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Environmental challenges are rarely confined to national, disciplinary, or linguistic domains. Convergent solutions require international collaboration and equitable access to new technologies and practices. The ability of international, multidisciplinary and multilingual research teams to work effectively can be challenging. A major impediment to innovation in diverse teams often stems from different understandings of the terminology used. These can vary greatly according to the cultural and disciplinary backgrounds of the team members. In this paper we take an empirical approach to examine sources of terminological confusion and their effect in a technically innovative, multidisciplinary, multinational, and multilingual research project, adhering to Open Science principles. We use guided reflection of participant experience in two contrasting teams—one applying Deep Learning (Artificial Intelligence) techniques, the other developing guidance for Open Science practices—to identify and classify the terminological obstacles encountered and reflect on their impact. Several types of terminological incongruities were identified, including fuzziness in language, disciplinary differences and multiple terms for a single meaning. A novel or technical term did not always exist in all domains, or if known, was not fully understood or adopted. Practical matters of international data collection and comparison included an unanticipated need to incorporate different types of data labels from country to country, authority to authority. Sometimes these incongruities could be solved quickly, sometimes they stopped the workflow. Active collaboration and mutual trust across the team enhanced workflows, as incompatibilities were resolved more speedily than otherwise. Based on the research experience described in this paper, we make six recommendations accompanied by suggestions for their implementation to improve the success of similar multinational, multilingual and multidisciplinary projects. These recommendations are conceptual drawing on a singular experience and remain to be sources for discussion and testing by others embarking on their research journey. [ABSTRACT FROM AUTHOR]

Scientific teams are increasingly diverse in discipline, international scope and demographics. Diversity has been found to be a driver of innovation but also can be a source of interpersonal friction. Drawing on a mixed-method study of 22 scientific working groups, this paper presents evidence that team diversity has a positive impact on scientific output (i.e., the number of journal papers and citations) through the mediation of the interdisciplinarity of the collaborative process, as evidenced by publishing in and citing more diverse sources. Ironically these factors also seem to be related to lower team member satisfaction and perceived effectiveness, countered by the gender balance of the team. Qualitative data suggests additional factors that facilitate collaboration, such as trust and leadership. Our findings have implications for team design and management, as team diversity seems beneficial, but the process of integration can be difficult and needs management to lead to a productive and innovative process. [ABSTRACT FROM AUTHOR]

Highlights • Over 2000 measurements of plant belowground biomass were compiled. • Accurate generic allometric models could be developed. • These models were validated across stands where whole-plot excavation data were available. • Gains in prediction efficiencies using species-specific models were negligible. Abstract Accurate quantification of below-ground biomass (BGB) of woody vegetation is critical to understanding ecosystem function and potential for climate change mitigation from sequestration of biomass carbon. We compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB. We found that the relationship between BGB and stem diameter was generic, with a simple power-law model having a BGB prediction efficiency of 72–93% for four broad plant functional types: (i) shrubs and Acacia trees, (ii) multi-stemmed mallee eucalypts, (iii) other trees of relatively high wood density, and; (iv) a species of relatively low wood density, Pinus radiata D. Don. There was little improvement in accuracy of model prediction by including variables (e.g. climatic characteristics, stand age or management) in addition to stem diameter alone. We further assessed the generality of the plant functional type models across 11 contrasting stands where data from whole-plot excavation of BGB were available. The efficiency of model prediction of stand-based BGB was 93%, with a mean absolute prediction error of only 6.5%, and with no improvements in validation results when species-specific models were applied. Given the high prediction performance of the generalised models, we suggest that additional costs associated with the development of new species-specific models for estimating BGB are only warranted when gains in accuracy of stand-based predictions are justifiable, such as for a high-biomass stand comprising only one or two dominant species. However, generic models based on plant functional type should not be applied where stands are dominated by species that are unusual in their morphology and unlikely to conform to the generalised plant functional group models. [ABSTRACT FROM AUTHOR]

This paper discusses the process of retrieval and updating legacy data to allow on-line discovery and delivery. There are many pitfalls of institutional and non-institutional ecological data conservation over the long term. Interruptions to custodianship, old media, lost knowledge and the continuous evolution of species names makes resurrection of old data challenging. We caution against technological arrogance and emphasise the importance of international standards. We use a case study of a compiled set of continent-wide vegetation survey data for which, although the analyses had been published, the raw data had not. In the original study, publications containing plot data collected from the 1880s onwards had been collected, interpreted, digitised and integrated for the classification of vegetation and analysis of its conservation status across Australia. These compiled data are an extremely valuable national collection that demanded publishing in open, readily accessible online repositories, such as the Terrestrial Ecosystem Research Network (http://www.tern.org.au) and the Atlas of Living Australia (ALA: http://www.ala.org.au), the Australian node of the Global Biodiversity Information Facility (GBIF: http://www.gbif.org). It is hoped that the lessons learnt from this project may trigger a sober review of the value of endangered data, the cost of retrieval and the importance of suitable and timely archiving through the vicissitudes of technological change, so the initial unique collection investment enables multiple re-use in perpetuity. [ABSTRACT FROM AUTHOR]

