 0.967 and 0.960, respectively (Table 2). The selected model’s feature class was the threshold.
Hierodula tenuidentata is present in most of the European countries situated along the Black Sea and Mediterranean coast. After removing all imprecise or misidentified observations, the GBIF data indicate its occurrence, in addition to its native areas, in Southern Ukraine, Southern Republic of Moldova, Southern Romania, Bulgaria, North-Western Turkey, Greece, North Macedonia, Western Albania, Western Croatia, Northern Serbia, Southern Hungary, Slovenia, Northern Italy, and, to a lesser extent, South-Eastern France and Spain (Figure 1). Most of the data are located in close proximity to the Black Sea and Mediterranean Sea coastlines.
The climatic variables used to build the final model were: Isothermality (BIO 03), Mean Temperature of Warmest Quarter (BIO 10), Mean Diurnal Range (BIO 02), Mean Temperature of the Coldest Quarter (BIO 11), Annual Mean Temperature (BIO 01), Precipitation of Coldest Quarter (BIO 19), Annual Precipitation (BIO 12), Precipitation of Warmest Quarter (BIO 18), and Elevation (ELEV). The main variables influencing the distribution of Hierodula tenuidentata are temperature and precipitation, but no single variable showed very high importance. Isothermality had the highest contribution at 20%, followed by Mean Temperature of Warmest Quarter (14.8%) and Mean Diurnal Range of Temperature (12.5%). Hierodula tenuidentata prefers small thermal variations both on an annual and daily level, combined with high temperatures in summer, indicating its preference for tropical monsoon climate conditions similar to those found in India (Figure 2).
The predicted models, using all presence points and bioclimatic variables, identified new suitable areas for Hierodula tenuidentata. The main region for this species is located in the Eastern, Northern, and Western Black Sea, as well as in Greece, the Dalmatian Coast, and Italy, including their islands.
The number of distribution points for the Asian native range is very low, with only 65 points remaining after applying a spatial filter, limiting the prediction model for this area. However, in the Caucasus region, the situation is different, as there are 134 points available for a much smaller area. This makes the model more suitable for predicting the species’ range in this region. To evaluate the species’ range without relying on new locations, another prediction model was developed using only presence data from the native range. For this model, 199 distribution points (Asia and the Caucasus) for Hierodula tenuidentata, along with 19 bioclimatic variables and elevation data, were used. The AUC values for the training data and test data for the best model using the presence locations from the native areas were 0.988 and 0965, respectively, with the selected feature class being the threshold.
The native-locations model predicts a suitable distribution range for 88.6% of the new European locations (Figure 3). The locations which are not in the predicted distribution range are in inhabited areas, mostly in large cities such as Kyiv (UA), Chernihiv (UA), Ljubljana (SI), Innsbruck (AU), Klaus (AU), Athens (GR), Patra (GR), Chania (GR), and others.
The final model was built using three climatic variables (Figure 4): Annual Mean Temperature (BIO 01), Mean Temperature of Warmest Quarter (BIO 10), and Mean Diurnal Range (BIO 02). Together, these variables contributed 55.2% to the model.

