deletions | additions       diff --git a/introduction_1.tex b/introduction_1.tex  index aa41ae2..0fe2667 100644  --- a/introduction_1.tex  +++ b/introduction_1.tex 
...
\section{Introduction}   In recent years, the scientic scientific  study of complexity in ecological systems has increasedthe  understanding of a broad range of phenomena such as ecological richness, abundance, and hierarchical structure. As result a result,  different approximations were have been  exploredin focus  to developa  mathematical formalism formalisms in order  to represent the ecological complexity as ecological indicator (Parrot, 2005). The advantage of an a  complexity indicator in ecostytems ecosystems  is the posibility possibility  of relation with ecological integrity, diversity and/or resilience, for example. Relation with the spatial and temporal scales, with the structure and function as well are desired (Parro, 2010). In spite of the valuable efforts in ecological modeling over the last decade to take into account the ecological interactions in much detail (Petrovskii and Petrovskay, 2012), more explorations are requiered required  to explain ecological dynamics in terms of complexity. A start starting  point for complexity studies is considering that ecological systems exhibit properties like emergence, self-organization, and life. In addition addition,  there are two important propeties, essential properties,  homeostasis and autopoiesis, which supports the self-regulation and autonomy of the system. The above properties comes from the relevant interactiona interactions  among system's system’s  components and generates novel ingormation. information.  These interactions determine the future of ecosystems and its complex behavior. Novel information limits predictability, 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. Interactions are one the most important reason for complexity generation. To support the formal description of complexity, self-organization and emergence, information theory (Shannon, 19499) has been used in different ways, ways  as it can see in Prokopenko et al (2009). Formals aspects for homeostasis and autopoiesis can be found in Fernández et al., 2014. Considering there are multiple ways to describe the state of an ecosystem and taking ecosystem. Taking  into account that the balance between change (chaos) and stability (order) states has been proposed as a characteristic of complexity (Langton,**; Kaufmann), we can say that more chaotic systems produce more information (emergence) (emergence),  and more stable systems are more organized. Thus we propose, based on information theory, that complexity can be defined as the balance between emergence and self-organization (Gershenson & Fernández; Fernández et. al. 2015). This approach have has  been applied to some ecological systems (Fernández & Gershenson 2013; Fernández et al. 2013*eccs) with good results indicating that ecological dynamics can be described in terms of information. This papers expands expand  the useful of the application of complexity measuring applying formal expresions of complexity, self-organization, emergence, homeostasis and autopoiesis to the physiochemical, nutrients and biomass subsystems to four types of lakes located in an a  latitudinal gradiente (arctic, highland template, lowland template to tropical), in focus to evaluate the usefulness and bennefits in ecological systems. \cite{cite:0}