Nord University

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Qualitative content analysis consists of conventional, directed and summative approaches for data analysis. They are used for provision of descriptive knowledge and understandings of the phenomenon under study. However, the method underpinning directed qualitative content analysis is insufficiently delineated in international literature. This paper aims to describe and integrate the process of data analysis in directed qualitative content analysis. Various international databases were used to retrieve articles related to directed qualitative content analysis. A review of literature led to the integration and elaboration of a stepwise method of data analysis for directed qualitative content analysis. The proposed 16-step method of data analysis in this paper is a detailed description of analytical steps to be taken in directed qualitative content analysis that covers the current gap of knowledge in international literature regarding the practical process of qualitative data analysis. An example of "the resuscitation team members' motivation for cardiopulmonary resuscitation" based on Victor Vroom's expectancy theory is also presented. The directed qualitative content analysis method proposed in this paper is a reliable, transparent, and comprehensive method for qualitative researchers. It can increase the rigour of qualitative data analysis, make the comparison of the findings of different studies possible and yield practical results.

The genome of the Atlantic cod has been sequenced, and genomic analysis reveals an immune system that differs significantly from that in other vertebrates. The major histocompatibility complex (MHC) II has been lost, as have some other genes that are essential for MHC II function. But there is an expansion in the number of MHC I genes and a unique composition for its toll-like receptor family. These compensatory changes in both adaptive and innate immunity mean that cod is no more susceptible to disease than most other vertebrates. These findings challenge current models of vertebrate immune evolution, and may facilitate the development of targeted vaccines for disease management in aquaculture. Atlantic cod (Gadus morhua) is a large, cold-adapted teleost that sustains long-standing commercial fisheries and incipient aquaculture1,2. Here we present the genome sequence of Atlantic cod, showing evidence for complex thermal adaptations in its haemoglobin gene cluster and an unusual immune architecture compared to other sequenced vertebrates. The genome assembly was obtained exclusively by 454 sequencing of shotgun and paired-end libraries, and automated annotation identified 22,154 genes. The major histocompatibility complex (MHC) II is a conserved feature of the adaptive immune system of jawed vertebrates3,4, but we show that Atlantic cod has lost the genes for MHC II, CD4 and invariant chain (Ii) that are essential for the function of this pathway. Nevertheless, Atlantic cod is not exceptionally susceptible to disease under natural conditions5. We find a highly expanded number of MHC I genes and a unique composition of its Toll-like receptor (TLR) families. This indicates how the Atlantic cod immune system has evolved compensatory mechanisms in both adaptive and innate immunity in the absence of MHC II. These observations affect fundamental assumptions about the evolution of the adaptive immune system and its components in vertebrates.

Climate change mitigation has become the central theme for many policy initiatives, as such, the European Union (EU) member countries are working assiduously to achieve the emission targets. To provide policy direction in achieving the emission targets, this study investigated the drivers essential to attaining the Sustainable Development Goals in regards to reducing environmental pollution in EU member countries. A balanced panel of 16-EU countries from 1997 to 2014 was estimated with Panel Pool Mean Group Autoregressive distributive lag (PMG-ARDL) model. The study traced the equilibrium relationship between ecological footprint, real gross domestic product, trade openness, fertility rate, renewable and non-renewable energy consumption - suggested by both Kao and Pedroni cointegration tests. The PMG-ARDL analysis confirmed the role of non-renewable energy consumption in depleting environmental quality while renewable energy consumption was found to improve environmental sustainability. Interestingly, the unexpected long-run fertility-ecological footprint nexus was connected with the divergent fertility rate information of the EU member countries. Although, country-specific policy approach is essential, however, such a framework should be compatible with the region's overall Sustainable Development Goals. The call for diversification of existing energy portfolios by either incorporating or enhancing renewable energy technologies is essential to sustain the current success strides of most member states. Thus, the EU needs to strengthen its commitments to achieving the emission targets by decarbonizing and sustaining its economic growth trajectory.

