Abstract
Eutrophication is accelerating the recent expansion of oxygen-depleted coastal marine environments. Several bolivinid foraminifera are abundant in these oxygen-depleted settings, and take up nitrate through the pores in their shells for denitrification. This makes their pore density a possible nitrate proxy. This study documents three aspects related to the porosity of bolivinids. 1. A new automated image analysis technique to determine the number of pores in bolivinids is tested. 2. The pore patterns of Bolivina spissa from five different ocean settings are analysed. The relationship between porosity, pore density and mean pore size significantly differs between the studied locations. Their porosity is mainly controlled by the size of the pores at the Gulf of Guayaquil (Peru), but by the number of pores at other studied locations. This might be related to the presence of a different cryptic Bolivina species in the Gulf of Guayaquil. 3. The pore densities of closely related bolivinids in core-top samples are calibrated as a bottom-water nitrate proxy. Bolivina spissa and Bolivina subadvena showed the same correlation between pore density and bottom-water nitrate concentrations, while the pore density of Bolivina argentea and Bolivina subadvena accumeata is much higher.
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Introduction
Oceanic oxygen concentrations are predicted to decrease globally affecting the stability of marine ecosystems1,2,3,4. Global warming accelerates ongoing ocean deoxygenation5,6, and expansion of oxygen minimum zones (OMZs)1,2,7. Increased ocean warming enhances upper-ocean stratification8, reduces ventilation, and has implications for biological productivity7 as well as carbon, nitrogen9 and phosphorus cycling10 in the oceans. These processes are amplified by the large-scale use of chemical nitrogenous fertilizers to satisfy global demand for food production which drastically disrupts the nitrogen cycle11,12. Oxygen is a major influence on the marine nitrogen cycle in the global oceans6 as some microbial processes require oxygen while others are inhibited by it8. When oxygen concentrations drop below ~ 4.5 µmol/kg, nitrate becomes the major electron acceptor for respiration replacing oxygen, a condition called suboxic13,14,15. The continued expansion of suboxia results in the loss of fixed nitrogen via denitrification14,16, a dissimilatory process in which nitrate (NO3-) is ultimately converted into dinitrogen gas17. Therefore, denitrification reduces the supply of NO3- in global oceans14,16. Nitrogen fixation, nitrification, and denitrification are major processes in the nitrogen cycle that are mainly facilitated by bacteria18, while lower oxygen concentrations can either enhance or inhibit these processes14. Therefore, the nitrogen cycling in OMZs is different from the rest of the open ocean15. Approximately 30–50% of fixed nitrogen loss in the world’s oceans occurs in oxygen minimum and deficient zones14. Quantitative paleo-reconstruction of nitrate levels could provide a comprehensive understanding of how the different processes mentioned above interacted in the past. This will help us to predict future changes in marine nutrient budgets and possible impacts of eutrophication.
Foraminifera are a group of amoeboid protists that are abundant in marine environments19, and account for a major part of benthic denitrification in the OMZs20,21,22. Many calcareous foraminiferal tests (shells) are porous. The pores in benthic foraminiferal tests play an important role in facilitating gas exchange and osmoregulation between the foraminifera and the environment23. The pore density (number of pores per unit area), mean pore size (average pore sizes of one individual), and shape of pores are important morphological features that vary among different taxa24,25,26. The porosity (% of the area of the tests occupied by the pores), and pore density of foraminifera are likely driven by environmental factors. Factors that have been suggested include latitude, water density27,28,29,30, temperature, salinity31, oxygen, and nitrate concentrations24,32,33,34. Porosity might also be genetically encoded25,35. Porosity is a species-specific trait that can be used to distinguish certain pseudocryptic species such as Ammonia spp.25. Nevertheless, within a single species phenotypic plasticity exists. Thus, porosity can be influenced by environmental conditions, and hence used as a paleoproxy.
Porosity in benthic foraminifera plays an important role in adaptation strategies by facilitating gas exchange through larger pore areas in low oxic conditions24,32,36. Cell organelles involved in respiration (i.e. mitochondria) are more abundant around the inner pore surfaces of species living in oxygen-depleted conditions than in well-oxygenated conditions23. In some foraminiferal species, increased gas exchange can be attained by either increasing the number of pores or by increasing the surface area of the test (or shell)37. However, the function of pores may vary among species because of their difference in evolutionary history38.
The shallow oxygen minimum zones of the Eastern Pacific have large standing stocks of benthic foraminiferal species39. Several benthic foraminiferal species living in oxygen-depleted environments perform complete denitrification, which is rare amongst eukaryotes40. Denitrification is the preferred respiration pathway in several foraminiferal species from oxygen-depleted environments, making these eukaryotes an important part of benthic nitrogen cycling in some environments41. Previously, it has been found that benthic foraminifera living in oxygen- or nitrate-depleted environments have higher pore density and porosity than those living under well-oxygenated conditions or high ambient nitrate concentrations24,32,33. Therefore, pore parameters of fossil shells are promising proxies for paleo oxygen and nitrate concentrations.
