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Physiological adaptations for inhabiting transitional environments with strongly variable abiotic conditions can sometimes be displayed as behavioural shifts. A striking example might be found in bivalve species that inhabit estuaries characterised by fluctuations in environment. The opening and closing of their valves, so called gaping activity, represents behaviour that is required for two key physiological functions: food intake and respiration. Linking valve-gaping behaviour to environmental drivers can greatly improve our understanding and modelling of bivalve bioenergetics . Nowadays large data sets on gaping activity can be collected with automated sensors, but interpretation is difficult due to the large amount of environmental drivers and the intra-individual variability. This study aims to understand whether an unsupervised machine learning method (k-means clustering) can be used to identify patterns in gaping activity. Two commercially important congener mussels , Mytilus galloprovincialis and Mytilus edulis inhabiting two transitional coastal areas, the Venice Lagoon and the Wadden Sea , were fitted with sensors to monitor valve-gaping, while a comprehensive set of environmental parameters was also monitored. Data were analysed by applying three times a k-mean algorithm to the gaping time series. In the 1st analyses, the algorithm was applied to the overall gaping time series, including daily variations. We identified at both sites three clusters that were characterised by different average daily gaping aperture. The algorithm was subsequently reapplied to relate daily means of gaping to environmental conditions, being temperatures, oxygen saturation and chlorophyll levels. This 2nd analyses revealed that mean gaping aperture was mainly linked to food availability. A 3rd follow-up analysis aimed at exploring daily patterns. This third analysis again revealed consistent patterns amongst the two sites, where two clusters emerged that showed different degrees of oscillatory behaviour. There was however no obvious relationship between this fine scale oscillatory behaviours and environmental variables, but in the Venice Lagoon there was a site effect. Overall, we show that clustering algorithms can disentangle behavioural patterns within complex series of big data. The latter offers new opportunities to improve site-specific bioenergetic bivalve models by rephrasing the clearance and respiration terms based on the mean gaping aperture, provided that further laboratory experimentations are conducted to extrapolate parameters linking aperture with energy inputs and outputs.

Invasive species managers utilise species records to inform management. These data can also be used in Species Distribution Models (SDM) to predict future spread or potential invasion of new areas. However, issues with non-equilibrium (also called disequilibrium) can cause difficulties in modelling invasive species that have not fully colonised their potential distribution and, in addition, sampling bias can result from a lack of information on survey effort, a particular issue for presence only modelling techniques. Geographical confounds are unavoidable when building iSDMs but there are methods that allow prediction to be optimised. We used maximum entropy (Maxent) to model suitable habitat for invasive Reeve's muntjac deer (Muntiacus reevesi) throughout Great Britain and Ireland comparing several methods that aimed to address invasive Species Distribution Modelling (iSDM) bias including spatial filtering, weighted background points and targeted background points built at varying spatial extents. Model evaluation metrics suggested that the model, which explicitly failed to account for non-equilibrium at the full extent of Great Britain and Ireland using random background points, predicted the species' current invasive range best. This highlighted that negative environmental relationships are likely to represent uncolonised areas rather than habitat selection and thus, low predicted suitability of uncolonised areas was misleading. Of the models that dealt with non-equilibrium conceptually best, by restricting the training extent to their current invasive range or core range, and utilised targeted background points accounting for survey effort (cells with other deer species recorded as present yet with no records for muntjac) as the best model evaluation metric, yielded relatively poor predictive performance. This implied limited habitat selectivity or avoidance within the colonised range which, when spatially extrapolated, suggested virtually all regions in Great Britain and Ireland may be vulnerable to future muntjac invasion.

