The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.

Focused Identification of Germplasm (FIGS) is a new method to select plant genetic resources for the improvement of food crops. Traditional cultivars (landraces) and crop wild relatives (CWR) provide a valuable source for novel alleles in crop improvement programs, but conserved landraces and CWR often lack important documentation. Genebank collections worldwide provide ready access to plant genetic resources including online documentation. However, incomplete documentation, and in particular the lack of relevant characterization and evaluation data (traits), often limit the efficient use of plant genetic resources. This current study demonstrates how trait mining with the new FIGS method can be used to predict missing trait information for landraces. Ecogeographic data from the location of origin for 14 Nordic landraces of barley (Hordeum vulgare L.) was successfully correlated to morphological traits using a modern multilinear data modeling method (multilinear partial least squares [N-PLS]). This result suggests that trait mining can efficiently be used as a targeted germplasm selection method and complement or replace the current core selection method in situations when the requirements for the trait mining method are fulfilled. Copyright © 2010. Crop Science Society of America . Crop Science Society of America