This article explores whether co-creation offers a viable path for the public sector. After an initial account of the transformation of the public sector from a legal authority and a service provider to an arena of co-creation, it defines co-creation and provides some empirical examples. This is followed by a discussion of the risks and benefits of co-creation as well as the drivers and barriers that may stimulate or hamper its expansion. The article also reflects on how institutional design, public leadership, and systemic change can advance co-creation. The conclusion summarizes the findings by setting out some researchable propositions.

It has been known for decades that there are severe problems associated with null-hypothesis significance testing (NHST) based on arbitrary P-value thresholds (e.g., P = 0.05).A small literature review indicates that much of the current research in ecology and evolution is still disregarding the warnings and frequently relies on binary decisions based on P-values to report statistical significance.While the P-value itself is a sound mathematical concept that does not have to be banned when used correctly, we should stop using the term ‘statistical significance’ and replace it with a gradual notion of evidence.Language matters and ‘evidence’ is an intuitive concept that honestly reflects what the data really tell us.To facilitate rewriting scientific results, we offer generic examples of how to translate (binary) significance language into a gradual language of evidence. Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the ‘good practices’ that exist in other fields. Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the ‘good practices’ that exist in other fields. The P-value is probably the most commonly used and yet the most hotly debated statistical measure employed for the interpretation of quantitative research outcomes (e.g., [1.Goodman S.N. Toward evidence-based medical statistics. 1: the P value fallacy.Ann. Intern. Med. 1999; 130: 995-1004Crossref PubMed Scopus (858) Google Scholar, 2.Murtaugh P.A. In defense of P values.Ecology. 2014; 95: 611-617Crossref PubMed Scopus (202) Google Scholar, 3.Nuzzo R. Statistical errors.Nature. 2014; 506: 150-152Crossref PubMed Scopus (910) Google Scholar, 4.Goodman S.N. Aligning statistical and scientific reasoning.Science. 2016; 352: 1180-1182Crossref PubMed Scopus (66) Google Scholar, 5.Benjamin D. et al.Redefine statistical significance.Nat. Hum. Behav. 2017; 2: 6-10Crossref Scopus (1136) Google Scholar]). The P-value is, in essence, the main ingredient in null-hypothesis significance testing (NHST), where the existence of an effect of interest is evaluated following a recipe-like procedure. In the almost 100-year-long history of P-values, the respective practice of NHST has continually been criticized in literally hundreds of articles (e.g., [6.Berkson J. Tests of significance considered as evidence.J. Am. Stat. Assoc. 1942; 219: 325-335Crossref Scopus (160) Google Scholar, 7.Rozenboom W.W. The fallacy of the null-hypothesis significance test.Psychol. Bull. 1960; 57: 416-428Crossref PubMed Scopus (452) Google Scholar, 8.Cox D.R. Statistical significance tests.Br. J. Clin. Pharmacol. 1982; 14: 325-331Crossref PubMed Scopus (66) Google Scholar, 9.Cohen J. The earth is round (p < .05).Am. Psychol. 1994; 49: 997-1003Crossref Scopus (2725) Google Scholar, 10.Ioannidis J.P.A. Why most published research findings are false.PLoS Med. 2005; 2e124Crossref PubMed Scopus (5576) Google Scholar, 11.Wasserstein R.L. Lazar N.A. The ASA’s statement on p-values: context, process, and purpose.Am. Stat. 2016; 70: 129-133Crossref Scopus (2876) Google Scholar, 12.Amrhein V. et al.Retire statistical significance.Nature. 2019; 567: 305-307Crossref PubMed Scopus (1100) Google Scholar]). Eventually, statisticians shocked their audience with articles entitled ‘Why most published research findings are false’ [10.Ioannidis J.P.A. Why most published research findings are false.PLoS Med. 2005; 2e124Crossref PubMed Scopus (5576) Google Scholar] and ‘The statistical crisis in science’ [13.Gelman A. Loken E. The statistical crisis in science.Am. Sci. 2014; 102: 460-465Crossref Scopus (388) Google Scholar], essentially condemning the reliance on P-values and NHST to assess the statistical significance of effects. One key problem is the mistaking of statistical significance for scientific importance, even though the myth that lower P-values automatically imply higher relevance was debunked a long time ago (e.g., [8.Cox D.R. Statistical significance tests.Br. J. Clin. Pharmacol. 1982; 14: 325-331Crossref PubMed Scopus (66) Google Scholar,14.Goodman S.N. A dirty dozen: twelve P-value misconceptions.Semin. Hematol. 2008; 45: 135-140Crossref PubMed Scopus (347) Google Scholar]). In addition, the P-value is often misinterpreted (see for instance [14.Goodman S.N. A dirty dozen: twelve P-value misconceptions.Semin. Hematol. 