As biological and linguistic diversity, the world's cultural diversity is on decline. However, to date there are no estimates of the rate at which the specific cultural traits of a group disappear, mainly because we lack empirical data to assess how the cultural traits of a given population change over time. Here we estimate changes in cultural traits associated to the traditional knowledge of wild plant uses among an Amazonian indigenous society. We collected data among 1151 Tsimane' Amerindians at two periods of time. Results show that between 2000 and 2009, Tsimane' adults experienced a net decrease in the report of plant uses ranging from 9% (for the female subsample) to 26% (for the subsample of people living close to towns), equivalent to a 1 to 3 % per year. Results from a Monte Carlo simulation show that the observed changes were not the result of randomness. Changes were more acute for men than for women and for informants living in villages close to market towns than for informants settled in remote villages. The Tsimane' could be abandoning their traditional knowledge as they perceive that this form of knowledge do not equip them well to deal with the new socio-economic and cultural conditions they face nowadays.

Data gathered through community-based forest monitoring (CBFM) programs may be as accurate as those gathered by professional scientists, but acquired at a much lower cost and capable of providing more detailed data about the occurrence, extent and drivers of forest loss, degradation and regrowth at the community scale. In addition, CBFM enables greater survey repeatability. Therefore, CBFM should be a fundamental component of national forest monitoring systems and programs to measure, report and verify (MRV) REDD+ activities. To contribute to the development of more effective approaches to CBFM, in this paper we assess: (1) the feasibility of using small, low-cost drones (i.e., remotely piloted aerial vehicles) in CBFM programs; (2) their potential advantages and disadvantages for communities, partner organizations and forest data end-users; and (3) to what extent their utilization, coupled with ground surveys and local ecological knowledge, OPEN ACCESSForests 2014, 5 1482 would improve tropical forest monitoring. To do so, we reviewed the existing literature regarding environmental applications of drones, including forest monitoring, and drew on our own firsthand experience flying small drones to map and monitor tropical forests and training people to operate them. We believe that the utilization of small drones can enhance CBFM and that this approach is feasible in many locations throughout the tropics if some degree of external assistance and funding is provided to communities. We suggest that the use of small drones can help tropical communities to better manage and conserve their forests whilst benefiting partner organizations, governments and forest data end-users, particularly those engaged in forestry, biodiversity conservation and climate change mitigation projects such as REDD+.

Forest degradation affects forest structure, composition and diversity, carbon stocks, functionality and ecosystem processes. It is known to contribute significantly to global carbon emissions, but there is uncertainty about the relative size of these emissions. This is largely because while deforestation, or long-term forest clearance, has been successfully monitored using remote sensing (RS) technology, there are more difficulties in using RS to quantify forest degradation, in which the area remains as forest, but with an altered structure, composition and function. A major challenge in estimating emissions from forest degradation is that in addition to identifying the areas affected, the amount of biomass loss over time in a given area must be estimated. Contributory challenges to mapping, monitoring and quantifying forest degradation include the complexity of the concept of degradation, limitations in the spatial and temporal resolution of RS sensors, and the inherent complexity of detecting degradation caused by different disturbance processes and forest uses. We take the innovative approach of dividing the studies reviewed by the specific type of forest disturbance that is being monitored (selective logging, fires, shifting cultivation and fuelwood extraction etc.), since these different activities will result in different signatures in the canopy and thus may determine the type of RS technology that may best be applied.

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