Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA

Abstract Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulner...

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Autores principales:	Andrew T. Taylor, Thomas Hafen, Colt T. Holley, Alin González, James M. Long
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2020
Materias:	conservation biology ecological niche model fisheries management Maxent riverscape ecology Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/3932f52abb9047f4a2bafdfcbdd426de
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spelling	oai:doaj.org-article:3932f52abb9047f4a2bafdfcbdd426de2021-11-04T13:06:09ZSpatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA2045-775810.1002/ece3.5913https://doaj.org/article/3932f52abb9047f4a2bafdfcbdd426de2020-01-01T00:00:00Zhttps://doi.org/10.1002/ece3.5913https://doaj.org/toc/2045-7758Abstract Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AICC‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AICC‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.Andrew T. TaylorThomas HafenColt T. HolleyAlin GonzálezJames M. LongWileyarticleconservation biologyecological niche modelfisheries managementMaxentriverscape ecologyEcologyQH540-549.5ENEcology and Evolution, Vol 10, Iss 2, Pp 705-717 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	conservation biology ecological niche model fisheries management Maxent riverscape ecology Ecology QH540-549.5
spellingShingle	conservation biology ecological niche model fisheries management Maxent riverscape ecology Ecology QH540-549.5 Andrew T. Taylor Thomas Hafen Colt T. Holley Alin González James M. Long Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA
description	Abstract Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AICC‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AICC‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.
format	article
author	Andrew T. Taylor Thomas Hafen Colt T. Holley Alin González James M. Long
author_facet	Andrew T. Taylor Thomas Hafen Colt T. Holley Alin González James M. Long
author_sort	Andrew T. Taylor
title	Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA
title_short	Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA
title_full	Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA
title_fullStr	Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA
title_full_unstemmed	Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA
title_sort	spatial sampling bias and model complexity in stream‐based species distribution models: a case study of paddlefish (polyodon spathula) in the arkansas river basin, usa
publisher	Wiley
publishDate	2020
url	https://doaj.org/article/3932f52abb9047f4a2bafdfcbdd426de
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Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA

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