We determined pore parameters mean pore size, pore density, and porosity of the shallow infaunal species Bolivina spissa (see Fig. 1). Many bolivinids have an affinity for low-oxygen environments42. Bolivina spissa is well adapted to low oxygen conditions32,43, and has the ability to denitrify41, which makes it a promising species that might facilitate quantitative NO3- reconstructions.
(a) Scanning electron microscopic images of B. spissa collected from Mexican Margin (MAZ-1E-04), (water depth: 1463 m) and (b) their total area relative to first (oldest) ~ ten chambers within 50,000–70,000 μm2 measured using ZEN lite software.
We used foraminiferal specimens retrieved from five oxygen-depleted locations around the Pacific: the Gulf of Guayaquil (core M77/2-59-01), the Mexican Margin (core MAZ-1E-04), the Sea of Okhotsk (core MD01-2415), and “core-top” (i.e. surface-sediment) samples from Sagami Bay, and the continental margin of Costa Rica (Quepos Slide, core SO206-43-MUC) (Fig. 2). Here, we present a non-destructive, fast and statistically robust method for quantitatively describing the morphometrics in benthic foraminiferal tests. We applied an automated image recognition technique on scanning electron microscope (SEM) images using a deep learning algorithm to analyse the morphological features of B. spissa. Deep learning is a type of machine learning which is used to identify objects in images and allows to process data in a way according to user’s interest44.
Map showing site locations studied: Gulf of Guayaquil (M77/2-59-01, depth: 997 m), Mexican Margin (MAZ-1E-04, depth: 1463 m), Sea of Okhotsk (MD01-2415, depth: 822 m), core-top samples from Costa Rica (Quepos Slide, SO206-43-MUC, depth: 568 m), and Sagami Bay (Japan, depth: 1410 m). The map was produced using Ocean Data View (Schlitzer, R., Ocean Data View, odv.awi.de, 2017).
We studied (1) the interdependence between the pore density, porosity and mean pore size of B. spissa to demonstrate whether total porosity is mainly influenced by the number or the size of pores and (2) whether porosity or pore density can be used as a robust proxy for bottom-water nitrate [NO3–]BW reconstructions. Finally, we compare the pore density between B. spissa, Bolivina subadvena, Bolivina subadvena accumeata, and Bolivina argentea, and provide an extended nitrate vs. pore density calibration for B. spissa and B. subadvena from different locations around the Pacific.
Results
Comparison between manual and automated pore density analyses
Pore density measurements showed a 0–20% difference between manual and automated methods with an average individual difference at 4.2%. There was no significant difference in the mean pore density of all 31 specimens between the manual (0.0059 ± 0.0002 P µm–2; 1 SEM) and the automated (0.0059 ± 0.0002 P µm–2; 1 SEM) image analyses (T-test, p = 0.99). In three out of 31 cases the difference was 0% and the algorithm was counting exactly the same number of pores that have been recognized manually (Supplementary Table ST1). Only two specimens of B. subadvena showed a relatively high offset (10% and 20%). The original training of the algorithm is based on B. spissa. For future studies, which include a closer analysis of other species, we recommend an individual training for each species.
Automated pore measurements with and without manual corrections
There was no significant difference for porosity (t = 0.31, p = 0.75) and pore density (t = 0.58, p = 0.56) obtained through automated image analysis with and without manual corrections, where artefacts of the automated image analyses were manually removed (Supplementary Tables ST2 and ST3).
Interdependence between pore parameters of B. spissa.
The overall porosity values of all locations varied between 2.66% and 16.03% with a mean (± SD) of 8.52% (± 2.14%). The mean pore size varied between 5.98 µm2 and 47.62 µm2 with a mean (± SD) of 17.83 µm2 (± 3.83 µm2). The overall pore density varied between 0.002 P/µm–2 to 0.009 P/µm–2 with a mean (± SD) of 0.004 P/µm–2 (± 0.001 P/µm–2).
Specimens of B. spissa from the Gulf of Guayaquil, (M77/2-59-01) had the lowest porosity (7.14% ± 1.62%) and mean pore size (17.13 µm2 ± 4.37 µm2) of all analysed locations. The specimens from the Sea of Okhotsk, (MD01-2415) had the highest porosity (10.83% ± 1.66%) and mean pore size (20.67 µm2 ± 3.54 µm2). The mean pore density was not significantly different for the core-top samples (Costa Rica and Sagami Bay) and the down core samples from the Mexican Margin (MAZ-1E-04) and the Sea of Okhotsk. The pore density at the Gulf of Guayaquil (0.0043 P/µm2 ± 0.0008 P/µm2) was lower than at the other locations (Supplementary Table ST4).
