Quaternary Vegetation Dynamics – The African Pollen Database

Runge, Jürgen; Gosling, William D.; Lézine, Anne-Marie; Scott, Louis

doi:10.1201/9781003162766

Cited by 4 publications

(2 citation statements)

References 568 publications

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“…We argue that the very heterogeneity of past cultural and ecological data is what necessitates formal, transdisciplinary model-building approaches; stating problems in clear probabilistic and/or mathematical terms is the only way to directly account for the sheer variety of different data sources and their inherent issues. Computational modelling makes it possible to analyze and synthesize information from different sources, such as Neotoma [153], SESHAT [154], various continental-scale pollen databases [155][156][157], ArchaeoGlobe [158], LandCover6k [159], People3k [160], the Paleobiology database (https://paleobiodb.org/ accessed on 1 June 2022), HYDE [161], and the Global Biodiversity Information Facility (https://www.gbif.org/ accessed on 1 June 2022). To account for often wildly varying quality, including but not limited to coverage, resolution, or the various inherent uncertainties of, for example, chronometric, species identification, and cultural data, such computational models can be constructed in such a way as to incorporate insights from a variety of sources and disciplines concurrently.…”

Section: Data Challengesmentioning

confidence: 99%

Developing Transdisciplinary Approaches to Sustainability Challenges: The Need to Model Socio-Environmental Systems in the Longue Durée

et al. 2022

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Human beings are an active component of every terrestrial ecosystem on Earth. Although our local impact on the evolution of these ecosystems has been undeniable and extensively documented, it remains unclear precisely how our activities are altering them, in part because ecosystems are dynamic systems structured by complex, non-linear feedback processes and cascading effects. We argue that it is only by studying human–environment interactions over timescales that greatly exceed the lifespan of any individual human (i.e., the deep past or longue durée), we can hope to fully understand such processes and their implications. In this article, we identify some of the key challenges faced in integrating long-term datasets with those of other areas of sustainability science, and suggest some useful ways forward. Specifically, we (a) highlight the potential of the historical sciences for sustainability science, (b) stress the need to integrate theoretical frameworks wherein humans are seen as inherently entangled with the environment, and (c) propose formal computational modelling as the ideal platform to overcome the challenges of transdisciplinary work across large, and multiple, geographical and temporal scales. Our goal is to provide a manifesto for an integrated scientific approach to the study of socio-ecological systems over the long term.

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Section: Data Challengesmentioning

confidence: 99%

Developing Transdisciplinary Approaches to Sustainability Challenges: The Need to Model Socio-Environmental Systems in the Longue Durée

et al. 2022

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“…The potential is especially high for fossil pollen data, which have a long history of community data assembly and curation, recently aided by a series of data mobilization efforts (e.g. Latin America in progress), Africa (Ivory et al, 2020; Runge et al, 2021) and the Indo‐Pacific (in progress). Such data assemblages allow a deepened understanding of vegetation dynamics across various spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

A guide to the processing and standardization of global palaeoecological data for large‐scale syntheses using fossil pollen

Flantua

Mottl

Felde

et al. 2023

Global Ecol Biogeogr

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Aim Palaeoecological data are crucial for comprehending large‐scale biodiversity patterns and the natural and anthropogenic drivers that influence them over time. Over the last decade, the availability of open‐access research databases of palaeoecological proxies has substantially increased. These databases open the door to research questions needing advanced numerical analyses and modelling based on big‐data compilations. However, compiling and analysing palaeoecological data pose unique challenges that require a guide for producing standardized and reproducible compilations. Innovation We present a step‐by‐step guide of how to process fossil pollen data into a standardized dataset compilation ready for macroecological and palaeoecological analyses. We describe successive criteria that will enhance the quality of the compilations. Though these criteria are project and research question‐dependent, we discuss the most important assumptions that should be considered and adjusted accordingly. Our guide is accompanied by an R‐workflow—called FOSSILPOL—and corresponding R‐package—called R‐Fossilpol—that provide a detailed protocol ready for interdisciplinary users. We illustrate the workflow by sourcing and processing Scandinavian fossil pollen datasets and show the reproducibility of continental‐scale data processing. Main Conclusions The study of biodiversity and macroecological patterns through time and space requires large‐scale syntheses of palaeoecological datasets. The data preparation for such syntheses must be transparent and reproducible. With our FOSSILPOL workflow and R‐package, we provide a protocol for optimal handling of large compilations of fossil pollen datasets and workflow reproducibility. Our workflow is also relevant for the compilation and synthesis of other palaeoecological proxies and as such offers a guide for synthetic and cross‐disciplinary analyses with macroecological, biogeographical and palaeoecological perspectives. However, we emphasize that expertise and informed decisions based on palaeoecological knowledge remain crucial for high‐quality data syntheses and should be strongly embedded in studies that rely on the increasing amount of open‐access palaeoecological data.

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Relative Pollen Productivity Estimates for Major Plant Taxa in Middle Subtropical China

Wan

Huang

Chen

et al. 2023

Land

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Relative pollen productivity (RPP) is a key parameter for quantitative reconstruction of past vegetation cover. However, RPP estimates are rarely obtained in the subtropical and tropical regions. In this study, the extended R-value (ERV) model was used to estimate RPP for major plant taxa in the evergreen broadleaved and mixed forests in middle subtropical China based on soil samples and vegetation data from 23 sites. The best result was obtained with the combinations of ERV sub-model 3 and Prentice’s or 1/d vegetation distance-weighting method. The relevant source area of pollen (RSAP) of the soil samples was estimated to be ca. 500 m. RPP on the basis of ERV sub-model 3 and Prentice’s model was obtained for seven taxa and the ranking is as follows: Castanopsis (1 ± 0), Ilex (0.352 ± 0.031), Mallotus (0.221 ± 0.028), Liquidambar (0.115 ± 0.007), Cyclobalanopsis (0.107 ± 0.006), Camelia (0.033 ± 0.001), Symplocos (0.010 ± 0.002). RPPs for Cyclobalanopsis, Camelia, Ilex, and Symplocos which are dominant elements in the subtropical evergreen broadleaved forests were first obtained. Our result demonstrates a significant effect of pollen dispersal models on the estimates of RPPs. The RPPs obtained in this study provide an important basis for quantitative vegetation reconstruction in the subtropical region of China.

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Quaternary Vegetation Dynamics – The African Pollen Database

Cited by 4 publications

References 568 publications

Developing Transdisciplinary Approaches to Sustainability Challenges: The Need to Model Socio-Environmental Systems in the Longue Durée

Developing Transdisciplinary Approaches to Sustainability Challenges: The Need to Model Socio-Environmental Systems in the Longue Durée

A guide to the processing and standardization of global palaeoecological data for large‐scale syntheses using fossil pollen

Relative Pollen Productivity Estimates for Major Plant Taxa in Middle Subtropical China

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