4. Discussion

The ecological niche models have commonly been used to study the ecology and distribution of invasive species and to develop conservation strategies [1]. SDMs are a powerful tool which is used in conservation, ecology, and biogeography, especially for predicting gaps in their distribution range [27]. The key factors that influence the accuracy of the species distribution models are the sampling range and sample size [1].
In Europe, Hierodula tenuidentata superficially resembles Mantis religiosa (Linnaeus, 1758), but this European species has the entire tegminae of the same color, while in Hierodula tenuidentata, the stigma is white. Moreover, the oothecae of Hierodula tenuidentata differ from those of Mantis religiosa and can be easily recognized in the field [28].
The present study focuses on the distribution of Hierodula tenuindentata and the variables that may influence its occurrence in new sites. Two sets of models were created using ecological niche modeling through MaxEnt, based on data obtained from GBIF. The first model analyzes the distribution range of the species by utilizing the available observations from both native and new areas.
The natural range of Hierodula tenuidentata comprises India, Nepal, Borneo, Kazakhstan, Turkmenistan, Tajikistan and Sunda Island, and Hierodula transcaucasica originally inhabited Armenia, Azerbaijan, Georgia, Afghanistan, Iran, Pakistan, Nepal, Kalmykia (European Russia), Caucasus, Tajikistan, Uzbekistan, and Kazakhstan [15]. Considering Hierodula transcaucasica to be a synonym of Hierodula tenuidentata [18], the natural range of Hierodula tenuidentata extends from India to Caucasus. Although it was recorded in Crimea a century ago [29] and in northwest Turkey, near the Georgia border, in 1973 [30], there were no records in Europe for about 100 years until some specimens were recorded again in Crimea and Ukraine in 2016, mostly near the Black Sea and Sea of Azov coasts [31]. It is hard to believe that the species was overlooked by everyone in Europe for 100 years, given its size and general appearance and the fact that in the last few years it started to be relatively easy to spot in certain areas of Europe. In just a few years, the species was recorded from many European countries, as follows: Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Greece, Italy, Moldova, North Macedonia, Romania, Serbia, Slovenia, Spain, and Ukraine [32]. The species is also known from France [33]. Many of these records are from urban and suburban areas, where the heat island effect, combined with local warming due to climate change, may have facilitated the species’ spread and colonization of new areas [32]. Taking into account the rapid detection of the species in many European countries in a short interval of time, after a long period with no records in Europe, and the species presence in many urban/suburban areas, the best hypothesis for European colonization was attributed to human-aided introduction, probably combined with a natural expansion from its original range [18,28,32]. Even though human intervention may have played a role in the species’ European colonization, our distribution model strongly suggests that the colonization was more likely due to natural expansion from its original range. Probably the species started to colonize Eastern Europe from Caucasus at a low rate that intensified in the last years due to less restrictive cold conditions during winter in eastern Europe determined by climate change.
The second model analyzes the species distribution niche using only observations from its native areas. Most European areas predicted by this model align with available data, with only a few observations falling outside the model range. These observations are limited to large cities with developed trade routes, indicating human-mediated transportation of the species via tracks, ferries, trains, or airplanes. Some populations seem to be restricted to urban regions or to certain parks within a city [28,32], suggesting a non-natural dispersion of the species. Additional evidence of human-mediated transportation was observed when an individual was seen hitchhiking on a boat in the Black Sea a few years ago [34].
The distribution of Hierodula tenuidentata in Asia has a very low representation in GBIF. However, by modeling the species’ native range using available presence data from India to Caucasus, we can observe a predicted distribution range in those European areas where we have recorded observations in GBIF.
Based on the available occurrence locations downloaded from GBIF [16,17] for Hierodula tenuidentata, we can safely assume that the species has expanded naturally to Europe by following the climatic niches provided by the continuous mountain ranges, such as the Himalaya–Hindu Kush–Elburz–Caucasus, acting as an ecological bridge from India to Europe. Most of the observations are concentrated near the Black Sea, with occasional sightings along major rivers like the Dnipro, Danube, or Po. The species’ absence from areas with large harbors or commercial routes, but with suitable climatic niche, such as most of the southern half of Italy, the Mediterranean part of France and Spain, further supports the argument for natural expansion. Moreover, a large concentration of occurrence data is visible in southeastern Europe, particularly along Black Sea Coast up to Greece.
Commercial routes such as trucks, ships, or airplanes may have aided in the expansion of the species, but it is of secondary importance compared to natural expansion. The species’ presence in remote areas such as the Danube Delta and other seaside areas with no international trading activity strongly suggests that natural dispersion is the primary factor driving its expansion.
It is worth noting that Hierodula tenuidentata has a high degree of mobility [35] in its newly colonized areas. While it is unclear how far they can travel, their high mobility is an important factor in the species’ ability to expand its range over time.
The species’ distribution across Asia is poorly studied and there are large gaps in countries with limited research on insects such as Pakistan, Afghanistan, Turkmenistan, Uzbekistan, Tajikistan, or Kazakhstan (Figure 5). Both the predicted models, based on native locations and present observations, show a very low occurrence probability in this area and where it has been detected, the range is limited. However, it is possible that the Hindu Kush and Elburz Mountain ranges offer suitable climatic niches for the species occurrence on their most humid and sheltered slopes, which may not be well defined in bioclimatic data from WorldClim. The observation of the species in the mountains of Central Asia (as on the border of Tian Shan mountains), a region with poor trade and low transportation accessibility, suggests the possibility of natural expansion towards the west, along the Hindu Kush and Elburz mountains, eventually reaching the Caspian Sea and the Caucasus region. Hence, the missing link overlapping Tajikistan and Afghanistan area may simply be due to a lack of observations in those regions.
It is likely that the species’ distribution is continuous from India to Europe, and the apparent “absence” between the Himalayan–Central Asia range and the Caucasus is simply due to a lack of data. The slopes of the Himalayas in India and the mountains of southern Central Asia (Hindu Kush) certainly offer ecological niches similar to those found in the Caucasus or India. However, these areas may not appear on the prediction model due to the lack of available data and the resolution of WorldClim data, which have no way of capturing the local topoclimatic conditions that the species certainly exploits.
The fact that Hierodula tenuidentata is present in North-West India, in Assam, despite the model not predicting its occurrence, could indicate that the modeling resolution has limitations.
Climate change could play a role in the expansion, as higher temperatures during the cold season may be beneficial for the species. However, it is important to consider whether the survival of eggs could be affected by prolonged exposure to low temperatures. This dependence on higher temperature is evident in its presence along coastal areas such as the Black Sea, where observations are concentrated along the coast with milder winter temperatures moderated by the sea. The high concentration of observations on the southern coast of Crimean Peninsula exemplifies the climatic niche for this species outside its native range, as the region’s mountain shelter provides suitable conditions for the species.
We can expect the distribution of Hierodula tenuidentata to expand in the near future across Italy, South-Eastern France, Spain, and even North-Western Africa based on its present distribution niche.
As a larger species than European Mantis species, its expansion could potentially impact native populations. However, since it is a natural process, it is difficult to mitigate its influence on local species. To understand its impact, further ecological studies are needed to examine its interactions with local mantis or other predator species in newly occupied areas.

