Changing floodscapes in the Amazon basin: a functional understanding wetland forest adaptations and susceptibility to anthropogenic change


Large meandering rivers are the most iconic feature of the Amazon basin. Channelling the largest volume of water on Earth, these rivers flood more than 350,000 km2 of wetlands in the Amazon basin every year, hosting a large share of Amazonia’s biological diversity, an enormous number of endemic species, and significantly contributing to the regional carbon balance. More than 70% of the human population in the Amazon also lives on or within a few kilometres from floodplains. Over millennia, plants, animals, and people have closely adapted to this annual inundation cycle, known as the “flood pulse”, which in some regions can reach water levels of 10-12 meters.

Floodplain forests comprise almost 80% of the riverine wetlands in the Amazon, and yet they remain severely understudied when compared to terrestrial tropical forests. This is a pressing issue as these forests are increasingly threatened by extreme flood and drought events and the expansion of hydropower enterprises. Disruptions of natural flooding cycles can strongly impact floodplain ecosystems over thousands of square kilometres, leading to irreversible ecological changes. To prevent such catastrophic losses, we must rapidly improve our understanding of how the diversity and function of Amazonian floodplain forests will respond to human-induced hydrological changes.

There is statistical evidence that ecosystem function, community composition and species diversity are strongly tied to flooding patterns, but the ecological mechanisms behind these responses and how they may be affected by inundation changes are still poorly known. Modern functional ecology approaches are a promising avenue to elucidate these mechanisms, as flood survival adaptations should be reflected in traits related to growth rates, biomass allocation, leaf morphology and biochemistry, and whole-plant architecture. Still, only two studies to date have applied a functional approach to Amazon wetlands. As functional traits related to plant resource acquisition (e.g. leaf area) can be decoupled from flood tolerance traits (e.g. tissue porosity), we expect that multiple coexisting survival strategies may be developed by wetland plants, and the diversity of these strategies will in turn determine processes and services such as carbon storage and emission, wood provision, fisheries yields and maintenance of biodiversity.

This PhD opportunity is part of a ground-breaking project that will combine contemporary plant functional ecology with cutting-edge 3D remote sensing and real-time environmental monitoring to produce the most advanced understanding of how the diversity, structure and functioning of Amazonian flooded forests respond to changes in climate and inundation. Fully funded by a special National Geographic Society program, we will springboard the current knowledge on the mechanisms by which hydrology and other processes affect community assembly and ecosystem functioning in these forests, which is essential for predicting and managing the effects of human-induced changes The broad aims of this project are to document the still poorly known functional diversity of Amazonian floodplain forests, and to test hypotheses and forecast scenarios regarding the responses of taxonomic and functional forest composition to hydrological disturbances, as well as the resulting changes in carbon cycling and ecosystem service provision. Within this scope, the are multiple avenues to be pursued for a PhD dissertation, including tropical botany, functional ecology and community assembly, plant morphological and ecophysiological adaptation strategies, as well as 3D remote sensing methods and ecological forecasting.


The overarching research questions of the project are 1) “How diverse are the flood adaptation strategies of trees in Amazonian wetland forests?” and 2) “How can knowing these strategies allow us to better understand and predict the future impacts of anthropogenic hydrological changes?”.

To answer these questions, we will obtain unprecedented coupled measurements of plant functional traits, whole-tree and whole-stand architecture, and near-real time inundation dynamics at the individual tree level. Our project will be the first to combine cutting-edge ecological and remote sensing approaches to obtain such measurements in Amazonian floodplain forests. Data collection will occur over a network of forest plots covering the full inundation gradient, where we will carry tree species inventories, and then sample all species for functional traits related to resource and water use adaptation. We will also propose and test new functional traits that can be directly related to flood tolerance. At each plot we will install water level loggers with centimetre vertical accuracy and deploy terrestrial and airborne laser scanning to obtain 3D reconstructions of local topography and tree/stand architecture at near sub-centimetre accuracy. The 3D models of ground topography and individual tree architecture will be precisely aligned with the water level monitoring, allowing us to precisely determine the daily inundation conditions experienced by each individual tree.

This intensive data collection will be carried over a 30+ day field expedition during the low water season of 2022 (Oct-Nov), at the Mamirauá Sustainable Development Reserve (MSDR) in the Brazilian Amazonia. The MSDR is the largest Brazilian protected area dedicated to wetland conservation, a recognized UNESCO World Heritage site and a RAMSAR site representing Amazon wetlands. It offers unparalleled wetland research infrastructure in the tropics, including a network of well-equipped floating research stations, ground and water vehicles, full logistical support for research expeditions, and a vetted pool of highly trained field guides that can be hired from the local communities that co-manage the reserve.

