The mangrove forests of Southeast Asia are highly biodiverse and provide multiple ecosystem services upon which millions of people depend. Mangroves enhance fisheries and coastal protection, and store among the highest densities of carbon of any ecosystem globally. Mangrove forests have experienced extensive deforestation owing to global demand for commodities, and previous studies have identified the expansion of aquaculture as largely responsible. The proportional conversion of mangroves to different land use types has not been systematically quantified across Southeast Asia, however, particularly in recent years. In this study we apply a combined geographic information system and remote sensing method to quantify the key proximate drivers (i.e., replacement land uses) of mangrove deforestation in Southeast Asia between 2000 and 2012. Mangrove forests were lost at an average rate of 0.18% per year, which is lower than previously published estimates. In total, more than 100,000 ha of mangroves were removed during the study period, with aquaculture accounting for 30% of this total forest change. The rapid expansion of rice agriculture in Myanmar, and the sustained conversion of mangroves to oil palm plantations in Malaysia and Indonesia, are identified as additional increasing and under-recognized threats to mangrove ecosystems. Our study highlights frontiers of mangrove deforestation in the border states of Myanmar, on Borneo, and in Indonesian Papua. To implement policies that conserve mangrove forests across Southeast Asia, it is essential to consider the national and subnational variation in the land uses that follow deforestation.

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How do people decide whether to try to retrieve an answer to a problem or to compute the answer by some other means? The authors report 2 experiments showing that this decision is based on problem familiarity rather than on retrievability of some answer (correct or incorrect), even when problem familiarization occurred 24 hr earlier. These effects at the level of the individual problem solver and the results reported by L. M. Reder and E E. Ritter (1992) are well fit with the same parameter values in a spreading-activation computational model of feeling of knowing in which decisions to retrieve or compute an answer are based on the familiarity or activation levels of the problem representation. The authors therefore argue that strategy selection is governed by a familiarity-based feeling-of-knowing process rather than by a process that uses the availability of the answer or some form of race between retrieving and computing the answer.When given any problem to solve, the problem solver may choose to retrieve a previously computed solution from memory or choose to compute the answer by using some reasoning strategy (e.g., using an algorithm, inferencing, or making plausibility judgments). This decision between retrieving and computing is used in a wide range of problem domains. In academic domains, the decision is important in tasks varying in complexity from simple arithmetic (e.g., 9 + 6) to fact verification in story comprehension (e.g., Did the heir to the hamburger chain love his wife?) to economics (e.g., What is the effect of a value added tax on supply and demand?). In everyday problem domains, the decision is also important in a wide range of tasks such as navigating a path to the grocery store or calling someone on the phone.How is the decision between retrieval and reasoning

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