In general, there was a significant linear correlation between mean pore size and porosity (coefficient of determination, R2 = 0.27, p = 3.19E-93, Fig. 3a; Supplementary Table ST5) for all the analysed specimens. We observed strong regional differences in R2 among the studied sites. The R2 was highest for the specimens from the Gulf of Guayaquil (R2 = 0.45, p = 5.91E-89, Fig. 3a), and lowest for the specimens from core-top samples (R2 = 0.05, p = 0.047, Fig. 3a). We found a significant linear correlation between porosity and pore density (R2 = 0.42, p = 1.36E-15, Fig. 3b; Supplementary Table ST6) among all the sampling locations with the highest R2 of 0.45 at the Mexican Margin, while the specimens from the Gulf of Guayaquil showed the weakest correlation (R2 = 0.1, p = 3.21E-17, Fig. 3b) between porosity and pore density.
Relationship between (a) porosity vs mean pore size (b) porosity vs pore density (c) pore density vs mean pore size of B. spissa specimens from Gulf of Guayaquil (M77/2-59-01), Mexican Margin (MAZ-1E-04), Sea of Okhotsk (MD01-2415), and the core-top samples (Sagami Bay and Costa Rica). Total number of specimens utilized, n = 1344.
All analysed specimens showed a significant but weak negative linear correlation between pore density and mean pore size (R2 = 0.085, p = 1.34E-27, Fig. 3c; Supplementary Table ST7). We found a higher R2 for the core-top samples collected from Costa Rica and Sagami Bay (R2 = 0.4, p = 7.18E-10, Fig. 3c), and the weakest for the samples from the Gulf of Guayaquil (R2 = 0.20, p = 4.52E-35, Fig. 3c).
The combined data from all studied locations clearly fall apart into two distinguishable clusters for both porosity and pore density: “Cluster 1” (black dashed circle Fig. 3a), grouped most of the specimens belonging to the Gulf of Guayaquil (n = 669), and “Cluster 2” (red dashed circle, Fig. 3a), consisted of specimens belonging to the Mexican Margin (n = 445), the Sea of Okhotsk (144), and the core-top samples (n = 76). The porosity was significantly different between Cluster 1 and Cluster 2 (W = 50,716; p < 2.2e -16). This also accounts for the pore density (W = 79,726, p < 2.2e-16) and the mean pore size (W = 170,008; p = 4.49e-15). All data have been included in the Supplementary Table ST8.
Inter-species comparison of pore parameters and pore density vs [NO3 –]BW calibration in the core-top samples
While core-top specimens of B. spissa and B. subadvena from Costa Rica (Quepos Slide), and Sagami Bay (Japan) had a very similar pore density, pore densities of B. subadvena accumeata and B. argentea were around 50–300% higher (Fig. 4; Supplementary Table ST9). The new data for B. spissa and B. subadvena from Quepos Slide and Sagami Bay fit well into the pore density correlation with [NO3–]BW that has been found for B. spissa from the Peruvian OMZ32 (Fig. 4). There was a highly significant linear correlation between the pore density of B. spissa and B. subadvena from Peru, Costa Rica, and Sagami Bay (R2 = 0.93, p < 0.0001, Fig. 4b). The data of B. subadvena accumeata and B. argentea were offset from this linear regression (Fig. 4a). The relationships between the pore density of B. spissa and B. subadvena from core-top samples (Costa Rica and Sagami Bay) and bottom-water oxygen (R2 = 0.43, p = 0.028; Supplementary Fig. SF1), temperature (R2 = 0.50, p = 0.015; Supplementary Fig. SF2), salinity (R2 = 0.41, p = 0.035; Supplementary Fig. SF3), and water depth (R2 = 0.48, p = 0.018; Supplementary Fig. SF4) has been analysed to test, if nitrate is the main factor that controls the pore density. These correlations are significant (R2 varies between 0.41 and 0.50; P varies between 0.015 and 0.035) but clearly weaker than the correlation of the pore density to nitrate (R2 = 0.93, p = 1.4E-6; Fig. 4b). The data for bottom-water oxygen, temperature, salinity and water depth from core-top samples have been included in Supplementary Table ST10.
Correlation between the mean pore density of different closely related Bolivina species from core-top samples and [NO3–]BW. If no species name is indicated, the analysed species was B. spissa. The specimens of B. subadvena, B. subadvena accumeata and B. argentea are all from location SO206-43-MUC off Costa Rica, except the one specimen of B. subadvena at ~ 42 μmol/kg [NO3–]BW that was collected at Sagami Bay (Japan). The linear fit (all data) has been applied to all available data for B. spissa and B. subadvena, except B. subadvena accumeata and B. argentea. (a) Pore density vs [NO3–]BW plot including all analysed Bolivina species. (b) Pore density vs [NO3–]BW plot only including B. spissa and B. argentea. The linear fit (Peru) alone was the published correlation from Glock et al.32,45 and only included B. spissa collected off Peru. Error bars are the standard error of the mean (1SEM).
