2018
DOI: 10.1038/s41598-018-22967-6
|View full text |Cite
|
Sign up to set email alerts
|

Lack of impact of the El Hierro (Canary Islands) submarine volcanic eruption on the local phytoplankton community

Abstract: The eruption of a submarine volcano south of El Hierro Island (Canary Islands) in October 2011 led to major physical and chemical changes in the local environment. Large amounts of nutrients were found at specific depths in the water column above the volcano associated with suboxic layers resulting from the oxidation of reduced chemical species expelled during the eruptive phase. It has been suggested that the fertilization with these compounds enabled the rapid restoration of the ecosystem in the marine reser… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

2
9
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
3
2
1
1

Relationship

0
7

Authors

Journals

citations
Cited by 17 publications
(11 citation statements)
references
References 35 publications
2
9
0
Order By: Relevance
“…After that initial phase, a significant increase in the abundance of the mesozooplankton was observed, particularly after 2017, suggesting that probably some environmental conditions could affect the abundance and structure of the zooplankton community. Previous to the eruption, low productivity was observed around El Hierro Island (Arístegui et al, 1997;Hernández-León and Ikeda, 2005) as well as during the first years of the post-eruptive phase (Gómez-Letona et al, 2018). However, continuous injection of inorganic nutrients during the post-eruptive phase from Tagoro submarine volcano, particularly silicates, has been reported in the area (González-Vega et al, 2020), yielding nutrient fluxes similar to those measured in the eastern Canary Islands more influenced and closer to the NW-African coastal upwelling.…”
Section: Seasonal and Interannual Zooplankton Abundancementioning
confidence: 67%
See 1 more Smart Citation
“…After that initial phase, a significant increase in the abundance of the mesozooplankton was observed, particularly after 2017, suggesting that probably some environmental conditions could affect the abundance and structure of the zooplankton community. Previous to the eruption, low productivity was observed around El Hierro Island (Arístegui et al, 1997;Hernández-León and Ikeda, 2005) as well as during the first years of the post-eruptive phase (Gómez-Letona et al, 2018). However, continuous injection of inorganic nutrients during the post-eruptive phase from Tagoro submarine volcano, particularly silicates, has been reported in the area (González-Vega et al, 2020), yielding nutrient fluxes similar to those measured in the eastern Canary Islands more influenced and closer to the NW-African coastal upwelling.…”
Section: Seasonal and Interannual Zooplankton Abundancementioning
confidence: 67%
“…Currently, the Tagoro volcano exhibits new benthic habitats thriving around the main and secondary craters, colonized by small hydrozoan colonies with a high diversity of annelids, arthropods, cnidarians, and mollusks as the first colonizers (Sotomayor-García et al, 2019). Although in other submarine volcanic areas the hydrothermal vent emissions have been observed to have a considerable effect on the diversity, abundance, biology, and ecology of the planktonic communities (Tarasov, 2006), no clear impact has yet been proved on the local phytoplankton community at Tagoro volcano (Gómez-Letona et al, 2018). Monitoring of the new submarine volcano could provide a unique opportunity to study the resilience of the pelagic ecosystem in El Hierro after an acute volcanic disturbance as well as to depict the effect of the subsequent diffuse volcanic emissions on the zooplankton community.…”
Section: Introductionmentioning
confidence: 99%
“…2 0 0 6 -0 8 -1 5 2 0 0 6 -1 0 -2 1 2 0 0 6 -1 2 -2 7 2 0 0 7 -0 3 -0 4 2 0 0 7 -0 5 -1 0 2 0 0 7 -0 7 -1 6 2 0 0 7 -0 9 -2 1 2 0 0 7 -1 1 -2 7 2 0 0 8 -0 2 -0 3 2 0 1 7 -0 8 -1 4 2 0 1 7 -1 1 -0 5 2 0 1 7 -1 2 -2 7 2 0 1 8 -0 2 -1 7 2 0 1 8 -0 4 -1 1 2 0 1 8 -0 6 -0 2 2 0 1 8 -0 7 -2 4 2 0 1 8 -0 9 -1 4 2 0 1 8 -1 1 -0 5 2 0 1 8 -1 2 -2 8 c, d) Chlorophyll time series for ~1 year prior to and after the eruption for a pixel close to the lava flow's ocean entry. The identification of chlorophyll is more challenging under these conditions due to discoloration associated with high concentrations of sulfur compounds (Gómez-Letona et al, 2018). This pattern may thus partly reflect shifts in local chemistry and color rather than solely changes in chlorophyll; in situ data, e.g., Wilson et al (2019), are required to verify any changes in chlorophyll concentrations (Gómez-Letona et al, 2018).…”
Section: Lava-water Interactions and Ocean Fertilization: Hypotheses And Insights From Modern Eruptionsmentioning
confidence: 99%
“…(c, d) Chlorophyll time series for ~1 year prior to and after the eruption for a pixel close to the lava flow's ocean entry. The identification of chlorophyll is more challenging under these conditions due to discoloration associated with high concentrations of sulfur compounds(Gómez-Letona et al, 2018). This pattern may thus partly reflect shifts in local chemistry and color rather than solely changes in chlorophyll; in situ data, e.g.,Wilson et al (2019), are required to verify any changes in chlorophyll concentrations(Gómez-Letona et al, 2018).…”
mentioning
confidence: 99%
“…In particular, the diffusion of volcanic ash cloud is affected by multiple factors, such as meteorological conditions (i.e., wind direction, wind speed, temperature, humidity, rainfall, etc. ), the weight of volcanic ash debris particles and the physiochemical reactions of volcanic gases with the surrounding atmosphere [19]- [25], so the evolution rule is the key to exact monitor and simulate of the volcanic ash cloud diffusion. At present, being widely used in the machine learning, image analysis and recognition, disease prediction and financial data mining, long short term memory (LSTM) neural network can not only effectively handle the relatively long intervals and delays in time series, but also accurately predict the subsequent development trends in the time series [26].…”
Section: Introductionmentioning
confidence: 99%