Bias and variance in model results associated with spatial scaling of measurements for parameterization in regional assessments

Models are central to global change analyses, but they are often parameterized using data that represent only a portion of heterogeneity in a region. This creates uncertainty in the results and constrains the reliability of model inferences. Our objective was to evaluate the uncertainty associated with differential scaling of parameterization data to model soil organic carbon stock changes as a function of US agricultural land use and management. Specifically, we compared analyses in which model parameters were derived from field experimental data that were scaled to the entire US vs. the same data scaled to climate regions within the country. We evaluated the effect of differential scaling on both bias and variance in model results. Model results had less variance by scaling data to the entire country because of a larger sample size for deriving individual parameter values, although there was a relatively large bias associated with this parameterization, estimated at 2.7 Tg C yr^?1. Even with the large bias, resulting confidence intervals from the two parameterizations had considerable overlap for the estimated national rate of SOC change (i.e. 77% overlap in those intervals). Consequently, the results were relatively similar when focusing on the uncertainty rather than solely on the mean estimate. In contrast, large biases created less overlap in confidence intervals for the change rates within individual climate regions, compared with the national estimates. For example, the overlap in resulting intervals from the two parameterizations was only 32% for the warm temperate moist region, with a corresponding bias of 3.1 Tg C yr^?1. These findings demonstrate that there is a greater risk of making erroneous inferences because of large biases if models are parameterized with broader scale information, such as an entire country, and then used to address impacts at a finer spatial scale, such as sub‐regions within a country. In addition, the study demonstrates a trade‐off between variance and bias in model results that depends on the scaling of data for model parameterization.     

Microbial community structure and global trace gases

Global change can affect soil processes by either altering the functioning of existing organisms or by restructuring the community, modifying the fundamental physiologies that drive biogeochemical processes. Thus, not only might process rates change, but the controls over them might also change. Moreover, previously insignificant processes could become important. These possibilities raise the question ‘Will changes in climate and land use restructure microbial communities in a way that will alter trace gas fluxes from an ecosystem?’ Process studies indicate that microbial community structure can influence trace gas dynamics at a large scale. For example, soil respiration and CH₄ production both show ranges of temperature response among ecosystems, indicating differences in the microbial communities responsible. There are three patterns of NH₄⁺ inhibition of CH₄ oxidation at the ecosystem scale: no inhibition, immediate inhibition, and delayed inhibition; these are associated with different CH₄ oxidizer communities. Thus, it is possible that changes in climate, land-use, and disturbance regimes could alter microbial communities in ways that would substantially alter trace gas fluxes; we discuss the data supporting this conclusion. We also discuss approaches to developing research linking microbial community structure and activity to the structure and functioning of the whole ecosystem. Modern techniques allow us to identify active organisms even if they have not been cultivated; in combination with traditional experimental approaches we should be able to identify the linkages between these active populations and the processes they carry out at the ecosystem level. Finally, we describe scenarios of how global change could alter trace gas fluxes by altering microbial communities and how understanding the microbial community dynamics could improve our ability to predict future trace gas fluxes.     

History as a cause of area effects: an illustration from Cerion on Great Inagua, Bahamas

The two parts of this paper work towards the common aim of setting contexts for and documenting explanations based on historical contingencies. The first part is a review of area effects in Cepaea. We discuss the original definitions and explanations, emphasizing the debate of adaptationist vsstochastic approaches, but arguing that the contrast of historical contingency vs. selective fit to environment forms a more fruitful and fundamental context in discussing the origin of area effects. We argue that contingencies of bottlenecks and opening of formerly unsuited habitats may explain the classic area effects of Cepaea better than selectionist accounts originally proposed. The second part is a documentation of an area effect within Cerion columnaon the northern coast of Great Inagua, Bahamas. Historical explanations are often plagued by insufficiency of preserved information, but the Inagua example provides an unusual density of data, with several independent criteria all pointing to the same conclusion. Shells in the area effect are squat and flat-topped in contrast with typical populations of long, thin, tapering shells living both east and west of the area effect. The flat-topped area effect is a result of introgression with a propagule of the C. dimidiatum stock (living on nearby Cuba, and most apically flattened of all Cerion). Fossils of this propagule were found fully cemented into highly indurated fossil soil crusts within the region of the current area effect. Multivariate morphometry, based on complex patterns of covariation, not just intermediacy in single characters, identifies the area effect samples as hybrids between this propagule and typical C. columna. Genetic analysis has identified three unexpected alleles in area effect samples only, and in no other snails of any other Cerion taxon anywhere else on Inagua. We hypothesize that the flattopped area effect did not arise as a selective response to local environments within C. columna, but by introgression from a fortuitously introduced propagule of the C. dimidiaium complex. The unexpected alleles therefore represent genetic phantoms of C. dimidiatum's former presence or are hybrizymes—novel alleles produced by interspecific hybridization     

Factors Affecting Nest Survival of Greater Sage-Grouse in Northcentral Montana

Abstract: We studied greater sage-grouse (Centrocercus urophasianus) in northcentral Montana, USA, to examine the relationship between nest success and habitat conditions, environmental variables, and female sage-grouse characteristics. During 2001-2003, we radiomarked 243 female greater sage-grouse, monitored 287 nests, and measured 426 vegetation plots at 4 sites in a 3,200-km² landscape. Nest survival varied with year, grass canopy cover, daily precipitation with a 1-day lag effect, and nesting attempt. In all years, daily survival rate increased on the day of a rain event and decreased the next day. There was temporal variation in nest success both within and among years: success of early (first 28 d of nesting season) nests ranged from 0.238 (SE = 0.080) in 2001 to 0.316 (SE = 0.055) in 2003, whereas survival of late (last 28 d of nesting season) nests ranged from 0.276 (SE = 0.090) in 2001 to 0.418 (SE = 0.055) in 2003. Renests experienced higher survival than first nests. Grass cover was the only important model term that could be managed, but direction and magnitude of the grass effect varied. Site, shrub and forb canopy cover, and Robel pole reading were less useful predictors of nest success; however, temporal and spatial variation in these habitat covariates was low during our study. We note a marked difference between both values and interpretations of apparent nest success, which have been used almost exclusively in the past, and maximum-likelihood estimates used in our study. Annual apparent nest success (0.46) was, on average, 53% higher than maximum-likelihood estimates that incorporate individual, environmental, and habitat covariates. The difference between estimates was variable (range = +8% to +91%). Management of habitats for nesting sage-grouse should focus on increasing grass cover to increase survival of first nests and contribute to favorable conditions for renesting, which should be less likely if survival of first nests increases.