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Dynamic Macroecology

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The dynamic macroecology group studies different aspects of ecology on large spatial and temporal scales, from the Pleistocene to the Anthropocene, related to the question

“How do species and their traits change in space and time, particularly with global change?”

We explore

the current and past distributions of species or functional groups and their traits and niches

the effect of climate and other factors on biodiversity
the spatio-temporal dynamics of plant (particularly tree) species and their ranges interacting with the environment, ecophysiology, population dynamics, species migration and climate and land-use change

the evolution in time and geographical space of species niches and traits and biological communities and assemblages

Main methods we develop and work with

climate (change) data scaling and provision

dynamic ecological models
model scaling
statistical methods in ecology, e.g., species distribution models, phylogenetic comparative methods, trait analyses

FURTHER INFORMATION

Projects

Global blue-green richness differences

This project contributes to the Blue Green Biodiversity Research Initiative - an Eawag-WSL collaboration focusing on Biodiversity at the interface of aquatic and terrestrial ecosystems.

Climatic extremes such as droughts, heat waves, and late frost events, are predicted to increase with ongoing climate change and are therefore of primary concern for environmental research. We will develop such data on extreme climatic events on a global scale from 1950-2100.

FutureWeb

FutureWeb will integrate predictive biogeography, geography, biostatistics and trophic web ecology to derive an ensemble of scenarios of the impact of global change on multi-trophic vertebrate biodiversity, ecosystem functioning and the provision of ecosystem services in Europe.

Simulating dynamics and spread of vegetation and interacting species

The spatio-temporal DGVM LPJ-GM is extended with modules for dynamics and spread of interacting species and tested in case studies with different interactors

FORClimateS

The project aims to aggregate and synthesize existing information on different spatial and temporal scales to provide decision-makers with relevant and robust information on short- and long-term risks for forests and forest functions and on adaptation strategies under different climate futures

Ecology of Interactions

Hummingbird-plant interactions across elevation gradients in Central and South America

Joint models in biodiversity modeling

This project aims to evaluate advantages and potential pitfalls of JSDMs in biodiversity modeling

Automatisierte Erfassung invasiver Neophyten an Autobahnen

Highways are important corridors for the spread of invasive species. In this project, an automated detection and mapping of invasive alien plant species based on artificial intelligence (Deep Learning) and computer vision was developed and applied on highways of the Swiss Central Plateau.

Photoperiod and ciracian clocks - effects on adaptation and acclimation

Do evolutionary constraints on photoperiod and circadian clocks hinder adaptation and acclimation of plants to climate change? With this project, we will provide the mechanistic basis and a conceptual framework to understand how climatic and daylight/photoperiod cues jointly affects tree functioning.

Can all agriculture in Europe changed to organic production?

What happens when all agriculture in Europe is changed to organic production? This project investigates how the transition to organic production can be made and at a truly European scale combining 11 universities and 15- multi actors from 9 EU countries and 3 associated countries.

SpeedMind

The SpeedMind project will focuses on floristic data from Switzerland as a pilot system to set up and study the benefits of data-mining and machine learning techniques for facilitating biodiversity assessments.

DEForScen

Current plastic response of plants to climate change may be reflected by tree health. We investigate tree health data collected on ca. 6000 plots across Europe since the 1980s to figure out whether changes and trends in defoliation permit to identify species-specific response to climate change.

Tree species migration in DGVMs

Combining very efficient methods for simulation of seed dispersal and for local vegetation dynamics allows for large scale spatiotemporal vegetation dynamics simulations in DGVMs

Shiny Server

The R package "Shiny" offers a simple web application framework for R. Within this internal project we use a dedicated Shiny server to evaluate the capability of web-based Shiny applications for existing work processes and projects at WSL.

ClimSens - Climate Sensitivity of plants and animals in Switzerland

This project assesses the sensitivity of (mostly) plant and animal species to expected climatic changes.

STACCATO

STACCATO is a BiodivERsA project carried out in collaboration with 10 European Institutions and investigates direct effects of climate and land-use change on biodiversity, as well as indirect effects on ecosystem services.

FORHYCS

A hydrological model is coupled to a forest model to better assess the influence of changing drivers to forests and the hydrological cycle

Forest dynamics in Switzerland - pattern, driving forces and ecological implications

Aim of the project is the reconstruction of forest changes in Switzerland (since 1840) and Canton of Zurich (since 1667) to study forest dynamics and search for transition processes.

FORECOM Forest cover changes in mountainous regions

In the Polish-Swiss joint research project Forecom we will analyze historical forest cover changes and their drivers in the Carpatians and Alps. We will develop scenarios of potential future forest cover changes and assess the impact of past and future changes on key ecosystem services.

