Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of “Economic Pressure on Land” (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future land-cover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a “business as usual” and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD + debate: if restricted to forests, “cross-biome leakage” would severely reduce REDD + effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD + would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD + remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage

Mechanisms Underlying the Confined Diffusion of Cholera Toxin B-Subunit in Intact Cell Membranes

Multivalent glycolipid binding toxins such as cholera toxin have the capacity to cluster glycolipids, a process thought to be important for their functional uptake into cells. In contrast to the highly dynamic properties of lipid probes and many lipid-anchored proteins, the B-subunit of cholera toxin (CTxB) diffuses extremely slowly when bound to its glycolipid receptor GM1 in the plasma membrane of living cells. In the current study, we used confocal FRAP to examine the origins of this slow diffusion of the CTxB/GM1 complex at the cell surface, relative to the behavior of a representative GPI-anchored protein, transmembrane protein, and fluorescent lipid analog. We show that the diffusion of CTxB is impeded by actin- and ATP-dependent processes, but is unaffected by caveolae. At physiological temperature, the diffusion of several cell surface markers is unchanged in the presence of CTxB, suggesting that binding of CTxB to membranes does not alter the organization of the plasma membrane in a way that influences the diffusion of other molecules. Furthermore, diffusion of the B-subunit of another glycolipid-binding toxin, Shiga toxin, is significantly faster than that of CTxB, indicating that the confined diffusion of CTxB is not a simple function of its ability to cluster glycolipids. By identifying underlying mechanisms that control CTxB dynamics at the cell surface, these findings help to delineate the fundamental properties of toxin-receptor complexes in intact cell membranes

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD

Cascading Impacts of Bark Beetle-Caused Tree Mortality on Coupled Biogeophysical and Biogeochemical Processes

Recent, large‐scale outbreaks of bark beetle infestations have affected millions of hectares of forest in western North America, covering an area similar in size to that impacted by fire. Bark beetles kill host trees in affected areas, thereby altering water supply, carbon storage, and nutrient cycling in forests; for example, the timing and amount of snow melt may be substantially modified following bark beetle infestation, which impacts water resources for many western US states. The quality of water from infested forests may also be diminished as a result of increased nutrient export. Understanding the impacts of bark beetle outbreaks on forest ecosystems is therefore important for resource management. Here, we develop a conceptual framework of the impacts on coupled biogeophysical and biogeochemical processes following a mountain pine beetle (Dendroctonus ponderosae) outbreak in lodgepole pine (Pinus contorta Douglas var latifolia) forests in the weeks to decades after an infestation, and highlight future research needs and management implications of this widespread disturbance event

Identifying knowledge gaps in seagrass research and management: An Australian perspective

© 2016 Elsevier Ltd Seagrass species form important marine and estuarine habitats providing valuable ecosystem services and functions. Coastal zones that are increasingly impacted by anthropogenic development have experienced substantial declines in seagrass abundance around the world. Australia, which has some of the world's largest seagrass meadows and is home to over half of the known species, is not immune to these losses. In 1999 a review of seagrass ecosystems knowledge was conducted in Australia and strategic research priorities were developed to provide research direction for future studies and management. Subsequent rapid evolution of seagrass research and scientific methods has led to more than 70% of peer reviewed seagrass literature being produced since that time. A workshop was held as part of the Australian Marine Sciences Association conference in July 2015 in Geelong, Victoria, to update and redefine strategic priorities in seagrass research. Participants identified 40 research questions from 10 research fields (taxonomy and systematics, physiology, population biology, sediment biogeochemistry and microbiology, ecosystem function, faunal habitats, threats, rehabilitation and restoration, mapping and monitoring, management tools) as priorities for future research on Australian seagrasses. Progress in research will rely on advances in areas such as remote sensing, genomic tools, microsensors, computer modeling, and statistical analyses. A more interdisciplinary approach will be needed to facilitate greater understanding of the complex interactions among seagrasses and their environment