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For species that inhabit environments where resource availability may be unpredictable, balance of resource allocation to life‐history traits can have heightened consequences for survival, reproduction, and ultimately, fitness. Acquisition and allocation of energy to maintenance, capital gain and reproduction should be in tune with the landscape an animal inhabits—environmental severity, food availability and population size all influence the resources animals have and dictate the ways they should be allocated.In seasonal environments, animals that experience periods of extreme resource limitation (e.g. harsh winters) may favour allocation of resources to body reserves to secure their survival at the cost of reproduction (i.e. risk averse). In contrast, the same accumulation of body reserves may not be necessary to survive in relatively benign landscapes where instead, allocation to reproduction is favoured (i.e. risk prone).According to the theory of risk‐sensitive allocation of resources, when animals are exposed to unprecedented or life‐threatening conditions, they may shift resource allocation to favour building capital over allocation in reproduction to preempt against encountering another life‐threatening event in the future.Using data from a long‐term project on a highly site‐faithful and long‐lived species, mule deer (Odocoileus hemionus), we evaluated how a life‐threatening winter and the associated changes in resource availability resulting from a population reduction influenced how animals acquired and allocated energy to survival (i.e. fat accumulation).Per capita precipitation, and the associated reduction in population abundance after the severe winter, had a positive influence of accrual of fat over summer. After the extreme physiological stress of a hard winter, deer starting spring with low body reserves accumulated 2.8 percentage points more fat over summer compared with before the experience of a bad winter and had an increased probability of recruiting fewer offspring. Fat stores can interact with environment, life history and behaviour to influence survival during periods of resource scarcity.For a long‐lived herbivore, we documented shifts in risk tolerance associated with fat accrual in preparation for winter, supporting the notion that risk‐sensitive allocation of resources may be plastic—an essential adaptation for animals to cope with rapidly changing landscapes. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Animals are inherently tied to nutritional resources of the landscape. Added cost of coping with environmental stressors, like disease, can exacerbate nutritional limitations. Pneumonia, a respiratory disease caused primarily by bacterial pathogens, has caused massive declines in populations of bighorn sheep (Ovis canadensis) throughout western North America. Nevertheless, population dynamics following a pneumonia epizootic vary across populations, which has spawned the idea that ecological conditions may play a role in regulating populations after a die‐off. We used 2 bighorn sheep populations with contrasting dynamics to test the hypothesis that habitat quality on summer range affects population dynamics following a pneumonia epizootic. We sampled over 700 vegetation transects and quantified nutrient content of 2,093 forage samples of 127 genera on summer ranges to compare habitat quality (macro‐ and micronutrients in forage, biomass, plant cover, and species diversity) between the ranges. The population exhibiting growth, higher recruitment, and better nutritional condition had over double the herbaceous biomass in their core foraging areas in summer than the population exhibiting decline, lower recruitment, and poorer nutritional status. The population experiencing growth also had more macro‐ and micronutrients available on their summer range as a function of higher biomass along with higher species diversity. Although winter range often is considered the nutritional bottleneck for ungulates, we demonstrate that the conditions of summer range can have consequences for population dynamics. Habitat quality should be incorporated when considering management objectives and population recovery of large herbivores in the presence of disease; interactions between habitat quality and population dynamics still apply and likely are amplified with additional stressors like disease. [ABSTRACT FROM AUTHOR]

Robust parameter design is a methodology that involves experimental design and strategic modeling for the purpose of simultaneously reducing system variability while achieving a target specification for the system mean. Modeling results obtained under unrealistic assumptions about the underlying data distribution may mislead practitioners when it comes to improving system quality. Using classical normal theory modeling techniques for right‐skewed experimental data in dual‐response modeling has some drawbacks, such as obtaining a misleading fit and estimated optimal operating conditions that are potentially far from the true optimal values. Moreover, using inappropriate modeling methods can create a chain of degradation in the production phase and subsequently result in poor system performance. This paper proposes a new dual‐response surface modeling approach when the quality characteristic of a system follows a gamma distribution. The procedure and its advantages are illustrated using the well‐known printing process experiment. [ABSTRACT FROM AUTHOR]

