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Does society benefit from encouraging or discouraging private infectious disease-risk mitigation? Private individuals routinely mitigate infectious disease risks through the adoption of a range of precautions, from vaccination to changes in their contact with others. Such precautions have epidemiological consequences. Private disease-risk mitigation generally reduces both peak prevalence of symptomatic infection and the number of people who fall ill. At the same time, however, it can prolong an epidemic. A reduction in prevalence is socially beneficial. Prolongation of an epidemic is not. We find that for a large class of infectious diseases, private risk mitigation is socially suboptimal-either too low or too high. The social optimum requires either more or less private mitigation. Since private mitigation effort depends on the cost of mitigation and the cost of illness, interventions that change either of these costs may be used to alter mitigation decisions. We model the potential for instruments that affect the cost of illness to yield net social benefits. We find that where a disease is not very infectious or the duration of illness is short, it may be socially optimal to promote private mitigation effort by increasing the cost of illness. By contrast, where a disease is highly infectious or long lasting, it may be optimal to discourage private mitigation by reducing the cost of disease. Society would prefer a shorter, more intense, epidemic to a longer, less intense epidemic. There is, however, a region in parameter space where the relationship is more complicated. For moderately infectious diseases with medium infectious periods, the social optimum depends on interactions between prevalence and duration. Basic reproduction numbers are not sufficient to predict the social optimum.

Governments worldwide are recognising ecosystem services as an approach to address sustainability challenges. Decision-makers need credible and legitimate measurements of ecosystem services to evaluate decisions for trade-offs to make wise choices. Managers lack these measurements because of a data gap linking ecosystem characteristics to final ecosystem services. The dominant method to address the data gap is benefit transfer using ecological data from one location to estimate ecosystem services at other locations with similar land cover. However, benefit transfer is only valid once the data gap is adequately resolved. Disciplinary frames separating ecology from economics and policy have resulted in confusion on concepts and methods preventing progress on the data gap. In this study, we present a 10-step approach to unify concepts, methods and data from the disparate disciplines to offer guidance on overcoming the data gap. We suggest: (1) estimate ecosystem characteristics using biophysical models, (2) identify final ecosystem services using endpoints and (3) connect them using ecological production functions to quantify biophysical trade-offs. The guidance is strategic for public policy because analysts need to be: (1) realistic when setting priorities, (2) attentive to timelines to acquire relevant data, given resources and (3) responsive to the needs of decision-makers., (© 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.)

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as "economic epidemiology" or "epidemiological economics," the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.

Government policies are needed when people's behaviors fail to deliver the public good. Those policies will be most effective if they can stimulate long-term changes in beliefs and norms, creating and reinforcing the behaviors needed to solidify and extend the public good.It is often the short-term acceptability of potential policies, rather than their longer-term efficacy, that determines their scope and deployment. The policy process should consider both time scales. The academy, however, has provided insufficient insight on the coevolution of social norms and different policy instruments, thus compromising the capacity of decision makers to craft effective solutions to the society's most intractable environmental problems. Life scientists could make fundamental contributions to this agenda through targeted research on the emergence of social norms.

The most unique feature of Earth is the existence of life, and the most extraordinary feature of life is its diversity. Approximately 9 million types of plants, animals, protists and fungi inhabit the Earth. So, too, do 7 billion people. Two decades ago, at the first Earth Summit, the vast majority of the world's nations declared that human actions were dismantling the Earth's ecosystems, eliminating genes, species and biological traits at an alarming rate. This observation led to the question of how such loss of biological diversity will alter the functioning of ecosystems and their ability to provide society with the goods and services needed to prosper.

Hierarchical competition-colonization models have been used to explain limiting similarities among species, successional dynamics, and species loss under habitat destruction. This class of models assumes that there is an inverse relationship between competitive ability and colonization ability and that competitively superior species exclude competitively inferior species when both occupy the same site. This hierarchical model of performance trade-offs, however, exhibits some unusual behaviors in the high-diversity limit, including infinitesimally close species packing, pathologically slow dynamics, and fundamentally important regularities in trait-abundance relationships. In particular, under the condition of constant mortality across species, a 3/2-power-law relationship emerges between abundance and fecundity under infinite packing (abundance of a species with fecundity f is inversely proportional to f to the 3/2 power). In this article, we explore the high-diversity limit of the hierarchical competition-colonization model, with particular emphasis on patterns of species packing, species-abundance relationships, and system stability. Because of the potential for pathologically slow dynamics following perturbations and infinitesimally close species packing in the high-diversity limit for this class of models, the models may need to be modified to include more realistic mechanisms governing the extent and timing of interspecific competitive exclusion in order to effectively capture the structure and dynamics of real-world ecosystems.

By examining the consequences of simultaneous mutualism and competition between plants and decomposers, we show that testable predictions about nutrient allocation in ecosystems follow from the assumption that decomposers allocate for their own growth the fraction of mineralized nutrient that maximizes their population biomass, leaving the remainder available for plant uptake. Available data for a wide variety of ecosystems are nearly all consistent with the predicted quantitative relationships among nitrogen flow rates and nitrogen fractions in plants, decomposers, and nonliving organic matter. Our predictions are robust against changes in the detailed structure of the nutrient-cycle models we use for our derivations.