Unmanned aerial vehicles (UAVs) paired with a structure from motion (SfM) algorithm (UAV-SfM) can be used to derive canopy height models (CHMs) for individual tree crown delineation (ITCD). ITCD algorithms normally perform well in coniferous forests, but their capabilities in broadleaf or mixed forests are still challenging. In this study, we investigated the application of three ITCD algorithms using UAV-based high-resolution imagery in a broadleaf Hyrcanian forest. Three uneven-aged sites including a high-density (HD), a medium-density (MD), and a low-density (LD) stand were selected located in Noor city in Mazandaran province (Iran). Three marker-controlled segmentation algorithms, i.e., inverse watershed segmentation (IWS), local maxima (LM), and region growing (RG) were tested for a series of CHMs generated from point clouds derived by a structure from motion algorithm, across a range of spatial resolutions and a Gaussian filter with varying sigma. The delineation results were validated using field inventory data. False positives outnumbered false negatives for fine resolution CHMs. The highest overall accuracy was achieved for a spatial resolution of 100 cm using the RG algorithm and the IWS algorithm. Also, the effect of different forest structures, CHM filtering, and different tree species on the accuracy of tree delineation algorithms were evaluated. Overall, the selected delineation algorithms influenced the success of ITCD in a way that the RG algorithm generated significantly more accurate results than the other two algorithms. The RG algorithm was the most appropriate approach for the individual tree crown delineation.

To better understand the impact of ocean acidification on marine ecosystems, an important ongoing research priority for marine scientists is to characterize present-day pH variability. Following recent technological advances, autonomous pH sensor deployments in shallow coastal marine environments have revealed that pH dynamics in coastal oceans are more variable in space and time than the discrete, open-ocean measurements that are used for ocean acidification projections. Data from these types of deployments will benefit the research community by facilitating the improved design of ocean acidification studies as well as the identification or evaluation of natural and human-influenced pH variability. Importantly, the collection of ecologically relevant pH data and a cohesive, user-friendly integration of results across sites and regions requires (1) effective sensor operation to ensure high-quality pH data collection and (2) efficient data management for accessibility and broad reuse by the marine science community. Here, we review the best practices for deployment, calibration, and data processing and quality control, using our experience with Durafet®-based pH sensors as a model. Next, we describe information management practices for streamlining preservation and distribution of data and for cataloging different types of pH sensor data, developed in collaboration with two U.S. Long Term Ecological Research (LTER) sites. Finally, we assess sensor performance and data recovery from 73 SeaFET deployments in the Santa Barbara Channel using our quality control guidelines and data management tools, and offer recommendations for improved data yields. Our experience provides a template for other groups contemplating using SeaFET technology as well as general steps that may be helpful for the design of data management for other complex sensors.

The mosquito species is one of most important insect vectors of several diseases, namely, malaria, filariasis, Japanese encephalitis, dengue, and so on. In particular, in recent years, as the number of people who enjoy outdoor activities in urban areas continues to increase, information about mosquito activity is in demand. Furthermore, mosquito activity prediction is crucial for managing the safety and the health of humans. However, the estimation of mosquito abundances frequently involves uncertainty because of high spatial and temporal variations, which hinders the accuracy of general mechanistic models of mosquito abundances. For this reason, it is necessary to develop a simpler and lighter mosquito abundance prediction model. In this study, we tested the efficacy of the artificial neural network (ANN), which is a popular empirical model, for mosquito abundance prediction. For comparison, we also developed a multiple linear regression (MLR) model. Both the ANN and the MLR models were applied to estimate mosquito abundances in 2-year observations in Yeongdeungpo-gu, Seoul, conducted using the Digital Mosquito Monitoring System (DMS). As input variables, we used meteorological data, including temperature, wind speed, humidity, and precipitation. The results showed that performances of the ANN model and the MLR model are almost same in terms of R and root mean square error (RMSE). The ANN model was able to predict the high variability as compared to MLR. A sensitivity analysis of the ANN model showed that the relationships between input variables and mosquito abundances were well explained. In conclusion, ANNs have the potential to predict fluctuations in mosquito numbers (especially the extreme values), and can do so better than traditional statistical techniques. But, much more work needs to be conducted to assess meaningful time delays in environmental variables and mosquito numbers.

