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Improving invasive species management using predictive phenology models: an example from brown marmorated stink bug (Halyomorpha halys) in Japan
BACKGROUND In order to better understand the population dynamics of invasive species in their native range, we developed two predictive phenological models using the ubiquitous invasive insect pest, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), as the model organism. Our work establishes a zer...
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Published in: | Pest management science 2021-12, Vol.77 (12), p.5489-5497 |
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creator | Kamiyama, Matthew T Matsuura, Kenji Yoshimura, Tsuyoshi Yang, Chin‐Cheng Scotty |
description | BACKGROUND
In order to better understand the population dynamics of invasive species in their native range, we developed two predictive phenological models using the ubiquitous invasive insect pest, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), as the model organism. Our work establishes a zero‐inflated negative binomial regression (ZINB) model, and a general additive mixed model (GAMM) based on 11 years of black light trap monitoring of H. halys at three locations in Japan.
RESULTS
The ZINB model indicated that degree days (DD) have a significant effect on the trap catch of adult H. halys, and that precipitation has no effect. A dataset generated by 1000 simulations from the ZINB suggested that higher predicted trap catches equated to a lower probability of encountering a zero‐count. The GAMM produced a cubic regression smooth curve which forecasts the seasonal phenology of H. halys as following a bell‐shaped trend in Japan. Critical DD points during the field season in Japan included 261 DD for first H. halys adult detection and 1091 DD for peak activity.
CONCLUSIONS
This study establishes the first models capable of forecasting native H. halys population dynamics based on DD. These robust models practically improve population forecasting of H. halys in the future and help fill gaps in knowledge pertaining to its native phenology, thus ultimately contributing to the progression of efficient management of this globally invasive species. © 2021 Society of Chemical Industry.
The population dynamics of Halyomorpha halys follow a single peaked trend throughout the field season in Japan, as estimated by predictive degree day‐based phenology models. |
doi_str_mv | 10.1002/ps.6589 |
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In order to better understand the population dynamics of invasive species in their native range, we developed two predictive phenological models using the ubiquitous invasive insect pest, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), as the model organism. Our work establishes a zero‐inflated negative binomial regression (ZINB) model, and a general additive mixed model (GAMM) based on 11 years of black light trap monitoring of H. halys at three locations in Japan.
RESULTS
The ZINB model indicated that degree days (DD) have a significant effect on the trap catch of adult H. halys, and that precipitation has no effect. A dataset generated by 1000 simulations from the ZINB suggested that higher predicted trap catches equated to a lower probability of encountering a zero‐count. The GAMM produced a cubic regression smooth curve which forecasts the seasonal phenology of H. halys as following a bell‐shaped trend in Japan. Critical DD points during the field season in Japan included 261 DD for first H. halys adult detection and 1091 DD for peak activity.
CONCLUSIONS
This study establishes the first models capable of forecasting native H. halys population dynamics based on DD. These robust models practically improve population forecasting of H. halys in the future and help fill gaps in knowledge pertaining to its native phenology, thus ultimately contributing to the progression of efficient management of this globally invasive species. © 2021 Society of Chemical Industry.
