Tobeña, Marta; Prieto, Rui; Machete, Miguel; Silva, Mónica A. (2016): Mapped cetacean habitat suitability and richness in the Azores, doi:10.1594/PANGAEA.864511 Related to submitted manuscript: Tobeña, Marta ; Prieto, Rui ; Machete, Miguel; Silva, Mónica A. (2016): Modelling the potential distribution and richness of cetaceans in the Azores from Fisheries Observer Program Data. Frontiers in Marine Science. doi:xxxxxx --------------------------------------------------------------------------------------------------------------------- This folder contains the following files: - CetaceanMaxentModels.lyr This ArcGIS compatible file contains maps of richness and cetacean distribution in Azores, split in two Layer groups "Individual maps" and " Richness maps". The individual maps are outputs from cetacean maxent model results obtained with MaxEnt software, in logistic format and stored with ArcGis10.2 in four functional species groups layers: 1) baleen whales (genus Balaneoptera)(whales: code for this functional group used in the richness maps); Balaenoptera acutorostrata (BAC:code for this species used in the models) Balaenoptera borealis(BBO) Balaenoptera physalus(BPH) Balaenoptera musculus(BMU) 2) sperm and beaked whales (genera Physeter, Mesoplodon, Hyperoodon, and Ziphius) (deep:code for this functional group used in the richness maps) Physeter macrocephalus(PMA) Ziphius cavirostris(ZCA) Hyperoodon ampullatus(HAM) Mesoplodon spp.(MES) 3) small Delphinids (genera Delphinus, Stenella, and Tursiops); (dol:code for this functional group used in the richness maps) Tursiops truncatus(TTR) Stenella frontalis(SFR) Stenella coeruleoalba(SCO) Delphinus delphis(DDE) 4) large Delphinids (genera Globicephala, Grampus, Orcinus, and Pseudorca). (lrgdol:code for this functional group used in the richness maps) Orcinus orca(OOR) Grampus griseus(GGR) Pseudorca crassidens(PCR) Globicephala macrorhynchus(GMA) These individuals maps were used to create the richness maps by functional groups. Projection: WGS_1984 Extent: Top: 42,2250015832 Left: -33,775034796 Right: -22,5083651596 Bottom: 34,4250000232 Cell Size(x,y): 0,01666667; 0,01666667 Dimensions: x = 676 ; y = 468 ; Variables: double x(x) ; double y(y) ; float SCO_may49_avg.asc(x, y) ; SCO_may49_avg.asc:long_name = "SCO_may49_avg.asc" ; SCO_may49_avg.asc:missing_value = -9999 ; Global attributes: Conventions = "CF-1.0" ; Source_Software = "Esri ArcGIS" ; - Inidividualmaps.lyr Monthly (April-September) species distribution maps The 96 monthly (April-September) maps(.asc) of potential species distribution based on the MaxEnt final SDMs. We created test-SDMs for each species by splitting presences into training and test datasets using a 10-fold cross-validation procedure to estimate predictive performance on held-out folds (Elith et al., 2011; Peterson et al., 2011). It is generally assumed that MaxEnt models with mean test-AUC values between 0.7 and 0.9 perform reasonably well and that models with mean test-AUC above 0.9 have a high performance (Phillips et al., 2006; Duan et al., 2014). Projection: WGS_1984 Extent: Top: 42,2250015832 Left: -33,775034796 Right: -22,5083651596 Bottom: 34,4250000232 Cell Size(x,y): 0,01666667; 0,01666667 Dimensions: x = 676 ; y = 468 ; Variables: double x(x) ; double y(y) ; float SCO_may49_avg.asc(x, y) ; SCO_may49_avg.asc:long_name = "SCO_may49_avg.asc" ; SCO_may49_avg.asc:missing_value = -9999 ; Global attributes: Conventions = "CF-1.0" ; Source_Software = "Esri ArcGIS" ; - Richnessmaps.lyr Monthly (April-September) cetacean richness maps We produced 24 monthly (April-September) cetacean species richness maps (GRID). These maps were created by combining (stacking) the individual species prediction maps created in MaxEnt, to produce stacked species distribution models (S-SDMs) for each month evaluated in this study. Here we used the software ENM Tools (Warren et al., 2010) to standardize raw scores from each species prediction maps so that all scores within the geographic space summed to 1, making predictions comparable among SDMs. The resulting processed maps were then combined in ArcGIS by summing the standardized raw scores from equivalent cells to create the final monthly species relative richness maps, following the advice from Calabrese et al. (2014). These maps do not intend to give an estimate of how many species are present in a given site, but only where cetacean richness is expected to be higher when compared to adjacent areas. Abbreviations(apr-april; may-may; jun-june; jul-july; aug-august; sep-september) Projection: WGS_1984 Extent: WGS_1984 Extent: Top: 42,2250015832 Left: -33,775034796 Right: -22,5083651596 Bottom: 34,4250000232 Cell Size(x,y): 0,01666667; 0,01666667 Dimensions: x = 676 ; y = 468 ; Variables: double x(x) ; double y(y) ; float apr4whales.asc(x, y) ; apr4whales:long_name = " apr4whales" ; apr4whales:missing_value = -9999 ; Global attributes: Conventions = "CF-1.0" ; Source_Software = "Esri ArcGIS" ; ------------------------------------------------------------------------------------------------------------------- If you have any questions, please contact: - Marta Tobeña; martatobena@gmail.com - Rui Prieto; rprieto@mare-centre.pt --------------------------------------------------------------------------------------------------------------------- References: - Calabrese, J. M., Certain, G., Kraan, C. and Dormann, C. F. (2014), Stacking species distribution models and adjusting bias by linking them to macroecological models. Global Ecology and Biogeography, 23: 99–112. doi:10.1111/geb.12102 - Duan, R.-Y., Kong, X.-Q., Huang, M.-Y., Fan, W.-Y., and Wang, Z.-G. (2014). The predictive performance and stability of six species distribution models. PLoS ONE 9, e112764. doi: 10.1371/journal.pone.0112764. - Dudík, M., Phillips, S.J., and Schapire, R.E. (2007). Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. Journal of Machine Learning Research 8, 1217-1260. - Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., and Yates, C.J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17, 43-57. - Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., and Araújo, M.B. (2011). Ecological niches and geographic distributions. Princeton and Oxford: Princeton University Press. - Phillips, S.J., Anderson, R.P., and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231-259. - Warren, D.L., Glor, R.E., and Turelli, M. (2010). ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607-611. doi: 10.1111/j.1600-0587.2009.06142.x.