While the effects of climate change on top predators are well documented, the role of predation on ecosystem level carbon production is poorly developed, despite it being a logical consequence of trophic dynamics. Trophic cascade effects have shown predator mediated changes in primary production, but we predict that predators should lower the overall biomass capacity of any system with top down control. Through a simple Bayesian belief network model of a typical marine foodweb, we show that predator removal, as is common through activities such as fishing and shark finning, results in higher biomasses of lower trophic level fish and zooplankton, resulting in higher net carbon production by the system. In situations common throughout much of the ocean, where activities such as shark finning and over fishing reduce the highest tropic levels, the probability of net carbon production increasing in the model was ~60%, and unlike previous studies on simple food chains, trophic cascade effects were not present. While the results are preliminary, and sources of uncertainty in data and models are acknowledged, such results provide even more strength to the argument to protect open sea fish stocks, and particularly large predators such as sharks, cetaceans and game fish.

The rise of informatics has presented new opportunities for analyzing, visualizing, and interacting with data across the sciences, and biodiversity science is no exception. Recently, comprehensive datasets on the geographic distributions of species have been assembled that represent a thorough accounting of a given taxonomic group of species (e.g. birds, mammals, etc.), and which form critical tools for both basic biology and conservation. However, these databases present several challenges for visualization, interaction, and participation for users across a broad range of scientists and the public. In support of the development of a new comprehensive ant biodiversity database containing over 1.7 million records, we developed a new client–server web-mapping application, antmaps.org , to visualize and interact with the geographic distributions of all 15,050 ant species and aggregate patterns of their diversity and biogeography. Our application development approach was based on user-centered design principles of usability engineering, human-computer interaction, and cartography. The resulting application is highly focused on providing efficient and intuitive access to geographic biodiversity data using a client–server interaction that allows users to query and retrieve data on the fly. This is achieved with a backend solution to efficiently work with large volumes of geospatial data. The usability and utility of the final version of the application was measured based on effectiveness, efficiency and user satisfaction, and assessed using questionnaires, usability lab studies and surveys. While the development of antmaps.org was motivated by a particular ant biodiversity dataset, the basic framework, design, and functionality are not specific to ants and could be used to interact with biodiversity data of any taxonomic group.

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey-predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries. We would like to thank Johan Van Der Molen from CEFAS for providing the ERSEM model outputs, the ICES DATRAS database for the North Sea IBTS data and Historical Catch Statistics, ICES North Sea Integrated Assessment Working Group (WGINOSE) and the organisations which provide data for the ICES assessment process, in particular SAHFOS who have provided the North Sea plankton data, Chiara Franco for general advice and the Natural Environment Research Council, UK (NE/ J01642X/1)who has provided the funding of this research. We gratefully acknowledge support from the European Commission (OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; no: 603773) for David Maxwell and support from CEFAS for Andrew Kenny and David Maxwell

Habitat conservation, and hence conservation of biodiversity hinges on knowledge of the spatial distribution of habitats, not least those that are particularly valuable or vulnerable. In offshore Norway, benthic habitats are systematically surveyed and described by the national programme MAREANO (Marine AREAl database for NOrwegian waters). Benthic habitats and biotopes are defined in terms of the species composition of their epibenthic megafauna. Some habitats are of special conservation interest on account of their intrinsic value and/or vulnerability (e.g., long-lived species, rareness, to comply with international regulations such as OSPAR). In Norway, off Nordland and Troms, the following habitats of special interest can be found: Umbellula encrinus Stands, Radicipes sp. Meadows, Deep Sea Sponge Aggregations, Seapen and Burrowing Megafauna Communities, Hard Bottom Coral Gardens. In this paper, we used underwater video data collected within the MAREANO programme to define and describe benthic habitats and biotopes of special interest, and to map the geographic distribution thereof by means of habitat modelling. We first evaluated the community structure of each habitat in the list using a SIMPROF test. We determined that the class Deep Sea Sponge Aggregations, as defined by OSPAR, had to be split into at least three classes. We then re-defined seven new types of ecological features, including habitats and biotopes that were sufficiently homogeneous. Then we modelled the spatial distributions of these habitats and biotopes using Conditional Inference Forests. Since the purpose of the distribution maps is to support spatial planning we classified the heat maps using density thresholds. The accuracy of models ranged from fair to excellent. Hard Bottom Coral Gardens were the most rare habitat in terms of total area predicted (224 km2, 0.3% of the area modelled), closely followed by Radicipes Meadows (391 km2, 0.6%). Soft Bottom Demosponges (Geodid sponges and other taxa) represent the largest habitat, with a predicted area of 9288 km2 (14%). Distribution maps of classes defined by habitat-forming species (Hard Bottom Coral Gardens) were more reliable than those defined by a host of species, or where no single species was a clear habitat provider (e.g. Seapen and Burrowing Megafauna Communities). We also put forward that a scale of patchiness larger than the scale of observation, and homogeneity of the community both play a role in model performance, and hence in map usefulness. These along with density threshold values based on observed data should all be taken into account in marine classifications and habitat definitions.

