A comparison of joint species distribution models for percent cover data
Abstract Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular ha...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14437 |
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| author | Pekka Korhonen Francis K. C. Hui Jenni Niku Sara Taskinen Bert van derVeen |
| author_facet | Pekka Korhonen Francis K. C. Hui Jenni Niku Sara Taskinen Bert van derVeen |
| author_sort | Pekka Korhonen |
| collection | DOAJ |
| description | Abstract Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular has proven popular for building JSDMs, being able to handle many response types including presence‐absence data, biomass, overdispersed and/or zero‐inflated counts. We extend latent variable models to handle percent cover response variables, with vegetation, sessile invertebrate and macroalgal cover data representing the prime examples of such data arising in community ecology. Sparsity is a commonly encountered challenge with percent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, that is, have 0% or 100% cover, respectively, rendering the use of beta distribution inadequate. We propose two JSDMs suitable for percent cover data, namely a hurdle beta model and an ordered beta model. We compare the two proposed approaches to a beta distribution for shifted responses, transformed presence‐absence data and an ordinal model for percent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological percent cover data. |
| format | Article |
| id | doaj-art-b0bd0a9a30a3437f8219ad4c1e893bee |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-b0bd0a9a30a3437f8219ad4c1e893bee2024-12-04T05:28:48ZengWileyMethods in Ecology and Evolution2041-210X2024-12-0115122359237210.1111/2041-210X.14437A comparison of joint species distribution models for percent cover dataPekka Korhonen0Francis K. C. Hui1Jenni Niku2Sara Taskinen3Bert van derVeen4Department of Mathematics and Statistics University of Jyväskylä Jyväskylä FinlandResearch School of Finance, Actuarial Studies and Statistics The Australian National University Canberra Australian Capital Territory AustraliaFaculty of Sport and Health Sciences University of Jyväskylä Jyväskylä FinlandDepartment of Mathematics and Statistics University of Jyväskylä Jyväskylä FinlandDepartment of Mathematical Sciences Norwegian University of Science and Technology Trondheim NorwayAbstract Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at both the species‐ and the community‐level. The family of generalised linear latent variable models in particular has proven popular for building JSDMs, being able to handle many response types including presence‐absence data, biomass, overdispersed and/or zero‐inflated counts. We extend latent variable models to handle percent cover response variables, with vegetation, sessile invertebrate and macroalgal cover data representing the prime examples of such data arising in community ecology. Sparsity is a commonly encountered challenge with percent cover data. Responses are typically recorded as percentages covered per plot, though some species may be completely absent or present, that is, have 0% or 100% cover, respectively, rendering the use of beta distribution inadequate. We propose two JSDMs suitable for percent cover data, namely a hurdle beta model and an ordered beta model. We compare the two proposed approaches to a beta distribution for shifted responses, transformed presence‐absence data and an ordinal model for percent cover classes. Results demonstrate the hurdle beta JSDM was generally the most accurate at retrieving the latent variables and predicting ecological percent cover data.https://doi.org/10.1111/2041-210X.14437beta regressioncommunity‐level modellinglatent variable modelordinationpercent cover datazero‐inflation |
| spellingShingle | Pekka Korhonen Francis K. C. Hui Jenni Niku Sara Taskinen Bert van derVeen A comparison of joint species distribution models for percent cover data Methods in Ecology and Evolution beta regression community‐level modelling latent variable model ordination percent cover data zero‐inflation |
| title | A comparison of joint species distribution models for percent cover data |
| title_full | A comparison of joint species distribution models for percent cover data |
| title_fullStr | A comparison of joint species distribution models for percent cover data |
| title_full_unstemmed | A comparison of joint species distribution models for percent cover data |
| title_short | A comparison of joint species distribution models for percent cover data |
| title_sort | comparison of joint species distribution models for percent cover data |
| topic | beta regression community‐level modelling latent variable model ordination percent cover data zero‐inflation |
| url | https://doi.org/10.1111/2041-210X.14437 |
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