the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Nov 2025
New planktonic foraminifera-derived transfer function for the South Atlantic Ocean: Palaeoceanographic implications for the Brazil- Malvinas Confluence
Abstract. Planktonic foraminiferal assemblages are extensively used for reconstructing sea surface temperature through the application of transfer functions. Nonetheless, it has been observed that several parameters present throughout the water column also influence compositional changes within these assemblages. Selection of driving factors and evaluation of transfer function performances are method-specific processes that require the combination of prior ecological knowledge and objective variable selection approaches. In this study, we compiled a 171 core-top samples dataset of planktonic foraminifera and productivity-related variables to quantify the relationship between the assemblages and modern productivity conditions in the South Atlantic Ocean. Multivariate statistical analyses revealed that planktonic foraminiferal species were related to austral summer nitrate, explaining an independent and significant proportion of variance in the species data. We evaluated different prediction models, and estimated their performances considering spatial autocorrelation. The calibration model Weighted Averaging with tolerance downweighting and inverse deshrinking (WATOL_inv) with h-block cross validation showed a regression coefficient of r2cv = 0.938, with a root-mean-square error of prediction RMSEP = 1.578 սmol l-1. The resulting transfer function was applied then to sediment core GeoB2806-4 (~37° S – 53° W; 3500 m) in order to reconstruct variations of summer nitrate concentration during the Holocene. Our reconstructed summer nitrate shows a general decreasing trend from early to mid-Holocene associated with increased biological uptake, and a later increase of it towards the late Holocene. We suggest that changes in summer surface nitrate concentration are linked to the latitudinal shifts of the Brazil-Malvinas Confluence. Understanding the displacement of the Confluence, and the associated shifts in the upper layers’ nutrient availability, is crucial to evaluate the implications of these changes on the local to regional ecosystem dynamics and trophic structure, particularly when considering future climate projections.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-5531', Anonymous Referee #1, 05 Dec 2025
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AC1: 'Reply on RC1', Paula Albarracin, 13 Feb 2026
We would like to thank the reviewer for their careful reading of our manuscript and their constructive comments. Below we have copied the review in full and provide our response in bold text.
We feel that thanks to these suggestions the manuscript will improve considerably and hope that our proposed revision will meet the criteria for publication in Climate of the Past.
Paula B. Albarracin
On behalf of all authors.
R1.2:My main concern is that the authors do not demonstrate that the nitrate concentration is more important than temperature (or other physical environmental variables) for planktonic foraminifera species assembly. The authors restrict their analysis to nutrient and productivity related variables, but a priori ruling out the influence of temperature is no evidence that it plays no role and that an (independent) reconstruction of nitrate concentration is possible.
We appreciate the reviewer’s perspective regarding the dominant role of temperature in structuring planktonic foraminifera communities, a view supported also by global analyses of plankton diversity gradients (Ibarbalz et al. 2019; Righetti et al. 2019). However, we would like to clarify that our choice of nitrate as a target for reconstruction does not imply that temperature is unimportant, but rather that it is not the sole driver of community structure, especially at regional scales. We also recognize that sea-surface temperature represents the primary control on planktonic foraminifera distributions in the South Atlantic, as documented in previous studies (i.e., Kucera et al., 2005). Based on the well established premise that temperature is the main factor that structures (modern) planktonic foraminiferal assemblages, the scientific challenge addressed here is whether a transfer function can be developed to reconstruct secondary environmental gradients, such as productivity-related variables. From an ecosystem perspective, environmental drivers are rarely independent because they are coupled through feedbacks and indirect interactions (Wootton, 2002; Chafron et al., 2021; Gleich et al., 2025).While we agree that temperature acts as the main variable determining the presence or absence of species at a global latitudinal scale (Ibarbalz et al. 2019; Righetti et al. 2019) which is consistent with the metabolic theory of ecology (Brown et al. 2004), secondary factors such as nitrate concentration or turbulence (i.e., Righetti et al. 2019), trophic status, and influence of coastal waters (Piredda et al. 2017) can generate nonmonotonic ecological responses.
