Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Efficient Bayesian analysis of spatial occupancy models
Species conservation initiatives play an important role in ecological studies. Occupancy models have been a useful tool for ecologists to make inference about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environme...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
University of Cape Town
2020
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867614269663608832 |
|---|---|
| access_status_str | Open Access |
| author | Bleki, Zolisa |
| author2 | Clark, Allan |
| author_browse | Bleki, Zolisa Clark, Allan |
| author_facet | Clark, Allan Bleki, Zolisa |
| author_sort | Bleki, Zolisa |
| collection | Thesis |
| description | Species conservation initiatives play an important role in ecological studies. Occupancy models have been a useful tool for ecologists to make inference about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environmental variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the single-season spatial occupancy model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial random effect in our sampler. We also develop OccuSpytial, a statistical package implementing our Gibbs sampler in the Python programming language. The aim of this study is to highlight the computational efficiency that can be obtained by employing several techniques, which include exploiting the sparsity of the precision matrix of the ICAR model and also making use of Polya-Gamma latent variables to obtain closed form expressions for the posterior conditional distributions of the parameters of interest. An algorithm for efficiently sampling from the posterior conditional distribution of the spatial random effects parameter is also developed and presented. To illustrate the sampler's performance a number of simulation experiments are considered, and the results are compared to those obtained by using a Gibbs sampler incorporating Restricted Spatial Regression (RSR) to specify the spatial random effect. Furthermore, we fit our model to the Helmeted guineafowl (Numida meleagris) dataset obtained from the 2nd South African Bird Atlas Project database in order to obtain a distribution map of the species. We compare our results with those obtained from the RSR variant of our sampler, those obtained by using the stocc statistical package (written using the R programming language), and those obtained from not specifying any spatial information about the sites in the data. It was found that using RSR to specify spatial random effects is both statistically and computationally more efficient that specifying them using ICAR. The OccuSpytial implementations of both ICAR and RSR Gibbs samplers has significantly less runtime compared to other implementations it was compared to. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32469 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:49:21.932Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | University of Cape Town |
| publisherStr | University of Cape Town |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32469 Efficient Bayesian analysis of spatial occupancy models Bleki, Zolisa Clark, Allan detection probability Markov Chain Monte Carlo Occupancy Modelling Spatial Modelling Species Occurrence Species conservation initiatives play an important role in ecological studies. Occupancy models have been a useful tool for ecologists to make inference about species distribution and occurrence. Bayesian methodology is a popular framework used to model the relationship between species and environmental variables. In this dissertation we develop a Gibbs sampling method using a logit link function in order to model posterior parameters of the single-season spatial occupancy model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial random effect in our sampler. We also develop OccuSpytial, a statistical package implementing our Gibbs sampler in the Python programming language. The aim of this study is to highlight the computational efficiency that can be obtained by employing several techniques, which include exploiting the sparsity of the precision matrix of the ICAR model and also making use of Polya-Gamma latent variables to obtain closed form expressions for the posterior conditional distributions of the parameters of interest. An algorithm for efficiently sampling from the posterior conditional distribution of the spatial random effects parameter is also developed and presented. To illustrate the sampler's performance a number of simulation experiments are considered, and the results are compared to those obtained by using a Gibbs sampler incorporating Restricted Spatial Regression (RSR) to specify the spatial random effect. Furthermore, we fit our model to the Helmeted guineafowl (Numida meleagris) dataset obtained from the 2nd South African Bird Atlas Project database in order to obtain a distribution map of the species. We compare our results with those obtained from the RSR variant of our sampler, those obtained by using the stocc statistical package (written using the R programming language), and those obtained from not specifying any spatial information about the sites in the data. It was found that using RSR to specify spatial random effects is both statistically and computationally more efficient that specifying them using ICAR. The OccuSpytial implementations of both ICAR and RSR Gibbs samplers has significantly less runtime compared to other implementations it was compared to. 2020-12-30T10:18:00Z 2020-12-30T10:18:00Z 2020 Master Thesis Masters MSc http://hdl.handle.net/11427/32469 eng application/pdf University of Cape Town Department of Statistical Sciences Faculty of Science |
| spellingShingle | detection probability Markov Chain Monte Carlo Occupancy Modelling Spatial Modelling Species Occurrence Bleki, Zolisa Efficient Bayesian analysis of spatial occupancy models |
| thesis_degree_str | Master's |
| title | Efficient Bayesian analysis of spatial occupancy models |
| title_full | Efficient Bayesian analysis of spatial occupancy models |
| title_fullStr | Efficient Bayesian analysis of spatial occupancy models |
| title_full_unstemmed | Efficient Bayesian analysis of spatial occupancy models |
| title_short | Efficient Bayesian analysis of spatial occupancy models |
| title_sort | efficient bayesian analysis of spatial occupancy models |
| topic | detection probability Markov Chain Monte Carlo Occupancy Modelling Spatial Modelling Species Occurrence |
| url | http://hdl.handle.net/11427/32469 |
| work_keys_str_mv | AT blekizolisa efficientbayesiananalysisofspatialoccupancymodels |