What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition

Seagrass condition, resilience and ecosystem services are affected by the below-ground tissues (BGr) but these are rarely monitored. In this study we compiled historical data across northern Australia to investigate biomass allocation strategies in 13 tropical seagrass species. There was sufficient...

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Autores principales:	C.J. Collier, L.M. Langlois, K.M. McMahon, J. Udy, M. Rasheed, E. Lawrence, A.B. Carter, M.W. Fraser, L.J. McKenzie
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Seagrass Biomass Monitoring Species diversity Australia Great barrier reef Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/636c06f841bd46bca91d7a30471c76ff
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spelling	oai:doaj.org-article:636c06f841bd46bca91d7a30471c76ff2021-12-01T04:37:04ZWhat lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition1470-160X10.1016/j.ecolind.2020.107156https://doaj.org/article/636c06f841bd46bca91d7a30471c76ff2021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20310955https://doaj.org/toc/1470-160XSeagrass condition, resilience and ecosystem services are affected by the below-ground tissues (BGr) but these are rarely monitored. In this study we compiled historical data across northern Australia to investigate biomass allocation strategies in 13 tropical seagrass species. There was sufficient data to undertake statistical analysis for five species: Cymodocea serrulata, Halophila ovalis, Halodule uninervis, Thalassia hemprichii, and Zostera muelleri. The response of below-ground biomass (BGr) to above-ground biomass (AGr) and other environmental and seagrass community composition predictor variables were assessed using Generalized Linear Models. Environmental data included: region, season, sediment type, water depth, proximity to land-based sources of pollution, and a light stress index. Seagrass community data included: species diversity and dominant species class (colonising, opportunistic or persistant) based on biomass. The predictor variables explained 84–97% of variance in BGr on the log-scale depending on the species. Multi-species meadows showed a greater investment into BGr than mono-specific meadows and when dominated by opportunistic or persistent seagrass species. This greater investment into BGr is likely to enhance their resistance to disturbances if carbohydrate storage reserves also increase with biomass. Region was very important for the estimation of BGr from AGr in four species (not in C. serrulata). No temporally changing environmental features were included in the models, therefore, they cannot be used to predict local-scale responses of BGr to environmental change. We used a case study for Cairns Harbour to predict BGr by applying the models to AGr measured at 362 sites in 2017. This case study demonstrates how the model can be used to estimate BGr when only AGr is measured. However, the general approach can be applied broadly with suitable calibration data for model development providing a more complete assessment of seagrass resources and their potential to provide ecosystem services.C.J. CollierL.M. LangloisK.M. McMahonJ. UdyM. RasheedE. LawrenceA.B. CarterM.W. FraserL.J. McKenzieElsevierarticleSeagrassBiomassMonitoringSpecies diversityAustraliaGreat barrier reefEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107156- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Seagrass Biomass Monitoring Species diversity Australia Great barrier reef Ecology QH540-549.5
spellingShingle	Seagrass Biomass Monitoring Species diversity Australia Great barrier reef Ecology QH540-549.5 C.J. Collier L.M. Langlois K.M. McMahon J. Udy M. Rasheed E. Lawrence A.B. Carter M.W. Fraser L.J. McKenzie What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
description	Seagrass condition, resilience and ecosystem services are affected by the below-ground tissues (BGr) but these are rarely monitored. In this study we compiled historical data across northern Australia to investigate biomass allocation strategies in 13 tropical seagrass species. There was sufficient data to undertake statistical analysis for five species: Cymodocea serrulata, Halophila ovalis, Halodule uninervis, Thalassia hemprichii, and Zostera muelleri. The response of below-ground biomass (BGr) to above-ground biomass (AGr) and other environmental and seagrass community composition predictor variables were assessed using Generalized Linear Models. Environmental data included: region, season, sediment type, water depth, proximity to land-based sources of pollution, and a light stress index. Seagrass community data included: species diversity and dominant species class (colonising, opportunistic or persistant) based on biomass. The predictor variables explained 84–97% of variance in BGr on the log-scale depending on the species. Multi-species meadows showed a greater investment into BGr than mono-specific meadows and when dominated by opportunistic or persistent seagrass species. This greater investment into BGr is likely to enhance their resistance to disturbances if carbohydrate storage reserves also increase with biomass. Region was very important for the estimation of BGr from AGr in four species (not in C. serrulata). No temporally changing environmental features were included in the models, therefore, they cannot be used to predict local-scale responses of BGr to environmental change. We used a case study for Cairns Harbour to predict BGr by applying the models to AGr measured at 362 sites in 2017. This case study demonstrates how the model can be used to estimate BGr when only AGr is measured. However, the general approach can be applied broadly with suitable calibration data for model development providing a more complete assessment of seagrass resources and their potential to provide ecosystem services.
format	article
author	C.J. Collier L.M. Langlois K.M. McMahon J. Udy M. Rasheed E. Lawrence A.B. Carter M.W. Fraser L.J. McKenzie
author_facet	C.J. Collier L.M. Langlois K.M. McMahon J. Udy M. Rasheed E. Lawrence A.B. Carter M.W. Fraser L.J. McKenzie
author_sort	C.J. Collier
title	What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
title_short	What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
title_full	What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
title_fullStr	What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
title_full_unstemmed	What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
title_sort	what lies beneath: predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/636c06f841bd46bca91d7a30471c76ff
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What lies beneath: Predicting seagrass below-ground biomass from above-ground biomass, environmental conditions and seagrass community composition

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