Biodiversität und Raumentwicklung

Das Projekt «Biodiversität und Raumentwicklung» untersucht Zusammenhänge zwischen Biodiversität und Raumentwicklung auf Landschaftsebene. Die Resultate sind kartographisch dargestellt und können zur Visualisierung von Szenarien der Landschaftsentwicklung eingesetzt werden.

Revision of the Red List of fern and vascular plants

The accessory study of the revision of the red list of fern and vascular plants aims to evaluate the practicability of using statistical methods as a tool for revisions and quality control of red lists.

The need for high resolution

The goal of this project is to provide current and scenario climate grids of resolutions adequate for ecological studies and applications.

Genetic Variation

Die Klimerwärmung dürfte den Wald auf vielen Standorten unter Stress setzen. Wir untersuchen, ob die genetische Variation der Waldbäume es ihnen erlaubt, sich an den Klimawandel anzupassen. Wir fokussieren dabei auf die drei häufigsten Baumarten in Schweizer Wäldern, die Fichte, die Tanne und die Buche.

Bark Beetle Modeling

The most important forest insect in Europe, i.e. the bark beetle Ips typographus, is likely to become even more important with climate change. In this project, we evaluate the driving factors of recent bark beetle infestations by means of statistical modeling and develop bark beetle population and tree susceptibility models for infestation risk analysis.

Waldausdehnung

Ziel des Projektes ist es, zu verbesserten Grundlagen für die politische Lenkung der Waldflächenzunahme im Schweizer Berggebiet beizutragen.

TreeMig

A spatio-temporal forest modelled has been developed to simulate forest dynamics and species migration under a changing climate.

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Publications

Zellweger, F.; De Frenne, P.; Lenoir, J.; Vangansbeke, P.; Verheyen, K.; Bernhardt-Römermann, M.; Baeten, L.; Hédl, R.; Berki, I.; Brunet, J.; Van Calster, H.; Chudomelová, M.; Decocq, G.; Dirnböck, T.; Durak, T.; Heinken, T.; Jaroszewicz, B.; Kopecký, M.; Máliš, F.; ... Coomes, D., 2020: Response to comment on "forest microclimate dynamics drive plant responses to warming". Science, 370, 6520: eabd6193 (3 pp.). doi: 10.1126/science.abd6193

Moos, C.; Khelidj, N.; Guisan, A.; Lischke, H.; Randin, C.F., 2020: A quantitative assessment of rockfall influence on forest structure in the Swiss Alps. European Journal of Forest Research, doi: 10.1007/s10342-020-01317-0

Visser, A.W.; Brun, P.; Chakraborty, S.; Spaanheden Dencker, T.; Van Denderen, P.D.; Van Gemert, R.; Van Someren Gréve, H.; Heilmann, I.; Holm, M.W.; Jónasdóttir, S.H.; Kenitz, K.M.; Kiørboe, T.; Lindegren, M.; Mariani, P.; Nielsen, L.T.; Pancic, M.; Payne, M.; Pécuchet, L.; Schnedler-Meyer, N.A.; ... Andersen, K.H., 2020: Seasonal strategies in the world's oceans. Progress in Oceanography, 189: 102466 (16 pp.). doi: 10.1016/j.pocean.2020.102466

Fritsch, M.; Lischke, H.; Meyer, K.M., 2020: Scaling methods in ecological modelling. Methods in Ecology and Evolution, 11, 11: 1368-1378. doi: 10.1111/2041-210X.13466

Kattge, J.; Bönisch, G.; Díaz, S.; Lavorel, S.; Prentice, I.C.; Leadley, P.; Tautenhahn, S.; Werner, G.D.A.; Aakala, T.; Abedi, M.; Cailleret, M.; Cherubini, P.; McFadden, I.R.; Moretti, M.; Rossi, C.; Trotsiuk, V.; Wirth, C., 2020: TRY plant trait database – enhanced coverage and open access. Global Change Biology, 26, 1: 119-188. doi: 10.1111/gcb.14904

Brun, P.; Psomas, A.; Ginzler, C.; Thuiller, W.; Zappa, M.; Zimmermann, N.E., 2020: Large‐scale early‐wilting response of Central European forests to the 2018 extreme drought. Global Change Biology, 26: 7021-7035. doi: 10.1111/gcb.15360

Petter, G.; Mairota, P.; Albrich, K.; Bebi, P.; Brůna, J.; Bugmann, H.; Haffenden, A.; Scheller, R.M.; Schmatz, D.R.; Seidl, R.; Speich, M.; Vacchiano, G.; Lischke, H., 2020: How robust are future projections of forest landscape dynamics? Insights from a systematic comparison of four forest landscape models. Environmental Modelling and Software, 134: 104844 (13 pp.). doi: 10.1016/j.envsoft.2020.104844