Since pores were manually counted for the core-top pore density dataset off Peru from Glock et al.32, no data was available for the porosity of these specimens. A comparison of the porosity in tests of core-top samples of B. spissa from Costa Rica (9.5% ± 0.2%; 1SEM; N = 39) and Sagami Bay (9.1% ± 0.2%; 1SEM; N = 37) showed no significant difference between these two locations (p = 0.25). The Costa Rica [NO3–]BW was lower and there was a significant difference in the pore density between these two locations (p = 8.7E-5, Fig. 4). This indicated that the pore density of B. spissa might be more sensitive to changes in the [NO3–]BW than the porosity. In addition, while the pore density of B. subadvena fit very well into the pore density-[NO3–]BW correlation of B. spissa (see Fig. 4), the porosity of B. subadvena was significantly higher than the porosity of B. spissa (10.9% ± 0.5% for B. subadvena vs. 9.5% ± 0.2% for B. spissa from Costa Rica; p = 0.0002).
Discussion
Evaluation of the automatic image recognition technique
Our study tested the application of a newly developed automated image recognition method for the detection of pore parameters of the benthic foraminiferal species B. spissa. This method can be used to accurately measure pore parameters such as the mean pore size, porosity, and pore density of B. spissa. This allows a high and efficient sample throughput (less than 1 min for one specimen) compared to manual analysis (5–6 min for one specimen) of pores. This automated deep learning approach produces results statistically identical to manual analyses. No significant improvement is found, if the results from the deep learning image analyses are manually corrected by removing artefacts from the images.
Both manual determination of pores using SEM images32,46,47,48 and automated measurements49,50, have advantages and disadvantages. For example, manual methods can be laborious and time-consuming. The fully automated method by Tetard et al.50 is rapid, allows quick generation of data, and the image acquisition and processing require no monitoring, however, it needs a very specific setup and is destructive, since the specimens are broken to shards. The semi-automatic method by Petersen et al.49 can produce reliable data in a short amount of time, minimizes artefacts related to the curvature of the tests, and gives information on pore area, perimeter, and circularity indexes but focuses only on a small part of the shell, which limits the amount of data per specimen.
By contrast, porosity measurements using deep-learning as applied in this study are non-destructive and automatically determines various pore parameters on the fully visible test surface. Moreover, the fully automated method is reproducible in comparison to manual methods where the analyses are performed by different operators. The application of a non-destructive method allows the use of the foraminifera for other analyses, thereby providing the possibility to use a single sample population for a multiproxy paleo reconstruction.
Although this automated method generates large datasets, proper attention should be given to the processing of curved specimens of B. spissa, because the curvature can create difficulties in counting the exact number of pores. Therefore, we suggest utilizing specimens with flat surfaces.
Variation of pore patterns in B. spissa from different environments
All specimens of B. spissa that have been analysed showed a positive but weak correlation between the porosity and the mean pore size (R2 = 0.27, p < 0.05; Fig. 3a). Certain foraminifera species increase their porosity by increasing the size of their pores to facilitate electron acceptor uptake from the environment49,51. The strongest correlation between mean pore size and porosity at the Gulf of Guayaquil (M77/2-59-01) suggests that individuals at this location tend to increase the porosity by increasing their mean pore size rather by increasing its pore density. Similar observations were documented on Ammonia spp. that typically dwells in shallow marine environments such as tidal mudflats25. These species tend to increase their porosity by building fewer but larger pores, which has been suggested to ensure optimal shell stability34,49. The notable weaker correlation between porosity and mean pore size, for the other analysed sites (R2 between 0.05 and 0.12, Fig. 3a) implies that most of the analysed B. spissa do not control their porosity by modifying the size of the pores. This weak correlation between porosity and the mean pore size in B. spissa is an indicator that the size of the pores is only a secondary control on overall porosity of B. spissa at most of the studied locations.
The strongest significant linear correlation between porosity and pore density has been found at the Mexican Margin (MAZ-1E-04) (Fig. 3b), which suggests that B. spissa adjusts its porosity by adapting the number of pores and not the pore-size. Specimens from the Gulf of Guayaquil are exceptional as they show only a weak correlation between porosity and pore density (R2 = 0.1, Fig. 3b). Nevertheless, the negative correlation between pore density and mean pore size among the studied sites (Fig. 3c) are in good agreement with previous studies on Ammonia spp.34,49. Mechanical constraints like shell stability could be a controlling factor leading to the inverse relationship between pore density and mean pore size34. Our new data shows that, except in the Gulf of Guayaquil, B. spissa mainly controls its porosity by the number of pores.
