Prioritization Approach Research Articles

A dynamic failure mode and effects analysis for train systems failures risk assessment using FCM and prospect theory

Improving the reliability of railway train systems and preventing potential failures in the train operation process is one of the most significant tasks. The failure mode and effects analysis (FMEA) is the most effective and widely applied technique for identification, evaluation, and prevention risk of potential failures in diverse fields. Nevertheless, current risk prioritization approaches for FMEA overlook the transfer of decision makers’ risk preferences under different risk states of potential failures. In addition, little attrition has been paid to addressing the risk prioritization problems in FMEA under a dynamic environment. In order to bridge these research gaps, this paper proposes a dynamic prioritization approach for FMEA by integrating the Fuzzy Cognitive Map (FCM) and the prospect theory. First, improved weighted arithmetic averaging (WAA) operator based on the similarity measure is constructed to aggregate each decision maker’s evaluation information. Then, the FCM is applied to obtain the risk matrix and interaction relationships among failures under different risk states. Next, the dynamic prospect theory is built to determine the risk priority of each failure by considering the risk preference of decision makers, in which the dynamic weight functions are derived based on the risk matrix under different risk states. Finally, the proposed dynamic risk prioritization approach for FMEA is tested by the failures risk analysis of the railway train bogie system in the railway train systems. The comparison study is conducted to demonstrate the reliability and rationality of the proposed risk prioritization approach.

Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.

Examining the geometry of streets through accessibility: new insights from streetspace allocation analysis

This paper describes streetspace allocation analysis, a method that uses street cross-sections to measure footway and carriageway widths and quantify a key parameter of street design citywide. The resulting network-based streetspace allocation metrics are employed on a proof-of-concept study of train station service areas in London, applying shortest-path analysis under a place and walking prioritisation approach. Overall, streetspace allocation statistics for London confirm the citywide predominance of space allocated for vehicular transport over pedestrian uses. A comparison of the current distribution and proposed re-allocation of streetspace on streets near stations allows for the investigation of the effects of streetspace enhancements, which tend to be beneficial in reducing pedestrian movement impedance and extending service areas. The methods presented here can offer valuable analytical capacity for developing new transit-oriented schemes and designing place-based streets that support sustainable transport and sustainable urban development.

Abstract 14391: The Transcriptional Landscape of Liver Sinusoidal Endothelial Cells

Introduction: Human liver endothelial cells (HLEC) are a heterogeneous cell population. Single cell transcriptomics and immunostaining assays have categorized HLEC into two major groups according to their location within the liver lobules (zone 1 or periportal HLEC and zones 2/3 or central venous (CV) HLEC). Hypothesis: Cellular deconvolution of the bulk transcriptome of HLEC will highlight important properties of HLECs and their transcriptional regulators. Methods: We uniformly processed all bulk RNA-seq data from HLECs in Gene Expression Omnibus. We used CIBERSORTx with human liver single cell RNA-seq data to perform cell type deconvolution of bulk HLEC RNA-seq. We compared the transcriptome of HLEC subpopulations to that of HUVEC. We then applied the HOMER motif finding algorithm on the promoters of genes up/downregulated in HLEC compared to HUVEC. Results: We analyzed data from 10 HLEC samples and 107 HUVEC samples from 37 independent experiments. Principal component analysis revealed that 6 of the 10 HLEC samples have robust transcriptional differences compared to HUVEC. CIBERSORTx showed that those 6 HLEC samples correspond to CV HLECs and can be identified by FCGR2B expression. Differential gene expression analysis revealed enrichment of inflammatory and coagulation pathways in CV HLEC and enrichment of proliferation and mitosis pathways in HUVEC. CV HLEC showed increased expression of factors VIII, X, XII, Protein S and antithrombin III. We validated gene expression at the protein level by Western blot. A transcription factor (TF) prioritization approach using HOMER motif enrichment revealed a potential role for E2F, and RXR-RAR TF as primary determinants of the differences between HUVEC and CV HLEC. Conclusions: We identify and characterize CV HLEC as a distinct endothelial subtype relevant to inflammation and coagulation. We also identify downregulation of E2F and upregulation of RXR-RAR TF as potential key regulators of the CV HLEC transcriptome.

