Nonbreeding-Season Drivers of Population Dynamics in Seasonal Migrants: Conservation Parallels Across Taxa

For seasonal migrants, logistical constraints have often limited conservation efforts to improving survival and reproduction during the breeding season only. Yet, mounting empirical evidence suggests that events occurring throughout the migratory life cycle can critically alter the demography of many migrant species. Herein, we build upon recent syntheses of avian migration research to review the role of non-breeding seasons in determining the population dynamics and fitness of diverse migratory taxa, including salmonid fishes, marine mammals, ungulates, sea turtles, butterflies, and numerous bird groups. We discuss several similarities across these varied migrants: (i) non-breeding survivorship tends to be a strong driver of population growth; (ii) non-breeding events can affect fitness in subsequent seasons through seasonal interactions at individual- and population-levels; (iii) broad-scale climatic influences often alter non-breeding resources and migration timing, and may amplify population impacts through covariation among seasonal vital rates; and (iv) changes to both stationary and migratory non-breeding habitats can have important consequences for abundance and population trends. Finally, we draw on these patterns to recommend that future conservation research for seasonal migrants will benefit from: (1) more explicit recognition of the important parallels among taxonomically diverse migratory animals; (2) an expanded research perspective focused on quantification of all seasonal vital rates and their interactions; and (3) the development of detailed population projection models that account for complexity and uncertainty in migrant population dynamics.

An Estimate of Nest Loss in Canada Due to Industrial Forestry Operations

Annual loss of nests by industrial (nonwoodlot) forest harvesting in Canada was estimated using two avian point-count data sources: (1) the Boreal Avian Monitoring Project (BAM) dataset for provinces operating in this biome and (2) available data summarized for the major (nonboreal) forest regions of British Columbia. Accounting for uncertainty in the proportion of harvest occurring during the breeding season and in avian nesting densities, our estimate ranges from 616 thousand to 2.09 million nests. Estimates of the impact on numbers of individuals recruited into the adult breeding population were made based on the application of survivorship estimates at various stages of the life cycle. Future improvements to this estimate are expected as better and more extensive avian breeding pair density estimates become available and as provincial forestry statistics become more refined, spatially and temporally. The effect of incidental take due to forestry is not uniform and is disproportionately centered in the southern boreal. Those species whose ranges occur primarily in these regions are most at risk for industrial forestry in general and for incidental take in particular. Refinements to the nest loss estimate for industrial forestry in Canada will be achieved primarily through the provision of more accurate estimates of the area of forest harvested annually during the breeding season stratified by forest type and Bird Conservation Region (BCR). A better understanding of survivorship among life-history stages for forest birds would also allow for better modeling of the effect of nest loss on adult recruitment. Finally, models are needed to project legacy effects of forest harvesting on avian populations that take into account forest succession and accompanying cumulative effects of landscape change.

Dynamics of convectively driven banded jets in the laboratory

The banded organization of clouds and zonal winds in the atmospheres of the outer planets has long fascinated observers. Several recent studies in the theory and idealized modeling of geostrophic turbulence have suggested possible explanations for the emergence of such organized patterns, typically involving highly anisotropic exchanges of kinetic energy and vorticity within the dissipationless inertial ranges of turbulent flows dominated (at least at large scales) by ensembles of propagating Rossby waves. The results from an attempt to reproduce such conditions in the laboratory are presented here. Achievement of a distinct inertial range turns out to require an experiment on the largest feasible scale. Deep, rotating convection on small horizontal scales was induced by gently and continuously spraying dense, salty water onto the free surface of the 13-m-diameter cylindrical tank on the Coriolis platform in Grenoble, France. A “planetary vorticity gradient” or “β effect” was obtained by use of a conically sloping bottom and the whole tank rotated at angular speeds up to 0.15 rad s−1. Over a period of several hours, a highly barotropic, zonally banded large-scale flow pattern was seen to emerge with up to 5–6 narrow, alternating, zonally aligned jets across the tank, indicating the development of an anisotropic field of geostrophic turbulence. Using particle image velocimetry (PIV) techniques, zonal jets are shown to have arisen from nonlinear interactions between barotropic eddies on a scale comparable to either a Rhines or “frictional” wavelength, which scales roughly as (β/Urms)−1/2. This resulted in an anisotropic kinetic energy spectrum with a significantly steeper slope with wavenumber k for the zonal flow than for the nonzonal eddies, which largely follows the classical Kolmogorov k−5/3 inertial range. Potential vorticity fields show evidence of Rossby wave breaking and the presence of a “hyperstaircase” with radius, indicating instantaneous flows that are supercritical with respect to the Rayleigh–Kuo instability criterion and in a state of “barotropic adjustment.” The implications of these results are discussed in light of zonal jets observed in planetary atmospheres and, most recently, in the terrestrial oceans.