2008; 45: 135-140Crossref PubMed Scopus (347) Google Scholar] for a list of 12 P-value misconceptions), illustrating that understanding what the P-value actually means is not as simple as it seems. Formally, the P-value is the probability of observing an outcome that is at least as extreme as an observed data summary, under the assumption that a certain hypothesis, the so-called null hypothesis (H0), is true (Box 1). The null hypothesis thereby implies that a specific mathematical model is correct, for example that the data are normally distributed with a prespecified mean. In NHST we can only do two things: we can either reject H0 or we can not reject it. If we cannot reject H0, that is, when P lies above a predefined threshold (usually P > 0.05), it is incorrect to conclude that ‘...there was no effect...’ or that ‘the null hypothesis is true’. In fact, H0 cannot be proven and ‘absence of evidence is not evidence of absence’ [15.Altman D.G. Bland J.M. Absence of evidence is not evidence of absence.Br. Med. J. 1995; 311: 485Crossref PubMed Scopus (1123) Google Scholar], reflecting that NHST is an intrinsically asymmetric procedure. Similarly, the P-value is often interpreted as the probability that H0 is true, a misconception that is persistent despite it having been pointed out repeatedly (e.g., [8.Cox D.R. Statistical significance tests.Br. J. Clin. Pharmacol. 1982; 14: 325-331Crossref PubMed Scopus (66) Google Scholar,11.Wasserstein R.L. Lazar N.A. The ASA’s statement on p-values: context, process, and purpose.Am. Stat. 2016; 70: 129-133Crossref Scopus (2876) Google Scholar,14.Goodman S.N. A dirty dozen: twelve P-value misconceptions.Semin. Hematol. 2008; 45: 135-140Crossref PubMed Scopus (347) Google Scholar]).Box 1The P-valueDefinition: the P-value is the probability of observing a specific data summary (e.g., an average) that is at least as extreme as the one observed, given that the null hypothesis (H0) is correct.Example: a prominent example is a case where H0 assumes that a certain data summary (denoted as test statistic) has a standard normal distribution. Given that the observed value z of the test statistic is derived from the data, the P-value is thus the probability that we would see such an extreme, or an even more extreme, value given that H0 was in fact true. The P-value thus reflects how likely it is that we see a specific outcome if H0 holds.The graphical example in Figure I shows the meaning of the P-value, once for an observed value of z = 1.96 for a relatively clear positive effect (left) and once for an observed value z = − 0.84 for a negative, but less clear, effect. The shaded areas under the curve represent the P-values, that is, the probabilities that the observed values of z or more extreme values occur under H0. The P-value for z = 1.96 is thus P = 0.025 + 0.025 = 0.05, and the P-value for z = − 0.84 is P = 0.2 + 0.2 = 0.4.The asymmetric nature of the P-value: one major misunderstanding about the P-value is that it is often believed to say something about the probability that H0 is true. This is not the case. Instead, the probability that H0 is true, given a certain data summary (i.e., a test statistic) from the data, is given as:P(H0∣data summary)=P(data summary∣H0)⋅P(H0)P(data summary)[I] This implies that we would have to specify a prior guess for P(H0). Even if this was possible, we would need to calculate the prior density for the observed data summary P(data summary), which is not trivial in most circumstances. Definition: the P-value is the probability of observing a specific data summary (e.g., an average) that is at least as extreme as the one observed, given that the null hypothesis (H0) is correct. Example: a prominent example is a case where H0 assumes that a certain data summary (denoted as test statistic) has a standard normal distribution. Given that the observed value z of the test statistic is derived from the data, the P-value is thus the probability that we would see such an extreme, or an even more extreme, value given that H0 was in fact true. The P-value thus reflects how likely it is that we see a specific outcome if H0 holds. The graphical example in Figure I shows the meaning of the P-value, once for an observed value of z = 1.96 for a relatively clear positive effect (left) and once for an observed value z = − 0.84 for a negative, but less clear, effect. The shaded areas under the curve represent the P-values, that is, the probabilities that the observed values of z or more extreme values occur under H0. The P-value for z = 1.96 is thus P = 0.025 + 0.025 = 0.05, and the P-value for z = − 0.84 is P = 0.2 + 0.2 = 0.4. The asymmetric nature of the P-value: one major misunderstanding about the P-value is that it is often believed to say something about the probability that H0 is true. This is not the case. Instead, the probability that H0 is true, given a certain data summary (i.e., a test statistic) from the data, is given as:P(H0∣data summary)=P(data summary∣H0)⋅P(H0)P(data summary)[I] This implies that we would have to specify a prior guess for P(H0). Even if this was possible, we would need to calculate the prior density for the observed data summary P(data summary), which is not trivial in most circumstances. More recently, several high-profile papers have brought the same old controversy to the attention of the broader scientific community [3.Nuzzo R. Statistical errors.Nature. 