Modeling the potential distribution and richness of cetaceans in the Azores from Fisheries Observer Program data

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Marine Science 2 (2016): 202, doi:10.3389/fmars.2016.00202.Marine spatial planning and ecological research call for high-resolution species distribution data. However, those data are still not available for most marine large vertebrates. The dynamic nature of oceanographic processes and the wide-ranging behavior of many marine vertebrates create further difficulties, as distribution data must incorporate both the spatial and temporal dimensions. Cetaceans play an essential role in structuring and maintaining marine ecosystems and face increasing threats from human activities. The Azores holds a high diversity of cetaceans but the information about spatial and temporal patterns of distribution for this marine megafauna group in the region is still very limited. To tackle this issue, we created monthly predictive cetacean distribution maps for spring and summer months, using data collected by the Azores Fisheries Observer Programme between 2004 and 2009. We then combined the individual predictive maps to obtain species richness maps for the same period. Our results reflect a great heterogeneity in distribution among species and within species among different months. This heterogeneity reflects a contrasting influence of oceanographic processes on the distribution of cetacean species. However, some persistent areas of increased species richness could also be identified from our results. We argue that policies aimed at effectively protecting cetaceans and their habitats must include the principle of dynamic ocean management coupled with other area-based management such as marine spatial planning.This work was supported by FEDER funds, through the Competitiveness Factors Operational Programme - COMPETE, by national funds, through FCT - Foundation for Science and Technology, under project TRACE (PTDC/ MAR/74071/2006), and by regional funds, through DRCT/SRCTE, under projects MAPCET (M2.1.2/F/012/2011) and 2020 (M2.1.2/I/026/2011). We acknowledge funds provided by FCT to MARE, through the strategic project UID/MAR/04292/2013. RP is supported by an FCT postdoctoral grant (SFRH/BPD/108007/2015); MAS is supported by Program Investigator FCT (IF/00943/2013) and MT was supported by a research fellowship under the Exploratory project (IF/00943/2013/CP1199/CT0001) that also paid the fees for this open-access publication. IF/00943/2013 and IF/00943/2013/CP1199/CT0001 are funded by FSE and MCTES, through POPH and QREN