A structured analysis of risk to important wildlife elements in three Australian Wildlife Conservancy sanctuaries

Where data and information are lacking, structured expert risk assessments can provide a powerful tool to progress natural resource planning. In many situations, practitioners make informal assessments of risk within small groups that typically constitute employees. In this study, we report on three small (in terms of experts) structured expert‐based risk assessment case studies conducted by expert employees of a not‐for‐profit organisation (Australian Wildlife Conservancy) to demonstrate the utility of the approach. The case studies were carried out for three wildlife sanctuaries managed by AWC: Faure Island, Karakamia and Paruna. The likelihood that a set of direct risk factors would cause management failure for sets of important wildlife elements in the three sanctuaries was elicited from the small group of ecological experts. The analysis was couched in terms of a management aim to not lose species from each wildlife element over the management period of 25 years with current management. The experts believed, in particular, that increasing temperature and decreasing water availability associated with climate change were likely to impact significantly upon the vegetation elements and water‐reliant fauna associated with the sanctuaries. Some vegetation elements were also thought likely to be at risk of over‐grazing, unsuitable fire regimes and, in some cases, disease. In addition to predation by exotic predators at one sanctuary, the experts identified additional direct risk factors for various fauna elements associated with expected changes to the vegetation elements, including reduction in food availability, nesting habitat, and generally important life media. From the risk analyses, a preliminary conceptual model was developed to underpin monitoring and to indicate areas for possible management intervention and research. The case studies demonstrate that even in a small workplace team, structured risk assessments can be efficiently accomplished and can provide expedient and transparent information that effectively captures and aggregates the views of the experts.     

Comparing urban food system characteristics and actions in US and Indian cities from a multi‐environmental impact perspective: Toward a streamlined approach

Food action plans in many global cities articulate interest in multiple objectives including reducing in‐ and trans‐boundary environmental impacts (water, land, greenhouse gas (GHG)). However, there exist few standardized analytical tools to compare food system characteristics and actions across cities and countries to assess trade‐offs between multiple objectives (i.e., health, equity) with environmental outcomes. This paper demonstrates a streamlined model applied for analysis of four cities with varying characteristics across the United States and India, to quantify system‐wide water, energy/GHG, and land impacts associated with multiple food system actions to address health, equity, and environment. Baseline diet analysis finds key differences between countries in terms of meat consumption (Delhi 4; Pondicherry 16; United States 59, kg/capita/year), and environmental impact of processing of the average diet (21%, 19%, <1%, <1% of community‐wide GHG‐emissions for New York, Minneapolis, Delhi, and Pondicherry). Analysis of supply chains finds city average distance (food‐miles) varies (Delhi 420; Pondicherry 200; United States average 1,640 km/t‐food) and the sensitivity of GHG emissions of food demand to spatial variability of energy intensity of irrigation is greater in Indian than US cities. Analysis also finds greater pre‐consumer waste in India versus larger post‐consumer accumulations in the United States. Despite these differences in food system characteristics, food waste management and diet change consistently emerge as key strategies. Among diet scenarios, all vegetarian diets are not found equal in terms of environmental benefit, with the US Government's recommended vegetarian diet resulting in less benefit than other more focused targeted diet changes.     

Implementation of a dose calculation algorithm based on Monte Carlo simulations for treatment planning towards MRI guided ion beam therapy

Magnetic resonance guidance in particle therapy has the potential to improve the current performance of clinical workflows. However, the presence of magnetic fields challenges the current algorithms for treatment planning. To ensure proper dose calculations, compensation methods are required to guarantee that the maximum deposited energy of deflected beams lies in the target volume. In addition, proper modifications of the intrinsic dose calculation engines, accounting for magnetic fields, are needed. In this work, an algorithm for proton treatment planning in magnetic fields was implemented in a research treatment planning system (TPS), matRad. Setup-specific look up tables were generated using a validated MC model for a clinical proton beamline (62.4 – 215.7 MeV) interacting with a dipole magnet (B = 0–1 T). The algorithm was successfully benchmarked against MC simulations in water, showing gamma index (2%/2mm) global pass rates higher than 96% for different plan configurations. Additionally, absorbed depth doses were compared with experimental measurements in water. Differences within 2% and 3.5% in the Bragg peak and entrance regions, respectively, were found. Finally, treatment plans were generated and optimized for magnetic field strengths of 0 and 1 T to assess the performance of the proposed model. Equivalent treatment plans and dose volume histograms were achieved, independently of the magnetic field strength. Differences lower than 1.5% for plan quality indicators (D_2%, D_50%, D_90%, V_95% and V_105%) in water, a TG119 phantom and an exemplary prostate patient case were obtained. More complex treatment planning studies are foreseen to establish the limits of applicability of the proposed model.     

