Recognition of screening out hierarchical toxic contaminants tuned by quantified pseudo-components from complex engineering co-combustion.

Co-combustion of industrial and municipal solid wastes has emerged as the most promising disposal technology, yet its effect on unknown contaminants generation remains rarely revealed due to waste complexity. Hence, six batches of large-scale engineering experiments were designed in an incinerator of 650 t/d, which overcame the inauthenticity and deviation of laboratory tests. 953-1772 non-targeted compounds were screened in fly ash. Targeting the impact of co-combustion, a pseudo-component matrix model was innovatively integrated to quantitatively extract nine components from complex wastes grouped into biomass and plastic. Thus, the influence was evaluated across eight dimensions, covering molecular characteristics and toxicity. The effect of co-combustion with biomass pseudo-components was insignificant. However, co-combustion with high ratios of plastic pseudo-components induced higher potential risks, significantly promoting the formation of unsaturated hydrocarbons, highly unsaturated compounds (DBE≥15), and cyclic compounds by 19 %- 49 %, 17 %- 31 %, and 7 %- 27 %, respectively. Especially, blending with high ratios of PET plastic pseudo-components produced more species of contaminants. Unique 2 Level I toxicants, bromomethyl benzene and benzofuran-2-carbaldehyde, as well as 4 Level II toxicants, were locked, receiving no concern in previous combustion. The results highlighted risks during high proportion plastics co-combustion, which can help pollution reduction by tuning source wastes to enable healthy co-combustion.

Quantifying the impact of upscaled parameters on radionuclide transport in three-dimensional fracture-matrix systems.

Assessing the long-term safety of geological repositories for high-level radioactive waste is critically dependent on understanding radionuclide transport in multi-scale fractured rocks. This study explores the influence of upscaled parameters on radionuclide movement within a three-dimensional fracture-matrix system using a discrete fracture-matrix (DFM) model. The developed numerical simulation workflow includes creating a random discrete fracture network, meshing of the fractures and matrix, assigning upscaled parameters, and conducting finite element simulations. We simulated the spatiotemporal evolution of radionuclide concentrations in the fractures and matrix over a century, revealing significant spatial heterogeneity driven by a heterogeneous seepage field. Employing geostatistics-based upscaling methods, we predicted the effective ranges of crucial solute transport parameters at the field scale. The matrix diffusion coefficient, matrix distribution coefficient, and longitudinal dispersivity were upscaled by factors of 2.0-3.0, 2.5-4.0, and 10-104, respectively, based on laboratory-scale measurements. Incorporating these upscaled parameters into the DFM model, we analyzed their impact on radionuclide transport. Our findings demonstrate that an upscaled matrix diffusion coefficient and matrix distribution coefficient result in a delayed transport of radionuclides in fractures by enhancing mass transfer between the fractures and rock matrix, while an upscaled longitudinal dispersivity accelerates transport by advancing the positions of concentration peaks. Sensitivity analysis revealed that the matrix distribution coefficient is the most impactful, followed by dispersivity and matrix diffusion coefficient. These insights are important for minimizing parameter uncertainties and enhancing the accuracy of predictions concerning radionuclide transport in multi-scale fractured rocks.

Analysis of the natural collapse course of non-traumatic osteonecrosis of the femoral head based on the matrix model.

BACKGROUND: There are many predictions about the progression of natural collapse course of osteonecrosis of the femoral head. Here, we aimed to combine the three classical prediction methods to explore the progression of the natural collapse course. METHODS: This retrospective study included 127 patients admitted to our hospital from October 2016 to October 2017, in whom the femoral head had not collapsed. Logistic regression analysis was performed to determine the collapse risk factors, and Kaplan-Meier survival curves were used for femoral head survival analysis. The collapse rate of the femoral head was recorded within 5 years based on the matrix model. The specificity of the matrix model was analyzed using the receiver operating characteristic curve. RESULTS: A total of 127 patients with a total of 202 hips were included in this study, and 98 hips collapsed during the follow-up period. Multivariate logistics regression analysis showed that the predictive ability of the matrix model was stronger than Association Research Circulation Osseous staging, Japanese Investigation Committee classification, and area (P < 0.05). Kaplan-Meier survival curve showed that the median survival time of femoral head in patients was 3 years. The result of the receiver operating characteristic curve analysis showed that the area under the curve (AUC) of the matrix model had better predictive value (AUC = 0.771, log-rank test: P < 0.001). CONCLUSION: We creatively combined the three classical prediction methods for evaluating the progression of the natural collapse course based on the matrix model and found that the higher the score of the matrix model, the higher the femoral head collapse rate. Specifically, the matrix model has a potential value in predicting femoral head collapse and guiding treatment selection.

