Abstract:The increased availability of global scale environmental and biological data is enabling more objective, data‐driven, classifications of the oceans. However, comparisons of seasonal differences at a global scale have been limited to ocean color and derived variables. Here, we used long‐term averages of 18 ocean variables in summer and winter to classify the seasonal ocean surface waters through principle components analysis and k‐means clustering. We identified 11 distinct areas that fit the definition of “eco… Show more
“…Similarly, based on ginland analyses key variables are related to high temperature velocity of change (sst_max_sl) and sea surface salinity (SSS) of the freshest month (biogeo09), indicating that reproduction and survival are important forces acting on sardines in the area. The only variables that reflected northern poorness to southern richness were mean PAR (photosynthetically active radiation) and bottom nitrate, which have been found to vary on a seasonal basis (Zhao & Costello, 2019). Nevertheless, not strictly looking into the seasonal variation of the environmental variables with time (we only looked at average values for the period May–October approximating the conditions prior to the species' reproductive season, Gordó‐Vilaseca et al, 2021) inevitably disregards any seasonal/temporal changes occurring as well as their spatial extent, with the role of seasonality remaining to be further assessed in future studies.…”
Section: Discussionmentioning
confidence: 99%
“…Overall, our observed responses to local environments comply with the environmental conditions prevailing in those areas and their deterioration (Ramírez et al, 2021). According to the marine regionalization of Zhao and Costello (2019), there are extreme changes in PAR, waves and wind in the northern sites (i.e., GSA07a, GSA07b and GSA06a) during winter (December–February) but high PAR during summer (June–August) at the southern sites with consequent effects on productivity and temperature, especially during the reproductive period. The above, coupled with rapid changes in temperature (Ramírez et al, 2018, 2021), could be held responsible for such differences in physical conditions between the north and the south that might be reinforced by periods of lower connectivity with areas subject to milder pressures of extreme environmental variability and where healthier fish are found, such as the GSA06c sampling site (as in Hidalgo et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…The above, coupled with rapid changes in temperature (Ramírez et al, 2018(Ramírez et al, , 2021, could be held responsible for such differences in physical conditions between the north and the south that might be reinforced by periods of lower connectivity with areas subject to milder pressures of extreme environmental variability and where healthier fish are found, such as the GSA06c sampling site (as in Hidalgo et al, 2019). These intensively fluctuating conditions constrain species either to tolerate such variation, adjust their activity, growth and reproduction in response to seasons, or evolve into distinct biogeographical assemblages (Zhao & Costello, 2019).…”
Section: Is the North/south Gradient Solely Affected By The Environme...mentioning
By evaluating genetic variation across the entire genome, one can address existing questions in a novel way while raising new ones. The latter include how different local environments influence adaptive and neutral genomic variation within and among populations, providing insights into local adaptation of natural populations and their responses to global change. Here, under a seascape genomic approach, ddRAD data of 4609 SNPs from 398 sardines (Sardina pilchardus) collected in 11 Mediterranean and one Atlantic site were generated. These were used along with oceanographic and ecological information to detect signals of adaptive divergence with gene flow across environmental gradients. The studied sardines constitute two clusters (FST=0.07), a pattern attributed to outlier loci, highlighting putative local adaptation. The trend in the number of days with sea surface temperature above 19oC, critical threshold Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site.for successful sardine spawning, was crucial at all levels of population structuring with implications on species' key biological processes. Outliers link candidate SNPs to the region's environmental heterogeneity. Our findings provide evidence for a dynamic equilibrium where population structure is maintained by physical and ecological factors under the opposing influences of migration and selection. This dynamic in nature system, warrants continuous monitoring under a seascape genomic approach that might benefit from a temporal and more detailed spatial dimension. Our results may contribute to complementary studies aimed at providing deeper insights into the mechanistic processes underlying population structuring. Those are key for understanding and predicting future changes and responses of this highly exploited species in the face of climate change.