Planar chromatographic super-hyphenations for rapid dereplication

AbstractAnalytical chemistry offers important tools that provide insight and step-by-step understanding. However, natural samples containing many thousands of different compounds are subject to natural variance which makes it complex for artificial intelligence and human understanding. Instead of the prevailing comprehensive separation/detection approach, a prioritization approach is discussed for dereplication that focuses on the most necessary to discover. It is extremely helpful to combine on the same surface two disciplines, i.e. chemistry for separation of mixtures and biology for detection of biological effects, to filter out and prioritize the important compounds that then need to be identified. Complex mixtures are separated in parallel with imaging high-performance thin-layer chromatography, detected non-targeted with planar multiplex bioassays to prioritize compounds and elute the important ones to orthogonal column chromatography−diode array detection−high-resolution mass spectrometry for further characterization. Super-hyphenations reduce thousands of compounds to a manageable number of important active compounds. Its potential lies not only in the prioritization of compounds, but also detection of unknown compounds that were not previously the focus of analysts. An image is worth a thousand words and an effect image even more so. On-surface multiplex bioassays enable the differentiation of opposing signals/effects of compounds in a complex natural sample, which only makes understanding possible. On-surface metabolization enables the study of any changes in the effects through digestion or de-/toxification (S9 liver system). By these new tools applied on the same surface, the mechanisms of action of complex mixtures can be better understood. The latest open-source 2LabsToGo system combines the chemistry and biology laboratory in a miniaturized system developed for wider use of this innovative technique.

Security Requirements Prioritization Techniques: A Survey and Classification Framework

Security requirements Engineering (SRE) is an activity conducted during the early stage of the SDLC. SRE involves eliciting, analyzing, and documenting security requirements. Thorough SRE can help software engineers incorporate countermeasures against malicious attacks into the software’s source code itself. Even though all security requirements are considered relevant, implementing all security mechanisms that protect against every possible threat is not feasible. Security requirements must compete not only with time and budget, but also with the constraints they inflect on a software’s availability, features, and functionalities. Thus, the process of security requirements prioritization becomes an integral task in the discipline of risk-analysis and trade-off-analysis. A sound prioritization technique provides guidance for software engineers to make educated decisions on which security requirements are of topmost importance. Even though previous research has proposed various security requirement prioritization techniques, none of the existing research efforts have provided a detailed survey and comparative analysis of existing techniques. This paper uses a literature survey approach to first define security requirements engineering. Next, we identify the state-of-the-art techniques that can be adopted to impose a well-established prioritization criterion for security requirements. Our survey identified, summarized, and compared seven (7) security requirements prioritization approaches proposed in the literature.

Empirical validation of a family-member prioritization approach to maximize statistical power in missing person cases

In order to prioritize the exhumation of the most informative reference relatives to increase the statistical power of a reference group, a conditional simulation approach for missing person identification that combines both exclusion and inclusion power in reference families has been previously developed. The aim of this study is to empirically validate this approach by comparing its predicted theoretical prioritization model with the observed changes in statistical power in real cases of our laboratory, in which new relatives had already been added. We conclude that this approach is a reliable tool to choose the most appropriate reference relatives to complete a family group and improve the identification power of a Missing Person (MP).

Common genetic risk factors in ASD and ADHD co-occurring families.

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two major neurodevelopmental disorders that frequently co-occur. However, the genetic mechanism of the co-occurrence remains unclear. The New Jersey Language and Autism Genetics Study (NJLAGS) collected more than 100 families with at least one member affected by ASD. NJLAGS families show a high prevalence of ADHD and provide a good opportunity to study shared genetic risk factors for ASD and ADHD. The linkage study of the NJLAGS families revealed regions on chromosomes 12 and 17 that are significantly associated with ADHD. Using whole-genome sequencing data on 272 samples from 73 NJLAGS families, we identified potential risk genes for ASD and ADHD. Within the linkage regions, we identified 36 genes that are associated with ADHD using a pedigree-based gene prioritization approach. KDM6B (Lysine Demethylase 6B) is the highest-ranking gene, which is a known risk gene for neurodevelopmental disorders, including ASD and ADHD. At the whole-genome level, we identified 207 candidate genes from the analysis of both small variants and structure variants, including both known and novel genes. Using enrichment and protein-protein interaction network analyses, we identified gene ontology terms and pathways enriched for ASD and ADHD candidate genes, such as cilia function and cation channel activity. Candidate genes and pathways identified in our study improve the understanding of the genetic etiology of ASD and ADHD and will lead to new diagnostic or therapeutic interventions for ASD and ADHD in the future.

