Ecology

To enhance the legibility of the results, the habituation phase was marked as the H mean score from habituation trials 5 to 7, which served as a reference value for further analyses, while the test trials were marked as T1, T2, and T3, respectively. Novelty, i.e. addition or change of objects in zone C, was introduced in the first test trial T1.The initial four habituation trials have not been presented here, as they served only as a habituation phase and not as an element of the comparative analysis of the animals’ response to novelty.The data was analysed using a General Linear Model procedure GLM, with repeated measurements H, T1, T2, T3 as within-subject factors, followed by an LSD PostHoc test which involved a comparison of the habituation phase H with the three test trials T1, T2 and T3. Bonferroni correction for multiple comparisons was employed. Differences were considered significant for p ≤ 0.05. Data analysis was carried out using JASP v. 0.14.1 software, an open-source project supported by the University of Amsterdam.Time spent in the transporterThe amount of time spent in the transporter, excluding the latency to leave the transporter (that is, the amount of time from the moment the transporter was opened until the rat first entered the experimental apparatus), was measured for each group.In the ADD group, the analysis showed a significant main effect of trial: F(3, 39) = 5.033, p = 0.005, Eta2 = 0.279 (Wilks’ Lambda). A post-hoc analysis showed a significant decrease in the time spent in the transporter in the first and third test trials compared to the habituation phase (T1: p = 0.008, d = 1.090; T3: p = 0.017, d = 0.982).In the CMPLX group, the analysis showed a significant main effect of trial: F(3, 36) = 8.695, p  More

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SampleA total of 620 carnivore tooth pits were included in the present study. These samples included tooth marks produced by;

Brown Bears (Ursus arctos, Ursidae, 69 pits)

Spotted Hyenas (Crocuta crocuta, Hyaenidae, 86 pits)

Wolves (Canis lupus, Canidae, 80 pits)

African Wild Dogs (Lycaon pictus, Canidae, 89 pits)

Foxes (Vulpes vulpes, Canidae, 53 pits)

Jaguars (Panthera onca, Felidae, 77 pits)

Leopards (Panthera pardus, Felidae, 84 pits)

Lions (Panthera leo, Felidae, 82 pits)