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. doi: 10.1111/jbi.13985

Brun, P.; Thuiller, W.; Chauvier, Y.; Pellissier, L.; Wüest, R.O.; Wang, Z.; Zimmermann, N.E., 2020: Model complexity affects species distribution projections under climate change. Journal of Biogeography, 47, 1: 130-142. doi: 10.1111/jbi.13734

Beckman, N.G.; Aslan, C.E.; Rogers, H.S.; Kogan, O.; Bronstein, J.L.; Bullock, J.M.; Hartig, F.; HilleRisLambers, J.; Zhou, Y.; Zurell, D.; Brodie, J.F.; Bruna, E.M.; Cantrell, R.S.; Decker, R.R.; Efiom, E.; Fricke, E.C.; Gurski, K.; Hastings, A.; Johnson, J.S.; ... Zambrano, J., 2020: Advancing an interdisciplinary framework to study seed dispersal ecology. AoB PLANTS, 12, 2: plz048 (18 pp.). doi: 10.1093/aobpla/plz048

Scherrer, D.; Vitasse, Y.; Guisan, A.; Wohlgemuth, T.; Lischke, H., 2020: Competition and demography rather than dispersal limitation slow down upward shifts of trees' upper elevation limits in the Alps. Journal of Ecology, 108, 6: 2416-2430. doi: 10.1111/1365-2745.13451

Molinari, C.; Carcaillet, C.; Bradshaw, R.H.W.; Hannon, G.E.; Lehsten, V., 2020: Fire-vegetation interactions during the last 11,000 years in boreal and cold temperate forests of Fennoscandia. Quaternary Science Reviews, 241: 106408 (12 pp.). doi: 10.1016/j.quascirev.2020.106408

Baltensweiler, A.; Brun, P.; Pranga, J.; Psomas, A.; Zimmermann, N.E.; Ginzler, C., 2020: Räumliche Analyse von Trockenheitssymptomen im Schweizer Wald mit Sentinel-2-Satellitendaten. Schweizerische Zeitschrift für Forstwesen, 171, 5: 298-301. doi: 10.3188/szf.2020.0298

Lembrechts, J.J.; Aalto, J.; Ashcroft, M.B.; De Frenne, P.; Kopecký, M.; Lenoir, J.; Luoto, M.; Maclean, I.M.D.; Roupsard, O.; Fuentes‐Lillo, E.; Pellissier, L.; Pitteloud, C.; Wipf, S.; Descombes, P.; Zellweger, F.; Frei, E.R.; Rixen, C.; Rossi, C.; Nijs, I., 2020: SoilTemp: a global database of near-surface temperature. Global Change Biology, 26, 11: 6616-6629. doi: 10.1111/gcb.15123

Feldmeier, S.; Schmidt, B.R.; Zimmermann, N.E.; Veith, M.; Ficetola, G.F.; Lötters, S., 2020: Shifting aspect or elevation? The climate change response of ectotherms in a complex mountain topography. Diversity and Distributions, 26, 11: 1483-1495. doi: 10.1111/ddi.13146

Descombes, P.; Walthert, L.; Baltensweiler, A.; Meuli, R.G.; Karger, D.N.; Ginzler, C.; Zurell, D.; Zimmermann, N.E., 2020: Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography, 43, 10: 1448-1463. doi: 10.1111/ecog.05117

Csilléry, K.; Buchmann, N.; Fady, B., 2020: Adaptation to drought is coupled with slow growth, but independent from phenology in marginal silver fir (Abies alba Mill.) populations. Evolutionary Applications, 13, 9: 2357-2376. doi: 10.1111/eva.13029

Abrahamczyk, S.; Wohlgemuth, T.; Nobis, M.; Nyffeler, R.; Kessler, M., 2020: Shifts in food plant abundance for flower-visiting insects between 1900 and 2017 in the canton of Zurich, Switzerland. Ecological Applications, 30, 6: e2138 (11 pp.). doi: 10.1002/eap.2138

Iosifescu Enescu, I.; Hanimann, D.; Karger, D.N.; Plattner, G.; Haas-Artho, D.; Kurup Buchholz, R.; Espona, L.; Zimmermann, N.E.; Pellissier, L.; Hägeli, M., 2020: Challenges for integrating Web-EGIS functionalities in the environmental research data portal EnviDat. In: 2020: 18th Swiss geoscience meeting 2020, Zürich, Switzerland. (3 pp.).

Buholzer, S.; De Micheli, A.; Marazzi, B.; Nobis, M.; Rometsch, S.; Schmidt, L.; Schönenberger, N., 2020: Eindringlinge in der Landwirtschaft. Invasive Neophyten. UFA-Revue, 30-33.

20 from 471 publications

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Staff