The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour. To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves). Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations. We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value. We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing. [ABSTRACT FROM AUTHOR]

Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semiarid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/). [ABSTRACT FROM AUTHOR]

Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above-ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalization (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9-356 Mg ha−1). Losses in efficiency of prediction were <1% if generalized models were used in place of species-specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand-level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species-specific models is only warranted when gains in accuracy of stand-based predictions are relatively high (e.g. high-value monocultures). [ABSTRACT FROM AUTHOR]

Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e., drought, flooding, cyclones and fire) that can alter ecosystem composition, structure and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology at the continental scale using the Enhanced Vegetation Index (EVI), calculated from MODerate resolution Imaging Spectroradiomter (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e., tropical savannas) to regions where seasonal variation is minimal (i.e., tropical rainforests) or high but irregular (i.e., arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understory, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve current understanding of Australian ecosystems. To facilitate sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/). [ABSTRACT FROM AUTHOR]

The natural diet of the camaenid land snail Thersites mitchellae (Cox, 1864) was investigated by examination of the faecal contents of specimens collected from a range of substrates. The composition of faecal pellets from 22 snails obtained from three different substrates was determined. The results demonstrate that T. mitchellae has a generalist feeding strategy that varies with substrate. Fungal material contributed a high proportion of the diet, suggesting that coarse woody debris (a common fungal substrate) may be an important requirement for populations of T. mitchellae in rainforestassociated habitats. Thersites mitchellae was the first species for which a critical habitat determination was made under the New South Wales Threatened Species Conservation Act 1995. This study adds to our knowledge of the biology of this poorly known land snail. Future studies would benefit from obtaining data pertaining to the timing and frequency of fungal dispersal and substrate preferences to gain further understanding about the availability of fungi as a food source. [ABSTRACT FROM AUTHOR]

Mitchell's Rainforest Snail Thersites mitchellae Cox 1864 is listed as `endangered' under the NSW Threatened Species Conservation Act 1995, as `critically endangered' under the Environment Protection and Biodiversity Conservation Act I 999, and is on the IUCN Red List. Over the last 30 years, fewer than 20 live snails have been formally recorded in Museum and National Parks records. From the discovery of shells of the species, and a known habitat at Stotts Island in the Tweed River, it is understood that the species is highly specific in its habitat preference, occurring in `Lowland Rainforest on Floodplain' and the margins of Melaleuca quinquenervia swamps in northern New South Wales, Australia. `Lowland Rainforests on Floodplain' is itself listed as an endangered community. In this paper an initial description of the behaviour of the first substantial population discovered beyond Stotts Island is described. Like many land snails it was found to be nocturnal and sensitive to light. It appears to be highly sensitive to atmospheric humidity, and while active was mainly found on logs and leaf litter. Suggestions are made for profitable future studies that would provide a better understanding of its behaviour and habitat requirements, and consequently facilitate further discovery of the species and contribute to its conservation. [ABSTRACT FROM AUTHOR]

The camphor tree, Cinnamomum camphora (L.) Nees et Eberm., is a major environmental weed in parts of eastern Australia, particularly in northeastern New South Wales. It occurs in this region in two chemotypic forms, discriminated on the basis of leaf oil: camphor and 1,8-cineole. Oil was extracted from various parts of trees of each of these chemotypes: leaf, fruit, branch, trunk and root. Analysis of the oil revealed that, for the camphor-type, camphor content was greater in leaves than in other tree parts, where cineole and safrole were also present in substantial proportions; and, for the cineole-type, 1,8-cineole, which with lesser quantities of sabinene and citronellol dominated the leaf oil, is reduced in significance in the trunk where camphor is also an important constituent. [ABSTRACT FROM AUTHOR]

Advances in science, both today and in the future, will depend on the openness, accessibility and reusability of data, software, samples, and data products. In particular, environmental, ecological, and geological data represent single observations of dynamics and thus are critically important in aggregate. Similarly, global data on many phenomena and systems are essential for many applications to society, including weather prediction, hazard mitigation, water use, and GPS navigation, and thus have major economic benefits. Yet, large amounts of research data related to the Earth and its ecosystems are either not well preserved or preserved at all. And when they are preserved, the creator of those data is not always identified, resulting in difficulties in attributing the data resulting in proper credit. In some scientific domains, the practice of preserving data in a trusted repository is improving. During the last 18 months the Enabling FAIR Data project has made significant improvements in the common policies across the Earth, space, and environmental science journals and repositories. Many journals are now in the process of transitioning new practices for data that require data no longer be archived in the supplementary information of a paper, but instead placed in a repository with proper metadata, persistent identifier, and links to the manuscript and data creators. More recently a new grant has been awarded by the Belmont Forum to a group of international stakeholders working on the infrastructure of data sharing, reuse, and credit to make sure that all the elements needed for automated attribution are in place. Credit for data promotes data preservation, resulting in increases of data reuse. We are partnered with communities such as Earth Science Information Partners (ESIP) and Research Data Alliance (RDA) as well as organizations such as ORCiD and DataCite to address continued challenges with data attribution and credit. In this presentation we will review the project objectives, planned timelines, and outreach efforts planned to improve automated attribution and credit for data. [ABSTRACT FROM AUTHOR]