RT info:eu-repo/semantics/article
T1 Economic mapping and assessment of Cymodocea nodosa meadows as nursery grounds for commercially important fish species. A case study in the Canary Islands
A1 Arbelo Pérez, Manuel Imeldo
A1 Casas Más, Enrique José
A1 Martín-García, Laura
A1 Otero-Ferrer, Francisco
A1 Tuya, Fernando
A1 Haroun, Ricardo
A2 Física
K1 Cymodocea nodosa
K1 seagrass meadows
K1 habitat suitability mapping
K1 ecosystem services
K1 value transfer methodology
K1 Canary Islands
AB Cymodocea nodosa seagrass meadows provide several socio-economically ecosystem services, including nurseries for numerous species of commercial interest. These seagrasses are experiencing a worldwide decline, with global loss rates approaching 5% per year, mainly related to coastal human activities. Cymodocea nodosa, the predominant seagrass in the Canary Archipelago (Spain), is also exposed to these threats, which could lead to habitat loss or even local disappearance. In this case study, we estimated the potential economic value of Cymodocea nodosa seagrass meadows for local fisheries at an archipelago scale. Habitat suitability maps were constructed using MAXENT 3.4.1, a software for modelling species distributions by applying a maximum entropy machinelearning method, from a set of environmental variables and presence and background records extracted from historical cartographies. This model allows characterising and assessing the C. nodosa habitat suitability, overcoming the implicit complexity derived from seasonal changes in this species highly dynamic meadows and using it as a first step for the mapping and assessment of ecosystem services. In a second step, value transfer methodologies were used, along with published economic valuations of commerciallyinteresting fish species related to C. nodosa meadows. We estimate that the potential monetary value of these species can add up to more than 3 million euros per year for the entire Archipelago. The simplicity of the proposed methodology facilitates its repeatability in other similar regions, using freely available data and hence, being suitable for data-scarce scenarios.
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/36915
UL http://riull.ull.es/xmlui/handle/915/36915
LA en
NO https://doi.org/10.3897/oneeco.6.e70919
DS Repositorio institucional de la Universidad de La Laguna
RD 24-nov-2024