2014; 506: 150-152Crossref PubMed Scopus (910) Google Scholar,4.Goodman S.N. Aligning statistical and scientific reasoning.Science. 2016; 352: 1180-1182Crossref PubMed Scopus (66) Google Scholar,12.Amrhein V. et al.Retire statistical significance.Nature. 2019; 567: 305-307Crossref PubMed Scopus (1100) Google Scholar,16.Claridge-Change A. Assam P.N. Estimation statistics should replace significance testing.Nat. Methods. 2016; 13: 108-109Crossref Scopus (42) Google Scholar]. The discussion was boosted by a statement on the misuse and misinterpretation of the P-value and statistical significance that the American Statistical Association (ASA) published in March 2016 [11.Wasserstein R.L. Lazar N.A. The ASA’s statement on p-values: context, process, and purpose.Am. Stat. 2016; 70: 129-133Crossref Scopus (2876) Google Scholar]. This was the first time in the history of the Association that such a policy statement had been released, underlining the importance the ASA Board assigned to the topic. The confusion around the P-values’ use is exacerbated by the fact that, ironically, it is not actually the definition of the P-value that is the problem. Rather, the issues arise from the way the P-value is used in NHST to make binary decisions (significant versus nonsignificant, there is an effect versus there is no effect) based on a sharp, arbitrary cutoff, typically P = 0.05 (though recent arguments speak for lower limits, see [5.Benjamin D. et al.Redefine statistical significance.Nat. Hum. Behav. 2017; 2: 6-10Crossref Scopus (1136) Google Scholar]). When it was originally developed, the P-value was indeed not meant to be used the way it is used today. Fisher, who suggested the P-value [17.Fisher R.A. The arrangement of field experiments.J. Minist. Agric. 1926; 33: 503-515Google Scholar], used the term ‘significance’ only to indicate that an observed outcome was worth closer investigation, and emphasized that H0 would be rejected only if follow-up experiments also ‘rarely failed to achieve significance’, while he opposed using the P-value for ‘automatic inference’ (see, e.g., [4.Goodman S.N. Aligning statistical and scientific reasoning.Science. 2016; 352: 1180-1182Crossref PubMed Scopus (66) Google Scholar]). Long-term misuse of P-values has fostered questionable research (e.g., et research in ecology and PubMed Scopus Google like model based on P-values, and the results are known with the of have in and and the to results more often (i.e., has to a of positive findings that has to a severe scientific crisis (see, e.g., [10.Ioannidis J.P.A. Why most published research findings are false.PLoS Med. 2005; 2e124Crossref PubMed Scopus (5576) Google The crisis in Scopus Google the on 2016; PubMed Google Scholar]). and for use of the P-value are still hotly debated and no on a way is in D. et al.Redefine statistical significance.Nat. Hum. Behav. 2017; 2: 6-10Crossref Scopus (1136) Google Scholar,11.Wasserstein R.L. Lazar N.A. The ASA’s statement on p-values: context, process, and purpose.Am. Stat. 2016; 70: 129-133Crossref Scopus (2876) Google Scholar,12.Amrhein V. et al.Retire statistical significance.Nature. 2019; 567: 305-307Crossref PubMed Scopus (1100) Google J. et can see statistical 2019; Scopus Google Scholar] to a In the of the we in that have about how to report their findings and some to report P-values The confusion is also for by the of the of the and to P-values D. Scopus Google Scholar]. the P-value would be a case of the out with the Despite statisticians still that the P-value is a statistical when interpreted D. to 2017; 14: Scholar]. to using the P-value of for a long The most prominent examples are like the or or (see, e.g., The of the is what alternative we to the 2019; PubMed Scopus Google Scholar] for an when are used to make binary for example the of in model when the null effect lies in a or when a certain threshold of a we are not from an It for be that model based on the can be into P-value (e.g., P.A. 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Review Panel of the ERS LAM Task Force L ymphangioleiomyomatosis (LAM) is a rare lung disease, which occurs sporadically or in association with the genetic disease tuberous sclerosis complex (TSC) Sporadic LAM affects ,1 in 400,000 adult females; in TSC, LAM occurs in 30-40% of adult females