Depending on your choice of research questions within the major framework, data analysis will include botanical identification, statistical analysis of taxonomic and functional diversity and its relationship to inundation patterns, and advanced causal inference on the emergence of distinct survival strategies within floodplain tree species. It may also include acquisition and processing of ground and drone-based laser scanning data, and the development and application of expert algorithms to identify individual trees and extract precise 3D models to be described by mathematical and statistical models of whole-plant and whole-stand architecture. You may also focus on the development of empirical or mechanistic, individual based models of forest succession and survival and test different future disturbance scenarios.

Project Timeline

Year 1

During year 1 you will perform an extensive literature review to familiarise yourself with theory and methods related to the ecological functioning of the Amazon floodplain and multidisciplinary theoretical background and methods within the context of te project. You will also participate on frequent research team meetings, including recognised specialists in plant ecology, biogeochemistry and remote sensing from multiple countries, and will contribute with the planning and experimental design of the expedition.

Based on these you will identify the research questions that will comprise your dissertation, from within the opportunities offered by the larger project scope. Depending on your entry date, you will be able to participate on IAPETUS-related or other training activities during summer 2021, and you will have the opportunity to join the full field expedition to the Amazon during October-November 2022.

Year 2

During the first semester of year 2, you will work on the organisation, processing and analysis of the data collected during the expedition, in relation to your research questions, and continue participating on team meetings to discuss data organisation, analysis and interpretation, which may lead to your participation as a collaborator on additional scientific manuscripts. At the start of the second semester of year two you will commence more directed analytical work, towards a first dissertation chapter/scientific manuscript addressing your first research question, ideally for submission by the end of year 2. If relevant, you may also use the preliminary data and results obtained to support your own independent applications for additional funding to support further training, data collection and or data analysis, beyond what is already covered by the overarching grant. You will also have ample chance throughout the year to engage with the training opportunities offered by IAPETUS and by Universities of Stirling and Newcastle.

Year 3

During year three you will continue to work on answering your research questions as dissertation chapters/journal articles, as well as interacting with project team members as a collaborator. You will ideally submit a second manuscript for publication before the start of the second semester of year 3 and present your work on at least one relevant international conference during this year. You will also receive targeted training and mentoring related to academic and career development, as offered by the supporting universities and IAPETUS, to prepare you for entering the job market.

Year 3.5

During year 3.5 you will finalise you dissertation work and submit your third manuscript for publication. As you finalise your degree, you will be coached and supported in preparation for your next career stage.

& Skills

You will receive training in the frontier of knowledge in tropical forest ecology, ecosystem ecology and functional plant ecology and ecophysiology. You will also acquire advanced scientific programming skills, will become comfortable using multiple statistical methods and modelling approaches, and based on your chosen research questions, you will receive training on field and laboratory methods and instrumentation in plant ecology and physiology, biogeochemistry, LiDAR surveying and remote sensing, and/or individual-based modelling. Through IAPETUS and the supporting universities, you will have ample opportunity to engage with training on research practices, writing and speaking skills, career development and other specific technical training.

References & further reading

Albernaz, A. L. et al. (2012) Tree species compositional change and conservation implications in the white-water flooded forests of the Brazilian Amazon. J. Biogeogr. 39, 869–883.

Barichivich, J. et al. (2018) Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4, eaat8785.

Hess, L. L. et al. (2015). Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar. Wetlands 35, 745–756.

Householder, J. E. et al. (2021). Modeling the Ecological Responses of Tree Species to the Flood Pulse of the Amazon Negro River Floodplains. Frontier in Ecology and Evolution 9: 195.

Luize, B. G. et al. (2018). The tree species pool of Amazonian wetland forests: Which species can assemble in periodically waterlogged habitats? PLOS ONE 13, e0198130.

Mori, G. B. et al. (2019) Trait divergence and habitat specialization in tropical floodplain forests trees. PLoS One 14, e0212232.

Mori, G. B et al (2021). Edaphic characteristics drive functional traits distribution in Amazonian floodplain forests. Plant Ecology 222: 349–360.

Parolin, P. (2009). Submerged in darkness: adaptations to prolonged submergence by woody species of the Amazonian floodplains. Ann. Bot. 103, 359–376.

Resende, A. F. et al. (2019). Massive tree mortality from flood pulse disturbances in Amazonian floodplain forests: The collateral effects of hydropower production. Sci. Total Environ. 659, 587–598.

Schöngart, et al. (2021). The shadow of the Balbina dam: A synthesis of over 35 years of downstream impacts on floodplain forests in Central Amazonia. Aquatic Conservation 31, 1117–1135.

Wittmann, F. et al. (2006). Tree species composition and diversity gradients in white-water forests across the Amazon Basin. J. Biogeogr. 33, 1334–1347.

Further Information

Thiago Silva, University of Stirling,

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