Factors Affecting Interannual Movements of Snowy Plovers

We studied the interannual movements of 361 individually color-banded adult Snowy Plovers (Charadrius alexandrinus nivosus) at Great Salt Lake, Utah from 1990 to 1993. In northern Utah, Snowy Plovers nested in a dynamic environment; suitable breeding habitat declined by 50% at two study areas in four years. Male Snowy Plovers were more site faithful than females; 40% of males exhibited fidelity compared with 26% of females (P \u3c 0.01). However, as the amount of available suitable nesting habitat declined, male site fidelity diminished, whereas female fidelity remained relatively constant. We found strong evidence that female site fidelity was affected by nesting success in the previous year. Females that nested unsuccessfully were less likely than successful females to exhibit site fidelity the following year; males did not exhibit this nest-success bias. In addition, unsuccessful females breeding at sites with high densities of nests tended to disperse the following year, whereas male site fidelity did not appear to be affected by either a study site\u27s overall nesting success the previous year or a study site\u27s nest density the previous year. Female avoidance of areas with high densities of nests may be an antipredator strategy. Snowy Plovers in northern Utah have biparental incubation duties, but only males care for broods. Familiarity with brood-rearing areas was one plausible explanation for male-biased fidelity. However, we could not eliminate an alternative hypothesis that both focal study sites represented scarce breeding areas due to the presence of freshwater, and male Snowy Plovers preferred to use the same areas rather than disperse. We propose that more landscape-level studies are needed to address questions concerning local and regional movement patterns

A Landscape-Level Assessment of Whitebark Pine Regeneration in the Rocky Mountains, USA

Whitebark pine (Pinus albicaulis Engelm.) has recently experienced high mortality due to multiple stressors, and future population viability may rely on natural regeneration. We assessed whitebark pine seedling densities throughout the US Rocky Mountains and identified stand, site, and climatic variables related to seedling presence based on data from 1,217 USDA Forest Service Forest Inventory and Analysis plots. Although mean densities were highest in the whitebark pine forest type, 83% of sites with seedlings present occurred in non-whitebark pine forest types, and the highest densities occurred in the lodgepole pine forest type. To identify factors related to whitebark pine seedling presence, we compared the results generated from three statistical models: logistic regression, classification tree, and random forests. All three models identified cover of grouse whortleberry (Vaccinium scoparium Leiberg ex Coville) as an important predictor, two models distinguished live and dead whitebark pine basal area and elevation, and one model recognized seasonal temperature. None of the models identified forest type as an important predictor. Understanding these factors may help managers identify areas where natural regeneration of whitebark pine is likely to occur, including sites in non-whitebark pine forest types

Development and Validation of Spatially Explicit Habitat Models for Cavity-nesting Birds in Fishlake National Forest, Utah

The ability of USDA Forest Service Forest Inventory and analysis (FIA) generated spatial products to increase the predictive accuracy of spatially explicit, macroscale habitat models was examined for nest-site selection by cavity-nesting birds in Fishlake National Forest, Utah. One FIA-derived variable (percent basal area of aspen trees) was significant in the habitat model; however, the incorporation of FIA stand structure information did not increase model accuracy. Cavity-nesting birds respond strongly to nest-tree attributes unable to be modeled spatially for this study. Future modeling efforts should focus on larger taxa (e.g., ungulates) and richness/diversity studies

Gap analysis: a geographic approach for assessing national biological diversity

The global concern with reduction in biodiversity has generated responses in the United States, such as the Endangered Species Act (ESA). Although the ESA has had some effect, the species-by-species approach presents a problem because it does not consider the broad ecological principles of biodiversity including the need for balance between different species and their combined influence on a given habitat. There is an implicit assumption that national parks, wildlife sanctuaries, and other protected areas provide for conservation needs. However, these areas have not necessarily been delineated on the basis of animal habitat zones or ecologically significant units. Gap Analysis is an evaluation method providing a systematic approach for assessing the protection afforded biodiversity in a given area. It uses geographic information systems to identify gaps in biodiversity protection that may be filled by the establishment of new preserves or changes in land-use practices. Gap Analysis has three primary layers: (1) distribution of vegetation types delineated from satellite imagery, (2) land ownership, and (3) distribution of vegetation types delineated from satellite imagery, habitat preference models. Vegetation classification procedures using satellite image or aerial photograph analysis are linked to wildlife/ habitat databases. Gap analysis includes seral as well as climax vegetation, and classes must be compatible with those used in neighboring states. The examples of these procedures for the Utah Gap Analysis are given with some reference to Gap Analysis in other states. The overall approach provides a logical base for evaluating and protecting national biological diversity

Rodent-Mediated Interactions Among Seed Species of Differing Quality in a Shrubsteppe Ecosystem

