Accessible ecology: synthesis of the long, deep, and broad

Data synthesis for environmental management: A case study of Chesapeake Bay

Synthesizing large, complex data sets to inform resource managers towards effective environmental stewardship is a universal challenge. In Chesapeake Bay, a well-studied and intensively monitored estuary in North America, the challenge of synthesizing data on water quality and land use as factors related to a key habitat, submerged aquatic vegetation, was tackled by a team of scientists and resource managers operating at multiple levels of governance (state, federal). The synthesis effort took place over a two-year period (2016-2018), and the results were communicated widely to a) scientists via peer review publications and conference presentations; b) resource managers via web materials and workshop presentations; and c) the public through newspaper articles, radio interviews, and podcasts. The synthesis effort was initiated by resource managers at the United States Environmental Protection Agencys' Chesapeake Bay Program and 16 scientist participants were recruited from a diversity of organizations. Multiple short, immersive workshops were conducted regularly to conceptualize the problem, followed by data analysis and interpretation that supported the preparation of the synthetic products that were communicated widely. Reflections on the process indicate that there are a variety of structural and functional requirements, as well as enabling conditions, that need to be considered to achieve successful outcomes from synthesis efforts.

Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data

The growing availability of genetic data sets, in combination with machine learning frameworks, offers great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we analysed repurposed data using machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 species using open-access scripts and various databases. After identifying important predictors of nucleotide diversity with random forest regression, we conducted follow-up analyses to examine the roles of phylogenetic history, geography, and demographic processes on intraspecific diversity. Although life history traits were not important predictors for this data set, we found significant phylogenetic signal in genetic diversity within amphibians. We also found that salamander species at northern latitudes contained low genetic diversity. Data repurposing and machine learning provide valuable tools for detecting patterns with relevance for conservation, but concerted efforts are needed to compile meaningful data sets with greater utility for understanding global biodiversity.

Habitat Heterogeneity and Connectivity: Effects on the Planktonic Protist Community Structure at Two Adjacent Coastal Sites (the Lagoon and the Gulf of Venice, Northern Adriatic Sea, Italy) Revealed by Metabarcoding

The Lagoon of Venice (LoV) and the Gulf of Venice (GoV), two adjacent coastal Long Term Ecological Research (LTER) sites in the northern Adriatic Sea, represent a transitional/marine coupled ecosystem under the influence of regional and local factors. In this study, these sites were sampled on four dates from April 2016 to February 2017 for environmental DNA and relevant abiotic variables, aiming to assess the relative importance of habitat heterogeneity and connectivity in structuring the protist community. High Throughput Sequencing of V4-18S rRNA gene from 56 samples collected at seven stations produced ca 6 million reads, grouped into 7,336 Operational Taxonomic Units (OTUs) at 97% similarity, which were affiliated to protists belonging to 34 taxonomic groups. The whole community was dominated by Bacillariophyta, especially in spring-summer in the LoV, and by Dinophyta, mainly in the GoV. Ciliophora, Syndiniales, and Cryptophyceae were the next more abundant groups. The community structure varied across the seasons and was different in the two ecosystems, which shared 96% of the reads but showed a high proportion of OTUs distributed preferentially in one of the two sites (specialists) and a different partitioning of trophic categories. GoV specialists were mainly Dinophyceae (>56%), followed by Syndiniales and Bacillariophyta, while the LoV specialists were distributed among several groups, including Bacillariophyta, Syndiniales, Ciliophora, Cryptophyceae, and Trebouxiophyceae. The main abiotic drivers of the differences between protist communities were salinity and temperature, which however explained a minor part of the variance (17%), pointing at a higher relevance of biotic factors and inter-taxa relationships. This was more evident in the LoV, where the network analysis highlighted a higher number of OTUs’ connections than in the GoV. Overall, the metabarcoding approach allowed to depict the composition of the whole protist community in the lagoon and adjacent coastal waters with high resolution, revealing many taxa so far not reported in the area. In addition, despite no clear barrier to dispersal processes, differences in the relative abundance and temporal variability of local protist communities indicate that environmental heterogeneity, in these adjacent and connected ecosystems, can be strong enough to allow for ecological segregation.

