Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.     

Microsites of seed arrival: spatio–temporal variations in complex seed‐disperser networks

Microsites where seeds arrive during the dispersal process determine plant reproductive success, affecting the quality of dispersal. Despite their crucial role for plant recruitment, very few studies have addressed spatio–temporal variations in microsites of seed arrival in complex seed‐disperser networks. Using an endozoochorous dispersal system, we characterized the microsites of seed arrival of eight fleshy‐fruited plant species dispersed by five mammal species during two consecutive seasons across three sites in a Mediterranean environment (n = 383 feces with seeds; 261 453 seeds). We evaluated spatial and temporal variations in the probability of a seed to arrive at open microsites or at microsites with varying plant cover, considering selection by frugivores and assessing the extent to which seeds of particular species arrived under conspecifics or heterospecifics. We found strong spatio–temporal variations in the amounts of seeds of the eight target species arriving at different microsites. These variations were strongly driven by frugivores’ selection of different landscape elements (i.e. open areas and microsites dominated by different plant species), which differed from expectations based on their local availability. In general, more seeds than expected arrived at vacant (open) microsites. Using bipartite network graphs to connect seeds with their arrival microsites, we found that the proportion of seeds of fleshy‐fruited species arriving near conspecifics or heterospecifics, or at vacant microsites, varied depending on the target plant species, but also on the frugivore species dispersing it, on the study site and on the dispersal season. Our study revealed marked spatio–temporal variations in the microsites of seed arrival, which will potentially have implications for the quality of dispersal effectiveness, ultimately affecting plant population dynamics and community structure. Such a strong context‐dependence in the microsites of seed arrival is likely to confer resilience against unpredictable environmental conditions, like those typical of Mediterranean ecosystems.     

Identifying footprints of selection in stocked brown trout populations: a spatio‐temporal approach

Studies of interactions between farmed and wild salmonid fishes have suggested reduced fitness of farmed strains in the wild, but evidence for selection at the genic level is lacking. We studied three brown trout populations in Denmark which have been significantly admixed with stocked hatchery trout (19–64%), along with two hatchery strains used for stocking. The wild populations were represented by contemporary samples (2000–2006) and two of them by historical samples (1943–1956). We analysed 61 microsatellite loci, nine of which showed putative functional relationships [expressed sequence tag (EST)‐linked or quantitative trait loci]. F_ST‐based outlier tests provided support for diversifying selection at chromosome regions marked by three loci, two anonymous and one EST‐linked. Patterns of differentiation suggested that the loci were candidates for being under diversifying hitch‐hiking selection in hatchery vs. wild environments. Analysis of hatchery strain admixture proportions showed that in one wild population, two of the loci showed significantly lower admixture proportions than the putatively neutral loci, implying contemporary selection against alleles introduced by hatchery strain trout. In the most strongly admixed population, however, there was no evidence for selection, possibly because of immigration by stocked trout overcoming selection against hatchery‐derived alleles or supportive breeding practices allowing hatchery strain trout to escape natural selection. To our knowledge, this is the first study demonstrating footprints of selection in wild salmonid populations subject to spawning intrusion by farmed fish.     

Sun‐induced chlorophyll fluorescence from high‐resolution imaging spectroscopy data to quantify spatio‐temporal patterns of photosynthetic function in crop canopies

Passive detection of sun‐induced chlorophyll fluorescence (SIF) using spectroscopy has been proposed as a proxy to quantify changes in photochemical efficiency at canopy level under natural light conditions. In this study, we explored the use of imaging spectroscopy to quantify spatio‐temporal dynamics of SIF within crop canopies and its sensitivity to track patterns of photosynthetic activity originating from the interaction between vegetation structure and incoming radiation as well as variations in plant function. SIF was retrieved using the Fraunhofer Line Depth (FLD) principle from imaging spectroscopy data acquired at different time scales a few metres above several crop canopies growing under natural illumination. We report the first maps of canopy SIF in high spatial resolution. Changes of SIF were monitored at different time scales ranging from quick variations under induced stress conditions to seasonal dynamics. Natural changes were primarily determined by varying levels and distribution of photosynthetic active radiation (PAR). However, this relationship changed throughout the day demonstrating an additional physiological component modulating spatio‐temporal patterns of SIF emission. We successfully used detailed SIF maps to track changes in the canopy's photochemical activity under field conditions, providing a new tool to evaluate complex patterns of photosynthesis within the canopy.     