The population dynamics of Halyomorpha halys follow a single peaked trend throughout the field season in Japan, as estimated by predictive degree day‐based phenology models.</description><identifier>ISSN: 1526-498X</identifier><identifier>EISSN: 1526-4998</identifier><identifier>DOI: 10.1002/ps.6589</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Black light ; Forecasting ; Halyomorpha halys ; Indigenous species ; Insects ; integrated pest management ; Introduced species ; Invasive insects ; Invasive species ; Nonnative species ; pentatomidae ; pest monitoring ; Phenology ; Population dynamics ; Population forecasting ; Regression models ; Statistical analysis ; zero‐inflation</subject><ispartof>Pest management science, 2021-12, Vol.77 (12), p.5489-5497</ispartof><rights>2021 Society of Chemical Industry.</rights><rights>Copyright © 2021 Society of Chemical Industry</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3229-3aff09b69deaec7f67b9030f8f54d81823713cafd016dd0c6c4a594ce9f20f6d3</citedby><cites>FETCH-LOGICAL-c3229-3aff09b69deaec7f67b9030f8f54d81823713cafd016dd0c6c4a594ce9f20f6d3</cites><orcidid>0000-0003-0967-5170</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Kamiyama, Matthew T</creatorcontrib><creatorcontrib>Matsuura, Kenji</creatorcontrib><creatorcontrib>Yoshimura, Tsuyoshi</creatorcontrib><creatorcontrib>Yang, Chin‐Cheng Scotty</creatorcontrib><title>Improving invasive species management using predictive phenology models: an example from brown marmorated stink bug (Halyomorpha halys) in Japan</title><title>Pest management science</title><description>BACKGROUND
In order to better understand the population dynamics of invasive species in their native range, we developed two predictive phenological models using the ubiquitous invasive insect pest, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), as the model organism. Our work establishes a zero‐inflated negative binomial regression (ZINB) model, and a general additive mixed model (GAMM) based on 11 years of black light trap monitoring of H. halys at three locations in Japan.
RESULTS
The ZINB model indicated that degree days (DD) have a significant effect on the trap catch of adult H. halys, and that precipitation has no effect. A dataset generated by 1000 simulations from the ZINB suggested that higher predicted trap catches equated to a lower probability of encountering a zero‐count. The GAMM produced a cubic regression smooth curve which forecasts the seasonal phenology of H. halys as following a bell‐shaped trend in Japan. Critical DD points during the field season in Japan included 261 DD for first H. halys adult detection and 1091 DD for peak activity.
CONCLUSIONS
This study establishes the first models capable of forecasting native H. halys population dynamics based on DD. These robust models practically improve population forecasting of H. halys in the future and help fill gaps in knowledge pertaining to its native phenology, thus ultimately contributing to the progression of efficient management of this globally invasive species. © 2021 Society of Chemical Industry.
The population dynamics of Halyomorpha halys follow a single peaked trend throughout the field season in Japan, as estimated by predictive degree day‐based phenology models.</description><subject>Black light</subject><subject>Forecasting</subject><subject>Halyomorpha halys</subject><subject>Indigenous species</subject><subject>Insects</subject><subject>integrated pest management</subject><subject>Introduced species</subject><subject>Invasive insects</subject><subject>Invasive species</subject><subject>Nonnative species</subject><subject>pentatomidae</subject><subject>pest monitoring</subject><subject>Phenology</subject><subject>Population dynamics</subject><subject>Population forecasting</subject><subject>Regression models</subject><subject>Statistical analysis</subject><subject>zero‐inflation</subject><issn>1526-498X</issn><issn>1526-4998</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp10UtLAzEQAOBFFHziXwh4UJFqNtvdbrxJ8VERFFTwtqTJpI3uJjGz29p_4U82teJB8DTD8DEPJkn2U3qaUsrOPJ4WecnXkq00Z0Wvz3m5_puXL5vJNuIrpZRzzraSz1Hjg5sZOyHGzgSaGRD0IA0gaYQVE2jAtqTDpfABlJHt0vgpWFe7yYI0TkGN50RYAh-i8TUQHVxDxsHNbewRGhdEC4pga-wbGXcTcnQj6oWLdT8VZBpzPI7Tya3wwu4mG1rUCHs_cSd5vrp8Gt707u6vR8OLu57MGOO9TGhN-bjgCgTIgS4GY04zqkud91WZliwbpJkUWtG0UIrKQvZFzvsSuGZUFyrbSY5WfeP57x1gWzUGJdS1sOA6rFgeeZbTAYv04A99dV2wcbuoeJ4xXvIiqsOVksEhBtCVDyaev6hSWi0_U3mslp-J8mQl56aGxX-senj81l8eFJGn</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Kamiyama, Matthew