A Palaeoenvironmental Investigation of Amazonian Lowland Sensitivity to Climatic Drivers Using Pollen-based Modelling Approaches

Informacje na temat projektu

PALOMA

Identyfikator umowy o grant: 101105420

DOI

10.3030/101105420

Projekt został zamknięty

Data podpisania przez KE 17 Lipca 2023

Data rozpoczęcia 1 Kwietnia 2024

Data zakończenia 31 Marca 2026

Finansowanie w ramach

• Marie Skłodowska-Curie Actions (MSCA)

Koszt całkowity

Brak danych

Wkład UE

€ 165 312,96

Koordynowany przez

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
Spain

Periodic Reporting for period 1 - PALOMA (A Palaeoenvironmental Investigation of Amazonian Lowland Sensitivity to Climatic Drivers Using Pollen-based Modelling Approaches)

Okres sprawozdawczy: 2024-04-01 do 2026-03-31

Podsumowanie kontekstu i ogólnych celów projektu

This MSCA project was entitled “A Palaeoenvironmental Investigation of Amazonian Lowland Sensitivity to Climatic Drivers Using Pollen-Based Modelling Approaches”, hereafter Project PALOMA (European Fellowship, Grant ID 101105420), and it was hosted at Geosciences Barcelona (GEO3BCN-CSIC). Its overarching aim was to initiate the first steps towards reconstructing past environmental and climatic changes in Amazonia, more specifically the lowlands of north-western Amazonia, using quantitative pollen-based climatic modelling, in order to understand the sensitivity of this biome to climatic drivers during the Holocene (last 11,700 years).
Being able to accurately reconstruct past climatic changes in the Amazon, and use these findings as past analogues of what we could face in the ongoing global change scenario to inform conservation initiatives and relevant stakeholders, is an increasingly urgent matter. Considering the current rate of CO2 emissions, the ‘safe’ limit of 2°C warming will likely be exceeded by 2100. Projected disruptions to the balance of hydrological and evapotranspiration cycles may lead to forest dieback and in turn trigger changes in rainfall and increased fire events and send Amazonia down an irreversible path. As such, protecting and conserving globally-important carbon sinks like the Amazon represents the strongest approach for defence against climate change. Within the last region, the lowlands of NW Amazonia are particularly crucial in the regulation of Amazonia’s hydrological cycles as this region receives most of the water input from the Andes and delivers it into the Amazon River, meaning than any changes here will likely affect the rest of the Basin and ultimately the rest of the world. Therefore, reconstructing past changes in climate and environment in Amazonia can help us evaluate the response and sensitivity of these ecosystems to external drivers, including climatic variability. We can use this information to forecast the potential shifts the vegetation may undergo in the ongoing climatic change, and even anticipate undesirable scenarios.
Fossil pollen grains, preserved in sedimentary deposits such as lakes or peatlands, enable the reconstruction of past vegetation composition through time and the drivers that led to changes in assemblages, such as climatic variability and human impact. While some long-term (1000+ years) records of climate have been recovered from the Amazonian region in the past, these rely on interpretations of several proxies (like pollen) or indicators of climate that are often hard to disentangle from other processes, limiting our ability to compare past changes to present-day climatic observations or future projections.
In this context, project PALOMA sought to attempt the development and application of quantitative pollen-based climate reconstructions using modern pollen datasets to calibrate statistical relationships between vegetation and climate. This approach is based on the principle that plant taxa occupy specific climatic ranges, and that pollen assemblages reflect the composition of surrounding vegetation. By compiling modern pollen data across climatic and environmental gradients and linking these to observed climatic variables, it is possible to establish the relationship between pollen and climatic parameters such as temperature and precipitation. These relationships can then be used to calibrate fossil pollen records using transfer function models and reconstruct past climate variables. The overall objectives of the project were: 1) Enhance the modern pollen dataset for Amazonian lowlands and improve the representation of different taphonomic sources; and 2) Apply quantitative methods for pollen-based climate reconstruction to fossil records from NW Amazonia.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

