Contain or eradicate? Optimizing the management goal for Australian acacia invasions in the face of uncertainty

Aim To identify whether eradication or containment is expected to be the most cost-effective management goal for an isolated invasive population when knowledge about the current extent is uncertain. Location Global and South Africa. Methods We developed a decision analysis framework to analyse the best management goal for an invasive species population (eradication, containment or take no action) when knowledge about the current extent is uncertain. We used value of information analysis to identify when investment in learning about the extent will improve this decision-making and tested the sensitivity of the conclusions to different parameters (e.g. spread rate, maximum extent, and management efficacy and cost). The model was applied to Acacia paradoxa DC, an Australian shrub with an estimated invasive extent of 310 ha on Table Mountain, South Africa. Results Under the parameters used, attempting eradication is cost-effective for infestations of up to 777 ha. However, if the invasion extent is poorly known, then attempting eradication is only cost-effective for infestations estimated as 296 ha or smaller. The value of learning is greatest (maximum of 8% saving) when infestation extent is poorly known and if it is close to the maximum extent for which attempting eradication is optimal. The optimal management action is most sensitive to the probability that the action succeeds (which depends on the extent), with the discount rate and cost of management also important, but spread rate less so. Over a 20-year time-horizon, attempting to eradicate A. paradoxa from South Africa is predicted to cost on average ZAR 8 million if the extent is known, and if our current estimate is poor, ZAR 33.6 million as opposed to ZAR 32.8 million for attempting containment. Main conclusions Our framework evaluates the cost-effectiveness of attempting eradication or containment of an invasive population that takes uncertainty in population extent into account. We show that incorporating uncertainty in the analysis avoids overly optimistic beliefs about the effectiveness of management enabling better management decisions. For A. paradoxa in South Africa, attempting to eradicate is likely to be cost-effective, particularly if resources are allocated to better understand and improve management efficacy.Centre of Excellence for Invasion Biolog

Variational Monte Carlo study of the ground state properties and vacancy formation energy of solid para-H2 using a shadow wave function

A Shadow Wave Function (SWF) is employed along with Variational Monte Carlo techniques to describe the ground state properties of solid molecular para-hydrogen. The study has been extended to densities below the equilibrium value, to obtain a parameterization of the SWF useful for the description of inhomogeneous phases. We also present an estimate of the vacancy formation energy as a function of the density, and discuss the importance of relaxation effects near the vacant site

Approximative treatment of 5f-systems with partial localization due to intra-atomic correlations

Increasing experimental and theoretical evidence points towards a dual nature of the 5$f$ electrons in actinide-based strongly correlated metallic compounds, with some 5$f$ electrons being localized and others delocalized. In a recent paper (PRB xxx, 2004), we suggested the interplay of intra-atomic correlations as described by Hund's rules and a weakly anisotropic hopping (hybridization) as a possible mechanism. The purpose of the present work is to provide a first step towards a microscopic description of partial localization in solids by analyzing how well various approximation schemes perform when applied to small clusters. It is found that many aspects of partial localization are described appropriately both by a variational wavefunction of Gutzwiller type and by a treatment which keeps only those interactions which are present in LDA+U calculations. In contrast, the energies and phase diagram calculated within the Hartree Fock approximation show little resemblence with the exact results. Enhancement of hopping anisotropy by Hund's rule correlations are found in all approximations.Comment: 9 pages, 9 figure

Disordered Boson Systems: A Perturbative Study

A hard-core disordered boson system is mapped onto a quantum spin 1/2 XY-model with transverse random fields. It is then generalized to a system of spins with an arbitrary magnitude S and studied through a 1/S expansion. The first order 1/S expansion corresponds to a spin-wave theory. The effect of weak disorder is studied perturbatively within such a first order 1/S scheme. We compute the reduction of the speed of sound and the life time of the Bloch phonons in the regime of weak disorder. Generalizations of the present study to the strong disordered regime are discussed.Comment: 27 pages, revte

Critical Currents and Vortex States at Fractional Matching Fields in Superconductors with Periodic Pinning

