Analyzing diversification rate heterogeneity across phylogenies allows us to explore all manner of questions, including why Australia has such an incredible diversity of lizards and snakes.
Within the tree of life there are differences in speciation and extinction rates over time and across lineages. Biologists have long been interested in how speciation rates change as a function of ecological opportunity or whether key innovations lead to increases in the rate of speciation. Exploring this rate variation and examining how clades differ in terms of their diversification dynamics can help us to understand why species diversity varies so dramatically in time and space. Learning more about the relationship between traits and diversification rates is especially important because it has the potential to reveal the causes of pervasive variation in species richness among clades and across geographic regions.
Could we use the plants in this swamp forest to predict the diversity of other species?
Local communities and regional biotas are built of hundreds, if not thousands, of species. Most of these species are small-bodied and discreet lifeforms. So it’s no wonder that naturalists have almost always focused their attention on conspicuous species of their particular liking. Why plants then? Well, plants are practical and efficient. They “stand still and wait to be counted”, as the eminent population biologist John Harper put it. No matter the weather, from spring to autumn. There are enough plant species to show contrasts between sites, and yet they can usually be identified to species level in the field.
You Can’t Predict the Diversity of Beetles from Lichens… Can You?
To me, the ‘citizen scientist’ label feels a little patronising – conveying an image of people co-opted en masse for top-down, scientist-led, large-scale biological surveys. That said, scientist-led surveys can offer valid contributions to conservation and the documentation of the effects of climate change (among other objectives). They also engage the public (not least children) in science, although volunteers usually have an interest in natural history and science already. For me though, the real excitement comes in following a bottom-up path: making my own discoveries and approaching scientists for assistance with my projects.
Robert Colwell at the Boreas Pass in Colorado, USA
ROB: I grew up on a working ranch in the Colorado mountains, surrounded on three sides by National Forest and a National Wilderness Area. My mother, an ardent amateur naturalist, taught me and my sister the local native flora and fauna and our father instilled a respect for the land in us. For my doctoral research at the University of Michigan, I studied insect biodiversity in Colorado and Costa Rica at several elevations. The challenges of estimating the number of species (species richness) and understanding why some places are species-rich and others species-poor has fascinated me ever since. Continue reading →
This month’s issue contains two Applications articles and two Open Access articles, all of which are freely available.
– Plant-O-Matic: A free iOS application that combines the species distribution models with the location services built into a mobile device to provide users with a list of all plant species expected to occur in the 100 × 100 km geographic grid cell corresponding to the user’s location.
– RClone: An R package built upon genclone software which includes functions to handle clonal data sets, allowing:
Checking for data set reliability to discriminate multilocus genotypes (MLGs)
Ascertainment of MLG and semi-automatic determination of clonal lineages (MLL)
Genotypic richness and evenness indices calculation based on MLGs or MLLs
Describing several spatial components of clonality
Shannon entropy and its exponential have been extensively used to characterize uncertainty, diversity and information-related quantities in ecology, genetics, information theory, computer science and many other fields. Its mathematical expression is given in the figure below.
In the 1950s Shannon entropy was adopted by ecologists as a diversity measure. It’s interpreted as a measure of the uncertainty in the species identity of an individual randomly selected from a community. A higher degree of uncertainty means greater diversity in the community.
Unlike species richness which gives equal weight to all species, or the Gini-Simpson index that gives more weight to individuals of abundant species, Shannon entropy and its exponential (“the effective number of common species” or diversity of order one) are the only standard frequency-sensitive complexity measures that weigh species in proportion to their population abundances. To put it simply: it treats all individuals equally. This is the most natural weighing for many applications. Continue reading →
A key property of biodiversity is that it is not evenly distributed around the world. In other words, different sites are usually home to different biological communities. Quantifying the differences among biological communities is a major step towards understanding how and why biodiversity is distributed in the way it is.
The term beta diversity was introduced by R.H. Whittaker in 1960. He defined it as “the extent of change in community composition, or degree of community differentiation, in relation to a complex-gradient of environment, or a pattern of environments”. In his original paper, Whittaker proposed several ways to quantify beta diversity. In its simplest form (which we will call strict sense or multiplicative beta diversity), beta diversity is defined as the ratio between gamma (regional) and alpha (local) diversities (Whittaker, 1960; Jost, 2007). Therefore, it is the effective number of distinct compositional units in the region (Tuomisto, 2010). Essentially, beta diversity quantifies the number of different communities in the region. So it’s clear that beta diversity does not only account for the relationship between local and regional diversity, but also informs about the degree of differentiation among biological communities. This is because alpha and gamma diversities are different if (and only if) the biological communities within the region are different.
It’s easy to demonstrate how beta diversity varies from the minimum to the maximum differentiation of local assemblages in a region. For simplicity, we will quantify biological diversity as species richness (number of species), but it’s important to remember that alpha, beta and gamma diversities can also be defined to account for richness and relative abundances (see Jost, 2007 for a detailed explanation). When local assemblages are all identical (minimum differentiation), alpha diversity equals gamma diversity, and beta diversity equals 1 (figure below).
As you may know, today (Friday 22 May) is the United Nations Day for Biodiversity and we are celebrating by highlighting some of the best papers that have been published on biodiversity in Methods in Ecology and Evolution. This is by no means an exhaustive list and you can find many more articles on similar topics on the Wiley Online Library (remember, if you are a member of the BES, you can access all Methods articles free of charge).
If you would like to learn more about the International Day for Biological Diversity, you may wish to visit the Convention on Biological Diversity website, follow them on Twitter or check out today’s hashtag: #IBD2015.
Without further ado though, here are a few of the best Methods papers on Biological Diversity:
We begin with an Open Access article from one of our Associate Editors, Douglas Yu (et al.). This article was published in the August issue of 2012 and focuses on the metabarcoding of arthropods. The authors present protocols for the extraction of ecological, taxonomic and phylogenetic information from bulk samples of arthropods. They also demonstrate that metabarcoding allows for the precise estimation of pairwise community dissimilarity (beta diversity) and within-community phylogenetic diversity (alpha diversity), despite the inevitable loss of taxonomic information.