Predictive systems ecology

Accounting for herbaceous communities in process-based models will advance our understanding of "grassy" ecosystems

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process-based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

Managing the effects of multiple stressors on wildlife populations in their ecosystems: developing a cumulative risk approach

Assessing cumulative effects of human activities on ecosystems is required by many jurisdictions, but current science cannot meet regulatory demands. Regulations define them as effect(s) of one human action combined with other actions. Here we argue for an approach that evaluates the cumulative risk of multiple stressors for protected wildlife populations within their ecosystems. Monitoring effects of each stressor is necessary but not sufficient to estimate how multiple stressors interact to affect wildlife populations. Examining the mechanistic pathways, from cellular to ecological, by which stressors affect individuals can help prioritize stressors and interpret how they interact. Our approach uses health indicators to accumulate the effects of stressors on individuals and to estimate changes in vital rates, driving population status. We advocate using methods well-established in human health and integrating them into ecosystem-based management to protect the health of commercially and culturally important wildlife populations and to protect against risk of extinction for threatened species. Our approach will improve abilities to conserve and manage ecosystems but will also demand significant increases in research and monitoring effort. We advocate for increased investment proportional to the economic scale of human activities in the Anthropocene and their pervasive effects on ecology and biodiversity.

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework

It is known that microorganisms are essential for the functioning of ecosystems, but the extent to which microorganisms respond to different environmental variables in their natural habitats is not clear. In the current study, we present a methodological framework to quantify the covariation of the microbial community of a habitat and environmental variables of this habitat. It is built on theoretical considerations of systems ecology, makes use of state-of-the-art machine learning techniques and can be used to identify bioindicators. We apply the framework to a data set containing operational taxonomic units (OTUs) as well as more than twenty physicochemical and geographic variables measured in a large-scale survey of European lakes. While a large part of variation (up to 61%) in many environmental variables can be explained by microbial community composition, some variables do not show significant covariation with the microbial lake community. Moreover, we have identified OTUs that act as "multitask" bioindicators, i.e., that are indicative for multiple environmental variables, and thus could be candidates for lake water monitoring schemes. Our results represent, for the first time, a quantification of the covariation of the lake microbiome and a wide array of environmental variables for lake ecosystems. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes that are essential for functioning and stability of lake ecosystems.

A guide to ecosystem models and their environmental applications

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition

Although metacommunity ecology has been a major field of research in the last decades, with both conceptual and empirical outputs, the analysis of the temporal dynamics of metacommunities has only emerged recently and consists mostly of repeated static analyses. Here we propose a novel analytical framework to assess metacommunity processes using path analyses of spatial and temporal diversity turnovers. We detail the principles and practical aspects of this framework and apply it to simulated datasets to illustrate its ability to decipher the respective contributions of entangled drivers of metacommunity dynamics. We then apply it to four empirical datasets. Empirical results support the view that metacommunity dynamics may be generally shaped by multiple ecological processes acting in concert, with environmental filtering being variable across both space and time. These results reinforce our call to go beyond static analyses of metacommunities that are blind to the temporal part of environmental variability.

Occupancy versus colonization-extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood-dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large-scale occupancies of species with differing landscape-scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization-extinction models, conducting the modeling at alternative spatial scales and using fine- or coarse-resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization-extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization-extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization-extinction models over occupancy models, modeling the process at the finest resource-unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.

Ecological response to altered rainfall differs across the Neotropics

There is growing recognition that ecosystems may be more impacted by infrequent extreme climatic events than by changes in mean climatic conditions. This has led to calls for experiments that explore the sensitivity of ecosystems over broad ranges of climatic parameter space. However, because such response surface experiments have so far been limited in geographic and biological scope, it is not clear if differences between studies reflect geographic location or the ecosystem component considered. In this study, we manipulated rainfall entering tank bromeliads in seven sites across the Neotropics, and characterized the response of the aquatic ecosystem in terms of invertebrate functional composition, biological stocks (total invertebrate biomass, bacterial density) and ecosystem fluxes (decomposition, carbon, nitrogen). Of these response types, invertebrate functional composition was the most sensitive, even though, in some sites, the species pool had a high proportion of drought-tolerant families. Total invertebrate biomass was universally insensitive to rainfall change because of statistical averaging of divergent responses between functional groups. The response of invertebrate functional composition to rain differed between geographical locations because (1) the effect of rainfall on bromeliad hydrology differed between sites, and invertebrates directly experience hydrology not rainfall and (2) the taxonomic composition of some functional groups differed between sites, and families differed in their response to bromeliad hydrology. These findings suggest that it will be difficult to establish thresholds of "safe ecosystem functioning" when ecosystem components differ in their sensitivity to climatic variables, and such thresholds may not be broadly applicable over geographic space. In particular, ecological forecast horizons for climate change may be spatially restricted in systems where habitat properties mediate climatic impacts, and those, like the tropics, with high spatial turnover in species composition.

