RT info:eu-repo/semantics/article
T1 Macroalgae niche modelling: a two-step approach using remote sensing and in situ observations of a native and an invasive Asparagopsis
A1 Arbelo Pérez, Manuel Imeldo
A1 Casas Más, Enrique José
A1 Fernandez, Marc
A1 Gil, Artur
A1 Yesson, Chris
A1 Prestes, Afonso
A1 Moreu-Badia, Ignacio
A1 Neto, Ana
A2 Física
K1 Asparagopsis armata
K1 Asparagopsis taxiformis
K1 Ecological niche modelling
K1 Remote sensing
K1 Unmanned aerial vehicle
AB Wearefacing a global loss of biodiversity due to climate change. This will lead to unpredictable changes in ecosystems, affecting the goods and services they provide introduction of non-indigenous marine species. This represents one of the major threats to marine biodiversity and therefore, there is a strong need to assess, map and monitor these alien species. The appearance of non-indigenous species is especially dangerous in fragile ecosystems and it is of great importance to better understand the invasion mechanisms of these invasive species. This is the case for invasive alga Asparagopsis armata, present in the Azores Archipelago. In this study we propose a methodology to define the realized ecological niche of this invasive alga, alongside the native Asparagopsis taxiformis, to understand better its distribution and potential impact on native communities and ecosystem services. These objectives comply with the EU Biodiversity strategy for 2020 goals and the need to map and assess ecosystems and their services. The lack of reliable high-resolution data makes this a challenging task. Within this scope, we propose a combination of Remote Sensing, Unmanned Aerial Vehicle based imagery together with in-situ field data to build ecological niche modelling approaches as a cost-effective methodology to identify and characterize vulnerable marine ecosystems. Our results show that this combination can help achieve monitoring, leading to a better understanding of ecological niches and the consequences of non-indigenous species invasion in fragile ecosystems, like small islands, when faced with limited data
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/36919
UL http://riull.ull.es/xmlui/handle/915/36919
LA en
NO https://doi.org/10.1007/s10530-021-02554-z
DS Repositorio institucional de la Universidad de La Laguna
RD 19-may-2024