According to the scale at which these environmental factors seem to operate (i.e., regional, subregional, local), these variables can act as "fine-scale filters" that determine relative abundances and, hence, the final community structure (Del Bello et al. 2012). Thus, parameters with limited explanatory power in global temperature-dominated models may still play structurally meaningful roles within regional systems. Consistent with this framework, our results indicate that planktonic foraminiferal communities respond to an interconnected environmental landscape in which nitrate contributes significantly to assemblage structure, even though temperature explains the largest fraction of variance.
In summary, reconstructing nitrate is therefore not intended to challenge the primacy of temperature, but to capture the nutritional state of the ocean, which can decouple from temperature under certain (paleo)oceanographic conditions. Because nitrogen availability is a primary limiting factor for marine phytoplankton (Basu and Mackey, 2018) and future climate change is expected to alter temperature, pH, and nutrient regimes (Chafron et al., 2021), excluding nitrate solely because it is secondary at the global scale would overlook the multidimensional ecological niche of planktonic foraminifera and constrain our ability to understand marine ecosystem dynamics and vulnerability (Piredda et al., 2017; Del Bello et al., 2012).
The study by Lessa et al in itself is not sufficient proof as it is based on plankton tows and the implications for sedimentary assemblages, which are vertically and temporally integrated, is not yet clear.
We agree with the Reviewer that plankton tow data cannot be directly equated with sedimentary assemblages, which integrate signals over longer temporal and vertical scales. However, we would like to clarify that our interpretation does not rely on the study by Lessa et al. (2020). That study is cited solely as supporting evidence showing that so-called secondary environmental factors can influence the structure of modern planktonic foraminiferal communities within the mixed layer in the South Atlantic.
All core-top sedimentary assemblages (thanatocoenoses) are time-averaged. Nevertheless, the development of transfer functions is inherently based on the assumption that the uppermost sediment centimeters represent modern mean environmental conditions, which are themselves also temporally integrated. Thus, sedimentary assemblages from the calibration dataset and the reconstructions are treated consistently within the same temporal and spatial framework. Therefore, our analysis remains consistent in terms of scale and signal integration, and the comparison between modern and fossil assemblages is conducted under equivalent assumptions.
The authors may be right, but they need to redo their analysis with a wider suite of environmental variables (including temperature) to prove this. Only if this analysis reveals that nitrate is an important predictor of assemblage composition that can be disentangled from the temperature effect can they proceed with the reconstruction.
Following the reviewer’s recommendation, we have now carried out an exploratory multivariate analysis. We applied a Multiple Factor Analysis (MFA) to analyze jointly (i) the planktonic foraminiferal assemblage dataset; and two major groups of abiotic descriptors resolved seasonally: (ii) nutrient and productivity-related variables (iron, chlorophyll-a, Silicate, nitrate and phosphate); and (iii) physical variables (temperature and salinity). The MFA provides a symmetric framework to investigate patterns of covariation among multiple datasets measured on the same samples, without assuming any a priori causal hierarchy among variables (Dray et al., 2003). The results of this analysis confirm that winter sea-surface temperature emerges as the variable most strongly associated with the main ecological gradient whereas productivity-related variables, including nutrients and chlorophyll-a, show a strong contribution to the first and subsequent dimensions, indicating that their influence on assemblage composition can be statistically distinguished from the temperature signal.
This analysis would be incorporated into the new version of the manuscript, with a concise description in the main text and full methodological and graphical documentation provided in the Supplementary Material.
In this regard it is surprising to see that the authors compare their reconstructions of nitrate concentration with reconstructions of seawater temperature based on exactly the same species assemblages, but there is no indication that is possible to derive independent reconstructions of both variables
As mentioned above, our aim was to explore the influence of secondary environmental variables that are commonly overlooked in planktonic foraminifera–based reconstructions.
The observed correspondence between reconstructed nitrate concentration and temperature-based reconstructions for core GeoB2806-4 reflect shared underlying oceanographic processes controlling nutrient availability. Nevertheless, in response to the reviewer’s concern, we have decided to remove the annual mixed-layer temperature reconstruction and retain the totally independent reconstruction of the latitudinal migration of the Brazil–Malvinas Confluence (Voigt et al. 2015), as both signals reflect the same underlying oceanographic mechanism: the northern advection of the Malvinas Current. This modification would be implemented in the new version of the manuscript.
