New theoretical and analytical approaches for quantifying landscape dynamics and its effects on ecosystem function in the hyper-dynamic Amazon wetlands

Overview

Large rivers and associated wetlands are the most iconic landscapes of the Amazon basin. Together, river floodplain systems cover more than 400,000 km2 of the Amazon basin, twice the area of the UK. Resulting from both long- and short-term interactions between climate, hydrology and geology, these are among the most complex natural landscapes in the world, forming ever-changing mosaics of habitats that are annually flooded. Unfortunately, these fascinating systems are also severely threatened by an increasing frequency of extreme hydrological events, as well as hydrological disruptions caused by hydropower damming and by basin-wide deforestation.

In this project, you will test the limitations of traditional landscape ecology approaches and propose new methods to describe and quantify landscape dynamics in the hyper-dynamic Amazonian floodplains systems. You will then demonstrate how such spatial and temporal dynamics influence the movement and distribution of living organisms, and how observed and expected hydrological changes may impact the ecology, evolution and/or human society in the Amazonian floodplains

Floodplain landscapes are shaped by tectonic processes and climatic oscillations that determine erosion and deposition, resulting in river channel migrations that continuously sculpt the landscape (see https://tinyurl.com/ygwsjrvs for a satellite time-lapse in the central Amazon). These dynamics have led to a naturally and highly fragmented landscape, where habitat patches form a complex shifting mosaic. In these systems, traditional matrix-corridor-landscape approaches cannot adequately represent this complexity, and thus new conceptual and analytical models are necessary to understand landscape organization and dynamics in large tropical floodplains.

On top of that, floodplain landscapes also alternate seasonally between aquatic and terrestrial environments; a terrestrial forest observed in October will be flooded by 10+ meters of water depth in June. This so-called flood pulse is the key driver of most ecological and biogeochemical processes in the floodplain, and it also shapes the movement, dispersal and reproductive strategies, and eco-evolutionary dynamics of plants and animals. Yet, there are no studies to date that adequately quantify the influence of these hyper-dynamic landscape changes on animal and plant biology, and how they will be impacted by current and future anthropogenic changes. Therefore, in this project you will have the chance to significantly advance the existing knowledge on the role of landscape dynamics on the ecology and evolution of floodplain organisms, with direct implications to conservation and landscape management in the Amazon and other tropical floodplains.

The suggested structure for this project is to: 1) Comprehensively evaluate the current literature in landscape ecology and physical geography to improve upon or propose new theoretical models for representing and understanding such hyper-dynamic landscapes, 2) Demonstrate and quantify the influence of floodplain landscape dynamics over plant and/or animal life in the floodplain by applying state of the art movement simulation, analysis, and visualization methods with both synthetic and observed organism movement data; 3) Discuss how the observed and expected hydrological extremes and current patterns of anthropogenic land use change may impact landscape dynamics and ecosystem function in Amazonian floodplain environments.

Within these central themes there will be considerable scope to develop your own research questions trough a combination of remote sensing and GIS methods, ecological and/or hydrological modelling, and landscape genetics and/or meta-population/meta-community approaches for fauna and/or flora species, such as manatees, caimans, the Arapaima fish and jaguars, or several plant species with different dispersal, establishment and flood tolerance mechanisms.

Click on an image to expand

Image Captions

A) Landscape changes at the Curuai floodplain, southern bank of the Amazon. Left: extreme drought in 2005; Right: extreme flood in 2009. Images are false color composites from the Landsat TM satellite. Vegetation appears as green, bare soil appears as red, and water appears as blue. B) Habitat distribution and flood duration in the Mamirauá Sustainable Development Floodplain, Central Amazon. All images by Thiago Silva.

Methodology

The first element of your PhD work will be a comprehensive review of the published literature on landscape ecology, movement ecology, meta-population/community theory, and floodplain hydrology and ecology in tropical wetlands, to acquire mastery over these subjects. Once you achieve it, you will then draw from these usually disconnected disciplinary areas to identify, improve on and/or propose an entirely new theoretical model for representing, quantifying and understanding landscape dynamics in floodplain environments. You will then define a suite of computational experimental approaches to test the assumptions of your proposed method, making use of existing remote sensing and GIS tools to quantify landscape dynamics and computational models for simulating dispersal scenarios. To validate the results of your simulations, will will then use pre-existing or newly acquired data on species movement and dispersal dynamics. Finally, you will combine model and observational results to project and evaluate scenarios of the them impacts of climate change, extreme events and land cover change on landscape dynamics and associate eco-evolutionary processes and ecosystem services. You are expected to disseminate your findings in the form of peer-reviewed manuscripts, conference presentations and accessible science communication materials for lay audiences.

Project Timeline

Year 1

During year 1, you will review the literature to identify key knowledge gaps and to support you in selecting, improving and/or proposing a theoretical and methodological approach to model and quantify landscape dynamics in floodplain environments. You will also participate on specific training courses focused on these topics and on relevant computer, laboratory and/or field methods. This initial theoretical work is likely to lead to an initial version your first thesis chapter/journal publication at the end of year 1, representing a relevant contribution to the theoretical landscape ecology literature.