Anticipating the ecological effects of climate change to inform natural resource climate adaptation planning represents one of the primary challenges of contemporary conservation science. Species distribution models have become a widely used tool to generate first-pass estimates of climate change impacts to species probabilities of occurrence. There are a number of technical challenges to constructing species distribution models that can be alleviated by the use of scientific workflow software. These challenges include data integration, visualization of modeled predictor–response relationships, and ensuring that models are reproducible and transferable in an adaptive natural resource management framework. We used freely available software called VisTrails Software for Assisted Habitat Modeling (VisTrails:SAHM) along with a novel ecohydrological predictor dataset and the latest Coupled Model Intercomparison Project 5 future climate projections to construct species distribution models for eight forest and shrub species in the Greater Yellowstone Ecosystem in the Northern Rocky Mountains USA. The species considered included multiple species of sagebrush and juniper, Pinus flexilis, Pinus contorta, Pseudotsuga menziesii, Populus tremuloides, Abies lasciocarpa, Picea engelmannii, and Pinus albicaulis. Current and future species probabilities of occurrence were mapped in a GIS by land ownership category to assess the feasibility of undertaking present and future management action. Results suggested that decreasing spring snowpack and increasing late-season soil moisture deficit will lead to deteriorating habitat area for mountain forest species and expansion of habitat area for sagebrush and juniper communities. Results were consistent across nine global climate models and two representative concentration pathway scenarios. For most forest species their projected future distributions moved up in elevation from general federal to federally restricted lands where active management is currently prohibited by agency policy. Though not yet fully mature, custom scientific workflow software shows considerable promise to ease many of the technical challenges inherent in modeling the potential ecological impacts of climate change to support climate adaptation planning.

Thermal infrared video can provide essential information about bird and bat activity for risk assessment studies, but the analysis of recorded video can be time-consuming and may not extract all of the available information. Automated processing makes continuous monitoring over extended periods of time feasible, and maximizes the information provided by video. This is especially important for collecting data in remote locations that are difficult for human observers to access, such as proposed offshore wind turbine sites. We developed new processing algorithms for single camera thermal video that automate the extraction of two-dimensional bird and bat flight tracks, and that characterize the extracted tracks to support animal identification and behavior inference. The algorithms consist of video peak store followed by background masking and perceptual grouping to extract flight tracks. The extracted tracks are automatically quantified in terms that could then be used to infer animal taxonomy and possibly behavior, as described in the companion article from Cullinan, et al. [“Classification of birds and bats using flight tracks.” Ecological Informatics, 27:55–63]. The developed automated processing was evaluated using six video clips containing a total of 184 flight tracks. The detection rate was 81% and the false positive rate was 17%. In addition to describing the details of the algorithms, we suggest models for interpreting thermal imaging information.

Characterization of the diets of upper-trophic pelagic predators that consume forage species is a key ingredient in the development of ecosystem-based fishery management plans, conservation of marine predators, and ecological and economic modeling of trophic interactions. Here we present the California Current Predator Diet Database (CCPDD) for the California Current region of the Pacific Ocean over the past century, assimilating over 190 published records of predator food habits for over 100 predator species and 32 categories of forage taxa (species or groups of similar species). Literature searches targeted all predators that consumed forage species: seabirds, cetaceans, pinnipeds, bony and cartilaginous fishes, and a predatory invertebrate. Diet data were compiled into a relational database. Analysis of the CCPDD highlighted differences in predator diet data availability based on geography, time period and predator taxonomy, as well as prominent prey categories. The top 5 forage taxa with the most predators included juvenile rockfish, northern anchovy, euphausiid krill, Pacific herring and market squid. Predator species with abundant data included Pacific hake, common murre, and California sea lion. Most diet data were collected during the summer; the lack of winter data will restrict future use of the CCPDD to understand seasonal patterns in predator diet unless more such data become available. Increased synthesis of historical information can provide new resources to understand patterns in the role of forage species in predator diet. Increased publication and/or accessibility of long-term datasets and data-sharing will further foster the synthesis of information intended to inform the management, conservation and understanding of marine food webs.

