Prof. Dr. Loïc Pellissier

Prof. Dr.  Loïc Pellissier

Prof. Dr. Loïc Pellissier

Ökosysteme u. Landschaftsevolution

CHN F 29.2

Universitätstrasse 16

8092 Zürich


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Research Interests

As Professor of Ecosystem and Landscape Evolution, my research focuses on the study of the interaction between biological agents and landscapes across a variety of spatial and temporal scales. The formulation of theoretical models such as MacArthur & Wilson's Theory of Island Biogeography or Hanski’s meta-community model, allowed one to unravel the mechanisms underlying spatial patterns of biodiversity. Following this line of research, I develop eco-evolutionary theories to explain the origins of spatial biodiversity gradients at different spatial and temporal scales that I confront with empirical data. I integrate ecological and evolutionary mechanisms with deep time paleo-environmental reconstructions to unravel the processes that have shaped extant biodiversity gradients.

I use fundamental knowledge on the formation of biodiversity gradients to advance the realism of model inferences about the consequence of global changes on biodiversity. I develop and use technology to better quantify and monitor biodiversity in landscapes in space and time. As part of a highly interdisciplinary field in biological systems science, my research uses methods ranging from population genetics and phylogenetics to understand the mechanisms of speciation, community ecology and genetic metabarcoding to investigate species distributions and their interactions, and statistical, mathematical and process-based modelling to understand current patterns and forecast biodiversity under scenarios of global changes.

Modelling the eco-evolutionary processes shaping biodiversity

One of the main objectives of my research is the investigation of the general principles underlying the spatial organization of biodiversity. The observed extant spatial organisation of biological systems at large scale should represent the legacy of millions of years of evolution under past geo-climatic environmental changes. Landscape Ecology research increasingly integrate influential ecological theories for gaining mechanistic inferences on biodiversity patterns, but these theories usually do not integrate mechanisms of evolution or speciation. In my research, I develop both mathematical and agent-based models to provide an eco-evolutionary understanding of the spatial organisation of biodiversity. For instance, we use mathematical formulation of biological processes in the form of partial differential equations applied to dynamic spatial networks to study the interaction between populations and the landscape dynamic (Boussange et al. in preparation). We further developed more complex and realistic agent-based models to understand the origin of biodiversity.

We showed that a mechanistic model of speciation, extinction and dispersal bounded by the distribution of shallow reef habitats over several million years of change produced realistic biodiversity dynamics matching patterns in fossil records and phylogenies (Leprieur et al. 2016 Nature Communications, Gaboriau et al. 2019 Proc. Roy. Soc. B.) and offered a realistic simulated organisation of the global functional structure of marine reef fishes (Donati et al. 2019 Ecography). A major advantage of process-based models is that those produce emerging patterns, which help form intuition and generate new hypotheses. For instance, in Leprieur et al. (2016), we proposed that the coral triangle in South East Asia is the result of the movement and integration of two fauna, one from the Western Tethys (proto-Mediterranean Sea) and one from the northern Coast of Australia paralleling a Wallace’s theory (1859) for the marine system. To make these new methods widely available to researchers, we published the code in open in the form of R packages (Hagen et al. 2021 Plos Biol), which offer a new simulation tool to better understand the origin of biodiversity.

Documenting the current organisation of biodiversity

The ecological interactions between the species and the landscape together generate biodiversity patterns. In my research, I develop a range of methods to map and monitor biodiversity in space and time. Within my research group, we developed a data processing pipeline to map species ranges from occurrences data compiled from multiple databases, using a combination of spatial statistics and machine learning. Data processing pipelines allowed for instance producing species range maps for most marine fishes globally (Albouy et al. 2019 Nature Ecology and Evolution) and for the tropical marine Americas (Polanco et al. 2020 Journal of Biogeography). We are currently improving the data processing pipeline toward mapping the range of a majority of tree species globally from over 40 different databases to inform forest restoration (Pellissier, Crowther et al., in preparation).

