Comparing bark thickness: testing methods with bark–stem data from two South African fire‐prone biomes

AIMS: Bark thickness–stem diameter relationships are non‐linear above a stem diameter threshold in many woody species, which makes relative bark thickness measures dependent on the range of stem diameters that are sampled. This influences the appropriateness of different methods for comparing fire r...

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Veröffentlicht in:Journal of vegetation science 2014-09, Vol.25 (5), p.1247-1256
Hauptverfasser: Hempson, Gareth P, Midgley, Jeremy J, Lawes, Michael J, Vickers, Karen J, Kruger, Laurence M, Pausas, Juli
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container_issue 5
container_start_page 1247
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creator Hempson, Gareth P
Midgley, Jeremy J
Lawes, Michael J
Vickers, Karen J
Kruger, Laurence M
Pausas, Juli
description AIMS: Bark thickness–stem diameter relationships are non‐linear above a stem diameter threshold in many woody species, which makes relative bark thickness measures dependent on the range of stem diameters that are sampled. This influences the appropriateness of different methods for comparing fire responses of woody plants across studies. Here we develop a framework for bark thickness comparisons by evaluating relative bark thickness estimates and bark thickness predictions obtained from linear and curved models fitted to raw and log‐transformed bark–stem data. We use this framework to contrast bark thickness among fynbos and savanna plant functional groups. LOCATION: Fynbos (17 species) and savanna (21 species) systems in South Africa. METHODS: The linear subset of bark–stem data was identified using a three‐step procedure. Linear regressions (with and without an intercept) and curved models (allometric and modified exponential models) were fitted to the linear subset and complete raw bark–stem data set, respectively. In addition, linear regression models were fitted to the log‐transformed complete bark–stem data set. Regression slopes and bark thickness predictions obtained from these different approaches were compared, to determine which method provides the most robust metric for comparing bark thickness. Bark thickness was compared among fynbos resprouter guilds and acacias from low‐ and high‐fire savannas. RESULTS: The slope of the regression model fitted to the linear subset of raw bark–stem data provides a reliable metric for general comparisons of relative bark thickness. Bark thickness predictions from the curved and log models were comparable at 20‐cm stem diameter, but the log model underestimated bark thickness at 5 cm for certain species. Relative bark thickness was significantly higher in: (1) fynbos fire resisters and epicormic resprouters than in non‐resprouters; and (2) acacias from high‐ vs low‐fire savannas. CONCLUSIONS: The slope of the regression model fitted to the linear subset of raw bark–stem data is a useful metric for bark thickness comparisons across studies, and compares favourably with bark thickness predictions derived from models fitted to the complete bark–stem data set. Fynbos and savanna trends support the proposition that relative bark thickness reflects differences in woody plant responses to fire and indicate the modal fire regime of ecosystems.
doi_str_mv 10.1111/jvs.12171
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This influences the appropriateness of different methods for comparing fire responses of woody plants across studies. Here we develop a framework for bark thickness comparisons by evaluating relative bark thickness estimates and bark thickness predictions obtained from linear and curved models fitted to raw and log‐transformed bark–stem data. We use this framework to contrast bark thickness among fynbos and savanna plant functional groups. LOCATION: Fynbos (17 species) and savanna (21 species) systems in South Africa. METHODS: The linear subset of bark–stem data was identified using a three‐step procedure. Linear regressions (with and without an intercept) and curved models (allometric and modified exponential models) were fitted to the linear subset and complete raw bark–stem data set, respectively. In addition, linear regression models were fitted to the log‐transformed complete bark–stem data set. Regression slopes and bark thickness predictions obtained from these different approaches were compared, to determine which method provides the most robust metric for comparing bark thickness. Bark thickness was compared among fynbos resprouter guilds and acacias from low‐ and high‐fire savannas. RESULTS: The slope of the regression model fitted to the linear subset of raw bark–stem data provides a reliable metric for general comparisons of relative bark thickness. Bark thickness predictions from the curved and log models were comparable at 20‐cm stem diameter, but the log model underestimated bark thickness at 5 cm for certain species. Relative bark thickness was significantly higher in: (1) fynbos fire resisters and epicormic resprouters than in non‐resprouters; and (2) acacias from high‐ vs low‐fire savannas. CONCLUSIONS: The slope of the regression model fitted to the linear subset of raw bark–stem data is a useful metric for bark thickness comparisons across studies, and compares favourably with bark thickness predictions derived from models fitted to the complete bark–stem data set. Fynbos and savanna trends support the proposition that relative bark thickness reflects differences in woody plant responses to fire and indicate the modal fire regime of ecosystems.