Data sharing is the practice of making data available for use by others. Ecologists are increasingly generating and sharing an immense volume of data. Such data may serve to augment existing data collections and can be used for synthesis efforts such as meta-analysis, for parameterizing models, and for verifying research results (i.e., study reproducibility). Large volumes of ecological data may be readily available through institutions or data repositories that are the most comprehensive available and can serve as the core of ecological analysis. Ecological data are also employed outside the research context and are used for decision-making, natural resource management, education, and other purposes. Data sharing has a long history in many domains such as oceanography and the biodiversity sciences (e.g., taxonomic data and museum specimens), but has emerged relatively recently in the ecological sciences.A review of several of the large international and national ecological research programs that have emerged since the mid-1900s highlights the initial failures and more recent successes as well as the underlying causes—from a near absence of effective policies to the emergence of community and data sharing policies coupled with the development and adoption of data and metadata standards and enabling tools. Sociocultural change and the move towards more open science have evolved more rapidly over the past two decades in response to new requirements set forth by governmental organizations, publishers and professional societies. As the scientific culture has changed so has the cyberinfrastructure landscape. The introduction of community-based data repositories, data and metadata standards, software tools, persistent identifiers, and federated search and discovery have all helped promulgate data sharing. Nevertheless, there are many challenges and opportunities especially as we move towards more open science. Cyberinfrastructure challenges include a paucity of easy-to-use metadata management systems, significant difficulties in assessing data quality and provenance, and an absence of analytical and visualization approaches that facilitate data integration and harmonization. Challenges and opportunities abound in the sociocultural arena where funders, researchers, and publishers all have a stake in clarifying policies, roles and responsibilities, as well as in incentivizing data sharing. A set of best practices and examples of software tools are presented that can enable research transparency, reproducibility and new knowledge by facilitating idea generation, research planning, data management and the dissemination of data and results.

Classification of birds and bats that use areas targeted for offshore wind farm development is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea is transect-surveys conducted by trained individuals in boats or planes, or analysis of imagery collected from aerial surveys. These methods can be costly and pose safety concerns so that observation times are limited to daylight hours and fair weather. We propose an alternative method based on analysis of thermal video that could be recorded autonomously. We present a framework for building models to classify birds and bats and their associated behaviors from their flight tracks. As an example, we developed a discriminant model for theoretical flight paths and applied it to data ( N = 64 tracks) extracted from 5-minute video clips. The agreement between model- and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and the number of different path types was reduced. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of gulls and swallows ( N ≥ 18) was on average 73% and 85% correct, respectively. Model classification of bats and terns ( N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models could be improved if the distance between the camera and the target was known.

The modelling and simulation tool ECOBAS was extended in order to include special features supporting the development of ecological models. The «Graphical Model Editor» allows the connection of at least 2 modules in order to build a whole model to run simulations. With the ECOBAS simulation system the model can be tested extensively in order to find appropriate parameter sets («Parameter analysis» and «Parameter estimation») and to identify critical parameters («Sensitivity analysis»). The «Interaction Analysis» shows the internal dependencies of a model. ECOBAS integrates the steps of ecological modelling and creates well readable and complete documentations within one working step, supports modularization of models and the user is rid of the technical and numerical aspects of modelling. Hence ECOBAS is setting up complete, consistent and syntactical correct models. All new features of the ECOBAS-system will be introduced by applying it on the existing ecosystem model EMMO.