Risk Assessment of Passenger Behaviors That Influence Accident Type and Severity in Metro Operation.

Background: The unsafe behavior of passengers frequently causes metro operation accidents. This research aims to establish a model for evaluating the risk of unsafe behavior among subway passengers and for assessing the severity of different types of accidents caused by passenger unsafe behavior. Methods: A risk assessment model that combines the Interaction Matrix (IM) model with a Monte Carlo algorithm was established to quantitatively test the risk of unsafe behavior among passengers. Based on the initial data of 234 cases, the behavioral risks in accidents were simulated, and the resulting risks follow a normal distribution. After analyzing the differences in behavioral risk distribution characteristics, the targeted risk mitigation countermeasures were obtained. Results: Results showed that there are 12 kinds of unsafe behaviors related to 4 metro operation accident types. Among them, crowded stampede caused by four kinds of passengers' unsafe behavior has the highest risk mean (µ) of 5.14, followed by escalator injury (4.72), pinched by a shielding barrier (4.42) and fall injury (4.14). Conclusion: The severity of different types of accidents caused by different unsafe behaviors of passengers was obtained, which can provide a basis for targeted risk mitigation strategies and measures.

Demographic and population response of the threatened coral Acropora cervicornis (Scleractinia, Acroporidae) to fireworm corallivory

Introduction: The fireworm Hermodice carunculata is a widespread polychaete that can prey upon many coral species. However, few studies have examined the effect of fireworm predation on coral demographics during non-outbreak periods. Objective: To determine whether predation by H. carunculata compromised the growth, survival, and population performance of the threatened coral Acropora cervicornis. Methods: Nursery-reared coral fragments (n = 99) were fixed to the bottom of Punta Melones reef in the Island Municipality of Culebra, Puerto Rico. Predation activity and its demographic consequences on coral outplants were assessed from December 2020 to August 2022. Susceptibility to predation was compared between colonies collected directly from the reef and those originating from outside sources (e.g., coral nurseries). With the demographic data, simple size-based population matrix models were developed to 1) examine whether fireworm predation led to a significant decline in population growth rate (λ), 2) determine the demographic transition(s) that contribute the most to λ, and 3) determining the demographic transition(s) that accounted for differences in λ when comparing scenarios that considered either only predated colonies or both predated and non-predated outplants. Results: Predation increased over time, being more frequently observed in the area with the highest topographic relief and on colonies foreign to the study site. Outplants that were partially consumed grew significantly slower than non-predated colonies; however, predation did not threaten their survival. The likelihood of being attacked by the fireworm increased with branching complexity. The estimated λ for a scenario considering only predated colonies was 0.99, whereas, for a scenario where both predated and non-predated colonies were considered, λ was 0.91. Population growth, under the two scenarios, was mainly influenced by the probability of a large colony surviving and remaining at the largest size. Conclusions: Although predation can negatively impact coral growth, the relatively high survival rate of predated colonies compensates for the adverse effect. Since survival is the demographic transition that contributes most to population growth, it could be concluded that under a non-outbreak scenario, fireworm predation may not be the primary cause of A. cervicornis population decline.