Samples originated from a number of different sources, including animals kept in parks as well as wild animals. Samples obtained from wild animals included those produced by foxes as well as wolves. The only sample containing both wild and captive animals was the wolf sample. Preliminary data from these tooth pits revealed animals in captivity to have highly equivalent tooth pit morphologies to wild animals ((vert d vert ) = 0.125, p = 9.0e−14, BFB = 1.4e+11), while tooth scores revealed otherwise ((vert d vert ) = 0.152, p = 0.99, BFB = 3.7e+01 against (H_{a})). Under this premise, and so as to avoid the influence of confounding variables that go beyond the scope of the present study, tooth scores were excluded from the present samples and are under current investigation (data in preperation). Nevertheless, other research have shown tooth pits to be more informative than tooth scores when considering morphology20,23.When working with tooth mark morphologies, preference is usually given to marks found on long bone diaphyses. This is preferred considering how diaphyses are denser than epiphyses, and are thus more likely to survive during carnivore feeding. Nevertheless, when working with captive or semi-captive animals, controlling the bones that carnivores are fed is not always possible. This is due to the rules and regulations established by the institution where these animals are kept64. While this was not an issue for the majority of the animals used within the present study, in the case of P. pardus, animals were only fed ribs in articulation with other axial elements. In light of this, a careful evaluation on the effects this may have on the analogy of our samples was performed (Supplementary Appendix 2). These reflections concluded that in order to maintain a plausible analogy with tooth marks produced by other animals on diaphyses, tooth marks could only be used if found on the shaft of bovine ribs closest to the tuburcle, coinciding with the posterior and posterior-lateral portions of the rib, and farthest away from the costochondral junction65. This area of the rib corresponds to label RI3 described by Lam et al.65. Moreover, with a reported average cortical thickness of 2.3mm (± 0.13 mm) and Bone Mineral Density of (4490 kg/m^{3} [213.5, 334.6])66, bovine ribs are frequently employed in most bone simulation experiments used in agricultural as well as general surgical sciences. Finally, considering the grease, muscle and fat content of typical domestic bovine individuals67, alongside the general size of P. pardus teeth, it was concluded that the use of rib elements for this sample was the closest possible analogy to the tooth marks collected from other animals.Carnivores were fed a number of different sized animals, also dependent in most cases on the regulations established by the institution where these animals are kept64. Nevertheless, recent research has found statistical similarities between tooth marks found on different animals25, with the greatest differences occurring between large and small sized animals. Needless to say, considering the typical size of prey some of these carnivores typically consume, this factor was not considered of notable importance for the present study25 (Supplementary Appendix 1).For the purpose of comparisons, animals were split into 5 groups according to ecosystem as well as taxonomic family. From an ecological perspective, two datasets were defined; (1) the Pleistocene European Taxa dataset containing U. arctos, V. vulpes, C. crocuta, P. pardus, P. leo and C. lupus; and (2) the African Taxa dataset containing C. crocuta, P. pardus, L. pictus and P. leo. When considering taxonomic groupings, animals were separated into 3 groups, including; (1) the Canidae dataset, including V. vulpes, L. pictus and C. lupus; (2) the Felidae dataset, including P. pardus, P. onca and P. leo; and (3) a general Taxonomic Family dataset, including all Canidae in the same group, all Felidae in the same group, followed by Hyaenidae and Ursidae. Some complementary details on each of these carnivores have been included in Supplementary Appendix 1.All experiments involving carnivores were performed in accordance with the relevant ethical guidelines as set forth by park keepers and general park regulations. No animals were sacrificed specifically for the purpose of these experiments. Likewise, carnivores were not manipulated or handled at any point during the collection of samples. Collection of chewed bones were performed directly by park staff and assisted by one of the authors (JY). The present study followed the guidelines set forth by ARRIVE (https://arriveguidelines.org/) wherever necessary. No licenses or permits were required in order to perform these experiments. Finally, in the case of animals in parks, bone samples were provided by the park according to normal feeding protocols. More details can be consulted in the Extended Samples section of the supplementary files.3D modelling and landmark digitisationDigital reconstructions of tooth marks were performed using Structured