Use of Maxent for predictive habitat mapping of CWC in the Bari canyon Bargain Annaëlle Foglini Federica, Bonaldo Davide, Pairaud Ivane & Fabri Marie-Claire Ifremer Mediterranée & CNR Bologna (ISMAR)

BARI CANYON 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide2

Modelisation Processings  Relate species occurrence data (distribution = biological data) with environmental predictor variables (EGVs = Ecogeographic variables)  Explain the contribution of each environmental variable to the species distribution  Produce continuous maps of potential species or habitat

Méthode générale Steps of the CWC predictive habitat modelisation 1.Data acquisition Species recordsEnvironmental data 2.Model settings Choose of statistical Methods Evaluate Variable contribution Model assessment  Is the model better than random model ? 3.Model applications Predictive habitat mapsSites comparisonsMarine planification tool

Maxent software Software nt/ by Steven Phillips, Miro Dudik and Rob Schapire, with support from AT&T Labs- Research, Princeton University, and the Center for Biodiversity and Conservation, American Museum of Natural History 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide5

25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide6 Opening the software

25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide7 Opening the software To enter the species data To enter the Eco- geographic variables Parameters of the model

Use of ADELIE-SIG (Ifremer) on video transects and digital images  CWC presence points  datapoints have been reduced to one presence points per cell of EGVs resolution (BARI = 20*20m) Data acquisition : CWC presence points

Species files in Maxent software Files need to be in.csv Export the table from arcgis into.txt, open in Excel, and change the file in this form : Species/long/lat The file must be then save in.csv Open the.csv file with wordpad and change « ; » with «, » Open the file in maxent 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide9

1. Use of the high resolution bathymetry data With arcgis, and Benthic Terrain Modeler module ( e8c83d220e7926) e8c83d220e7926 limit the bathymetric data to the canyon (bathymetry under -180m) compute benthic indices at different resolution Data acquisition : EGVs Surface Area to Planar Area Bathymetric Position Index (3, 9, 17, 25, 33, 65) Curvature Eastness Northness Rugeness (3, 5, 11 m resolution) Slope Terrain roughness Orientation Topography Profile Plan

1.Use of hydrodynamic data at 1km resolution  Data from 1rst of november to 28th june  Climatic event from 25th Jan. To 14th Feb. interpolate the measure points to the bathymetric resolution (20 m) with natural neighbor. Data acquisition : EGVs Current speed Max Mean SD Water Density Temperature Water Salinity Max Mean SD Max Mean SD Max Mean SD

EGVs study Variables need to be non-correlated in a model A statistical analysis is thus necessary to choose the best non-correlated ones before using the model (PCA, dendrogram…) The model is also better if the sample points incompass all the variable value range -> Need to compare the EVGs values at each points to the global EVGs values 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide12

EVGs finally selected for CWC in the Bari canyon 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide13 Slope Ruggeness at 11 pixels resolution Bathymetric Position Index at 25 pixels resolution Bathymetric Position Index at 65 pixels resolution Northness Eastness Profile curvature Transversal curvature Mean water current speed Mean water salinity Mean water density Maximum water temperature

EGV files in Maxent software Files need to be in.asc Export the table from arcgis into.asc Open the directory layer containing all the files with Maxent Then, choose the EVGs that will be use in the model, selecting the good type of variable (continuous or categorical) 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide14

Software settings 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide15 Feature types to be use during training. « Hinge » only has been selected, which is according to Philipps & Dudik (2008) a good approximation of the global distribution of the species Settings has many options for Maxent model In « basic option », the regularization multiplier has been changed to « 3 », to avoid over-fitting 10 replicates, using crossvalidate has been selected to test the model The other parameters has been left In « Advanced option », the default prevalence has been changed to « 0.7 », as the dive to study CWC were not totaly random The other parameters has been left

25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide16 Software settings This options have been selected to do jackniffe tests and study each variable importance for CWC distribution The logistic format has been left, to have a probability of suitability for each pixel, from 0 to 100 %. Define here the output directory And then RUN the model

MAXENT outputs One html file, that summaries all results All replicates html files Many tables and graphs 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide17

MAXENT outputs ROC curves (and AUC values) Maps CWC distribution response to variable variations Variable importance in the final model table Jackniffe tests 25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide18

25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide19 MAXENT outputs for CWC distribution in the Bari Canyon The ROC curve is very high, with an AUC value close to 1 (0.99) The results of the habitat mapping show two main areas for CWC distribution in the BARI canyon (left map), with no big differences between replicates (SD very low, map on the right)

25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide20 MAXENT outputs for CWC distribution in the Bari Canyon The importance of each variable in the final model shows that Slope, rugosity and water current speed are the main variable to explain CWC distribution Jackniffe tests on training gain had the same conclusions, including also the mean water density in the main variables The absence of the salinity in the model also decrease the most the final gain, showing that this variable is essential in CWC distribution

25/11/2015Bargain Annaelle – Modélisation des coraux d’eau froide21 ModelHigh probability area Threshold Maxent3.9 km²0.6 Conclusions