BioEO
Functional Biodiversity & Earth Observation Lab
Discovering the effects of environmental changes on Latin American forest ecosystems
In this project we are studying how past and near present changes in environmental conditions (e.g. climate and land use change) are modifying the forests composition. However, above looking at these changes based on just taxonomic entities, we consider this beneath the lens of functional traits. We use the functional traits of plants because these are the 'tools' that allow species to adapt to the new environmental conditions or shift towards more suitable locations.
At the focus of this project are two main questions:
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Are forests shifting in trait composition? First we analyse whether Latin American forest communities are shifting in trait composition, and if such changes are due to modification in environmental conditions.
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What is the potential for multispectral reflectance approaches to aid mapping of canopy traits at large-scale? Scaling trait patterns provides large-scale information on trait variation through time and space, and allows us to track them across this. Here we look at the application of high spectral, spatial and temporal resolution satellite remote sensing provided by Sentinel-2 satellite, and multispectral drone-based remote sensing, to track tree canopy functional traits across large spatial scales in South America.
Effects of a changing environment in Chilean forests
Our team at Oxford University along with local partners in Chile from the Universidad Austral, are working on setting up new plots along a latitudinal gradient from Santiago all the way south to Magallanes in Chile. We have selected six different locations across the gradient and established two 1 ha plots per site. Our sites are in: i) Las Cabras (34 South) ii) Parque Nacional 7 Tazas (35.4 South) iii) San Pablo de Tregua (39.6 South) iv) Correntoso (41.5 South) v) Reserva Tapanandra (45 South) and vi) Reserva Nacional de Magallanes (53 South).
In Chile we are carrying out plot censuses for all vegetation equal or above 10 cm DBH and sampling their soil characteristics, as well as LiDAR vegetation structure with the ZEB1 handheld equipment, multispectral imagery with the Inspire 1 drone, and the ALTUM MicaSense camera capturing 5 spectral bands, and one thermal.
Interdependence of tropical forests and soil biota as seen by remote sensing
We cannot adequately understand or simulate how tropical forests respond to environmental change without the knowledge of the local feedback between plants and their soil biota (e.g. fungi and bacterial communities) as these play a major role in the resilience of forest ecosystems. Current approaches that model vegetations responses to a changing climate tend to ignore such soil-plant feedbacks simply because we do not know how they are interconnected across forest ecosystems. The biodiversity-rich tropical forests, which are home to more than 50% of global diversity, urgently need answers to these questions as evidence shows how climate change is affecting their biodiversity levels.
Effects of vegetation structure and landscape simplification on bee diversity
In our research, we include the heterogeneity that exists within patches of remnant forest in studies that assess the effects of habitat loss on pollinators. The study is focused at Cerrado; a diverse brazilian savannah that is highly threatened by land use change. We studied patches of natural and restored evergreen riparian forest and deciduous forests within private farms, which are protected by environmental law. The sampling areas were in the state of Goiás, in the central plateau of Brazil, and in the state of Mato Grosso, in the transition between Cerrado and Amazon forest.
There are three main questions on this research:
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Are the vegetation structure and plant traits affect the richness and abundance of bees that occurred in small fragments of natural habitats within farms? We discuss the importance of the vegetation structure and plant traits as a source of nesting and feeding resources for bees. Then, we sampled the bees within forest patches across private farms using bait- and pan-traps and sampled the vegetation using LiDAR and multispectral imagery.
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Is the heterogeneity of those structural aspects or plant traits buffer the negative effects of loss of natural habitat at the landscape?
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Are the vegetation structure and plant traits being important drivers of bee diversity at a larger scale (tropical region)? We will perform SDM to understand how the distribution and richness of tropical bees respond to the relationship between their traits, the vegetation structure and plant traits and climatic conditions.
Coarse UAV remote sensing to predict grassland plant & invertebrate biodiversity
In this project we are studying the propensity of multispectral and LiDAR data gathered via drone / Unmanned Aerial Vehicle (UAV) surveys at relatively coarse spatial resolutions, to accurately predict grassland plant and invertebrate biodiversity. This references in-situ ecological data gathered by the Leverhulme Centre for Nature Recovery (LCNR) in Park Farm, a series of permanent pasture grasslands newly adopted for research in Oxford, forming part of their regenerative farming gradient of study sites.
Applying the vegetation spectral and structural diversity hypotheses, we investigate how well the coefficient of variation in vegetation's reflectance between pixels across 10 wavelength bands, partitioned within and between communities, as well as the structural diversity of hedgerows and grassland, can explain the variation in these measures of biodiversity between survey sites. The ability of which would be highly beneficial to further the development of cost-effective, accurate monitoring of biodiversity, able to be extended remotely across large spatial scales.
Publication PDFs are available to read & download at the adjacent buttons, for accessibility
Doughty, C.E., Gaillard, C., Burns, P., Keany, J., Abraham, A., Malhi, Y.S., Aguirre-Gutierrez, J., Koch, G., Jantz, P., Shenkin, A. and Tang, H. (2023). Tropical forests are mainly unstratified especially in Amazonia and regions with lower fertility or higher temperatures. doi:https://doi.org/10.1088/2752-664x/ace723.