Whole-genome sequencing of the seagrass Zostera, representing the first marine angiosperm genome to be fully sequenced, provides insight into the evolutionary changes associated with a transition to a marine environment in this angiosperm lineage. The seagrass Zostera marina, or eelgrass, is widely distributed throughout the Northern Hemisphere. It is therefore of considerable ecological importance but — as with other seagrasses — its coastal habitats are among the world's most threatened ecosystems. Jeanine Olsen and colleagues report the whole-genome sequence of Zostera. Their analyses provide insights into the evolutionary changes associated with the 'back to the sea' reverse evolutionary trajectory that has occurred in this angiosperm lineage, including the loss of the entire repertoire of stomatal genes, and the presence of sulfated cell-wall polysaccharides that are more macro-algal-like than plant-like. Seagrasses colonized the sea1 on at least three independent occasions to form the basis of one of the most productive and widespread coastal ecosystems on the planet2. Here we report the genome of Zostera marina (L.), the first, to our knowledge, marine angiosperm to be fully sequenced. This reveals unique insights into the genomic losses and gains involved in achieving the structural and physiological adaptations required for its marine lifestyle, arguably the most severe habitat shift ever accomplished by flowering plants. Key angiosperm innovations that were lost include the entire repertoire of stomatal genes3, genes involved in the synthesis of terpenoids and ethylene signalling, and genes for ultraviolet protection and phytochromes for far-red sensing. Seagrasses have also regained functions enabling them to adjust to full salinity. Their cell walls contain all of the polysaccharides typical of land plants, but also contain polyanionic, low-methylated pectins and sulfated galactans, a feature shared with the cell walls of all macroalgae4 and that is important for ion homoeostasis, nutrient uptake and O2/CO2 exchange through leaf epidermal cells. The Z. marina genome resource will markedly advance a wide range of functional ecological studies from adaptation of marine ecosystems under climate warming5,6, to unravelling the mechanisms of osmoregulation under high salinities that may further inform our understanding of the evolution of salt tolerance in crop plants7.