The spatio‐temporal forest patch dynamics inferred from the fine‐scale synchronicity in growth chronology

Question: Abrupt increments in tree radial growth chronology are associated with gap formations derived from disturbances. If a forest has been primarily controlled by fine‐scale disturbances such as single tree‐fall, do these release events spatio‐temporally synchronize at a fine scale such as 10 m and 5 years? Is it possible to quantify spatio‐temporal patterns of synchronicity from tree rings and long‐term inventories, and associate them with spatial forest patch dynamics? How and to what extent can we reconstruct the fine‐scale synchronized growth and spatio‐temporal forest patch dynamics from currently available information? Location: Cores were taken from Abies sachalinensis trees in a coniferous/deciduous mixed forest in the Shiretoko Peninsula, Hokkaido, northern Japan. Methods: We first eliminated short‐term fluctuations and highlighted growth trends over the mid‐term using a time‐series smoothing technique. This helped identify release events, we then conducted fine‐scale spatial analyses on released A. sachalinensis primarily with cluster analysis. Results: We specified the unit scale of synchronicity at 10 m, and classified released A. sachalinensis trees into spatially separated regions. Only once during the recent 50 years was extensive synchronicity over 40 m found. Most of the released A. sachalinensis were isolated, with non‐released A. sachalinensis present in nearby, implying imperfect synchronization. The ambiguous 20–30 m A. sachalinensis patches present in the current forest were the result of connected and overlapping patches smaller than 10 m associated with different disturbances and different responses of understorey trees. Conclusion: Tree‐ring series, long‐term census and fine‐scale spatio‐temporal analyses revealed that this forest community has been controlled by two types of disturbance: frequent small disturbances such as single tree‐fall and less frequent multiple tree‐falls.     

Accounting for uncertainty in colonisation times: a novel approach to modelling the spatio‐temporal dynamics of alien invasions using distribution data

A novel, yet generic, Bayesian approach to parameter inference in a stochastic, spatio‐temporal model of dispersal and colonisation is developed and applied to the invasion of a region by an alien plant species. The method requires species distribution data from multiple time points, and accounts for temporal uncertainty in colonisation times inherent in such data. Covariates, such as climate parameters, altitude and land use, which capture variation in the suitability of sites for plant colonisation, are easily incorporated into the model. The method assumes no local extinction of occupied sites and thus is primarily applicable to modelling distribution data at relatively coarse spatial resolutions of plant species whose range is expanding over time. The implementation of the model and inference algorithm are illustrated through application to British floristic atlas data for the widespread alien Heracleum mantegazzianum (giant hogweed) assessed at a 10 × 10 km resolution in 1970 and 2000. We infer key characteristics of this species, predict its future spread, and use the resulting fitted model to inform a simulation‐based assessment of the methodology. Simulated distribution data are used to validate the inference algorithm. Our results suggest that the accuracy of inference is not sensitive to the number of distribution time points, requiring only that there are at least two points in time when distributions are mapped. We demonstrate the utility of the modelling approach by making future forecasts and historic hindcasts of the distribution of giant hogweed in Great Britain. Giant hogweed is one of the worst alien plants in Britain and has rapidly increased its range since 1970, yet we highlight that a further 20% of land area remains susceptible to colonisation by this species. We use the robustness of this case study to discuss the potential for modelling distribution data for other species and at different spatial scales.     

Finding the missing link: Disulfide‐containing proteins via a high‐throughput proteomics approach

Top‐down proteomics have recently started to gain attention as a novel method to provide insight into the structure of proteins in their native state, specifically the number and location of disulfide bridges. However, previous techniques still relied on complex and time‐consuming protein purification and reduction reactions to yield useful information. In this issue of Proteomics, Zhao et al. (high‐throughput screening of disulfide‐containing proteins in a complex mixture, Proteomics 2013, 13, 3256–3260) devise a clever and rapid method for high‐throughput determination of disulfides in proteins via reduction by tris(2‐carboxyethyl)phosphine. Their work provides the foundation necessary to undertake more complex experiments in biological samples.     

GO‐At : in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO‐At (gene ontology prediction in A. thaliana), a method that combines five data types (co‐expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two‐step approach, GO‐At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the ‘best hit’ from the first list, and achieves success rates of up to 79%. GO‐At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO‐At’s ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi‐associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web‐based implementation of GO‐At ( http://www.bioinformatics.leeds.ac.uk/goat ) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.     

Representation of selected‐reaction monitoring data in the mzQuantML data standard

The mzQuantML data standard was designed to capture the output of quantitative software in proteomics, to support submissions to public repositories, development of visualization software and pipeline/modular approaches. The standard is designed around a common core that can be extended to support particular types of technique through the release of semantic rules that are checked by validation software. The first release of mzQuantML supported four quantitative proteomics techniques via four sets of semantic rules: (i) intensity‐based (MS¹) label free, (ii) MS¹ label‐based (such as SILAC or N¹⁵), (iii) MS² tag‐based (iTRAQ or tandem mass tags), and (iv) spectral counting. We present an update to mzQuantML for supporting SRM techniques. The update includes representing the quantitative measurements, and associated meta‐data, for SRM transitions, the mechanism for inferring peptide‐level or protein‐level quantitative values, and support for both label‐based or label‐free SRM protocols, through the creation of semantic rules and controlled vocabulary terms. We have updated the specification document for mzQuantML (version 1.0.1) and the mzQuantML validator to ensure that consistent files are produced by different exporters. We also report the capabilities for production of mzQuantML files from popular SRM software packages, such as Skyline and Anubis.     

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.     

Modeling seasonality in space‐time infectious disease surveillance data

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one‐step‐ahead predictions if all three components allow for seasonal variation.