Light Surface Scanning (SLSS)68. The equipment used in the present study was the DAVID SLS-2 Structured Light Surface Scanner located in the C.A.I. Archaeometry and Archaeological Analysis lab of the Complutense University of Madrid (Spain). This equipment consists of a DAVID USB CMOS Monochrome 2-Megapixel camera and ACER K11 LED projector. Both the camera and the projector were connected to a portable ASUS X550VX personal laptop (8 GB RAM, Intel® CoreTM i5 6300HQ CPU (2.3 GHz), NVIDIA GTX 950 GPU) via USB and HDMI respectively. The DAVID’s Laser Scanner Professional Edition software is stored in a USB Flash Drive. Equipment were calibrated using a 15 mm markerboard, using additional macro lenses attached to both the projector and the camera in order to obtain optimal resolution at this scale. Once calibrated the DAVID SLS-2 produces a point cloud density of up to 1.2 million points which can be exported for further processing via external software.The landmark configuration used for this study consists of a total of 30 landmarks (LMs)21; 5 fixed Type II landmarks18 and a (5 times 5) patch of semilandmarks69 (Fig. S2). Of the 5 fixed landmarks, LM1 and LM2 mark the maximal length (l) of each pit. For the correct orientation of the pit, LM1 can be considered to be the point along the maximum length furthest away from the perpendicular axis marking the maximum width (w). LM2 would therefore be the point closest to said perpendicular axis (see variables (d_{1}) and (d_{2}) in Fig. S2 for clarification). LM3 and LM4 mark the extremities of the perpendicular axis (w) with LM3 being the left-most extremity and LM4 being the right-most extremity. LM5 is the deepest point of the pit. The semilandmark patch is then positioned over the entirety of the pit, so as to capture the internal morphology of the mark.Landmark collection was performed using the free Landmark Editor software (v.3.0.0.6.) by a single experienced analyst. Inter-analyst experiments prior to landmark collection revealed the landmark model to have a robustly defined human-induced margin of error of 0.14 ± 0.09 mm (Median ± Square Root of the Biweight Midvariance). Detailed explanations as well as an instructional video on how to place both landmarks and semilandmarks can be consulted in the Supplementary Appendix and main text of Courtenay et al.21.Geometric morphometricsOnce collected, landmarks were formatted as morphologika files and imported into the R free software environment (v.3.5.3, https://www.r-project.org/). Initial processing of these files consisted in the orthogonal tangent projection into a new normalized feature space. This process, frequently referred to as Generalized Procrustes Analysis (GPA), is a valuable tool that allows for the direct comparison of landmark configurations18,19,70. GPA utilises different superimposition procedures (translation, rotation and scaling) to quantify minute displacements of individual landmarks in space71. This in turn facilitates the comparison of landmark configurations, as well as hypothesis testing, using multivariate statistical analyses. Nevertheless, considering observations made by Courtenay et al.20,21,25 revealed tooth mark size to be an important conditioning factor in their morphology, prior analyses in allometry were also performed72. From this perspective, allometric analyses first considered the calculation of centroid sizes across all individuals; the square root of the sum of squared distances of all landmarks of an object from their centroid18. These calculations were then followed by multiple regressions to assess the significance of shape-size relationships. For regression, the logarithm of centroid sizes were used. In cases where shape-size relationships proved significant, final superimposition procedures were performed excluding the scaling step of GPA (form).In addition to these analyses, preliminary tests were performed to check for the strength of phylogenetic signals73. This was used as a means of testing whether groups of carnivores produced similar tooth pits to other members of the same taxonomic family. For details on the phylogenies used during these tests, consult Fig. S1 and Supplementary Appendix 1.For the visualisation of morphological trends and variations, Thin Plate Splines (TPS) and central morphological tendencies were calculated19,71. From each of these mean landmark configurations, for ease of pattern visualisation across so many landmarks, final calculations were performed using Delaunay 2.5D Triangulation algorithms74 creating visual meshes of these configurations in Python (v.3.7.4, https://www.python.org/).Once normalised, landmark coordinates were processed using dimensionality reduction via Principal Components Analyses (PCA). In order to identify the optimal number of Principal Component Scores (PC Scores) that best represented morphological variance, permutation tests were performed calculating the observed variance explained by each PC with the permuted variance over 50 randomized iterations75. Multivariate Analysis of Variance (MANOVA) tests