R1: “My second concern is with the selection of the samples included in the calibration dataset. In the method section it is written that they use over 300 samples, but in the results fewer than 200 are mentioned. It is unclear why some samples were excluded (and what the effect is). On a more fundamental level, what is the rationale of working with a subset of the available data (Siccha and Kucera 2017)? Please explain as this likely influences the transfer function model and the reconstruction”.
We appreciate the reviewer’s concern, and understand that this section was probably not clear enough in the original manuscript.
The compilation of Siccha and Kucera (2017) encompasses global environmental gradients and oceanographic regimes. Even when first transfer-function approaches often relied on global or supra-regional calibration datasets, subsequent methodological work has shown that partitioning calibration datasets into sub-basin or regional subsets can substantially improve model performance and ecological interpretability (e.g., Kucera et al., 2005). In this study, we therefore decided to work with a subset of the Siccha and Kucera dataset in order to regionalize the calibration and better resolve secondary gradients. Including the 300 samples of the South Atlantic in a single calibration framework inevitably increases the secondary gradients reconstruction uncertainty.
Sites were selected using an objective criterion based on Euclidean distance in environmental space, ensuring coverage of the full range of the reconstructed variable while focusing on locations influenced by key, climate-dependent oceanographic processes. This strategy prevents the dominant primary gradient from masking secondary gradients, which would otherwise hinder the evaluation of their influence on planktonic foraminiferal assemblage structure.
By adopting this approach, the calibration dataset retains the full variability of the reconstructed environmental variable while improving reconstruction accuracy. The criteria used for sample selection and the resulting number of sites would be better described in the revised Methods section.
R1: “My third major concern is about what nitrate concentrations actually tell about the environment. The authors seemingly use nutrient concentrations and productivity interchangeably (e.g. L 545 “4.4 Holocene paleoproductivity reconstruction in the BMC (WSA)”), but they are different. The nutrient concentration reflects what is left over after utilisation by primary producers and it is hence not related to primary production in a straightforward way. The fact that nitrate concentration and chlorophyll-a concentration (probably a better indicator of productivity) plot perpendicular to each other in the RDA plots underscores this. So if it is possible to reconstruct nitrate concentrations in the South Atlantic, then a reinterpretation of the results is still needed”.
We agree with the reviewer´s concern. Therefore, in the new version of the manuscript we would replace the term “productivity” by “biological uptake” when referring to nutrient concentration interpretation to more accurately reflect the processes being described.
The purpose of the RDA is to identify the dominant environmental gradients structuring planktonic foraminifera species assemblages. Nitrate_summer (together with Silicate) forms the longest vector and explains the largest fraction of constrained variance (Kent, 2012). Chlorophyll-a, while ecologically meaningful, captures a different aspect of the system related to the biological response, and therefore occupies an orthogonal position in the ordination space. The near-orthogonality between Nitrate_summer and chlorophyll-a simply indicates a weak linear correlation, meaning that these variables describe different and largely independent dimensions of the environment (ter Braak, 1994).
MINOR COMMENTS
L1: the title is vague as to what this transfer function is actually for.
We appreciate the reviewer’s comment. We have modified the title following the suggestion in order to improve its clarity.
Title: Can planktonic foraminifera-derived transfer function for secondary environmental variables yield meaningful results? Implications for the subtropical western South Atlantic
L109-111: “In order to…” I don’t understand the reasoning here…. please describe the selection process (see also above).
The section 2.1.1 Surface samples are entirely rewritten following the main concerns of the reviewer.
L124: “The temporal ranges…” please describe better. I can see how temperature is affected by global warming, but the effect on the other variables is less clear. For most variables there is also observational and climatology data available for earlier times, so why not use those if the effect of global change is a concern?
We understand the reviewer’s concern regarding the temporal representativeness of the environmental variables. This methodological decision follows the criteria outlined by Hohmann et al. (2020). To clarify this point, we have now explicitly included this reference:“Following the criteria proposed by Hohmann et al. (2020), the temporal ranges selection was chosen to minimize the influence of recent anthropogenic warming on the environmental datasets.”
L126: “Therefore, we analyzed seasonal averages…” I don’t understand the “therefore” here. How does this reduce the influence of global warming?