Beyond species-level diversity, we used data processing pipelines to map genetic diversity in fishes globally and model its underlying drivers (Manel et al. 2020 Nature Communications). We are developing field based approaches to collect biodiversity information, using remote sensing, or genetic-based technology such as environmental DNA (eDNA) to quantify sequence diversity or RAD-sequencing to measure species genomic diversity (Donati et al. 2021 Proc. Roy. Soc. B.). For instance, we are coupling traditional vegetation mapping with hyperspectral remote sensing to generate high resolution mapping of plant species distribution in alpine grasslands (Fopp et al., in preparation). We contribute to efforts in the development and application of eDNA in the marine environments and tested the methods in over 30 different regions in the Mediterranean, Caribbean Sea, or Pacific Ocean. We are evaluating the ability of eDNA to gather information of entire assemblages in just in a few minutes in the field toward long term monitoring of those ecosystems (Polanco et al. 2021 Environmental DNA, Juhel et al. 2020). Using a similar molecular approach, we documented the interactions between plant and herbivores along elevation gradients to understand the interdependence of biodiversity via trophic networks (Pitteloud et al. 2021 Journal of Biogeography). In all, this work investigating the organisation of biodiversity can be used to calibrate mechanistic models forecasting the consequences of global changes on ecosystems.

Forecasts of species assemblages under global changes

With increasing global change, landscapes are expected to become even more dynamic as a result of human exploitation and climate change, thus reshaping the ecological conditions for species. Mechanistic models can help provide forecasts of species assemblages under scenarios of global changes. In this domain, I apply statistical models to forecast the consequence of global change on landscapes (Gerecke et al. 2019 Palgrave Communication), based on correlative association between a species presence or abundance and environmental predictors (Grünig et al. 2020 Global Change Biology). Our research has demonstrated that climate change is expected to largely reshape the connectivity of landscapes, e.g. the separation of marine organisms between the Atlantic and the Pacific (Wisz et al. 2015 Nature Climate Change), or the connectivity of Caribou populations within the island archipelago of northern Canada as sea ice melts (Jenkins et al. 2016 Biology Letters). Correlative models may provide unrealistic forecasts when other mechanisms determine species ranges such as dispersal or biotic interactions. Therefore, beyond statistical models of global change, we also develop process-based models to generate forecasts of ecosystem responses to global changes.

We have developed a trait-based mechanistic model of plant community assembly integrating competition and herbivory in order to quantify the potential lags in ecological responses to climate change (Alexander et al. 2018 Global Change Biology, Pellissier et al. 2018 Functional Ecology). Such process-based models in the form of differential equations can simulate ecological interactions along environmental gradients, and in turn, be used to infer the response of mountain systems to climate change (Alexander et al. 2018 Global Change Biology, Pellissier et al. 2018 Functional Ecology). The future development of this model will allow evaluating the link between biodiversity, ecosystem functioning and its sensitivity to global changes.

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Selected publications

Albouy, C., Archambault, P., Appeltans, W., Araújo, M. B., Beauchesne, D., Cazelles, K., ... & Gravel, D. (2019). The marine fish food web is globally connected. Nature Ecology & Evolution, 3(8), 1153-1161.

Alexander, J. M., Chalmandrier, L., Lenoir, J., Burgess, T. I., Essl, F., Haider, S., ... & Pellissier, L. (2018). Lags in the response of mountain plant communities to climate change. Global Change Biology, 24(2), 563-579.

Donati, G. F. A., Parravicini, V., Leprieur, F., Hagen, O., Gaboriau, T., Heine, C., ... & Pellissier, L. (2019). A process‐based model supports an association between dispersal and the prevalence of species traits in tropical reef fish assemblages. Ecography, 42(12), 2095-2106.