were then performed on these select PCs to assess the significance of multivariate morphological variance among samples.Geometric Morphometric applications were programmed in the R programming language (Sup. Appendix 8).Robust statisticsWhile GPA is known to normalize data76, this does not always hold true. Under this premise, caution must be taken when performing statistical analyses on these datasets. Taking this into consideration, prior to all hypothesis testing, normality tests were also performed. These included Shapiro tests and the inspection of Quantile–Quantile graphs. In cases where normality was detected, univariate hypothesis tests were performed using traditional parametric Analysis of Variance (ANOVA). For multivariate tests, such as MANOVA, calculations were derived using the Hotelling-Lawley test-statistic. When normality was rejected, robust alternatives to each of these tests were chosen. In the case of univariate testing, the Kruskal–Wallis non-parametric rank test was prefered, while for MANOVA calculations, Wilk’s Lambda was used.Finally, in light of some of the recommendations presented by The American Statistical Association (ASA), as debated in Volume 73, Issue Sup1 of The American Statistician77,78, the present study considers p-values of ( >2sigma ) from the mean to indicate only suggestive support for the alternative hypothesis ((H_{a})). (p ; > ; 0.005), or where possible, (3sigma ) was therefore used as a threshold to conclude that (H_{a}) is “significant”. In addition, Bayes Factor Bound (BFB) values (Eq. 1) have also been included alongside all corresponding p-Values79. Unless stated otherwise, BFBs are reported as the odds in favor of the alternative hypothesis (BFB:1). More details on BFB, Bayes Factors and the (p ; > ; 3sigma ) threshold have been included in Supplementary Appendix 3. General BFB calibrations in accordance with Benjamin and Berger’s Recommendation 0.379, as well as False Positive Risk values according to Colquhoun’s proposals80, have also been included in Table S20 of Supplementary Appendix 3.$$begin{aligned} BFB = frac{1}{-e ; p ; log (p)} end{aligned}$$
(1)
All statistical applications were programmed in the R programming language (Sup. Appendix 8).Computational learningComputational Learning employed in this study consisted of two main types of algorithm; Unsupervised and Supervised algorithms. The concept of “learning” in AI refers primarily to the creation of algorithms that are able to extract patterns from raw data (i.e. “learn”), based on their “experience” through the construction of mathematical functions38,81. The basis of all AI learning activities include the combination of multiple components, including; linear algebra, calculus, probability theory and statistics. From this, algorithms can create complex mathematical functions using many simpler concepts as building blocks38. Here we use the term “Computational Learning” to refer to a very large group of sub-disciplines and sub-sub-disciplines within AI. Deep Learning and Machine Learning are terms frequently used (and often debated), however, many more branches and types of learning exist. Under this premise, and so as to avoid complication, the present study has chosen to summarise these algorithms using the term “Computational”.Similar to the concepts of Deep and Machine Learning, many different types of supervision exist. The terms supervised and unsupervised refer to the way raw data is fed into the algorithm. In most literature, data will be referred to via the algebraic symbol x, whether this be a vector, scalar or matrix. The objective of algorithms are to find patterns among a group of x. In an unsupervised context, x is directly fed into the algorithm without further explanation. Algorithms are then forced to search for patterns that best explain the data. In the case of supervised contexts, x is associated with a label or target usually denominated as y. Here the algorithm will try and find the best means of mapping x to y. From a statistical perspective, this can be explained as (pleft( y vert x right) ). In sum, unsupervised algorithms are typically used for clustering tasks, dimensionality reduction or anomaly detection, while supervised learning is typically associated with classification tasks or regression.The workflow used in the present study begins with dimensionality reduction, as explained earlier with the use of PCA. While preliminary experiments were performed using non-linear dimensionality reduction algorithms, such as t-distributed Stochastic Neighbor Embedding (t-SNE)82 and Uniform Manifold Approximation and Projection (UMAP)83, PCA was found to be the most consistent across all datasets, a point which should be developed in detailed further research. Once dimensionality reduction had been performed, and prior to any advanced computational modelling, datasets were cleaned using unsupervised Isolation Forests (IFs)84. Once anomalies had been removed, data augmentation was performed using two different unsupervised approaches; Generative Adversarial Networks (GANs)38,39,40,41 and Markov Chain Monte Carlo (MCMC) sampling44. Data augmentation was performed for two primary reasons; (1) the simulation of larger datasets to ensure supervised algorithms have enough information to train from, and (2) to balance datasets so each sample has the same size. Both MCMCs and GANs were trialed and tested using robust statistics to evaluate quality of augmented data41. Once the best model had been determined, each of the datasets were augmented so they had a total sample size of (n = 100). In the case of the Taxonomic Family dataset, augmentation was performed until all samples had the same size as the largest sample.Once augmented, samples were used for the training of supervised classification models. Two classification models were tried and tested; Support Vector Machines (SVM)85 and Neural Support Vector Machines (NSVM)86,87. NSVMs are an extension of SVM using Neural Networks (NNs)38 as feature extractors, in substituting the kernel functions typically used in SVMs. Hyperparameter optimization for both SVMs and NSVMs were performed using Bayesian Optimization Algorithms (BOAs)88.Supervised computational applications were performed in both the R and Python programming languages (Sup. Appendix 8). For full details on both unsupervised and supervised computational algorithms, consult the Extended Methods section of the Supplementary Materials.Evaluation of supervised learning algorithms took into account a wide array of different popular evaluation metrics in machine and deep learning. These included; Accuracy, Sensitivity, Specificity, Precision, Recall, Area Under the receiver operator characteristic Curve (AUC), the F-Measure (also known as the F1 Score), Cohen’s Kappa ((kappa )) statistic, and model Loss. Each of these metrics, with the exception of loss, are calculated using confusion matrices, measuring the ratio of correctly classified individuals (True Positive & True Negative) as well as miss-classified individuals (False Positive & False Negative). For more details see Supplementary Appendix 6.Accuracy is simply reported as either a decimal (left[ 0, 1right] ) or a percentage. Accuracy is a metric often misinterpreted, as explained in Supplementary Appendix 6, and should always be considered in combination with other values, such as Sensitivity or Specificity. Both Sensitivity and Specificity are values reported as decimals (left[ 0, 1right] ), and are used to evaluate the proportion of correct classifications and miss-classifications. AUC values are derived from receiver operator characteristic curves, a method used to balance and graphically represent the rate of correctly and incorrectly classified individuals. The closest the curve gets to reaching the top left corner of the graph, the better the classifier, while diagonal lines in the graph represent a random classifier (poor model). In order to quantify the curvature of the graph, the area under the curve can be calculated (AUC), with (AUC=1) being a perfect classifier and (AUC=0.5) being a random classifier. The (kappa ) statistic is a measure of observer reliability, usually employed to test the agreement between two systems. When applied to confusion matrix evaluations, (kappa ) can be used to assess the probability that a model will produce an output (hat{y}) that coincides with the real output y. (kappa ) values typically range between (left[ 0, 1right] ), with (kappa =1) meaning perfect agreement, (kappa =0) being random agreement, and (kappa =0.8) typically used as a threshold to define a near-perfect or perfect algorithm.While in the authors’ opinion, AUC, Sensitivity and Specificity values are the most reliable evaluation metrics for studies of this type (Supp. Appendix 6), for ease of comparison with other papers or authors who choose to use other metrics, we have also included Precision, Recall and F-Measure values. Precision and Recall values play a similar role to sensitivity and specificity, with recall being equivalent to sensitivity, and precision being the calculation of the number of correct positive predictions made. Precision and Recall, however, differ from their counterparts in being more robust to imbalance in datasets. F-Measures are a combined evaluation of these two measures. For more details consult Supplementary Appendix 6.Loss metrics were reported using the Mean Squared Error (Eq. 2);$$begin{aligned} MSE = frac{1}{n} sum _{i = 1}^{n} left( y_{i} – hat{y}_{i} right) ^{2} end{aligned}$$
(2)
Loss values are interpreted considering values closest to 0 as an indicator of greater confidence when using the model to make new predictions.Final evaluation metrics were reported when using algorithms to classify only the original samples, without augmented data. Augmented data was, therefore, solely used for training and validation. Finally, so as to assess the impact data augmentation has on supervised learning algorithms, algorithms were also trained on the raw data. This was performed using 70% of the raw data for training, while the remaining 30% was used as a test set. More

