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Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre - PubMed

  • ️Wed Jan 01 2020

Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre

Domenico D'Alelio et al. Sci Rep. 2020.

Abstract

Phytoplankton play key roles in the oceans by regulating global biogeochemical cycles and production in marine food webs. Global warming is thought to affect phytoplankton production both directly, by impacting their photosynthetic metabolism, and indirectly by modifying the physical environment in which they grow. In this respect, the Bermuda Atlantic Time-series Study (BATS) in the Sargasso Sea (North Atlantic gyre) provides a unique opportunity to explore effects of warming on phytoplankton production across the vast oligotrophic ocean regions because it is one of the few multidecadal records of measured net primary productivity (NPP). We analysed the time series of phytoplankton primary productivity at BATS site using machine learning techniques (ML) to show that increased water temperature over a 27-year period (1990-2016), and the consequent weakening of vertical mixing in the upper ocean, induced a negative feedback on phytoplankton productivity by reducing the availability of essential resources, nitrogen and light. The unbalanced availability of these resources with warming, coupled with ecological changes at the community level, is expected to intensify the oligotrophic state of open-ocean regions that are far from land-based nutrient sources.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1

Study system and working hypothesis: productivity changes at the BATS site. (A) Geographic location of the BATS time series site in the western boundary of the North Atlantic gyre. (B) Vertical extension of the sampling site, in comparison with the shelf profile (the surface water layer is shown in green). (C) Factors that potentially affect primary production and a conceptual scheme for the decrease of phytoplankton biomass in the surface waters as warming proceeds.

Figure 2
Figure 2

Long term series at the BATS station. (A) Time series for the environmental and biological variables investigated herein. (B) Time series of annual means from 1990 to 2016; units in the y-axis are the same as in (A).

Figure 3
Figure 3

Associated trends of temperature and net primary productivity at the BATS site. The annual averaged time-series of temperature and net primary productivity (NPP) are shown, in comparison with the results of time-windowed Seasonal Kendall tests (window = 10 years) conducted on the monthly-averaged time series. In the uppermost plot, red boxes highlight periods of significant warming; in the lowermost plot, green and blue boxes highlight periods of significant increasing and decreasing of NPP, respectively. In both plots, arrows indicate overlapping between subsequent windows showing statistically significant ten-years trends.

Figure 4
Figure 4

Results of genetic programming analyses on the BATS dataset. (A) Comparisons between observed values of net primary productivity (NPP) and those predicted by ten different GP experiments; the regression coefficient ‘r’ and the Mean Absolute Error ‘E’ are indicated for each experiment. The Mean Absolute Error in Table 1 is the average of values indicated in this figure. The experiments with the best match between real and predicted data are in bold. (B) Synthetic representation of the impact of different variables (N = nitrates; P = phosphates; Lut-Zea = lutein-zeaxanthin; Si = silicates; J-day = Julian day; Y-day = the day of the year, i.e. a parameter used to express the progress of seasons; T = temperature; MLD = mixed layer depth, ΔD = density gradient; Chl b = chlorophyll b; Chl a = chlorophyll a; Fuco = fucoxanthin) on net primary productivity NPP, according to ten GP experiments, coded as (1–10) in the graphs; green and blue arrows indicate either positive or negative impacts of a variable on NPP, respectively; arrow thickness is proportional to the magnitude of the impact.

Figure 5
Figure 5

Systemic impact on net primary productivity at the BATS site. Overall impact of different variables (N = nitrates; P = phosphates; Lut-Zea = lutein-zeaxanthin; Si = silicates; J-day = Julian day; Y-day = the day of the year; T = temperature; MLD = mixed layer depth, ΔD = density gradient; Chl b = chlorophyll b; Chl a = chlorophyll a; Fuco = fucoxanthin) on net primary productivity NPP, according to the average of ten GP experiments shown in Fig. 4; green and blue arrows indicate either positive or negative impacts of a variable on NPP, respectively; arrow thickness is proportional to the magnitude of the impact.

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