A comparison of presence-only analytical techniques and their application in forest pest modeling
Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Munro, Holly L. [verfasserIn] Montes, Cristián R. [verfasserIn] Gandhi, Kamal J.K. [verfasserIn] Poisson, Miguel A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Ecological informatics - Amsterdam [u.a.] : Elsevier, 2006, 68 |
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| Übergeordnetes Werk: |
volume:68 |
| DOI / URN: |
10.1016/j.ecoinf.2021.101525 |
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| Katalog-ID: |
ELV007535902 |
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| 245 | 1 | 0 | |a A comparison of presence-only analytical techniques and their application in forest pest modeling |
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| 520 | |a Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. | ||
| 650 | 4 | |a Generalized additive model | |
| 650 | 4 | |a Gradient boosting | |
| 650 | 4 | |a Insects | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a MaxEnt | |
| 650 | 4 | |a Nonnative species | |
| 650 | 4 | |a Random forest | |
| 700 | 1 | |a Montes, Cristián R. |e verfasserin |4 aut | |
| 700 | 1 | |a Gandhi, Kamal J.K. |e verfasserin |4 aut | |
| 700 | 1 | |a Poisson, Miguel A. |e verfasserin |4 aut | |
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10.1016/j.ecoinf.2021.101525 doi (DE-627)ELV007535902 (ELSEVIER)S1574-9541(21)00316-2 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Munro, Holly L. verfasserin aut A comparison of presence-only analytical techniques and their application in forest pest modeling 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. Generalized additive model Gradient boosting Insects Machine learning MaxEnt Nonnative species Random forest Montes, Cristián R. verfasserin aut Gandhi, Kamal J.K. verfasserin aut Poisson, Miguel A. verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 68 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 68 |
| spelling |
10.1016/j.ecoinf.2021.101525 doi (DE-627)ELV007535902 (ELSEVIER)S1574-9541(21)00316-2 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Munro, Holly L. verfasserin aut A comparison of presence-only analytical techniques and their application in forest pest modeling 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. Generalized additive model Gradient boosting Insects Machine learning MaxEnt Nonnative species Random forest Montes, Cristián R. verfasserin aut Gandhi, Kamal J.K. verfasserin aut Poisson, Miguel A. verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 68 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 68 |
| allfields_unstemmed |
10.1016/j.ecoinf.2021.101525 doi (DE-627)ELV007535902 (ELSEVIER)S1574-9541(21)00316-2 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Munro, Holly L. verfasserin aut A comparison of presence-only analytical techniques and their application in forest pest modeling 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. Generalized additive model Gradient boosting Insects Machine learning MaxEnt Nonnative species Random forest Montes, Cristián R. verfasserin aut Gandhi, Kamal J.K. verfasserin aut Poisson, Miguel A. verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 68 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 68 |
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10.1016/j.ecoinf.2021.101525 doi (DE-627)ELV007535902 (ELSEVIER)S1574-9541(21)00316-2 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Munro, Holly L. verfasserin aut A comparison of presence-only analytical techniques and their application in forest pest modeling 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. Generalized additive model Gradient boosting Insects Machine learning MaxEnt Nonnative species Random forest Montes, Cristián R. verfasserin aut Gandhi, Kamal J.K. verfasserin aut Poisson, Miguel A. verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 68 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 68 |
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10.1016/j.ecoinf.2021.101525 doi (DE-627)ELV007535902 (ELSEVIER)S1574-9541(21)00316-2 DE-627 ger DE-627 rda eng 610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl Munro, Holly L. verfasserin aut A comparison of presence-only analytical techniques and their application in forest pest modeling 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. Generalized additive model Gradient boosting Insects Machine learning MaxEnt Nonnative species Random forest Montes, Cristián R. verfasserin aut Gandhi, Kamal J.K. verfasserin aut Poisson, Miguel A. verfasserin aut Enthalten in Ecological informatics Amsterdam [u.a.] : Elsevier, 2006 68 Online-Ressource (DE-627)506285960 (DE-600)2218079-5 (DE-576)25927349X 1878-0512 nnns volume:68 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 Ökologie: Allgemeines 42.11 Biomathematik Biokybernetik AR 68 |
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Munro, Holly L. @@aut@@ Montes, Cristián R. @@aut@@ Gandhi, Kamal J.K. @@aut@@ Poisson, Miguel A. @@aut@@ |
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Munro, Holly L. |
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Munro, Holly L. ddc 610 fid BIODIV bkl 42.90 bkl 42.11 misc Generalized additive model misc Gradient boosting misc Insects misc Machine learning misc MaxEnt misc Nonnative species misc Random forest A comparison of presence-only analytical techniques and their application in forest pest modeling |
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610 333.7 DE-600 BIODIV DE-30 fid 42.90 bkl 42.11 bkl A comparison of presence-only analytical techniques and their application in forest pest modeling Generalized additive model Gradient boosting Insects Machine learning MaxEnt Nonnative species Random forest |
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Munro, Holly L. Montes, Cristián R. Gandhi, Kamal J.K. Poisson, Miguel A. |
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a comparison of presence-only analytical techniques and their application in forest pest modeling |
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A comparison of presence-only analytical techniques and their application in forest pest modeling |
| abstract |
Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. |
| abstractGer |
Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. |
| abstract_unstemmed |
Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV007535902</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524150925.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230507s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ecoinf.2021.101525</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV007535902</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1574-9541(21)00316-2</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="a">333.7</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.11</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Munro, Holly L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A comparison of presence-only analytical techniques and their application in forest pest modeling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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="520" ind1=" " ind2=" "><subfield code="a">Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized additive model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gradient boosting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Insects</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MaxEnt</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonnative species</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random forest</subfield></datafield><datafield tag="700" ind1="1" ind2=" 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