Introducción: El gusano de fuego Hermodice carunculata es un poliqueto común que puede depredar muchas especies de coral. Sin embargo, pocos estudios han examinado el efecto de la depredación del gusano de fuego en la demografía de los corales durante periodos sin brotes poblacionales. Objetivo: Este estudio tuvo como objetivo determinar si la depredación por H. carunculata compromete el crecimiento, la supervivencia y el desempeño poblacional del coral amenazado Acropora cervicornis. Métodos: Fragmentos de coral criados en vivero (n = 99) se fijaron al fondo del arrecife Punta Melones en la Isla Municipio de Culebra, Puerto Rico. La actividad de depredación y sus consecuencias demográficas en los trasplantes de coral se evaluaron desde diciembre de 2020 hasta agosto de 2022. Se comparó la susceptibilidad a la depredación entre las colonias recolectadas directamente del arrecife y las que se originaron en fuentes externas (p. ej., viveros de coral). Con los datos demográficos, se desarrollaron modelos matriciales simples de población basados en el tamaño para 1) examinar si la depredación del gusano de fuego causa una disminución significativa en la tasa de crecimiento de la población (λ), 2) determinar las transiciones demográficas que más contribuyen a λ, y 3) determinar la(s) transición(es) demográfica(s) que explican las diferencias en λ al comparar escenarios que consideraron solo colonias depredadas o la combinación de colonias depredadas y no depredadas. Resultados: La depredación aumentó con el tiempo, observándose con mayor frecuencia en la zona de mayor relieve topográfico y en colonias ajenas al sitio de estudio. Los trasplantes consumidos parcialmente crecieron significativamente más lento que las colonias no depredadas; sin embargo, la depredación no amenazó su supervivencia. La probabilidad de ser atacado por el gusano de fuego aumentó con la complejidad morfológica de la colonia. El λ estimado para un escenario que consideraba solo las colonias depredadas fue de 0.99, mientras que, para un escenario en el que se consideraron tanto las colonias depredadas como las no depredadas, λ fue de 0.91. El crecimiento de la población, en ambos escenarios, estuvo influenciado principalmente por la probabilidad de que una colonia grande sobreviviera y permaneciera en el tamaño más grande. Conclusiones: Aunque la depredación puede tener un impacto negativo en el crecimiento de los corales, una tasa de supervivencia relativamente alta de las colonias depredadas compensa los efectos adversos. Dado que la supervivencia es la transición demográfica que más contribuye al crecimiento de la población, se podría concluir que, en un escenario sin brotes, la depredación por gusanos de fuego no debe ser la causa principal de la disminución de la población de A. cervicornis.

A Bayesian multi-stage modelling framework to evaluate impacts of energy development on wildlife populations: an application to greater sage-grouse (Centrocercus urophasianus).

Increased demand for domestic production of renewable energy has led to expansion of energy infrastructure across western North America. Much of the western U.S. comprises remote landscapes that are home to a variety of vegetation communities and wildlife species, including the imperiled sagebrush ecosystem and indicator species such as greater sage-grouse (Centrocercus urophasianus). Geothermal sources in particular have potential for continued development across the western U.S. but impacts to greater sage-grouse and other species are unknown. To address this information gap, we describe a novel two-pronged methodology that analyzes impacts of geothermal energy production on pattern and process of greater sage-grouse populations using (a) before-after control-impact (BACI) measures of population growth and lek absence rates and (b) concurrent-to-operation evaluations of demographic rates. Growth and absence rate analyses utilized 14 years of lek survey data collected prior (2005-2011) and concurrent (2012-2018) to geothermal operations at two sites in Nevada, USA. Demographic analyses utilized relocation data, restricted inference to concurrent years, and incorporated 17 additional control sites. Demographic results were applied to >100 potential geothermal sites distributed across the study region to generate spatially explicit predictions of unrealized population-level impacts.•State-space and generalized linear models yield estimates of population growth and lek absence rates, respectively, before and after the onset of geothermal energy production; distances ranging from 2-30 km are evaluated as alternative control-impact footprint hypotheses; this provides inference about the spatial extent as well as the magnitude of impacts associated with geothermal development.•Estimation of important population demographic rates are implemented to investigate the processes by which geothermal energy development might reduce population growth; independent estimates of confounding, environmental effects from 17 control sites are made spatially explicit within 'impact' models to establish baseline conditions otherwise masked by collinearity.•Population matrix models are built using estimates from demographic analyses to provide landscape mapping of impacts associated with potential geothermal sites.

