Macroalgae niche modelling: a two-step approach using remote sensing and in situ observations of a native and an invasive Asparagopsis

  1. Fernandez, Marc 22
  2. Casas, Enrique 1
  3. Arbelo, Manuel 1
  4. Moreu‐Badia, Ignacio 22
  5. Gil, Artur 22
  6. Neto, Ana 22
  7. Yesson, Chris 3
  8. Prestes, Afonso 22
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    GRID grid.10041.34

  2. 2 University of the Azores
    info

    University of the Azores

    Ponta Delgada, Portugal

    GRID grid.7338.f

  3. 3 Institute of Zoology
    info

    Institute of Zoology

    Bratislava, Eslovaquia

    GRID grid.425138.9

Journal:
Biological Invasions

ISSN: 1387-3547

Year of publication: 2021

Type: Article

Export: RIS
DOI: 10.1007/s10530-021-02554-z GOOGLE SCHOLAR

Metrics

Cited by

  • Scopus Cited by: 0 (20-11-2021)

JCR (Journal Impact Factor)

(Indicator corresponding to the last year available on this portal, year 2020)
  • Year 2020
  • Journal Impact Factor: 3.133
  • Best Quartile: Q1
  • Area: BIODIVERSITY CONSERVATION Quartile: Q1 Rank in area: 15/60 (Ranking edition: SCIE)
  • Area: ECOLOGY Quartile: Q2 Rank in area: 64/166 (Ranking edition: SCIE)

SCImago Journal Rank

(Indicator corresponding to the last year available on this portal, year 2020)
  • Year 2020
  • SJR Journal Impact: 1.167
  • Best Quartile: Q1
  • Area: Ecology Quartile: Q1 Rank in area: 50/392
  • Area: Ecology, Evolution, Behavior and Systematics Quartile: Q1 Rank in area: 90/658

CiteScore

(Indicator corresponding to the last year available on this portal, year 2020)
  • Year 2020
  • CiteScore of the Journal : 5.2
  • Area: Ecology, Evolution, Behavior and Systematics Percentile: 87
  • Area: Ecology Percentile: 86

Journal Citation Indicator (JCI)

(Indicator corresponding to the last year available on this portal, year 2020)
  • Year 2020
  • Journal Citation Indicator (JCI): 0.92
  • Best Quartile: Q1
  • Area: BIODIVERSITY CONSERVATION Quartile: Q1 Rank in area: 12/64
  • Area: ECOLOGY Quartile: Q2 Rank in area: 53/178

Abstract

We are facing 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. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG

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