We thank the reviewer for pointing this out. We agree that the use of “therefore” was unclear in this context, and we have revised the manuscript and improve the logical flow of the sentence.
L131: “Sediment core GeoB2806-4 (37°50’S - 53°08.6’W, 3500 m depth; García Chapori et al., 2015) was used for testing the transfer function developed here.” Testing implies that the true nitrate concentrations during the Holocene were known. Please reword as this is not a test, but an application.
We thank the reviewer for pointing this out. We agree that the term “testing” was misleading. We have therefore revised the manuscript and reworded this sentence to clarify that the transfer function is applied to the sediment core rather than tested against known values.
L153: “morphotypes” should be subspecies (Morard et al. 2019).
We thank the reviewer for pointing out this mistake. We mention the two subspecies Globigerinoides ruber albus and Globigerinoides ruber ruber in replacement of the morphotypes “white” and “pink” following Morard et al. (2019).
L154: why were rare species excluded from the analysis?
This is a very common procedure when multivariate analyses are performed. This decision follows the criteria of Fatela and Taborda (2002). To clarify and strengthen this methodological choice, we have added additional references supporting this approach (e.g., Birks et al., 2010; Lopes et al., 2010).
L155: how does log transformation standardise the variance?
This is a very common procedure when multivariate analyses are performed. We follow Aitchison and Egozcue (2005), who suggested that the log-ratio transformation is based on a one-to-one correspondence between compositional vectors and their associated log-ratio vectors, such that any statement about compositions can be reformulated in terms of log ratios, and vice versa. The main advantage of this transformation is that it removes the problem of a constrained sample space, the unit simplex, into an unconstrained multivariate real space, thereby allowing the application of standard multivariate techniques. To clarify this point, we have added the corresponding reference to the manuscript.
L203: which dissimilarity metric was used. And why five analogues?
We thank the reviewer for noticing this mistake regarding the MAT. We use the Square Chord Distance as dissimilarity metrics and ten instead of five analogs. This has now been corrected in the manuscript.
Transfer function performance: I don’t understand why the authors assess the transfer function not immediately using h-block crossvalidation and neither why they don’t evaluate all models in this way. Perhaps a different model than the WATOL_inv one performs better with h-block CV than shown in Table 4?
The assessment of spatial autocorrelation is first performed on the calibration dataset, following Telford and Birks (2009). Only if significant spatial autocorrelation is detected, it is necessary to apply h-block cross-validation in order to obtain unbiased performance estimates in spatially structured environments. H-block cross-validation omits all samples within h kilometres of the test sample and is therefore specifically designed to account for spatial dependence. Based on this framework, model performance was first evaluated using “standard” cross-validation to identify the most suitable model, and h-block cross-validation was subsequently applied to account for spatial autocorrelation and provide a conservative performance estimate. This approach ensures methodological consistency while avoiding unnecessary penalization of all models prior to assessing spatial structure.Also, is the evaluation of the performance based on the R2 and RMSE sufficient? I realise that this is the usual approach, but the authors show two other metrics (related to bias), but don’t use them.
In transfer function development, model performance is commonly evaluated using the root mean square error of prediction (RMSEP) together with complementary statistics such as the coefficient of determination (r²), mean bias, and maximum bias (e.g., Birks, 1995; Barrows et al., 2000; Barrows and Juggins, 2005; Simpson, 2007, 2011). While bias-related metrics provide useful diagnostic information, RMSEP remains the primary criterion for selecting models for palaeoenvironmental reconstructions, as it directly quantifies overall predictive error and is generally used as the uncertainty estimate applied to fossil reconstructions (Birks et al., 2010). For this reason, RMSEP is considered sufficient as the main metric for model performance evaluation in this study. Additionally r² is also considered.
