Learning Deep Belief Networks from Non-Stationary Streams

Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. © 2012 Springer-Verlag

Global change synergies and trade-offs between renewable energy and biodiversity

Reliance on fossil fuels is causing unprecedented climate change and is accelerating environmental degradation and global biodiversity loss. Together, climate change and biodiversity loss, if not averted urgently, may inflict severe damage on ecosystem processes, functions and services that support the welfare of modern societies. Increasing renewable energy deployment and expanding the current protected area network represent key solutions to these challenges, but conflicts may arise over the use of limited land for energy production as opposed to biodiversity conservation. Here, we compare recently identified core areas for the expansion of the global protected area network with the renewable energy potential available from land-based solar photovoltaic, wind energy and bioenergy (in the form of Miscanthus 9 giganteus). We show that these energy sources have very different biodiversity impacts and net energy contributions. The extent of risks and opportunities deriving from renewable energy development is highly dependent on the type of renewable source harvested, the restrictions imposed on energy harvest and the region considered, with Central America appearing at particularly high potential risk from renewable energy expansion. Without restrictions on power generation due to factors such as production and transport costs, we show that bioenergy production is a major potential threat to biodiversity, while the potential impact of wind and solar appears smaller than that of bioenergy. However, these differences become reduced when energy potential is restricted by external factors including local energy demand. Overall, we found that areas of opportunity for developing solar and wind energy with little harm to biodiversity could exist in several regions of the world, with the magnitude of potential impact being particularly dependent on restrictions imposed by local energy demand. The evidence provided here helps guide sustainable development of renewable energy and contributes to the targeting of global efforts in climate mitigation and biodiversity conservation

Food supply and bioenergy production within the global cropland planetary boundary

Supplying food for the anticipated global population of over 9 billion in 2050 under changing climate conditions is one of the major challenges of the 21st century. Agricultural expansion and intensification contributes to global environmental change and risks the long-term sustainability of the planet. It has been proposed that no more than 15% of the global ice-free land surface should be converted to cropland. Bioenergy production for land-based climate mitigation places additional pressure on limited land resources. Here we test normative targets of food supply and bioenergy production within the cropland planetary boundary using a global land-use model. The results suggest supplying the global population with adequate food is possible without cropland expansion exceeding the planetary boundary. Yet this requires an increase in food production, especially in developing countries, as well as a decrease in global crop yield gaps. However, under current assumptions of future food requirements, it was not possible to also produce significant amounts of first generation bioenergy without cropland expansion. These results suggest that meeting food and bioenergy demands within the planetary boundaries would need a shift away from current trends, for example, requiring major change in the demand-side of the food system or advancing biotechnologies

Standardized Assessment of Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in Sight

Extinction rates in the Anthropocene are three orders of magnitude higher than background and disproportionately occur in the tropics, home of half the world’s species. Despite global efforts to combat tropical species extinctions, lack of high-quality, objective information on tropical biodiversity has hampered quantitative evaluation of conservation strategies. In particular, the scarcity of population-level monitoring in tropical forests has stymied assessment of biodiversity outcomes, such as the status and trends of animal populations in protected areas. Here, we evaluate occupancy trends for 511 populations of terrestrial mammals and birds, representing 244 species from 15 tropical forest protected areas on three continents. For the first time to our knowledge, we use annual surveys from tropical forests worldwide that employ a standardized camera trapping protocol, and we compute data analytics that correct for imperfect detection. We found that occupancy declined in 22%, increased in 17%, and exhibited no change in 22% of populations during the last 3–8 years, while 39% of populations were detected too infrequently to assess occupancy changes. Despite extensive variability in occupancy trends, these 15 tropical protected areas have not exhibited systematic declines in biodiversity (i.e., occupancy, richness, or evenness) at the community level. Our results differ from reports of widespread biodiversity declines based on aggregated secondary data and expert opinion and suggest less extreme deterioration in tropical forest protected areas. We simultaneously fill an important conservation data gap and demonstrate the value of large-scale monitoring infrastructure and powerful analytics, which can be scaled to incorporate additional sites, ecosystems, and monitoring methods. In an era of catastrophic biodiversity loss, robust indicators produced from standardized monitoring infrastructure are critical to accurately assess population outcomes and identify conservation strategies that can avert biodiversity collapse. © 2016 Beaudrot et al