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Study site and target foraminiferal speciesThe Maldivian archipelago is a partially drowned carbonate platform within the central, equatorial Indian Ocean. It consists of two rows of north–south orientated atolls, which encompass an Inner Sea. The lowermost neritic carbonate unit sits upon volcanic bedrock and has been dated back to the Eocene19 with continuous drift deposition, within the Inner Sea, starting ~ 12.9 Ma at the establishment of the modern South Asian Monsoon (SAM)35,36. This seasonally reversing, major climatic system has an impact on both the regional precipitation patterns as well as physiochemical oceanographic properties (Fig. 1). The summer southwest SAM brings warm, wet conditions to the Indian subcontinent, as well as higher saline surface waters from the Arabian Sea into the Maldives region. In comparison, the winter northeast SAM results in cool, dry continental conditions and transports lower salinity water from the Bay of Bengal into the central, equatorial Indian Ocean. As a result, the Maldives seasonal salinity depth profiles can vary significantly, yet due to its tropical location the seasonal sea water temperatures are relatively stable.Three symbiont-bearing foraminiferal species are used in this study: Amphistegina lessonii, Globigerinoides ruber (white) and Trilobatus sacculifer (with sac-like final chamber):Amphistegina lessonii is a larger benthic, symbiont-bearing (diatoms) foraminiferal species. It has a shallow depth range (0–50 m)37,38,39 and is globally abundant in tropical coral reef, benthic foraminiferal shoal and general carbonate shelf settings40. Similarly to corals, amphisteginids have been shown to bleach under high temperatures/high irradiance levels with the new development of the Amphistegina Bleaching Index (ABI) as an indicator of photo-inhibitory stress in coral reef settings41,42. From ~ 30 °C this species starts showing signs of thermal stress, with bleaching and mortality reported for temperatures  > 31 °C11,12.Globigerinoides ruber (w) hosts dinoflagellate endosymbionts and is the most common planktonic foraminiferal species in tropical-subtropical waters13 state that while G. ruber (w) is generally considered one of the shallowest-dwelling species, its depth distribution does vary in relation to regional ecological conditions. It has a particular relation to the nutricline depth in less turbid, oligotrophic conditions43 which has been confirmed for the Maldives28. It is omnivorous, however in comparison to other omnivorous, symbiont-bearing species, it has demonstrated an elevated adaptation for consuming phytoplankton protein over zooplankton protein13. From culture experiments, it has a broad temperature (14–31 °C) and salinity (22–49 PSU) tolerance, and has been reported as the most tolerant species to low sea surface salinity (SSS)13. This species occurs year-round and has a fortnightly reproduction13.Trilobatus sacculifer is a planktonic foraminiferal species abundant in tropical-subtropical surface waters and as such is extensively used in paleo-reconstructions. It hosts dinoflagellate endosymbionts yet is omnivorous, feeding predominantly on calanoid copepods13. It is a euryhaline species, with a broad salinity (24–47 PSU) and temperature (14–32 °C) tolerance. Similarly to G. ruber (w), this species occurs year-round and has a monthly reproduction on a synodic lunar cycle13. While a shallow dwelling species, it is generally reported to live slightly deeper in the water column, in comparison to G. ruber (w)28,30,44.SamplingAll planktonic foraminiferal specimens (G. ruber (w) and T. sacculifer (w/s)) for the geochemical analysis (δ18Oc and Mg/Ca) originate from the International Ocean Discovery Program (IODP) Expedition 359, Site U1467 (4° 51.0274′ N, 73° 17.0223′ E) drilled in 2015 within the Inner Sea of the Maldivian archipelago at a water depth of 487 m19. The age model for these samples was adopted from a previous study45 which is based on the correlation of their long-term (0–1800 kyr) Site 359-U1467 C. mabahethi and G. ruber (w) δ18Oc records to the stacked reference curve of46. Recent surface sediment samples (mudline A and B: representing the sample from the sediment/water interface), as well as three samples from the peak of MIS9e (U1467C, 2H6, 0–1 cm; U1467C, 2H6, 15–16 cm; U1467C, 2H6, 18–19 cm) and MIS11c (U1467B, 3H2, 147–148 cm; U1467B, 3H3, 9–10 cm; U1467B, 3H3, 12–13 cm) were analysed19,28 (sample locations are shown on Fig. 3). The mudline is identified as Recent, likely representing the last few hundred years, based on the presence of Rose Bengal (1 g/L) stained ostracods and benthic foraminifera. The study by45 has verified that diagenetic influences, within this shallow, carbonate environment, are not a concern for foraminiferal geochemical compositions over the investigated time-interval (MIS1-11).Rose Bengal stained A. lessonii specimens were obtained from modern surface rubble samples collected by hand, at 10 m water depth, during the 2015 International Union for Conservation of Nature (IUCN) REGENERATE cruise47 (Supplementary Table 6). Samples were collected from the reefs of two islands, Maayafushi and Rasdhoo, both located within the central part of the Maldivian archipelago. As the foraminifera shells were stained pink, it implies they were living at the time of collection. These specimens were used for stable isotopic analysis and their reconstructed temperatures represent modern (a cumulative signal encompassing their lifespan of four to twelve months48) conditions (Supplementary Tables 5–6). A full explanation of the Rose Bengal protein stain for foraminifera is detailed in49.