</description><identifier>ISSN: 1100-9233</identifier><identifier>EISSN: 1654-1103</identifier><identifier>DOI: 10.1111/jvs.12171</identifier><language>eng</language><publisher>Oxford: Opulus Press</publisher><subject>Acacia ; Agronomy. Soil science and plant productions ; Allometric model ; Animal and plant ecology ; Animal, plant and microbial ecology ; Bark ; Biological and medical sciences ; data collection ; ecosystems ; Fire ecology ; fire regime ; Forest ecology ; Forest trees ; Fundamental and applied biological sciences. Psychology ; Fynbos ; General agronomy. Plant production ; linear models ; Linear regression ; Modified exponential model ; Plant ecology ; plant response ; prediction ; Regression analysis ; Relative bark thickness ; Savanna ; Savannas ; Stem diameter ; Use of agricultural and forest wastes. 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This influences the appropriateness of different methods for comparing fire responses of woody plants across studies. Here we develop a framework for bark thickness comparisons by evaluating relative bark thickness estimates and bark thickness predictions obtained from linear and curved models fitted to raw and log‐transformed bark–stem data. We use this framework to contrast bark thickness among fynbos and savanna plant functional groups. LOCATION: Fynbos (17 species) and savanna (21 species) systems in South Africa. METHODS: The linear subset of bark–stem data was identified using a three‐step procedure. Linear regressions (with and without an intercept) and curved models (allometric and modified exponential models) were fitted to the linear subset and complete raw bark–stem data set, respectively. In addition, linear regression models were fitted to the log‐transformed complete bark–stem data set. Regression slopes and bark thickness predictions obtained from these different approaches were compared, to determine which method provides the most robust metric for comparing bark thickness. Bark thickness was compared among fynbos resprouter guilds and acacias from low‐ and high‐fire savannas. RESULTS: The slope of the regression model fitted to the linear subset of raw bark–stem data provides a reliable metric for general comparisons of relative bark thickness. Bark thickness predictions from the curved and log models were comparable at 20‐cm stem diameter, but the log model underestimated bark thickness at 5 cm for certain species. Relative bark thickness was significantly higher in: (1) fynbos fire resisters and epicormic resprouters than in non‐resprouters; and (2) acacias from high‐ vs low‐fire savannas. CONCLUSIONS: The slope of the regression model fitted to the linear subset of raw bark–stem data is a useful metric for bark thickness comparisons across studies, and compares favourably with bark thickness predictions derived from models fitted to the complete bark–stem data set. Fynbos and savanna trends support the proposition that relative bark thickness reflects differences in woody plant responses to fire and indicate the modal fire regime of ecosystems.</description><subject>Acacia</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Allometric model</subject><subject>Animal and plant ecology</subject><subject>Animal, plant and microbial ecology</subject><subject>Bark</subject><subject>Biological and medical sciences</subject><subject>data collection</subject><subject>ecosystems</subject><subject>Fire ecology</subject><subject>fire regime</subject><subject>Forest ecology</subject><subject>Forest trees</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Fynbos</subject><subject>General agronomy. Plant production</subject><subject>linear models</subject><subject>Linear regression</subject><subject>Modified exponential model</subject><subject>Plant ecology</subject><subject>plant response</subject><subject>prediction</subject><subject>Regression analysis</subject><subject>Relative bark thickness</subject><subject>Savanna</subject><subject>Savannas</subject><subject>Stem diameter</subject><subject>Use of agricultural and forest wastes. 