To achieve our objectives, the project was centred around two main research work packages (WP).
WP1 focused on compiling the Amazonian Modern Pollen Database (AMPD), which now represents the most comprehensive taxonomically harmonised calibration dataset for the region, achieved through extensive collaboration with an international network of researchers across Europe, North America and South America, and fieldwork campaigns for the collection of new samples in Peru and Ecuador. The final AMPD comprises >460 samples from Amazonian lowlands, exceeding previous datasets for this region; samples were subjected to rigorous harmonisation and linked with climate data from each site, providing a very valuable calibration dataset. One of the most innovative contributions involved designing and testing a new type of pollen trap aimed at sampling pollen influx in rivers, with the goal of sampling different modes of pollen transport and understand taphonomic processes, in order to better understand the relationship between modern pollen deposition and the surrounding vegetation and climate.
WP2 involved the implementation of multiple modelling approaches on several fossil pollen records from Peru and Ecuador, using the AMPD for calibration. Modelling reconstructions including transfer function techniques, probabilistic palaeoclimate reconstruction methods and generalised additive modelling (GAMs). While these models require further refining, they represent the first application of these methods to fossil pollen records in Amazonia, and they show promising results that demonstrate evidence of past climate variation. These analytical methodologies led to the creation of an open-source Python package and interactive interface that enables quantitative reconstructions through proxies such as pollen and rigorous validation of results. This new tool has valuable potential for making these modelling methods accessible to the international palaeoecological community and maximise analytical capabilities.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

The AMPD constitutes the most comprehensive, up-to-date, taxonomically harmonised modern pollen calibration dataset from Amazonia, with particular focus on the lowlands, yet compiled. This provides empirical basis for pollen-based reconstructions in the region. The AMPD was then used to investigate diversity gradients and community structure across the Amazon Basin, to identify climatically-related vegetation distribution patterns and to evaluate the ecological representativeness and calibration potential of the dataset.
The new river pollen trap design and related methodological approach, which was tested in rivers in the NW Amazon in Peru and Ecuador, represents the first device specifically made for time-integrated, instream pollen sampling method under continuous and natural water flow, enabling the investigation of fluvial pollen transport in Amazonia and potentially elsewhere as well. Our investigation (submitted to Journal of Ecology) revealed that the fluvial pollen signal reflects the composition of vegetation occurring along the river’s course, although the representation of vegetation communities depends on the size, sediment load, and discharge of the river. This device may also provide a new tool for calibrating the waterborne pollen component in pollen records. In addition, we also used the deployment of river traps in Peru to study the taphonomic processes that might influence the pollen signal by combining different pollen collectors (river traps, aerial traps, lake traps, surface samples) and relate them to modern vegetation data.
First application of quantitative pollen-based climatic reconstruction techniques to Amazonian fossil records, proving that this method is applicable in this region if enough calibration data is available, though further tests are required to ensure the accuracy of the models. So far, comparison of results with regional climate records allowed the identification of broad coeval trends between reconstructed climatic variables and other records of temperature, precipitation and ENSO (El Niño Southern Oscillation) variability. These comparisons also proved useful for identifying discrepancies between reconstruction outputs and regional records.
The application of Latent Dirichlet Allocation (LDA) methods to tropical pollen is innovative, and helped to identify shifts in ecological assemblages through time. This is a valuable method for the tropics, where the high diversity, distribution overlap (same species occurring in different environments) and large variation in taxonomic identification (several families identified to family or genus and few to species level) mean that few taxa can carry a meaningful climatic or ecological signal. The implementation of this method has opened future research avenues to implement LDA-derived topics for transfer functions rather than pollen data, which opens a promising new direction for reducing noise in complex tropical fossil pollen records.
Our Python package provides a fully open-source, accessible and interactive interface for proxy-based palaeoclimatic reconstruction (not just pollen), offering a valuable and transparent tool that can enhance the accuracy and validity of results. The application of this tool on our datasets helped to improve accuracy of the reconstructions, validate results, and reveal climatic gradients across the Amazon Basin.