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Models of upland species’ distributions are improved by accounting for geodiversity

CONTEXT: Recent research suggests that novel geodiversity data on landforms, hydrology and surface materials can improve biodiversity models at the landscape scale by quantifying abiotic variability more effectively than commonly used measures of spatial heterogeneity. However, few studies consider...

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Autores principales: Bailey, Joseph J., Boyd, Doreen S., Field, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404796/
https://www.ncbi.nlm.nih.gov/pubmed/30930538
http://dx.doi.org/10.1007/s10980-018-0723-z
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author Bailey, Joseph J.
Boyd, Doreen S.
Field, Richard
author_facet Bailey, Joseph J.
Boyd, Doreen S.
Field, Richard
author_sort Bailey, Joseph J.
collection PubMed
description CONTEXT: Recent research suggests that novel geodiversity data on landforms, hydrology and surface materials can improve biodiversity models at the landscape scale by quantifying abiotic variability more effectively than commonly used measures of spatial heterogeneity. However, few studies consider whether these variables can account for, and improve our understanding of, species’ distributions. OBJECTIVES: Assess the role of geodiversity components as macro-scale controls of plant species’ distributions in a montane landscape. METHODS: We used an innovative approach to quantifying a landscape, creating an ecologically meaningful geodiversity dataset that accounted for hydrology, morphometry (landforms derived from geomorphometric techniques), and soil parent material (data from expert sources). We compared models with geodiversity to those just using topographic metrics (e.g. slope and elevation) and climate data. Species distribution models (SDMs) were produced for ‘rare’ (N = 76) and ‘common’ (N = 505) plant species at 1 km(2) resolution for the Cairngorms National Park, Scotland. RESULTS: The addition of automatically produced landform geodiversity data and hydrological features to a basic SDM (climate, elevation, and slope) resulted in a significant improvement in model fit across all common species’ distribution models. Adding further geodiversity data on surface materials resulted in a less consistent statistical improvement, but added considerable conceptual value to many individual rare and common SDMs. CONCLUSIONS: The geodiversity data used here helped us capture the abiotic environment’s heterogeneity and allowed for explicit links between the geophysical landscape and species’ ecology. It is encouraging that relatively simple and easily produced geodiversity data have the potential to improve SDMs. Our findings have important implications for applied conservation and support the need to consider geodiversity in management. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10980-018-0723-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-64047962019-03-27 Models of upland species’ distributions are improved by accounting for geodiversity Bailey, Joseph J. Boyd, Doreen S. Field, Richard Landsc Ecol Research Article CONTEXT: Recent research suggests that novel geodiversity data on landforms, hydrology and surface materials can improve biodiversity models at the landscape scale by quantifying abiotic variability more effectively than commonly used measures of spatial heterogeneity. However, few studies consider whether these variables can account for, and improve our understanding of, species’ distributions. OBJECTIVES: Assess the role of geodiversity components as macro-scale controls of plant species’ distributions in a montane landscape. METHODS: We used an innovative approach to quantifying a landscape, creating an ecologically meaningful geodiversity dataset that accounted for hydrology, morphometry (landforms derived from geomorphometric techniques), and soil parent material (data from expert sources). We compared models with geodiversity to those just using topographic metrics (e.g. slope and elevation) and climate data. Species distribution models (SDMs) were produced for ‘rare’ (N = 76) and ‘common’ (N = 505) plant species at 1 km(2) resolution for the Cairngorms National Park, Scotland. RESULTS: The addition of automatically produced landform geodiversity data and hydrological features to a basic SDM (climate, elevation, and slope) resulted in a significant improvement in model fit across all common species’ distribution models. Adding further geodiversity data on surface materials resulted in a less consistent statistical improvement, but added considerable conceptual value to many individual rare and common SDMs. CONCLUSIONS: The geodiversity data used here helped us capture the abiotic environment’s heterogeneity and allowed for explicit links between the geophysical landscape and species’ ecology. It is encouraging that relatively simple and easily produced geodiversity data have the potential to improve SDMs. Our findings have important implications for applied conservation and support the need to consider geodiversity in management. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10980-018-0723-z) contains supplementary material, which is available to authorized users. Springer Netherlands 2018-10-28 2018 /pmc/articles/PMC6404796/ /pubmed/30930538 http://dx.doi.org/10.1007/s10980-018-0723-z Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research Article
Bailey, Joseph J.
Boyd, Doreen S.
Field, Richard
Models of upland species’ distributions are improved by accounting for geodiversity
title Models of upland species’ distributions are improved by accounting for geodiversity
title_full Models of upland species’ distributions are improved by accounting for geodiversity
title_fullStr Models of upland species’ distributions are improved by accounting for geodiversity
title_full_unstemmed Models of upland species’ distributions are improved by accounting for geodiversity
title_short Models of upland species’ distributions are improved by accounting for geodiversity
title_sort models of upland species’ distributions are improved by accounting for geodiversity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404796/
https://www.ncbi.nlm.nih.gov/pubmed/30930538
http://dx.doi.org/10.1007/s10980-018-0723-z
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