Foreseeing the future of mutualistic communities beyond collapse

Changing conditions may lead to sudden shifts in the state of ecosystems when critical thresholds are passed. Some well-studied drivers of such transitions lead to predictable outcomes such as a turbid lake or a degraded landscape. Many ecosystems are, however, complex systems of many interacting species. While detecting upcoming transitions in such systems is challenging, predicting what comes after a critical transition is terra incognita altogether. The problem is that complex ecosystems may shift to many different, alternative states. Whether an impending transition has minor, positive or catastrophic effects is thus unclear. Some systems may, however, behave more predictably than others. The dynamics of mutualistic communities can be expected to be relatively simple, because delayed negative feedbacks leading to oscillatory or other complex dynamics are weak. Here, we address the question of whether this relative simplicity allows us to foresee a community's future state. As a case study, we use a model of a bipartite mutualistic network and show that a network's post-transition state is indicated by the way in which a system recovers from minor disturbances. Similar results obtained with a unipartite model of facilitation suggest that our results are of relevance to a wide range of mutualistic systems.

Mechanistic simulations predict that thermal and hydrological effects of climate change on Mediterranean trout cannot be offset by adaptive behaviour, evolution, and increased food production

Streamflow is a main driver of fish population dynamics and is projected to decrease in much of the northern hemisphere, especially in the Mediterranean region, due to climate change. However, predictions of future climate effects on cold-water freshwater fish populations have typically focused only on the ecological consequences of increasing temperatures, overlooking the concurrent and interacting effects of climate-driven changes in streamflow regimes. Here, we present simulations that contrasted the consequences of changes in thermal regime alone versus the combined effects of changes in thermal regime and streamflow for resident trout populations in distinct river types with different sensitivities to climatic change (low-altitude main river vs. high-altitude headwaters). We additionally assessed the buffering effect of increased food production that may be linked to warming. We used an eco-genetic individual-based model that integrates the behavioural and physiological effects of extrinsic environmental drivers -temperature and flow- with intrinsic dynamics -density-dependence, phenotypic plasticity and evolutionary responses - across the entire trout life cycle, with Mediterranean brown trout Salmo trutta as the model species. Our simulations indicated that: (1) Hydrological change is a critical dimension of climate change for the persistence of trout populations, in that neither river type supported viable populations under strong rates of flow change, even under scenarios of increased food production. (2) Climate-change-related environmental change most affects the largest, oldest trout via increased metabolic costs and decreased energy inputs. In both river types, populations persisted under extreme warming alone but became dominated by younger, smaller fish. (3) Density-dependent, plastic and evolutionary changes in phenology and life-history traits provide trout populations with important resilience to warming, but strong concurrent shifts in streamflow could exceed the buffering conferred by such intrinsic dynamics.

Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts

Quantitative models play an increasing role in exploring the impact of global change on biodiversity. To win credibility and trust, they need validating. We show how expert knowledge can be used to assess a large number of empirical species niche models constructed for the British vascular plant and bryophyte flora. Key outcomes were (a) scored assessments of each modeled species and niche axis combination, (b) guidance on models needing further development, (c) exploration of the trade-off between presenting more complex model summaries, which could lead to more thorough validation, versus the longer time these take to evaluate, (d) quantification of the internal consistency of expert opinion based on comparison of assessment scores made on a random subset of models evaluated by both experts. Overall, the experts assessed 39% of species and niche axis combinations to be "poor" and 61% to show a degree of reliability split between "moderate" (30%), "good" (25%), and "excellent" (6%). The two experts agreed in only 43% of cases, reaching greater consensus about poorer models and disagreeing most about models rated as better by either expert. This low agreement rate suggests that a greater number of experts is required to produce reliable assessments and to more fully understand the reasons underlying lack of consensus. While area under curve (AUC) statistics showed generally very good ability of the models to predict random hold-out samples of the data, there was no correspondence between these and the scores given by the experts and no apparent correlation between AUC and species prevalence. Crowd-sourcing further assessments by allowing web-based access to model fits is an obvious next step. To this end, we developed an online application for inspecting and evaluating the fit of each niche surface to its training data.

Will natural resistance result in populations of ash trees remaining in British woodlands after a century of ash dieback disease?

