In this paper, we compare the relationship between scale and period in ecological pattern analysis and wavelet analysis. We also adapt a commonly used wavelet, the Morlet, to ecological pattern analysis. Using Monte Carlo assessments, we apply methods of statistical significance test to wavelet analysis for pattern analysis. In order to understand the inherent strength and weakness of the Morlet and the Mexican Hat wavelets, we also investigate and compare the properties of two frequently used wavelets by testing with field data and four artificial transects of different typical patterns which is often encountered in ecological research. It is shown that the Mexican Hat provides better detection and localization of patch and gap events over the Morlet, whereas the Morlet offers improved detection and localization of scale over the Mexican Hat. There is always a trade-off between the detection and localization of scale versus patch and gap events. Therefore, the best composite analysis is the combination of their advantages. The properties of wavelet in dealing with ecological data may be affected by characteristics intrinsic to wavelet itself. The peaks of different scales in isograms of wavelet power spectrum from the Mexican Hat may overlap with each other. Alternatively, these peaks of different scales in isograms of wavelet power spectrum may combine with each other unless the size of the analyzed scales is significantly different. These overlapping or combining lead to combining of peaks for different scales, or the masking of trough between peaks of different scales in the scalogram.Ecologists should combine all the information in scalogram and isograms of wavelet coefficient and wavelet power spectrum from different wavelets, which can provide us a broader view and precise pattern information.

With projected lifespans of many decades, infrastructure initiatives such as Europe’s Distributed System of Scientific Collections (DiSSCo), USA’s Integrated Digitized Biocollections (iDigBio), National Specimen Information Infrastructure (NSII) of China and Australia’s digitisation of national research collections (NRCA Digital) aim at transforming today’s slow, inefficient and limited practices of working with natural science collections. The need to borrow specimens (plants, animals, fossils or rocks) or physically visit collections, and absence of linkages to other relevant information represent significant impediments to answering today’s scientific and societal questions. A logical extension of the Internet, Digital Object Architecture (Kahn and Wilensky 2006) offers a way of grouping, managing and processing fragments of information relating to a natural science specimen. A ‘digital specimen’ acts as a surrogate in cyberspace for a specific physical specimen, identifying its actual location and authoritatively saying something about its collection event (who, when, where) and taxonomy, as well as providing links to high-resolution images. A digital specimen exposes supplementary information about related literature, traits, tissue samples and DNA sequences, chemical analyses, environmental information, etc. stored elsewhere than in the natural science collection itself. By presenting digital specimens as a new layer between data infrastructure of natural science collections and user applications for processing and interacting with information about specimens and collections, it’s possible to seamlessly organise global access spanning multiple collection-holding institutions and sources. Virtual collections of digital specimens with unique identifiers offer possibilities for wider, more flexible, and ‘FAIR’ (Findable, Accessible, Interoperable, Reusable) access for varied research and policy uses: recognising curatorial work, annotating with latest taxonomic treatments, understanding variations, working with DNA sequences or chemical analyses, supporting regulatory processes for health, food, security, sustainability and environmental change, inventions/products critical to the bio-economy, and educational uses. Adopting a digital specimen approach is expected to lead to faster insights for lower cost on many fronts. We propose that realising this vision requires a new TDWG standard. OpenDS is a specification of digital specimen and other object types essential to mass digitisation of natural science collections and their digital use. For five principal digital object types corresponding to major categories of collections and specimens’ information, OpenDS defines structure and content, and behaviours that can act upon them: Digital specimen: Representing a digitised physical specimen, contains information about a single specimen with links to related supplementary information; Storage container: Representing groups of specimens stored within a single container, such as insect tray, drawer or sample jar; Collection: Information about characteristics of a collection; Organisation: Information about the legal-entity owning the specimen and collection to which it belongs; and, Interpretation: Assertion(s) made on or about the specimen such as determination of species and comments. Digital specimen: Representing a digitised physical specimen, contains information about a single specimen with links to related supplementary information; Storage container: Representing groups of specimens stored within a single container, such as insect tray, drawer or sample jar; Collection: Information about characteristics of a collection; Organisation: Information about the legal-entity owning the specimen and collection to which it belongs; and, Interpretation: Assertion(s) made on or about the specimen such as determination of species and comments. Secondary classes gather presentation/preservation characteristics (e.g., herbarium sheets, pinned insects, specimens in glass jars, etc.), the general classification of a specimen (i.e., plant, animal, fossil, rock, etc.) and history of actions on the object (provenance). Equivalencing concepts in ABCD 3.0 and EFG extension for geo-sciences, OpenDS is also an ontology extending OBO Foundry’s Biological Collection Ontology (BCO) (Walls et al. 2014) from bco:MaterialSample, which has preferred label dwc:specimen from Darwin Core, thus linking it also with that standard. OpenDS object content can be serialized to specific formats/representations (e.g. JSON) for different exchange and processing purposes.

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