Biogeographic and Ecologic Patterns in Calcareous Nannoplankton in the Atlantic and Pacific Oceans During the Terminal Cretaceous

Calcareous nannoplankton biogeography in the Cretaceous ocean has been analyzed from their floral composition at a time-slice spanning the upper parts of the Micula prinsii Zone (approximately the latest 10-60 kyr of the Cretaceous) at DSDP (Deep Sea Drilling Project) sites from low (160) through middle (3 T) paleolatitudes in both the Northern and the Southern Hemisphere. The study is based on relative abundance data of 44 species at Sites 356, 525A, and 527 from the South Atlantic, Sites 384 and 548A from the North Atlantic, and Site 465A from the Pacific Ocean.La biogeografía de nanoplancton calcáreo en el océano Cretácico se ha analizado a partir de su composición floral en un intervalo de tiempo que abarca las partes superiores de la zona Micula prinsii (aproximadamente los últimos 10-60 kyr del Cretácico) en DSDP (Deep Sea Drilling Project). sitios desde bajas (160) hasta medias (3 T) paleolatitudes en el hemisferio norte y sur. El estudio se basa en datos de abundancia relativa de 44 especies en los Sitios 356, 525A y 527 del Atlántico Sur, los Sitios 384 y 548A del Atlántico Norte y el Sitio 465A del Océano Pacífico

Comparison of statistical and artificial neural network techniques for estimating past sea surface temperatures from planktonic foraminifer census data

International audienceWe present the first detailed and rigorous comparison of six different computational techniques used to reconstruct sea surface temperatures (SST) from planktonic foraminifer census data. These include the Imbrie-Kipp transfer functions (IKTF), the modem analog technique (MAT), the modem analog technique with similarity index (SIMMAX), the revised analog method (RAM), and, for the first time, a set of back propagation artificial neural networks (ANN) trained on a large faunal data set, including a modification where geographical information was added among the input variables (ANND). By training the techniques on an identical database, we were able to explore the differences in SST reconstructions resulting solely from the use of different mathematical methods. The comparison indicates that while the IKTF technique consistently shows the worst performance, ANN and RAM perform slightly better than MAT and that the inclusion of the geographical information into the training database (SIMMAX and ANND) further improves the accuracy of modem SST estimates. However, when applied to an independent validation data set and an additional fossil data set, the results did not conform to this ranking. The largest differences in the reconstructed SST values occurred between groups of techniques with different approaches to SST reconstruction; that is, ANN and ANND produced SST reconstructions significantly different from those produced by RAM, SIMMAX, and MAT. The application of the various techniques to the validation data set, which allowed comparison of SST reconstructions with instrumental records, suggests that artificial neural networks might provide better paleo-SST estimates than the other techniques. 1. Introduction One of the most remarkable consequences of Charles Lyell's uniformitarian principle for the field of paleoceanography has been the opportunity to use the relationship between the distribution of modem faunas and floras and present-day physical conditions in the ocean to reconstruct climatic variations in the Quaternary period. In addition, if this relationship were expressed in the form of a mathematical formula, past climatic variations could be quantified in standard physical scales and units. This appealing prospect was discovered early in paleoceanographical studies, and quantitative reconstruction of Quaternary climate change by means of fossil faunas has become a standard and routinely applied procedure. Some early attempts at quantifying the relationship between species abundances and observed physical parameters relied on relatively simple mathematical methods [see Hutson, 1977]. The straggle for improvement in the precision of the estimates caused researchers to resort to more complex, often computer-intensive, statistical methods. To date, three different approaches have been used to quantify the relationship between faunal data and physical properties of the environment. The first approach, known as the Imbrie-Kipp transfer function (IKTF) method [Imbrie and Kipp, 1971], utilizes the standard statistical technique of Q mode principal component analysis to decompose the variation in the faunal data into a smaller number of variables that are then regressed upon the known physical parameters. Hutson [1980] developed an alternative approach: His modem analog technique (MAT) does not generate a unique calibration formula between faunal data and physical properties. Instead, this method searches the database of modem faunas for samples with assemblages that most resemble the fossil assemblage. The environment representing the fossil sample is then reconstructed from the physical properties recorded in the best modem analog samples. While the IKTF approach relies upon the assumption that the reconstruction of species' responses to physical parameters will yield the most reliable estimates of past environments, MAT, a true incarnate of Lyell's uniformitarianism, resorts solely to searching for modem situations most similar to that observed in a fossil sample. Significant improvements of this approach include the modem analog with similarity index (SIMMAX) method [Pfiau-mann e! al., 1996] and the revised analog method (RAM) [14/ael-broeck et al., 1998]. The third approach, using artificial neural networks (ANN), a branch of artificial intelligence, relies on the sole assumption that there, indeed, is a relationship between the distribution of modem faunas and the physical properties of the environment. ANNs have the ability to overcome problems of fuzzy and nonlinear relationships between sets of input and output variables. This computer-intensive approach is based on an algorithm that has the ability of autonomous "learning" of a relationship between two groups of numbers [14/assetman, 1989; Beale and Jackson, 1990]. Once trained, the neural network serves as a unique transfer function, yet at the same time this highly nonlinear and recurrent function is so complex that it has the ability to simulate a decision algorithm. The utility of ANN in reconstructing past environmental conditions has been recently demonstrated by Malmgren and Nordlurid [ 1997]. The existence of different approaches to sea surface temperature (SST) reconstruction inevitably raises questions of whether there 52

Late Pleistocene-Holocene radiolarian paleotemperatures in the Norwegian Sea based on Artificial Neural Networks 224 (2005) 311 332

Artificial Neural Networks (ANN) were trained by using an extensive radiolarian census dataset from the Nordic (Greenland, Norwegian, and Iceland) Seas. The regressions between observed and predicted Summer Sea Temperature (SST) indicate that lower error margins and better correlation coefficients are obtained for 100 m (SST100) compared to 10 m (SST10) water depth, and by using a subset of species instead of all species. The trained ANNs were subsequently applied to radiolarian data from two Norwegian Sea cores, HM 79-4 and MD95-2011, for reconstructions of SSTs through the last 15,000 years. The reconstructed SST is quite high during the Bolling-Allerod, when it reaches values only found later during the warmest phase of the Holocene. The climatic transitions in and out of the Younger Dryas are very rapid and involve a change in SST100 of 6.2 and 6.8 degrees C, taking place over 440 and 140 years, respectively. SST100 remains at a maximum during the early Holocene, and this Radiolarian Holocene Optimum Temperature Interval (RHOTI) predates the commonly recognized middle Holocene Climatic Optimum (HCO). During the 8.2 ka event, SST100 decreases by ca. 3 degrees C, and this episode marks the establishment of a cooling trend, roughly spanning the middle Holocene (until ca. 4.2 ka). Successively, since then and through the late Holocene, SST100 follows instead a statistically significant warming trend. The general patterns of the reconstructed SSTs agree quite well with previously obtained results based on application of Imbrie and Kipp Transfer Functions (IKTF) to the same two cores for SST0. A statistically significant cyclic component of our SST record (period of 278 years) has been recognized. This is close to the de Vries or Suess cycle, linked to solar variability, and documented in a variety of other high-resolution Holocene records