Multiple drivers of plant diversity in coastal dunes: A Mediterranean experience

In light of the global biodiversity loss, syntheses of the available knowledge about drivers of biodiversity are becoming increasingly important. However, despite the high number of studies analyzing patterns of plant species diversity, few attempts have been made to synthesize findings within different ecosystems. In this work, the relative role of a wide set of predictors imputable to three conceptual-methodological domains (abiotic, human-mediated disturbance and landscape domain, hereafter AD, DD and LD) was simultaneously analyzed in 644 random plots distributed along the coastal dunes of Central Italy. Native species richness and focal species cover, both field-recorded, were used as response variables. Predictors pertaining to the three domains were derived from both field surveys and high-resolution remotely sensed imagery (LiDAR and orthophotos). To test how AD, DD and LD affect native species richness and focal species cover, a GLM and a linear model were fitted respectively. The three domains were then ranked according to their relative importance. Although the role of the three domains was always significant, they turned out to unequally contribute to the explanation of native species richness and focal species cover patterns. For Mediterranean coastal dune ecosystems, AD appears to be the key biodiversity driver, followed by DD and LD. Our results suggest that as long as human disturbance is limited, plant diversity will distribute according to species abiotic tolerances, regardless of habitat loss and fragmentation per se. Representing a first effort towards a synthesis of plant diversity drivers in coastal dunes, this work points to the importance, in Mediterranean coastal dune ecosystems, of zonation dynamics, whose occurrence should be addressed as a priority issue by efficient conservations strategies.

The tragedy of the biodiversity data commons: a data impediment creeping nigher?

Researchers are embracing the open access movement to facilitate unrestricted availability of scientific results. One sign of this willingness is the steady increase in data freely shared online, which has prompted a corresponding increase in the number of papers using such data. Publishing datasets is a time-consuming process that is often seen as a courtesy, rather than a necessary step in the research process. Making data accessible allows further research, provides basic information for decision-making and contributes to transparency in science. Nevertheless, the ease of access to heaps of data carries a perception of ‘free lunch for all’, and the work of data publishers is largely going unnoticed. Acknowledging such a significant effort involving the creation, management and publication of a dataset remains a flimsy, not well established practice in the scientific community. In a meta-analysis of published literature, we have observed various dataset citation practices, but mostly (92%) consisting of merely citing the data repository rather than the data publisher. Failing to recognize the work of data publishers might lead to a decrease in the number of quality datasets shared online, compromising potential research that is dependent on the availability of such data. We make an urgent appeal to raise awareness about this issue.

Using a linked table-based structure to encode self-describing multiparameter spatiotemporal data

Multiparameter data with both spatial and temporal components are critical to advancing the state of environmental science. These data and data collected in the future are most useful when compared with each other and analyzed together, which is often inhibited by inconsistent data formats and a lack of structured documentation provided by researchers and (or) data repositories. In this paper we describe a linked table-based structure that encodes multiparameter spatiotemporal data and their documentation that is both flexible (able to store a wide variety of data sets) and usable (can easily be viewed, edited, and converted to plottable formats). The format is a collection of five tables (Data, Locations, Params, Data Sets, and Columns), on which restrictions are placed to ensure data are represented consistently from multiple sources. These tables can be stored in a variety of ways including spreadsheet files, comma-separated value (CSV) files, JavaScript object notation (JSON) files, databases, or objects in a software environment such as R or Python. A toolkit for users of R statistical software was also developed to facilitate converting data to and from the data format. We have used this format to combine data from multiple sources with minimal metadata loss and to effectively archive and communicate the results of spatiotemporal studies. We believe that this format and associated discussion of data and data storage will facilitate increased synergies between past, present, and future data sets in the environmental science community.

