Dr. Christine I. B. Wallis
| Alumni |
Contact
Deutschhausstraße 12
35037 Marburg
35037 Marburg
Curriculum vitae
2018 - current: Postdoc (MARA bridging scholarship)
2014 - 2018: Scientific assistant and PhD student in the DFG project C2 at the LCRS and Laboratory for Conservation Ecology Marburg
2011 - 2013: Study of "Physical Geography of Human-Environmental System" at the Universität zu Berlin
3/2011-5/2011: Internship at Estación Cientifica San Francisco, Ecuador (DFG-Forschergruppe 812)
2009-2011: Study of Geography at the University of Marburg
Scientific interest
- Habitat structure
- Wildlife ecology
- Biodiversity functioning
- Remote sensing & GIS
Publications
Articles
2021 - Wallis, C.I.B.; Tiede, Y.; Beck, E.; Böhning-Gaese, K.; Brandl, R.; Donoso, D.A.; Espinosa, C.I.; Fries, A.; Homeier, J.; Inclan, D.; Leuschner, C.; Maraun, M.; Mikolajewski, K.; Neuschulz, E.L.; Scheu, S.; Schleuning, M.; Suárez, J.P.; Tinoco, B.A.; Farwig, N. & Bendix, J. (2021): Biodiversity and ecosystem functions depend on environmental conditions and resources rather than the geodiversity of a tropical biodiversity hotspot. Scientific Reports 11(1), 24530.
2019 - Wallis, C.I.B.; Homeier, J.; Peña, J.; Brandl, R.; Farwig, N. & Bendix, J. (2019): Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data. Remote Sensing of Environment 225, 77-92.
2017 - Tiede, Y.; Schlautmann, J.; Donoso, D.A.; Wallis, C.I.B.; Bendix, J.; Brandl, R. & Farwig, N. (2017): Ants as indicators of environmental change and ecosystem processes. Ecological Indicators 83, 527–537.
- Wallis, C.I.B.; Brehm, G.; Donoso, D.A.; Fiedler, K.; Homeier, J.; Paulsch, D.; Süßenbach, D.; Tiede, Y.; Brandl, R.; Farwig, N. & Bendix, J. (2017): Remote sensing improves prediction of tropical montane species diversity but performance differs among taxa. Ecological Indicators 1(1), 1-11.
2016 - Wallis, C.I.B.; Paulsch, D.; Zeilinger, J.; Silva, B.; Curatola Fernández, G.F.; Brandl, R.; Farwig, N. & Bendix, J. (2016): Contrasting performance of Lidar and optical texture models in predicting avian diversity in a tropical mountain forest. Remote Sensing of Environment 174, 223-232.