Land reclamation associated with natural gas development has become increasingly important to mitigate land surface disturbance in western North America. Since well pads occur on sites with multiple land use and ownership, the progress and outcomes of these efforts are of interest to multiple stakeholders including industry, practitioners and consultants, regulatory agents, private landowners, and the scientific community. Reclamation success criteria often vary within, and among, government agencies and across land ownership type. Typically, reclamation success of a well pad is judged by comparing vegetation cover from a single transect on the pad to a single transect in an adjacent reference site and data are collected by a large number of technicians with various field monitoring skills. We utilized "SamplePoint" image analysis software and a spatially balanced sampling design, called balanced acceptance sampling, to demonstrate how spatially explicit quantitative data can be used to determine if sites are meeting various reclamation success criteria and used chi‐square tests to show how sites in vegetation percent cover differ from a statistical standpoint. This method collects field data faster than traditional methods. We demonstrate how quantitative and spatially explicit data can be utilized by multiple stakeholders, how it can improve upon current reference site selection, how it can satisfy reclamation monitoring requirements for multiple regulatory agencies, how it may help improve future seed mix selection, and discuss how it may reduce costs for operations responsible for reclamation and how it may reduce observer bias. [ABSTRACT FROM AUTHOR]

The Ashtabula River covers an area that lies in northeast Ohio, USA, flowing into the central basin of Lake Erie at the city of Ashtabula where its drainage covers an area of 355 km2. In the mid‐1900s, several chemical production companies began operation along tributaries of the Ashtabula, and over time, discharges from these facilities left the lower Ashtabula River heavily contaminated with polychlorinated biphenyls (PCBs), heavy metals, and a suite of other agents. This pollution lead to the designation of the lower Ashtabula River as one of the most contaminated sites in the Great Lakes watershed, as defined in the 1987 Great Lakes Water Quality Agreement. This manuscript is a case study used to demonstrate the advantages of noninferiority testing over the classical hypothesis testing framework to demonstrate when one or more biological variables at the impacted site are no worse or inferior to the distribution of the same biological variable(s) at a reference site. In order to implement this approach, it is essential to define a tolerance level that represents a meaningful difference in parameter values. We demonstrate the use of Bayesian logistic regression for developing a data‐driven approach for tolerance level determination when consensus on the tolerance level value cannot be reached. [ABSTRACT FROM AUTHOR]

