When population mortality outweighs fertility rates, the long-term survival of the population is not sustainable, resulting in species extinction. Given the current extinction crisis there is an urgent need to maximize the effectiveness of conservation management programs. For many such strategies, well-informed demographic models provide rigorous predictions of population fate, which can be critical for successful conservation. However, the accuracy of these models hinges on the availability of demographic data. Despite the importance of demographic data to inform management of threatened species, there has been no global assessment of demographic information available. We standardized the taxonomy and terms describing traits across 24 databases on demography and/or with demographic life history traits. We developed a Demographic Index of Species Knowledge (DISKo) that shows data availability for fertility and survival, which are the essential data to understand population dynamics, and therefore demographics. Our results show that demographic data for both fertility and mortality is shockingly scarce. This is the case for even the best-studied tetrapod groups. The data with the highest quality (i.e. to be able to forecast population fate) is available for only 3.4% of mammals and just 1% of bird species. For amphibians and reptiles this figure is less than 1% (0.2% for amphibians and 0.3% for reptiles). Knowledge is also geographically biased with glaring data gaps in the tropics. The low number of species with demographic data is surprising because major efforts to tackle comparative questions in ecology and evolution have resulted in the development of numerous species-specific trait databases (e.g. PanTHERIA, Amniote life-history database, AnAge). Our results illustrate the distribution of demographic information across trait-type, phylogeny and space and provide a useful tool for optimizing future research priorities. In addition, they highlight the importance of standardizing terminology and units across parallel database efforts. The next step is to link DISKo with a Genetic Knowledge index that we are developing.
Traditionally, in biodiversity studies, the scientists who collected the data were also the people who analyzed it and published the associated scientific papers. The advent of DiGIR and Species Analyst software by Vieglais and collaborators, however, spawned a revolution in how biodiversity studies were undertaken. It became possible for scientists to electronically collate data from disparate museum collections and spurred the growth of niche modeling. This new approach helped create the Global Biodiversity Information Facility (GBIF) and programs such as iDigBio. The GBIF portal now serves as a directory to open source biodiversity data from museums, government agencies and non-governmental organizations (NGOs) around the world. In addition, it allows for the complete separation of data collection and data analysis. However, this separation of data collector and data analyst has generated a new concern. How can GBIF and data analysts have confidence in the data especially when it comes from a variety of sources? The traditional answer has been using data quality measures but studies in other fields such as social science, psychology, economics and computer science show that trust also plays a role. Here we develop a model that includes both elements of data quality and trust to understand the confidence one might have in biodiversity data. This modeling effort can lead to new policies that increase the perception of data quality and the knowledge among data suppliers, data aggregators such as GBIF data and data analysts. The purpose is find ways to increase the use of biodiversity data from aggregators to meet conservation goals. DiGIR = Distributed Generic Information Retrieval
Biologists' trust and use of aggregated biodiversity data are suffering because of persistent criticisms of the quality of these data for basic and applied analyses. Individually, one can interpret each criticism as a problem of data quality local to some taxonomic group or geographic region. Indeed, biodiversity aggregators often respond by pointing critics toward correcting errors at their source. We will show, however, that these disputes over data quality are better understood as reflecting systemic flaws in the design of the aggregation process. As a result, fundamental change is needed to effectively address issues of trust in big biodiversity data. In particular, the design change must expand the roles available to researchers as established by data aggregators, such that the interests and views of bottom-up, high-quality content providers are more directly represented. We will outline steps towards alternative, provenance-aware design solutions that promote the formation and maintenance of high-quality biodiversity data packages. Our discussion focuses on the unitary taxonomic syntheses ("backbones") created by biodiversity data aggregators. We show how the aggregation process can lead to a loss of data unity at the system level when different data sources adhere to conflicting taxonomic perspectives. Many aggregators follow a design paradigm that requires one taxonomic hierarchy to organize all data at a given time. They achieve this unitary representation of the data using combinations of algorithmic and social practices governed by feasibility constraints rather than principles grounded in taxonomic theory. Eliminating taxonomic conflict between input sources in this manner often results in a hierarchy that no longer corresponds to the view of any particular source – it is a synthesis nobody believes in. Biodiversity data users and contributors frequently regard the quality of these novel classification theories as deficient. We will show how the Darwin Core (DwC) standard plays a critical role in the design of the aggregation process. We carefully separate causes for poor aggregation that are rooted in failures on the data provision or DwC implementation side, versus systemic DwC flaws in the context of aggregation. For the latter, we outline specific syntactic and semantic solutions - often but not always represented in the Taxonomic Concept Transfer Schema - to achieve suitable aggregation outcomes. We conclude that improved aggregation designs must increase the power allocated to individual (or co-authoring) experts and their heterogeneous views to act as intermediary license-providers for the formation of trusted, big biodiversity data.
Traditionally, knowledge is kept by individuals and not by institutions. This weakens an institution’s ability to progress and be competitive. Evidence in the literature suggests gaps in the processes of knowledge elicitation and acquisition. For a complex domain such as biodiversity, new mechanisms are needed to acquire, record and manage knowledge, preferably with a high level of expressiveness, which includes tacit knowledge. There is academic consensus that tacit knoweldge can aggregate semantics to structural instruments of knowledge. In this research, the tacit knowledge considered is scientific. This knowledge is not necessarily formalizable, but must be capable of systematization, associated with a logical process. In this domain, experts may not have the necessary skills to carry out the process of acquiring knowledge without the participation of an analyst. The problem of knowledge communication and transference amongst individuals within an organization must be dealt with. The open question is: how to establish the ideal conditions that allow experts to communicate their knowledge? Much of the power of human expertise is the result of experience, gained through years, and represented as heuristics. Often the expertise becomes so common that the experts have difficultly describing specific tasks. In other cases, the knowledge is distributed throughout the organization and most of the time resides in the minds of experts. The lack of attention to the differences between experts and the level of knowledge they possess, can affect the efficiency of the process of knowledge elicitation, and the quality of the knowledge acquired. The kind of knowledge that needs to be elicited must be considered too. To browse through the variety of Knowledge Elicitation Techniques (KETs), it is necessary to identify the most appropriate method for a particular situation. It must be considered that: there are different kinds of knowledge, of experts and expertise; different ways of representing knowledge, which can help elicitation, validation and reuse of knowledge; different ways to use knowledge, so that the elicitation process can be guided by the use purpose of the elicited knowledge; and therefore, KETs should be chosen appropriately to meet the contingencies. Among the KETs taxonomies available, we consider only the individual tacit knowledge elicitation methods that permit the participation of the analyst. Interview is the method used in the context of this research. The elicited knowledge must be stored and managed for further use. An architecture to register the elicited knowledge is under development.
