Biomedical subjects

Brandon T Bestelmeyer

Publications and source records attributed to Brandon T Bestelmeyer.

3 recordsLinked to original sources

PubMed2006

Soil-geomorphic heterogeneity governs patchy vegetation dynamics at an arid ecotone.

Soil properties are well known to affect vegetation, but the role of soil heterogeneity in the patterning of vegetation dynamics is poorly documented. We asked whether the location of an ecotone separating grass-dominated and sparsely vegetated areas reflected only historical variation in degradation or was related to variation in inherent soil properties. We then asked whether changes in the cover and spatial organization of vegetated and bare patches assessed using repeat aerial photography reflected self-organizing dynamics unrelated to soil variation or the stable patterning of soil variation. We found that the present-day ecotone was related to a shift from more weakly to more strongly developed soils. Parts of the ecotone were stable over a 60-year period, but shifts between bare and vegetated states, as well as persistently vegetated and bare states, occurred largely in small (<40 m2) patches throughout the study area. The probability that patches were presently vegetated or bare, as well as the probability that vegetation persisted and/or established over the 60-year period, was negatively related to surface calcium carbonate and positively related to subsurface clay content. Thus, only a fraction of the landscape was susceptible to vegetation change, and the sparsely vegetated area probably featured a higher frequency of susceptible soil patches. Patch dynamics and self-organizing processes can be constrained by subtle (and often unrecognized) soil heterogeneity.

Ecosystem↗

PubMed2004

Cross-scale interactions, nonlinearities, and forecasting catastrophic events.

Catastrophic events share characteristic nonlinear behaviors that are often generated by cross-scale interactions and feedbacks among system elements. These events result in surprises that cannot easily be predicted based on information obtained at a single scale. Progress on catastrophic events has focused on one of the following two areas: nonlinear dynamics through time without an explicit consideration of spatial connectivity [Holling, C. S. (1992) Ecol. Monogr. 62, 447-502] or spatial connectivity and the spread of contagious processes without a consideration of cross-scale interactions and feedbacks [Zeng, N., Neeling, J. D., Lau, L. M. & Tucker, C. J. (1999) Science 286, 1537-1540]. These approaches rarely have ventured beyond traditional disciplinary boundaries. We provide an interdisciplinary, conceptual, and general mathematical framework for understanding and forecasting nonlinear dynamics through time and across space. We illustrate the generality and usefulness of our approach by using new data and recasting published data from ecology (wildfires and desertification), epidemiology (infectious diseases), and engineering (structural failures). We show that decisions that minimize the likelihood of catastrophic events must be based on cross-scale interactions, and such decisions will often be counterintuitive. Given the continuing challenges associated with global change, approaches that cross disciplinary boundaries to include interactions and feedbacks at multiple scales are needed to increase our ability to predict catastrophic events and develop strategies for minimizing their occurrence and impacts. Our framework is an important step in developing predictive tools and designing experiments to examine cross-scale interactions.

Animals↗

PubMed2004

Land management in the American southwest: a state-and-transition approach to ecosystem complexity.

State-and-transition models are increasingly being used to guide rangeland management. These models provide a relatively simple, management-oriented way to classify land condition (state) and to describe the factors that might cause a shift to another state (a transition). There are many formulations of state-and-transition models in the literature. The version we endorse does not adhere to any particular generalities about ecosystem dynamics, but it includes consideration of several kinds of dynamics and management response to them. In contrast to previous uses of state-and-transition models, we propose that models can, at present, be most effectively used to specify and qualitatively compare the relative benefits and potential risks of different management actions (e.g., fire and grazing) and other factors (e.g., invasive species and climate change) on specified areas of land. High spatial and temporal variability and complex interactions preclude the meaningful use of general quantitative models. Forecasts can be made on a case-by-case basis by interpreting qualitative and quantitative indicators, historical data, and spatially structured monitoring data based on conceptual models. We illustrate how science- based conceptual models are created using several rangeland examples that vary in complexity. In doing so, we illustrate the implications of designating plant communities and states in models, accounting for varying scales of pattern in vegetation and soils, interpreting the presence of plant communities on different soils and dealing with our uncertainty about how those communities were assembled and how they will change in the future. We conclude with observations about how models have helped to improve management decision-making.

Conservation of Natural Resources↗