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Soil science and plant productions</topic><topic>Allometric model</topic><topic>Animal and plant ecology</topic><topic>Animal, plant and microbial ecology</topic><topic>Bark</topic><topic>Biological and medical sciences</topic><topic>data collection</topic><topic>ecosystems</topic><topic>Fire ecology</topic><topic>fire regime</topic><topic>Forest ecology</topic><topic>Forest trees</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Fynbos</topic><topic>General agronomy. Plant production</topic><topic>linear models</topic><topic>Linear regression</topic><topic>Modified exponential model</topic><topic>Plant ecology</topic><topic>plant response</topic><topic>prediction</topic><topic>Regression analysis</topic><topic>Relative bark thickness</topic><topic>Savanna</topic><topic>Savannas</topic><topic>Stem diameter</topic><topic>Use of agricultural and forest wastes. Biomass use, bioconversion</topic><topic>Vegetation</topic><topic>woody plants</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hempson, Gareth P</creatorcontrib><creatorcontrib>Midgley, Jeremy J</creatorcontrib><creatorcontrib>Lawes, Michael J</creatorcontrib><creatorcontrib>Vickers, Karen J</creatorcontrib><creatorcontrib>Kruger, Laurence M</creatorcontrib><creatorcontrib>Pausas, Juli</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Journal of vegetation science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hempson, Gareth P</au><au>Midgley, Jeremy J</au><au>Lawes, Michael J</au><au>Vickers, Karen J</au><au>Kruger, Laurence M</au><au>Pausas, Juli</au><au>Pausas, Juli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparing bark thickness: testing methods with bark–stem data from two South African fire‐prone biomes</atitle><jtitle>Journal of vegetation science</jtitle><addtitle>J Veg Sci</addtitle><date>2014-09</date><risdate>2014</risdate><volume>25</volume><issue>5</issue><spage>1247</spage><epage>1256</epage><pages>1247-1256</pages><issn>1100-9233</issn><eissn>1654-1103</eissn><abstract>AIMS: Bark thickness–stem diameter relationships are non‐linear above a stem diameter threshold in many woody species, which makes relative bark thickness measures dependent on the range of stem diameters that are sampled. This influences the appropriateness of different methods for comparing fire responses of woody plants across studies. Here we develop a framework for bark thickness comparisons by evaluating relative bark thickness estimates and bark thickness predictions obtained from linear and curved models fitted to raw and log‐transformed bark–stem data. We use this framework to contrast bark thickness among fynbos and savanna plant functional groups. LOCATION: Fynbos (17 species) and savanna (21 species) systems in South Africa. METHODS: The linear subset of bark–stem data was identified using a three‐step procedure. Linear regressions (with and without an intercept) and curved models (allometric and modified exponential models) were fitted to the linear subset and complete raw bark–stem data set, respectively. In addition, linear regression models were fitted to the log‐transformed complete bark–stem data set. Regression slopes and bark thickness predictions obtained from these different approaches were compared, to determine which method provides the most robust metric for comparing bark thickness. Bark thickness was compared among fynbos resprouter guilds and acacias from low‐ and high‐fire savannas. RESULTS: The slope of the regression model fitted to the linear subset of raw bark–stem data provides a reliable metric for general comparisons of relative bark thickness. Bark thickness predictions from the curved and log models were comparable at 20‐cm stem diameter, but the log model underestimated bark thickness at 5 cm for certain species. Relative bark thickness was significantly higher in: (1) fynbos fire resisters and epicormic resprouters than in non‐resprouters; and (2) acacias from high‐ vs low‐fire savannas. CONCLUSIONS: The slope of the regression model fitted to the linear subset of raw bark–stem data is a useful metric for bark thickness comparisons across studies, and compares favourably with bark thickness predictions derived from models fitted to the complete bark–stem data set. Fynbos and savanna trends support the proposition that relative bark thickness reflects differences in woody plant responses to fire and indicate the modal fire regime of ecosystems.</abstract><cop>Oxford</cop><pub>Opulus Press</pub><doi>10.1111/jvs.12171</doi><tpages>10</tpages></addata></record>
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source Jstor Complete Legacy; Wiley Online Library Journals Frontfile Complete
subjects Acacia
Agronomy. Soil science and plant productions
Allometric model
Animal and plant ecology
Animal, plant and microbial ecology
Bark
Biological and medical sciences
data collection
ecosystems
Fire ecology
fire regime
Forest ecology
Forest trees
Fundamental and applied biological sciences. Psychology
Fynbos
General agronomy. Plant production
linear models
Linear regression
Modified exponential model
Plant ecology
plant response
prediction
Regression analysis
Relative bark thickness
Savanna
Savannas
Stem diameter
Use of agricultural and forest wastes. Biomass use, bioconversion
Vegetation
woody plants
title Comparing bark thickness: testing methods with bark–stem data from two South African fire‐prone biomes
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