Across-model spread and shrinking in predicting peatland carbon dynamics under global change.

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO2 levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO2 (eCO2 ) concentrations in the measurements, simulated NEP responded positively to eCO2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Demographic Analysis of Shortfin Mako Shark (Isurus oxyrinchus) in the South Pacific Ocean.

The shortfin mako shark (Isurus oxyrinchus) demonstrates low productivity and is thus relatively sensitive to fishing. Natural mortality (M) and fishing mortality (F) data are critical to determine their population dynamics. However, catch and fishing effort data are unavailable for this species in the South Pacific Ocean, making stock assessments difficult. Demographic quantitative methods aid in analyzing species with limited data availability. We used a two-sex stage-structured matrix population model to examine the demographic stock status of mako sharks. However, data-limited models to determine fishery management strategies have limitations. We performed Monte Carlo simulations to evaluate the effects of uncertainty on the estimated mako shark population growth rate. Under unfished conditions, the simulations demonstrated that the mako sharks showed a higher finite population growth rate in the 2-year reproductive cycle compared to the 3-year reproductive cycle. Protecting immature mako sharks led to a higher population growth rate than protecting mature mako sharks. According to the sex-specific data, protecting immature male and female sharks led to a higher population growth rate than protecting mature male and female sharks. In conclusion, sex-specific management measures can facilitate the sustainable mako shark conservation and management.

Modeling temporal dynamics of genetic diversity in stage-structured plant populations with reference to demographic genetic structure.

Predicting temporal dynamics of genetic diversity is important for assessing long-term population persistence. In stage-structured populations, especially in perennial plant species, genetic diversity is often compared among life history stages, such as seedlings, juveniles, and flowerings, using neutral genetic markers. The comparison among stages is sometimes referred to as demographic genetic structure, which has been regarded as a proxy of potential genetic changes because individuals in mature stages will die and be replaced by those in more immature stages over the course of time. However, due to the lack of theoretical examination, the basic property of the stage-wise genetic diversity remained unclear. We developed a matrix model which was made up of difference equations of the probability of non-identical-by-descent of each life history stage at a neutral locus to describe the dynamics and the inter-stage differences of genetic diversity in stage-structured plant populations. Based on the model, we formulated demographic genetic structure as well as the annual change rate of the probability of non-identical-by-descent (denoted as η). We checked if theoretical expectations on demographic genetic structure and η obtained from our model agreed with computational results of stochastic simulation using randomly generated 3,000 life histories. We then examined the relationships of demographic genetic structure with effective population size Ne, which is the determinants of diversity loss per generation time. Theoretical expectations on η and demographic genetic structure fitted well to the results of stochastic simulation, supporting the validity of our model. Demographic genetic structure varied independently of Ne and η, while having a strong correlation with stable stage distribution: genetic diversity was lower in stages with fewer individuals. Our results indicate that demographic genetic structure strongly reflects stable stage distribution, rather than temporal genetic dynamics, and that inferring future genetic diversity solely from demographic genetic structure would be misleading. Instead of demographic genetic structure, we propose η as an useful tool to predict genetic diversity at the same time scale as population dynamics (i.e., per year), facilitating evaluation on population viability from a genetic point of view.

Growing up is hard to do: a demographic model of survival and growth of Caribbean octocoral recruits.

Background: Among species with size structured demography, population structure is determined by size specific survival and growth rates. This interplay is particularly important among recently settled colonial invertebrates for which survival is low and growth is the only way of escaping the high mortality that small colonies are subject to. Gorgonian corals settling on reefs can grow into colonies of millions of polyps and can be meters tall. However, all colonies start their benthic lives as single polyps, which are subject to high mortality rates. Annual survival among these species increases with size, reflecting the ability of colonies to increasingly survive partial mortality as they grow larger. Methods: Data on survival and growth of gorgonian recruits in the genera Eunicea and Pseudoplexaura at two sites on the southern coast of St John, US Virgin Islands were used to generate a stage structured model that characterizes growth of recruits from 0.3 cm until they reach 5 cm height. The model used the frequency distributions of colony growth rates to incorporate variability into the model. Results: High probabilities of zero and negative growth increase the time necessary to reach 5 cm and extends the demographic bottleneck caused by high mortality to multiple years. Only 5% of the recruits in the model survived and reached 5 cm height and, on average, recruits required 3 y to reach 5 cm height. Field measurements of recruitment rates often use colony height to differentiate recruits from older colonies, but height cannot unambiguously identify recruits due to the highly variable nature of colony growth. Our model shows how recruitment rates based on height average recruitment and survival across more than a single year, but size-based definitions of recruitment if consistently used can characterize the role of supply and early survival in the population dynamics of species.

