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Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend that researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.

PurposeThe aim of the paper is to assess the role and effectiveness of a proposed novel strategy for Life Cycle Inventory (LCI) data collection in the food sector and associated supply chains. The study represents one of the first of its type and provides answers to some of the key questions regarding the data collection process developed, managed and implemented by a multinational food company across the supply chain.MethodsAn integrated LCI data collection process for confectionery products was developed and implemented by Nestlé, a multinational food company. Some of the key features includes (1) management and implementation by a multinational food company; (2) types of roles to manage, provide and facilitate data exchange; (3) procedures to identify key products, suppliers and customers; (4) LCI questionnaire and cover letter and (5) data quality management based on the pedigree matrix. Overall, the combined features in an integrated framework provide a new way of thinking about the collection of LCI data from the perspective of a multinational food company.Results and discussionThe integrated LCI collection framework spanned across 5 months and resulted in 87 new LCI datasets for confectionery products from raw material, primary resource use, emission and waste release data collected from suppliers across 19 countries. The data collected was found to be of medium to high quality compared with secondary data. However, for retailers and waste service companies, only partially completed questionnaires were returned. Some of the key challenges encountered during the collection and creation of data included lack of experience, identifying key actors, communication and technical language, commercial compromise, confidentiality protection and complexity of multi-tiered supplier systems. A range of recommendations are proposed to reconcile these challenges which include standardisation of environmental data from suppliers, concise and targeted LCI questionnaires and visualising complexity through drawings.ConclusionsThe integrated LCI data collection process and strategy has demonstrated the potential role of a multinational company to quickly engage and act as a strong enabler to unlock latent data for various aspects of the confectionery supply chain. Overall, it is recommended that the research findings serve as the foundations to transition towards a standardised procedure which can practically guide other multinational companies to considerably increase the availability of LCI data.

In ecological modeling of the habitat of a species, it can be prohibitively expensive to determine species absence. Presence-only data consist of a sample of locations with observed presences and a separate group of locations sampled from the full landscape, with unknown presences. We propose an expectation-maximization algorithm to estimate the underlying presence-absence logistic model for presence-only data. This algorithm can be used with any off-the-shelf logistic model. For models with stepwise fitting procedures, such as boosted trees, the fitting process can be accelerated by interleaving expectation steps within the procedure. Preliminary analyses based on sampling from presence-absence records of fish in New Zealand rivers illustrate that this new procedure can reduce both deviance and the shrinkage of marginal effect estimates that occur in the naive model often used in practice. Finally, it is shown that the population prevalence of a species is only identifiable when there is some unrealistic constraint on the structure of the logistic model. In practice, it is strongly recommended that an estimate of population prevalence be provided.

The increasing awareness on climate issues in the built environment places a greater responsibility on the different actors to map the building emissions, reduce and optimise the use of materials, and thereby lower the environmental footprint. With several countries enforcing legally binding CO 2 limits to assess and benchmark the negative environmental side effects from buildings using the LCA method, it is presumable that practitioners from the industry will look for higher availability of data found from Environmental Product Declarations (EPDs). As the availability of data more than likely will increase drastically over the years, the study provides an extensive look into the world of digitalised EPDs, and how to use the format to extract a comprehensive number of EPD data. The extraction of data from the ECO Platform leads to a total of 1478 entities, and when adding EPDs from EPD Denmark this study scrutinises 1644 EPDs in total, from 4 EPD Program Operators (EPD-POs). The extraction process highlights the need for transparency and more mutual agreements in the documentation methods. Further, the study scratches the surface of the revised European EPD Standard EN15804, and what the changes and the transition will mean for the applicability and transparency in the building sector and for LCA models when the majority of emissions from GWP will increase.

The objective of the study was to analyze researchers’ compliance with their data availability statement (DAS) from manuscripts published in open-access journals with the mandatory DAS. We analyzed all articles from 333 open-access journals published during January 2019 by BioMed Central. We categorized types of the DAS. We surveyed corresponding authors who wrote in the DAS that they would share the data. Consent to participate in the study was sought for all included manuscripts. After accessing raw data sets, we checked whether data were available in a way that enabled reanalysis. Of 3556 analyzed articles, 3416 contained the DAS. The most frequent DAS category (42%) indicated that the data sets are available on reasonable request. Among 1792 manuscripts in which the DAS indicated that authors are willing to share their data, 1669 (93%) authors either did not respond or declined to share their data with us. Among 254 (14%) of 1792 authors who responded to our query for data sharing, only 123 (6.8%) provided the requested data. Even when authors indicate in their manuscript that they will share data upon request, the compliance rate is the same as for authors who do not provide the DAS, suggesting that the DAS may not be sufficient to ensure data sharing.

The concept of sustainable and responsible (SR) investments expresses that every investment should be based on the SR investor's code of ethics. To a large extent the allocation of SR investments to more sustainable companies and ethical practices is based on the environmental, social, and corporate governance (ESG) scores provided by rating agencies. However, a thorough investigation of ESG scores is a neglected topic in the literature. This paper uses Thomson Reuters ASSET4 ESG ratings to analyze the influence of firm size, a company's available resources for providing ESG data, and the availability of a company's ESG data on the company's sustainability performance. We find a significant positive correlation between the stated variables, which can be explained by organizational legitimacy. The results raise the question of whether the way the ESG score measures corporate sustainability gives an advantage to larger firms with more resources while not providing SR investors with the information needed to make decisions based on their beliefs. Due to our results, SR investors and scholars should reopen the discussion about: what sustainability rating agencies measure with ESG scores, what exactly needs to be measured, and if the sustainable finance community can reach their self-imposed objectives with this measurement.

