Biodiversity Informatics 

About: Biodiversity Informatics is an academic journal published by University of Kansas. The journal publishes majorly in the area(s): Biodiversity informatics & Environmental niche modelling. It has an ISSN identifier of 1546-9735. Over the lifetime, 88 publications have been published receiving 4108 citations.

Interpretation of models of fundamental ecological niches and species' distributional areas

Abstract: Estimation of the dimensions of fundamental ecological niches of species to predict their geographic distributions is increasingly being attempted in systematics, ecology, conservation, public health, etc. This technique is often (of necessity) based on data comprising records of presences only. In recent years, modeling approaches have been devised to estimate these interrelated expressions of a species' ecology, distributional biology, and evolutionary history—nevertheless, a formal basis in ecological and evolutionary theory has largely been lacking. In this paper, we outline such a formal basis to clarify the use of techniques applied to the challenge of estimating 'ecological niches;' we analyze example situations that can be modeled using these techniques, and clarify interpretation of results.

Uses and requirements of ecological niche models and related distributional models

Abstract: Modeling approaches that relate known occurrences of species to landscape features to discover ecological properties and predict geographic occurrences have seen extensive recent application in ecology, systematics, and conservation. A key component in this process is estimation or characterization of species' distributions in ecological space, which can then be useful in understanding their potential distributions in geographic space. Hence, this process is often termed ecological niche modeling or (less boldly) species distribution modeling. Applications of this approach vary widely in their aims, products, and requirements; this variety is reviewed herein, examples are provided, and differences in data needs and possible interpretations are discussed.

More complex distribution models or more representative data

Abstract: Distribution models for species are increasingly used to summarize species’ geography in conservation analyses. These models use increasingly sophisticated modeling techniques, but often lack detailed examination of the quality of the biological occurrence data on which they are based. I analyze the results of the best comparative study of the performance of different modeling techniques, which used pseudo-absence data selected at random. I provide an example of variation in model accuracy depending on the type of absence information used, showing that good model predictions depend most critically on better biological data.

Climate Change and Biodiversity: Some Considerations in Forecasting Shifts in Species' Potential Distributions

Abstract: Global climate change and its broad spectrum of effects on human and natural systems has become a central research topic in recent years; biodiversity informatics tools?particularly ecological niche modeling (ENM)?have been used extensively to anticipate potential effects on geographic distributions of species. Misuse of these tools, however, is counterproductive, as biased conclusions might be reached. In this paper, I discuss some issues related to niche theory, geographic distributions, data quality, and algorithms, all of which are relevant when using ENM in climate change projections for biodiversity. This assortment of opinions and ideas is presented in the hope that ENM applications to climate change questions can be made more realistic and more predictive.

Approaches to estimating the universe of natural history collections data

Abstract: This contribution explores the problem of recognizing and measuring the universe of specimen-level data existing in Natural History Collections around the world, in absence of a complete, world-wide census or register. Estimates of size seem necessary to plan for resource allocation for digitization or data capture, and may help represent how many vouchered primary biodiversity data (in terms of collections, specimens or curatorial units) might remain to be mobilized. Three general approaches are proposed for further development, and initial estimates are given. Probabilistic models involve crossing data from a set of biodiversity datasets, finding commonalities and estimating the likelihood of totally obscure data from the fraction of known data missing from specific datasets in the set. Distribution models aim to find the underlying distribution of collections’ compositions, figuring out the occult sector of the distributions. Finally, case studies seek to compare digitized data from collections known to the world to the amount of data known to exist in the collection but not generally available or not digitized. Preliminary estimates range from 1.2 to 2.1 gigaunits, of which a mere 3% at most is currently web-accessible through GBIF’s mobilization efforts. However, further data and analyses, along with other approaches relying more heavily on surveys, might change the picture and possibly help narrow the estimate. In particular, unknown collections not having emerged through literature are the major source of uncertainty.