National scale habitat suitability analysis to evaluate and improve conservation areas for a mature forest specialist species

Species distribution modelling is an important tool to inform conservation, particularly if combined with site prioritisation approaches. Results can then be used in both suggestion of new protected areas as well as evaluation of the existing ones. We applied MaxEnt analysis to a species of mature forests, the Eurasian Pygmy Owl ( Glaucidium passerinum ), which is recognised as a biodiversity indicator species. Furthermore, we used habitat suitability and uncertainty maps in site prioritisation for conservation and evaluation of the existing nature conservation area (NCA) network. We found species to be strongly positively associated with time since forestry activities at a local scale (25ha) and abundance of mature forests from local through home range (450ha) to landscape (1960ha) scales. The sum of habitat suitability as a proxy for apparent population allowed us to estimate that the existing NCAs hold only 23% of the population. We found 68% of priority sites (PS) for species conservation to be outside NCAs. Strict forestry restrictions form the most suitable conservation regimes, with more than 51% of PS in NCA network having insufficient regimes. Inclusion of PS in NCAs would increase the network to 26% of the national territory with 41% of the species apparent population. Currently, the most suitable conservation regimes cover less than 2% of the country area. • Eurasian pygmy owl reveals unprotected mature forest concentrations. • Site prioritisation highlight gaps within and between currently protected areas. • 68% of priority sites (PS) for a species are outside any conservation area. • 51% of PS in protected areas have insufficient conservation regime for species. • PS inclusion in protected areas would increase the network to only 26% of land.

Prioritization of Water Use Rights in Ethiopia: Exploring the Perspectives and Practices in the Governance of Awash River Basin

Water governance mainly strives to ensure equitable and efficient allocation and prioritization of use rights. The scarcity of water resources in any setting can be a cause for conflict, wastage, depletion, and/or pollution. Prioritization of water use rights is considered as important principle of water governance to protect the human right to water, accommodate interests, avoid conflict, wastage and allocate available volume of water to potential users. This article explores the perspectives and practices of prioritization approach of water governance in Awash River basin. Qualitative research approach was employed, and the findings indicate that there are multiple interpretations by the key actors of the priority ladder amidst general and insufficient policy and legal frameworks. The practice also indicates that water allocation plan for the medium and large-scale water users are stated on papers whereas actual allocations are made based on convenience. It is critical to devise water reform and set prioritizing principles and standards. Moreover there is the need to apply the most feasible and comprehensive approach in water governance and avail the essential resources and technologies.

Kentsel Dönüşüm Alanlarının Analitik Hiyerarşi Süreci Yöntemi ile Belirlenmesi

Kentsel dönüşüm, yapısal düzenleme, sürdürülebilir çevresel performans ve sosyo-ekonomik bütünleşme dengesinin gözetildiği bir düzendir. Yapı-çevre-insan ilişkisinin yeniden kurulmasında öngörülen hedeflere ulaşılabilmesi için göstergelerin tanımlanması ve sürdürülebilirliğin sağlanması önemlidir. Çalışmada, kentsel dönüşümde gösterge temelli önceliklendirme yaklaşımı kurgulanarak yer seçim kararlarının alınma süreçlerinde karar destek sürecini besleyen analitik hiyerarşi süreci yöntemi kullanılmıştır. Göstergelerin ölçülebilir, değerlendirmeye açık ve denetlenebilir olması sürdürülebilir yerleşim alanları oluşturulması için bir fırsattır. Kentsel dönüşüm projelerinin olumlu sonuçlarını artırabilmek için yapısal, çevresel ve sosyo-ekonomik bileşenleri kapsayan kriterler tanımlanmıştır. Uzman görüşleriyle kriterler ağırlıklandırılarak kentsel dönüşüm alanlarının belirlenmesinde dikkate alınması gerekebilecek bileşenlerin öncelik sıraları ortaya konmuştur. Önerilen yaklaşım ile kentsel dönüşüm projelerinin öncelikli olarak hangi alandan başlanması ve planlanması gerekliliği tespit edilmiştir.

