Point process models for presence-only analysis
1. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper, we review point process models, some of their advantages and some com...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Wiley-Blackwell Publishing Ltd.
2015
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/35654 |
| _version_ | 1848754553920421888 |
|---|---|
| author | Renner, I. Elith, J. Baddeley, Adrian Fithian, W. Hastie, T. Phillips, S. Popovic, G. Warton, D. |
| author_facet | Renner, I. Elith, J. Baddeley, Adrian Fithian, W. Hastie, T. Phillips, S. Popovic, G. Warton, D. |
| author_sort | Renner, I. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 1. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. 3. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudo-absences or ‘background points’) objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. 4. Point process models are related to some common approaches to presence-only species distribution modelling, which means that a variety of different software tools can be used to fit these models, including MAXENT or generalised linear modelling software. |
| first_indexed | 2025-11-14T08:42:15Z |
| format | Journal Article |
| id | curtin-20.500.11937-35654 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:42:15Z |
| publishDate | 2015 |
| publisher | Wiley-Blackwell Publishing Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-356542017-09-13T15:25:36Z Point process models for presence-only analysis Renner, I. Elith, J. Baddeley, Adrian Fithian, W. Hastie, T. Phillips, S. Popovic, G. Warton, D. species distribution modelling Gibbs processes MAXENT Cox processes pseudo-absences 1. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. 3. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudo-absences or ‘background points’) objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. 4. Point process models are related to some common approaches to presence-only species distribution modelling, which means that a variety of different software tools can be used to fit these models, including MAXENT or generalised linear modelling software. 2015 Journal Article http://hdl.handle.net/20.500.11937/35654 10.1111/2041-210X.12352 Wiley-Blackwell Publishing Ltd. unknown |
| spellingShingle | species distribution modelling Gibbs processes MAXENT Cox processes pseudo-absences Renner, I. Elith, J. Baddeley, Adrian Fithian, W. Hastie, T. Phillips, S. Popovic, G. Warton, D. Point process models for presence-only analysis |
| title | Point process models for presence-only analysis |
| title_full | Point process models for presence-only analysis |
| title_fullStr | Point process models for presence-only analysis |
| title_full_unstemmed | Point process models for presence-only analysis |
| title_short | Point process models for presence-only analysis |
| title_sort | point process models for presence-only analysis |
| topic | species distribution modelling Gibbs processes MAXENT Cox processes pseudo-absences |
| url | http://hdl.handle.net/20.500.11937/35654 |