Biomedical subjects

D C Bardos

Publications and source records attributed to D C Bardos.

3 recordsLinked to original sources

PubMed2006

Modeling species-habitat relationships with spatially autocorrelated observation data.

Spatial autocorrelation in wildlife observation data arises when extrinsic environmental processes and patterns that influence the spatial distribution of wildlife are themselves spatially structured, or when species are subject to intrinsic population processes, causing contagion or dispersion effects. Territoriality, Allee effects, dispersal limitations, and social clustering are examples of intrinsic processes. Both forms of autocorrelation can violate the assumptions of generalized linear regression models, resulting in biased estimation of model coefficients and diminished predictive performance. Such consequences may be avoided for extrinsic autocorrelation when autocorrelated environmental variables are available for use as model covariates, whereas intrinsic spatial autocorrelation requires an alternative modeling approach. The autologistic model provides an approach suited to the binary observations often obtained in wildlife surveys, but its performance has not been tested across widely varying sampling intensities or strengths of intrinsic spatial structure. Here we use simulated data to test the autologistic model under a range of sampling conditions. The autologistic model obtains better fits and substantially better predictive performance than the standard logistic regression model over the full range of sampling designs and intensities tested. We provide a simple Bayesian implementation of the autologistic model, which until now has not been achieved with standard statistical software alone. A step-by-step procedure is given for characterizing and modeling spatial autocorrelation in binary observation data, along with computer code for fitting autologistic models in WinBUGS, a freeware Bayesian analysis package. This approach avoids normal approximations to the pseudo-likelihood, in contrast to previous Bayesian applications of the autologistic model. We provide a sample application of the autologistic model, fitted to survey data for a gliding marsupial in southeastern Australia.

Bayes Theorem↗

PubMed2005

Probabilistic Gompertz model of irreversible growth.

Characterizing organism growth within populations requires the application of well-studied individual size-at-age models, such as the deterministic Gompertz model, to populations of individuals whose characteristics, corresponding to model parameters, may be highly variable. A natural approach is to assign probability distributions to one or more model parameters. In some contexts, size-at-age data may be absent due to difficulties in ageing individuals, but size-increment data may instead be available (e.g., from tag-recapture experiments). A preliminary transformation to a size-increment model is then required. Gompertz models developed along the above lines have recently been applied to strongly heterogeneous abalone tag-recapture data. Although useful in modelling the early growth stages, these models yield size-increment distributions that allow negative growth, which is inappropriate in the case of mollusc shells and other accumulated biological structures (e.g., vertebrae) where growth is irreversible. Here we develop probabilistic Gompertz models where this difficulty is resolved by conditioning parameter distributions on size, allowing application to irreversible growth data. In the case of abalone growth, introduction of a growth-limiting biological length scale is then shown to yield realistic length-increment distributions.

Age Factors↗

PubMed2000

Nonlinear cell response to strong electric fields.

The response of living cells to externally applied electric fields is of widespread interest. In particular, the intensification of electric fields across cell membranes is believed to be responsible, through membrane rupture and reversible membrane breakdown processes, for certain types of tissue damage in electrical trauma cases which cannot be attributed to Joule heating. Large elongated cells such as skeletal muscle fibres are particularly vulnerable to such damage. Previous theoretical studies of field intensification across cell membranes in such cells have assumed the membrane current to be linear in the applied field (Ohmic membrane conductivity) and were limited to sinusoidal applied fields. In this paper, we investigate a simple model of a long cylindrical cell, corresponding to nerve or skeletal muscle cells. Employing the electroquasistatic approximation, a system of coupled first-order differential equations for the membrane electric field is derived which incorporates arbitrary time dependence in the external field and nonlinear membrane response (non-Ohmic conductivity). The behaviour of this model is investigated for a variety of applied fields in both the linear and highly nonlinear regimes. We find that peak membrane fields predicted by the nonlinear model are approximately twice as intense, for low-frequency electrical trauma conditions, as those of the linear theory.

Algorithms↗