Novel pests and diseases are becoming increasingly common, and often cause additional mortality to host species in the newly contacted communities. This can alter the structure of the community up to, and including, the extinction of host species. In the last 20 years, ash dieback (ADB) disease has spread into Europe from East Asia. It has caused substantial mortality in ash tree (Fraxinus excelsior L.) populations. However, a proportion of the individuals in most populations appear to be less susceptible to ADB and resistance seems to have high heritability. These observations have led to suggestions that ash populations may be sustainable after the disease. In order to test this hypothesis, I modified an existing model of UK woodland (parametrized for Wytham Woods, Oxfordshire) to take into account the impact of ADB and allowed offspring to inherit resistance traits from their parent. The results suggest that ash populations would still exist in 100 years, but at lower levels than they are currently. For example, when the initial proportion of resistant individuals is about 10% and heritability of resistance is 0.5, then the population of ash falls to about one-third of present levels. The proportion of individuals initially resistant to ADB had a larger effect on population size after 100 years than the heritability of resistance. The fact that the initial size of the resistant population is important to achieve a high population size in the presence of ADB suggests that a selective breeding programme with the intention of augmenting the natural ash populations would be beneficial.

Let's Train More Theoretical Ecologists - Here Is Why

A tangled web of vicious circles, driven by cultural issues, has prevented ecology from growing strong theoretical roots. Now this hinders development of effective conservation policies. To overcome these barriers in view of urgent societal needs, we propose a global network of postgraduate theoretical training programs.

The importance and adaptive value of life-history evolution for metapopulation dynamics

The spatial configuration and size of patches influence metapopulation dynamics by altering colonisation-extinction dynamics and local density dependency. This spatial forcing as determined by the metapopulation typology then imposes strong selection pressures on life-history traits, which will in turn feed back on the ecological metapopulation dynamics. Given the relevance of metapopulation persistence for biological conservation, and the potential rescuing role of evolution, a firm understanding of the relevance of these eco-evolutionary processes is essential. We here follow a systems' modelling approach to quantify the importance of spatial forcing and experimentally observed life-history evolution for metapopulation demography as quantified by (meta)population size and variability. We therefore developed an individual-based model matching an earlier experimental evolution with spider mites to perform virtual translocation and invasion experiments that would have been otherwise impossible to conduct. We show that (a) metapopulation demography is more affected by spatial forcing than by life-history evolution, but that life-history evolution contributes substantially to changes in local- and especially metapopulation-level population sizes, (b) extinction rates are minimised by evolution in classical metapopulations, and (c) evolution is optimising individual performance in metapopulations when considering the importance of more cryptic stress resistance evolution. Ecological systems' modelling opens up a promising avenue to quantify the importance of eco-evolutionary feedbacks in spatially structured populations. Metapopulation sizes are especially impacted by evolution, but its variability is mainly determined by the spatial forcing. Eco-evolutionary dynamics can increase the persistence of classical metapopulations. Conservation of genetic variation and, hence, adaptive potential is thus not only essential in the face of environmental change; it also generates putative rescuing feedbacks that impact metapopulation persistence.

Indicators of transitions in biological systems

In the face of global biodiversity declines, predicting the fate of biological systems is a key goal in ecology. One popular approach is the search for early warning signals (EWSs) based on alternative stable states theory. In this review, we cover the theory behind nonlinearity in dynamic systems and techniques to detect the loss of resilience that can indicate state transitions. We describe the research done on generic abundance-based signals of instability that are derived from the phenomenon of critical slowing down, which represent the genesis of EWSs research. We highlight some of the issues facing the detection of such signals in biological systems - which are inherently complex and show low signal-to-noise ratios. We then document research on alternative signals of instability, including measuring shifts in spatial autocorrelation and trait dynamics, and discuss potential future directions for EWSs research based on detailed demographic and phenotypic data. We set EWSs research in the greater field of predictive ecology and weigh up the costs and benefits of simplicity vs. complexity in predictive models, and how the available data should steer the development of future methods. Finally, we identify some key unanswered questions that, if solved, could improve the applicability of these methods.

Synergistic impacts of habitat loss and fragmentation on model ecosystems

Habitat loss and fragmentation are major threats to biodiversity, yet separating their effects is challenging. We use a multi-trophic, trait-based, and spatially explicit general ecosystem model to examine the independent and synergistic effects of these processes on ecosystem structure. We manipulated habitat by removing plant biomass in varying spatial extents, intensities, and configurations. We found that emergent synergistic interactions of loss and fragmentation are major determinants of ecosystem response, including population declines and trophic pyramid shifts. Furthermore, trait-mediated interactions, such as a disproportionate sensitivity of large-sized organisms to fragmentation, produce significant effects in shaping responses. We also show that top-down regulation mitigates the effects of land use on plant biomass loss, suggesting that models lacking these interactions—including most carbon stock models—may not adequately capture land-use change impacts. Our results have important implications for understanding ecosystem responses to environmental change, and assessing the impacts of habitat fragmentation.

