Signals from the Soil in Tropical Forest Ecosystems

Overview

Soil water availability plays a critical role in ecological processes; in tropical forest ecosystems it can be a key driver determining plant species distributions and forest structure (e.g. Detto et al. 2013; Kupers et al. 2019a). However, we know relatively little about the processes underlying these patterns. For example, how do spatial and temporal variations in soil water availability affect plant physiological function, growth and mortality? And, do interspecific differences in ecological responses to soil hydraulic properties ultimately drive community dynamics, assembly and recovery in tropical forests?

Determining the connections between soil water availability and plant responses is ever more pressing under environmental change. In parts of the tropics, hydrological stresses (drought and flooding) are anticipated to increase and/or predictions are highly uncertain; plants which have evolved under ever-wet conditions may be exposed to more frequent or extreme water shortages. We need to better understand the nature of hydrological stresses and how plants respond to make predictions about mortality, carbon sequestration and compositional and biodiversity change under future climate scenarios. Simultaneously, human modification and degradation of forests is altering precipitation and soil water availability at various scales; we need to better understand these environmental impacts to predict future trajectories of plant communities in modified ecosystems.

To address these challenges, soil water availability data are needed at higher spatial and temporal resolution than measured currently (Kupers et al. 2019b), to capture variability and events of interest. For instance, it has been shown that a dry spell of just six days affected survival of pioneer seedlings (Dalling & Hubbell 2002). The project proposes to apply recent advances in information communication technology to install a new soil moisture wireless sensor network (WSN) (e.g. Corke et al. 2010; Nundloll et al. 2019). While WSNs are increasingly being used in agricultural settings, communication and battery life limitations still exist in remote areas so it will be necessary to develop methods to optimize data capture and storage. Combining these high resolution field measurements with remotely sensed data and process-based models can provide a much needed framework for upscaling to broader landscapes and prediction under future scenarios.

Relatively few studies have examined soil water variability and drought impacts in tropical forests of Southeast Asia. This project will utilise permanent forest inventory plots in Sabah, Malaysian Borneo where trees and seedlings are censused at regular intervals. The project proposes to (i) design and install a new soil moisture WSN, and use the spatio-temporal data gained from the WSN to (ii) determine how soil moisture regimes vary according to canopy cover and other environmental correlates, (iii) relate soil hydrological properties to key plant responses, such as leaf water potential and community dynamics and (iv) relate field data to remotely sensed soil moisture data, and parameterise a hydrological model allowing fine-scale prediction of soil water availability across heterogeneous landscapes.

Click on an image to expand

Image Captions

Topo.png – “Topographic variability can be a major driver of soil water movement” reproduced with permission from L. Banin
Seedling.png – “A Parashorea tomentella seedling in the understorey of a Bornean forest” reproduced with permission from L. Banin

Methodology

We anticipate that the project will involve two field seasons in Danum Valley, Sabah, Malaysian Borneo, the first in old growth forest and the second in recovering logged forest, facilitated by collaboration with Prof David Burslem, University of Aberdeen & PI for the ForestGEO site. The project will require the design and development of appropriate WSNs, with additional guidance from Prof Gordon Blair at Lancaster University, bringing expertise in information communication and environmental sensor network technology. These will be deployed in the field with the aim of delivering real-time data capture. The student may wish to use an adaptive sampling approach to optimise field data collection.
The student will relate field data to remotely sensed data (e.g. Sentinel 1 and 2; Gao et al. 2017) and parameterise a hydrological model (e.g. using Topmodel [Buytaert 2018] or the Soil Water Energy and Transpiration [SWEAT] model [Marthews et al. 2008]), allowing fine-scale prediction of soil water availability across heterogeneous landscapes.

Project Timeline

Year 1

Stage one: Review of literature; method development
The student will develop their understanding of interactions between hydrology and ecology in tropical forest systems. Simultaneously, the student will engage in sensor and communication technology methods for soil monitoring and adaptive sampling (Collaborating Partner, G. Blair, Lancaster University). Training needs will be identified and development activities planned. The student will refine the research objectives, key hypotheses, methods and sampling regimes. The first year will culminate in the first field season, where the soil sensor network will be deployed, tested and calibrated in the old growth forest.