ABSTRACT The white-winged dove ( Zenaida asiatica) serves an important ecological role as a diurnal pollinator of the saguaro cactus in the Sonoran desert and an economic role as a highly sought after game bird in North America. White-winged doves are intimately linked to anthropogenic changes on the landscape and because of this, have experienced dramatic population fluctuations over the last 75 years in response, both positively and negatively, to anthropogenic changes on the landscape. To understand the factors driving population growth and decline of migratory species like the white-winged dove, it is imperative we study resource use on both their breeding and wintering grounds. To understand how populations are distributed on the wintering grounds, we tested an alternative to band recovery approaches by using stable isotope analysis. Before we could use isotope analysis to link breeding and wintering locations for this species, we first needed to determine if hydrogen (δ2H) and carbon (δ13C) stable isotopes in feather tissue (δ2Hf and δ13Cf, respectively) could differentiate among populations of white-winged doves across their breeding range in Texas, New Mexico, and Arizona. δ2Hf and δ13Cf not only differentiated between populations of white-winged doves that breed in the United States, but δ2Hf also provided further differentiation in white-winged doves that breed in native Sonoran Desert and agricultural habitats in the western portion of their range. Ecological processes associated with desert resources and anthropogenic influences, specifically saguaro cacti and irrigated crops, largely determined δ2Hf in some white-winged doves in Arizona whereas δ2H of precipitation (δ2Hp) largely determined δ2Hf of doves in New Mexico and Texas. This study highlights the usefulness of stable isotope analysis to differentiate populations of animals across the landscape and the insight isotopes can provide into habitat and resource use. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Aging is a major risk factor for cardiovascular disease. It has previously been shown that protein levels of cathepsin K, a lysosomal cysteine protease, are elevated in the failing heart and that genetic ablation of cathepsin K protects against pressure overload-induced cardiac hypertrophy and contractile dysfunction. Here we test the hypothesis that cathepsin K knockout alleviates age-dependent decline in cardiac function. Cardiac geometry, contractile function, intracellular Ca2+ properties, and cardiomyocyte apoptosis were evaluated using echocardiography, fura-2 technique, immunohistochemistry, Western blot and TUNEL staining, respectively. Aged (24-month-old) mice exhibited significant cardiac remodeling (enlarged chamber size, wall thickness, myocyte cross-sectional area, and fibrosis), decreased cardiac contractility, prolonged relengthening along with compromised intracellular Ca2+ release compared to young (6-month-old) mice, which were attenuated in the cathepsin K knockout mice. Cellular markers of senescence, including cardiac lipofuscin, p21 and p16, were lower in the aged-cathepsin K knockout mice compared to their wild-type counterpart. Mechanistically, cathepsin K knockout mice attenuated an age-induced increase in cardiomyocyte apoptosis and nuclear translocation of mitochondrial apoptosis-inducing factor ( AIF). In cultured H9c2 cells, doxorubicin stimulated premature senescence and apoptosis. Silencing of cathepsin K blocked the doxorubicin-induced translocation of AIF from the mitochondria to the nuclei. Collectively, these results suggest that cathepsin K knockout attenuates age-related decline in cardiac function via suppressing caspase-dependent and caspase-independent apoptosis. [ABSTRACT FROM AUTHOR]

Experimenting with limited resources often means that we are trying to get more out of a single experiment and balance competing goals. Selecting a best response surface design when simultaneously optimizing multiple criteria requires carefully choosing measures and scales of different design criteria and then balancing the trade-offs between the criteria. This paper illustrates a decision-making process using a Pareto frontier to identify good design candidates and a Utopia point approach for selection of an optimal design based on several competing criteria. The Pareto approach shows substantial improvement over the classic desirability function method by offering the user greater flexibility in quantifying the robustness of designs to weight specifications and the sensitivity of the solutions to different choices of weights, scales, and metrics. Graphical methods are used for summarizing and extracting useful information for improved decision-making. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In many experimental situations, practitioners are confronted with costly, time consuming, or hard-to-change (HTC) factors. These practical or economic restrictions on randomization can be accommodated with a split-plot design structure that minimizes the manipulation of the HTC factors. Selecting a good design is a challenging task and requires knowledge of the opportunities and restrictions imposed by the experimental apparatus and an evaluation of statistical performance among competing designs. Building on the well-established evaluation criteria for the completely randomized context, we emphasize the unique qualitative and quantitative evaluation criteria for split-plot designs. An example from hypersonic propulsion research is used to demonstrate the consideration of multiple design evaluation criteria. Published in 2007 by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Comparisons between different designs have traditionally focused on balancing the quality of estimation or prediction relative to the overall size of the design. For split-plot designs with two levels of randomization, the total number of observations may not accurately summarize the true cost of the experiment, because different costs are likely associated with setting up the whole and subplot levels. In this paper, we present several flexible measures for design assessment based on D-, G- and V-optimality criteria that take into account potentially different cost structures for the split-plot designs. The new measures are illustrated with two examples: a 23 factorial experiment for first-order models, where all possible designs are considered, and selective designs for a three-factor second-order model. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

