Submitted by Cláudia Bueno (claudiamoura18@gmail.com) on 2016-05-19T19:46:17Z No. of bitstreams: 2 Dissertação - André Felipe Alves de Andrade - 2016.pdf: 2007118 bytes, checksum: 69341ce5bc6c459857d1feff5d92366e (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-20T13:54:17Z (GMT) No. of bitstreams: 2 Dissertação - André Felipe Alves de Andrade - 2016.pdf: 2007118 bytes, checksum: 69341ce5bc6c459857d1feff5d92366e (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-05-20T13:54:17Z (GMT). No. of bitstreams: 2 Dissertação - André Felipe Alves de Andrade - 2016.pdf: 2007118 bytes, checksum: 69341ce5bc6c459857d1feff5d92366e (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-02-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Coral reefs are of extreme importance to both nature and society, due to being responsible for several services and harbouring hundreds of species. Despite such critical importance, reef corals current suffered heavy losses since the Anthropocene, with 20% of world´s corals damaged beyond recovery due to human pressure and coastal development. This scenario is even worse, since corals are especially vulnerable to climate change and the entire ecosystem could go extinct by 2050. In this study we focus on comparing the already established impacts from human development and the yet happen losses from climate change on Brazilian corals, a unique fauna that still have gaps in knowledge. We created environmental suitability models for 24 species and quantified individual losses from both climate change and human activities. From the individual results we derived an overall pattern, in which we found out that future losses from climate alteration are equivalent to current losses from human activities. We then used the spatial distribution of those activities and key areas for conservation, determined with software Zonation, to select six areas in the Brazilian exclusive economic zone where proactive and reactive conservation strategies should be implanted, given its importance to biodiversity and concentrated anthropogenic impacts. Overall suitability losses were of approximately 30% for both sources and 60% of the areas will continue to be suitable in the future. Therefore, Brazilian corals will experience heavy losses from climate, especially the loss of highly suitable areas, which are compared to effects from human economic activities. Coral situation is likely to be even worse, if we were to consider bleaching, ocean acidification and diseases, events expected to increase with the rising temperature. / Ecological Niche Modelling (ENM) is widely used for conservation purposes, predicting species invasion, evolutionary aspects and a whole array of applications. However, for most cases, evaluating the efficiency of those models poses as problematic, as commonly used methods (i.e. random methods) do not assure the required independence between data used to create the model and data used to evaluate the model. We developed a new transferabilitybased framework that ensures the much-needed independence between subsets. We created an alternate approach that geographically splits occurrence datasets, while intrinsically controls issues related to previous transferability approaches, such as overfitting, extrapolation and sampling bias. We used 26 Atlantic coral species to perform three different geographical divisions quantifying the effect of different splits on model predictive efficiency. We demonstrate that transferability should be used as an effective method to evaluate ENMs. Geographical split of the area in deciles proved as a reliable evaluation method, assuring independence between datasets and being less prone to common transferability issues. Our odds-and-evens framework provides improvements to the ongoing debate of ENMs evaluating by its transferability. This new method corrects the issue of artificiality causing sampling bias and overfitting, common in previous methodologies, while also is less prone to extrapolation issues, a common problem in transferability approaches. Moreover, the framework appears as a feasible and useful alternative to the problematic and commonly used random partition of datasets evaluation.