Impact of the Nordic Arthroplasty Register Association (NARA) collaboration on demographics, methods and revision rates in knee arthroplasty: a register-based study from NARA 2000–2017

Background and purpose: We have previously observed differences in treatment and outcome of knee arthroplasties in the Nordic countries. To evaluate the impact of Nordic collaboration in the last 15 years we aimed to compare patient demographics, methods, and revision rates in primary knee arthroplasties among the 4 Nordic countries.Patients and methods: We included 535,051 primary knee arthroplasties reported 2000-2017 from the Nordic Arthroplasty Register Association (NARA) database. Kaplan-Meier analysis (KM) and restricted mean survival time (RMST) analysis were used to evaluate the cumulative revision rate (CRR) and RMST estimates with 95% confidence intervals (CI) and to compare countries in relation to risk of revision for any reason.Results: After 2010, the increase in incidence of knee arthroplasty plateaued in Sweden and Denmark but continued to increase in Finland and Norway. In 2017 the incidence was highest in Finland with 226 per 105 person-years, while it was less than 150 per 105 in the 3 other Nordic countries. In total knee arthroplasties performed for osteoarthritis (OA), overall CRR at 15 years for revision due to any reason was higher in Denmark (CRR 9.6%, 95% CI 9.2-10), Norway (CRR 9.1%, CI 8.7-9.5), and Finland (CRR 7.0%, CI 6.8-7.3) compared with Sweden (CRR 6.6%, CI 6.4-6.8). There were differences among the countries in use of implant brand and type, fixation, patellar component, and use of unicompartmental knee arthroplasty.Interpretation: We evinced a slowing growth of incidence of knee arthroplasties in the Nordic countries after 2010 with Finland having the highest incidence. We also noted substantial differences among the 4 Nordic countries, with Sweden having a lower risk of revision than the other countries. No impact of NARA could be demonstrated and CRR did not improve over time.</p

TRY plant trait database – enhanced coverage and open access

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

Organizing principles for vegetation dynamics

Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.Integrating natural selection and other organizing principles into next-generation vegetation models could render them more theoretically sound and useful for earth system applications and modelling climate impacts.Environmental Biolog

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, 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