Attitudes and norms affecting scientists' data reuse

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.

Combining community resurvey data to advance global change research

More and more ecologists have started to resurvey communities sampled in earlier decades to determine long-term shifts in community composition and infer the likely drivers of the ecological changes observed. However, to assess the relative importance of, and interactions among, multiple drivers joint analyses of resurvey data from many regions spanning large environmental gradients are needed. In this paper we illustrate how combining resurvey data from multiple regions can increase the likelihood of driver-orthogonality within the design and show that repeatedly surveying across multiple regions provides higher representativeness and comprehensiveness, allowing us to answer more completely a broader range of questions. We provide general guidelines to aid implementation of multi-region resurvey databases. In so doing, we aim to encourage resurvey database development across other community types and biomes to advance global environmental change research.

Open Data in Global Environmental Research: The Belmont Forum's Open Data Survey

This paper presents the findings of the Belmont Forum’s survey on Open Data which targeted the global environmental research and data infrastructure community. It highlights users’ perceptions of the term “open data”, expectations of infrastructure functionalities, and barriers and enablers for the sharing of data. A wide range of good practice examples was pointed out by the respondents which demonstrates a substantial uptake of data sharing through e-infrastructures and a further need for enhancement and consolidation. Among all policy responses, funder policies seem to be the most important motivator. This supports the conclusion that stronger mandates will strengthen the case for data sharing.

Data management challenges in analysis and synthesis in the ecosystem sciences

Open-data has created an unprecedented opportunity with new challenges for ecosystem scientists. Skills in data management are essential to acquire, manage, publish, access and re-use data. These skills span many disciplines and require trans-disciplinary collaboration. Science synthesis centres support analysis and synthesis through collaborative 'Working Groups' where domain specialists work together to synthesise existing information to provide insight into critical problems. The Australian Centre for Ecological Analysis and Synthesis (ACEAS) served a wide range of stakeholders, from scientists to policy-makers to managers. This paper investigates the level of sophistication in data management in the ecosystem science community through the lens of the ACEAS experience, and identifies the important factors required to enable us to benefit from this new data-world and produce innovative science. ACEAS promoted the analysis and synthesis of data to solve transdisciplinary questions, and promoted the publication of the synthesised data. To do so, it provided support in many of the key skillsets required. Analysis and synthesis in multi-disciplinary and multi-organisational teams, and publishing data were new for most. Data were difficult to discover and access, and to make ready for analysis, largely due to lack of metadata. Data use and publication were hampered by concerns about data ownership and a desire for data citation. A web portal was created to visualise geospatial datasets to maximise data interpretation. By the end of the experience there was a significant increase in appreciation of the importance of a Data Management Plan. It is extremely doubtful that the work would have occurred or data delivered without the support of the Synthesis centre, as few of the participants had the necessary networks or skills. It is argued that participation in the Centre provided an important learning opportunity, and has resulted in improved knowledge and understanding of good data management practices.

Transdisciplinary synthesis for ecosystem science, policy and management: The Australian experience

Mitigating the environmental effects of global population growth, climatic change and increasing socio-ecological complexity is a daunting challenge. To tackle this requires synthesis: the integration of disparate information to generate novel insights from heterogeneous, complex situations where there are diverse perspectives. Since 1995, a structured approach to inter-, multi- and trans-disciplinary(1) collaboration around big science questions has been supported through synthesis centres around the world. These centres are finding an expanding role due to ever-accumulating data and the need for more and better opportunities to develop transdisciplinary and holistic approaches to solve real-world problems. The Australian Centre for Ecological Analysis and Synthesis (ACEAS <http://www.aceas.org.au>) has been the pioneering ecosystem science synthesis centre in the Southern Hemisphere. Such centres provide analysis and synthesis opportunities for time-pressed scientists, policy-makers and managers. They provide the scientific and organisational environs for virtual and face-to-face engagement, impetus for integration, data and methodological support, and innovative ways to deliver synthesis products. We detail the contribution, role and value of synthesis using ACEAS to exemplify the capacity for synthesis centres to facilitate trans-organisational, transdisciplinary synthesis. We compare ACEAS to other international synthesis centres, and describe how it facilitated project teams and its objective of linking natural resource science to policy to management. Scientists and managers were brought together to actively collaborate in multi-institutional, cross-sectoral and transdisciplinary research on contemporary ecological problems. The teams analysed, integrated and synthesised existing data to co-develop solution-oriented publications and management recommendations that might otherwise not have been produced. We identify key outcomes of some ACEAS working groups which used synthesis to tackle important ecosystem challenges. We also examine the barriers and enablers to synthesis, so that risks can be minimised and successful outcomes maximised. We argue that synthesis centres have a crucial role in developing, communicating and using synthetic transdisciplinary research.

