Frank Breiner

Funktion

Gastwissenschafter

Biodiversität und Naturschutzbiologie

Lebensraumdynamik

Kontakt

frank.breiner(at)wslto make life hard for spam bots.ch

+41 44 739 24 18+41 44 739 24 18

Eidg. Forschungsanstalt WSL
Zürcherstrasse 111
8903 Birmensdorf

Standort

Birmensdorf

Postdoc Betreuung durch

Dr. Ariel Bergamini, Dr. Michael Nobis (beide WSL) und Prof. Dr. Antoine Guisan (Universität Lausanne)

Wissenschaftliche Interessen

Räumliche und ökologische Verbreitung seltener Pflanzenarten in der Schweiz, Artverbreitungsmodelle, Rote Listen, alpine Ökologie.

Auszeichnungen

MCED award 2015 für innovative Beiträge zur ökologischen Modellierung in der Kategorie Doktoranden und Postdocs.

Lebenslauf

seit 2016	PostDoc in der Spatial Ecology Group, Universität Lausanne
2011-2016	Doktorand an der WSL und Uni Lausanne PhD in Life Science an der Universität Lausanne
April 2011	M.Sc. Arbeit am Lehrstuhl für Pflanzenökologie: "Resurvey of a GLORIA Target Region in the Swiss National Park"
2008-2011	M.Sc. Biodiversität und Ökologie (Uni Bayreuth)
2005-2008	B.Sc. Geoökologie (Uni Bayreuth)

WEITERFÜHRENDE INFORMATIONEN

Projekte

Revision der Roten Liste der Farn- und Blütenpflanzen

In der Begleitstudie zur Revision der Roten Liste der Farn- und Blütenpflanzen wird überprüft, ob statistische Methoden als Hilfsmittel bei der Erstellung und bei der Qualitätskontrolle von Roten Listen sinnvoll eingesetzt werden können.

WSL-Publikationen

Wissenschaftliche Artikel Peer-reviewt

Della Rocca, F.; Bogliani, G.; Breiner, F.T.; Milanesi, P., 2019: Identifying hotspots for rare species under climate change scenarios: improving saproxylic beetle conservation in Italy. Biodiversity and Conservation, 28, 2: 433-449. doi: 10.1007/s10531-018-1670-3

Breiner, F.T.; Nobis, M.P.; Bergamini, A.; Guisan, A., 2018: Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution, 9, 4: 802-808. doi: 10.1111/2041-210X.12957

Breiner, F.T.; Bergamini, A., 2018: Improving the estimation of area of occupancy for IUCN Red List assessments by using a circular buffer approach. Biodiversity and Conservation, 27, 9: 2443-2448. doi: 10.1007/s10531-018-1555-5

Steinbauer, M.J.; Grytnes, J.; Jurasinski, G.; Kulonen, A.; Lenoir, J.; Pauli, H.; Rixen, C.; Winkler, M.; Bardy-Durchhalter, M.; Barni, E.; Bjorkman, A.D.; Breiner, F.T.; Burg, S.; Dawes, M.A.; Czortek, P.; Delimat, A.; Dullinger, S.; Erschbamer, B.; Felde, V.A.; ... Wipf, S., 2018: Accelerated increase in plant species richness on mountain summits is linked to warming. Nature, 556, 7700: 231-234. doi: 10.1038/s41586-018-0005-6

Rechsteiner, C.; Zellweger, F.; Gerber, A.; Breiner, F.T.; Bollmann, K., 2017: Remotely sensed forest habitat structures improve regional species conservation. Remote Sensing in Ecology and Conservation, 3, 4: 247-258. doi: 10.1002/rse2.46

Alle anzeigen

Weitere wissenschaftliche Publikationen

Scheurer, T.; Camenisch, M.; Breiner, F.; Walther, G.-R., 2013: Berggipfel: Pflanzen im Wärme-Stresstest. In: Haller, H.; Eisenhut, A.; Haller, R. (eds), 2013: Atlas des Schweizerischen Nationalparks : die ersten 100 Jahre. Bern, Haupt. 222-223.

Zeitschriften und Broschüren

Breiner, F.; Walther, G.; Camenisch, M., 2011: Neue Gipfelstürmer unter den Pflanzen. Cratschla, 4-5. doi: 10.5169/seals-418825

ESMs

ESMs – Ensembles of Small Models for R

What are ESMs?

Ensembles of Small Models (ESMs) are simple small models, e.g. bivariate models of only two predictors, which are averaged to an ensemble weighted by the mean of a cross-validated evaluation score (e.g. AUC).

What is the advantage of ESMs?

In Species Distribution Modeling it is often difficult to model rare species because only few occurrences are available and models are prone to overfitting. ESMs avoid overfitting without losing explanatory power through variable reduction by averaging small models. ESMs are therefore especially advantageous for modeling rare species.

How to start?

Here, we supply a commented R script to build ESMs in R. ESM functions are provided as part of the R package ecospat (Broenniman et al. 2016).

Further information?

Please have also a look at our blog post for methods in ecology and evolution. We would appreciate it very much if you leave a comment.

ESMs were granted with the MCED-award 2015.

Download

ESM functions are available in the R package ecospat (Broenniman et al. 2016).

Here we provide an example using original data from Breiner et al. (2015) and show how ESMs could be build step-by-step:<br/>

ESM-scripts.zip

References

Breiner F.T.; Guisan A.; Bergamini A.; Nobis M.P., 2015:

Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6: 1210-1218.

Broenniman O.; Di Cola V.; Guisan A., 2016: ecospat: Spatial Ecology Miscellaneous Methods. R package version 2.0. Available from World Wide Web <http://cran.r-project.org/package=ecospat>

Lomba, A.; Pellissier, L.; Randin, C.F.; Vicente, J.; Moreira, F.; Honrado, J.; Guisan A., 2010:

Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143, 2647–2657.

Range size estimation

This R-function quantifies the range size of a species using standard IUCN criteria (AOO, EOO), alphahulls or various options to estimate range size from binary predictions derived from Species Distribution Models (i.e. modelled range size within EOO, EOO around modelled range size and EOO around modelled occupied range size).