The different trends at different locations indicate that long-term environmental conditions or genetic factors likely play a pivotal role in contributing to the morphological differences in benthic foraminifera since the sediment cores cover periods of ~ 20 kyrs. Especially at the Gulf of Guayaquil, the pore parameters showed significant differences to the other studied locations. We speculate that these differences could be related either to the mechanism of electron acceptor uptake or to genetic factors. Benthic foraminifera can actively migrate within the sediment to their preferred microhabitat52,53,54 which exposes them to an oxygen/nitrate concentration gradient. The habitat preference of B. spissa in oxygen-deficient zones necessitates the use of alternate electron acceptors like nitrate for respiration41. In nitrate-depleted habitats, B. spissa optimizes its nitrate accumulation by building more pores to efficiently take up nitrate resulting in higher pore density32. Previous observations found that the cell size of many denitrifying foraminifers is limited by nitrate availability instead of oxygen41. Several denitrifying foraminiferal species, including B. spissa, have been shown to encode a NO3– transporter in their genome and transcriptome55,56. This means by using these NO3– transporters they can actively pump NO3– into their cells, since NO3– is a charged ion. This NO3– can be stored as intracellular nitrate (ICN) which can be utilized as a source of energy for metabolic activities21,40,57,58,59 via complete denitrification during oxygen-depleted conditions.
Biogeochemical controls on the pore patterns in the Gulf of Guayaquil
The site from where core M77/2-59-01 was retrieved (3.95° S, 81.23° W) is outside core oxygen minimum zone off Peru. The modern oxygen concentration recorded closest to this site is 55 µmol/kg, which is higher than at the other studied locations (38–47 µmol/kg)60. When oxygen concentration increases above a certain threshold, there will be less overall denitrification61,62 resulting in higher nitrate availability. We speculate that if there is more nitrate in the Gulf of Guayaquil relative to the other studied locations in the modern ocean, this was likely also the case in the past. This is supported by a sedimentary nitrogen isotope record on the same core M77/2-59-01 by Mollier-Vogel et al.63 and Mollier-Vogel et al.64, which indicated that pelagic denitrification was low at this location over the entire last deglaciation. The regional differences in the patterns at Gulf of Guayaquil could be an adaptation to the continuously higher nitrate availability at this site.
Genetic controls on the pore patterns in the Gulf of Guayaquil
The B. spissa specimens from the Gulf of Guayaquil are, except for their pore characteristics, morphologically similar to the B. spissa from the other locations but could be a different phylogenetic strain. Observations of Ammonia specimens by Hayward et al.46 suggested that genetically different species can also be morphologically distinguished. Later studies found genetically well-separated species of the Ammonia genus, which have earlier been considered as eco-phenotypes of Ammonia, can now be morphologically distinguished by their pore patterns and other subtle morphological features25. Similarly, it is possible to have the existence of genetic variation and cryptic species within a B. spissa morpho-group due to the wide geographical distances, and variability in ecological conditions that separated oxygen-depleted regions in the Pacific. Nevertheless, the phylotypes of B. spissa without a combined morphometric molecular analysis would be very difficult to discriminate as a separate species.
An extended modern pore density vs. nitrate calibration
Since there are studies that use either the pore density or porosity to reconstruct past environmental conditions32,33,45,65,66 we intended to address whether pore density or porosity is a better proxy for quantitative nitrate reconstructions. Although pore density in B. spissa shows a significant correlation to nitrate (Fig. 4b), the correlation between porosity and nitrate availability has not been systematically tested, yet. In addition, an extension of the local nitrate vs. pore density calibration for the Peruvian OMZ32 to other regions and foraminiferal species would increase the applicability of this proxy.
Figure 4 shows the relationship between pore density in other bolivinids and [NO3–]BW from core-top samples at different locations of the Pacific. The linear correlation between the pore density of B. spissa and B. subadvena and [NO3–]BW is highly significant and much stronger than the correlation to oxygen, temperature, salinity or water depth (Supplementary Figs. SF1 to SF4), making their pore density a promising proxy for present and past [NO3–]BW. This also suggests a close phylogenetic relationship with similar metabolic adaptations of both species. Indeed, B. spissa was originally classified as a variant of B. subadvena with the name B. subadvena var. spissa67 and 7 out of 7 Bolivina species that have been tested for denitrification were able to denitrify and 11 out of 12 analysed species intracellularly stored nitrate [Ref.68 and references therein]. Although B. subadvena accumeata is still considered a subspecies of B. subadvena, the pore characteristics are distinct from either B. spissa or B. subadvena. The pore density of B. argentea is elevated compared to the other species as it tends to build numerous but very small pores (see Fig. 5).
Scanning electron microscopic images of bolivinids (a) B. spissa, (b) B. subadvena (c) B. subadvena accumeata, and (d) B. argentea.
Therefore, the pore density of B. spissa and B. subadvena both can be used to reconstruct past [NO3–]BW conditions according to the following equation (Eq. 1), However, B. subadvena accumeata and B. argentea should be avoided, when the calibration shown in Eq. (1) is used. Future studies will show, if the latter two species also show species-specific relationships that might be used for paleoceanographic reconstructions.
where PD is the pore density.