Year 2

Stage two: Field data collection & modelling; placement
Data from the first field season will be analysed and evaluated. Incoming soil data will be continue to be accessed and analysed. The student will trial linking these field data with existing soil process models and/or freely available remotely sensed data. The student will plan and undertake their second field season in the logged forest.

Year 3

Stage three: Analysis and theory development
The student will conduct appropriate statistical and/or modelling analyses, addressing their key hypotheses, and prepare research for their thesis and publication. The student will be encouraged to present their work at a scientific conference.

Year 3.5

In this period, the thesis will be finalised for submission and papers for publication.

Training
& Skills

This interdisciplinary project will give the student a unique opportunity to gain a range of technical skills, including field ecology, sensing technology and networks and computer-based (statistical and modelling) skills, representing a comprehensive training experience.

Stage 1 will deliver a grounding in soil, ecosystem and ecological theory; principles of information communication technology in soil monitoring networks; hypothesis formulation and experimental design and statistical theory as appropriate to the design of the project; key field skills relating to soil moisture measurement and plant functional traits.

During Stage 2 the student will apply and consolidate field skills learnt in Stage 1. They will also gain important transferable skills including leadership, teamwork and organisation developed through implementing independent fieldwork as well as communicating science via various media and to different audiences.

Stage 3 will involve further training and application of statistical and/or modelling techniques, with the opportunity to contribute to modelling developments in this field. The student will also consolidate communication techniques and scientific writing.

References & further reading

Buyteart, W. (2018). Implementation of the Hydrological Model TOPMODEL in R. https://cran.r-project.org/web/packages/topmodel/topmodel.pdf.

Corke, P., Wark, T., Jurdak, R., Hu, W., Valencia, P. & Moore, D. (2010). Environmental Wireless Sensor Networks. Proceedings of the IEEE. 98, 1903 – 1917. 10.1109/JPROC.2010.2068530.

Dalling, J. & Hubbell, S. (2002). Seed Size, Growth Rate and Gap Microsite Conditions As Determinants of Recruitment Success for Pioneer Species. Journal of Ecology. 90, 557-568.

Detto M, Muller-Landau HC, Mascaro J, Asner GP (2013) Hydrological Networks and Associated Topographic Variation as Templates for the Spatial Organization of Tropical Forest Vegetation. PLoS ONE. 8, 10, e76296.

Gao, Q., Zribi, M., Escorihuela, M. & Baghdadi, N. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors. 17, 1966.

Kupers, S., Engelbrecht, B, Hernández, A., Wright, S. J., Wirth, C., & Rüger, N. (2019a). Growth responses to soil water potential indirectly shape local species distributions of tropical forest seedlings. DOI: 10.1111/1365-2745.13096.

Kupers, S., Wirth, C., Engelbrecht, B, & Rüger, N. (2019b). Dry season soil water potential maps of a 50 hectare tropical forest plot on Barro Colorado Island, Panama. Scientific Data. 6, 63. DOI: 10.1038/s41597-019-0072-z.

Marthews, T., Burslem, D., Paton, S., Yangüez, F. & Mullins, C. (2008). Soil drying in a tropical forest: Three distinct environments controlled by gap size. Ecological Modelling. 216, 3-4, 369-384.

Nundloll, V., Porter, B., Blair, G., Emmett, B., Jones, D., Chadwick, D., Winterbourn, B., Beattie, P., Dean, G., Shaw, R., Shelley, W., Brown, M. & Ullah, I. (2019). The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment. Future Internet. 11, 6, 129. https://doi.org/10.3390/fi11060129.

Further Information

We look forward to applications from highly motivated, enthusiastic and independent applicants. A strong background in physical, biological or environmental sciences is highly desirable. For informal inquiries about the project, please contact Dr Lindsay Banin via email libanin [at] ceh.ac.uk (Tel: 0131 445 8432)

Apply Now