Improving the identification of priority areas for conserving neotropical biodiversity : assessing uncertainties in spatial conservation prioritization

Critical thresholds in habitat reduction besides multiple threats such as climate change are leading to a biodiversity decline (Newbold et al. 2016; Betts et al. 2017; Melo et al. 2018; Roque et al. 2018), consistently predicted in multiple scenarios (Jenkins 2003; Thomas et al. 2004; Pereira et al. 2010; Bellard et al. 2012). Accordingly, extinction rates of species are now 8 to 100 times higher than those previously known (Ceballos et al. 2015). This biodiversity crisis imposes new challenges on conservation planning for prioritizing locations and efforts to guarantee the persistence of species and ecosystems over time (Meir et al. 2004; Wilson et al. 2016; Reside et al. 2018). The most common initiatives in conservation planning for safeguarding biodiversity and counteract biodiversity decline continue to be the expansion of the protected areas (PAs) systems (Jenkins & Joppa 2009; Watson et al. 2014). However, in a world increasingly dominated by human transformation processes (Ellis & Ramankutty 2008; Ellis 2011) and with limited economic resources for conservation (Wilson et al. 2009; Waldron et al. 2013), it is critical to expand this view and integrate conservation practices in human-modified landscapes (Ellis 2013). The adequate allocation of the limited resources available for biodiversity conservation is crucial for achieving global, national, and sub-national conservation targets (Dobrovolski et al. 2014; Pouzols et al. 2014; Venter et al. 2014; Di Marco et al. 2016; Espirito-Santo et al. 2017). Consequently, a critical issue in spatial conservation prioritization should be to minimize the uncertainty about what and where to conserve, in order to maximize the success of conservation planning (Regan et al. 2009; Wilson et al. 2009). Thus, I tested in this thesis how robustness of the planning process may be increased by applying the three principles proposed in the systematic conservation planning framework, (1) representation, (2) persistence, and (3) cost-efficiency (Margules & Sarkar 2007), but also by highlighting the variability of the conservation prioritization process to diverse approaches using different information (Langford et al. 2011). I evaluated the impact of several approaches and analyzed the sensitivity of the identification of priority areas for conservation in the Neotropics, one of the most biodiverse regions in the world, and also one with the most urgent needs of identifying conservation priorities. I present a comprehensive spatial conservation prioritization that addressed the composition (8563 spp.), structure (663 ecosystems), and function (5382 ecological groups) of biodiversity, showing the regional variability of conservation priorities according to the biodiversity attribute represented (Chapter 1). Also, I applied different approaches of costs and factors associated with the persistence of Neotropical biodiversity, and framed them in the land-sharing/sparing model to propose possible conservation actions in the region (Chapter 2). Finally, I evaluated multiple variables related to the systematic conservation planning principles, representation, persistence, and cost-efficiency, to analyze what factors better guide conservation priorities in the Neotropics across regional and national levels (Chapter 3). My results showed that Aichi 11 target for conserving 17% of terrestrial biodiversity in the Neotropics cannot be fulfilled with the current PAs. The prioritized areas proposed, as well as PAs, meet on average 60% of conservation targets, but being 60% more efficient in the extent of area selected. I show that in the most threatened biodiversity hotspots, the effectiveness and representativeness of biodiversity depend strictly on the inclusion of new conservation areas. Comparing biodiversity attributes, I found a higher level of surrogacy using a compositional approach explained by the number of biodiversity features and their overlapping levels. However, conservation priorities vary regionally, suggesting that the use of a single biodiversity attribute, may lead to inaccurate selections of conservation priorities (Chapter 1). I also found that costs are more influential in the selection of conservation areas than persistence measures. Using the variability in the priority areas found, we identified regions where land acquisition would be most suitable for conservation and restoration (land-sparing), and areas where conservation agreements would be more appropriate to implement restoration or wildlife-friendly farming conservation strategies (land-sharing) (Chapter 2). Through a sensitivity analysis, we found that representativeness of biodiversity, and particularly the proportion of natural habitats guides conservation priorities at the regional level, while the factors associated with costs and persistence are more critical at the Neotropical level. In the Neotropics, conservation priorities responded to the spatial conservation prioritization model, favoring complementarity and reducing conservation costs (Chapter 3). However, conservation planning at the regional level should consider several mechanisms to guarantee the success of biodiversity protection over time (Chapters 1, 2 and 3). Finally, I concluded that there is no single approach or a combination of them, which cover the whole variability in spatial information on biodiversity, costs, and persistence to conservation. Regional differences in conservation areas selected influence conservation planning differently, consequently leading to identify distinct priorities and conservation actions related. The regional variability in conservation areas selection found indicates more different strategies that should be applied to conservation planning, since biodiversity attributes, and cost and persistence approaches guide the selection differently (Chapters 1 and 2). However, despite variability in conservation areas selection, we could identify priority areas where coincidences over multiple approaches occur (Chapters 1 and 2). In these areas, the uncertainty in defining priority areas is reduced, supporting the definition of more robust conservation priorities and actions by identifying those factors related to conservation areas selection (Chapter 3). These irreplaceable areas were located in critical and threatened global biodiversity hotspots, e.g., the Chaco, the Atlantic forest, the Pantanal, Cerrado, and Caatinga regions in Brazil, and the moist and dry forests of northern Andes (Chapters 1 and 2).

• Tipo de recurso: doctoralThesis
• Fecha de publicación: 2020
• Institución: Pontificia Universidad Javeriana
• Repositorio: Repositorio Universidad Javeriana
• Idioma: spa
• OAI Identifier: oai:repository.javeriana.edu.co:10554/50602
• Acceso en línea: http://hdl.handle.net/10554/50602; https://doi.org/10.11144/Javeriana.10554.50602
• Palabra clave: Planeación para la conservación; Representatividad; Sustituto; Efectividad; Integralidad; Tierra compartida; Tierra excluida; Árboles aleatorios; Neotropico; Priorización espacial; Conservation planning; Representativeness; Surrogacy; Effectiveness; Comprehensiveness; Land sharing; Land sparing; Random forest; Neotropics; Spatial prioritization; Doctorado en estudios ambientales y rurales - Tesis y disertaciones académicas; Tierra - Zona Tropical; Árboles - Zona Tropical; Trópicos; Conservación de la biodiversidad forestal - Zona Tropical
• Rights: openAccess
• License: Atribución-NoComercial-SinDerivadas 4.0 Internacional