Author Contributions

Conceptualization, A.-M.P. and E.S.B.; methodology, A.-M.P. and E.S.B.; software, A.-M.P. and E.S.B.; validation, A.-M.P. and E.S.B.; formal analysis A.-M.P., L.S. and E.S.B.; investigation, A.-M.P. and E.S.B.; resources, A.-M.P. and E.S.B.; data curation, A.-M.P. and E.S.B.; writing—original draft preparation, A.-M.P. and E.S.B.; writing—review and editing, A.-M.P., L.S. and E.S.B. All authors have read and agreed to the published version of the manuscript.

Funding

Acknowledgment is given to infrastructure support from the Operational Program Competitiveness 2014–2020, Axis 1, under POC/448/1/1 Research infrastructure projects for public R&D institutions/Sections F 2018, through the Research Center with Integrated Techniques for Atmospheric Aerosol Investigation in Romania (RECENT AIR) project, under grant agreement MySMIS.

Acknowledgments

This work was supported by the Agigea Bird Observatory. We wish to thank all our volunteers. We wish to thank also Petronel Spaseni and to the anonymous reviewers for their improvements on a previous version of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Predicted distribution model for Hierodula tenuidentata using all available presence locations. The prediction map shows hotter red areas (values close to one) having higher Hierodula tenuidentata climate suitability. Green circles mark the known Hierodula tenuidentata occurrence points.
Figure 1. Predicted distribution model for Hierodula tenuidentata using all available presence locations. The prediction map shows hotter red areas (values close to one) having higher Hierodula tenuidentata climate suitability. Green circles mark the known Hierodula tenuidentata occurrence points.
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Figure 2. Response curve (BIO03—Isothermality, BIO10—Mean Temperature of Warmest Quarter, BIO02—Mean Diurnal Range of Temperature). The red line is the mean value for the 10 Maxent runs, and the blue bar represents ± standard deviation.
Figure 2. Response curve (BIO03—Isothermality, BIO10—Mean Temperature of Warmest Quarter, BIO02—Mean Diurnal Range of Temperature). The red line is the mean value for the 10 Maxent runs, and the blue bar represents ± standard deviation.
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Figure 3. Predicted distribution model for Hierodula tenuidentata using only native locations. The prediction map shows hotter red areas (values close to one) having higher Hierodula tenuidentata climate suitability. Green circles mark the known Hierodula tenuidentata occurrence points and the blue circles define native occurrence points.
Figure 3. Predicted distribution model for Hierodula tenuidentata using only native locations. The prediction map shows hotter red areas (values close to one) having higher Hierodula tenuidentata climate suitability. Green circles mark the known Hierodula tenuidentata occurrence points and the blue circles define native occurrence points.
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Figure 4. Response curve (BIO01—Annual Mean Temperature, BIO10—Mean Temperature of Warmest Quarter, BIO02—Mean Diurnal Range of Temperature). The red line is the mean value for the 10 Maxent runs, and the blue bar represents ± standard deviation.
Figure 4. Response curve (BIO01—Annual Mean Temperature, BIO10—Mean Temperature of Warmest Quarter, BIO02—Mean Diurnal Range of Temperature). The red line is the mean value for the 10 Maxent runs, and the blue bar represents ± standard deviation.
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Figure 5. Predicted distribution model for Hierodula tenuidentata and the native countries (blue lines) where it was recorded in the scientific literature. The prediction map shows hotter red areas (values close to one) having higher Hierodula tenuidentata climate suitability. Green circles mark the known Hierodula tenuidentata occurrence points and the blue circles define native occurrence points.
Figure 5. Predicted distribution model for Hierodula tenuidentata and the native countries (blue lines) where it was recorded in the scientific literature. The prediction map shows hotter red areas (values close to one) having higher Hierodula tenuidentata climate suitability. Green circles mark the known Hierodula tenuidentata occurrence points and the blue circles define native occurrence points.