“…Similarly, based on ginland analyses key variables are related to high temperature velocity of change (sst_max_sl) and sea surface salinity (SSS) of the freshest month (biogeo09), indicating that reproduction and survival are important forces acting on sardines in the area. The only variables that reflected northern poorness to southern richness were mean PAR (photosynthetically active radiation) and bottom nitrate, which have been found to vary on a seasonal basis (Zhao & Costello, 2019). Nevertheless, not strictly looking into the seasonal variation of the environmental variables with time (we only looked at average values for the period May–October approximating the conditions prior to the species' reproductive season, Gordó‐Vilaseca et al, 2021) inevitably disregards any seasonal/temporal changes occurring as well as their spatial extent, with the role of seasonality remaining to be further assessed in future studies.…”
Section: Discussionmentioning
confidence: 99%
“…Overall, our observed responses to local environments comply with the environmental conditions prevailing in those areas and their deterioration (Ramírez et al, 2021). According to the marine regionalization of Zhao and Costello (2019), there are extreme changes in PAR, waves and wind in the northern sites (i.e., GSA07a, GSA07b and GSA06a) during winter (December–February) but high PAR during summer (June–August) at the southern sites with consequent effects on productivity and temperature, especially during the reproductive period. The above, coupled with rapid changes in temperature (Ramírez et al, 2018, 2021), could be held responsible for such differences in physical conditions between the north and the south that might be reinforced by periods of lower connectivity with areas subject to milder pressures of extreme environmental variability and where healthier fish are found, such as the GSA06c sampling site (as in Hidalgo et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…The above, coupled with rapid changes in temperature (Ramírez et al, 2018(Ramírez et al, , 2021, could be held responsible for such differences in physical conditions between the north and the south that might be reinforced by periods of lower connectivity with areas subject to milder pressures of extreme environmental variability and where healthier fish are found, such as the GSA06c sampling site (as in Hidalgo et al, 2019). These intensively fluctuating conditions constrain species either to tolerate such variation, adjust their activity, growth and reproduction in response to seasons, or evolve into distinct biogeographical assemblages (Zhao & Costello, 2019).…”
Section: Is the North/south Gradient Solely Affected By The Environme...mentioning
By evaluating genetic variation across the entire genome, one can address existing questions in a novel way while raising new ones. The latter include how different local environments influence adaptive and neutral genomic variation within and among populations, providing insights into local adaptation of natural populations and their responses to global change. Here, under a seascape genomic approach, ddRAD data of 4609 SNPs from 398 sardines (Sardina pilchardus) collected in 11 Mediterranean and one Atlantic site were generated. These were used along with oceanographic and ecological information to detect signals of adaptive divergence with gene flow across environmental gradients. The studied sardines constitute two clusters (FST=0.07), a pattern attributed to outlier loci, highlighting putative local adaptation. The trend in the number of days with sea surface temperature above 19oC, critical threshold Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site.for successful sardine spawning, was crucial at all levels of population structuring with implications on species' key biological processes. Outliers link candidate SNPs to the region's environmental heterogeneity. Our findings provide evidence for a dynamic equilibrium where population structure is maintained by physical and ecological factors under the opposing influences of migration and selection. This dynamic in nature system, warrants continuous monitoring under a seascape genomic approach that might benefit from a temporal and more detailed spatial dimension. Our results may contribute to complementary studies aimed at providing deeper insights into the mechanistic processes underlying population structuring. Those are key for understanding and predicting future changes and responses of this highly exploited species in the face of climate change.
“…This advances previous MPA classifications for the world (Selig et al, ) and Coral Triangle (Asaad, Lundquist, Erdmann, & Costello, ) that lacked ecosystem layers for biodiversity mapping. Likewise, following the similar approach we explored potential variations along the boundaries between marine ecosystems due to seasonal change (Zhao & Costello, ). We found that there were little change in tropic zones, latitudinal changes in high latitudes, and more complicated changes in other regions.…”
Almost all classifications of the world ocean are based on expert opinion or ad hoc management areas. A quantitative analysis of environmental variables may provide a more objective basis for mapping and classifying the oceans to support data management, reporting, and conservation efforts. Here, we used long‐term averages of 20 ocean variables to classify the ocean surface waters using PCA and k‐means clustering. We identified seven distinct areas that fit the definition of “ecosystems,” that is, enduring regions demarcated by environmental characteristics. Of all the variables, temperature had the greatest importance and correlated with many other variables. However, some variables had uniquely significant effects on the classification, namely slope, surface current, pH, and wave height. Thus, while the present classification is robust for available data, future analyses with variables not presently available may improve it. How the ecosystems correlate with species richness, endemicity, or abundance will inform on the factors that most influence species' abundance, and thus support global modeling of the effects of climate change, for example, with regard to biological carbon fluxes.
“…A striking feature is the five subtropical gyres with the lowest primary productivity that have been referred to as "ocean deserts" [38]. They did not change greatly across the seasons [39], but they are strongly influenced by vertical mixing and nutrient delivery [30]. The centers of the gyres are easily located and displayed in Figure 2b with Chl-a of 0.033 mg/m 3 (G1), 0.043 mg/m 3 (G2), 0.044 mg/m 3 (G3), 0.022 mg/m 3 (G4), and 0.036 mg/m 3 (G5), respectively.…”
The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.
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