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Citation: https://doi.org/10.5194/egusphere-2025-5531-AC1
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AC1: 'Reply on RC1', Paula Albarracin, 13 Feb 2026
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RC2: 'Comment on egusphere-2025-5531', Anonymous Referee #2, 19 Dec 2025
The manuscript from Albarracin et al. explores the possibility to establish a transfer function linking nutrients and planktonic foraminifera in the South Atlantic Ocean. To do so, the study combines environmental variables extracted from data compilations (e.g., WOA, MODIS) and planktonic foraminifera assemblages from surface sediment samples (Siccha and Kucera 2017 ; Garcia Chapori and Kucera 2019 ; new material). They provide new foraminifera counts and use multiple statistical and analytical approaches to define which nutrient is best correlated/predicting species assemblages (here – nitrate) and propose a function to reconstruct its concentration throughout the holocene as a « proof of concept ».
The paper presents an important piece of work with extensive data handling and analyses and the authors address a very relevant question: what else than temperature (can) predict planktonic foraminifera distribution? I therefore believe that this study deserves attention.
While I support this explorative question and original approach, it seems that the authors did not consider the « classical » explanatory variable of planktonic foraminifera distribution – Temperature - at all. This means that they did not check for e.g., possible co-variance of nutrients concentration and temperature in the region. It could well be that in the current situation, even considering « temperature », « nitrate » would be the variable to retain, but I strongly believe that it should be tested, together with other variables such as salinity. I therefore recommend the authors to re-run their analyses including the classical variables and then, if nitrates (or any other nutrient) still comes out strong – pursue with their current reasoning.
Please find hereafter a couple of other detailed and minor points:
L.43, maybe also read and cite Strack et al., 2023?
2.1.2, I would recommend the authors to include a Table and/or Figure that summarizes the information and show the span around the variables for each calculated seasonal means.
An idea could be to make ODV maps of the environmental variables for the different seasons. This would visually keep their aesthetic but help the reader see the distribution of these elements and better assess direct links with the foraminifera’s population dynamic.
In Figure 6, deep dwellers such as G. truncatulinoides plot next to surface sp. such as G. ruber while having completely different ecology which to me doesn’t really support the introduction of the discussion. The findings of Lessa are based on tow samples and represent a snapshot rather than smoothed annual/pluriannual signal similar to the one « stored » in the surface sediments of this study.
L.420 to 425, in my opinion, what controls the presence of the species found in the South WSA is very likely temperature – the authors might miss that, not including temperature as a first level analysis, before looking at the nutrients etc…
Citation: https://doi.org/10.5194/egusphere-2025-5531-RC2 -
AC2: 'Reply on RC2', Paula Albarracin, 13 Feb 2026
We would like to thank the reviewer for their careful reading of our manuscript and their constructive comments. Below we have copied the review in full and provide our response in bold text.
We feel that thanks to these suggestions the manuscript will improve considerably and hope that our proposed revision will meet the criteria for publication in Climate of the Past.
Paula B. Albarracin
On behalf of all authors.
While I support this explorative question and original approach, it seems that the authors did not consider the «classical» explanatory variable of planktonic foraminifera distribution – Temperature - at all. This means that they did not check for e.g., possible co-variance of nutrients concentration and temperature in the region. It could well be that in the current situation, even considering «temperature», «nitrate» would be the variable to retain, but I strongly believe that it should be tested, together with other variables such as salinity. I therefore recommend the authors to re-run their analyses including the classical variables and then, if nitrates (or any other nutrient) still comes out strong – pursue with their current reasoning.
We appreciate the reviewer’s perspective regarding the classical explanatory variable of planktonic foraminifera distribution. However, we would like to clarify that the scientific challenge addressed here is whether a transfer function can be developed to reconstruct secondary environmental gradients (i.e., nitrate), especially at regional scales, due to parameters with limited explanatory power in global temperature-dominated models may still play structurally meaningful roles within regional systems.
Following the reviewer’s recommendation, we have now carried out an exploratory multivariate analysis. We applied a Multiple Factor Analysis (MFA) to analyze jointly (i) the planktonic foraminiferal assemblage dataset; and two major groups of abiotic descriptors resolved seasonally: (ii) nutrient and productivity-related variables (iron, chlorophyll-a, silicate, nitrate and phosphate); and (iii) physical variables (temperature and salinity). The MFA provides a symmetric framework to investigate patterns of covariation among multiple datasets measured on the same samples, without assuming any a priori causal hierarchy among variables (Dray et al., 2003). The results of this analysis confirm that winter sea-surface temperature emerges as the variable most strongly associated with the main ecological gradient whereas productivity-related variables, including nutrients and chlorophyll-a, show a strong contribution to the first and subsequent dimensions, indicating that their influence on assemblage composition can be statistically distinguished from the temperature signal.
