Rapid climate change from north Andean Lake Fúquene pollen records driven by obliquity: implications for a basin-wide biostratigraphic zonation for the last 284 ka

Bogotá-Ángel, Raúl Giovanni; Groot, M.H.M.; Hooghiemstra, H.; Lourens, Lucas Joost; Linden, M. van der; Berrío, Juan Carlos

doi:10.1016/j.quascirev.2011.08.003

Cited by 57 publications

(46 citation statements)

References 63 publications

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“…In terms of quantifying Plio-Pleistocene temperature fluctuations, the palaeoecological history of the páramos has been studied extensively (e.g. Cleef, 1979;Hooghiemstra, 1984;Hooghiemstra & Van der Hammen, 2004;Van der Hammen, 1974;Van der Hammen & Cleef, 1986) because of the unique high elevation fossil pollen records that cover most of the Pleistocene (Bogotá-A, Hooghiemstra, & Berrio, 2016;Bogotá-Angel et al, 2011;Groot, Hooghiemstra, Berrio, & Giraldo, 2013;Groot et al, 2011;Torres, Hooghiemstra, Lourens, & Tzedakis, 2013). Under current conditions, the páramos form isolated archipelagos of 'alpine (sky) islands' (McCormack, Huang, & Knowles, 2009;Sklenář et al, 2014) but the rich collection of fossil pollen sequences throughout this region (Flantua et al, 2015) show that the páramos underwent substantial elevational shifts during the Pleistocene, resulting in extensive changes in surface area and connectivity (Flantua et al, 2014;Hooghiemstra & Van der Hammen, 2004;Sklenář et al, 2014;Van der Hammen, 1974).…”

Section: Introductionmentioning

confidence: 99%

The flickering connectivity system of the north Andean páramos

Flantua

O’Dea

Onstein

et al. 2019

Journal of Biogeography

151

149

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Aim To quantify the effect of Pleistocene climate fluctuations on habitat connectivity across páramos in the Northern Andes. Location Northern Andes. Methods The unique páramos habitat underwent dynamic shifts in elevation in response to changing climate conditions during the Pleistocene. The lower boundary of the páramos is defined by the upper forest line, which is known to be highly responsive to temperature. Here, we reconstruct the extent and connectivity of páramos over the last 1 million years (Myr) by reconstructing the upper forest line from the long fossil pollen record of Funza09, Colombia, and applying it to spatial mapping on modern topographies across the Northern Andes for 752 time slices. Data provide an estimate of how often and for how long different elevations were occupied by páramos and estimate their connectivity to provide insights into the role of topography in biogeographical patterns of páramos. Results Our findings show that connectivity amongst páramos of the Northern Andes was highly dynamic, both within and across mountain ranges. Connectivity amongst páramos peaked during extreme glacial periods but intermediate cool stadials and mild interstadials dominated the climate system. These variable degrees of connectivity through time result in what we term the ‘flickering connectivity system’. We provide a visualization (video) to showcase this phenomenon. Patterns of connectivity in the Northern Andes contradict patterns observed in other mountain ranges of differing topographies. Main conclusions Pleistocene climate change was the driver of significant elevational and spatial shifts in páramos causing dynamic changes in habitat connectivity across and within all mountain ranges. Some generalities emerge, including the fact that connectivity was greatest during the most ephemeral of times. However, the timing, duration and degree of connectivity varied substantially among mountain ranges depending on their topographical configuration. The flickering connectivity system of the páramos uncovers the dynamic settings in which evolutionary radiations shaped the most diverse alpine biome on Earth.

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Section: Introductionmentioning

confidence: 99%

The flickering connectivity system of the north Andean páramos

Flantua

O’Dea

Onstein

et al. 2019

Journal of Biogeography

151

149

View full text Add to dashboard Cite

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“…5. We compute the following common F1 score = 2 * precision * recall precision + recall (4) where T P refers to true positives, T N to true negatives, F P to false positives, and F N 221 to false negatives. Precission, recall and F1 score were computed as an average weighted 222 by the number of images in each class.…”

Section: Experimental Design 214mentioning

confidence: 99%

Precise automatic classification of 46 different pollen types with convolutional neural networks

Sevillano

Holt

Aznarte

2020

Preprint

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In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and farmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable. 2 scientific fields. An estimated 40% of the world's population experience seasonal allergic 3 rhinitis (SAR) driven by exposure to pollen [1]. Pollen forecasting, informed by 4 examination of airborne pollen has become a key tool for management of SAR [2]. 5 Pollen is also very important for quality verification of honey [3], reconstructing past 6 vegetation to understand past changes in climate change [4], biodiversity [5], and human 7 impacts [6] and as a forensic tool [7]. Common to all these areas is the need for 8 experienced analysts to spend considerable amounts of time identifying and counting 9 pollen on slides. While other branches of science have been transformed by the 10 technological advances of recent decades, palynology is languishing, with the practical 11 methodology of pollen counting having hardly advanced much beyond that of the 1950s. 12 But this is not for want of trying. Flenley [8] was the first to call attention to the 13 need and potential of automation of pollen counting. A handful of early attempts were 14 published in the later decades of the 20th century, but the rapid increase in capability 15 in computational intelligence over the early part of the 21st century resulted in 16 considerable acceleration in the field during this time, with numerous attempts at 17 partial or complete automation of palynology appearing in the literature, summarised 18 in [9] and [10]. 19While the results of the existing studies can be regarded as promising, they are 20 rather limited in that they typically deal with a relatively small number of taxa (max 21 February 5, 2020 1/14 30, mean 8), and success/accuracy rates vary. While some palynological applications 22 may require a lower level of taxonomic diversity than others, it is arguable that many 23 'real world' pollen problems will require higher diversity than that of most of the 24 existing studies. For example, Stillman and Flenley, (1996) suggested that the minimum 25 number of taxa for paleoecological applications would be around 40 types. 26Recently, Sevillano and Aznarte ( [10]) presented an example of pollen classification 27 which applied deep learning convolu...

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“…However, higher-frequency cycles (O and P) have been interpreted in several records from Quaternary terrestrial archives (Berger and Loutre 2010;Bogota-A et al 2011;Kashiwaya et al 2010;Prokopenko et al 2006).…”

Section: Sub-milanković Cycles Over the Past 75 Kamentioning

confidence: 99%

“…Even though orbital cycles have been interpreted in numerous marine records (Barthes et al 1999;Golovchenko et al 1990;Jarrard and Arthur 1989;Molinie and Ogg 1990a) and several lacustrine sedimentary sequences (Bogota-A et al 2011;Kashiwaya et al 2010; Fig. 1 a Bathymetric map of Lake Van, seismic lines from pre-site surveys and targeted drillsites within the PALEO-VAN project (Litt et al 2009).…”

Section: Introductionmentioning

confidence: 99%