Hannu Saarenmaa

The roles and contributions of Biodiversity Observation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study

The Aichi Biodiversity Targets of the United Nations’ Strategic Plan for Biodiversity set ambitious goals for protecting biodiversity from further decline. Increased efforts are urgently needed to achieve these targets by 2020. The availability of comprehensive, sound and up-to-date biodiversity data is a key requirement to implement policies, strategies and actions to address biodiversity loss, monitor progress towards biodiversity targets, as well as to assess the current status and future trends of biodiversity. Key gaps, however, remain in our knowledge of biodiversity and associated ecosystem services. These are mostly a result of barriers preventing existing data from being discoverable, accessible and digestible. In this paper, we describe what regional Biodiversity Observation Networks (BONs) can do to address these barriers using the European Biodiversity Observation Network (EU BON) as an example. We conclude that there is an urgent need for a paradigm shift in how biodiversity data are collected, stored, shared and streamlined in order to tackle the many sustainable development challenges ahead. We need a shift towards an integrative biodiversity information framework, starting from collection to the final interpretation and packaging of data. This is a major objective of the EU BON project, towards which progress is being made.

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence‐only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi‐source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter‐ or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi‐source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA‐based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.

A suite of essential biodiversity variables for detecting critical biodiversity change

Key global indicators of biodiversity decline, such as the IUCN Red List Index and the Living Planet Index, have relatively long assessment intervals. This means they, due to their inherent structure, function as late‐warning indicators that are retrospective, rather than prospective. These indicators are unquestionably important in providing information for biodiversity conservation, but the detection of early‐warning signs of critical biodiversity change is also needed so that proactive management responses can be enacted promptly where required. Generally, biodiversity conservation has dealt poorly with the scattered distribution of necessary detailed information, and needs to find a solution to assemble, harmonize and standardize the data. The prospect of monitoring essential biodiversity variables (EBVs) has been suggested in response to this challenge. The concept has generated much attention, but the EBVs themselves are still in development due to the complexity of the task, the limited resources available, and a lack of long‐term commitment to maintain EBV data sets. As a first step, the scientific community and the policy sphere should agree on a set of priority candidate EBVs to be developed within the coming years to advance both large‐scale ecological research as well as global and regional biodiversity conservation. Critical ecological transitions are of high importance from both a scientific as well as from a conservation policy point of view, as they can lead to long‐lasting biodiversity change with a high potential for deleterious effects on whole ecosystems and therefore also on human well‐being. We evaluated candidate EBVs using six criteria: relevance, sensitivity to change, generalizability, scalability, feasibility, and data availability and provide a literature‐based review for eight EBVs with high sensitivity to change. The proposed suite of EBVs comprises abundance, allelic diversity, body mass index, ecosystem heterogeneity, phenology, range dynamics, size at first reproduction, and survival rates. The eight candidate EBVs provide for the early detection of critical and potentially long‐lasting biodiversity change and should be operationalized as a priority. Only with such an approach can science predict the future status of global biodiversity with high certainty and set up the appropriate conservation measures early and efficiently. Importantly, the selected EBVs would address a large range of conservation issues and contribute to a total of 15 of the 20 Aichi targets and are, hence, of high biological relevance.

The development of a digitising service centre for natural history collections.

Abstract Digitarium is a joint initiative of the Finnish Museum of Natural History and the University of Eastern Finland. It was established in 2010 as a dedicated shop for the large-scale digitisation of natural history collections. Digitarium offers service packages based on the digitisation process, including tagging, imaging, data entry, georeferencing, filtering, and validation. During the process, all specimens are imaged, and distance workers take care of the data entry from the images. The customer receives the data in Darwin Core Archive format, as well as images of the specimens and their labels. Digitarium also offers the option of publishing images through Morphbank, sharing data through GBIF, and archiving data for long-term storage. Service packages can also be designed on demand to respond to the specific needs of the customer. The paper also discusses logistics, costs, and intellectual property rights (IPR) issues related to the work that Digitarium undertakes.

BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology

BackgroundMaking forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as “Web services”) and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust “in silico” science. However, use of this approach in biodiversity science and ecology has thus far been quite limited.ResultsBioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible ‘virtual laboratory’, free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity.ConclusionsOur work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.