This analysis would be incorporated into the new version of the manuscript, with a concise description in the main text and full methodological and graphical documentation provided in the Supplementary Material.
MINOR COMMENTS
L43: maybe also read and cite Strack et al., 2023?
We appreciate the reviewer’s suggestion. The existence of non-analog ecological communities in South America is well known, especially in LGM pollen assemblages (i.e., Francisquini et al. 2020 and references therein). However, one of the fundamental assumptions of the Transfer Function approach is that the relationship between individual species abundances and environmental parameters remains stable over time, not the assemblage structure as Strack et al. (2023) suggest. Therefore, we consider that their contribution would not provide additional insights to our manuscript.
Section 2.1.2. Selected environmental variables: I would recommend the authors to include a Table and/or Figure that summarizes the information and show the span around the variables for each calculated seasonal means. An idea could be to make ODV maps of the environmental variables for the different seasons. This would visually keep their aesthetic but help the reader see the distribution of these elements and better assess direct links with the foraminifera’s population dynamic.
We appreciate the reviewer's suggestion. We will include a Table that summarizes the information of the seven environmental variables analyzed seasonally (temperature, salinity, phosphate, silicate, nitrate, chlorophyll a, iron) in Supplementary Material. In our opinion, the RDA is enough to help the reader see the relationship between the environmental variables analyzed and the foraminiferal assemblages.
Figure 6, deep dwellers such as G. truncatulinoides plot next to surface sp. such as G. ruber while having completely different ecology which to me doesn’t really support the introduction of the discussion.
We appreciate the reviewer’s opinion; however, we respectfully disagree. The RDA run in this contribution does not discriminate between depth habitat, prey selection or form of nitrogen consumed by foraminifera. It rather reflects whether species are primarily associated with high or low dissolved nitrate concentrations. Nevertheless, following the reviewer’s suggestion, we have added a sentence that we believe better aligns Figure 6 with the introduction of the Discussion section.
The findings of Lessa are based on tow samples and represent a snapshot rather than smoothed annual/pluriannual signal similar to the one « stored » in the surface sediments of this study.
We agree that plankton tow data cannot be directly equated with sedimentary assemblages, which integrate signals over longer temporal and vertical scales. However, we would like to clarify that our interpretation does not rely on the study by Lessa et al. (2020). That study is cited solely as supporting evidence showing that so-called secondary environmental factors can influence the structure of modern planktonic foraminiferal communities within the mixed layer in the South Atlantic.
All surface sedimentary assemblages are time-averaged. However, Boltovskoy et al. (1996) analyzed the foraminiferal species distribution in the western South Atlantic using tow samples collected from the upper 150 m of the water column. Their findings were consistent with those reported by Imbrie and Kipp (1971), Kucera et al. (2005), Siccha and Kucera (2017), and García Chapori and Kucera (2019), all of which were based on surface sediment samples. In our opinion, this validates the development of transfer functions which are based on the assumption that the uppermost sediment centimeters represent modern conditions.
L420-425 R2: in my opinion, what controls the presence of the species found in the South WSA is very likely temperature – the authors might miss that, not including temperature as a first level analysis, before looking at the nutrients etc…
We agree that temperature is an important control on planktonic foraminiferal distribution, especially at global scales. However, our calibration dataset shows that temperature alone cannot explain the assemblage patterns recognized in the South Atlantic.
As we mentioned above, the MFA confirms that winter sea-surface temperature is the most strongly associated variable with the main ecological gradient, whereas productivity-related variables show a strong contribution to the first and subsequent dimensions. This suggests that productivity-related variables can be statistically distinguished from the temperature signal and supports our interpretation that, while temperature is the main variable, secondary environmental variables are required to explain the full structure of planktonic foraminiferal assemblages in our calibration dataset.
The new version of the manuscript will include the discussion of the role of the winter temperature.