The effect of doubled CO2 and model basic state biases on the monsoon-ENSO system. II: Changing ENSO regimes

Integrations of a fully-coupled climate model with and without flux adjustments in the equatorial oceans are performed under 2×CO2 conditions to explore in more detail the impact of increased greenhouse gas forcing on the monsoon-ENSO system. When flux adjustments are used to correct some systematic model biases, ENSO behaviour in the modelled future climate features distinct irregular and periodic (biennial) regimes. Comparison with the observed record yields some consistency with ENSO modes primarily based on air-sea interaction and those dependent on basinwide ocean wave dynamics. Simple theory is also used to draw analogies between the regimes and irregular (stochastically forced) and self-excited oscillations respectively. Periodic behaviour is also found in the Asian-Australian monsoon system, part of an overall biennial tendency of the model under these conditions related to strong monsoon forcing and increased coupling between the Indian and Pacific Oceans. The tropospheric biennial oscillation (TBO) thus serves as a useful descriptor for the coupled monsoon-ENSO system in this case. The presence of obvious regime changes in the monsoon-ENSO system on interdecadal timescales, when using flux adjustments, suggests there may be greater uncertainty in projections of future climate, although further modelling studies are required to confirm the realism and cause of such changes.

Uncertainty assessment of a process-based integrated catchment model of phosphorus

Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.

Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density

Models developed to identify the rates and origins of nutrient export from land to stream require an accurate assessment of the nutrient load present in the water body in order to calibrate model parameters and structure. These data are rarely available at a representative scale and in an appropriate chemical form except in research catchments. Observational errors associated with nutrient load estimates based on these data lead to a high degree of uncertainty in modelling and nutrient budgeting studies. Here, daily paired instantaneous P and flow data for 17 UK research catchments covering a total of 39 water years (WY) have been used to explore the nature and extent of the observational error associated with nutrient flux estimates based on partial fractions and infrequent sampling. The daily records were artificially decimated to create 7 stratified sampling records, 7 weekly records, and 30 monthly records from each WY and catchment. These were used to evaluate the impact of sampling frequency on load estimate uncertainty. The analysis underlines the high uncertainty of load estimates based on monthly data and individual P fractions rather than total P. Catchments with a high baseflow index and/or low population density were found to return a lower RMSE on load estimates when sampled infrequently than those with a tow baseflow index and high population density. Catchment size was not shown to be important, though a limitation of this study is that daily records may fail to capture the full range of P export behaviour in smaller catchments with flashy hydrographs, leading to an underestimate of uncertainty in Load estimates for such catchments. Further analysis of sub-daily records is needed to investigate this fully. Here, recommendations are given on load estimation methodologies for different catchment types sampled at different frequencies, and the ways in which this analysis can be used to identify observational error and uncertainty for model calibration and nutrient budgeting studies. (c) 2006 Elsevier B.V. All rights reserved.

Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK

An integrated approach to climate change impact assessment is explored by linking established models of regional climate (SDSM), water resources (CATCHMOD) and water quality (INCA) within a single framework. A case study of the River Kennet illustrates how the system can be used to investigate aspects of climate change uncertainty, deployable water resources, and water quality dynamics in upper and lower reaches of the drainage network. The results confirm the large uncertainty in climate change scenarios and freshwater impacts due to the choice of general circulation model (GCM). This uncertainty is shown to be greatest during summer months as evidenced by large variations between GCM-derived projections of future tow river flows, deployable yield from groundwater, severity of nutrient flushing episodes, and Long-term trends in surface water quality. Other impacts arising from agricultural land-use reform or delivery of EU Water Framework Directive objectives under climate change could be evaluated using the same framework. (c) 2006 Elsevier B.V. All rights reserved.