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Table
Table 1. Environmental data used for predicted distribution model for the Hierodula tenuidentata at a spatial resolution of 30 arc-seconds (~1 km2 at the equator).
Table 1. Environmental data used for predicted distribution model for the Hierodula tenuidentata at a spatial resolution of 30 arc-seconds (~1 km2 at the equator).
Variable NameVariable AbbreviationVariable Recording
Minimum air temperature T_minMonthly minimum temperature (°C): 1970–2000
Maximum air temperature T_maxMonthly maximum temperature (°C): 1970–2000
Mean air temperatureT_medMonth mean temperature (°C): 1970–2000
Precipitation amount per dayPpMonth mean precipitations (mm): 1970–2000
Solar radiationSrSolar radiation (kJ m-2 day-1) for 1970–2000
Wind speedWsWind speed (m s-1) for 1970–2000
Water vapor pressure WvpWater vapor pressure (kPa): 1970–2000
Annual Mean TemperatureBIO 01Annual Mean Temperature (°C): 1970–2000
Mean Diurnal RangeBIO 02Mean Diurnal Range (Mean of monthly (max temp–min temp)): 1970–2000
IsothermalityBIO 03Isothermality (BIO2/BIO7) (×100): 1970–2000
Temperature SeasonalityBIO 04Temperature Seasonality (standard deviation ×100): 1970–2000
Max Temperature of Warmest MonthBIO 05Max Temperature of Warmest Month (°C): 1970–2000
Min Temperature of Coldest MonthBIO 06Min Temperature of Coldest Month (°C): 1970–2000
Temperature Annual RangeBIO 07Temperature Annual Range (BIO5-BIO6): 1970–2000
Mean Temperature of Wettest QuarterBIO 08Mean Temperature of Wettest Quarter (°C): 1970–2000
Mean Temperature of Driest QuarterBIO 09Mean Temperature of Driest Quarter (°C): 1970–2000
Mean Temperature of Warmest QuarterBIO 10Mean Temperature of Warmest Quarter (°C): 1970–2000
Mean Temperature of Coldest QuarterBIO 11Mean Temperature of Coldest Quarter (°C): 1970–2000
Annual PrecipitationBIO 12Annual Precipitation (mm): 1970–2000
Precipitation of Wettest MonthBIO 13Precipitation of Wettest Month (mm): 1970–2000
Precipitation of Driest MonthBIO 14Precipitation of Driest Month (mm): 1970–2000
Precipitation SeasonalityBIO 15Precipitation Seasonality (Coefficient of Variation)
Precipitation of Wettest QuarterBIO 16Precipitation of Wettest Quarter (mm): 1970–2000
Precipitation of Driest QuarterBIO 17Precipitation of Driest Quarter (mm): 1970–2000
Precipitation of Warmest QuarterBIO 18Precipitation of Warmest Quarter (mm): 1970–2000
Precipitation of Coldest QuarterBIO 19Precipitation of Coldest Quarter (mm): 1970–2000
ElevationElevElevation (m)
Table
Table 2. The evaluation of the statistics test for 10-folds cross-validation for the Hierodula tenuidentata distribution models, using all available observation (complete) and only native presence.
Table 2. The evaluation of the statistics test for 10-folds cross-validation for the Hierodula tenuidentata distribution models, using all available observation (complete) and only native presence.
Distribution ModelAUC TrainAUC TestAUC DifferenceAUC sd
Complete (native and new locations)0.9670.9600.070.007
Native presence0.9880.9650.0250.015
AUC train—Area Under the Curve for train set, AUC test—Area Under the Curve for test set, AUC difference—Area Under the Curve for the difference between AUC train and AUC test and AUC sd—Area Under the Curve standard deviation.
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Pintilioaie, A.-M.; Sfîcă, L.; Baltag, E.S. Climatic Niche of an Invasive Mantid Species in Europe: Predicted New Areas for Species Expansion. Sustainability 2023, 15, 10295. https://doi.org/10.3390/su151310295

AMA Style

Pintilioaie A-M, Sfîcă L, Baltag ES. Climatic Niche of an Invasive Mantid Species in Europe: Predicted New Areas for Species Expansion. Sustainability. 2023; 15(13):10295. https://doi.org/10.3390/su151310295

Chicago/Turabian Style

Pintilioaie, Alexandru-Mihai, Lucian Sfîcă, and Emanuel Stefan Baltag. 2023. "Climatic Niche of an Invasive Mantid Species in Europe: Predicted New Areas for Species Expansion" Sustainability 15, no. 13: 10295. https://doi.org/10.3390/su151310295

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