Statistical modeling of patterns in annual reproductive rates
Reproduction by individuals is typically recorded as count data (e.g., number of fledglings from a nest or inflorescences on a plant) and commonly modeled using Poisson or negative binomial distributions, which assume that variance is greater than or equal to the mean. However, distributions of repr...
| Autor principal: | Brooks, Mollie E. |
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| Outros Autores: | Kristensen, Kasper, Darrigo, Maria Rosa, Rubim, Paulo, Uríarte, Ma?ia, Bruna, Emilio M., Bolker, Benjamin M. |
| Grau: | Artigo |
| Idioma: | English |
| Publicado em: |
Ecology
2020
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| Acesso em linha: |
https://repositorio.inpa.gov.br/handle/1/16653 |
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oai:repositorio:1-16653 |
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dspace |
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oai:repositorio:1-16653 Statistical modeling of patterns in annual reproductive rates Brooks, Mollie E. Kristensen, Kasper Darrigo, Maria Rosa Rubim, Paulo Uríarte, Ma?ia Bruna, Emilio M. Bolker, Benjamin M. Ecological Modeling Fecundity Herb Poisson Ratio Population Density Rainfall Reproductive Effort Songbird Heliconia Acuminata Animals Longitudinal Study Poisson Distribution Reproduction Statistical Model Animal Linear Models Longitudinal Studies Models, Statistical Poisson Distribution Reproduction Reproduction by individuals is typically recorded as count data (e.g., number of fledglings from a nest or inflorescences on a plant) and commonly modeled using Poisson or negative binomial distributions, which assume that variance is greater than or equal to the mean. However, distributions of reproductive effort are often underdispersed (i.e., variance < mean). When used in hypothesis tests, models that ignore underdispersion will be overly conservative and may fail to detect significant patterns. Here we show that generalized Poisson (GP) and Conway-Maxwell-Poisson (CMP) distributions are better choices for modeling reproductive effort because they can handle both overdispersion and underdispersion; we provide examples of how ecologists can use GP and CMP distributions in generalized linear models (GLMs) and generalized linear mixed models (GLMMs) to quantify patterns in reproduction. Using a new R package, glmmTMB, we construct GLMMs to investigate how rainfall and population density influence the number of fledglings in the warbler Oreothlypis celata and how flowering rate of Heliconia acuminata differs between fragmented and continuous forest. We also demonstrate how to deal with zero-inflation, which occurs when there are more zeros than expected in the distribution, e.g., due to complete reproductive failure by some individuals. © 2019 by the Ecological Society of America 2020-06-15T21:35:36Z 2020-06-15T21:35:36Z 2019 Artigo https://repositorio.inpa.gov.br/handle/1/16653 10.1002/ecy.2706 en Volume 100, Número 7 Restrito Ecology |
| institution |
Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional |
| collection |
INPA-RI |
| language |
English |
| topic |
Ecological Modeling Fecundity Herb Poisson Ratio Population Density Rainfall Reproductive Effort Songbird Heliconia Acuminata Animals Longitudinal Study Poisson Distribution Reproduction Statistical Model Animal Linear Models Longitudinal Studies Models, Statistical Poisson Distribution Reproduction |
| spellingShingle |
Ecological Modeling Fecundity Herb Poisson Ratio Population Density Rainfall Reproductive Effort Songbird Heliconia Acuminata Animals Longitudinal Study Poisson Distribution Reproduction Statistical Model Animal Linear Models Longitudinal Studies Models, Statistical Poisson Distribution Reproduction Brooks, Mollie E. Statistical modeling of patterns in annual reproductive rates |
| topic_facet |
Ecological Modeling Fecundity Herb Poisson Ratio Population Density Rainfall Reproductive Effort Songbird Heliconia Acuminata Animals Longitudinal Study Poisson Distribution Reproduction Statistical Model Animal Linear Models Longitudinal Studies Models, Statistical Poisson Distribution Reproduction |
| description |
Reproduction by individuals is typically recorded as count data (e.g., number of fledglings from a nest or inflorescences on a plant) and commonly modeled using Poisson or negative binomial distributions, which assume that variance is greater than or equal to the mean. However, distributions of reproductive effort are often underdispersed (i.e., variance < mean). When used in hypothesis tests, models that ignore underdispersion will be overly conservative and may fail to detect significant patterns. Here we show that generalized Poisson (GP) and Conway-Maxwell-Poisson (CMP) distributions are better choices for modeling reproductive effort because they can handle both overdispersion and underdispersion; we provide examples of how ecologists can use GP and CMP distributions in generalized linear models (GLMs) and generalized linear mixed models (GLMMs) to quantify patterns in reproduction. Using a new R package, glmmTMB, we construct GLMMs to investigate how rainfall and population density influence the number of fledglings in the warbler Oreothlypis celata and how flowering rate of Heliconia acuminata differs between fragmented and continuous forest. We also demonstrate how to deal with zero-inflation, which occurs when there are more zeros than expected in the distribution, e.g., due to complete reproductive failure by some individuals. © 2019 by the Ecological Society of America |
| format |
Artigo |
| author |
Brooks, Mollie E. |
| author2 |
Kristensen, Kasper Darrigo, Maria Rosa Rubim, Paulo Uríarte, Ma?ia Bruna, Emilio M. Bolker, Benjamin M. |
| author2Str |
Kristensen, Kasper Darrigo, Maria Rosa Rubim, Paulo Uríarte, Ma?ia Bruna, Emilio M. Bolker, Benjamin M. |
| title |
Statistical modeling of patterns in annual reproductive rates |
| title_short |
Statistical modeling of patterns in annual reproductive rates |
| title_full |
Statistical modeling of patterns in annual reproductive rates |
| title_fullStr |
Statistical modeling of patterns in annual reproductive rates |
| title_full_unstemmed |
Statistical modeling of patterns in annual reproductive rates |
| title_sort |
statistical modeling of patterns in annual reproductive rates |
| publisher |
Ecology |
| publishDate |
2020 |
| url |
https://repositorio.inpa.gov.br/handle/1/16653 |
| _version_ |
1787141624930762752 |
| score |
11.653393 |