Purpose For the development of any life cycle assessment study, the practitioner frequently integrates primary data collected on-field, with background data taken from various life cycle inventory databases which are part of most commercial LCA software packages. However, such data is often not generally applicable to all product systems since, especially concerning the agri-food sector, available datasets may not be fully representative of the site specificity of the food product under examination. In this context, the present work investigates the background, sources and methodological aspects that characterise the most known commercial databases containing agri-food data, with a focus on four agri-food supply chains (olive oil, wine, wheat products and citrus fruit), which represent an important asset for the Italian food sector. Methods Specifically, the paper entails a review of currently available LCI databases and their datasets with a twofold scope: firstly, to understand how agri-food data is modelled in these databases for a coherent and consistent representation of regional scenarios and to verify whether they are also suitable for the Italian context and, secondly, to identify and analyse useful and relevant methodological approaches implemented in the existing LCI databases when regional data are modelled. Results Based on the aforementioned review, it is possible to highlight some problems which may arise when developing an LCI pertaining to the four Italian agri-food supply chains, namely: 1. The need for specific inventory datasets to tackle the specificities of agri-food product systems. 2. The lack of datasets, within the existing DBs, related to the Italian context and to the abovementioned supply chains. In fact, at present, in the currently available LCI DBs, there are very few (or in some cases none) datasets related to Italian wine, olive oil, wheat-based products and citrus fruit. The few available datasets often contain some data related to the Italian context but also approximate data with that of product systems representing other countries. Furthermore, the present study allowed to identify and discuss the main aspects to be used as starting elements for modelling regional data to be included in a future Italian LCI database of the abovementioned four supply chains. Conclusions The results of the present study represent a starting point for the collection of data and its organisation, in order to develop an Italian LCI agri-food database with datasets which are representative of the regional specificities of four agri-food supply chains which play an important role in the Italian economy.

The combination of Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B data provides a new perspective in remote sensing application for terrestrial monitoring. Jointly, these four sensors together offer global 10–30-m multi-spectral data coverage at a higher temporal revisit frequency. In this study, combinations of four sensors were used to examine the revisit interval by modelled orbit swath information. To investigate different factors that could influence data availability, an analysis was carried out for one year based on daytime surface observations of Landsat-8 and Sentinel-2A -2B. We found that (i) the global median average of revisit intervals for the combination of four sensors was 2.3 days; (ii) the global mean average number of surface observations was 141.4 for the combination of Landsat-8 and Sentinel-2A -2B; (iii) the global mean average cloud-weighted number of observations for the three sensors combined was 81.9. Three different locations were selected to compare with the cloud-weighted number of observations, and the results show an appropriate accuracy. The utility of combining four sensors together and the implication for terrestrial monitoring are discussed.

PurposeThe objective of this paper was to provide LCA practitioners with recommendations and a framework for upscaling emerging technologies by reviewing upscaling methods applied so far in ex ante life cycle assessment (LCA).MethodsWeb of Science was searched for articles published between 1990 and 2019 (April) using different variations of the term “ex ante LCA” as keywords. Suitable studies were reviewed to understand the key characteristics and main methodological principles of upscaling methods.Results and discussionA total of 18 studies were selected for literature review. Review results showed that most studies reported what a hypothetical upscaled technology would look like in the future. All studies described how they estimated data; they applied different data estimation methods, using process simulation, manual calculations, molecular structure models (MSMs) and proxies. Since the review results showed that most ex ante LCA studies followed similar upscaling steps, we developed a framework for the upscaling of emerging technologies in ex ante LCA consisting of three main steps: (1) projected technology scenario definition, (2) preparation of a projected LCA flowchart, and (3) projected data estimation. Finally, a decision tree was developed based on the review results that provides recommendations for LCA practitioners regarding the upscaling procedure in ex ante LCA.ConclusionsOur findings can be useful for LCA practitioners aiming at upscaling in ex ante LCA. We provide an overview of upscaling methods used in ex ante LCA and introduce a framework describing the steps involved in the upscaling process and a decision tree recommending an up-scaling procedure. The results show that in theory all data estimation methods described in this paper can be applied to estimate material flows, energy flows, and elementary flows (emissions and natural resource use). Finally, since different kinds of expertise are required for upscaling in ex ante LCA, we recommend that technology experts from different fields are involved in performing ex ante LCA, e.g., technology developers, LCA practitioners, and engineers.

During the last decade, there has been a proliferation of statistical methods for studying resource selection by animals. While statistical techniques are advancing at a fast pace, there is confusion in the conceptual understanding of the meaning of various quantities that these statistical techniques provide. Terms such as selection, choice, use, occupancy and preference often are employed as if they are synonymous. Many practitioners are unclear about the distinctions between different concepts such as ‘probability of selection,’ ‘probability of use,’ ‘choice probabilities’ and ‘probability of occupancy’. Similarly, practitioners are not always clear about the differences between and relevance of ‘relative probability of selection’ vs. ‘probability of selection’ to effective management. Practitioners also are unaware that they are using only a single statistical model for modelling resource selection, namely the exponential probability of selection, when other models might be more appropriate. Currently, such multimodel inference is lacking in the resource selection literature. In this paper, we attempt to clarify the concepts and terminology used in animal resource studies by illustrating the relationships among these various concepts and providing their statistical underpinnings.