δ18Oc stable isotopic analysisAll samples were initially washed using a 32 μm sieve to remove the finer clay and silt fractions. Subsequently, they were air dried and sieved into discrete sizes for foraminiferal picking. To ensure enough calcite for the measurements, all specimens for Individual Foraminifera Analysis (IFA) for both G. ruber (w) and T. sacculifer (w/s) (n = 632) were picked from the 355–400 μm size fraction. In addition, traditional whole-shell (pooled) measurements for G. ruber (w) (n = 24) were conducted on specimens from the 212–400 μm fraction (2–5 pooled specimens). Trilobatus sacculifer (w/s) traditional whole-shell analysis (n = 21) was measured on specimens (2 pooled specimens) from the 300–355 μm fraction. The majority of these pooled measurements are obtained from28,45,50,51 (Supplementary Table 1). Amphistegina lessonii measurements were run on single specimens  > 250 μm in size. Prior to stable isotopic analysis, all shells were briefly cleaned (1–2 s) by ultrasonication in Milli-Q water to remove any adhering particles. All stable isotopic measurements were conducted at the School of GeoSciences at the University of Edinburgh on a Thermo Electron Delta + Advantage mass spectrometer integrated with a Kiel carbonate III automated extraction line. Samples were reacted with 100% phosphoric acid (H3PO4) at 90 °C for 15 min, with the evolved CO2 gas collected in a liquid nitrogen coldfinger and analysed compared to a reference gas. All samples are corrected using an internal laboratory standard and expressed as parts per mil (‰) relative to Vienna Pee Dee Belemnite (VPDB). Replicate measurements of the standards give the instrument an analytical precision (1σ) of ~ 0.05 ‰ for δ18O and δ13C.Mg/Ca analysisThe Mg/Ca data is obtained from28,45,50,51 (Supplementary Table 1). Each G. ruber (w) Mg/Ca analysis (n = 17; 212–250 μm in size) was conducted on 30 pooled specimens by inductively coupled plasma optical emission spectrometry (ICP-OES) on a Thermoscientific iCap 6300 (dual viewing) at the Institute of Geosciences of the Goethe-University of Frankfurt. All samples were initially cleaned (1–2 s) by ultrasonication in Milli-Q water and then the standard oxidative cleaning protocol of52 followed to prevent clay mineral contamination. The final centrifuged sample solution was diluted with an yttrium solution (1 mg/l) prior to measurement to allow for the correction of matrix effects. In addition, before each analysis five calibration solutions were measured to allow for intensity ratio calibrations. All element/Ca measurements were standardized using an internal consistency standard (ECRM 752–1, 3.761 mmol/mol Mg/Ca). Furthermore, the elements Al, Fe, and Mn were screened and blanks periodically run to monitor for further signs of contamination during the analyses.Establishment of present and past seawater temperaturesPrior to temperature calculations, we test the IFA distributions for normality using the Shapiro‐Wilk test and the Fisher–Pearson coefficient of skewness with bootstrap confidence intervals, to define the skewness of the datasets53 (Supplementary Table 3). The Recent G. ruber (w) and T. sacculifer (w/s) and MIS11c T. sacculifer are normally distributed. In the case of both MIS9e datasets and the MIS11c G. ruber population, the null hypothesis that the data are normally distributed (p ≤ 0.05) is rejected (Supplementary Table 3). Considering bioturbation within the sediment record is a possibility, we use two methods to identify and remove outliers in the IFA datasets. Firstly, the inter-quartile range (IQR) is used for each δ18Oc dataset, which defines a measurement as an outlier if it falls outside the range [Q1 − 1.5 (Q3 − Q1), Q3 + 1.5 (Q3 − Q1)], with IQR = Q3 − Q1 and Q3 and Q1 representing the third and first quartile of the dataset20. But if there is considerable reworking, the IQR method would not necessarily identify reworked glacial measurements (highest δ18Oc values) within the interglacial samples. As such, the Recent IFA datasets, which are both normally distributed, are used to further set a rudimentary cut-off point for the highest δ18Oc (= lowest temperatures) value to expect during past interglacial minima periods for both G. ruber (w) and T. sacculifer (w/s) (this is discussed further in the Supplementary Materials, Supplementary Figs. 1–3).There are innumerable analytical techniques (e.g., traditional mass spectrometry, secondary-ion mass spectrometry, laser ablation inductively coupled plasma mass spectrometry), proxies (Mg/Ca, δ18O, clumped isotopes, TEX86, Uk’37) as well as target medians (e.g., calcitic shells of foraminifera, aragonitic coral skeletons, ice, lipids, alkenones) which are used in marine paleo-temperature reconstructions. Furthermore, different methods exist in the literature to calculate temperature estimates using both planktonic foraminiferal δ18Oc and Mg/Ca measurements with innumerable species-specific δ18O-temperature and Mg/Ca-temperature equations reported20,23,30,54,55,56. Moreover, due to the exponential nature of the Mg/Ca-temperature equations, if inappropriately applied, offsets in the upper temperature range are exacerbated. Additional considerations are species-specific offsets and differential geochemical compositions within the shell (e.g., high versus low Mg banding, gametogenic calcite). Trilobatus sacculifer gametogenic calcite has been reported to be significantly enriched in Mg in comparison to the rest of the shell57. As T. sacculifer specimens selected for use in this study underwent reproduction, indicated by the presence of a sac-like final chamber58, we can expect their Mg/Ca ratios to be biased. As such, to avoid overestimates we chose to use only G ruber (w, pooled) Mg/Ca and δ18Oc data to calculate representative δ18Osw values for each time interval, for use with both the G. ruber (w) and T. sacculifer (w/s) δ18Oc IFA datasets. Considering both planktonic species are considered as shallow-dwellers with similar living depths and an affinity for the DCM, the utilisation of common δ18Osw values is applicable13,28,30.The G. ruber Mg/Ca-temperature Eq. (1) from55 (temperature calibration range: ~ 22–27 °C), similarly applied in the Maldivian study of28, was used in this study:$$Mg/Ca=0.34left(pm 0.08right)mathrm{exp}(0.102left(pm 0.010right)*T)$$
(1)
The applied δ18O-temperature species-specific equations (Eqs. 2 and 3) were previously utilised in the local study by28. Both the G. ruber (Eq. 2) and T. sacculifer (Eq. 3) equations are from the Indian Ocean study of59 (temperature calibration range: ~ 20–31 °C):$$T=12.75-5({delta }^{18}{O}_{c}-{delta }^{18}{O}_{sw})$$
(2)
$$T=11.95-5.26({delta }^{18}{O}_{c}-{delta }^{18}{O}_{sw})$$
(3)
Using the above equations, the range in temperature estimates are obtained as follows (Fig. 4):

