Habitat conversion and global avian biodiversity loss

The magnitude of the impacts of human activities on global biodiversity has been documented at several organizational levels. However, although there have been numerous studies of the effects of local-scale changes in land use (e.g. logging) on the abundance of groups of organisms, broader continental or global-scale analyses addressing the same basic issues remain largely wanting. None the less, changing patterns of land use, associated with the appropriation of increasing proportions of net primary productivity by the human population, seem likely not simply to have reduced the diversity of life, but also to have reduced the carrying capacity of the environment in terms of the numbers of other organisms that it can sustain. Here, we estimate the size of the existing global breeding bird population, and then make a first approximation as to how much this has been modified as a consequence of land-use changes wrought by human activities. Summing numbers across different land-use classes gives a best current estimate of a global population of less than 100 billion breeding bird individuals. Applying the same methodology to estimates of original land-use distributions suggests that conservatively this may represent a loss of between a fifth and a quarter of pre-agricultural bird numbers. This loss is shared across a range of temperate and tropical land-use types

Drought events and their effects on vegetation productivity in China

Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982–2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)

The Global Landsat Archive: Status, Consolidation, and Direction

New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had twoadditional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program

On the use of marker data to determine the kinetics of the digestive behaviour of feeds

A model of the transport process that follows the progress of digesta successively through the small intestine of a monogastric is investigated. The process is multi-phase and multi-constituent, as described in detail by Bastianelli et al. [J. Anim. Sci., 74:1873–1887, 1996]. The model describes the movement of marker substances that are used to obtain data on the interactions between the intestinal sections and digesta with differing levels of soluble fibre. A multi-stage process is modelled by a set of coupled first order linear differential equations. Solutions of steady and initial value problems provide information on the transfer rates of the processes. Properties of the solutions as functions of system parameters are examined. References M. Renton, J. Hanan and K. Burrage, Using the canonical modelling approach to simplify the simulation of function in functional-structural plant models. New Phytologist, 166:845–857, 2005. doi:10.1111/j.1469-8137.2005.01330.x D. Bastianelli, D. Sauvant and A. Rerat, Mathematical modeling of digestion and nutrient absorption in pigs. J. Animal Science, 74:1873–1887, 1996. http://www.journalofanimalscience.org/content/74/8/1873.abstract R. G. Lentle and P. W. M. Janssen, Manipulating Digestion with Foods designed to Change the Physical Characteristics of digesta. Critical Reviews in Food Science and Nutrition, 50:130–145, 2010. doi:10.1080/10408390802248726 J. France, J. H. M. Thornley, M. S. Dhanoa and R. C. Siddons, On the mathematics of digesta flow kinetics. Journal of Theoretical Biology, 113:743–758, 1985. doi:10.1016/S0022-5193(85)80191-0 A. Mazanov and J. V. Nolan, Simulation of the dynamics of nitrogen metabolism in sheep. British Journal of Nutrition, 35:149–174, 1976. doi:10.1079/BJN19760017 A. Mazanov, Stability of Multi-pool Models with Lags. Journal of Theoretical Biology, 59:429–442, 1976. doi:10.1016/0022-5193(76)90181-

Evapotranspiration in Northern Eurasia : impact of forcing uncertainties on terrestrial ecosystem model estimates

The ecosystems in Northern Eurasia (NE) play an important role in the global water cycle and the climate system. While evapotranspiration (ET) is a critical variable to understand this role, ET over this region remains largely unstudied. Using an improved version of the Terrestrial Ecosystem Model with five widely used forcing data sets, we examine the impact that uncertainties in climate forcing data have on the magnitude, variability, and dominant climatic drivers of ET for the period 1979-2008. Estimates of regional average ET vary in the range of 241.4-335.7mmyr(-1) depending on the choice of forcing data. This range corresponds to as much as 32% of the mean ET. Meanwhile, the spatial patterns of long-term average ET across NE are generally consistent for all forcing data sets. Our ET estimates in NE are largely affected by uncertainties in precipitation (P), air temperature (T), incoming shortwave radiation (R), and vapor pressure deficit (VPD). During the growing season, the correlations between ET and each forcing variable indicate that T is the dominant factor in the north and P in the south. Unsurprisingly, the uncertainties in climate forcing data propagate as well to estimates of the volume of water available for runoff (here defined as P-ET). While the Climate Research Unit data set is overall the best choice of forcing data in NE according to our assessment, the quality of these forcing data sets remains a major challenge to accurately quantify the regional water balance in NE

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

A Land Cover Map of Africa. Carte de l'Occupation du Sol de l'Afrique.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions

State of the Climate in 2010

Several large-scale climate patterns influenced climate conditions and weather patterns across the globe during 2010. The transition from a warm El Niño phase at the beginning of the year to a cool La Niña phase by July contributed to many notable events, ranging from record wetness across much of Australia to historically low Eastern Pacific basin and near-record high North Atlantic basin hurricane activity. The remaining five main hurricane basins experienced below- to well-below-normal tropical cyclone activity. The negative phase of the Arctic Oscillation was a major driver of Northern Hemisphere temperature patterns during 2009/10 winter and again in late 2010. It contributed to record snowfall and unusually low temperatures over much of northern Eurasia and parts of the United States, while bringing above-normal temperatures to the high northern latitudes. The February Arctic Oscillation Index value was the most negative since records began in 1950. The 2010 average global land and ocean surface temperature was among the two warmest years on record. The Arctic continued to warm at about twice the rate of lower latitudes. The eastern and tropical Pacific Ocean cooled about 1°C from 2009 to 2010, reflecting the transition from the 2009/10 El Niño to the 2010/11 La Niña. Ocean heat fluxes contributed to warm sea surface temperature anomalies in the North Atlantic and the tropical Indian and western Pacific Oceans. Global integrals of upper ocean heat content for the past several years have reached values consistently higher than for all prior times in the record, demonstrating the dominant role of the ocean in the Earth’s energy budget. Deep and abyssal waters of Antarctic origin have also trended warmer on average since the early 1990s. Lower tropospheric temperatures typically lag ENSO surface fluctuations by two to four months, thus the 2010 temperature was dominated by the warm phase El Niño conditions that occurred during the latter half of 2009 and early 2010 and was second warmest on record. The stratosphere continued to be anomalously cool. Annual global precipitation over land areas was about five percent above normal. Precipitation over the ocean was drier than normal after a wet year in 2009. Overall, saltier (higher evaporation) regions of the ocean surface continue to be anomalously salty, and fresher (higher precipitation) regions continue to be anomalously fresh. This salinity pattern, which has held since at least 2004, suggests an increase in the hydrological cycle. Sea ice conditions in the Arctic were significantly different than those in the Antarctic during the year. The annual minimum ice extent in the Arctic—reached in September—was the third lowest on record since 1979. In the Antarctic, zonally averaged sea ice extent reached an all-time record maximum from mid-June through late August and again from mid-November through early December. Corresponding record positive Southern Hemisphere Annular Mode Indices influenced the Antarctic sea ice extents. Greenland glaciers lost more mass than any other year in the decade-long record. The Greenland Ice Sheet lost a record amount of mass, as the melt rate was the highest since at least 1958, and the area and duration of the melting was greater than any year since at least 1978. High summer air temperatures and a longer melt season also caused a continued increase in the rate of ice mass loss from small glaciers and ice caps in the Canadian Arctic. Coastal sites in Alaska show continuous permafrost warming and sites in Alaska, Canada, and Russia indicate more significant warming in relatively cold permafrost than in warm permafrost in the same geographical area. With regional differences, permafrost temperatures are now up to 2°C warmer than they were 20 to 30 years ago. Preliminary data indicate there is a high probability that 2010 will be the 20th consecutive year that alpine glaciers have lost mass. Atmospheric greenhouse gas concentrations continued to rise and ozone depleting substances continued to decrease. Carbon dioxide increased by 2.60 ppm in 2010, a rate above both the 2009 and the 1980–2010 average rates. The global ocean carbon dioxide uptake for the 2009 transition period from La Niña to El Niño conditions, the most recent period for which analyzed data are available, is estimated to be similar to the long-term average. The 2010 Antarctic ozone hole was among the lowest 20% compared with other years since 1990, a result of warmer-than-average temperatures in the Antarctic stratosphere during austral winter between mid-July and early September. List of authors and affiliations... .3 Abstract 16 1. Introduction 17 2. Global Climate 27 a. Overview .. 