Sampling uncertainty properties of cloud fraction estimates from random transect observations

[1] Cloud cover is conventionally estimated from satellite images as the observed fraction of cloudy pixels. Active instruments such as radar and Lidar observe in narrow transects that sample only a small percentage of the area over which the cloud fraction is estimated. As a consequence, the fraction estimate has an associated sampling uncertainty, which usually remains unspecified. This paper extends a Bayesian method of cloud fraction estimation, which also provides an analytical estimate of the sampling error. This method is applied to test the sensitivity of this error to sampling characteristics, such as the number of observed transects and the variability of the underlying cloud field. The dependence of the uncertainty on these characteristics is investigated using synthetic data simulated to have properties closely resembling observations of the spaceborne Lidar NASA-LITE mission. Results suggest that the variance of the cloud fraction is greatest for medium cloud cover and least when conditions are mostly cloudy or clear. However, there is a bias in the estimation, which is greatest around 25% and 75% cloud cover. The sampling uncertainty is also affected by the mean lengths of clouds and of clear intervals; shorter lengths decrease uncertainty, primarily because there are more cloud observations in a transect of a given length. Uncertainty also falls with increasing number of transects. Therefore a sampling strategy aimed at minimizing the uncertainty in transect derived cloud fraction will have to take into account both the cloud and clear sky length distributions as well as the cloud fraction of the observed field. These conclusions have implications for the design of future satellite missions. This paper describes the first integrated methodology for the analytical assessment of sampling uncertainty in cloud fraction observations from forthcoming spaceborne radar and Lidar missions such as NASA's Calipso and CloudSat.

When smoke gets in our eyes: The multiple impacts of atmospheric black carbon on climate, air quality and health

With both climate change and air quality on political and social agendas from local to global scale, the links between these hitherto separate fields are becoming more apparent. Black carbon, largely from combustion processes, scatters and absorbs incoming solar radiation, contributes to poor air quality and induces respiratory and cardiovascular problems. Uncertainties in the amount, location, size and shape of atmospheric black carbon cause large uncertainty in both climate change estimates and toxicology studies alike. Increased research has led to new effects and areas of uncertainty being uncovered. Here we draw together recent results and explore the increasing opportunities for synergistic research that will lead to improved confidence in the impact of black carbon on climate change, air quality and human health. Topics of mutual interest include better information on spatial distribution, size, mixing state and measuring and monitoring. (c) 2006 Elsevier Ltd. All rights reserved.

An event-based approach to validating solar wind speed predictions: high-speed enhancements in the Wang-Sheeley-Arge model

One of the primary goals of the Center for Integrated Space Weather Modeling (CISM) effort is to assess and improve prediction of the solar wind conditions in near‐Earth space, arising from both quasi‐steady and transient structures. We compare 8 years of L1 in situ observations to predictions of the solar wind speed made by the Wang‐Sheeley‐Arge (WSA) empirical model. The mean‐square error (MSE) between the observed and model predictions is used to reach a number of useful conclusions: there is no systematic lag in the WSA predictions, the MSE is found to be highest at solar minimum and lowest during the rise to solar maximum, and the optimal lead time for 1 AU solar wind speed predictions is found to be 3 days. However, MSE is shown to frequently be an inadequate “figure of merit” for assessing solar wind speed predictions. A complementary, event‐based analysis technique is developed in which high‐speed enhancements (HSEs) are systematically selected and associated from observed and model time series. WSA model is validated using comparisons of the number of hit, missed, and false HSEs, along with the timing and speed magnitude errors between the forecasted and observed events. Morphological differences between the different HSE populations are investigated to aid interpretation of the results and improvements to the model. Finally, by defining discrete events in the time series, model predictions from above and below the ecliptic plane can be used to estimate an uncertainty in the predicted HSE arrival times.

Changes in the global sulfate burden due to perturbations in global CO2 concentrations

A large ensemble of general circulation model (GCM) integrations coupled to a fully interactive sulfur cycle scheme were run on the climateprediction.net platform to investigate the uncertainty in the climate response to sulfate aerosol and carbon dioxide (CO2) forcing. The sulfate burden within the model (and the atmosphere) depends on the balance between formation processes and deposition (wet and dry). The wet removal processes for sulfate aerosol are much faster than dry removal and so any changes in atmospheric circulation, cloud cover, and precipitation will feed back on the sulfate burden. When CO2 is doubled in the Hadley Centre Slab Ocean Model (HadSM3), global mean precipitation increased by 5%; however, the global mean sulfate burden increased by 10%. Despite the global mean increase in precipitation, there were large areas of the model showing decreases in precipitation (and cloud cover) in the Northern Hemisphere during June–August, which reduced wet deposition and allowed the sulfate burden to increase. Further experiments were also undertaken with and without doubling CO2 while including a future anthropogenic sulfur emissions scenario. Doubling CO2 further enhanced the increases in sulfate burden associated with increased anthropogenic sulfur emissions as observed in the doubled CO2-only experiment. The implications are that the climate response to doubling CO2 can influence the amount of sulfate within the atmosphere and, despite increases in global mean precipitation, may act to increase it.

