The New York Prosecutorial Conduct Commission and the Dawn of a New Era of Reform for Prosecutors

This Note discusses the history of the national dialogue regarding prosecutorial misconduct, analyzes recent state reforms, and proposes that New York\u27s standing Brady orders and prosecutorial conduct commission provide the blueprint for ushering in a new era of prosecutorial accountability

Sensitivity analysis as an aid in modelling and control of (poorly-defined) ecological systems

A literature review of the use of sensitivity analyses in modelling nonlinear, ill-defined systems, such as ecological interactions is presented. Discussions of previous work, and a proposed scheme for generalized sensitivity analysis applicable to ill-defined systems are included. This scheme considers classes of mathematical models, problem-defining behavior, analysis procedures (especially the use of Monte-Carlo methods), sensitivity ranking of parameters, and extension to control system design

Senior Night

This is a letter regarding the loss of my senior year of high school

Modeling for understanding v. modeling for numbers

Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecosystems 20 (2017): 215-221, doi:10.1007/s10021-016-0067-y.I draw a distinction between Modeling for Numbers, which aims to address how much, when, and where questions, and Modeling for Understanding, which aims to address how and why questions. For-numbers models are often empirical, which can be more accurate than their mechanistic analogues as long as they are well calibrated and predictions are made within the domain of the calibration data. To extrapolate beyond the domain of available system-level data, for-numbers models should be mechanistic, relying on the ability to calibrate to the system components even if it is not possible to calibrate to the system itself. However, development of a mechanistic model that is reliable depends on an adequate understanding of the system. This understanding is best advanced using a for-understanding modeling approach. To address how and why questions, for-understanding models have to be mechanistic. The best of these for-understanding models are focused on specific questions, stripped of extraneous detail, and elegantly simple. Once the mechanisms are well understood, one can then decide if the benefits of incorporating the mechanism in a for-numbers model is worth the added complexity and the uncertainty associated with estimating the additional model parameters.This work has been supported in part by NSF grants 0949420, 1026843, 1065587, 1107707, and 1503781.2017-11-1

Modeling coupled biogeochemical cycles

Author Posting. © Ecological Society of America, 2011. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Frontiers in Ecology and the Environment 9 (2011): 68–73, doi:10.1890/090223.Organisms require about 30 essential elements to sustain life. The cycles of these elements are coupled to one another through the specific physiological requirements of the organisms. Here, I contrast several approaches to modeling coupled biogeochemical cycles using an example of carbon, nitrogen, and phosphorus accumulation in a Douglas-fir (Pseudotsuga menziesii) forest ecosystem and the response of that forest to elevated atmospheric carbon dioxide concentrations and global warming. Which of these approaches is most appropriate is subject to debate and probably depends on context; nevertheless, this question must be answered if scientists are to understand ecosystems and how they might respond to a changing global environment.This work was supported by National Science Foundation (NSF) grant #DEB-0716067

An approach to modeling resource optimization for substitutable and interdependent resources

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rastetter, E. B., & Kwiatkowski, B. L. An approach to modeling resource optimization for substitutable and interdependent resources. Ecological Modelling, 425, (2020): 109033, doi:10.1016/j.ecolmodel.2020.109033.We develop a hierarchical approach to modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources. Substitutable resources are resources that fill the same metabolic or stoichiometric need of the organism. Interdependent resources are resources whose acquisition or expenditure are tightly linked (e.g., light, CO2, and water in photosynthesis and associated transpiration). We illustrate the approach by simulating the development of vegetation with four substitutable sources of N that differ only in the cost of their uptake and assimilation. As the vegetation develops, it uses the least expensive N source first then uses progressively more expensive N sources as the less expensive sources are depleted. Transition among N sources is based on the marginal yield of N per unit effort expended, including effort expended to acquire C to cover the progressively higher uptake costs. We illustrate the approach to interdependent resources by simulating the expenditure of effort to acquire light energy, CO2, and water to drive photosynthesis in vegetation acclimated to different conditions of soil water, atmospheric vapor pressure deficit, CO2 concentration, and light levels. The approach is an improvement on the resource optimization used in the earlier Multiple Element Limitation (MEL) model.This work was supported in part by the National Science Foundation under NSF grants 1651722, 1637459, 1603560, 1556772, 1841608. Any Opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation

Interactions among resource partitioning, sampling effect, and facilitation on the biodiversity effect: A modeling approach

Resource partitioning, facilitation, and sampling effect are the three mechanisms behind the biodiversity effect, which is depicted usually as the effect of plant-species richness on aboveground net primary production. These mechanisms operate simultaneously but their relative importance and interactions are difficult to unravel experimentally. Thus, niche differentiation and facilitation have been lumped together and separated from the sampling effect. Here, we propose three hypotheses about interactions among the three mechanisms and test them using a simulation model. The model simulated water movement through soil and vegetation, and net primary production mimicking the Patagonian steppe. Using the model, we created grass and shrub monocultures and mixtures, controlled root overlap and grass water-use efficiency (WUE) to simulate gradients of biodiversity, resource partitioning and facilitation. The presence of shrubs facilitated grass growth by increasing its WUE and in turn increased the sampling effect whereas root overlap (resource partitioning) had, on average, no effect on sampling effect. Interestingly, resource partitioning and facilitation interacted so the effect of facilitation on sampling effect decreased as resource partitioning increased. Sampling effect was enhanced by the difference between the two functional groups in their efficiency in using resources. Morphological and physiological differences make one group outperform the other, once those differences were established further differences did not enhance the sampling effect. In addition, grass WUE and root overlap positively influence the biodiversity effect but showed no interactions.Fil: Flombaum, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Sala, Osvaldo Esteban. Arizona State University. School of Life Sciences and School of Sustainability; Estados UnidosFil: Rastetter, Edward B.. Marine Biological Laboratory. The Ecosystem Center; Estados Unido