REFERENCES
Boltovskoy, E., Boltovskoy, D., Correa, N., and Brandini, F.: Planktic foraminifera from the southwestern Atlantic (30°–60° S): species-specific patterns in the upper 50 m, Marine Micropaleontology, 28, 53–72, https://doi.org/10.1016/0377-8398(95)00045-3, 1996.
Dray, S., Chessel, D., and Thioulouse, J.: Co-inertia analysis and the linking of ecological data tables, Ecology, 84, 3078–3089, 2003.
Francisquini, M. I., Lorente, F. L., Pessenda, L. C. R., Buso Junior, A. A., Mayle, F. E., Cohen, M. C. L., França, M. C., Bendassolli, J. A., Giannini, P. C. F., Schiavo, J. A., and Macario, K.: Cold and humid Atlantic rainforest during the Last Glacial Maximum, northern Espírito Santo state, southeastern Brazil, Quaternary Science Reviews, 244, 106489, https://doi.org/10.1016/j.quascirev.2020.106489, 2020.
García Chapori, N., and Kucera, M.: Planktonic foraminifera census counts from the western South Atlantic, PANGAEA, https://doi.org/10.1594/PANGAEA.907931, 2019.
Imbrie, J., and Kipp, N. G.: A new micropaleontological method for quantitative paleoclimatology: application to a late Pleistocene Caribbean core, in: The Late Cenozoic Glacial Ages, edited by: Turekian, K. K., Yale University Press, New Haven, 71–181, https://doi.org/10.1016/0033-5894(73)90051-3, 1971.
Kucera, M., Weinelt, M., Kiefer, T., Pflaumann, U., Hayes, A., Chen, M.-T., Mix, A. C., Barrows, T. T., Cortijo, E., Duprat, J., Juggins, S., and Waelbroeck, C.: Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific oceans, Quaternary Science Reviews, 24, 951–998, https://doi.org/10.1016/j.quascirev.2004.07.014, 2005.
Lessa, D., Morard, R., Jonkers, L., Venancio, I. M., Reuter, R., Baumeister, A., Albuquerque, A. L., and Kucera, M.: Distribution of planktonic foraminifera in the subtropical South Atlantic: depth hierarchy of controlling factors, Biogeosciences, 17, 4313–4342, https://doi.org/10.5194/bg-17-4313-2020, 2020.
Siccha, M., and Kucera, M.: ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples, Scientific Data, 4, 170109, https://doi.org/10.1038/sdata.2017.109, 2017.
Strack, T., Jonkers, L., Rillo, M. C., Hillebrand, H., and Kucera, M.: Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age, Nature Ecology & Evolution, https://doi.org/10.1038/s41559-022-01888-8, 2022.
Citation: https://doi.org/10.5194/egusphere-2025-5531-AC2
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AC2: 'Reply on RC2', Paula Albarracin, 13 Feb 2026
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Albarracin et al present a transfer function model that relates planktonic foraminifera abundance to seawater nitrate concentration. The model is based on a set of core top sediment species assemblages from the South Atlantic Ocean. Before building the transfer function model the authors assess which nutrient and productivity related variables could serve as independent predictors of the species assemblage composition and they evaluate several different models. The final model is used to reconstruct the nitrate concentration during the Holocene using fossil assemblages from a core in the Brazil-Malvinas Confluence.
Whereas there is ample evidence that on a global and basin-wide scale planktonic foraminifera species composition is best predicted by temperature (Morey et al. 2005; Rillo et al. 2021), there are also indications that on smaller spatial scales and in isolated basins the drivers of species assembly differ (Azibeiro et al. 2023). It is hence valuable to assess the influence of other variables than temperature on species assemblage composition and the study by Albarracin is therefore of potential interest for the readership of Climate of the Past. However, there are several issues including one fundamental methodological flaw in the study that prevent me from recommending the study for publication in its present form.
My main concern is that the authors do not demonstrate that the nitrate concentration is more important than temperature (or other physical environmental variables) for planktonic foraminifera species assembly. The authors restrict their analysis to nutrient and productivity related variables, but a priori ruling out the influence of temperature is no evidence that it plays no role and that an (independent) reconstruction of nitrate concentration is possible. The study by Lessa et al in itself is not sufficient proof as it is based on plankton tows and the implications for sedimentary assemblages, which are vertically and temporally integrated, is not yet clear. The authors may be right, but they need to redo their analysis with a wider suite of environmental variables (including temperature) to prove this. Only if this analysis reveals that nitrate is an important predictor of assemblage composition that can be disentangled from the temperature effect can they proceed with the reconstruction. In this regard it is surprising to see that the authors compare their reconstructions of nitrate concentration with reconstructions of seawater temperature based on exactly the same species assemblages, but there is no indication that is possible to derive independent reconstructions of both variables.