Dynamical Behavior of Two Interacting Double Quantum Dots in 2D Materials for Feasibility of Controlled-NOT Operation.

Two interacting double quantum dots (DQDs) can be suitable candidates for operation in the applications of quantum information processing and computation. In this work, DQDs are modeled by the heterostructure of two-dimensional (2D) MoS2 having 1T-phase embedded in 2H-phase with the aim to investigate the feasibility of controlled-NOT (CNOT) gate operation with the Coulomb interaction. The Hamiltonian of the system is constructed by two models, namely the 2D electronic potential model and the 4×4 matrix model whose matrix elements are computed from the approximated two-level systems interaction. The dynamics of states are carried out by the Crank-Nicolson method in the potential model and by the fourth order Runge-Kutta method in the matrix model. Model parameters are analyzed to optimize the CNOT operation feasibility and fidelity, and investigate the behaviors of DQDs in different regimes. Results from both models are in excellent agreement, indicating that the constructed matrix model can be used to simulate dynamical behaviors of two interacting DQDs with lower computational resources. For CNOT operation, the two DQD systems with the Coulomb interaction are feasible, though optimization of engineering parameters is needed to achieve optimal fidelity.

A novel approach for accurate quantification of lake residence time - Lake Kinneret as a case study.

Water residence time, which is affected by increasing water demands and climate change, plays a crucial role in lakes and reservoirs since it influences many natural physical and ecological processes that eventually impact the water quality of the waterbody. Thus, accurate quantification of the water residence time and its distribution is an important tool in lake management. In this study we present a novel approach for assessing the residence time in lakes and reservoirs. The approach is based on the Leslie matrix model that was originally developed for the analysis of age-structured biological population dynamics. In this approach the water in the lake is divided into different age classes each representing the time since the "parcel" of water entered the lake and provides an overall picture of the water age structure. The traditional approach for calculating residence times, which relies only on the lake volume and annual inflow or outflow volumes thereby disregarding any previous information, is very sensitive to large interannual variation. While the proposed approach produces the fraction and volume distribution curves of all age classes within the lake for each simulated timestep. Thus, in addition to mean residence time, the fraction of young water (FYW), quantifying the "young" fraction of water in the lake can be analyzed. The same is true for any other age class of water. The approach was applied to Lake Kinneret (Sea of Galilee) historical data collected over 32 years (1987-2018) and for prediction of long-term time series based on several future scenarios (inflows and outflows). It offers a more accurate quantification of the mean residence time of water in a lake and can easily be adapted to other waterbodies. Comparison of simulation results may serve as basis for determining the lake's management policy, by controlling the inflows and outflows, that will affect both the mean residence time and the fraction of "young/old" age classes of water.

Modelling the responses of partially migratory metapopulations to changing seasonal migration rates: From theory to data.

