Dispersion by wind of CO2 leaking from underground storage: Comparison of analytical solution with simulation

The concentration of CO₂ in air near the ground needs to be predicted to assess environmental and health risks from leaking underground storage. There is an exact solution to the advection–diffusion equation describing trace gases carried by wind when the wind profile is modeled with a power-law dependence on height. The analytical solution is compared with a numerical simulation of the coupled air–ground system with a source of CO₂ underground at the water table. The two methods produce similar results far from the boundaries, but the boundary conditions have a strong effect; the simulation imposes boundary conditions at the edge of a finite domain while the analytic solution imposes them at infinity. The reverse seepage from air to ground is shown in the simulation to be very small, and the large difference between time scales suggests that air and ground can be modeled separately, with gas emissions from the ground model used as inputs to the air model.     

Model selection criteria for overdispersed data and their application to the characterization of a host-parasite relationship

In the statistical modeling of a biological or ecological phenomenon, selecting an optimal model among a collection of candidates is a critical issue. To identify an optimal candidate model, a number of model selection criteria have been developed and investigated based on estimating Kullback’s (Information theory and statistics. Dover, Mineola, 1968) directed or symmetric divergence. Criteria that target the directed divergence include the Akaike (2nd international symposium on information theory. Akadémia Kiadó, Budapest, Hungary, pp 267–281, 1973, IEEE Trans Autom Control AC 19:716–723, 1974) information criterion, AIC, and the “corrected” Akaike information criterion (Hurvich and Tsai in Biometrika 76:297–307, 1989), AICc; criteria that target the symmetric divergence include the Kullback information criterion, KIC, and the “corrected” Kullback information criterion, KICc (Cavanaugh in Stat Probab Lett 42:333–343, 1999; Aust N Z J Stat 46:257–274, 2004). For overdispersed count data, simple modifications of AIC and AICc have been increasingly utilized: specifically, the quasi Akaike information criterion, QAIC, and its corrected version, QAICc (Lebreton et al. in Ecol Monogr 62(1):67–118 1992). In this paper, we propose analogues of QAIC and QAICc based on estimating the symmetric as opposed to the directed divergence: QKIC and QKICc. We evaluate the selection performance of AIC, AICc, QAIC, QAICc, KIC, KICc, QKIC, and QKICc in a simulation study, and illustrate their practical utility in an ecological application. In our application, we use the criteria to formulate statistical models of the tick (Dermacentor variabilis) load on a white-footed mouse (Peromyscus leucopus) in northern Missouri.     

Population biology of White-crowned Sparrows: Residence time and local movements of juveniles

Summary A field study of the ranging behavior of post-fledging White-crowned Sparrows (Zonotrichia leucophrys nuttalli) was carried out in undisturbed natural habitat in the Point Reyes National Seashore, Marin Co., California, USA. Forty-three first-brood juveniles, with tail feathers still growing, were banded at an estimated average age of 27 days and re-trapped an average of an additional 24 days for a total minimum time in residence in the immediate natal area of 51 days (Table 1).The distance from the site of first capture to subsequent recapture sites was about 60 m until age 35 days; then the distance increased to about 250 m by age 50 days (Fig. 2). These data indicate that the average juvenile White-crowned Sparrow completes at least the first 50 days of life in its natal area. Experimental work on song ontogeny has previously demonstrated that the sensitive period for song learning ends at about 50 days. Hence, young in our study population probably learn their natal dialect prior to any major dispersal movements. The correspondence between residence time and song learning would have the consequence of perpetuating song dialects as geographically structured populations.     

The effect of summer drought on the predictability of local extinctions in a butterfly metapopulation

The ecological impacts of extreme climatic events on population dynamics and community composition are profound and predominantly negative. Using extensive data of an ecological model system, we tested whether predictions from ecological models remain robust when environmental conditions are outside the bounds of observation. We observed a 10-fold demographic decline of the Glanville fritillary butterfly (Melitaea cinxia) metapopulation on the Åland islands, Finland in the summer of 2018 and used climatic and satellite data to demonstrate that this year was an anomaly with low climatic water balance values and low vegetation productivity indices across Åland. Population growth rates were strongly associated with spatiotemporal variation in climatic water balance. Covariates shown previously to affect the extinction probability of local populations in this metapopulation were less informative when populations were exposed to severe drought during the summer months. Our results highlight the unpredictable responses of natural populations to extreme climatic events.     

