Bosch, Samuel

De Clerck, Olivier

Tyberghein, Lennert

English

Ghent University Academic Bibliography

Samuel Bosch1,2, Lennert Tyberghein2, Olivier De Clerck1  1 Phycology Research Group, Ghent University, Krijgslaan 281-S8, 9000 Ghent, Belgium.  E-mail: samuel.bosch@ugent.be 2 Flanders Marine Institute (VLIZ), InnovOcean site, Wandelaarskaai 7. B-8400 Oostende. Predictor selection for species distribution modeling in a marine environment. MARSPEC (Sbrocco & Barber, 2013) have appeared but, with the notable exception of Barbet-Massin & Jetz (2014) addressing bird distribution models, little research has been done on which and how many predictors should be selected in order to obtain a realistic species distribution model (SDM). Predictor selection is, however, a crucial issue as the predictor set directly impacts the performance, the spatial and temporal transferability as well as the interpretation of the model. O ver the last few years increasing attention has been given to the development of species distribution models of marine species. New datasets with marine predictors like Bio-ORACLE (Tyberghein et al., 2012) and  What’s next ith the results of the 4 variable models we have a first indication of which predictors are valuable. Further experiments need to be performed to find out if the results are the same when changing algorithms (e.g. GLM, RF, BRT), number of predictors, set of species W and regularization parameters. Also more work has to be done to select the ranking procedure that returns the best predictors to include in an SDM of a species with unknown preferences. VLIZ YOUNG MARINE SCIENTISTS' DAY 20 February 2015 References Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., & De Clerck, O. (2012). Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21(2), 272–281. Sbrocco, E. J., & Barber, P. H. (2013). MARSPEC: ocean climate layers for marine spatial ecology. Ecology, 94(4), 979–979. Barbet-Massin, M., & Jetz, W. (2014). A 40-year, continent-wide, multispecies assessment of relevant climate predictors for species distribution modelling. Diversity and Distributions, 20(11), 1285–1295. Vandepitte, L., Bosch, S., Tyberghein, L., Waumans, F., Vanhoorne, B., Hernandez, F., … Mees, J. (2015). Fishing for data and sorting the catch: assessing the data quality, completeness and fitness for use of data in marine biogeographic databases. Database, 2015, bau125–bau125. Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … Lautenbach, S. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 027–046. Acknowledgement The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, the Hercules Foundation and the Flemish Government – department EWI. Correlation groups Representation of the rankings of predictors for every species. [Red = high rank, white = low rank of the average test AUC]. Predictors or correlation groups relevant for most species have a red bar. We can conclude that models which include [temperature], [chlorophyll], [salinity], [∆SST] and bathymetry and/or distance to shore have higher predictive power. T o find out which predictors are relevant to incorporate in an SDM we created models for a random set of 200 marine Methods species for which quality controlled (Vandepitte et al 2015) distribution records were derived from the international Ocean Biogeographic Information System (OBIS) (www.iobis.org). First we created a pairwise correlation (Pearson’s r) matrix for all 53 yearly rasters in BIO-ORACLE and MARSPEC. Based on this correlation matrix we removed 11 variables until all absolute pairwise correlations were smaller than 0.95. Then following the methodology in Dormann et al. (2013) 14 correlation groups were created with a maximum between group absolute correlation of 0.7. Using these correlation groups, SDMs were created for all possible four variable combinations (35031 per species) with MaxEnt on the UGent High Performance Cluster, making sure that two variables from the same correlation group did not occur in one model. From the ranking of the mean, median and maximum test AUC of the predictors from the different correlation groups and within the correlation groups we can infer which predictors are more suitable for building SDMs. Correlation matrix for all 53 variables from BIO-ORACLE and MARSPEC (prefixed with ‘m’).  * Variables with an asterix (*) were  not included in a correlation group because they had a pairwise correlation greater than 0.95 with another variable. More info and survey  at http://bit.ly/vliz2015 Chlorophyll  group Salinity group  ∆SST Topography  Temperature group 

Predictor selection for species distribution modeling in a marine environment

Bosch, S.

Tyberghein, L.

De Clerck, O.

