Scielo RSS <![CDATA[Revista chilena de historia natural]]> vol. 91 num. lang. en <![CDATA[SciELO Logo]]> <![CDATA[Protected areas in Chile: are we managing them?]]> Abstract Background Human population growth since the mid-1900s has been accompanied by an unsustainable use of natural resources and a corresponding impact on terrestrial and marine biota. In response, most states have established protected areas as tools to decrease biodiversity loss, being Chile one of the signatories of international conservation agreements such as the Convention on Biological Diversity (CBD) and the 2010 Aichi Targets. This study reviews the Chilean protected areas that have been created to date, with an emphasis on the existence and effectiveness of management plans for all terrestrial and marine protected areas. Effectiveness was individually evaluated using two filters: 1) the age of the management plan and 2) the first four steps of the Protected Areas Management Effectiveness (PAME) methodology recommended by the IUCN. Results We show that 84 out of a total of 145 protected areas (PAs), and only five out of a total of 20 marine protected areas (MPAs), have management plans. Only 12% (N = 16) of PAs are effectively managed; while in the marine realm, no MPA has an effective plan. Conclusions Our results show the lack of both the effectiveness of and updates to the management plans for the vast majority of the national territory and raise the following question: is it sustainable to continue adding protected areas to the national system even though it is clear that the existing support is insufficient to meet the minimum requirements for full implementation? <![CDATA[A modularity-based approach for identifying biodiversity management units]]> Abstract Background Taxon- and/or ecosystem-based definitions of management units typically focus on conspicuous species and physical habitat limits; these definitions implicitly assume that these classification systems are related to the mechanisms that determine biodiversity persistence. However, ecological theory shows that this assumption may not be supported. Herein, we introduce the use of modularity analysis for objectively identifying management units and topological roles that land cover type plays on species movement through the landscape. Methods As a case study, we used a coastal system in Uruguay, with 28 land cover types and five taxa (from plants to mammals). A modularity-based approach was used to identify subsets of habitats with biotic affinity, termed modules, across the different taxonomic groups. Modularity detects the tendency of some land cover types to have a higher probability of the mutual interchange of individuals than other land cover types. Based on this approach, pairs of habitats that co-occur in the same module across taxa were considered in the same biodiversity management units (BMU). In addition, the topological role of each habitat was determined based on the occurrence of species through the landscape. Results Our approach determined three management units that combine land cover types usually considered independent, but instead are interrelated by an occurrence-based ecological network as proxies of the potential flow of individual and land use. For each selected taxon, the specific topological role of each habitat was determined. Conclusions This approach provides an objective way of delineating spatial units for conservation assessment. We showed that land cover types within these spatial units could be identified as refuges for specific types of biodiversity, sources of propagules for neighboring or overall landscapes, or stepping-stones connecting sub-regions. The preservation of these topological roles might help maintain the mechanisms that drive biodiversity in the system. Interestingly, the role of land cover type was strongly contingent on the taxa being considered. The method is comprehensible, applicable to policy and decision-makers, and well-connected with ecological theory. Moreover, this approach complements existing methods, introduces novel quantitative uses of available information, determines criteria for land cover classification and identifies management units that are not evident through other approaches.