How can we identify and communicate the ecological value of deep-sea ecosystem services?

Submarine canyons are considered biodiversity hotspots which have been identified for their important roles in connecting the deep sea with shallower waters. To date, a huge gap exists between the high importance that scientists associate with deep-sea ecosystem services and the communication of this knowledge to decision makers and to the wider public, who remain largely ignorant of the importance of these services. The connectivity and complexity of marine ecosystems makes knowledge transfer very challenging, and new communication tools are necessary to increase understanding of ecological values beyond the science community. We show how the Ecosystem Principles Approach, a method that explains the importance of ocean processes via easily understandable ecological principles, might overcome this challenge for deep-sea ecosystem services. Scientists were asked to help develop a list of clear and concise ecosystem principles for the functioning of submarine canyons through a Delphi process to facilitate future transfers of ecological knowledge. These ecosystem principles describe ecosystem processes, link such processes to ecosystem services, and provide spatial and temporal information on the connectivity between deep and shallow waters. They also elucidate unique characteristics of submarine canyons. Our Ecosystem Principles Approach was successful in integrating ecological information into the ecosystem services assessment process. It therefore has a high potential to be the next step towards a wider implementation of ecological values in marine planning. We believe that successful communication of ecological knowledge is the key to a wider public support for ocean conservation, and that this endeavour has to be driven by scientists in their own interest as major deep-sea stakeholders.

Information visualisation for science and policy: engaging users and avoiding bias

Visualisations and graphics are fundamental to studying complex subject matter. However, beyond acknowledging this value, scientists and science-policy programmes rarely consider how visualisations can enable discovery, create engaging and robust reporting, or support online resources. Producing accessible and unbiased visualisations from complicated, uncertain data requires expertise and knowledge from science, policy, computing, and design. However, visualisation is rarely found in our scientific training, organisations, or collaborations. As new policy programmes develop [e.g., the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES)], we need information visualisation to permeate increasingly both the work of scientists and science policy. The alternative is increased potential for missed discoveries, miscommunications, and, at worst, creating a bias towards the research that is easiest to display.

Long-term climate sensitivity of grazer performance: a cross-site study

Climate change will affect grasslands in a number of ways, but the consequences of a warmer, drier world for grazers is uncertain. Predicting future grazer performance is complex since climate change affects both the quantity and quality of forage through a combination of processes that occur over a range of time scales. To better predict the consequences of climate change for grazer performance, a dataset was compiled of over a quarter million bison weights distributed across 22 US herds that span a large range of climates. Patterns of bison body mass among sites, age classes, and sexes were analyzed with respect to differences in geographic patterns of climate and interannual variation in climate. While short-term effects of climate variability are likely to depend on the magnitude and timing of precipitation during the year, grazers will be negatively affected by sustained hotter, drier conditions most likely associated with reductions in forage quality. Short-term, little effect of high temperatures on bison performance is observed, which suggests that the long-term effects of higher temperatures are likely to accrue over time as nitrogen availability in grasslands is reduced and forage quality declines. If relationships observed for bison are general for cattle, the economic consequences of higher temperatures due to decreased weight gain in US cattle could be on the order of US$1B per 1°C increase in temperature. Long-term monitoring of forage quality as well as native and domesticated grazer performance is recommended to better understand climate change effects on grazers.