Qualitative reasoning has been successfully used for ecological modelling, particularly when numerical data are not available. However, in order to further explore the potential of this modelling approach, it is important to discuss how to incorporate numerical data, if available, and to develop means to evaluate conceptual aspects and model outputs. This paper describes a study on qualitative model evaluation, in which numerical data about water quality are used to define different scenarios in a water basin, so that the outputs of simulations with the model can be compared to the actual system. The model was evaluated by independent experts, concerning its conceptual and operational aspects, and with respect to its predictive capability. The model was considered valid for the intended use, which is to increase the understanding of non-expert water managers.

Dynamic environmental models use a state transition function, external inputs and parameters to simulate the change of real-world processes over time. Modellers specify the state transition function and the external inputs required in the process calculation of each time step in a component model, a self-contained numerical module representing an individual spatio-temporal process. Depending on the application case of a component model – such as standalone execution or in an integrated model – the source of the external input needs to be specified. The required external inputs can thereby be obtained by a file operation in case of a standalone execution. Alternatively, required inputs can be obtained from other component models, in case the component model is part of an integrated model. Using different notations to specify these input requirements, however, requires a modification of the state transition function per application case and therefore would reduce the generic applicability of a component model. To address this problem, we propose the function object notation as a means to specify the input requirements of a component model. This function object notation provides modellers with a uniform syntax to express the input requirements within the state transition function. During component initialisation, the function objects can be parameterised with different external sources. In addition to a uniform syntax, the function object notation allows a modeller to specify a request-reply execution flow of the coupled models (i.e. a component requests data needed for its own progress from another component). We extend the request-reply execution approach to Monte Carlo simulations and implement a software framework prototype. Using this prototype, we build an exemplary integrated model by coupling components for land use change, hydrology and Eucalyptus tree growth at different temporal discretisations to obtain the probability for bioenergy plant growing in a hypothetical catchment. The presented approach allows modellers to specify input requirements in the state transition function independently from the source of external inputs and therefore increases the reusability of these component models.

An intricate network of interactions between organisms and their environment form the ecosystems that sustain life on earth. With a detailed understanding of these interactions, ecologists and biologists can make better informed predictions about the ways different environmental factors will impact ecosystems. Despite the abundance of research data on biotic and abiotic interactions, no comprehensive and easily accessible data collection is available that spans taxonomic, geospatial, and temporal domains. Biotic-interaction datasets are effectively siloed, inhibiting cross-dataset comparisons. In order to pool resources and bring to light individual datasets, specialized research tools are needed to aggregate, normalize, and integrate existing datasets with standard taxonomies, ontologies, vocabularies, and structured data repositories. Global Biotic Interactions (GloBI) provides such tools by way of an open, community-driven infrastructure designed to lower the barrier for researchers to perform ecological systems analysis and modeling. GloBI provides a tool that (a) ingests, normalizes, and aggregates datasets, (b) integrates interoperable data with accepted ontologies (e.g., OBO Relations Ontology, Uberon, and Environment Ontology), vocabularies (e.g., Coastal and Marine Ecological Classification Standard), and taxonomies (e.g., Integrated Taxonomic Information System and National Center for Biotechnology Information Taxonomy Database), (c) makes data accessible through an application programming interface (API) and various data archives (Darwin Core, Turtle, and Neo4j), and (d) houses a data collection of about 700,000 species interactions across about 50,000 taxa, covering over 1100 references from 19 data sources. GloBI has taken an open-source and open-data approach in order to make integrated species-interaction data maximally accessible and to encourage users to provide feedback, contribute data, and improve data access methods. The GloBI collection of datasets is currently used in the Encyclopedia of Life (EOL) and Gulf of Mexico Species Interactions (GoMexSI).

Emergent cooperative relations in ecosystems are ill understood, but have the potential to strongly improve evolutionary computing. On the other hand, eco-evolutionary computation has the potential to provide new insights in the structuring and functioning of ecosystems. Here we study ecosystem based problem solving in a co-evolutionary framework of predators (solvers) and prey (problems), extended with a population of scavengers, which can eat the remains of prey (that is, cooperate with the predators in solving the problems). We show that such an artificial ecosystem of predators, prey and scavengers, with a selection and fitness regime favoring specialization, self-organizes in space and time such that (1) problems are automatically decomposed in easier to solve parts, (2) the predator, prey and scavenger populations differentiate in sub-populations according to this decomposition, and (3) predators and scavengers automatically co-localize in space such that the problems are indeed solved by predator–scavenger combinations which together correctly approximate the target function. That is, the use of a spatial co-evolutionary ecosystem as information processing unit for evolutionary computation gives rise to an emergent structure of niches, each consisting of complementary partial solutions. As a result, ecosystem based solutions are preferred over individual-based solutions in solving the studied function approximation task.