Interactions among seeds, mediated by granivorous rodents, are likely to play a strong role in shrubsteppe ecosystem restoration. Past studies typically consider only pairwise interactions between preferred and less preferred seed species, whereas rangeland seedings are likely to contain more than 2 seed species, potentially leading to complex interactions. We examined how the relative proportion of seeds in a 3-species polyculture changes rodent seed selectivity (i.e., removal) and indirect interactions among seeds. We presented 2 rodent species, Peromyscus maniculatus (deer mice) andPerognathus parvus (pocket mice), in arenas with 3-species seed mixtures that varied in the proportion of a highly preferred, moderately preferred, and least preferred seed species, based on preferences determined in this study. We then conducted a field experiment in a pocket mouse–dominated ecosystem with the same 3-species seed mixtures in both “treated” (reduced shrub and increased forb cover) and “untreated” shrubsteppe. In the arena experiment, we found that rodents removed more of the highly preferred seed when the proportions of all 3 seeds were equal. Moderately preferred seeds experienced increased removal when the least preferred seed was in highest proportion. Removal of the least preferred seed increased when the highly preferred seed was in highest proportion. In the field experiment, results were similar to those from the arena experiment and did not differ between treated and untreated shrubsteppe areas. Though our results suggest that 3-species mixtures induce complex interactions among seeds, managers applying these results to restoration efforts should carefully consider the rodent community present and the potential fate of removed seeds

Performance of Vegetative Filter Strips with Varying Pollutant Source and Filter Strip Lengths

Vegetative filter strips (VFS) can reduce runoff losses of pollutants such as nitrogen (N) and phosphorus (P) from land areas treated with fertilizers. While VFS effectiveness is considered to depend on lengths of pollutant source and VFS areas, there is little experimental evidence of this dependence, particularly when the pollutant source is manure-treated pasture. This study assessed the effects of pollutant source area (fescue pasture treated with poultry litter) length and VFS (fescue pasture) length on VFS removal of nitrate N (NO3-N), ammonia N (NH3-N), total Kjeldahl N (TKN), ortho-P (PO4-P), total P (TP), total organic carbon (TOC), total suspended solids (TSS), and fecal coliform (FC) from incoming runoff. This research examined poultry litter-treated lengths of 6.1, 12.2, and 18.3 m, with corresponding VFS lengths of up to 18.3 m, 12.2 m, and 6.1 m, respectively. Runoff was produced from simulated rainfall applied to both the litter-treated and VFS areas at 50 mm/h for 1 h of runoff. Pollutant concentrations in runoff were unaffected by litter-treated length but demonstrated a first-order exponential decline with increasing VFS length except for TSS and FC. Runoff mass transport of NH3-N,TKN, PO4-P, TP and TOC increased with increasing litter-treated length (due to increased runoff) and decreased (approximately first-order exponential decline) with increasing VFS length when affected by VFS length. Effectiveness of the VFS in terms of NH3-N, TKN, PO4-P, TP and TOC removal from runoff ranged from 12-75, 22-67, 22-82, 21-66, and 8-30% respectively. The data from this study can help in developing and testing models that simulate VFS performance and thus aid in the design of VFS installed downslope of pasture areas treated with animal manure

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models.Random forests (RF) using covariates selected via recursive feature elimination was consistently most accurate, or was among the most accurate, classifiers sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. 43 Individual subgroup class accuracy was generally dependent upon the number of soil pedon 44 observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil-landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area

Dual theory of the superfluid-Bose glass transition in disordered Bose-Hubbard model in one and two dimensions

I study the zero temperature phase transition between superfluid and insulating ground states of the Bose-Hubbard model in a random chemical potential and at large integer average number of particles per site. Duality transformation maps the pure Bose-Hubbard model onto the sine-Gordon theory in one dimension (1D), and onto the three dimensional Higgs electrodynamics in two dimensions (2D). In 1D the random chemical potential in dual theory couples to the space derivative of the dual field, and appears as a random magnetic field along the imaginary time direction in 2D. I show that the transition from the superfluid state in both 1D and 2D is always controlled by the random critical point. This arises due to a coupling constant in the dual theory with replicas which becomes generated at large distances by the random chemical potential, and represents a relevant perturbation at the pure superfluid-Mott insulator fixed point. At large distances the dual theory in 1D becomes equivalent to the Haldane's macroscopic representation of disordered quantum fluid, where the generated term is identified with random backscattering. In 2D the generated coupling corresponds to the random mass of the complex field which represents vortex loops. I calculate the critical exponents at the superfluid-Bose glass fixed point in 2D to be \nu=1.38 and z=1.93, and the universal conductivity at the transition \sigma_c = 0.26 e_{*}^2 /h, using the one-loop field-theoretic renormalization group in fixed dimension.Comment: 25 pages, 6 Postscript figures, LaTex, references updated, typos corrected, final version to appear in Phys. Rev. B, June 1, 199