UQ in Biodiversity Change#

This is the main article for a series about quantifying biodiversity change.

See the following articles for more information on the various ways biodiversity is quantified:

There are various ways to summarize the information contained in the species abundance distribution:

See the following articles for methods for dealing with uncertainty when quantifying biodiversity change:


How do changes in the physical environment affect the structure and function of biological communities? This question is becoming increasingly important as human activity accelerates environmental change across the planet. The structure and function of of a given biological community is comprised of multiple components from taxonomic to genetic diversity and is generally summarized under the term “biodiversity.” Thus, a key question is how biodiversity is changing at local, regional, and global scales. Statistical models that relate environmental variables to various aspects of biodiversity are often used to understand and predict how changes in environmental conditions affect biodiversity and large amounts of biological and associated environmental data have been collected to this end. However, there are numerous sources of uncertainty in these data. We seek to understand how various sources of uncertainty affect our ability to make accurate predictions about biodiversity in the face of environmental change.

What is a biological community?#

Broadly defined, a community consists of all the species that interact with one another in a specific place and time. However, there is a large amount of flexibility for how a community is defined and operationalized for a given study. Often, a community is considered a subset of species that occupy the same trophic level and share similar living habits (a horizontal community sensu Vellend [2016]). Communities can encompass a range of scales such as the bacteria in an animal’s digestive tract, all the phytoplankton in a small pond, or all of the non-herbacious plants in a large forest.

Quantifying Biodiversity#

Biodiversity is a multifaceted concept that encompasses the taxonomic, functional, genetic, and phylogenetic diversity of a biological community. However, studies often address only a subset of these components so that the term biodiversity is often used in a narrower sense. For a number of reasons, studies often focus on taxonomic diversity of biological communities. In other words, biodiversity typically refers to the identity and abundances of species in a biological community and this is the definition we use here. For species where individuals can be clearly delineated, this involves taking a census of the community and recording the number of individuals observed for each species. For species where individuals are less easily distinguished, measures such as percentage of area covered or percentage of total biomass may be used instead. We will restrict ourselves to cases where individuals can be clearly distinguished and counted.

There are various ways of quantifying biodiversity. First, we may be interested in the number and pattern of abundances of species in a single community. The species abundance distribution, which records the abundances of each species in a community, is central to biodiversity studies. Examining the species abundance distribution of many different communities has led an apparent law of ecology: many species in a community are rare and only a few are abundant [McGill et al., 2007].

Many approaches to quantifying biodiversity of a single community essentially characterize different aspects of its species abundance distribution. In the parametric approach, the species abundance distribution is described by the parameters of a statistical distribution. This approach has been used to test theories about community assembly and detect changes due to anthropogenic pressure. See Species Abundance Distribution: Parametric Approaches for more information.

The non-parametric approach includes common biodiversity measures such as species richness, evenness, and diversity. Species richness measures the total number of species in a community while measures of evenness describe how total abundance is distribute amongst them. Diversity measures typically combine aspects of both richness and evenness into a single value. See Species Abundance Distribution: Non-parametric Approaches for more information.

We might also be interested in understanding the compositional variation between multiple communities, termed beta diversity. Various measures of multivariate analysis are typically used to compare the composition of multiple communities. See Species Abundance Distribution: Multivariate Approaches for more information.

A key step in many multivariate analyses of compositional variation is to calculate some measure of (dis)similarity between communities (see Species Abundance Distribution: Multivariate Approaches). However, this can be a problem when comparing communities with very different sets of species. Most measures of compositional dissimilarity are formulated in terms of shared species and are therefore bounded between 0 and 1. A dissimilarity of 0 indicates that both communities are identical and a dissimilarity of 1 indicates that the communities share no species in common. When analyzing compositional variation over large spatial and temporal scales, it is likely that many communities will share no species in common. The saturation of the dissimilarity metric therefore represents a barrier to quantifying changes in community composition over large spatial and temporal scales. More fundamentally, the comparison of biological communities is difficult because such datasets are high dimensional, containing abundances of hundreds or thousands of species. Compositional data can form complex structures, termed manifolds, in multivariate space. The complexity of such manifolds can introduce error in measures of compositional dissimilarity. In Analyzing Compositional Dissimilarity Using Diffusion Maps , we apply diffusion maps, a manifold learning method, to quantify compositional variation over large spatial and temporal scales.



Brian J McGill, Rampal S Etienne, John S Gray, David Alonso, Marti J Anderson, Habtamu Kassa Benecha, Maria Dornelas, Brian J Enquist, Jessica L Green, Fangliang He, and others. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecology letters, 10(10):995–1015, 2007.


Mark Vellend. The theory of ecological communities (mpb-57). In The theory of ecological communities (MPB-57). Princeton University Press, 2016.


Jordan A. Gault