Variance component analysis of body mass in a wild population of deer (Odocoileus virginianus): results from two decades of research

Context Long-term studies of large, vertebrate mammals using capture–recapture data are scarce, even though long-term ecological studies are requisite to understanding quantitative genetics and evolutionary processes that can be applied as part of management programs. Aims Objectives were to (1) partition components of variation in body mass to understand the differential effects of environmental variation on the sexes during ontogeny, to better prescribe habitat-improvement projects, and (2) estimate repeatability to assess potential for selection on body mass. Methods We used a 23-year dataset (1983–2005) of capture–recapture records of wild white-tailed deer (Odocoileus virginianus) to estimate components of variance and repeatability of body mass. We used an animal-model approach that employed the use of general linear mixed models and restricted maximum likelihood to adjust for the effects of age (i.e. fixed effect), and to partition the total phenotypic variance into among-individual (i.e. the deer), permanent environmental (i.e. year of birth) and temporary environmental (i.e. year of measurement and residual) effects (all modelled as random effects). Key results We found that body mass increased with age in both sexes, repeatability of body mass was 0.595 for females and 0.716 for males, and among-individual variation was more influential on body mass than were permanent and temporary environmental effects combined. Year of birth was more important in males than females, but changed during the course of ontogeny for both sexes. Year of measurement did not influence post-rut body mass in males, but did contribute to variation in body mass of females. Conclusions These long-term data offer insights into the sources of variation that influence body mass of deer, which can be used to understand how environmental sources of variation influence phenotypic traits, and for developing management plans and making selection decisions. Implications Knowledge of repeatability (as an upper limit to heritability) can be used to make management decisions related to selection, culling and breeding, whereas understanding environmental effects can lead to better management recommendations (e.g. habitat-improvement projects).

Hierarchical analysis of vegetation dynamics over 71 years: soil-rainfall interactions in a Chihuahuan Desert ecosystem

Proliferation of woody plants in grasslands and savannas is a persistent problem globally. This widely observed shift from grass to shrub dominance in rangelands worldwide has been heterogeneous in space and time largely due to cross-scale interactions among soils, climate, and land-use history. Our objective was to use a hierarchical framework to evaluate the relationship between spatial patterns in soil properties and long-term shrub dynamics in the northern Chihuahuan Desert of New Mexico, USA. To meet this objective, shrub patch dynamics from 1937 to 2008 were characterized at patch and landscape scales using historical imagery and a recent digital soils map. Effects of annual precipitation on patch dynamics on two soils revealed strong correlations between shrub growth on deep sandy soils and above-average rainfall years (r = 0.671, P = 0.034) and shrub colonization and below-average rainfall years on shallow sandy soils (r = 0.705, P = 0.023). Patch-level analysis of demographic patterns revealed significant differences between shrub patches on deep and shallow sandy soils during periods of above- and below-average rainfall. Both deep and shallow sandy soils exhibited low shrub cover in 1937 (1.0% +/- 2.3% and 0.3% +/- 1.3%, respectively [mean +/- SD]) and were characterized by colonization or appearance of new patches until 1960. However, different demographic responses to the cessation of severe drought on the two soils and increased frequency of wet years after 1960 have resulted in very different endpoints. In 2008 a shrubland occupied the deep sandy soils with cover at 19.8% +/- 9.1%, while a shrub-dominated grassland occurred on the shallow sandy soils with cover at 9.3% +/- 7.2%. Present-day shrub vegetation constitutes a shifting mosaic marked by the coexistence of patches at different stages of development. Management implications of this long-term multi-scale assessment of vegetation dynamics support the notion that soil properties may constrain grassland remediation. Such efforts on sandy soils should be focused on sites characterized by near-surface water-holding capacity, as those lacking available water-holding capacity in the shallow root zone pose challenges to grass recovery and survival.