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).