Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections
Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distributi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ramasamy, Maruthadurai [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Anzeiger für Schädlingskunde - Berlin : Blackwell Wissenschafts-Verl., 1925, 95(2021), 2 vom: 28. Juli, Seite 841-854 |
|---|---|
| Übergeordnetes Werk: |
volume:95 ; year:2021 ; number:2 ; day:28 ; month:07 ; pages:841-854 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10340-021-01411-1 |
|---|
| Katalog-ID: |
SPR046295887 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR046295887 | ||
| 003 | DE-627 | ||
| 005 | 20230507113354.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220222s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10340-021-01411-1 |2 doi | |
| 035 | |a (DE-627)SPR046295887 | ||
| 035 | |a (SPR)s10340-021-01411-1-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ramasamy, Maruthadurai |e verfasserin |0 (orcid)0000-0002-9680-7487 |4 aut | |
| 245 | 1 | 0 | |a Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 | ||
| 520 | |a Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. | ||
| 650 | 4 | |a Invasive pest |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Invasion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Global spread |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Forecasting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Modelling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Das, Bappa |4 aut | |
| 700 | 1 | |a Ramesh, R. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Anzeiger für Schädlingskunde |d Berlin : Blackwell Wissenschafts-Verl., 1925 |g 95(2021), 2 vom: 28. Juli, Seite 841-854 |w (DE-627)325162085 |w (DE-600)2034322-X |x 1439-0280 |7 nnns |
| 773 | 1 | 8 | |g volume:95 |g year:2021 |g number:2 |g day:28 |g month:07 |g pages:841-854 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10340-021-01411-1 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_647 | ||
| 912 | |a GBV_ILN_702 | ||
| 951 | |a AR | ||
| 952 | |d 95 |j 2021 |e 2 |b 28 |c 07 |h 841-854 | ||
| author_variant |
m r mr b d bd r r rr |
|---|---|
| matchkey_str |
article:14390280:2021----::rdciglmtcagipcsnoetawrdieitiuinfalry |
| hierarchy_sort_str |
2021 |
| publishDate |
2021 |
| allfields |
10.1007/s10340-021-01411-1 doi (DE-627)SPR046295887 (SPR)s10340-021-01411-1-e DE-627 ger DE-627 rakwb eng Ramasamy, Maruthadurai verfasserin (orcid)0000-0002-9680-7487 aut Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. Invasive pest (dpeaa)DE-He213 Invasion (dpeaa)DE-He213 Global spread (dpeaa)DE-He213 Forecasting (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Das, Bappa aut Ramesh, R. aut Enthalten in Anzeiger für Schädlingskunde Berlin : Blackwell Wissenschafts-Verl., 1925 95(2021), 2 vom: 28. Juli, Seite 841-854 (DE-627)325162085 (DE-600)2034322-X 1439-0280 nnns volume:95 year:2021 number:2 day:28 month:07 pages:841-854 https://dx.doi.org/10.1007/s10340-021-01411-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 AR 95 2021 2 28 07 841-854 |
| spelling |
10.1007/s10340-021-01411-1 doi (DE-627)SPR046295887 (SPR)s10340-021-01411-1-e DE-627 ger DE-627 rakwb eng Ramasamy, Maruthadurai verfasserin (orcid)0000-0002-9680-7487 aut Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. Invasive pest (dpeaa)DE-He213 Invasion (dpeaa)DE-He213 Global spread (dpeaa)DE-He213 Forecasting (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Das, Bappa aut Ramesh, R. aut Enthalten in Anzeiger für Schädlingskunde Berlin : Blackwell Wissenschafts-Verl., 1925 95(2021), 2 vom: 28. Juli, Seite 841-854 (DE-627)325162085 (DE-600)2034322-X 1439-0280 nnns volume:95 year:2021 number:2 day:28 month:07 pages:841-854 https://dx.doi.org/10.1007/s10340-021-01411-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 AR 95 2021 2 28 07 841-854 |
| allfields_unstemmed |
10.1007/s10340-021-01411-1 doi (DE-627)SPR046295887 (SPR)s10340-021-01411-1-e DE-627 ger DE-627 rakwb eng Ramasamy, Maruthadurai verfasserin (orcid)0000-0002-9680-7487 aut Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. Invasive pest (dpeaa)DE-He213 Invasion (dpeaa)DE-He213 Global spread (dpeaa)DE-He213 Forecasting (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Das, Bappa aut Ramesh, R. aut Enthalten in Anzeiger für Schädlingskunde Berlin : Blackwell Wissenschafts-Verl., 1925 95(2021), 2 vom: 28. Juli, Seite 841-854 (DE-627)325162085 (DE-600)2034322-X 1439-0280 nnns volume:95 year:2021 number:2 day:28 month:07 pages:841-854 https://dx.doi.org/10.1007/s10340-021-01411-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 AR 95 2021 2 28 07 841-854 |
| allfieldsGer |
