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\section{Introduction}   Traditionally, science has been reductionistic. Reductionism—the most popular approach in science—is not appropriate for studying biological and ecological systems, as it attempts to simplify and separate in order to predict their future behavior and states. Due to prediction difficulty, biological and ecological systems have been considered as complex. This “complexity” is due to the relevant interactions between components [18]. It is important to highlight that, etymologically, the term complexity comes from the Latin plexus, which means interwoven. In other words, something complex is difficult to separate. Including interactions in ecological studies, for complexity understanding is no easy. For example, it has been tried with global models that including the greatest number of variables, resulting also in serious deficiencies in predictability, especially for the limitation for the incorporation of all interactions ecosystem multi-elements and components (Moore et al. 2002). Alternative forms of explain de complex dynamics have been trying with the assessment of attributes like resilience and robustness (Ulanowicz et al. 2009). Also, ecological complexity has been related with stability. This way, complexity characterization has been supported in variables such as species richness (number of species), connectance (fraction of the possible interspecific interactions), interaction strength (effect of one species’ density on the growth rate of another specie) and evenness (abundance variance). Meanwhile, stability has been related with resilience (velocity to return to the equilibrium), resistance (variable’ grade of change) and variability (population density variance) (Pimm, 1984). However, these interpretations of interactions are conducts to find an explanation of functional complexity, than the evaluation of how complex is an ecosystem.  As complex systems, we need consider that biological and ecological systems have properties like emergence, self-organization, and life. It means, biological and ecological systems dynamics generate novel information from the relevant interactions among components. Interactions determine the future of systems and its complex behavior. Novel information limits predictability, as it is not included in initial or boundary conditions. It can be said that this novel information is emergent since it is not in the components, but produced by their interactions. Interactions can also be used by components to self-organize, i.e. produce a global pattern from local dynamics. The balance between change (chaos) and stability (order) states has been proposed as a characteristic of complexity. Since more chaotic systems produce more information (emergence) and more stable systems are more organized, complexity can be defined as the balance between emergence and self-organization. In addition, there are two properties that support the above processes: homeostasis refers to regularity of states in the system and autopoiesis that reflects autonomy.  Due to a plethora of definitions, notions, and measures of these concepts has have  been proposed, the authors proposed abstract measures of emergence, selforganization, complexity, homeostasis an autopoiesis based on information theory, in focus to clarify their meaning with formal definitions (Gershenson and Fernández, 2012). Now we proposes propose  apply to aquatic ecosystem formal measures developed. From the application to the case of study, an artic Artic  lake, we clarify the ecological meaning of these notions notions,  and we showed how the ecological dynamics can be described in terms of information. This way the study of the complexity in biological and ecological cases, now is easier. In the next section, we present a brief explanation of limnology, as the field that studies the lakes. In section 3 3,  we present the synthetic measures of results of emergence, selforganization, complexity, and homeostasis. Section 4 describes our experiments and results with the artic Artic  lake, which illustrate the useful of the proposed measures. This is followed by a discussion in Section 5. The article closes with proposals for future work and conclusions.