While the pore characteristics of denitrifying foraminifera are promising paleoproxies for past [NO3–]BW32,45, pore characteristics of the epifaunal species Cibicidoides and Planulina spp. that likely rely on O2 respiration seem to be good indicators for past bottom-water oxygen concentration [O2]BW33,65. Intriguingly, while the new data on B. subadvena, and B. spissa indicate that pore density is more sensitive to ambient [NO3–] variations than the total porosity, it appears that the opposite is the case for epifaunal species. In Cibicidoides and Planulina spp. porosity is more sensitive to ambient [O2] fluctuations than the pore density33,65.
Data from only two sites for the correlation between total porosity of bolivinids and [NO3–]BW are available. Future studies should address this issue and include both the pore density and total porosity. The fact that porosity of B. spissa from the Sagami Bay and Costa Rica core-tops are similar, but the pore density at Costa Rica is significantly higher indicates that the Sagami Bay specimens build larger pores than the specimens from Costa Rica.
The different pore characteristics of denitrifying bolivinids and the aerobic epifaunal species might be related to the mechanism of electron acceptor uptake. The uptake of O2 is limited by passive diffusion, since O2 is not charged and foraminifera have no respiratory organs that can actively take up O2. Thus, aerobic foraminifera can only increase the O2 uptake through the pores by increasing the area of pores on their test (i.e. total porosity), which can be done by either creating more pores (increase in pore density) or larger pores (increase in mean pore size). Some foraminifera species ensure better shell stability by increasing their porosity through building less but larger pores34. Thus, the increase of total porosity of epifaunal Cibicidoides and Planulina spp. might also be restricted by shell stability. They tend to build larger pores to increase their porosity, which might explain the weaker correlation between pore density and ambient [O2] compared to total porosity33,45. Denitrifying bolivinids can actively pump NO3– into their cells, since NO3– is a charged ion and they genetically encode nitrate transporters55,56. Thus, we hypothesize that the denitrifying bolivinids do not rely on the increase of total porosity but rather on the number of pores to enhance electron acceptor uptake. For the moment, the empiric correlation between the pore density of B. spissa and B. subadvena appears to be solid, since a deglacial pore density record of B. spissa from the Peruvian margin reconstructed similar [NO3–]BW as other proxies and various modeling studies45.
Conclusions
The application of automated image analysis through deep-learning provided a robust method for determining the pore patterns in the shallow infaunal benthic foraminiferal species B. spissa. The differences in pore patterns of B. spissa found between different studied locations suggest caution in the interpretation of the results. Nevertheless, our new data shows that, except for the Gulf of Guayaquil, B. spissa mainly controls its porosity by the number of pores. This gives additional validation that the pore density of B. spissa is a robust and reliable paleo-proxy for nitrate concentrations in bottom-waters. Quantitative reconstructions of past bottom-water nitrate concentrations could help us to predict the environmental and ecological impacts of future climate scenarios. Moreover, understanding the factors controlling porosity in bolivinids provides insight into benthic denitrification, which is indispensable for future biogeochemical studies. Future studies concerning foraminiferal porosity should consider both mean pore size and pore density, and a combined morphometric molecular approach for the complete description of foraminiferal pore patterns. As the presence of cryptic species within a morphogroup might complicate paleoceanographic interpretation of pore density or porosity in benthic foraminifera, the phylogenetic analyses of Bolivina species is highly relevant for better proxy validations.
Methods
Sampling of sediment cores
The piston core M77/2-059-1 (03° 57.01′ S, 81° 19.23′ W, recovery 13.59 m) was retrieved from the Gulf of Guayaquil at 997 m water depth during RV Meteor cruise M77/2 in 2008. The chronostratigraphy is based on accelerator mass spectrometry radiocarbon dating (AMS14C) of planktonic foraminifers, supported by benthic stable oxygen isotope (δ18O) stratigraphy from Uvigerina peregrina69,70. The CALYPSO giant piston core MD01-2415 (53° 57.09′ N, 149° 57.52′ E, recovery 46.23 m) was recovered from the northern slope of the Sea of Okhotsk at 822 m water depth during WEPAMA cruise MD122 of the R/V Marion Dufresne71,72. The chronostratigraphic framework of core MD01-2415 is based on a combination of stable oxygen-isotope stratigraphy, AMS14C dating, and orbital tuning72. The piston core MAZ-1E-04, Mexican Margin was collected on board the RV El Puma at a water depth of 1468 m. The core, SO206-43-MUC was retrieved in 2009 from a sea mound slope (Quepos Slide) off Costa Rica during RS Sonne cruise SO206 using a multicorer. Supernatant water of the multicorer SO206-43-MUC was carefully removed. Then, the core was gently pushed out of the multicorer tube. For the foraminiferal analyses, the core was cut into 10 mm thick slices (upto 20 cm depth) and samples were transferred to Whirl-Pack™ plastic bags and stored at a temperature of 4 °C.
