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Subfailure damage in ligaments was evaluated macroscopically from a structural perspective (referring to the entire ligament as a structure) and microscopically from a cellular perspective. Freshly harvested rat medial collateral ligaments (MCLs) were used as a model in ex vivo experiments. Ligaments were preloaded with 0.1 N to establish a consistent point of reference for length (and strain) measurements. Ligament structural damage was characterized by nonrecoverable difference in tissue length after a subfailure stretch. The tissue's mechanical properties (via stress vs. strain curves measured from a preloaded state) after a single subfailure stretch were also evaluated (n = 6 pairs with a different stretch magnitude applied to each stretched ligament). Regions containing necrotic cells were used to characterize cellular damage after a single stretch. It should be noted that the number of damaged cells was not quantified and the difference between cellular area and area of fluorescence is not known. Structural and cellular damage were represented and compared as functions of subfailure MCL strains. Statistical analysis indicated that the onset of structural damage occurs at 5.14% strain (referenced from a preloaded length). Subfailure strains above the damage threshold changed the shape of the MCL stress-strain curve by elongating the toe region (i.e., increasing laxity) as well as decreasing the tangential modulus and ultimate stress. Cellular damage was induced at ligament strains significantly below the structural damage threshold. This cellular damage is likely to be part of the natural healing process in mildly sprained ligaments.

Estimating cause‐specific mortality is often of central importance for understanding the dynamics of wildlife populations. Despite such importance, methodology for estimating and analyzing cause‐specific mortality has received little attention in wildlife ecology during the past 20 years. The issue of analyzing cause‐specific, mutually exclusive events in time is not unique to wildlife. In fact, this general problem has received substantial attention in human biomedical applications within the context of biostatistical survival analysis. Here, we consider cause‐specific mortality from a modern biostatistical perspective. This requires carefully defining what we mean by cause‐specific mortality and then providing an appropriate hazard‐based representation as a competing risks problem. This leads to the general solution of cause‐specific mortality as the cumulative incidence function (CIF). We describe the appropriate generalization of the fully nonparametric staggered‐entry Kaplan–Meier survival estimator to cause‐specific mortality via the nonparametric CIF estimator (NPCIFE), which in many situations offers an attractive alternative to the Heisey–Fuller estimator. An advantage of the NPCIFE is that it lends itself readily to risk factors analysis with standard software for Cox proportional hazards model. The competing risks‐based approach also clarifies issues regarding another intuitive but erroneous “cause‐specific mortality” estimator based on the Kaplan–Meier survival estimator and commonly seen in the life sciences literature.

Randomized intervention analysis (RIA) is used to detect changes in a manipulated ecosystem relative to an undisturbed reference system. It requires paired time series of data from both ecosystems before and after manipulation. RIA is not affected by non—normal errors in data. Monte Carlo simulation indicated that, even when serial autocorrelation was substantial, the true P value (i.e., from nonoautocorrelated data) was <.05 when the P value from autocorrelated data was <.01. We applied RIA to data from 12 lakes (3 manipulated and 9 reference ecosystems) over 3 yr. RIA consistently indicated changes after major manipulations and only rarely indicated changes in ecosystems that were not manipulated. Less than 3% of the data sets we analyzed had equivocal results because of serial autocorrelation. RIA appears to be a reliable method for determining whether a nonrandom change has occurred in a manipulated ecosystem. Ecological arguments must be combined with statistical evidence to determine whether the changes demonstrated by RIA can be attributed to a specific ecosystem manipulation.

Chronic wasting disease (CWD) is a fatal disease of deer, elk, and moose transmitted through direct, animal-to-animal contact, and indirectly, via environmental contamination. Considerable attention has been paid to modeling direct transmission, but despite the fact that CWD prions can remain infectious in the environment for years, relatively little information exists about the potential effects of indirect transmission on CWD dynamics. In the present study, we use simulation models to demonstrate how indirect transmission and the duration of environmental prion persistence may affect epidemics of CWD and populations of North American deer. Existing data from Colorado, Wyoming, and Wisconsin's CWD epidemics were used to define plausible short-term outcomes and associated parameter spaces. Resulting long-term outcomes range from relatively low disease prevalence and limited host-population decline to host-population collapse and extinction. Our models suggest that disease prevalence and the severity of population decline is driven by the duration that prions remain infectious in the environment. Despite relatively low epidemic growth rates, the basic reproductive number, R 0, may be much larger than expected under the direct-transmission paradigm because the infectious period can vastly exceed the host's life span. High prion persistence is expected to lead to an increasing environmental pool of prions during the early phases (i.e. approximately during the first 50 years) of the epidemic. As a consequence, over this period of time, disease dynamics will become more heavily influenced by indirect transmission, which may explain some of the observed regional differences in age and sex-specific disease patterns. This suggests management interventions, such as culling or vaccination, will become increasingly less effective as CWD epidemics progress.

In the transplantation of a kidney from a sibling donor who is mismatched with the recipient for one HLA haplotype, graft survival is higher when the donor has maternal HLA antigens not inherited by the recipient than when the donor has paternal HLA antigens not inherited by the recipient.

Researchers and wildlife managers increasingly find themselves in situations where they must deal with infectious wildlife diseases such as chronic wasting disease, brucellosis, tuberculosis, and West Nile virus. Managers are often charged with designing and implementing control strategies, and researchers often seek to determine factors that influence and control the disease process. All of these activities require the ability to measure some indication of a disease's foothold in a population and evaluate factors affecting that foothold. The most common type of data available to managers and researchers is apparent prevalence data. Apparent disease prevalence, the proportion of animals in a sample that are positive for the disease, might seem like a natural measure of disease's foothold, but several properties, in particular, its dependency on age structure and the biasing effects of disease-associated mortality, make it less than ideal. In quantitative epidemiology, the "force of infection," or infection hazard, is generally the preferred parameter for measuring a disease's foothold, and it can be viewed as the most appropriate way to "adjust" apparent prevalence for age structure. The typical ecology curriculum includes little exposure to quantitative epidemiological concepts such as cumulative incidence, apparent prevalence, and the force of infection. The goal of this paper is to present these basic epidemiological concepts and resulting models in an ecological context and to illustrate how they can be applied to understand and address basic epidemiological questions. We demonstrate a practical approach to solving the heretofore intractable problem of fitting general force-of-infection models to wildlife prevalence data using a generalized regression approach. We apply the procedures to Mycobacterium bovis (bovine tuberculosis) prevalence in bison (Bison bison) in Wood Buffalo National Park, Canada, and demonstrate strong age dependency in the force of infection as well as an increased mortality hazard in positive animals.