Among-individual and within-individual variation in expression of seasonal migration versus residence is widespread in nature and could substantially affect the dynamics of partially migratory metapopulations inhabiting seasonally and spatially structured environments. However, such variation has rarely been explicitly incorporated into metapopulation dynamic models for partially migratory systems. We, therefore, lack general frameworks that can identify how variable seasonal movements, and associated season- and location-specific vital rates, can control system persistence. We constructed a novel conceptual framework that captures full-annual-cycle dynamics and key dimensions of metapopulation structure for partially migratory species inhabiting seasonal environments. We conceptualize among-individual variation in seasonal migration as two variable vital rates: seasonal movement probability and associated movement survival probability. We conceptualize three levels of within-individual variation (i.e. plasticity), representing seasonal or annual variation in seasonal migration or lifelong fixed strategies. We formulate these concepts as a general matrix model, which is customizable for diverse life-histories and seasonal landscapes. To illustrate how variable seasonal migration can affect metapopulation growth rate, demographic structure and vital rate elasticities, we parameterize our general models for hypothetical short- and longer-lived species. Analyses illustrate that elasticities of seasonal movement probability and associated survival probability can sometimes equal or exceed those of vital rates typically understood to substantially influence metapopulation dynamics (i.e. seasonal survival probability or fecundity), that elasticities can vary non-linearly, and that metapopulation outcomes depend on the level of within-individual plasticity. We illustrate how our general framework can be applied to evaluate the consequences of variable and changing seasonal movement probability by parameterizing our models for a real partially migratory metapopulation of European shags Gulosus aristotelis assuming lifelong fixed strategies. Given observed conditions, metapopulation growth rate was most elastic to breeding season adult survival of the resident fraction in the dominant population. However, given doubled seasonal movement probability, variation in survival during movement would become the primary driver of metapopulation dynamics. Our general conceptual and matrix model frameworks, and illustrative analyses, thereby highlight complex ways in which structured variation in seasonal migration can influence dynamics of partially migratory metapopulations, and pave the way for diverse future theoretical and empirical advances.

Examination of the interaction between age-specific predation and chronic disease in the Greater Yellowstone Ecosystem.

Predators may create healthier prey populations by selectively removing diseased individuals. Predators typically prefer some ages of prey over others, which may, or may not, align with those prey ages that are most likely to be diseased. The interaction of age-specific infection and predation has not been previously explored and likely has sizable effects on disease dynamics. We hypothesize that predator cleansing effects will be greater when the disease and predation occur in the same prey age groups. We examine the predator cleansing effect using a model where both vulnerability to predators and pathogen prevalence vary with age. We tailor this model to chronic wasting disease (CWD) in mule deer and elk populations in the Greater Yellowstone Ecosystem, with empirical data from Yellowstone grey wolves and cougars. Model results suggest that under moderate, yet realistic, predation pressure from cougars and wolves independently, predators may decrease CWD outbreak size substantially and delay the accumulation of symptomatic deer and elk. The magnitude of this effect is driven by the ability of predators to selectively remove late-stage CWD infections that are likely the most responsible for transmission, but this may not be the age class they typically select. Thus, predators that select for infected young adults over uninfected juveniles have a stronger cleansing effect, and these effects are strengthened when transmission rates increase with increasing prey morbidity. There are also trade-offs from a management perspective-that is, increasing predator kill rates can result in opposing forces on prey abundance and CWD prevalence. Our modelling exploration shows that predators have the potential to reduce prevalence in prey populations when prey age and disease severity are considered, yet the strength of this effect is influenced by predators' selection for demography or body condition. Current CWD management focuses on increasing cervid hunting as the primary management tool, and our results suggest predators may also be a useful tool under certain conditions, but not necessarily without additional impacts on host abundance and demography. Protected areas with predator populations will play a large role in informing the debate over predator impacts on disease.

Modeling the evolution of herbicide resistance in weed species with a complex life cycle.

A growing number of weed species have evolved resistance to herbicides in recent years, which causes an immense financial burden to farmers. An increasingly popular method of weed control is the adoption of crops that are resistant to specific herbicides, which allows farmers to apply the herbicide during the growing season without harming the crop. If such crops are planted in the presence of closely related weed species, it is possible that resistance genes could transfer from the crop species to feral populations of the wild species via gene flow and become stably introgressed under ongoing selective pressure by the herbicide. We use a density-dependent matrix model to evaluate the effect of planting such crops on the evolution of herbicide resistance under a range of management scenarios. Our model expands on previous simulation studies by considering weed species with a more complex life cycle (perennial, rhizomatous weed species), studying the effect of environmental variation in herbicide effectiveness, and evaluating the role of common simplifying genetic assumptions on resistance evolution. Our model predictions are qualitatively similar to previous modeling studies using species with a simpler life cycle, which is, crop rotation in combination with rotation of herbicide site of action effectively controls weed populations and slows the evolution of herbicide resistance. We find that ignoring the effect of environmental variation can lead to an over- or under-prediction of the speed of resistance evolution. The effect of environmental variation in herbicide effectiveness depends on the resistance allele frequency in the weed population at the beginning of the simulation. Finally, we find that degree of dominance and ploidy level have a much larger effect on the predicted speed of resistance evolution compared to the rate of gene flow.