Dimensions of biodiversity loss: Spatial mismatch in land-use impacts on species, functional and phylogenetic diversity of European bees

Aim

Agricultural intensification and urbanization are important drivers of biodiversity change in Europe. Different aspects of bee community diversity vary in their sensitivity to these pressures, as well as independently influencing ecosystem service provision (pollination). To obtain a more comprehensive understanding of human impacts on bee diversity across Europe, we assess multiple, complementary indices of diversity.

Location

One Thousand four hundred and forty six sites across Europe.

Methods

We collated data on bee occurrence and abundance from the published literature and supplemented them with the PREDICTS database. Using Rao's Quadratic Entropy, we assessed how species, functional and phylogenetic diversity of 1,446 bee communities respond to land‐use characteristics including land‐use class, cropland intensity, human population density and distance to roads. We combined these models with statistically downscaled estimates of land use in 2005 to estimate and map—at a scale of approximately 1 km²—the losses in diversity relative to semi‐natural/natural baseline (the predicted diversity of an uninhabited grid square, consisting only of semi‐natural/natural vegetation).

Results

We show that—relative to the predicted local diversity in uninhabited semi‐natural/natural habitat—half of all EU27 countries have lost over 10% of their average local species diversity and two‐thirds of countries have lost over 5% of their average local functional and phylogenetic diversity. All diversity measures were generally lower in pasture and higher‐intensity cropland than in semi‐natural/natural vegetation, but facets of diversity showed less consistent responses to human population density. These differences have led to marked spatial mismatches in losses: losses in phylogenetic diversity were in some areas almost 20 percentage points (pp.) more severe than losses in species diversity, but in other areas losses were almost 40 pp. less severe.

Main conclusions

These results highlight the importance of exploring multiple measures of diversity when prioritizing and evaluating conservation actions, as species‐diverse assemblages may be phylogenetically and functionally impoverished, potentially threatening pollination service provision.

Constraint-based metabolic modelling of marine microbes and communities

Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12898-016-0103-y) contains supplementary material, which is available to authorized users.

A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees

Woodlands provide valuable ecosystem services, and it is important to understand their dynamics. To predict the way in which these might change, we need process‐based predictive ecological models, but these are necessarily very data intensive. We tested the ability of existing datasets to provide the parameters necessary to instantiate a well‐used forest model (SORTIE) for a well‐studied woodland (Wytham Woods). Only five of SORTIE's 16 equations describing different aspects of the life history and behavior of individual trees could be parameterized without additional data collection. One age class – seedlings – was completely missed as they are shorter than the height at which Diameter at Breast Height (DBH) is measured. The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time. This results in there being insufficient data to parameterize process‐based models in order to meet the societal demand for ecological prediction. We do not advocate ceasing the measurement of DBH, but we do recommend that those concerned with tree mensuration consider whether additional measures of trees could be added to their data collection protocols. We also see advantages in integrating techniques such as ground‐based LIDAR or remote sensing techniques with long‐term datasets to both preserve continuity with what has been performed in the past and to expand the range of measurements made.

Biodiversity scenarios neglect future land-use changes

Efficient management of biodiversity requires a forward-looking approach based on scenarios that explore biodiversity changes under future environmental conditions. A number of ecological models have been proposed over the last decades to develop these biodiversity scenarios. Novel modelling approaches with strong theoretical foundation now offer the possibility to integrate key ecological and evolutionary processes that shape species distribution and community structure. Although biodiversity is affected by multiple threats, most studies addressing the effects of future environmental changes on biodiversity focus on a single threat only. We examined the studies published during the last 25 years that developed scenarios to predict future biodiversity changes based on climate, land-use and land-cover change projections. We found that biodiversity scenarios mostly focus on the future impacts of climate change and largely neglect changes in land use and land cover. The emphasis on climate change impacts has increased over time and has now reached a maximum. Yet, the direct destruction and degradation of habitats through land-use and land-cover changes are among the most significant and immediate threats to biodiversity. We argue that the current state of integration between ecological and land system sciences is leading to biased estimation of actual risks and therefore constrains the implementation of forward-looking policy responses to biodiversity decline. We suggest research directions at the crossroads between ecological and environmental sciences to face the challenge of developing interoperable and plausible projections of future environmental changes and to anticipate the full range of their potential impacts on biodiversity. An intergovernmental platform is needed to stimulate such collaborative research efforts and to emphasize the societal and political relevance of taking up this challenge.