Identifying priority aquatic refuges to sustain freshwater biodiversity in intermittent streams in eastern Australia

AbstractThe hydrological variability of intermittent streams means that the spatial distribution of dry‐season aquatic refuges within river networks and the temporal dynamics of hydrological connectivity between them are critical for the persistence of aquatic biodiversity. Here, a new approach is demonstrated to identify surface water bodies as priority refuges for efficient conservation management of freshwater biodiversity in intermittent stream networks.Recently developed models of surface water extent and daily streamflow were used to represent spatio‐temporal variations in hydrological connectivity and surface water persistence within river networks of eastern Australia over a 107‐yr period. Using this information, systematic conservation planning was applied to prioritize aquatic areas for conservation of 25 fish species under two scenarios. One scenario identified priority refuges to complement those already occurring in protected areas, whereas the other did not consider protected area status.The priority networks identified concentrated on the main stems of river catchments where surface water was more likely to be persistent and aquatic refuges were more likely to be connected, but also included headwaters for rare fish species. All three set conservation targets for the 25 fish species can be met in the best solution of priority networks. Although the second scenario achieved the targets with a smaller size of priority network overall, it required more new aquatic refuges and was thus less efficient than the first scenario.The newly developed datasets are useful for freshwater conservation prioritization because they account for hydrological variability of intermittent streams. The systematic prioritization approach applied is transferable to other regions and freshwater taxa to identify aquatic refuges for biodiversity conservation within intermittent stream systems.

Engineering Characteristics Prioritization in Quality Function Deployment Using an Improved ORESTE Method with Double Hierarchy Hesitant Linguistic Information

Quality function deployment (QFD) is a customer-driven product development technique widely utilized to translating customer requirements into engineering characteristics for maximum customer satisfaction. Nonetheless, when used in real situations, the traditional QFD method has been criticized to have many deficiencies, e.g., in expressing experts’ uncertain assessments and prioritizing engineering characteristics. In this study, we propose a new engineering characteristics prioritization approach based on double hierarchy hesitant linguistic term sets (DHHLTSs) and the ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method to overcome the shortcomings of the traditional QFD. Specifically, the main contributions of this study to the literature are that the DHHLTSs are utilized to describe the hesitant relationship assessments between customer requirements and engineering characteristics provided by experts, and the ORESTE method is modified and used to determine the importance ranking orders of engineering characteristics. Finally, a case study and a comparison analysis are presented to illustrate the feasibility and practicability of the proposed QFD approach. The advantages of the new approach being proposed are higher flexibility in handling experts’ intricate and hesitant relationship evaluation information and effective in providing a reasonable prioritization of engineering characteristics in the practical QFD analysis.

A novel multi-criteria decision-making approach for prioritization of elective surgeries through formulation of “weighted MeNTS scoring system”

BackgroundPublicly funded healthcare system has long non-manageable elective surgery waiting lists due to the non-existence of systematic mathematical modelling that can assess the relative priority of patients on elective surgery waiting lists thus denying the provision of surgical support to the patients with higher urgency. Mostly the patients of general surgery are entertain with highly subjective “time-honoured” methods that are inadequate to measure and compare the urgency of surgical procedure. ObjectiveA methodology of assigning priorities to patients on elective surgery waiting lists has been presented in this paper using weighted criteria objectives. The objectives hve been chosen and assigned weights based on hospital conditions, and in consultation with the surgeons in hospital in Pakistan. MethodsThe proposed methodology presents two working contributions; first, a scoring mechanism based on MeNTS scoring system with weighted criterion that objectively translate the condition of patient prior to the surgical procedure; and second, a patient prioritization methodology to select patients for surgeries according to the corresponding scores. Detailed simulation results from actual patient data have been presented to evaluate the effectiveness of the proposed methodology, and its applicability and ease of use has been tested in real-time by surgeons while providing consultations to their patients. ResultsThe proposed methodology outperforms the traditional “first-come-first-serve” methodology as there was a 30% reduction in average waiting time in elective surgery waiting lists (from 4.246 to 2.956 days) with 103 (90%) of patients being entertained before or within the unprioritized surgeries time span, with 94 patients having surgery within 1 day of being on waiting list (an increase of 47 patients). Moreover, transparency and equity were also found in the adaptation of this strategy to prioritize the elective surgery patients. ConclusionsPrioritizing patients on elective surgery waiting lists is an important concern in surgical field. In most of the methodologies presented in earlier research, prioritization of patients for surgery is carried out subjectively. This study shows that the proposed technique has the potential to decrease the waiting times for patients on elective surgery waiting lists, as well as be presented as an objective methodology for preparing the elective surgery waiting lists to increase the transparency in waiting list.