Abstract A large array of species distribution model ( SDM ) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDM s in the context of multispecies data, including both joint SDM s that model multiple species together, and stacked SDM s that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study.

Salmon lice, Lepeophtheirus salmonis, are naturally occurring parasites of salmon in sea water. Intensive salmon farming provides better conditions for parasite growth and transmission compared with natural conditions, creating problems for both the salmon farming industry and, under certain conditions, wild salmonids. Salmon lice originating from farms negatively impact wild stocks of salmonids, although the extent of the impact is a matter of debate. Estimates from Ireland and Norway indicate an odds ratio of 1.1:1-1.2:1 for sea lice treated Atlantic salmon smolt to survive sea migration compared to untreated smolts. This is considered to have a moderate population regulatory effect. The development of resistance against drugs most commonly used to treat salmon lice is a serious concern for both wild and farmed fish. Several large initiatives have been taken to encourage the development of new strategies, such as vaccines and novel drugs, for the treatment or removal of salmon lice from farmed fish. The newly sequenced salmon louse genome will be an important tool in this work. The use of cleaner fish has emerged as a robust method for controlling salmon lice, and aquaculture production of wrasse is important towards this aim. Salmon lice have large economic consequences for the salmon industry, both as direct costs for the prevention and treatment, but also indirectly through negative public opinion.

This essay suggests changes to the theory of public value and, in particular, the strategic triangle framework, in order to adapt it to an emerging world where policy makers and managers in the public, private, voluntary and informal community sectors have to somehow separately and jointly create public value. One set of possible changes concerns what might be in the centre of the strategic triangle besides the public manager. Additional suggestions are made concerning how multiple actors, levels, arenas and/or spheres of action, and logics might be accommodated. Finally, possibilities are outlined for how the strategic triangle might be adapted to complex policy fields in which there are multiple, often conflicting organizations, interests and agendas. In other words, how might politics be more explicitly accommodated. The essay concludes with a number of research suggestions.

This article describes, analyses and discusses the development of the Nordic school model in three phases of the post‐war period, viewed in the light of the development of the political system throughout the period and in comparison with the development of the school system in the western world in this period. The “classical period” from 1945 until about 1970 is often referred to as the golden era of social democracy, during which a number of special characteristics were attributed to the model. First, the reforms were introduced on the basis of national policies drawn up by a strong and innovative state in association with business organisations and industry. The main objective was to involve the school in the realisation of social goals such as equal opportunity and community fellowship. School development is very largely determined by state‐managed conditions—“input management”. The Nordic model was regarded as an ideal for school development in western countries. The Nordic countries generally followed the same course, but at different tempos, with Sweden being the main source of inspiration. During the next phase, 1970–1980/85, the Nordic model was influenced by international, political new radicalism in which increasing importance was attached to pupils' individual emancipation, and there was greater local influence over school development as well as by the teaching profession. In the third and final phase, the Nordic school model was of less importance in comparison to other countries. Partly as a result of new globalisation and free markets, economic competition between nations gained greater influence over school philosophy and development. Technical and instrumental goals were prioritised at the expense of national and social unity. Evaluation of pupils' academic skills was intensified and became an important management tool—“output management”. State control diminished as a result of the decentralisation of power and of the increasing influence of international reports and resolutions on school reform in the Nordic countries. During the latter phase the dominant neo‐liberal education policy has been subjected to criticism from the culture conservative and social democratic/progressive side.

