Researchers and conservation managers largely agree on the relevance of traditional ecological knowledge for natural resource management in indigenous communities, but its prevalence and role as societies modernize are contested. We analyzed the transmission of traditional knowledge among rural local people in communities linked to protected areas in Doñana, southwestern Spain. We studied changes in knowledge related to local practices in agriculture and livestock farming among 198 informants from three generations that cover the period in which the area transited from an economy strongly dependent on local ecosystem services to a market economy with intensified production systems. Our results suggest an abrupt loss of traditional agricultural knowledge related to rapid transformations and intensification of agricultural systems, but maintenance of knowledge of traditional livestock farming, an activity allowed in the protected areas that maintains strong links with local cultural identity. Our results demonstrate the potential of protected areas in protecting remaining bodies of traditional ecological knowledge in developed country settings. Nevertheless, we note that strict protection in cultural-landscape-dominated areas can disrupt transmission of traditional knowledge if local resource users and related practices are excluded from ecosystem management.

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

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In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the final customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the fitness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial offer and customers show different willingness to consume depending on how well the assigned depot fits their preferences. Thus, we assume that different customer-depot assignment maps will lead to different customer-expenditure levels. As a consequence, market-segmentation strategies need to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to