Benefits of measurable population connectivity metrics for area-based marine management

The dispersal of larvae by ocean currents is likely to represent an increasingly important driver of marine population dynamics across fragmented habitats. A boost in availability of larval dispersal data from biophysical simulations has therefore led to routine calculations of population connectivity metrics that are used for area-based management decision support, including the placement of Marine Protected Areas (MPAs). However, connectivity-based decision support for area-based management is often complex, highly uncertain, and the associated conservation impact rarely if ever evaluated. In combination, these challenges risk stakeholder engagement, compliance, and overall management effectiveness. Here we use a case study representing multiple key fishery species on coral reefs in Indonesia to demonstrate that consideration of larval dispersal for MPA placement decision support could be critical to recover both fish populations and fisheries from depletion, thereby mitigating potentially severe impacts on coastal communities. Importantly, we further show that MPA placement decisions can be effective even if based on comparatively simple and empirically measurable dispersal characteristics. Maximizing larval export, expressed as the contribution of larvae from MPA candidate sites to total larval settlement in surrounding areas, for example, was found to be a broadly beneficial MPA placement prioritization approach. Across investigated fish families with diverse life histories, this strategy resulted in MPA network designs that increased catches by a factor of 1.3 ± 0.3 (mean ± SD) and total fish biomass by a factor of 3.2 ± 0.3 (9.7 ± 1.2 in no-fishing areas and 1.4 ± 0.3 in fished areas) compared to conditions without effectively managed or protected areas. Our findings are relevant for both the implementation and impact evaluation of global marine conservation policies, specifically in tropical biodiversity hotspots, such as Indonesia, where coral reefs are often overfished and increasingly threatened but local communities highly dependent on sustainable fisheries.

Efficiency in the cath lab: Pursuing value-based improvements following a sociotechnical approach

IntroductionCardiac catheterization laboratories (cath labs) are commonly designed to achieve the best clinical outcomes while being efficient. This study develops methods to support healthcare managers in analyzing the added value of improvement measures, following a value-based healthcare (VBHC) framework. MethodsBased on a sociotechnical approach, the applied methods aimed to increase efficiency, reduce duplication of tasks, cut costs, and improve patient care, while creating a more inclusive and trusting decision making process. Within the process of building a multicriteria evaluation model, meetings, interviews, and a decision conference were designed. In parallel, a workflow discrete event simulation model was built to replicate current patient flow and activities and to reproduce the impact of implementing improvement measures and variations in human resources. ResultsFour evaluation criteria were considered for workflow improvement: contribution to the efficiency of the admission process, compliance with schedules, agility among procedures, and human resources training. The designed improvement measures sought to 1) improve the start-up of the day, 2) improve pre-exam assessment, and 3) improve staff training. Results prioritize ‘Measure 2’ and demonstrate that improving human resources allocation increases efficiency and offers significant cost savings. ConclusionsThe proposed solution is in line with VBHC principles, as it improves efficiency and care delivery while optimizing costs. Simulation was an effective tool to replicate and explore possible changes. Multicriteria modeling proved to be a useful approach for a transparent prioritization and redesign of improvements in the healthcare sector.

