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Bioclimatic Modelling Identifies Suitable Habitat for the Establishment of the Invasive European Paper Wasp (Hymenoptera: Vespidae) across the Southern Hemisphere

Howse, Matthew W. F., Haywood, John, Lester, Philip J.·2020·Insects
DOI 10.3390/insects11110784PMC PMC7697767
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Abstract

Species distribution models (SDMs) are tools used by ecologists to help predict the spread of invasive species. Information provided by these models can help direct conservation and biosecurity efforts by highlighting areas likely to contain species of interest. In this study, two models were created to investigate the potential range expansion of Polistes dominula Christ (Hymenoptera: Vespidae) in the southern hemisphere. This palearctic species has spread to invade North and South America, South Africa, Australia, and more recently New Zealand. Using the BIOCLIM and MAXENT modelling methods, regions that were suitable for P. dominula were identified based on climate data across four regions in the southern hemisphere. In South America areas of central Chile, eastern Argentina, parts of Uruguay, and southern Brazil were identified as climatically suitable for the establishment of P. dominula. Similarly, southern parts of South Africa and Australia were identified by the model to be suitable as well as much of the North Island and east of the South Island of New Zealand. Based on outputs from both models, significant range expansion by P. dominula is possible across its more southern invaded ranges.

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Figure 1 Global distribution of P. dominula constructed from data retrieved from Global Biodiversity Information Facility database (GBIF) [18]. Red points indicate occurrences in an invaded range. Blue points indicate occurrences in the assumed native range [5,6]. In total 9246 occurrences were used in this study. Of the total, 3028 occurrences were from invaded ranges and 6218 were from the assumed native range.
Figure 1 Global distribution of P. dominula constructed from data retrieved from Global Biodiversity Information Facility database (GBIF) [18]. Red points indicate occurrences in an invaded range. Blue points indicate occurrences in the assumed native range [5,6]. In total 9246 occurrences were used in this study. Of the total, 3028 occurrences were from invaded ranges and 6218 were from the assumed native range.CC BY · PubMed Central
Figure 2 Raw outputs of both prediction models. The left images show the raw output given by BIOCLIM. The raw output of the BIOCLIM prediction is a value between 0 and 1 where the higher the number the more suitable the environment is. The BIOCLIM algorithm compares the environmental values of a cell to the median values of the environmental values of cells containing known occurrences of the target species. Percentile scores closest to 0.5 are most suitable so values over this are subtracted from 1. The resulting score is multiplied by 2 to get a final value between 0 and 1 [30]. The right images show the raw output given by MAXENT where the value is an approximate probability that the species will be present given the local environmental conditions [31,46]. The black crosses indicate known occurrences of P. dominula.
Figure 2 Raw outputs of both prediction models. The left images show the raw output given by BIOCLIM. The raw output of the BIOCLIM prediction is a value between 0 and 1 where the higher the number the more suitable the environment is. The BIOCLIM algorithm compares the environmental values of a cell to the median values of the environmental values of cells containing known occurrences of the target species. Percentile scores closest to 0.5 are most suitable so values over this are subtracted from 1. The resulting score is multiplied by 2 to get a final value between 0 and 1 [30]. The right images show the raw output given by MAXENT where the value is an approximate probability that the species will be present given the local environmental conditions [31,46]. The black crosses indicate known occurrences of P. dominula.CC BY · PubMed Central
Figure 3 Predicted presence/absence plots for each region based on the two model outputs. For both models, thresholds (0.005 and 0.38, respectively) are calculated as the raw output value at which the sum of the true positive and true negative rates is maximized. Locations where the raw output values are over these thresholds are denoted a present status (1) and highlighted in green. Locations where raw output values are lower than the threshold are denoted an absent status (0) and remain grey. Black crosses indicate known occurrences of P. dominula.
Figure 3 Predicted presence/absence plots for each region based on the two model outputs. For both models, thresholds (0.005 and 0.38, respectively) are calculated as the raw output value at which the sum of the true positive and true negative rates is maximized. Locations where the raw output values are over these thresholds are denoted a present status (1) and highlighted in green. Locations where raw output values are lower than the threshold are denoted an absent status (0) and remain grey. Black crosses indicate known occurrences of P. dominula.CC BY · PubMed Central
Figure 4 Area under the receiver operating characteristic curve (AUC) plots for BIOCLIM prediction (a) and MAXENT prediction (b). This value is a number between zero and one and represents how accurately a model predicts presence/absence. A model with an AUC value of 1 predicts presence/absence with 100% accuracy while a model with an AUC score of 0.5 is one that predicts presence or absence correctly 50% of the time [30].
Figure 4 Area under the receiver operating characteristic curve (AUC) plots for BIOCLIM prediction (a) and MAXENT prediction (b). This value is a number between zero and one and represents how accurately a model predicts presence/absence. A model with an AUC value of 1 predicts presence/absence with 100% accuracy while a model with an AUC score of 0.5 is one that predicts presence or absence correctly 50% of the time [30].CC BY · PubMed Central
Figure 5 Histograms showing the distribution of bioclimatic values at known occurrences of P. dominula. Left is the distribution of annual average temperature (Bio1) and on the right the distribution of precipitation of the coldest quarter (Bio19). These two variables were chosen from the full list of 15, based on their high permutation importance in the MAXENT modelling approach.
Figure 5 Histograms showing the distribution of bioclimatic values at known occurrences of P. dominula. Left is the distribution of annual average temperature (Bio1) and on the right the distribution of precipitation of the coldest quarter (Bio19). These two variables were chosen from the full list of 15, based on their high permutation importance in the MAXENT modelling approach.CC BY · PubMed Central
Figure 6 Plot of the average annual temperature (Bio1) bioclimatic layer at each region highlighted in this study. This variable had the highest permutation importance of 37.9%. Black crosses indicate known occurrences of P. dominula.
Figure 6 Plot of the average annual temperature (Bio1) bioclimatic layer at each region highlighted in this study. This variable had the highest permutation importance of 37.9%. Black crosses indicate known occurrences of P. dominula.CC BY · PubMed Central
Figure 7 Plot of the precipitation of the coldest quarter (Bio19) bioclimatic layer at each region highlighted in this study. This variable had the second highest permutation importance of 15.9%. Black crosses indicate known occurrences of P. dominula.
Figure 7 Plot of the precipitation of the coldest quarter (Bio19) bioclimatic layer at each region highlighted in this study. This variable had the second highest permutation importance of 15.9%. Black crosses indicate known occurrences of P. dominula.CC BY · PubMed Central

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Metadata

Authors
Howse, M. W. F., Haywood, J., & Lester, P. J.
Affiliations
  • School of Biological Sciences, Victoria University of Wellington, Wellington 6140, New Zealand
  • School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
ORCID
Venue
Insects
Publisher
MDPI AG
Published
2020-11-11
Volume
11
Issue
11
Pages
784
ISSN
2075-4450
DOI
10.3390/insects11110784
PMC
PMC7697767
Cited by
25
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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CoChat. (2026). Citation check: Bioclimatic Modelling Identifies Suitable Habitat for the Establishment of the Invasive European Paper Wasp (Hymenoptera: Vespidae) across the Southern Hemisphere. https://app.cochat.ai/papers/10.3390/insects11110784
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Howse, M. W. F., Haywood, J., & Lester, P. J. (2020). Bioclimatic Modelling Identifies Suitable Habitat for the Establishment of the Invasive European Paper Wasp (Hymenoptera: Vespidae) across the Southern Hemisphere. Insects. https://doi.org/10.3390/insects11110784

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