Dechant, B., Jens Kattge, Pavlick, R., Schneider, N., Sabatini, F., Álvaro Moreno-Martínez, Butler, E.E., Peter, Vallicrosa, H., Teja Kattenborn, Coline C. F. Boonman, Madani, N., Wright, I.P., Dong, N., Hannes Feilhauer, Josep Peñuelas, Jordi Sardans, Jesús Aguirre‐Gutiérrez, Reich, P.B. and Leitao, P. (2023). Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches. EarthArXiv (California Digital Library). doi:https://doi.org/10.31223/x58s97.
Zhang, H., Malhi, Y., Agne Gvozdevaite, Peprah, T., Boackye, M., Kasia Ziemińska, Adu‐Bredu, S., Jesús Aguirre‐Gutiérrez, Sandoval, D., Prentice, C. and Oliveras, I. (2023). Photosynthetic and water transport strategies of plants along a tropical forest aridity gradient: a test of optimality theory. bioRxiv (Cold Spring Harbor Laboratory). doi:https://doi.org/10.1101/2023.01.10.523419.
Bhalla, I.S., Aguirre‐Gutiérrez, J. and Whittaker, R.J. (2023). Batting for rice: The effect of bat exclusion on rice in North-East India. Agriculture, Ecosystems & Environment, 341, p.108196. doi:https://doi.org/10.1016/j.agee.2022.108196.
Doughty, C.E., Gaillard, C., Abraham, A., Burns, P., Keany, J., Jesús Aguirre‐Gutiérrez, Malhi, Y., Jantz, P., Koch, G., Shenkin, A. and Tang, H. (2022). Unstratified forests dominate the tropics especially in regions with lower fertility or higher temperatures. EcoEvoRxiv. doi:https://doi.org/10.32942/x2vc7t.
Bauman, D., Fortunel, C., Delhaye, G., Malhi, Y., Cernusak, L.A., Bentley, L.P., Rifai, S.W., Aguirre-Gutiérrez, J., Menor, I.O., Phillips, O.L., McNellis, B.E., Bradford, M., Laurance, S.G.W., Hutchinson, M.F., Dempsey, R., Santos-Andrade, P.E., Ninantay-Rivera, H.R., Chambi Paucar, J.R. and McMahon, S.M. (2022). Tropical tree mortality has increased with rising atmospheric water stress. Nature, [online] pp.1–6. doi:https://doi.org/10.1038/s41586-022-04737-7.
Aguirre‐Gutiérrez, J., Berenguer, E., Oliveras Menor, I., Bauman, D., Corral-Rivas, J.J., Nava-Miranda, M.G., Both, S., Ndong, J.E., Ondo, F.E., Bengone, N.N., Mihinhou, V., Dalling, J.W., Heineman, K., Figueiredo, A., González-M, R., Norden, N., Hurtado-M, A.B., González, D., Salgado-Negret, B. and Reis, S.M. (2022). Functional susceptibility of tropical forests to climate change. Nature Ecology & Evolution, 6(7), pp.878–889. doi:https://doi.org/10.1038/s41559-022-01747-6.
Liang, J., Javier, Picard, N., Zhou, M., Pijanowski, B.C., Jacobs, D.F., Reich, P.B., Crowther, T.W., Gert-Jan Nabuurs, de-Miguel, S., Fang, J., Woodall, C.W., Jens-Christian Svenning, Tommaso Jucker, Bastin, J.-F., Wiser, S.K., Ferry Slik, Hérault, B., Alberti, G. and Keppel, G. (2022). Co-limitation towards lower latitudes shapes global forest diversity gradients. Nature Ecology and Evolution, 6(10), pp.1423–1437. doi:https://doi.org/10.1038/s41559-022-01831-x.
Aguirre-Gutiérrez, J., Rifai, S., Shenkin, A., Oliveras, I., Bentley, L.P., Svátek, M., Girardin, C.A.J., Both, S., Riutta, T., Berenguer, E., Kissling, W.D., Bauman, D., Raab, N., Moore, S., Farfan-Rios, W., Figueiredo, A.E.S., Reis, S.M., Ndong, J.E., Ondo, F.E. and N’ssi Bengone, N. (2021). Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data. Remote Sensing of Environment, 252, p.112122. doi:https://doi.org/10.1016/j.rse.2020.112122.
Aguirre-Gutiérrez, J., Malhi, Y., Lewis, S.L., Fauset, S., Adu-Bredu, S., Affum-Baffoe, K., Baker, T.R., Gvozdevaite, A., Hubau, W., Moore, S., Peprah, T., Ziemińska, K., Phillips, O.L. and Oliveras, I. (2020). Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity. Nature Communications, 11(1). doi:https://doi.org/10.1038/s41467-020-16973-4.
Resources & presentations
Tropical forests and a changing climate: Analysis at the interface between trait-based ecology, forest dynamics and remote sensing
Dr. Jesús Aguirre-Gutiérrez
CSIR-FORIG, Ghana, January 2022
Understanding climate effects on tropical forests through trait-based ecology and remote sensing:
A presentation at CSIR-FORIG.