Open Marine Archive

Samuel Bosch1,2, Lennert Tyberghein2, Olivier De Clerck1 
 
1 Phycology Research Group, Ghent University, Krijgslaan 281-S8, 9000 Ghent, Belgium.  E-mail: samuel.bosch@ugent.be 
2 Flanders Marine Institute (VLIZ), InnovOcean site, Wandelaarskaai 7. B-8400 Oostende. 
Predictor selection for species distribution modeling 
in a marine environment. 
MARSPEC (Sbrocco & Barber, 2013) have appeared but, with the notable exception of Barbet-Massin & Jetz 
(2014) addressing bird distribution models, little research has been done on which and how many predictors 
should be selected in order to obtain a realistic species distribution model (SDM). Predictor selection is, however, 
a crucial issue as the predictor set directly impacts the performance, the spatial and temporal transferability as 
well as the interpretation of the model. 
O ver the last few years increasing attention has been given to the development of species distribution models of marine species. New datasets with marine predictors like Bio-ORACLE (Tyberghein et al., 2012) and  
What’s next 
ith the results of the 4 variable models we have a first indication of which predictors are valuable. Further experiments need to be 
performed to find out if the results are the same when changing algorithms (e.g. GLM, RF, BRT), number of predictors, set of species W 
and regularization parameters. Also more work has to be done to select the ranking procedure that returns the best predictors to include in 
an SDM of a species with unknown preferences. 
VLIZ YOUNG MARINE SCIENTISTS' DAY 
20 February 2015 
References 
Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., & De Clerck, O. (2012). Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21(2), 272–281. 
Sbrocco, E. J., & Barber, P. H. (2013). MARSPEC: ocean climate layers for marine spatial ecology. Ecology, 94(4), 979–979. 
Barbet-Massin, M., & Jetz, W. (2014). A 40-year, continent-wide, multispecies assessment of relevant climate predictors for species distribution modelling. Diversity and Distributions, 20(11), 1285–1295. 
Vandepitte, L., Bosch, S., Tyberghein, L., Waumans, F., Vanhoorne, B., Hernandez, F., … Mees, J. (2015). Fishing for data and sorting the catch: assessing the data quality, completeness and fitness for use of data in marine biogeographic databases. Database, 2015, bau125–bau125. 
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … Lautenbach, S. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 027–046. 
Acknowledgement 
The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, the Hercules Foundation and the Flemish Government – department EWI. 
Correlation groups 
Representation of the 
rankings of predictors for 
every species. [Red = high 
rank, white = low rank of the 
average test AUC]. Predictors 
or correlation groups 
relevant for most species 
have a red bar. We can 
conclude that models which 
include [temperature], 
[chlorophyll], [salinity], 
[∆SST] and bathymetry 
and/or distance to shore 
have higher predictive 
power. 
T o find out which predictors are relevant to incorporate in an SDM we created models for a random set of 200 marine 
Methods 
species for which quality controlled (Vandepitte et al 2015) 
distribution records were derived from the international Ocean 
Biogeographic Information System (OBIS) (www.iobis.org). First we 
created a pairwise correlation (Pearson’s r) matrix for all 53 yearly 
rasters in BIO-ORACLE and MARSPEC. Based on this correlation 
matrix we removed 11 variables until all absolute pairwise 
correlations were smaller than 0.95. Then following the 
methodology in Dormann et al. (2013) 14 correlation groups were 
created with a maximum between group absolute correlation of 
0.7. Using these correlation groups, SDMs were created for all 
possible four variable combinations (35031 per species) with 
MaxEnt on the UGent High Performance Cluster, making sure that 
two variables from the same correlation group did not occur in 
one model. From the ranking of the mean, median and maximum 
test AUC of the predictors from the different correlation groups 
and within the correlation groups we can infer which predictors 
are more suitable for building SDMs. 
Correlation matrix for all 53 variables from BIO-ORACLE and MARSPEC (prefixed with ‘m’).  
* Variables with an asterix (*) were  not included in a correlation group because they had a 
pairwise correlation greater than 0.95 with another variable. 
More info and survey  
at http://bit.ly/vliz2015 
Chlorophyll  
group 
Salinity 
group 
 
∆SST 
Topography  Temperature 
group 


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Predictor selection for species distribution modeling in a marine environment

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