Introduction
Ecologists are facing challenges to decipher a rich amount of biological and environmental information embedded in an ecological community. The classification of a set of taxonomic units into functional groups based on morphology and species traits has been widely used in ecological research (Litchman & Klausmeier, 2008; Usseglio-Polatera et al. 2000). If species are pooled into the same group based on similar morphological or physiological characteristics and developing ecological groups, that can help ecologists to better understand the interactions between biological communities and their environment. For example, stream macroinvertebrates have been categorized into functional feeding groups, such as scrapers, shredders, collector-gatherers, collector-filterers and predators. Logez et al. (2013) suggested that similar fish assemblage functional structures will be found in similar environmental conditions.
Categorizing phytoplankton by their traits and functions was attempted a few decades ago. Reynolds et al (2002) set a precedent in the classification of phytoplankton functional groups. Salmaso & Padisák (2007) developed the Morpho-Functional Groups based on the phytoplankton morphological and functional characteristics, such as body size, mobility, nutrient requirements and other features. Kruk et al (2010) applied morphology-based functional groups (MBFG) approach to cluster phytoplankton organisms, and seven groups were defined according to the main morphological traits of phytoplankton such as cell volume, presence of flagella and the ratio of surface area and volume. However, there are still some challenges for phytoplankton ecologists to apply functional concepts in phytoplankton research. For example, phytoplankton communities can be extremely rich (Reynolds 2006) but may differ from region to region due to ecological factors. Classifying species into different functional groups requires a extensive amount of knowledge on the autecology of each species, and such information may not be readily available in the literature. Physiological data are not available for all phytoplankton species (Weithoff 2003), which limits our abilities for developing a priori functional classification (Mieleitner et al. 2008). On the other hand, environmental assessment of lakes using phytoplankton is urgently needed by water quality managers, especially in some rapidly developing regions such as China because of serious water pollution. A great deal of phytoplankton ecological studies have been conducted in Europe (EC Parliament and Council 2000), North America (Arhonditsis et al., 2004) and China (Deng et al., 2014). The implementation of water programs have generated an enormous amount of phytoplankton data with large spatial scales using standardized field protocols. How to effectively use these ‘datasets’ to enhance our current understanding of phytoplankton assemblages with relation to their environments and water resource management still remains challenging.
In order to provide a better understanding of the ecological information of phytoplankton associations, we introduce an affinity analysis called association rule for identifying phytoplankton associations. Association rule is a machine-learning method for discovering co-occurrence relationships among activities performed by specific individuals or groups in a large database using simple statistical performance measures. There have been many successful business applications for applying the method in finance, telecommunication, marketing, retailing and web analysis (Chen et al. 2005). Association rule is used to understand the purchase behavior of customers in retail. For example, people often purchased flowers and then cards, purchased milk and then eggs, so we can then call flower+cards or milk+eggs associations. This information is helpful for cross-selling, up-selling and discount plans. For phytoplankton, Aulacoseira subarctica , Aulacoseira islandica and Cyclotella meneghiniana , Cyclotellastelligera co-exist in softer-water lakes (Reynolds et al., 2002).
In ecological studies, we assume that each sampling site or sampling date is a ‘transaction’ in a business setting and each species is an item and then develop the associations. Many researches focus on phytoplankton spatial and temporal variations in lakes and rivers, and therefore this study identified phytoplankton associations from spatially and temporally datasets, respectively. The main objective of this study was to use affinity analysis to aid identification of the candidate associations of phytoplankton and then assess the relationships between the candidate phytoplankton associations and environmental factors using the redundancy analysis (RDA).