Purpose This study proposes to use the Theory of Planned Behaviour to predict entrepreneurial intentions among students in five developing and nine developed countries. The purpose is to investigate whether entrepreneurial intention and its antecedents differ between developing and developed countries, and to test the theory in the two groups of countries. Design/methodology/approach A total of 2,225 students in 13 countries participated in this study by responding to a structured questionnaire in classrooms. Structural equation modelling was used to analyse the data. Findings The findings indicate that respondents from developing countries have stronger entrepreneurial intentions than those from developed countries. Moreover, the respondents from developing countries also score higher on the theory's antecedents of entrepreneurial intentions – attitudes, subjective norms, and perceived behavioural control – than respondents from developed countries. The findings support the Theory of Planned Behaviour in both developing and developed countries. Research limitations/implications The findings strongly support the Theory of Planned Behaviour. The measure of subjective norms used, a multiple‐item index encompassing the views of other people and motivation to comply with these, seems to have advantages over other measures of this concept. Practical implications Developing countries need to focus on the development of institutions that can support entrepreneurial efforts. At the same time, developed economies may need to accept that entrepreneurial intentions are dependent on the dynamism of an economic environment and possibly on risk‐perceiving behaviours. Originality/value While multiple‐country studies on entrepreneurship in developing and developed countries have been called for, no previous study has compared entrepreneurial intentions between developing and developed countries. The inclusion of developing countries provides a unique quasi‐experimental setting in which to test the theory.

Qualitative design consists of various approaches towards data collection, which researchers can use to help with the provision of both cultural and contextual description and interpretation of social phenomena. Qualitative content analysis (QCA) and thematic analysis (TA) as qualitative research approaches are commonly used by researchers across disciplines. There is a gap in the international literature regarding differences between QCA and TA in terms of the concept of a theme and how it is developed. Therefore, in this discussion paper we address this gap in knowledge and present differences and similarities between these qualitative research approaches in terms of the theme as the final product of data analysis. We drew on current multidisciplinary literature to support our perspectives and to develop internationally informed analytical notions of the theme in QCA and TA. We anticipate that improving knowledge and understanding of theme development in QCA and TA will support other researchers in selecting the most appropriate qualitative approach to answer their study question, provide high-quality and trustworthy findings, and remain faithful to the analytical requirements of QCA and TA.

Declines in European bird populations are reported for decades but the direct effect of major anthropogenic pressures on such declines remains unquantified. Causal relationships between pressures and bird population responses are difficult to identify as pressures interact at different spatial scales and responses vary among species. Here, we uncover direct relationships between population time-series of 170 common bird species, monitored at more than 20,000 sites in 28 European countries, over 37 y, and four widespread anthropogenic pressures: agricultural intensification, change in forest cover, urbanisation and temperature change over the last decades. We quantify the influence of each pressure on population time-series and its importance relative to other pressures, and we identify traits of most affected species. We find that agricultural intensification, in particular pesticides and fertiliser use, is the main pressure for most bird population declines, especially for invertebrate feeders. Responses to changes in forest cover, urbanisation and temperature are more species-specific. Specifically, forest cover is associated with a positive effect and growing urbanisation with a negative effect on population dynamics, while temperature change has an effect on the dynamics of a large number of bird populations, the magnitude and direction of which depend on species' thermal preferences. Our results not only confirm the pervasive and strong effects of anthropogenic pressures on common breeding birds, but quantify the relative strength of these effects stressing the urgent need for transformative changes in the way of inhabiting the world in European countries, if bird populations shall have a chance of recovering.

Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.