10.1007/s10340-021-01411-1 doi (DE-627)SPR046295887 (SPR)s10340-021-01411-1-e DE-627 ger DE-627 rakwb eng Ramasamy, Maruthadurai verfasserin (orcid)0000-0002-9680-7487 aut Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. Invasive pest (dpeaa)DE-He213 Invasion (dpeaa)DE-He213 Global spread (dpeaa)DE-He213 Forecasting (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Das, Bappa aut Ramesh, R. aut Enthalten in Anzeiger für Schädlingskunde Berlin : Blackwell Wissenschafts-Verl., 1925 95(2021), 2 vom: 28. Juli, Seite 841-854 (DE-627)325162085 (DE-600)2034322-X 1439-0280 nnns volume:95 year:2021 number:2 day:28 month:07 pages:841-854 https://dx.doi.org/10.1007/s10340-021-01411-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 AR 95 2021 2 28 07 841-854 |
| allfieldsSound |
10.1007/s10340-021-01411-1 doi (DE-627)SPR046295887 (SPR)s10340-021-01411-1-e DE-627 ger DE-627 rakwb eng Ramasamy, Maruthadurai verfasserin (orcid)0000-0002-9680-7487 aut Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. Invasive pest (dpeaa)DE-He213 Invasion (dpeaa)DE-He213 Global spread (dpeaa)DE-He213 Forecasting (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 Das, Bappa aut Ramesh, R. aut Enthalten in Anzeiger für Schädlingskunde Berlin : Blackwell Wissenschafts-Verl., 1925 95(2021), 2 vom: 28. Juli, Seite 841-854 (DE-627)325162085 (DE-600)2034322-X 1439-0280 nnns volume:95 year:2021 number:2 day:28 month:07 pages:841-854 https://dx.doi.org/10.1007/s10340-021-01411-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 AR 95 2021 2 28 07 841-854 |
| language |
English |
| source |
Enthalten in Anzeiger für Schädlingskunde 95(2021), 2 vom: 28. Juli, Seite 841-854 volume:95 year:2021 number:2 day:28 month:07 pages:841-854 |
| sourceStr |
Enthalten in Anzeiger für Schädlingskunde 95(2021), 2 vom: 28. Juli, Seite 841-854 volume:95 year:2021 number:2 day:28 month:07 pages:841-854 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Invasive pest Invasion Global spread Forecasting Modelling |
| isfreeaccess_bool |
false |
| container_title |
Anzeiger für Schädlingskunde |
| authorswithroles_txt_mv |
Ramasamy, Maruthadurai @@aut@@ Das, Bappa @@aut@@ Ramesh, R. @@aut@@ |
| publishDateDaySort_date |
2021-07-28T00:00:00Z |
| hierarchy_top_id |
325162085 |
| id |
SPR046295887 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046295887</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507113354.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220222s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10340-021-01411-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046295887</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10340-021-01411-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ramasamy, Maruthadurai</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9680-7487</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invasive pest</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invasion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Global spread</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Forecasting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modelling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Das, Bappa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramesh, R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Anzeiger für Schädlingskunde</subfield><subfield code="d">Berlin : Blackwell Wissenschafts-Verl., 1925</subfield><subfield code="g">95(2021), 2 vom: 28. Juli, Seite 841-854</subfield><subfield code="w">(DE-627)325162085</subfield><subfield code="w">(DE-600)2034322-X</subfield><subfield code="x">1439-0280</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:95</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:2</subfield><subfield code="g">day:28</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:841-854</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10340-021-01411-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_647</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">95</subfield><subfield code="j">2021</subfield><subfield code="e">2</subfield><subfield code="b">28</subfield><subfield code="c">07</subfield><subfield code="h">841-854</subfield></datafield></record></collection>
|
| author |
Ramasamy, Maruthadurai |
| spellingShingle |
Ramasamy, Maruthadurai misc Invasive pest misc Invasion misc Global spread misc Forecasting misc Modelling Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections |
| authorStr |
Ramasamy, Maruthadurai |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)325162085 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1439-0280 |
| topic_title |
Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections Invasive pest (dpeaa)DE-He213 Invasion (dpeaa)DE-He213 Global spread (dpeaa)DE-He213 Forecasting (dpeaa)DE-He213 Modelling (dpeaa)DE-He213 |
| topic |
misc Invasive pest misc Invasion misc Global spread misc Forecasting misc Modelling |
| topic_unstemmed |
misc Invasive pest misc Invasion misc Global spread misc Forecasting misc Modelling |
| topic_browse |
misc Invasive pest misc Invasion misc Global spread misc Forecasting misc Modelling |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Anzeiger für Schädlingskunde |
| hierarchy_parent_id |
325162085 |
| hierarchy_top_title |
Anzeiger für Schädlingskunde |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)325162085 (DE-600)2034322-X |