High-performance digitization of natural history collections: Automated imaging lines for herbarium and insect specimens

The digitization of natural history collections calls for new, efficient solutions. Digitization of millions of specimens, with reasonable digitization costs and high statistical repeatability requires increased automation and industrial-scale work- flows. However, the variation in specimen form, size and coloring creates challenges for digitization methodology, pushing development towards optional actions. In this paper, we report the results of the digitization of herbarium and beetle collections using automated imaging lines. The technology of the imaging lines was based on a common innovation, but the versions used were applied to either 2-D sheets or small 3-D objects. The aim was to develop processes for enhancing the digitization of natural history specimens, but at the same time, to produce end products with high quality. Results showed that the herbarium and beetle collections could be digitized by using automation at the rate of hundreds or thousands of individual specimens per day. This is 5-10 times faster than the more manual methods of digitization which were previously used. The produced data, images and specimen label data were uniform in quality and could be viewed within minutes after being produced. Results indicate that the efficiency of digitization can be raised for different types of natural history specimens by use of automation and well-defined processes, and the increase in production rate does not reduce the quality of the end-results.

An operational definition of essential biodiversity variables

The concept of essential biodiversity variables (EBVs) was proposed in 2013 to improve harmonization of biodiversity data into meaningful metrics. EBVs were conceived as a small set of variables which collectively capture biodiversity change at multiple spatial scales and within time intervals that are of scientific and management interest. Despite the apparent simplicity of the concept, a plethora of variables that describes not only biodiversity but also any environmental features have been proposed as potential EBV (i.e. candidate EBV). The proliferation of candidates reflects a lack of clarity on what may constitute a variable that is essential to track biodiversity change, which hampers the operationalization of EBVs and therefore needs to be urgently addressed. Here, we propose that an EBV should be defined as a biological state variable in three key dimensions (time, space, and biological organization) that is critical to accurately document biodiversity change.

Making species checklists understandable to machines - a shift from relational databases to ontologies.

BackgroundThe scientific names of plants and animals play a major role in Life Sciences as information is indexed, integrated, and searched using scientific names. The main problem with names is their ambiguous nature, because more than one name may point to the same taxon and multiple taxa may share the same name. In addition, scientific names change over time, which makes them open to various interpretations. Applying machine-understandable semantics to these names enables efficient processing of biological content in information systems. The first step is to use unique persistent identifiers instead of name strings when referring to taxa. The most commonly used identifiers are Life Science Identifiers (LSID), which are traditionally used in relational databases, and more recently HTTP URIs, which are applied on the Semantic Web by Linked Data applications.ResultsWe introduce two models for expressing taxonomic information in the form of species checklists. First, we show how species checklists are presented in a relational database system using LSIDs. Then, in order to gain a more detailed representation of taxonomic information, we introduce meta-ontology TaxMeOn to model the same content as Semantic Web ontologies where taxa are identified using HTTP URIs. We also explore how changes in scientific names can be managed over time.ConclusionsThe use of HTTP URIs is preferable for presenting the taxonomic information of species checklists. An HTTP URI identifies a taxon and operates as a web address from which additional information about the taxon can be located, unlike LSID. This enables the integration of biological data from different sources on the web using Linked Data principles and prevents the formation of information silos. The Linked Data approach allows a user to assemble information and evaluate the complexity of taxonomical data based on conflicting views of taxonomic classifications. Using HTTP URIs and Semantic Web technologies also facilitate the representation of the semantics of biological data, and in this way, the creation of more “intelligent” biological applications and services.

Global Infrastructures for Biodiversity Data and Services

GEO BON regards development of a global infrastructure in support of Essential Biodiversity Variables (EBVs) as one of its main objectives. To realise the goal, an understanding of the context within which such an infrastructure needs to operate is important (for instance, it is part of a larger drive towards research data infrastructures in support of open science?) and the information technology applicable to such infrastructures needs to be considered. The EBVs are likely to require very specific implementation guidelines once the community has defined them in detail. In the interim it is possible to anticipate the likely architecture for a GEO BON infrastructure, and to provide guidance to individual researchers, institutions, and regional or global initiatives in respect of best practice. The best practice guidelines cover general aspects applicable to all research infrastructures, the use of persistent identifiers, interoperability guidelines in respect of vocabularies, data services and meta-data management, and advice on the use of global infrastructure services and/or federated, standards-based implementations.