27 b. Temperature 36; 1. Surface temperature .. 36; 2. Lower tropospheric temperatures 37; 3. Lower stratospheric temperatures .. 38; 4. Lake temperature 39 c. Hydrologic cycle .. 40; I. Surface humidity .. 40; 2. Total column water vapor .41; 3. Precipitation . 42; 4. Northern Hemisphere continental snow cover extent ... 44; 5. Global cloudiness 45; 6. River discharge . 46; 7. Permafrost thermal state . 48; 8. Groundwater and terrestrial water storage .. 49; 9. Soil moisture ..52; 10. Lake levels 53 d. Atmospheric circulation 55; 1. Mean sea level pressure . 55; 2. Ocean surface wind speed 56 e. Earth radiation budget at top-of-atmosphere ... 58 f. Atmosphere composition ...59; 1. Atmosphere chemical composition ...59; 2. Aerosols 65; 3. Stratospheric ozone 67 g. Land surface properties . 68; 1. Alpine glaciers and ice sheets .. 68; 2. Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) ... 72; 3. Biomass burning ... 72; 4. Forest biomass and biomass change .74 3. Global Oceans 77 a. Overview .. 77 b. Sea surface temperatures .. 78 c. Ocean heat content .81 d. Global ocean heat fluxes ... 84 e. Sea surface salinity .. 86 f. Subsurface salinity ... 88 g. Surface currents ... 92; 1. Pacific Ocean 93; 2. Indian Ocean 94; 3. Atlantic Ocean . 95 h. Meridional overturning circulation observations in the subtropical North Atlantic . 95 i. Sea level variations ... 98 j. The global ocean carbon cycle 100; 1. Air-sea carbon dioxide fluxes 100; 2. Subsurface carbon inventory . 102; 3. Global ocean phytoplankton . 105 4. Tropics ... 109 a. Overview 109 b. ENSO and the tropical Pacific 109; 1. Oceanic conditions ... 109; 2. Atmospheric circulation: Tropics .110; 3. Atmospheric circulation: Extratropics ...112; 4. ENSO temperature and precipitation impacts .113 c. Tropical intraseasonal activity .113 d. Tropical cyclones 114; 1. Overview .114; 2. Atlantic basin ...115; 3. Eastern North Pacific basin .121; 4. Western North Pacific basin .. 123; 5. Indian Ocean basins .. 127; 6. Southwest Pacific basin 129; 7. Australian region basin 130 e. Tropical cyclone heat potential .. 132 f. Intertropical Convergence Zones . 134; 1. Pacific ... 134; 2. Atlantic 136 g. Atlantic multidecadal oscillation 137 h. Indian Ocean Dipole . 138 5. The arctic ... 143 a. Overview 143 b. Atmosphere 143 c. Ocean .. 145; 1. Wind-driven circulation . 145; 2. Ocean temperature and salinity 145; 3. Biology and geochemistry .. 146; 4. Sea level .. 148 d. Sea ice cover ... 148; 1. Sea ice extent . 148; 2. Sea ice age ... 149; 3. Sea ice thickness 150 e. Land .. 150; 1. Vegetation ... 150; 2. Permafrost ... 152; 3. River discharge ... 153; 4. Terrestrial snow 154; 5. Glaciers outside Greenland 155 f. Greenland ... 156; 1. Coastal surface air temperature . 156; 2. Upper air temperatures . 158; 3. Atmospheric circulation . 158; 4. Surface melt extent and duration and albedo . 159; 5. Surface mass balance along the K-Transect .. 159; 6. Total Greenland mass loss from GRACE . 160; 7. Marine-terminating glacier area changes .. 160 6. ANTARCTICA ..161 a. Overview .161 b. Circulation ...161 c. Surface manned and automatic weather station observations 163 d. Net precipitation ... 164 e. 2009/10 Seasonal melt extent and duration . 167 f. Sea ice extent and concentration .. 167 g. Ozone depletion 170 7. Regional climates ... 173 a. Overview 173 b. North America ... 173; 1. Canada 173; 2. United States .. 175; 3. México . 179 c. Central America and the Caribbean .. 182; 1. Central America 182; 2. The Caribbean ... 183 d. South America .. 186; 1. Northern South America and the Tropical Andes . 186; 2. Tropical South America east of the Andes .. 187; 3. Southern South America 190 e. Africa 192; 1. Northern Africa 192; 2. Western Africa .. 193; 3. Eastern Africa . 194; 4. Southern Africa .. 196; 5. Western Indian Ocean countries 198 f. Europe . 199; 1. Overview 199; 2. Central and Western Europe 202; 3. The Nordic and Baltic countries . 203; 4. Iberia 205; 5. Mediterranean, Italian, and Balkan Peninsulas .206; 6. Eastern Europe .. 207; 7. Middle East ..208 g. Asia ... 210; 1. Russia ... 210; 2. East Asia ..215; 3. South Asia 217; 4. Southwest Asia ...219 h. Oceania ...222; 1. Southwest Pacific ..222; 2. Northwest Pacific, Micronesia .. 224; 3. Australia .. 227; 4. New Zealand .. 229 8. SEASONAL SUMMARIES ... 233 Acknowledgments 237 Appendix: Acronyms and Abbreviations 238 References . 24