| title |
Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections |
| ctrlnum |
(DE-627)SPR046295887 (SPR)s10340-021-01411-1-e |
| title_full |
Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections |
| author_sort |
Ramasamy, Maruthadurai |
| journal |
Anzeiger für Schädlingskunde |
| journalStr |
Anzeiger für Schädlingskunde |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| container_start_page |
841 |
| author_browse |
Ramasamy, Maruthadurai Das, Bappa Ramesh, R. |
| container_volume |
95 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ramasamy, Maruthadurai |
| doi_str_mv |
10.1007/s10340-021-01411-1 |
| normlink |
(ORCID)0000-0002-9680-7487 |
| normlink_prefix_str_mv |
(orcid)0000-0002-9680-7487 |
| title_sort |
predicting climate change impacts on potential worldwide distribution of fall armyworm based on cmip6 projections |
| title_auth |
Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections |
| abstract |
Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstractGer |
Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 |
| container_issue |
2 |
| title_short |
Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections |
| url |
https://dx.doi.org/10.1007/s10340-021-01411-1 |
| remote_bool |
true |
| author2 |
Das, Bappa Ramesh, R. |
| author2Str |
Das, Bappa Ramesh, R. |
| ppnlink |
325162085 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10340-021-01411-1 |
| up_date |
2024-07-03T21:38:46.520Z |
| _version_ |
1803595522003435520 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046295887</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507113354.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220222s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10340-021-01411-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046295887</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10340-021-01411-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ramasamy, Maruthadurai</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9680-7487</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a highly destructive insect pest of several crop plants and threatening global food security. The current Coupled Model Intercomparison Project phase 6 (CMIP6) data set was analysed to predict the potential worldwide distribution of FAW under present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 emission scenario with 19 bioclimatic variables through maximum entropy (MaxEnt) niche modelling. The MaxEnt model predicted the potential distribution of S. frugiperda with area under the receiver operator curve (AUC) values of 0.915 and 0.910 during training and testing, respectively. Annual precipitation, annual mean temperature and isothermality were the strongest predictors of S. frugiperda distribution with 42.6%, 22.4% and 10% contributions, respectively. The recent CMIP6 models predicted higher suitability of FAW in North America, Africa and Asia under future climatic conditions. Global suitability of FAW is predicted to increase by 4.49% and 8.33% under SSP1-2.6 and SSP5-8.5 scenarios, respectively, compared to that of current climate conditions. Multimodel ensemble predicted the highest risk of invasion and spread of FAW by 2050 and 2070 under SSP5-8.5 scenario. The predictions could be used to forecast the potential spread of FAW and combating outbreaks well in advance. Our results will be an important guide for researchers, policymakers and governments to devise suitable management strategies against this highly invasive pest.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invasive pest</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invasion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Global spread</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Forecasting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Modelling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Das, Bappa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ramesh, R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Anzeiger für Schädlingskunde</subfield><subfield code="d">Berlin : Blackwell Wissenschafts-Verl., 1925</subfield><subfield code="g">95(2021), 2 vom: 28. Juli, Seite 841-854</subfield><subfield code="w">(DE-627)325162085</subfield><subfield code="w">(DE-600)2034322-X</subfield><subfield code="x">1439-0280</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:95</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:2</subfield><subfield code="g">day:28</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:841-854</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10340-021-01411-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_647</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">95</subfield><subfield code="j">2021</subfield><subfield code="e">2</subfield><subfield code="b">28</subfield><subfield code="c">07</subfield><subfield code="h">841-854</subfield></datafield></record></collection>
|
| score |
7.3996468 |