Abstract We develop a novel class of measures to quantify sample completeness of a biological survey. The class of measures is parameterized by an order q ≥ 0 to control for sensitivity to species relative abundances. When q = 0, species abundances are disregarded and our measure reduces to the conventional measure of completeness, that is, the ratio of the observed species richness to the true richness (observed plus undetected). When q = 1, our measure reduces to the sample coverage (the proportion of the total number of individuals in the entire assemblage that belongs to detected species), a concept developed by Alan Turing in his cryptographic analysis. The sample completeness of a general order q ≥ 0 extends Turing's sample coverage and quantifies the proportion of the assemblage's individuals belonging to detected species, with each individual being proportionally weighted by the ( q − 1)th power of its abundance. We propose the use of a continuous profile depicting our proposed measures with respect to q ≥ 0 to characterize the sample completeness of a survey. An analytic estimator of the diversity profile and its sampling uncertainty based on a bootstrap method are derived and tested by simulations. To compare diversity across multiple assemblages, we propose an integrated approach based on the framework of Hill numbers to assess (a) the sample completeness profile, (b) asymptotic diversity estimates to infer true diversities of entire assemblages, (c) non‐asymptotic standardization via rarefaction and extrapolation, and (d) an evenness profile. Our framework can be extended to incidence data. Empirical data sets from several research fields are used for illustration.

The containment of the spread of COVID-19 pandemic and limitations on commercial activities, mobility and manufacturing sector have significantly affected waste management. Waste management is critical to human development and health outcomes, especially during the COVID-19 pandemic. The invaluable service provided by the waste management sector ensures that the unusual heaps of waste that poses health risks and escalate the spread of COVID-19 is avoided. In this study, we assess the impact of COVID-19 pandemic on waste management by observing lockdown and social distancing measures. We found that the quantity of waste increased across countries observing the social distancing measure of staying at home. The intensification of single-use products and panic buying have increased production and consumption, hence thwarting efforts towards reducing plastic pollution. However, several countries have thus far instituted policies to ensure sustainable management of waste while protecting the safety of waste handlers.

China's carbon-embedded growth trajectory is gradually becoming a burden to environmental sustainability, hence, requires much attention. The complexity of human capital attributed emissions coupled with fossil fuel inclined energy utilization for industrialization underscores the failure of China to meet its mitigation target. We developed a policy-driven conceptual tool based on disaggregate energy utilization, human capital, trade, income level and natural resource exploitation in a carbon and environmental degradation function. Using a battery of statistics and econometric techniques such as neural network, SIMPLS, U test, dynamic ARDL Simulations, and Prais-Winsten first-order autoregressive [AR(1)] regression with robust standard errors, we examined the theme based on a data spanning 1961–2016. The study demonstrates that fossil fuel energy consumption and human capital are conducive catalysts for climate change. The instantaneous increase in renewable energy, environmental sustainability and income level has a diminishing effect on emissions and environmental degradation. The environmental Kuznets curve (EKC) hypothesis is validated in both emissions and degradation function — at a turning point of US$ 5469.79 and US$ 5863.70, respectively. The study highlights that the over-dependence on fossil fuel energy and natural resources for economic development, carbon-intensive trade and carbon-embedded human capital, thwart efforts to mitigating climate change and its impacts. Thus, the onus of responsibility for achieving a cleaner environment in China depends majorly on governmental policies that favour or dampens environmental sustainability.

In the HUNT Study, all residents aged ≥20 years in the Nord-Trøndelag region, Norway, have been invited to repeated surveys since 1984-86. The study data may be linked to local and national health registries.\nThe HUNT4 survey in 2017-19 included 56 042 participants in Nord-Trøndelag and 107 711 participants in the neighbouring Sør-Trøndelag region.\nThe HUNT4 data enable more long-term follow-up, studies of life course health trajectories and within-family studies.\nNew measures include body composition analysis using bioelectrical impedance; a 1-week accelerometer recording; physical and cognitive testing in older adults; measurements of haemoglobin and blood cell counts, HbA1c and phosphatidylethanol; and genotyping.\nResearchers can apply for HUNT data access from HUNT Research Centre if they have obtained project approval from the Regional Committee for Medical and Health Research Ethics, see [www.ntnu.edu/hunt/data].