Global scale water stages from SAR imagery (GlosSARi) to support global flood forecasting

Despite the success of studies attempting to integrate remotely sensed data and flood modelling and the need to provide near-real time data routinely on a global scale as well as setting up online data archives, there is to date a lack of spatially and temporally distributed hydraulic parameters to support ongoing efforts in modelling. Therefore, the objective of this project is to provide a global evaluation and benchmark data set of floodplain water stages with uncertainties and assimilation in a large scale flood model using space-borne radar imagery. An algorithm is developed for automated retrieval of water stages with uncertainties from a sequence of radar imagery and data are assimilated in a flood model using the Tewkesbury 2007 flood event as a feasibility study. The retrieval method that we employ is based on possibility theory which is an extension of fuzzy sets and that encompasses probability theory. In our case we first attempt to identify main sources of uncertainty in the retrieval of water stages from radar imagery for which we define physically meaningful ranges of parameter values. Possibilities of values are then computed for each parameter using a triangular ‘membership’ function. This procedure allows the computation of possible values of water stages at maximum flood extents along a river at many different locations. At a later stage in the project these data are then used in assimilation, calibration or validation of a flood model. The application is subsequently extended to a global scale using wide swath radar imagery and a simple global flood forecasting model thereby providing improved river discharge estimates to update the latter.

Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid.

Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.

Comparative advantage in demand: Experimental evidence of preferences for genetically modified food in the United States and European Union

The United States (US) exports more than US$6 billion in agricultural commodities to the European Union (EU) each year, but one issue carries the potential to diminish this trade: use of biotechnology in food production. The EU has adopted more stringent policies towards biotechnology than the US. Understanding differences in European and American policies towards genetically modified (GM) foods requires a greater understanding of consumers' attitudes and preferences. This paper reports results from the first large-scale, cross-Atlantic study to analyse consumer demand for genetically modified food in a non-hypothetical market environment. We strongly reject the frequent if convenient assumption in trade theory that consumer preferences are identical across countries: the median level of compensation demanded by English and French consumers to consume a GM food is found to be more than twice that in any of the US locations. Results have important implications for trade theory, which typically focusses on differences in specialization, comparative advantage and factor endowments across countries, and for on-going trade disputes at the World Trade Organization.

Trophic interactions in a changing world: modelling aboveground-belowground interactions

The rate and scale of human-driven changes can exert profound impacts on ecosystems, the species that make them up and the services they provide that sustain humanity. Given the speed at which these changes are occurring, one of society's major challenges is to coexist within ecosystems and to manage ecosystem services in a sustainable way. The effect of possible scenarios of global change on ecosystem services can be explored using ecosystem models. Such models should adequately represent ecosystem processes above and below the soil surface (aboveground and belowground) and the interactions between them. We explore possibilities to include such interactions into ecosystem models at scales that range from global to local. At the regional to global scale we suggest to expand the plant functional type concept (aggregating plants into groups according to their physiological attributes) to include functional types of aboveground-belowground interactions. At the scale of discrete plant communities, process-based and organism-oriented models could be combined into "hybrid approaches" that include organism-oriented mechanistic representation of a limited number of trophic interactions in an otherwise process - oriented approach. Under global change the density and activity of organisms determining the processes may change non-linearly and therefore explicit knowledge of the organisms and their responses should ideally be included. At the individual plant scale a common organism-based conceptual model of aboveground-belowground interactions has emerged. This conceptual model facilitates the formulation of research questions to guide experiments aiming to identify patterns that are common within, but differ between, ecosystem types and biomes. Such experiments inform modelling approaches at larger scales. Future ecosystem models should better include this evolving knowledge of common patterns of aboveground-belowground interactions. Improved ecosystem models are necessary toots to reduce the uncertainty in the information that assists us in the sustainable management of our environment in a changing world. (C) 2004 Elsevier GmbH. All rights reserved.