Gaboriau, T., Albouy, C., Descombes, P., Mouillot, D., Pellissier, L., & Leprieur, F. (2019). Ecological constraints coupled with deep-time habitat dynamics predict the latitudinal diversity gradient in reef fishes. Proceedings of the Royal Society B, 286(1911), 20191506.

Gerecke, M., Hagen, O., Bolliger, J., Hersperger, A. M., Kienast, F., Price, B., & Pellissier, L. (2019). Assessing potential landscape service trade-offs driven by urbanization in Switzerland. Palgrave Communications, 5(1), 1-13.

Grünig, M., Calanca, P., Mazzi, D., & Pellissier, L. (2020). Inflection point in climatic suitability of insect pest species in Europe suggests non‐linear responses to climate change. Global Change Biology, 26(11), 6338-6349.

Hagen, O., Flück, B., Fopp, F., Cabral, J. S., Hartig, F., Pontarp, M., ... & Pellissier, L. (2021). gen3sis: A gen eral e ngine for e co-e volutionary si mulation s of the processes that shape Earth’s biodiversity. PLoS Biology, 19(7), e3001340.

Jenkins, D. A., Lecomte, N., Schaefer, J. A., Olsen, S. M., Swingedouw, D., Côté, S. D., ... & Yannic, G. (2016). Loss of connectivity among island-dwelling Peary caribou following sea ice decline. Biology Letters, 12(9), 20160235.

Leprieur, F., Descombes, P., Gaboriau, T., Cowman, P. F., Parravicini, V., Kulbicki, M., ... & Pellissier, L. (2016). Plate tectonics drive tropical reef biodiversity dynamics. Nature Communications, 7(1), 1-8.

Manel, S., Guerin, P. E., Mouillot, D., Blanchet, S., Velez, L., Albouy, C., & Pellissier, L. (2020). Global determinants of freshwater and marine fish genetic diversity. Nature Communications, 11(1), 1-9.

Pellissier L., Anzini M., Maiorano L., Dubuis A., Pottier J., Vittoz P., Guisan A. (2013). Spatial predictions of land use transitions and associated threats to biodiversity: the case of forest regrowth in mountain grasslands. Applied Vegetation Science, 16, 227–236.

Pellissier, L., Descombes, P., Hagen, O., Chalmandrier, L., Glauser, G., Kergunteuil, A., ... & Rasmann, S. (2018). Growth‐competition‐herbivore resistance trade‐offs and the responses of alpine plant communities to climate change. Functional Ecology, 32(7), 1693-1703.

Pellissier L., Leprieur F., Parravicini V., Litsios G., Olesen S., Wisz M.S., ... & Mouillot D. (2014). Quaternary coral reef refugia preserved fish diversity. Science, 344, 1016-1019.

Pellissier L., Ndiribe C., Dubuis A., Pradervand JN., Salamin N., Guisan A., Rasmann S. (2013). Turnover of plant lineages shapes herbivore phylogenetic beta diversity along ecological gradients. Ecology Letters, 16, 600-608.

Pitteloud, C., Walser, J. C., Descombes, P., Novaes de Santana, C., Rasmann, S., & Pellissier, L. (2021). The structure of plant–herbivore interaction networks varies along elevational gradients in the European Alps. Journal of Biogeography, 48(2), 465-476.

Polanco F, A., Fopp, F., Albouy, C., Brun, P., Boschman, L., & Pellissier, L. (2020). Marine fish diversity in Tropical America associated with both past and present environmental conditions. Journal of Biogeography, 47(12), 2597-2610.

Polanco F. A., Marques, V., Fopp, F., Juhel, J. B., Borrero‐Pérez, G. H., Cheutin, M. C., ... & Pellissier, L. (2021). Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environmental DNA, 3(1), 142-156.

Wisz M.S. , Broennimann O., Grønkjær P., Hedeholm R. B., Rask Møller P. D. , Guisan A., ... & Pellissier L. (2015). Arctic warming will promote Atlantic-Pacific fish interchange. Nature Climate Change, 5, 261-265.


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