Year 2

During year 2, you will define the experimental and analytical methods necessary for testing your proposed model. This will include achieving mastery over the necessary computational tools and methods, and if fieldwork for collecting new data is deemed necessary, it will also be carried on year 2. You will then perform and analyze your simulation experiments and validate them using the observational data you compiled/sampled. This will be supported by further training activities on methods related to the approaches you have selected for your research, and part of the analytical work may also be further supported by a placement at a relevant foreign research institution, in line with your choices of experimental approaches and model organisms. You will also continue to work on and eventually submit your first manuscript/chapter for publication, and start the work towards your second draft manuscript / thesis chapter. You may also participate on a relevant scientific conference during this year, to present preliminary results and gather feedback on your analyses.

Year 3

During year 3, you will refine and submit your second chapter /manuscript draft and then use your theoretical and experimental findings to support analyses and/or further experiments to assess the impact of current and future anthropogenic changes using known scenarios of climate and land use change, and discuss its implications for ecosystem function, conservation and the provision of ecosystem services by Amazonian floodplains. By the end of year 3, you are expected to participate on an international high-impact conference to present and receive feedback from the overall results of you thesis. An international placement to support data analysis may also be sought during this year.

Year 3.5

You will use this time to finalize the writing and submission of your manuscripts/chapters and assemble your overall thesis document. You will also be expected to engage on training for job/grant applications and on higher education teaching, which are annually offered by University of Stirling.

Training
& Skills

Throughout the PhD any training needs will be continually assessed between the successful candidate and the supervisory team. There will be the opportunity to attend specific training events, including IAPETUS2 training events and choices among the 50+ training courses offered annually by the Postgraduate Researcher Skills Development Program of University of Stirling, including data analysis, coding, software training, scientific writing, scientific presentation and outreach, preparation for the job market, and higher education teaching. Formal training activities will be complemented by active supervision through regular meetings of the supervisory team with the student, starting with weekly meetings with the main supervisor and monthly meetings with the supervisory committee on year 1, progressing to meetings scheduled as needed as the candidate becomes more independent. In accordance to the progress of the thesis work, the candidate may also spend short periods working directly with the main supervisors or selected collaborators, at their home institutions in the UK or abroad. The candidate will also participate on the regular meetings of the Ecosystem Change research group of the Division of Biological and Environmental Sciences, of which you will be a member, attend the weekly departmental seminars, as present their work as well. Progress will be formally evaluated annually in the PGR review process of the University of Stirling.

References & further reading

Bishop-Taylor, R., Tulbure, M. G. & Broich, M. Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series. Landscape Ecology 33, 625-640 (2018).

Bocedi, G. et al. RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes. Methods in Ecology and Evolution 5, 388-396 (2014).

Cushman, S. A., Gutzweiler, K., Evans, J. S. & McGarigal, K. The Gradient Paradigm: A Conceptual and Analytical Framework for Landscape Ecology. in Spatial Complexity, Informatics, and Wildlife Conservation (eds. Cushman, S. A. & Huettmann, F.) 83-108 (Springer Japan, 2010). doi:10.1007/978-4-431-87771-4_5.

Demšar, U. & Virrantaus, K. Space-time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science 24, 1527-1542 (2010).

Ferreira-Ferreira, J. et al. Combining ALOS/PALSAR derived vegetation structure and inundation patterns to characterize major vegetation types in the Mamirauá Sustainable Development Reserve, Central Amazon floodplain, Brazil. Wetlands Ecology and Management 23, 41-59 (2015).

Fletcher, R. J., Burrell, N. S., Reichert, B. E., Vasudev, D. & Austin, J. D. Divergent Perspectives on Landscape Connectivity Reveal Consistent Effects from Genes to Communities. Current Landscape Ecolology Reports 1, 67-79 (2016).

Martensen, A. C., Saura, S. & Fortin, M.-J. Spatio-temporal connectivity: assessing the amount of reachable habitat in dynamic landscapes. Methods in Ecology and Evolution 8, 1253-1264 (2017).

Riotte-Lambert, L. & Matthiopoulos, J. Environmental Predictability as a Cause and Consequence of Animal Movement. Trends in Ecology & Evolution (2019) doi:10.1016/j.tree.2019.09.009.

Sheaves, M. Consequences of ecological connectivity: the coastal ecosystem mosaic. Marine Ecology Progress Series 391, 107-115 (2009).

Wimberly, M. C. Species Dynamics in Disturbed Landscapes: When does a Shifting Habitat Mosaic Enhance Connectivity? Landscape Ecol 21, 35-46 (2006).

Further Information

Please contact Dr. Thiago Silva by e-mail, thiago.sf.silva@stir.ac.uk, or by phone at b +44 798 888 6891 (mobile)

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