1.

The mean G. ruber (w) Mg/Ca measurements are used together with Eq. (1) to calculate a temperature estimate for each time point (Supplementary Table 1). Since the Mg/Ca calcification temperatures are based on 30 pooled specimens, they are considered to reflect mean calcification temperatures.

2.

The Mg/Ca derived temperature estimates are then used together with the mean traditional (pooled) G. ruber (w) δ18Oc data and Eq. (2) to calculate representative δ18Osw values for each time point (Supplementary Table 2). As these are calculated from pooled samples, they are considered to mirror mean δ18Osw values for both the Recent and fossil populations.

3.

The G. ruber (w) and T. sacculifer (w/s) IFA datasets are then used, together with the relevant species-specific δ18O-temperature equations and δ18Osw values, to calculate the spread in temperature estimates (Fig. 4, Supplementary Tables 3–4).

Trilobatus sacculifer (w/s) data from the glacial maxima of MIS12 are included in the study to illustrate the applicability of the IFA method, however, as they do not contribute to the discussion on bleaching thresholds, they are discussed further in the Supplementary Materials (Supplementary Figs. 1, 3).Finally, the temperature estimates for the shallow-dwelling symbiont-bearing benthic A. lessonii are obtained using the genus-specific δ18O-temperature equation of60 (Eq. 4) (Supplementary Tables 5–6).$$T=16.3-4.24({delta }^{18}{O}_{c}-{delta }^{18}{O}_{sw})$$
(4)
Considering the benthic specimens were deemed living at the time of collection (Rose Bengal stained), a mean regional surface (0 m) δ18Osw value (0.49 ‰) is used together with the δ18Oc data in the calculations (Supplementary Tables 5–6). More

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