We study vortex states and dynamics in 2D superconductors with periodic pinning at fractional sub-matching fields using numerical simulations. For square pinning arrays we show that ordered states form at 1/1, 1/2, and 1/4 filling fractions while only partially ordered states form at other filling fractions, such as 1/3 and 1/5, in agreement with recent imaging experiments. For triangular pinning arrays we observe matching effects at filling fractions of 1/1, 6/7, 2/3, 1/3, 1/4, 1/6, and 1/7. For both square and triangular pinning arrays we also find that, for certian sub-matching fillings, vortex configurations depend on pinning strength. For weak pinning, ordering in which a portion of the vortices are positioned between pinning sites can occur. Depinning of the vortices at the matching fields, where the vortices are ordered, is elastic while at the incommensurate fields the motion is plastic. At the incommensurate fields, as the applied driving force is increased, there can be a transition to elastic flow where the vortices move along the pinning sites in 1D channels and a reordering transition to a triangular or distorted triangular lattice. We also discuss the current-voltage curves and how they relate to the vortex ordering at commensurate and incommensurate fields.Comment: 14 figure

Nature of the quantum phase transitions in the two-dimensional hardcore boson model

We use two Quantum Monte Carlo algorithms to map out the phase diagram of the two-dimensional hardcore boson Hubbard model with near ($V_1$) and next near ($V_2$) neighbor repulsion. At half filling we find three phases: Superfluid (SF), checkerboard solid and striped solid depending on the relative values of $V_1$, $V_2$ and the kinetic energy. Doping away from half filling, the checkerboard solid undergoes phase separation: The superfluid and solid phases co-exist but not as a single thermodynamic phase. As a function of doping, the transition from the checkerboard solid is therefore first order. In contrast, doping the striped solid away from half filling instead produces a striped supersolid phase: Co-existence of density order with superfluidity as a single phase. One surprising result is that the entire line of transitions between the SF and checkerboard solid phases at half filling appears to exhibit dynamical O(3) symmetry restoration. The transitions appear to be in the same universality class as the special Heisenberg point even though this symmetry is explicitly broken by the $V_2$ interaction.Comment: 10 pages, 14 eps figures, include

Context-dependent representation of within- and between-model uncertainty: Aggregating probabilistic predictions in infectious disease epidemiology

Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches

Stable Coexistence of an Invasive Plant and Biocontrol Agent: A Parameterized Coupled Plant-Herbivore Model

1. Coupled plant-herbivore models, allowing feedback from plant to herbivore populations and vice versa, enable us to predict the impact of biocontrol agents on their target weed populations; however, they are rarely used in biocontrol studies. We describe the population biology of the invasive plant Echium plantagineum and the weevil Mogulones larvatus, a biocontrol agent, in Australia. In order to understand the dynamics of this plant-herbivore system, a series of coupled models of increasing complexity was developed. 2. A simple model was extended to include a seed bank, density-dependent plant fecundity, competition between weevil larvae and plant tolerance of herbivory, where below a threshold plants could compensate for larval feeding. Parameters and functional forms were estimated from experimental and field data. 3. The plant model, in the absence of the weevil, exhibited stable dynamics and provided a good quantitative description of field densities before the weevil was introduced. 4. In the coupled plant-herbivore model, density dependence in both plant fecundity and weevil larval competition stabilized the dynamics. Without larval competition the model was unstable, and plant tolerance of herbivory exacerbated this instability. This was a result of a time delay in plant response to herbivore densities. 5. Synthesis and applications. The coupled plant-herbivore model allowed us to predict whether stable coexistence of target plant and biocontrol agents was achievable at an acceptable level. We found this to be the case for the Echium-Mogulones system and believe that similar models would be of use when assessing new agents in this and other invasive plant biocontrol systems. Density dependence in new biocontrol agents should be assessed in order to determine whether it is likely to result in the aims of classical biocontrol: low, stable and sustainable populations of plant and herbivore. Further work should be done to characterize the strength of density dependence according to the niche occupied by the biocontrol agent, for example the strength and functional form of density dependence in stem borers may be quite different to that of defoliators

Collaborative Hubs: Making the Most of Predictive Epidemic Modeling

The COVID-19 pandemic has made it clear that epidemic models play an important role in how governments and the public respond to infectious disease crises. Early in the pandemic, models were used to estimate the true number of infections. Later, they estimated key parameters, generated short-term forecasts of outbreak trends, and quantified possible effects of interventions on the unfolding epidemic. In contrast to the coordinating role played by major national or international agencies in weather-related emergencies, pandemic modeling efforts were initially scattered across many research institutions. Differences in modeling approaches led to contrasting results, contributing to confusion in public perception of the pandemic. Efforts to coordinate modeling efforts in so-called “hubs” have provided governments, healthcare agencies, and the public with assessments and forecasts that reflect the consensus in the modeling community. This has been achieved by openly synthesizing uncertainties across different modeling approaches and facilitating comparisons between them

The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy