- Biodiversity modelling of an island-like, rare type of ecosystem
- Assessing the relative role of biological processes to the persistence of species
- Past and Future environmental change drivers on ecosystem connectivity
Peruvian lomas, also termed fog oasis, are unique ecosystems composed by highly endemic plant communities forming in small fog-rich areas of otherwise barren landscape of the hyper-arid coastal plains along the Pacific coast. Due to the necessary topographic and climatic conditions for fog formation and interception, their occurrence result in spatially isolated communities, whose connectivity depends on proximity and on dispersal ability of pollen and seeds. Seedling recruitment is highly dependent on facilitative processes, as woody species may induce higher dew formation and thus increasing water availability. This creates a potential feedback loop for the establishment of more species and individuals. However, both dispersal and recruitment might have been disrupted by humans due to widespread change of animal dispersers (loss of guanaco, introduction of goats and cattle), to overgrazing and to the use of woody material by human communities in the past. Moreover, extreme events, such as El Nino, may strongly impact the population dynamics, due to the direct impact on fog formation. Therefore, it is unclear how past humans might have influenced current community composition and structure, while the expected increase in extreme events due to impending climate change will likely impose unforeseen changes to these threatened ecosystems and their endemic species. To fill these knowledge gaps, the application of next generation dynamics models able to integrate relevant processes (dispersal and interactions) and be applicable to non-equilibrium conditions (e.g. human-induced climate change and connectivity loss) is fundamental. By manipulating dispersal, biotic interaction and human drivers in computer simulations, we can compare emerging results with current community patterns and formally evaluate what was the most likely scenario of past change that shaped current communities. The best process combination will then be used to explore potential future communities under a changing climate.
General objective: To understand and predict biodiversity dynamics in the island-like ecosystems of lomas. Specific objectives: 1) Adapt available models to integrate loma-specific dispersal processes and facilitation; 2) Assess the relative role of dispersal loss, climatic events (e.g. El Nino) and human-caused removal of facilitators to community assembly; 3) Explore the future of lomas under impending climate change.
Figure 1: Project study system, methods and experimental design. A) Fog oasis in the dry season (top), wet season (middle) and endemic species (bottom). B) Spatially explicit dynamic model MetaRange (Fallert et al. in prep), with environmental dimensions (top) and process structure (bottom). C) From MetaRange we can obtain spatial distribution of the simulated species (illustration of current lomas distribution – modified from Moat et al. 2021). D) Experimental design: Objective 1 (O1) is to adapt the model to the loma system and calibrate parameters with trait data; O2 is to vary scenarios of human-induced change to assess past influences; O3 explores the effects of climate change on future distribution of lomas.
HostUniversity of Birmingham
- Climate and Environmental Sustainability
- Organisms and Ecosystems
Objective 1: We will apply a recently developed population-based metacommunity model that simulates plants undergoing life-histories processes (Fig. 1B). This model will be extended to include loma-specific processes, namely animal dispersal and facilitation.
Objective 2: We will perform simulation experiments switching on and off dispersal and facilitation in environmental scenarios with and without extreme events. Emergent species’ ranges and community assembly will be compared to known patterns across the scenarios to evaluate the most likely process combination that shaped current lomas (Fig. 1C-D).
Objective 3: With the selected process combination, we will then explore future scenarios of increased frequency, extent and intensity of extreme events, such as El Nino, on lomas connectivity and community assembly.
As the bulk of the model is already developed (Fig. 1B), the student can start adapting the model, while gathering data for model calibration and validation. This will inform field work for collecting missing data.
Training and skills
Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.
The student will be trained in computational programming (e.g. R, Julia, Matlab), next generation mechanistic models for biodiversity dynamics, optimization algorithms, handling and management of big data, collaborative working, modern computational methods. These skills are fundamental for the next generation of eco-informaticians that can work with predictive models. Further skills: planning and execution of field work in the tropics, data curation, project management. Students providing evidence (e.g. via papers published in peer-review journals, course certificates and/or reference letters) for basic or advanced knowledge in computational skills will be preferred.
Partners and collaboration
The entire project will be performed in close collaboration with Dr. Tovar and Dr. Moat, who are experts in spatial analysis, lomas and local flora, building on data and ecosystem knowledge over the last 20 years. They will both take part in the planning of each objective, from identifying the exact study lomas and species, to having access to empirical data and planning field campaigns.
