Cumulative biomass curves describe past and present conditions of Large Marine Ecosystems. (1st October 2019)
- Record Type:
- Journal Article
- Title:
- Cumulative biomass curves describe past and present conditions of Large Marine Ecosystems. (1st October 2019)
- Main Title:
- Cumulative biomass curves describe past and present conditions of Large Marine Ecosystems
- Authors:
- Pranovi, Fabio
Libralato, Simone
Zucchetta, Matteo
Anelli Monti, Marco
Link, Jason S. - Abstract:
- Abstract: Implementing the Ecosystem Approach in marine ecosystems is moving from preliminary steps—dedicated to defining the optimal features for indicators and developing efficient indicator frameworks—towards an operational phase where multisector marine management decisions are executed using this information. Within this operational context, emergent ecosystem properties are becoming quite promising as they have been demonstrated to be globally widespread and repeatable, and to be quite effective in detecting significant state variations of complex systems. Biomass accumulation across TLs (CumB‐TL) combines two important emergent properties of an ecosystem (energy flow, in terms of transfer efficiency, and storage, expressed as biomass), both amenable to detecting rapid ecosystem change. However, for further application, it is crucial to understand which types of drivers an indicator is sensitive to and how robust it is in relation to modifications of the external conditions and/or the system state. Here we address some outstanding questions of these CumB‐TL curves related to their sensitivity to various drivers by carrying out a global scale assessment (using data from 62 LMEs) over six decades (1950–2010). We confirm the consistency of the S‐pattern across all the LMEs, independent from latitude, ecosystem, environmental conditions, and stress level. The dynamics of the curve shape showed a tendency to stretch (i.e. decrease of steepness), in the presence of externalAbstract: Implementing the Ecosystem Approach in marine ecosystems is moving from preliminary steps—dedicated to defining the optimal features for indicators and developing efficient indicator frameworks—towards an operational phase where multisector marine management decisions are executed using this information. Within this operational context, emergent ecosystem properties are becoming quite promising as they have been demonstrated to be globally widespread and repeatable, and to be quite effective in detecting significant state variations of complex systems. Biomass accumulation across TLs (CumB‐TL) combines two important emergent properties of an ecosystem (energy flow, in terms of transfer efficiency, and storage, expressed as biomass), both amenable to detecting rapid ecosystem change. However, for further application, it is crucial to understand which types of drivers an indicator is sensitive to and how robust it is in relation to modifications of the external conditions and/or the system state. Here we address some outstanding questions of these CumB‐TL curves related to their sensitivity to various drivers by carrying out a global scale assessment (using data from 62 LMEs) over six decades (1950–2010). We confirm the consistency of the S‐pattern across all the LMEs, independent from latitude, ecosystem, environmental conditions, and stress level. The dynamics of the curve shape showed a tendency to stretch (i.e. decrease of steepness), in the presence of external disturbance and conversely to increase in steepness and shift towards higher TL in the case of recovery from stressed conditions. Our results suggest the presence of three main types of ecosystem dynamics, those showing an almost continuous increase in ecological state over time, those showing a continuous decrease in ecological state over time, and finally those showing a mixed behaviour flipping between recovering and degrading phases. These robust patterns suggest that the CumB‐TL curve approach has some useful properties for use in further advancing the implementation of the Ecosystem Approach, allowing us to detect the state of a given marine ecosystem based on the dynamics of its curve shape, by using readily available time series data. The value of being able to identify conditions that might require management actions is quite high and, in many respects, represents the main objective in the context of an Ecosystem Approach, with large applications for detecting and responding to global changes in marine ecosystems. Abstract : Our results suggest the presence of three main types of ecosystem dynamics, those showing an almost continuous increase in ecological state over time, those showing a continuous decrease in ecological state over time, and finally those showing a mixed behavior flipping between recovering and degrading phases. These robust patterns suggest that the CumB‐TL curve approach has some useful properties for use in further advancing the implementation of the Ecosystem Approach, allowing us to detect the state of a given marine ecosystem on the basis of the dynamic of its curve shape, by using readily available time series data. … (more)
- Is Part Of:
- Global change biology. Volume 26:Number 2(2020)
- Journal:
- Global change biology
- Issue:
- Volume 26:Number 2(2020)
- Issue Display:
- Volume 26, Issue 2 (2020)
- Year:
- 2020
- Volume:
- 26
- Issue:
- 2
- Issue Sort Value:
- 2020-0026-0002-0000
- Page Start:
- 786
- Page End:
- 797
- Publication Date:
- 2019-10-01
- Subjects:
- cumulative biomass -- Ecosystem Approach -- ecosystem indicators -- emergent properties -- trophic level
Climatic changes -- Environmental aspects -- Periodicals
Troposphere -- Environmental aspects -- Periodicals
Biodiversity conservation -- Periodicals
Eutrophication -- Periodicals
551.5 - Journal URLs:
- http://www.blackwell-synergy.com/member/institutions/issuelist.asp?journal=gcb ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/gcb.14827 ↗
- Languages:
- English
- ISSNs:
- 1354-1013
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4195.358330
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25872.xml