Evaluation of the Intensive Treatment and Rehabilitation Program for Residential Treatment and Rehabilitation Centers (INTREPRET) in the Philippines: a study protocol for a randomized controlled trial.

BACKGROUND: The Philippines has been severely affected by the methamphetamine crisis. The government has launched a policy war against drug use, although the severe sanctions imposed on drug users have been criticized internationally. To help implement a more effective and humane approach to drug use, this study aimed to introduce a comprehensive treatment program for methamphetamine users based on cognitive-behavioral therapy (CBT) whose effectiveness will be evaluated through a randomized controlled trial. METHODS: Methamphetamine users admitted into government-run rehabilitation facilities are recruited and randomly assigned to either a CBT-based treatment program or existing therapeutic community (TC)-based treatment. The CBT treatment program was developed based on the Matrix Model that considers cultural and social factors in the Philippines. After 6 months of treatment, there will be a three-month follow-up, when the participants' drug use (tested through urine testing) and other psychological variables, including craving, coping skills, and well-being, will be compared. Potential participants are given a summary of the study and a consent form. The consent form is signed and dated by participants prior to their study participation. Ethical approval was obtained prior to the commencement of the study. DISCUSSION: This is the first randomized controlled trial to compare the residential CBT program and the TC model for methamphetamine users in the Philippines. The study aims to fill the current knowledge and capacity gaps by introducing a CBT-based treatment program to improve the psychosocial well-being of drug users in the Philippines. Moreover, if the effectiveness of the treatment program is demonstrated, anti-drug campaigns and severe sanctions against drug users may be reconsidered. TRIAL REGISTRATION: UMIN Clinical Trials Registry JPRN-UMIN000038597 . Registered on 15 November 2019. Protocol version October 17, 2021 ver.2.

Weighing the unknowns: Value of Information for biological and operational uncertainty in invasion management.

The management of biological invasions is a worldwide conservation priority. Unfortunately, decision-making on optimal invasion management can be impeded by lack of information about the biological processes that determine invader success (i.e. biological uncertainty) or by uncertainty about the effectiveness of candidate interventions (i.e. operational uncertainty). Concurrent assessment of both sources of uncertainty within the same framework can help to optimize control decisions.Here, we present a Value of Information (VoI) framework to simultaneously analyse the effects of biological and operational uncertainties on management outcomes. We demonstrate this approach with a case study: minimizing the long-term population growth of musk thistle Carduus nutans, a widespread invasive plant, using several insects as biological control agents, including Trichosirocalus horridus, Rhinocyllus conicus and Urophora solstitialis.The ranking of biocontrol agents was sensitive to differences in the target weed's demography and also to differences in the effectiveness of the different biocontrol agents. This finding suggests that accounting for both biological and operational uncertainties is valuable when making management recommendations for invasion control. Furthermore, our VoI analyses show that reduction of all uncertainties across all combinations of demographic model and biocontrol effectiveness explored in the current study would lead, on average, to a 15.6% reduction in musk thistle population growth rate. The specific growth reduction that would be observed in any instance would depend on how the uncertainties actually resolve. Resolving biological uncertainty (across demographic model combinations) or operational uncertainty (across biocontrol effectiveness combinations) alone would reduce expected population growth rate by 8.5% and 10.5% respectively.Synthesis and applications. Our study demonstrates that intervention rank is determined both by biological processes in the targeted invasive populations and by intervention effectiveness. Ignoring either biological uncertainty or operational uncertainty may result in a suboptimal recommendation. Therefore, it is important to simultaneously acknowledge both sources of uncertainty during the decision-making process in invasion management. The framework presented here can accommodate diverse data sources and modelling approaches, and has wide applicability to guide invasive species management and conservation efforts.

