Biogeography 4th Edition Lomolino Pdf 12 Ninos Flight Carbon
Cherly Fleitas
Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere-atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches.
In this article, a new version of a biogeography-based optimization algorithm with Levy flight distribution (LFBBO) is introduced and used for the optimum design of reinforced concrete cantilever retaining walls under seismic loading. The cost of the wall is taken as an objective function, which is minimized under the constraints implemented by the American Concrete Institute (ACI 318-05) design code and geometric limitations. The influence of peak ground acceleration (PGA) on optimal cost is also investigated. The solution of the problem is attained by the LFBBO algorithm, which is developed by adding Levy flight distribution to the mutation part of the biogeography-based optimization (BBO) algorithm. Five design examples, of which two are used in literature studies, are optimized in the study. The results are compared to test the performance of the LFBBO and BBO algorithms, to determine the influence of the seismic load and PGA on the optimal cost of the wall.
We compare the simulations of three biogeography models (BIOME2, Dynamic Global Phytogeography Model (DOLY), and Mapped Atmosphere-Plant Soil System (MAPSS)) and three biogeochemistry models (BIOME-BGC (BioGeochemistry Cycles), CENTURY, and Terrestrial Ecosystem Model (TEM)) for the conterminous United States under contemporary conditions of atmospheric CO2 and climate. We also compare the simulations of these models under doubled CO2 and a range of climate scenarios. For contemporary conditions, the biogeography models successfully simulate the geographic distribution of major vegetation types and have similar estimates of area for forests (42 to 46% of the conterminous United States), grasslands (17 to 27%), savannas (15 to 25%), and shrublands (14 to 18%). The biogeochemistry models estimate similar continental-scale net primary production (NPP; 3125 to 3772 1012 gC yr-1) and total carbon storage (108 to 118 1015 gC) for contemporary conditions. Among the scenarios of doubled CO2 and associated equilibrium climates produced by the three general circulation models (Oregon State University (OSU), Geophysical Fluid Dynamics Laboratory (GFDL), and United Kingdom Meteorological Office (UKMO)), all three biogeography models show both gains and losses of total forest area depending on the scenario (between 38 and 53% of conterminous United States area). The only consistent gains in forest area with all three models (BIOME2, DOLY, and MAPSS) were under the GFDL scenario due to large increases in precipitation. MAPSS lost forest area under UKMO, DOLY under OSU, and BIOME2 under both UKMO and OSU. The variability in forest area estimates occurs because the hydrologic cycles of the biogeography models have different sensitivities to increases in temperature and CO2. However, in general, the biogeography models produced broadly similar results when incorporating both climate change and elevated CO2 concentrations. For these scenarios, the NPP estimated by the
We compare the simulations of three biogeography models (BIOME2, Dynamic Global Phytogeography Model (DOLY), and Mapped Atmosphere-Plant Soil System (MAPSS)) and three biogeochemistry models (BIOME-BGC (BioGeochemistry Cycles), CENTURY, and Terrestrial Ecosystem Model (TEM)) for the conterminous United States under contemporary conditions of atmospheric CO2 and climate. We also compare the simulations of these models under doubled CO2 and a range of climate scenarios. For contemporary conditions, the biogeography models successfully simulate the geographic distribution of major vegetation types and have similar estimates of area for forests (42 to 46% of the conterminous United States), grasslands (17 to 27%), savannas (15 to 25%), and shrublands (14 to 18%). The biogeochemistry models estimate similar continental-scale net primary production (NPP; 3125 to 37721012 gCyr-1) and total carbon storage (108 to 1181015 gC) for contemporary conditions. Among the scenarios of doubled CO2 and associated equilibrium climates produced by the three general circulation models (Oregon State University (OSU), Geophysical Fluid Dynamics Laboratory (GFDL), and United Kingdom Meteorological Office (UKMO)), all three biogeography models show both gains and losses of total forest area depending on the scenario (between 38 and 53% of conterminous United States area). The only consistent gains in forest area with all three models (BIOME2, DOLY, and MAPSS) were under the GFDL scenario due to large increases in precipitation. MAPSS lost forest area under UKMO, DOLY under OSU, and BIOME2 under both UKMO and OSU. The variability in forest area estimates occurs because the hydrologic cycles of the biogeography models have different sensitivities to increases in temperature and CO2. However, in general, the biogeography models produced broadly similar results when incorporating both climate change and elevated CO2 concentrations. For these scenarios, the NPP estimated by the
Amazonian ecosystems provide key ecosystem services, such as regulating the amount and timing of water and carbon flows through the Amazon Basin. Land use in these ecosystems affects regional water balance, which in turn affects biogeography of aquatic ecosystems, including wetlands and floodplains. We combined a hydrological model (Terrestrial Hydrology Model with Biogeochemistry, THMB), remote sensing observations (Hess et al. 2003), and empirical data to identify the distribution of aquatic biogeographic types throughout the central Amazon basin over time. We explored how future land-use scenarios for the Amazon Basin through 2030 (Soares-Filho et al. 2004) would modify the spatial and temporal patterns of aquatic ecosystems as compared to a baseline of natural potential vegetation cover under historical climate variability for the 20th century. We calibrated monthly simulation results with remotely sensed observations of flooded area and extent of different wetland categories for high and low water periods over a 1.7 million sq. km region of the central Amazon. Two additional dimensions of floodplain biogeography (river size and color) were added to provide insight into the geographic distribution of key ecosystem types and their flooding seasonality. For historical conditions, the model results reproduced regional differences in seasonal flood extent and timing north and south of the Amazon mainstem, reflecting the dominant climatic regimes. Black-water streams and medium-sized rivers, followed by large white-water rivers, were the most extensive types across the study region. However much of the black water was in areas likely to be influenced by white-water rivers while flooded. The monthly extent of flooded areas dominated by woody vegetation was consistently more strongly seasonal than non-woody areas. Also, the extent of flooding in muddy and semi-muddy rivers and floodplains tended to be more highly seasonal than in black- and clear-water areas. We
Southern Ocean phytoplankton biogeography is important for the biogeochemical cycling of carbon, silicate, and the transport of macronutrients to lower latitudes. With the discovery of the "Great Calcite Belt" (GBC), revealing an unexpectedly high prevalence of calcifying phytoplankton in the subtropical frontal region between 40-55S, the relative importance of Southern Ocean coccolithophores for phytoplankton biomass, net primary productivity and the carbon cycle need to be revisited. Using a regional high-resolution model with an embedded ecosystem module (ROMS-BEC) for the Southern Ocean (24-78S) that has been extended to include an explicit representation of coccolithophores, we assess the environmental drivers of Southern Ocean coccolithophore biogeography over the course of the growing season. We thereby focus on biotic interactions and the relative importance of top-down (grazing) versus bottom-up factors (light, nutrient, temperature) controlling growth and abundance. In our simulation, coccolithophores are an important member of the Southern Ocean phytoplankton community, contributing 13% to annually integrated net primary productivity south of 30S. We estimate the integrated annual calcification rate to account for 40% of the satellite derived global estimate. Modeled coccolithophore biomass is highest in February and March in a latitudinal band between 40-55S, when diatoms become heavily silicate limited. This region is characterized by a number of divergent fronts with a low Si:Fe ratio of waters supplied to the mixed layer, supporting an increased growth of coccolithophores at the expense of diatoms. We find top down controls to be the major control on the relative abundance of diatoms and coccolithophores in the Southern Ocean. We perform iron and silicate fertilization experiments to assess the effects of changed nutrient availability on coccolithophore abundance in the GCB. We find that changes in nutrient stoichiometry significantly alter
aa06259810