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

Environmental Volatility, Development Decisions, and Software Volatility: A Longitudinal Analysis

Although product development research often focuses on activities prior to product launch, for long-lived, adaptable products like software, development can continue over the entire product life cycle. For managers of these products the challenges are to predict when and how much the products will change and to understand how their development decisions influence the timing and magnitude of future change activities. We develop a two-stage model that relates environmental volatility to product development decisions and product development decisions to software volatility. The model is evaluated using a data archive that captures changes over 20 years to a firm's environment, its managers' development choices, and its software products. In Stage 1 we find that higher environmental volatility leads to greater use of process technology and standard component designs but less team member rotation. Earlier development decisions strongly influence current development choices, especially for product design and process technology. In Stage 2 we find that increased use of standard component designs dampens future software volatility by decreasing the average rate and magnitude of change. Adding new team members increases product enhancements at a faster pace than more intense use of process technology but adds repairs at almost the same rate as enhancements.product development decisions, product volatility, product life cycle, software volatility, environmental volatility, software development, software evolution, contingency theory, software maintenance, team management, software process, standard designs, organizational inertia

TRY plant trait database - enhanced coverage and open access

10.1111/gcb.14904GLOBAL CHANGE BIOLOGY261119-18

Containment measures

OBSOLETE (project finished) - Description of containment measures during COVID'19 lockdown, in the context of SIlent Cities project. Please request access to Silent Cities if neede

Archived - General Information (DO NOT USE)

DO NOT USE - The goal of this component was to document the data collection process of the Silent Cities Dataset. This component is just left for archive