A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.     

Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data, as determined by stock productivity, types of harvest situations, and amount of measurement error. Overall, in terms of estimating optimal spawner abundance S_MSY, the Ricker density-dependence parameter β, and optimal harvest rate h_MSY, the Bayesian state-space model works best for informative data from low and variable harvest rate situations for high-productivity salmon stocks. The traditional stock-recruitment model (TSR) may be used for estimating α and h_MSY for low-productivity stocks from variable and high harvest rate situations. However, TSR can severely overestimate S_MSY when spawner abundance is measured with large error in low and variable harvest rate situations. We also found that there is substantial merit in using h_MSY (or benchmarks derived from it) instead of S_MSY as a management target.     

菌根类型对森林树木净初级生产力的影响

菌根是土壤真菌与植物根系形成的共生体,存在于绝大多数植物（90%）的根系和生境中。菌根共有7种类型,在生态系统的过程和功能方面都扮演着十分重要的角色。为了增强对菌根在森林生态系统中重要功能的理解,文章基于全球森林数据库,在全球尺度上研究了不同菌根类型对森林树木净初级生产力（NPP）的影响。结果表明,森林树木NPP随菌根类型的不同而不同,AM类型菌根森林的NPP[679.49 g.m-2.a-1（以C计）]要显著高于含ECM类型菌根的森林[479.00 g.m-2.a-1（以C计）];菌根类型的不同对森林树木地上和地下及其各组分NPP的影响和贡献也存在着显著的不同,AM类型菌根对地下NPP的贡献要高于ECM菌根,而ECM菌根对地上NPP的贡献则较大。菌根类型对地上、地下NPP组分的影响分析则表明,AM类型的菌根对树叶和细根NPP的贡献较大,而ECM类型菌根则对树木主干和枝NPP的贡献较大。可见,森林树木总体NPP及其各组分NPP都随着菌根类型的不同而存在显著的差异。     

Effects of Multiple Levels of Social Organization on Survival and Abundance

Abstract: Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group‐level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age‐specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities.     

丛枝菌根真菌对内蒙古草原大针茅群落的影响

使用真菌抑制剂在内蒙古草原开展原位试验,人为创造菌根受抑制和正常两种环境,通过分析菌根侵染率,测定大针茅群落的物种组成、丰富度和多样性等结构指标,分析丛枝菌根真菌对群落结构和净初级生产力的影响。试验结果表明,两种处理群落的菌根侵染率不同,苯菌灵有效地降低了植株的菌根侵染率。丛枝菌根真菌短期内未能对植物群落的物种丰富度、多样性产生影响,未能改变植物群落的结构和净初级生产力。但丛枝菌根真菌的存在,会对植物群落内不同植物种的地上部生物量实现再分配,降低了优势种垄断资源的能力,使群落内物种的生物量和营养元素含量趋于均匀,有利于保护关键种,有利于植被的恢复与重建。因此,研究结论为内蒙古退化草原生态系统的恢复和重建提供了重要的理论依据和参考价值。     

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking. ● Model is not stuck in local optimal solutions for high-dimensional problem. ● Model can estimate source parameters accurately with known river water levels. ● Both sudden spill incident and normal sewage inputs into the river can be tracked. ● Model is superior to the traditional approaches based on the test cases. Water quality restoration in rivers requires identification of the locations and discharges of pollution sources, and a reliable mathematical model to accomplish this identification is essential. In this paper, an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge. The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method (MCMC). The methodological framework is tested using a designed case of a sudden wastewater spill incident (i.e., source location, flow rate, and starting and ending times of the discharge) and a real case of multiple sewage inputs into a river (i.e., locations and daily flows of sewage sources). The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites. It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space. In comparison, the inverse models based on the popular random walk Metropolis (RWM) algorithm and microbial genetic algorithm (MGA) do not produce reliable estimates for the two scenarios even after multiple simulation runs, and they fall into locally optimal solutions. Since much more water level data are available than water quality data, the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data, especially for rivers receiving significant sewage discharges.     

单层平面索网玻璃幕墙结构研究现状综述

单层平面索网玻璃幕墙是点支式玻璃幕墙的一种重要型式,其幕墙玻璃的支承结构为单层平面索网结构,在国内外一些大型公共建筑中得到了广泛的应用。综述了单层平面索网结构的研究现状,重点阐述了其几何非线性特征、玻-索协同工作特性,以及该类结构的动力特性及动力响应等研究成果。同时,对单层平面索网玻璃幕墙结构研究中所存在的问题进行了分析,并对其发展趋势作了展望。     

浐河、灞河硝酸盐端元贡献比例——基于硝酸盐氮、氧同位素研究

近年来,河流氮污染一直是生物地球化学领域研究的热点问题。然而,识别水体硝酸盐来源、端元贡献比例及其在水体中存在生物转化(硝化、反硝化)过程,仍旧是氮循环研究的难点问题。本研究选取流经西安市的两条河流——浐河和灞河,测定其河水溶解态硝酸盐氮、氧同位素组成,并结合Bayesian同位素混合模型,有效识别了两条河流从源头到汇入渭河河口处,氮素来源的变化,同时,定量分析了其贡献比例的变化。结果显示,河流源头附近,土壤有机氮是河流硝酸盐主要来源,其贡献比例接近30%;河流中游,由于沿河农业活动的增加,同位素指示河流硝酸盐主要来源转化为化学肥料,其贡献比例接近25%;河流下游,由于城市用水的汇入,硝酸盐氮、氧同位素值偏正,主要位于污水及粪肥区间,指示硝酸盐含量较高的生活污水及工业废水的输入,其贡献比例能达到30%以上。通过本研究,研究者定性及半定量的区分和浐河、灞河氮素来源,为今后有效控制氮污染提供了理论基础。     

防风网防尘技术及其在我国大型煤炭港口的应用与发展对策

防风网是控制煤炭起尘与扩散的一种有效手段,其防尘效果主要与防风网的结构、设置方式有关.虽然防风网的防尘效果已得到广泛认可,但防风网并未广泛应用于我国大型煤炭港口,其原因主要在于:①我国煤堆场规模较大,而防风网的有效庇护区范围有限,仅在其下风向、约20倍网高距离的范围内,因此限制了防风网的应用;②防风网造价较为昂贵,每100 m的造价多在106元以上.为推动防风网防尘技术在我国大型煤炭港口的应用,应加强对适宜煤堆场规模、防风网的有效庇护距离、新型防风网材料等技术问题的研究,并选择我国北方沿海大型煤码头开展防尘示范工程.      

秦皇岛港煤堆场防风网风洞试验研究

介绍了秦皇岛港煤堆场防风网模型风洞试验的目的，方法和内容，提供了试验结果分析。试验结果表明，煤堆场防风网可以有效地降低堆场部分区域内的风速和紊流度，减少环境污染。