When categorical noise variables are present in the Robust Parameter Design (RPD) context, it is possible to reduce process variance by not only manipulating the levels of the control factors but also by adjusting the proportions associated with the levels of the categorical noise factor(s). When no adjustment factors exist or when the adjustment factors are unable to bring the process mean close to target, a popular approach for determining optimal operating conditions is to find the levels of the control factors that minimize the estimated mean squared error of the response. Although this approach is effective, engineers may have a difficult time translating mean squared error into quality. We propose the use of a parts per million defective objective function. Furthermore, we point out that in many situations the levels of the control factors are not equally desirable due to cost and/or time issues. We have termed these types factors non-uniform control factors. We propose the use of desirability functions to determine optimal operating conditions when non-uniform control factors are present and illustrate this methodology with an example from industry. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In industrial experiments, restrictions on the execution of the experimental runs or the existence of one or more hard-to-change factors often leads to split-plot experiments, where there are two types of experimental units and two independent randomizations. The resulting compound symmetric error structure, as well as the settings of whole-plot and subplot factors, play important roles in the performance of split-plot experiments. When the practitioner is interested in predicting the response, a response surface design for a second-order model such as a central composite design (CCD) is often used. The prediction variance of second-order designs under a split-plot error structure is often of interest. In this paper, fraction of design space (FDS) plots are adapted to split-plot designs. In addition to the global curve exploring the entire design space, sliced curves at various whole-plot levels are presented to study prediction performance for subregions in the design space. The different sizes of the constrained subregions are accounted for by the proportional size of the sliced curves. The construction and use of the FDS plots are demonstrated through two examples of the restricted CCD in split-plot schemes. We also consider the impact of the variance ratio on design performance. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Parameter design is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The assumption is that there are both controllable factors (control variables) and uncontrollable/difficult to control factors (noise variables) that operate on the quality characteristic of a process. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic while minimizing the variation imposed on the process via the noise variables. Parameter design was popularized in the mid 1980s by Japanese quality consultant Genichi Taguchi. A panel discussion edited by Nair summarized important responses to Taguchi's ideas and methodology. In the last decade, there have been many applications and new developments in this important area. This review paper focuses largely on the work done since 1992, but a historical perspective of parameter design is also given. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Dreissenid mussels (including the zebra mussel Dreissena polymorpha and the quagga mussel D. rostriformis) are among the world's most notorious invasive species, with large and widespread ecological and economic effects. However, their long‐term population dynamics are poorly known, even though these dynamics are critical to determining impacts and effective management. We gathered and analyzed 67 long‐term (>10 yr) data sets on dreissenid populations from lakes and rivers across Europe and North America. We addressed five questions: (1) How do Dreissena populations change through time? (2) Specifically, do Dreissena populations decline substantially after an initial outbreak phase? (3) Do different measures of population performance (biomass or density of settled animals, veliger density, recruitment of young) follow the same patterns through time? (4) How do the numbers or biomass of zebra mussels or of both species combined change after the quagga mussel arrives? (5) How does body size change over time? We also considered whether current data on long‐term dynamics of Dreissena populations are adequate for science and management. Individual Dreissena populations showed a wide range of temporal dynamics, but we could detect only two general patterns that applied across many populations: (1) Populations of both species increased rapidly in the first 1–2 yr after appearance, and (2) quagga mussels appeared later than zebra mussels and usually quickly caused large declines in zebra mussel populations. We found little evidence that combined Dreissena populations declined over the long term. Different measures of population performance were not congruent; the temporal dynamics of one life stage or population attribute cannot generally be accurately inferred from the dynamics of another. We found no consistent patterns in the long‐term dynamics of body size. The long‐term dynamics of Dreissena populations probably are driven by the ecological characteristics (e.g., predation, nutrient inputs, water temperature) and their temporal changes at individual sites rather than following a generalized time course that applies across many sites. Existing long‐term data sets on dreissenid populations, although clearly valuable, are inadequate to meet research and management needs. Data sets could be improved by standardizing sampling designs and methods, routinely collecting more variables, and increasing support. [ABSTRACT FROM AUTHOR]

The article presents the authors' views on heart rhythm abnormalities that are known both as "arrhythmias" or "dysrhythmias." The authors clarify confusion that has reigned over which term is correct. It is argued that "arrhythmia" appears to be "more correct" term. It is stated that dysrhythmia was considered a neologism that first appeared in the 1960s.