Human land uses reduce climate connectivity across North America

Climate connectivity, the ability of a landscape to promote or hinder the movement of organisms in response to a changing climate, is contingent on multiple factors including the distance organisms need to move to track suitable climate over time (i.e. climate velocity) and the resistance they experience along such routes. An additional consideration which has received less attention is that human land uses increase resistance to movement or alter movement routes and thus influence climate connectivity. Here we evaluate the influence of human land uses on climate connectivity across North America by comparing two climate connectivity scenarios, one considering climate change in isolation and the other considering climate change and human land uses. In doing so, we introduce a novel metric of climate connectivity, ‘human exposure’, that quantifies the cumulative exposure to human activities that organisms may encounter as they shift their ranges in response to climate change. We also delineate potential movement routes and evaluate whether the protected area network supports movement corridors better than non‐protected lands. We found that when incorporating human land uses, climate connectivity decreased; climate velocity increased on average by 0.3 km/year and cumulative climatic resistance increased for ~83% of the continent. Moreover, ~96% of movement routes in North America must contend with human land uses to some degree. In the scenario that evaluated climate change in isolation, we found that protected areas do not support climate corridors at a higher rate than non‐protected lands across North America. However, variability is evident, as many ecoregions contain protected areas that exhibit both more and less representation of climate corridors compared to non‐protected lands. Overall, our study indicates that previous evaluations of climate connectivity underestimate climate change exposure because they do not account for human impacts.     

国家公园规划制度功能定位与空间属性

我国将构建统一的国土空间规划体系, 整体谋划国土空间开发保护格局, 强化对各类专项规划的指导约束, 这必将对国家公园保护制度产生重大影响。本文分析了国土空间规划视角下国家公园规划制度的定位, 阐述了国家公园规划承载空间规划与发展规划两大体系的基本属性, 应在国土空间规划体系中具有优先权和前置权; 重点分析了各国家公园总体规划在国家规划体系中的功能与属性特征, 作为空间规划应承接好上位空间规划对国家公园的控制指标和管控要求, 作为建设规划应承接好上位或同域国民经济发展规划等对国家公园的目标任务要求, 作为管理规划要明确国家公园顺畅运行的要求。国家公园应在分区规划、控制性详细规划等专项规划方面寻求创新, 探索国家公园用途管制的实现模式。     

基于综合评价法的洞庭湖区绿地生态网络构建

绿地生态网络对于改善区域生态空间破碎化、生物多样性降低、生态系统服务供需不平衡及保障区域生态安全具有重要意义。本研究以洞庭湖区为例,在3S技术支持下,从生态系统服务功能、潜在生物多样性、形态空间格局的角度综合评价和识别生态源地及计算栅格单元的基本生态阻力;利用夜间灯光指数修正基本生态阻力;运用最小累积阻力模型识别生态廊道;构建加权评价模型,对源地的聚合度、离散度及廊道的生态连接贡献度进行评价;利用结构特征指数对综合网络、“源-汇”潜在网络及规划网络进行对比分析和评价。结果表明: 源地空间分布不均衡,林地、灌丛与水域三者面积之和占源地总面积的95.9%,位于研究区中部的洞庭湖湿地生态风险较高;源地离生态网络系统中心位置越近及到其他源地的平均最小累积阻力越小,聚合及离散的优势越强;高生态质量源地周围的中、高生态质量源地分布越密集,其聚合度、离散度越高;廊道离高生态质量的源地越近,表现为生态连接贡献度越大;林地、灌丛,尤其是河道在自然生态系统与人类社会系统之间起着非常重要的生态连接作用;“源-汇”规划绿道对“源-汇”潜在生态廊道形成了良好补充,与“源-汇”潜在网络相比,综合网络的α、β、γ、ρ指数分别提高123.1%、25.8%、26.2%、74.6%;与“源-汇”规划网络相比,α、β、γ、ρ指数分别提高了190.0%、31.1%、32.5%、114.6%。本研究结果能为洞庭湖区绿地生态网络构建、国土空间规划提供参考。     

New Caledonian crows plan for specific future tool use

The ability to plan for future events is one of the defining features of human intelligence. Whether non-human animals can plan for specific future situations remains contentious: despite a sustained research effort over the last two decades, there is still no consensus on this question. Here, we show that New Caledonian crows can use tools to plan for specific future events. Crows learned a temporal sequence where they were (a) shown a baited apparatus, (b) 5 min later given a choice of five objects and (c) 10 min later given access to the apparatus. At test, these crows were presented with one of two tool–apparatus combinations. For each combination, the crows chose the right tool for the right future task, while ignoring previously useful tools and a low-value food item. This study establishes that planning for specific future tool use can evolve via convergent evolution, given that corvids and humans shared a common ancestor over 300 million years ago, and offers a route to mapping the planning capacities of animals.     

Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PurposeTo predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).MethodsA data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.ResultsSuccessful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.ConclusionsMachine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.     

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against measured data under homogeneous and heterogeneous irradiation geometries

PurposeThis work compares Monte Carlo dose calculations performed using the RayStation treatment planning system against data measured on a Varian Truebeam linear accelerator with 6 MV and 10 MV FFF photon beams.MethodsThe dosimetric performance of the RayStation Monte Carlo calculations was evaluated in a variety of irradiation geometries employing homogeneous and heterogeneous phantoms. Profile and depth dose comparisons against measurement were carried out in relative mode using the gamma index as a quantitative measure of similarity within the central high dose regions.ResultsThe results demonstrate that the treatment planning system dose calculation engine agrees with measurement to within 2%/1 mm for more than 95% of the data points in the high dose regions for all test cases. A systematic underestimation was observed at the tail of the profile penumbra and out of field, with mean differences generally <0.5 mm or 1% of curve dose maximum respectively. Out of field agreement varied between evaluated beam models.ConclusionsThe RayStation implementation of photon Monte Carlo dose calculations show good agreement with measured data for the range of scenarios considered in this work and is deemed sufficiently accurate for introduction into clinical use.