Planning routes around the world: International evidence for southern route preferences

Three studies test whether the southern route preference, which describes the tendency for route planners to disproportionately select south- rather than north-going routes, can be attributed to regional elevation patterns; specifically, we ask whether this effect replicates in three topographically disparate international regions, one of which is characterized by higher elevations to the north and lower to the south (Padua, Italy), and two characterized by higher elevations to the south and lower to the north (Enschede, Netherlands; Sofia, Bulgaria). In all cases, we found strong evidence that route planners disproportionately select south- rather than north-going routes at rates exceeding chance. We conclude that the southern route preference is driven by strong associations between canonical direction and perceived effort of route traversal; these effects are somewhat perplexing given that such associations are not founded in the reality of physical space.     

Emission reductions from woody biomass waste for energy as an alternative to open burning

Woody biomass waste is generated throughout California from forest management, hazardous fuel reduction, and agricultural operations. Open pile burning in the vicinity of generation is frequently the only economic disposal option. A framework is developed to quantify air emissions reductions for projects that alternatively utilize biomass waste as fuel for energy production. A demonstration project was conducted involving the grinding and 97-km one-way transport of 6096 bone-dry metric tons (BDT) of mixed conifer forest slash in the Sierra Nevada foothills for use as fuel in a biomass power cogeneration facility. Compared with the traditional open pile burning method of disposal for the forest harvest slash, utilization of the slash for fuel reduced particulate matter (PM) emissions by 98% (6 kg PM/BDT biomass), nitrogen oxides (NOx) by 54% (1.6 kg NOx/BDT), nonmethane volatile organics (NMOCs) by 99% (4.7 kg NMOCs/BDT), carbon monoxide (CO) by 97% (58 kg CO/BDT), and carbon dioxide equivalents (CO2e) by 17% (0.38 t CO2e/BDT). Emission contributions from biomass processing and transport operations are negligible. CO2e benefits are dependent on the emission characteristics of the displaced marginal electricity supply. Monetization of emissions reductions will assist with fuel sourcing activities and the conduct of biomass energy projects.     

An Analysis of Water Data Systems to Inform the Open Water Data Initiative

Improving access to data and fostering open exchange of water information is foundational to solving water resources issues. In this vein, the Department of the Interior's Assistant Secretary for Water and Science put forward the charge to undertake an Open Water Data Initiative (OWDI) that would prioritize and accelerate work toward better water data infrastructure. The goal of the OWDI is to build out the Open Water Web (OWW). We therefore considered the OWW in terms of four conceptual functions: water data cataloging, water data as a service, enriching water data, and community for water data. To describe the current state of the OWW and identify areas needing improvement, we conducted an analysis of existing systems using a standard model for describing distributed systems and their business requirements. Our analysis considered three OWDI‐focused use cases—flooding, drought, and contaminant transport—and then examined the landscape of other existing applications that support the Open Water Web. The analysis, which includes a discussion of observed successful practices of cataloging, serving, enriching, and building community around water resources data, demonstrates that we have made significant progress toward the needed infrastructure, although challenges remain. The further development of the OWW can be greatly informed by the interpretation and findings of our analysis.     

Optimizing data usability in the remedial process

Large quantities of data are collected and evaluated throughout every stage of the remedial process. The usability of these data is often a function of the perceived “quality” of the data, with good data considered usable and bad data considered unusable. The assessment of data quality and usability has traditionally focused on analytical and other direct measurement errors and uncertainty. However, problems with the data that are measurement-related are usually a relatively minor portion of the total error and uncertainty. Error and uncertainty are introduced throughout every aspect of any remedial process, including planning errors, measurement errors, and interpretation errors. Although each error component is important, the errors and uncertainty associated with the design, collection, and interpretation of data are often much greater than measurement-related errors. Nevertheless, there is typically a disproportionate level of effort expended addressing the minor types of measurement errors when compared to other more important error components in the remedial process. However, the key to obtaining optimum data use requires the general redirection of Data Quality Assurance (DQA) activities from measurement-related errors/uncertainty to other important planning and interpretation elements. The elements considered essential for developing an effective approach for maximizing data usability include: focused project planning activities stressing a rigorous data quality objective process and a geostatistical approach to the design of the data collection program; the development of sensible and appropriate data validation/review objectives; and the development of realistic error and uncertainty limits for measurement/analytical activities.