Further details on how to contact the supervisor for this project and how to apply for this project can be found here:
For any enquiries related to this project please contact Dr. Juliano Sarmento Cabral, University of Birmingham; [email protected].
To apply to this project:
- You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2024.
- You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
- Please submit your application and complete the host institution application process via: https://sits.bham.ac.uk/lpages/LES068.htm. Please select the PhD Bioscience (CENTA) 2024/25 Apply Now button. The CENTA application form 2024 and CV can be uploaded to the Application Information section of the online form. Please quote CENTA 2024-B4 when completing the application form.
Applications must be submitted by 23:59 GMT on Wednesday 10th January 2024.
Learning model structure, adapting and applying the model. Field work will be carried out to collect missing parameter-relevant data (e.g. dispersal ability, seed traits, facilitation effects). Simulation experiments for describing the relative role of facilitation and dispersal to community assembly. Writing article #1.
Learning and implementation of past change scenarios, by designing simulation experiments varying the degree of human influences on both dispersal (change in pollinator and disperser communities) and facilitation (loss of woody species). Potential field work for calibrating species-specific parameters (e.g. dispersal and recruitment). Writing article #2.
Design of explorative scenarios of synergistically interacting climate change scenarios. Potential field work for calibrating species-specific parameters (e.g. dispersal and recruitment). Writing article #3 and thesis.
Basic programming skills are necessary and will be further developed along the project. Field work will be planned if necessary to gather missing parameter values. Further field work may be included depending on how the project progresses and on the candidate’s interests.
Cabral, J.S., Valente, L., and Hartig, F. (2017) ‘Mechanistic models in macroecology and biogeography: state-of-art and prospects’, Ecography, 40, pp. 267-280. https://doi.org/10.1111/ecog.02480
Cabral, J.S., Whittaker, R.J., Wiegand, K. and Kreft, H. (2019) ‘Assessing predicted isolation effects from the general dynamic model of island biogeography with an eco‐evolutionary model for plants’. Journal of Biogeography, 46 (7), pp.1569-1581.
Fallert. S. (2021) Predicting the future distribution and abundance of species: a mechanistic range model for Orthoptera in Bavaria. Master thesis: Faculty of Biology, University of Wuerzburg.
Moat, J., Orellana-Garcia, A., Tovar, C., Arakaki, M., Arana, C., Cano, A., Faundez, L., Gardner, M., Hechenleitner, P., Hepp, J. and Lewis, G. (2021) ‘Seeing through the clouds–Mapping desert fog oasis ecosystems using 20 years of MODIS imagery over Peru and Chile’. International Journal of Applied Earth Observation and Geoinformation, 103, p.102468. https://doi.org/10.1016/j.jag.2021.102468.
Sarmento Cabral, J., Jeltsch F., Midgley G.F., Higgins S.I., Phillips S.I., Rebelo A.G., Rouget M., Thuiller W., and Schurr F.M. (2013) ‘Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae’, Diversity and Distributions, 19, pp. 363-376. https://doi.org/10.1111/ddi.12011
Tovar, C., Sánchez-Infantas E., Texeira-Roth, V. (2018) ‘Plant community dynamics of lomas fog oasis of Central Peru after the extreme precipitation caused by the 1997-98 El Niño event’, PLoS ONE 13(1): e0190572
Tovar, C., Melcher, I., Kusumoto, B., Cuesta, F., Cleef, A., Meneses, R.I., Halloy, S., Llambí, L.D., Beck, S., Muriel, P. and Jaramillo, R. (2020) ‘Plant dispersal strategies of high tropical alpine communities across the Andes’. Journal of Ecology, 108 (5), pp.1910-1922. https://doi.org/10.1111/1365-2745.13416.
Tovar, C., Hudson, L., Cuesta, F., Meneses, R.I., Muriel, P., Hidalgo, O., Palazzesi, L., Suarez Ballesteros, C., Hammond Hunt, E., Diazgranados, M. and Hind, D.N. (2023) Strategies of diaspore dispersal investment in Compositae: the case of the Andean highlands. Annals of Botany, p.mcad099. https://doi.org/10.1093/aob/mcad099.