My second concern is with the selection of the samples included in the calibration dataset. In the method section it is written that they use over 300 samples, but in the results fewer than 200 are mentioned. It is unclear why some samples were excluded (and what the effect is). On a more fundamental level, what is the rationale of working with a subset of the available data (Siccha and Kucera 2017)? Please explain as this likely influences the transfer function model and the reconstruction.
My third major concern is about what nitrate concentrations actually tell about the environment. The authors seemingly use nutrient concentrations and productivity interchangeably (e.g. L 545 “4.4 Holocene paleoproductivity reconstruction in the BMC (WSA)”), but they are different. The nutrient concentration reflects what is left over after utilisation by primary producers and it is hence not related to primary production in a straightforward way. The fact that nitrate concentration and chlorophyll-a concentration (probably a better indicator of productivity) plot perpendicular to each other in the RDA plots underscores this. So if it is possible to reconstruct nitrate concentrations in the South Atlantic, then a reinterpretation of the results is still needed.
Minor comments
L1: the title is vague as to what this transfer function is actually for.
L109: “In order to…” I don’t understand the reasoning here.
L111: please describe the selection process (see also above).
L124: “The temporal ranges…” please describe better. I can see how temperature is affected by global warming, but the effect on the other variables is less clear. For most variables there is also observational and climatology data available for earlier times, so why not use those if the effect of global change is a concern?
L126: “Therefore, we analyzed seasonal averages…” I don’t understand the “therefore” here. How does this reduce the influence of global warming?
L131: “Sediment core GeoB2806-4 (37°50’S - 53°08.6’W, 3500 m depth; García Chapori et al., 2015) was used for testing the transfer function developed here.” Testing implies that the true nitrate concentrations during the Holocene were known. Please reword as this is not a test, but an application.
L153: “morphotypes” should be subspecies (Morard et al. 2019).
L154: why were rare species excluded from the analysis?
L155: how does log transformation standardise the variance?
L203: which dissimilarity metric was used. And why five analogues?
Transfer function performance: I don’t understand why the authors assess the transfer function not immediately using h-block crossvalidation and neither why they don’t evaluate all models in this way. Perhaps a different model than the WATOL_inv one performs better with h-block CV than shown in Table 4? Also, is the evaluation of the performance based on the R2 and RMSE sufficient? I realise that this is the usual approach, but the authors show two other metrics (related to bias), but don’t use them.
Azibeiro, Lucia A., Michal Kučera, Lukas Jonkers, Angela Cloke-Hayes, and Francisco J. Sierro. 2023. “Nutrients and Hydrography Explain the Composition of Recent Mediterranean Planktonic Foraminiferal Assemblages.” Marine Micropaleontology 179 (March): 102201.
Morard, Raphaël, Angelina Füllberg, Geert-Jan A. Brummer, et al. 2019. “Genetic and Morphological Divergence in the Warm-Water Planktonic Foraminifera Genus Globigerinoides.” PloS One 14 (12): e0225246.
Morey, Ann E., Alan C. Mix, and Nicklas G. Pisias. 2005. “Planktonic Foraminiferal Assemblages Preserved in Surface Sediments Correspond to Multiple Environment Variables.” Quaternary Science Reviews 24 (7-9): 925–950.
Rillo, Marina C., Skipton Woolley, and Helmut Hillebrand. 2021. “Drivers of Global Pre‐industrial Patterns of Species Turnover in Planktonic Foraminifera.” Ecography, ahead of print, December 13. https://doi.org/10.1111/ecog.05892.
Siccha, Michael, and Michal Kucera. 2017. “ForCenS, a Curated Database of Planktonic Foraminifera Census Counts in Marine Surface Sediment Samples.” Scientific Data 4 (August): 170109.