The sediment samples from central part of Sagami Bay were collected by a push core (inner diameter: 8.2 cm, tube length: 32.0 cm) using the manipulator of human occupied vehicle Shinkai6500 in 2021 (Table 1 shows the details of all sampling locations). The surface 2 cm of the sediment was subsampled by extruding from the push core tube and then kept frozen prior to an isolation of foraminifera. Bottom-water temperature, salinity, and dissolved oxygen concentrations were 2.3 °C, 34.5, and 56.4 µM, respectively, which were measured with the CTDO sensor (Seabird SBE19).
Sample processing
All sediment samples from the Gulf of Guayaquil (M77/2-59-01), Mexican Margin (MAZ-1E-04), Sea of Okhotsk (MD01-2415), Costa Rica (SO206-43-MUC), and the Sagami Bay were washed and wet-sieved through a 63 µm mesh sieve. The residues were dried in an oven at temperatures between 38 and 50 °C. Afterwards the samples were fractioned into the grain-size fractions of 63–125, 125–250, 250–315, 315–355, 355–400, and > 400 μm. Specimens of Bolivina spissa, Bolivina subadvena, Bolivina subadvena accumeata and Bolivina argentea were picked from the 125–250 μm fraction. Only megalospheric specimens of B. spissa, were used for the pore analysis.
Bottom-water nitrate analyses at core-top locations
Supernatant water was sampled for the analysis of bottom-water NO3– concentrations in a core replicate from the multicore deployment at Costa Rica, (SO206-43-MUC). For the bottom water sample, a total of 2 ml was passed through a cadmium (Cd) catalyst to reduce NO3– to NO2– (nitrite), which was then analysed on-board using photometry. The resulting concentration is a mixture of NO3–, and NO2–. Since NO2– is a transient intermediate species in the benthic nitrogen cycle and is generally present at lower concentrations than NO3–, the NO2– concentration determined is assumed to approximately represent the concentration of NO3–.
For Sagami Bay nitrate analyses, ~ 20 mL of overlying water was gently collected using a tube. The overlying water was filtered through a 0.45 µm membrane filter and then stored at − 25 °C before nutrient analyses back in land-based laboratory. Nutrient concentrations were measured with a continuous-flow analyzer (BL-Tech QUAATRO 2-HR system, Japan)73. The data for the Peruvian OMZ cores has been taken from32.
Bottom-water salinity, temperature and oxygen at core-top locations
Bottom-water conditions at the locations that have been used for the core-top calibrations are shown in Table 2. Salinity, oxygen and temperature for the Costa Rica core have been taken from the World Ocean Atlas location 24,671(B), 84.5° W, 8.5° N and 550 m depth60. At the Sagami Bay location bottom-water temperature, salinity, and dissolved oxygen concentrations were measured with the CTDO sensor (Seabird SBE19). Data for bottom-water oxygen and temperature at the locations from the Peruvian OMZ were taken from32. Salinity data for the Peruvian OMZ was taken from74, using the CTD-data at M77/1-501/CTD-RO-23.
Image acquisition
A total number of 23 sample depths from the Mexican Margin (MAZ-1E-04), 37 sample depths from the Gulf of Guayaquil (M77/2-59-01), 12 sample depths from the Sea of Okhotsk (MD01-2415), and 2 core-top samples from Sagami Bay (Japan) and Costa Rica, (SO206-43-MUC) were utilized. All specimens of B. spissa were mounted onto carbon pads and photographed using Scanning Electron Microscope (version: Hitachi Tabletop SEM TM4000 series). All images were captured at a magnification of 150x. Due to the more or less flat surface of B. spissa, pore openings were generally well-defined, and clearly distinguishable from the SEM images. The total area on the tests of the specimens were determined using the Zeiss ZEN lite software (version: ZEN 3.4 blue edition; https://www.zeiss.com/microscopy/de/produkte/software/zeiss-zen-lite.html).
Size normalization
To reduce ontogenetic effects, the total area equivalent to the first (oldest) ~ ten chambers (covering 50,000–70,000 µm2) were measured for the quantification of pore parameters32. The pore density increases with each newly built chamber (Fig. 5), related to a decrease in the surface/volume ratio with the size of the specimens. If the more recent 1–2 chambers would be analysed, only specimens within the same ontogenetic stage could be used. i.e. the size of the specimen and the number of chambers should be the same in all the chosen specimens. It is practically impossible to use only specimens having exactly the same number of chambers. By sticking to the oldest chambers of the foraminifer the ontogenetic effects are minimized by size normalization. Considering the short life span of foraminifera, the data from earlier ontogenetic stages still provide a proper representation of the present situation.
Moreover, the larger area provides a statistically robust, and larger dataset for each analysed specimen65.
