Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types
New methods for species distribution models (SDMs) utilise presence–absence (PA) data to correct the sampling bias of presence‐only (PO) data in a spatial point process setting. These have been shown to improve species estimates when both datasets are large and dense. However, is a PA dataset that i...
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ftunivtasmania:oai:eprints.utas.edu.au:40073 2023-05-15T18:25:51+02:00 Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types Peel, SL Hill, NA Foster, SD Wotherspoon, SJ Ghiglione, C Schiaparelli, S 2019 https://eprints.utas.edu.au/40073/ unknown Wiley-Blackwell Publishing Ltd. Peel, SL, Hill, NA orcid:0000-0001-9329-6717 , Foster, SD, Wotherspoon, SJ orcid:0000-0002-6947-4445 , Ghiglione, C and Schiaparelli, S 2019 , 'Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types' , Methods in Ecology and Evolution, vol. 10, no. 7 , pp. 1002-1014 , doi:10.1111/2041-210X.13196 <http://dx.doi.org/10.1111/2041-210X.13196>. Poisson point processes presence–absence data presence-only data sampling bias Southern Ocean Mollusca species distribution models stochastic simulation Article PeerReviewed 2019 ftunivtasmania https://doi.org/10.1111/2041-210X.13196 2022-01-17T23:18:30Z New methods for species distribution models (SDMs) utilise presence–absence (PA) data to correct the sampling bias of presence‐only (PO) data in a spatial point process setting. These have been shown to improve species estimates when both datasets are large and dense. However, is a PA dataset that is smaller and patchier than hitherto examined able to do the same? Furthermore, when both datasets are relatively small, is there enough information contained within them to produce a useful estimate of species’ distributions? These attributes are common in many applications. A stochastic simulation was conducted to assess the ability of a pooled data SDM to estimate the distribution of species from increasingly sparser and patchier datasets. The simulated datasets were varied by changing the number of presence–absence sample locations, the degree of patchiness of these locations, the number of PO observations, and the level of sampling bias within the PO observations. The performance of the pooled data SDM was compared to a PA SDM and a PO SDM to assess the strengths and limitations of each SDM. The pooled data SDM successfully removed the sampling bias from the PO observations even when the presence–absence data were sparse and patchy, and the PO observations formed the majority of the data. The pooled data SDM was, in general, more accurate and more precise than either the PA SDM or the PO SDM. All SDMs were more precise for the species responses than they were for the covariate coefficients. The emerging SDM methodology that pools PO and PA data will facilitate more certainty around species’ distribution estimates, which in turn will allow more relevant and concise management and policy decisions to be enacted. This work shows that it is possible to achieve this result even in relatively data‐poor regions. Article in Journal/Newspaper Southern Ocean University of Tasmania: UTas ePrints Southern Ocean Methods in Ecology and Evolution 10 7 1002 1014 |
institution |
Open Polar |
collection |
University of Tasmania: UTas ePrints |
op_collection_id |
ftunivtasmania |
language |
unknown |
topic |
Poisson point processes presence–absence data presence-only data sampling bias Southern Ocean Mollusca species distribution models stochastic simulation |
spellingShingle |
Poisson point processes presence–absence data presence-only data sampling bias Southern Ocean Mollusca species distribution models stochastic simulation Peel, SL Hill, NA Foster, SD Wotherspoon, SJ Ghiglione, C Schiaparelli, S Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
topic_facet |
Poisson point processes presence–absence data presence-only data sampling bias Southern Ocean Mollusca species distribution models stochastic simulation |
description |
New methods for species distribution models (SDMs) utilise presence–absence (PA) data to correct the sampling bias of presence‐only (PO) data in a spatial point process setting. These have been shown to improve species estimates when both datasets are large and dense. However, is a PA dataset that is smaller and patchier than hitherto examined able to do the same? Furthermore, when both datasets are relatively small, is there enough information contained within them to produce a useful estimate of species’ distributions? These attributes are common in many applications. A stochastic simulation was conducted to assess the ability of a pooled data SDM to estimate the distribution of species from increasingly sparser and patchier datasets. The simulated datasets were varied by changing the number of presence–absence sample locations, the degree of patchiness of these locations, the number of PO observations, and the level of sampling bias within the PO observations. The performance of the pooled data SDM was compared to a PA SDM and a PO SDM to assess the strengths and limitations of each SDM. The pooled data SDM successfully removed the sampling bias from the PO observations even when the presence–absence data were sparse and patchy, and the PO observations formed the majority of the data. The pooled data SDM was, in general, more accurate and more precise than either the PA SDM or the PO SDM. All SDMs were more precise for the species responses than they were for the covariate coefficients. The emerging SDM methodology that pools PO and PA data will facilitate more certainty around species’ distribution estimates, which in turn will allow more relevant and concise management and policy decisions to be enacted. This work shows that it is possible to achieve this result even in relatively data‐poor regions. |
format |
Article in Journal/Newspaper |
author |
Peel, SL Hill, NA Foster, SD Wotherspoon, SJ Ghiglione, C Schiaparelli, S |
author_facet |
Peel, SL Hill, NA Foster, SD Wotherspoon, SJ Ghiglione, C Schiaparelli, S |
author_sort |
Peel, SL |
title |
Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
title_short |
Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
title_full |
Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
title_fullStr |
Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
title_full_unstemmed |
Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
title_sort |
reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types |
publisher |
Wiley-Blackwell Publishing Ltd. |
publishDate |
2019 |
url |
https://eprints.utas.edu.au/40073/ |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
Peel, SL, Hill, NA orcid:0000-0001-9329-6717 , Foster, SD, Wotherspoon, SJ orcid:0000-0002-6947-4445 , Ghiglione, C and Schiaparelli, S 2019 , 'Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types' , Methods in Ecology and Evolution, vol. 10, no. 7 , pp. 1002-1014 , doi:10.1111/2041-210X.13196 <http://dx.doi.org/10.1111/2041-210X.13196>. |
op_doi |
https://doi.org/10.1111/2041-210X.13196 |
container_title |
Methods in Ecology and Evolution |
container_volume |
10 |
container_issue |
7 |
container_start_page |
1002 |
op_container_end_page |
1014 |
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1766207552009273344 |