Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria.
<p>Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecti...
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2025
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| _version_ | 1852017778998902784 |
|---|---|
| author | Laura Boehm Vock (22008756) |
| author2 | Lauren M. Mossman (22008759) Zoi Rapti (736044) Adam G. Dolezal (12342474) Sara M. Clifton (3343496) |
| author2_role | author author author author |
| author_facet | Laura Boehm Vock (22008756) Lauren M. Mossman (22008759) Zoi Rapti (736044) Adam G. Dolezal (12342474) Sara M. Clifton (3343496) |
| author_role | author |
| dc.creator.none.fl_str_mv | Laura Boehm Vock (22008756) Lauren M. Mossman (22008759) Zoi Rapti (736044) Adam G. Dolezal (12342474) Sara M. Clifton (3343496) |
| dc.date.none.fl_str_mv | 2025-08-07T17:43:40Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0325801.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_of_models_predicting_i_Varroa_i_intensity_with_spatial_component_For_each_variable_we_fit_an_additive_model_and_interaction_with_time_the_model_preferred_by_AIC_is_presented_here_This_table_includes_the_results_of_the_additive_mo/29855418 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Ecology Science Policy Plant Biology Biological Sciences not elsewhere classified div >< p 2020 – 2021 2018 – 2019 11 billion annually examine risk factors spatiotemporal statistical model several beekeeper behaviors varroa destructor </ honey bee pollination beekeeper behaviors varroa </ honey production world economy without </ temporal trends specialty crops pollination services nearby apiaries mite burden lower numbers insecticide load environmental factors behavioral predictors beekeepers lose 300 bees |
| dc.title.none.fl_str_mv | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_586be353dd965fddfbc241d54b2d0001 |
| identifier_str_mv | 10.1371/journal.pone.0325801.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29855418 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria.Laura Boehm Vock (22008756)Lauren M. Mossman (22008759)Zoi Rapti (736044)Adam G. Dolezal (12342474)Sara M. Clifton (3343496)BiotechnologyEcologyScience PolicyPlant BiologyBiological Sciences not elsewhere classifieddiv >< p2020 – 20212018 – 201911 billion annuallyexamine risk factorsspatiotemporal statistical modelseveral beekeeper behaviorsvarroa destructor </honey bee pollinationbeekeeper behaviorsvarroa </honey productionworld economywithout </temporal trendsspecialty cropspollination servicesnearby apiariesmite burdenlower numbersinsecticide loadenvironmental factorsbehavioral predictorsbeekeepers lose300 bees<p>Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria.</p>2025-08-07T17:43:40ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0325801.t003https://figshare.com/articles/dataset/Comparison_of_models_predicting_i_Varroa_i_intensity_with_spatial_component_For_each_variable_we_fit_an_additive_model_and_interaction_with_time_the_model_preferred_by_AIC_is_presented_here_This_table_includes_the_results_of_the_additive_mo/29855418CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298554182025-08-07T17:43:40Z |
| spellingShingle | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. Laura Boehm Vock (22008756) Biotechnology Ecology Science Policy Plant Biology Biological Sciences not elsewhere classified div >< p 2020 – 2021 2018 – 2019 11 billion annually examine risk factors spatiotemporal statistical model several beekeeper behaviors varroa destructor </ honey bee pollination beekeeper behaviors varroa </ honey production world economy without </ temporal trends specialty crops pollination services nearby apiaries mite burden lower numbers insecticide load environmental factors behavioral predictors beekeepers lose 300 bees |
| status_str | publishedVersion |
| title | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| title_full | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| title_fullStr | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| title_full_unstemmed | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| title_short | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| title_sort | Comparison of models predicting <i>Varroa</i> intensity, with spatial component. For each variable, we fit an additive model and interaction with time; the model preferred by AIC is presented here. This table includes the results of the additive model for Floral quality, Insecticide, and Nesting quality. The remaining models include the indicated variables and interaction with time. Note that management practices are correlated with location, leading to possible overfitting in models that include the management variable. Statistics presentedeak are: Akaike Information Criterion (AIC), % of Deviance explained (a metric similar to but more appropriate for negative binomial regression), Relative Error (RelErr = Mean(), Mean Absolute Erroreak (MAE = Mean()). The null model uses overall mean <i>Varroa</i> intensity as the predicted value (). The best model for each statistic is bolded. There is no clear winner among the models for all criteria. |
| topic | Biotechnology Ecology Science Policy Plant Biology Biological Sciences not elsewhere classified div >< p 2020 – 2021 2018 – 2019 11 billion annually examine risk factors spatiotemporal statistical model several beekeeper behaviors varroa destructor </ honey bee pollination beekeeper behaviors varroa </ honey production world economy without </ temporal trends specialty crops pollination services nearby apiaries mite burden lower numbers insecticide load environmental factors behavioral predictors beekeepers lose 300 bees |