T</creator><creator>Matsuura, Kenji</creator><creator>Yoshimura, Tsuyoshi</creator><creator>Yang, Chin‐Cheng Scotty</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>7SS</scope><scope>7ST</scope><scope>7T7</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0967-5170</orcidid></search><sort><creationdate>202112</creationdate><title>Improving invasive species management using predictive phenology models: an example from brown marmorated stink bug (Halyomorpha halys) in Japan</title><author>Kamiyama, Matthew T ; Matsuura, Kenji ; Yoshimura, Tsuyoshi ; Yang, Chin‐Cheng Scotty</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3229-3aff09b69deaec7f67b9030f8f54d81823713cafd016dd0c6c4a594ce9f20f6d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Black light</topic><topic>Forecasting</topic><topic>Halyomorpha halys</topic><topic>Indigenous species</topic><topic>Insects</topic><topic>integrated pest management</topic><topic>Introduced species</topic><topic>Invasive insects</topic><topic>Invasive species</topic><topic>Nonnative species</topic><topic>pentatomidae</topic><topic>pest monitoring</topic><topic>Phenology</topic><topic>Population dynamics</topic><topic>Population forecasting</topic><topic>Regression models</topic><topic>Statistical analysis</topic><topic>zero‐inflation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kamiyama, Matthew T</creatorcontrib><creatorcontrib>Matsuura, Kenji</creatorcontrib><creatorcontrib>Yoshimura, Tsuyoshi</creatorcontrib><creatorcontrib>Yang, Chin‐Cheng Scotty</creatorcontrib><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Pest management science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kamiyama, Matthew T</au><au>Matsuura, Kenji</au><au>Yoshimura, Tsuyoshi</au><au>Yang, Chin‐Cheng Scotty</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving invasive species management using predictive phenology models: an example from brown marmorated stink bug (Halyomorpha halys) in Japan</atitle><jtitle>Pest management science</jtitle><date>2021-12</date><risdate>2021</risdate><volume>77</volume><issue>12</issue><spage>5489</spage><epage>5497</epage><pages>5489-5497</pages><issn>1526-498X</issn><eissn>1526-4998</eissn><abstract>BACKGROUND
In order to better understand the population dynamics of invasive species in their native range, we developed two predictive phenological models using the ubiquitous invasive insect pest, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), as the model organism. Our work establishes a zero‐inflated negative binomial regression (ZINB) model, and a general additive mixed model (GAMM) based on 11 years of black light trap monitoring of H. halys at three locations in Japan.
RESULTS
The ZINB model indicated that degree days (DD) have a significant effect on the trap catch of adult H. halys, and that precipitation has no effect. A dataset generated by 1000 simulations from the ZINB suggested that higher predicted trap catches equated to a lower probability of encountering a zero‐count. The GAMM produced a cubic regression smooth curve which forecasts the seasonal phenology of H. halys as following a bell‐shaped trend in Japan. Critical DD points during the field season in Japan included 261 DD for first H. halys adult detection and 1091 DD for peak activity.
CONCLUSIONS
This study establishes the first models capable of forecasting native H. halys population dynamics based on DD. These robust models practically improve population forecasting of H. halys in the future and help fill gaps in knowledge pertaining to its native phenology, thus ultimately contributing to the progression of efficient management of this globally invasive species. © 2021 Society of Chemical Industry.
The population dynamics of Halyomorpha halys follow a single peaked trend throughout the field season in Japan, as estimated by predictive degree day‐based phenology models.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/ps.6589</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-0967-5170</orcidid></addata></record> |
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subjects | Black light Forecasting Halyomorpha halys Indigenous species Insects integrated pest management Introduced species Invasive insects Invasive species Nonnative species pentatomidae pest monitoring Phenology Population dynamics Population forecasting Regression models Statistical analysis zero‐inflation |
title | Improving invasive species management using predictive phenology models: an example from brown marmorated stink bug (Halyomorpha halys) in Japan |
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