We evaluated the relevance of threshold selection in species distribution models on the delimitation of areas of endemism, using as case study the North American mammals. We modeled 40 species of endemic mammals of the Nearctic region with Maxent, and transformed these models to binary maps using four different thresholds: minimum training presence, tenth percentile training presence, equal training sensitivity and specificity, and 0.5 logistic probability. We analyzed the binary maps with the optimality method in order to identify areas of endemism and compare our results regarding previous analyses. The majority of the species tend to have very low values for the minimum training presence, whereas most of the species have a value of the tenth percentile training presence around 0.5, and the equal training sensitivity and specificity was around 0.3. Only with the tenth percentile threshold we recovered three out of the four patterns of endemism identified in North America, and detected more endemic species.The best identification of areas of endemism was obtained using the tenth percentile training presence threshold, which seems to recover better the distributional area of the mammals analyzed.

This article presents a hybrid classification method combining image segmentation, GIS analysis, and visual interpretation, and its application to elaborate a multi-date cartographic database with 23 land use/cover (LUC) classes using SPOT 5 imagery for the Mexican state of Michoacan (~60,000 km 2 ). First, the resolution of an existing 1:100,000 LUC map produced through visual interpretation of 2007 SPOT images was improved. 2007 SPOT images were segmented, and each segment received the "majority" LUC category from the 1:100,000 map. Segments were characterized from the images (spectral indices) and the map (LUC class). A multivariate trimming was applied to detect "uncertain" segments presenting discrepancy between their spectral response and the LUC class assigned from the map. For these uncertain segments, a category was determined by digital classification, but a definitive category was assigned through visual interpretation. Finally, accuracy of the resulting LUC map was assessed. The same procedure was applied to downgrade (2004) and to update (2014) the map. The implemented method enabled us to improve the scale of an existing 2007 LUC map and to detect land use/cover changes in previous (downgrading) and later (updating) dates with an overall accuracy of 83.3% ± 3.1%. ARTICLE HISTORY

We propose and illustrate a multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) as a tool to bring nexus thinking into practice. MuSIASEM studies the relations over the structural and functional components of social-ecological systems that determine the entanglement of water, energy, and food flows in a complex metabolic pattern. MuSIASEM simultaneously considers various dimensions and multiple scales of analysis and therefore avoids the predicament of quantitative analysis based on reductionism (one dimension and one scale at the time). The different functional elements of society (the parts) are characterized using the concept of "processor," that is, a profile of expected inputs and outputs associated with the expression of a specific function. The processors of the functional elements of the social-ecological system can be either scaled-up to describe the metabolic pattern of the system as a whole, or scaled-down by considering the characteristics of its lower-level parts-i.e., the different processors associated with the structural elements required to express the specific function. An analysis of functional elements provides insight in the socioeconomic factors that pose internal constraints on the development of the system. An analysis of structural elements makes it possible to study the compatibility of the system with external constraints (availability of natural resources and ecological services) in spatial terms. The usefulness of the approach is illustrated in relation to an example of the use of charcoal in a rural village of Laos.

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