TY  - JOUR
A1  - Dormann, Carsten F.
A1  - Elith, Jane
A1  - Bacher, Sven
A1  - Buchmann, Carsten M.
A1  - Carl, Gudrun
A1  - Carre, Gabriel
A1  - Garcia Marquez, Jaime R.
A1  - Gruber, Bernd
A1  - Lafourcade, Bruno
A1  - Leitao, Pedro J.
A1  - Münkemüller, Tamara
A1  - McClean, Colin
A1  - Osborne, Patrick E.
A1  - Reineking, Bjoern
A1  - Schröder-Esselbach, Boris
A1  - Skidmore, Andrew K.
A1  - Zurell, Damaris
A1  - Lautenbach, Sven
T1  - Collinearity a review of methods to deal with it and a simulation study evaluating their performance
JF  - Ecography : pattern and diversity in ecology ; research papers forum
N2  - Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
Y1  - 2013
U6  - https://doi.org/10.1111/j.1600-0587.2012.07348.x
SN  - 0906-7590
SN  - 1600-0587
VL  - 36
IS  - 1
SP  - 27
EP  - 46
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - GEN
A1  - Jeltsch, Florian
A1  - Bonte, Dries
A1  - Pe'er, Guy
A1  - Reineking, Björn
A1  - Leimgruber, Peter
A1  - Balkenhol, Niko
A1  - Schröder-Esselbach, Boris
A1  - Buchmann, Carsten M.
A1  - Müller, Thomas
A1  - Blaum, Niels
A1  - Zurell, Damaris
A1  - Böhning-Gaese, Katrin
A1  - Wiegand, Thorsten
A1  - Eccard, Jana
A1  - Hofer, Heribert
A1  - Reeg, Jette
A1  - Eggers, Ute
A1  - Bauer, Silke
T1  - Integrating movement ecology with biodiversity research
BT  - exploring new avenues to address spatiotemporal biodiversity dynamics
N2  - Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of ‘movement ecology’. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide ‘mobile links’ between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through ‘equalizing’ and ‘stabilizing’ mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 401 
KW  - mobile links
KW  - species coexistence
KW  - community dynamics
KW  - biodiversity conservation
KW  - long distance movement
KW  - landscape genetics
KW  - individual based modeling
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-401177
ER  - 
TY  - JOUR
A1  - Jeltsch, Florian
A1  - Bonte, Dries
A1  - Peer, Guy
A1  - Reineking, Björn
A1  - Leimgruber, Peter
A1  - Balkenhol, Niko
A1  - Schröder-Esselbach, Boris
A1  - Buchmann, Carsten M.
A1  - Müller, Thomas
A1  - Blaum, Niels
A1  - Zurell, Damaris
A1  - Böhning-Gaese, Katrin
A1  - Wiegand, Thorsten
A1  - Eccard, Jana
A1  - Hofer, Heribert
A1  - Reeg, Jette
A1  - Eggers, Ute
A1  - Bauer, Silke
T1  - Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics
Y1  - 2013
UR  - http://download.springer.com/static/pdf/827/art%253A10.1186%252F2051-3933-1- 6.pdf?auth66=1394891271_f1a4cb74d6be42ee3f8872ef2ca22c24&ext=.pdf
U6  - https://doi.org/10.1186/2051-3933-1-6
ER  - 
TY  - CHAP
A1  - Sapir, N.
A1  - Rotics, S.
A1  - Kaatz, M.
A1  - Davidson, S.
A1  - Zurell, Damaris
A1  - Eggers, U.
A1  - Jeltsch, Florian
A1  - Nathan, R.
A1  - Wikelski, M.
T1  - Multi-year tracking of white storks (Ciconia ciconia) how the environment shapes the movement and behavior of a soaring-gliding inter-continental migrant
T2  - Integrative and comparative biology
Y1  - 2013
SN  - 1540-7063
VL  - 53
IS  - 3
SP  - E189
EP  - E189
PB  - Oxford Univ. Press
CY  - Cary
ER  -