Adaptive management as a tool to improve the conservation of endemic floras: the case of Sicily, Malta and their satellite islands

The presence of endemic species is among the fundamental criteria for characterizing the biodiversity of a territory. Analyzing species richness, extinction level and distribution drivers is an important preliminary step to set conservation priorities and test environmental policies. This study applied the concept of adaptive management to develop strategies for the conservation of endemic floras by considering, as a case study, Sicily, Malta and their neighboring small islands. Adaptive management can be defined as the systematic acquisition and application of reliable information to improve management over time. The development of adaptive conservation strategies aimed preliminary: (1) to quantify endemic plant diversity; (2) to assess the current IUCN knowledge; (3) to analyze the spatial patterns of species distribution in relation to number of colonized habitats, preferential habitats, altitudinal range, and bedrock; (4) to assess whether Natura 2000 network contributed significantly to increase the overlap between endemic distributional areas and protected surface. Strictly Sicilian endemics were 202 taxa amounting to 7.0 % of the whole native flora (c. 2900 taxa). The current picture of extinction risk is still incomplete because over 50 % of endemics were never assessed or assessed with old IUCN criteria. The spatial range size of endemics depended by 40 % on bedrock, and altitudinal and niche breadth. Habitat type did not influence the range size of endemics. The overlap between endemic distributional areas and protected surface increased from 41.3 to 63.3 % with Natura 2000. Adaptive management needs measurable goals to test the progressive improvement of conservation strategies over time, and the reduction in threatened species may be considered as an indicator of successful conservation outcomes. Feedback plays an important role in the periodic revision of biodiversity assessment, distribution modeling, and environmental management, which are fundamental to predict conservation outcomes in the face of extreme uncertainty. In particular, the exhaustive knowledge of the IUCN status is a primary step to implement adaptive measures of conservation, especially as regards endemic floras that are potentially more vulnerable to large-scale or unpredictable and stochastic threats.