Automated image analysis
A total number of 1344 fossil specimens of B. spissa sampled from five different sampling locations were analysed. Porosity measurements were made on 6–20 well-preserved specimens of B. spissa in each of the studied locations. The pore density, mean pore size, and porosity were determined with an automated image analyzing software Amira (version: AmiraTM 3D pro) using a previously trained deep-learning algorithm. The deep learning algorithm that has been used for this study is included in the Amira software package. We used a convolutional neural network model (UNet) backboned with a resnet18 model for the deep learning training. The deep learning algorithm was trained with manually segmented pores on 52 images of B. spissa. In total 17,649 pores have been segmented manually for the deep learning training.
Only those specimens that had a total area equivalent to at least 50,000 µm2 were used for the automated analysis. The main steps for porosity measurements in Amira were:
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Import of multiple SEM images.
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The deep learning algorithm to recognize the pores was applied on imported images.
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Only the oldest chambers that fit within the total area of 50,000 to 70,000 µm2 were taken into acccount (Fig. 1b). All chambers beyond this threshold were manually removed, using the segmentation tools in the Amira software.
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A table with all measured pore characteristics can be exported by the software at the end of each set of analysis.
Comparison of manual vs. automatic pore density determination
To assess the reliability of the deep learning algorithm pore density was determined manually for 31 specimens belonging to the species B. spissa (27 specimens) B. subadvena (3 specimens) and B. subadvena accumeata (1 specimen). For four additional specimens of B. argentea pore density was determined manually, since the pores in this species are very small and not recognized by the deep learning algorithm that was trained with images of B. spissa. The detailed procedure for manual pore density determinations are published in Glock et al.32.
Automated pore measurements with and without manual corrections
To explore whether manual corrections (i.e. corrections done on the specimens that were automatically pore analysed) made a significant difference on automated data, a total number of 858 specimens were randomly selected and analysed both with and without manual correction. To apply manual corrections, we removed all artefacts (i.e. unwanted particles on the surface of B. spissa) on each specimen during the automated image analysis and obtained porosity data. For the automated image analysis without manual corrections, we applied the method of analyzing each specimen without manually removing the artefacts. Statistical analysis was carried out to decide if the porosity data obtained through either of these methods were significantly different or not. The data have been included as supplementary information in Supplementary Tables ST2 and ST3.
The preliminary statistical analysis was carried out in Excel and verified using R. To test the normality of the samples, we used Shapiro–Wilk normality test whenever necessary. To determine the correlation between pore parameters, a linear ordinary least-square regression was used. For normal distributions, we used the parametric Student’s t test (t), and for non-normal distributions we used the non-parametric Wilcox test (W). All the data generated or analysed during this study have been included in the supplementary information files.
Data availability
All data generated or analysed during this study are included in the tables of this published article (and its Supplementary Information files).
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Acknowledgements
We are grateful to the micropaleontology group at the Universität Hamburg. We greatly acknowledge the help of Dr.Yvonne Milker with the SEM, Jutta Richarz, Kaya Oda for lab support, and student assistants Hanna Firrincielli and Hannah Krüger. In addition, we thank Anke Bleyer for performing the nitrate analyses during Sonne cruise So206. Funding was provided by the Deutsche Forschungsgemeinschaft (DFG) through both N.G.’s Heisenberg grant GL 999/3-1 and grant GL 999/4-1. Funding for the core MD01-2415 recovery was provided by the German Science Foundation (DFG) within project Ti240/11-1. We thank Yvon Balut, Agnes Baltzer, and the Shipboard Scientific Party of RV Marion Dufresne cruise WEPAMA 2001 for their kind support. The recovery of core M77-59 recovery was a contribution of the German Science Foundation (DFG) Collaborative Research Project “Climate–Biogeochemistry interactions in the Tropical Ocean” (SFB 754). The study is a contribution to the Cluster of Excellence ‘CLICCS—Climate, Climatic Change, and Society’, and a contribution to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg.
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Open Access funding enabled and organized by Projekt DEAL. We acknowledge financial support from the Open Access Publication Fund of Universität Hamburg.
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A.G.M. wrote the core manuscript, did the sample preparation, electron microscopy of the fossil foraminifera and image and statistical analyses of all samples. N.G. planned the sampling strategy and study design, did onboard sampling during So206 and did the electron microscopy and analyses of the core-top samples. G.S. hosted the research group, and provided access to SEM, and lab facilities at the Universität Hamburg. D.N. provided sampling material for cores MD01-2415 and M77/2-59-01. C.D. provided sampling material for core MAZ-1E-04 and H.N. and I.S. provided the core-top samples and environmental parameters from Sagami Bay. All authors contributed to discussing the data and writing the manuscript.
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Govindankutty Menon, A., Davis, C.V., Nürnberg, D. et al. A deep-learning automated image recognition method for measuring pore patterns in closely related bolivinids and calibration for quantitative nitrate paleo-reconstructions. Sci Rep 13, 19628 (2023). https://doi.org/10.1038/s41598-023-46605-y
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DOI: https://doi.org/10.1038/s41598-023-46605-y
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