BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology.

BACKGROUND: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. RESULTS: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. CONCLUSIONS: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.

High levels of effective long-distance dispersal may blur ecotypic divergence in a rare terrestrial orchid.

BACKGROUND: Gene flow and adaptive divergence are key aspects of metapopulation dynamics and ecological speciation. Long-distance dispersal is hard to detect and few studies estimate dispersal in combination with adaptive divergence. The aim of this study was to investigate effective long-distance dispersal and adaptive divergence in the fen orchid (Liparis loeselii (L.) Rich.). We used amplified fragment length polymorphism (AFLP)-based assignment tests to quantify effective long-distance dispersal at two different regions in Northwest Europe. In addition, genomic divergence between fen orchid populations occupying two distinguishable habitats, wet dune slacks and alkaline fens, was investigated by a genome scan approach at different spatial scales (continental, landscape and regional) and based on 451 AFLP loci. RESULTS: We expected that different habitats would contribute to strong divergence and restricted gene flow resulting in isolation-by-adaptation. Instead, we found remarkably high levels of effective long-distance seed dispersal and low levels of adaptive divergence. At least 15% of the assigned individuals likely originated from among-population dispersal events with dispersal distances up to 220 km. Six (1.3%) 'outlier' loci, potentially reflecting local adaptation to habitat-type, were identified with high statistical support. Of these, only one (0.22%) was a replicated outlier in multiple independent dune-fen population comparisons and thus possibly reflecting truly parallel divergence. Signals of adaptation in response to habitat type were most evident at the scale of individual populations. CONCLUSIONS: The findings of this study suggest that the homogenizing effect of effective long-distance seed dispersal may overwhelm divergent selection associated to habitat type in fen orchids in Northwest Europe.

Ability of matrix models to explain the past and predict the future of plant populations.

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

How do plant ecologists use matrix population models?

Matrix projection models are among the most widely used tools in plant ecology. However, the way in which plant ecologists use and interpret these models differs from the way in which they are presented in the broader academic literature. In contrast to calls from earlier reviews, most studies of plant populations are based on < 5 matrices and present simple metrics such as deterministic population growth rates. However, plant ecologists also cautioned against literal interpretation of model predictions. Although academic studies have emphasized testing quantitative model predictions, such forecasts are not the way in which plant ecologists find matrix models to be most useful. Improving forecasting ability would necessitate increased model complexity and longer studies. Therefore, in addition to longer term studies with better links to environmental drivers, priorities for research include critically evaluating relative/comparative uses of matrix models and asking how we can use many short-term studies to understand long-term population dynamics.