Post provided by Juan A. Balbuena
Most species in ecological communities are rare, whereas only a few are common. This distributional paradox has intrigued ecologists for decades, but the interpretation of species abundance distributions remains elusive. In this blog post, lead author Juan A. Balbuena discusses how their recently published Methods in Ecology and Evolution paper and R package ‘Fuzzy quantification of common and rare species in ecological communities (FuzzyQ)’, is a potentially valuable analytical tool in community ecology and conservation biology.
Because environments differ and organisms are astonishingly diverse, the list of things ecologists can be as certain as death and taxes is rather short. One of the few universal patterns in nature concerns the uneven distribution of species abundances in ecological communities. Invariably most species are rare, whereas a few are common. This distributional paradox has intrigued ecologists for over a century and a half.
As Darwin observed, “If we ask ourselves why this or that species is rare, we answer that something is unfavourable in its conditions of life; but what that something is, we can hardly ever tell.” A great deal of effort has been put on establishing the distribution patterns emerging from the categorization of species as common or rare.
However, a major problem of fitting models to species abundance distributions has been adjusting the data to a suitable theoretical distribution. Another problem is that commonness and rarity are terms that are in widespread use but there is insufficient consensus on what makes a species rare or common. So, today and despite the large literature on the topic, the factors that account for the commonness of species being rare still remain elusive.
The macroecology of biological invasions
My fellow co-authors and I have studied the community ecology of grey mullets (Mugillidae) parasites for nearly two decades. These fish occur in marine and brackish temperate waters all over the world, and harbour a quite specialized and unique parasite fauna.
The so-iuy mullet (Planiliza haematocheilus) native to the Amur River estuary and the Sea of Japan was deliberately introduced in the Black Sea and Sea of Azov in the Soviet era. After numerous attempts, a successful reproductive population was established in the early 1980s. The environmental conditions in the invaded area seem favorable to this species and its expansion has coincided with a sharp decline of native grey mullets. Thanks to an international research project financed by the European Commission, we were able to survey so-iuy and other local grey mullets for parasites in the native and invaded areas and assemble a host-parasite database that opened opportunities to explore the macroecological patterns of parasites communities in both areas.
At some point the question of how the balance and separation between rare and common parasite species is altered in the invaded area was brought up as a logical expansion of our studies. Since we felt that analytical tools in this area were lacking, we decided to devise our own application under the acronym FuzzyQ (which stands for Fuzzy Quantification of common and rare species in ecological communities.)
Dealing with rarity quantitatively
We anticipated that FuzzyQ could enhance our capacity to explain and monitor changes in species abundance distributions including, but not limited to, those resulting from biological invasions. In particular, we aimed at developing a method amenable to hypothesis testing and statistical modelling that could be very widely applied. A first challenge was how to define rarity. Looking at the abundance and occupancy of species seemed a reasonable approach to assess the commonness and rarity of species as these criteria have been widely used for this purpose. Given a community surveyed a number of sites, quadrats or any other convenient sampling units (e.g. fish individuals in our particular case), rare species are expected to show both low abundance and low occupancy of the sites.
With FuzzyQ, we shifted the focus from species categorization to a quantitative approach to place each species along a rare‐commonness gradient. The idea was to apply a fuzzy clustering algorithm to allocate each species in the community to the rare or common category by comparing their abundance and occupancy data. The clustering approach used in fuzzy classification is ideal for handling scenarios in which borderline observations and ambiguity challenge traditional binary classification, being most appropriate to analyse complex patterns in natural systems.
Relevance of FuzzyQ
FuzzyQ provides a convenient, simple-to-use, comparative toolkit that supplies simple and intuitive ecological indicators that can give clear, actionable insights into the nature of ecological communities. Returning to the parasite communities of so-iuy mullets, our results pointed to a sharper distinction between common and rare species in the introduced area than in the native one. This finding conforms with evidence showing that most native species were lost during the introduction and the majority of species in the introduced area were acquired from local grey mullet species. Since newly acquired parasite species are expected to lack specific adaptations to the new host, this would explain their pronounced rarity compared to rare species in the native area.
By providing a quantitative framework of commonness and rarity, FuzzyQ can decisively contribute to the advancement of community ecology. By means of experiments and/or meta-analyses it can facilitate evaluation of the roles played by demographic variables and species traits and can reveal regularities in community assemblage. In addition, FuzzyQ can be valuable for conservation biologists. For instance, it provides a simple way to monitor variation of commonness‐rarity patterns quantitatively over time or along geographical and environmental gradients, which can supply crucial information on ecosystem changes.
To read the full ‘Fuzzy quantification of common and rare species in ecological communities (FuzzyQ)’ Methods in Ecology and Evolution article, visit the journal website here.
Learn more about the FuzzyQ R package on CRAN here.