The maximum entropy (MaxEnt) method has gained widespread use for distribution modelling, mostly because of the practical simplicity offered by the maxent.jar software. Whilst MaxEnt was originally described as a machine learning method, recent studies have shown that the method can be explained in terms of maximum likelihood estimation. This opens for using MaxEnt with new settings and options, such as new model selection and model assessment criteria, and improved user control of the variable selection process. New practical tools are needed to explore the new opportunities and assess if they enhance model performance and ecological interpretability of the models. We present a new conceptual framework, the Modular and functionally Integrated component-based Approach (MIA) framework for practical distribution modelling by which the core components of the DM process are decoupled and then wrapped together more flexibly into component-based functional modules. Computational object-oriented and workflow approaches are integrated with ecological, statistical and modelling theory in order to handle the complexity associated with the full modelling process in a practical way. Objects (variables, functions, results, etc.) are defined according to specific modelling parameters. Properties (e.g., identities and content) are inherited between objects and new objects are created in a flexible and automated, yet traceable way. We operationalise this framework for MaxEnt by the MIA Toolbox (MIAT), a set of flexible, modular R-scripts (available in supplementary appendices) wrapped around maxent.jar and existing R-functions. MIAT covers the full range of options and settings for the maximum likelihood implementation of MaxEnt and provide flexible guidance of users through the DM process. A trail of models of increasing complexity is built to enhance traceability and interpretability, and to suit different modelling purposes. We briefly outline research questions that can be addressed by the MIAT.

We investigated the usefulness of a ground-based digital photography to evaluate seasonal changes in the aboveground green biomass and foliage phenology in a short-grass grassland in Japan. For ground-truthing purposes, the ecological variables of aboveground green biomass and spectral reflectance of aboveground plant parts were also measured monthly. Seasonal change in a camera-based index (rG: ratio of green channel) reflected the characteristic events of the foliage phenology such as the leaf-flush and leaf senescence. In addition, the seasonal pattern of the rG was similar to that of the aboveground green biomass throughout the year. Moreover, there was a positive linear relationship between rG and aboveground green biomass (R2=0.81, p

Accurate and reliable predictions of invasive species distributions are urgently needed by land managers for developing management plans and monitoring new potential areas of establishment. Presence-only species distribution models are commonly used in these evaluations, however they are rarely tested with independent data over time or compared with presence-absence models fit with the same presence data. Using Maxent, we developed a presence-only model of invasive cheatgrass (Bromus tectorum L.) distribution in Rocky Mountain National Park, Colorado, USA in 2007 fit with limited data, and then tested the model with independent presence and absence data collected between 2008 and 2013. This model was verified using threshold dependent and threshold independent evaluation metrics. Next, we developed a Maxent model with cheatgrass presence data from 2007 through 2013 (i.e. Maxent 2013), and compared this model to a presence-absence method (i.e., generalized linear model; GLM 2013) using the same data. Threshold dependent and threshold independent evaluation metrics suggested Maxent 2013 outperformed GLM 2013, and a two-tailed Wilcoxon signed rank test indicated relative probability outputs were not significantly different between the models in geographic space. Based on known presences and absences of cheatgrass collected in the field, the Maxent 2013 and GLM 2013 relative probability outputs were highly correlated at absence locations but less correlated at presence locations. A Kappa comparison of Maxent 2007 and Maxent 2013 binary output provides evidence that Maxent is robust when fit with limited data. Our results indicate Maxent is an appropriate model for use when land management objectives are supported by limited resources and thus require a conservative, but highly accurate estimate of habitat suitability for invasive species on the landscape.