Improvement projects with an environmental focus: A novel approach for prioritization

Employment of a systematic approach for prioritization of improvement projects and addressing the voice of the stakeholders are imperative for organizations in the current business circumstances. This study suggests a novel approach that employs multi-criteria decision-making (MCDM) methods for project prioritization. A combination of step-wise weight assessment ratio analysis (SWARA) method and house of quality (HOQ) is used in the calculation of the importance of criteria, followed by utilization of criteria importance in COmplex PRoportional ASsessment of alternatives with grey relations (COPRAS-G) for selection of the final project. It considers environmental effects alongside traditional criteria. Innovation in this study is in the combination of the MCDMs with HOQ and prioritizing projects considering the impact on the environment. The advantages are efficiency and simplicity. The practical focus of the approach is illustrated using a case example. The approach is used for the prioritization of projects that apply the Define–Measure–Analyze–Improve–Control (DMAIC) framework in a Six Sigma organization. The evaluation of three DMAIC projects suggested taking up Project C. The illustration has shown applicability in real-life situations. Thus, the novel approach identifies an environment-friendly project that satisfies the stakeholders the most. The utilization of Grey MCDM method makes the approach suitable for use in an uncertain environment.

Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks

The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by major European rivers. This study investigated the spatial distribution of chemicals in the Ukrainian shelf (the northwestern part of the Black Sea) and their main sources. Chemical occurrence data used in the study was generated within the Joint Black Sea Surveys (JBSS), which took place in 2016 and 2017 as a part of the EU/UNDP EMBLAS II project (www.emblasproject.org). During the JBSS, seawater samples were analyzed by a non-target screening workflow using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Open-source algorithms were applied to generate a combined dataset of 30,489 detected chemical signals and their intensities. Out of these, 35 compounds were tentatively identified by the application of a non-target screening identification workflow based on automated matching of their mass spectra against those in available mass spectral libraries. The dataset was used to generate images, representing spatial distribution of each of the signals. These images were then used as an input to a deep learning convolutional neural network classification model. The study resulted in the development of an open-source end-to-end workflow for the estimation of the pollution load by chemicals contributed by the two major inflowing rivers (Danube and Dnieper) and other, so far unidentified, sources. A dedicated dashboard was built to facilitate data visualization per detected signal/compound. The presented model proved to be especially useful at the prioritization of signals of unknown compounds, which is of key importance for the follow up structure elucidation efforts of bulky non-target screening data. The deep learning approach for peak prioritization of unknown chemicals in the environment has been used for the first time.

Relevance of Impact Categories and Applicability of Life Cycle Impact Assessment Methods from an Automotive Industry Perspective

Climate change impacts have been extensively addressed in academia, politics and industry for decades. However, particularly within the scientific community, the importance of considering further impact categories to ensure holistic environmental assessment and avoid burden shifting is strongly emphasized. Since considering all impact categories might become overwhelming for industry, a prioritization approach can support practitioners to focus their efforts on the most relevant impacts. Therefore, within this paper, an approach for the identification of relevant impact categories is developed for the automotive sector together with Volkswagen AG. The evaluation is conducted using a criteria set including criteria groups “relevance for automotive sector” and “relevance for stakeholders”. For the impact categories identified as relevant, an evaluation of LCIA methods is conducted considering the methodologies CML and ReCiPe 2016 and the methods recommended by PEF. The results demonstrate that climate change is by far the most relevant impact category followed by resource use, human toxicity and ecotoxicity from both automotive and stakeholder perspective. Based on the evaluation of the LCIA methods, a combination of different methods can be recommended. This work provides guidance for the automotive sector to prioritize its focus on the most relevant impact categories and to select applicable LCIA methods for their quantification.

Prioritization of public and private land to protect species at risk habitat

AbstractConservation budgets are limited, requiring strategic prioritization among actions to efficiently protect species. Systematic prioritization approaches typically determine locations for conservation that most effectively balance species protection with cost. Proxies for cost are frequently used in prioritizing land for protection. Here, we combine financial cost estimates for private land acquisition and species habitat models into a spatial prioritization to explore cost‐effective habitat protection, using a case study of species at risk in Ontario, Canada. Our findings suggest a key trade‐off, whereby protecting the areas with the greatest concentration of species at risk may not be the best strategy for protecting these species. Instead, protecting species at risk may be most cost effective in areas where species‐at‐risk richness is still relatively high, but land costs are relatively low, such as in central Ontario. However, the budget required to adequately protect species at risk through land purchase would be much larger than is currently available for conservation efforts, even if public lands are preferentially protected. Therefore, to effectively protect all species at risk in Ontario, we recommend the use of alternative conservation measures, such as easements and incentives for restoration on private land, to supplement already protected areas.