Matrix and agent-based modeling of threats to a diamond-backed terrapin population.

Population models are important tools for evaluating human impacts and potential management approaches on declining species. However, often studies are limited by constraints of the specific modeling approach. In this study we considered the persistence of a diamond-backed terrapin (Malaclemys terrapin) population using two distinct modeling approaches. Two of the models were deterministic matrix models. Analysis of the discrete non-spatial models showed that female adult survival rate had the largest positive impact on population growth while delaying sexual maturity decreased population growth. The matrix models also demonstrated that an increase in crab traps skewed the sex ratio of the population in favor of females. The third model was a stochastic agent-based formulation that evaluated how increases in the number of crab traps and frequency of nest disturbances affected the long-term viability of diamond-backed terrapins. The spatial agent-based model revealed how terrapin mortality was highly sensitive to the proximity of traps to the primary terrapin habitat. Results from this project improve our understanding of threats to diamond-backed terrapins and can be used to guide conservation efforts.

Microhabitats associated with solar energy development alter demography of two desert annuals.

Political and economic initiatives intended to increase energy production while reducing carbon emissions are driving demand for solar energy. Consequently, desert regions are now targeted for development of large-scale photovoltaic solar energy facilities. Where vegetation communities are left intact or restored within facilities, ground-mounted infrastructure may have negative impacts on desert-adapted plants because it creates novel rainfall runoff and shade conditions. We used experimental solar arrays in the Mojave Desert to test how these altered conditions affect population dynamics for a closely related pair of native annual plants: rare Eriophyllum mohavense and common E. wallacei. We estimated aboveground demographic rates (seedling emergence, survivorship, and fecundity) over 7 yr and used seed bank survival rates from a concurrent study to build matrix models of population growth in three experimental microhabitats. In drier years, shade tended to reduce survival of the common species, but increase survival of the rare species. In a wet year, runoff from panels tended to increase seed output for both species. Population growth projections from microhabitat-specific matrix models showed stronger effects of microhabitat under wetter conditions, and relatively little effect under dry conditions (lack of rainfall was an overwhelming constraint). Performance patterns across microhabitats in the wettest year differed between rare and common species. Projected growth of E. mohavense was substantially reduced in shade, mediated by negative effects on aboveground demographic rates. Hence, the rare species were more susceptible to negative effects of panel infrastructure in wet years that are critical to seed bank replenishment. Our results suggest that altered shade and water runoff regimes associated with energy infrastructure will have differential effects on demographic transitions across annual species and drive population-level processes that determine local abundance, resilience, and persistence.

Demography_Lab, an educational application to evaluate population growth: Unstructured and matrix models.

Training in Population Ecology asks for scalable applications capable of embarking students on a trip from basic concepts to the projection of populations under the various effects of density dependence and stochasticity. Demography_Lab is an educational tool for teaching Population Ecology aspiring to cover such a wide range of objectives. The application uses stochastic models to evaluate the future of populations. Demography_Lab may accommodate a wide range of life cycles and can construct models for populations with and without an age or stage structure. Difference equations are used for unstructured populations and matrix models for structured populations. Both types of models operate in discrete time. Models can be very simple, constructed with very limited demographic information or parameter-rich, with a complex density-dependence structure and detailed effects of the different sources of stochasticity. Demography_Lab allows for deterministic projections, asymptotic analysis, the extraction of confidence intervals for demographic parameters, and stochastic projections. Stochastic population growth is evaluated using up to three sources of stochasticity: environmental and demographic stochasticity and sampling error in obtaining the projection matrix. The user has full control on the effect of stochasticity on vital rates. The effect of the three sources of stochasticity may be evaluated independently for each vital rate. The user has also full control on density dependence. It may be included as a ceiling population size controlling the number of individuals in the population or it may be evaluated independently for each vital rate. Sensitivity analysis can be done for the asymptotic population growth rate or for the probability of extinction. Elasticity of the probability of extinction may be evaluated in response to changes in vital rates, and in response to changes in the intensity of density dependence and environmental stochasticity.