deletions | additions       diff --git a/Discussion.tex b/Discussion.tex  index 9504a44..91f13cd 100644  --- a/Discussion.tex  +++ b/Discussion.tex 
...
\section{Discussion}  other measures... transfer entropy, Tsallis entropy..   Compare results obtained \subsection{The ecological sense of the proposed measures.}     The proposed measures characterize the different ecological configurations and dynamics that elements of lakes acquire through their interactions. From simple mathematical expressions, based on probabilistic features, we can capture the properties and tendencies of the ecological systems, considering the scale at which they are described. In contrast with other complex computational methods, our approach permits analyze the properties and tendencies of any variables, in different subsystems, for one or more ecosystems. Also, we can change scale and apply the measures there, in order to determine at which scale the richness dynamics is representative. In instance, for Physico-chemical subsystem  in Arctic lakes, previous analysis shows that base 10 is very informative and represents the dynamics as base 8 or 16,34 and 64 (Fernández et al., 2014).     The integration of self-organization and emergence aspects into our C measure have advantages like the complex dynamics of an ecological system can be observed as the balance between regularity and change or variability. In this context, it can define which variable, process or ecosystem is more complex than other.     The characterization of the behavior of the biotic component, in front of environmental disturbances or environmental variability, can be complemented with the autopoiesis and homeostasis measures. It is an important feature that will be useful for studies of global ecological change. In general we suggest that systems with a higher complexity are more robust while those with a lower complexity are less.     Clarifying that chaos should not be confused with complexity (Gershenson, 2013), we highlight that our measures can distinguish between random and non-random ecological processes or variables. The  former papers. is related with very high emergence (high entropy), it implies too many changes and patterns destruction. The second implies very high self-organization (very low entropy); it prevents that complex patterns emerging. For further details, this randomness can be examined in the probability distribution for any process or variables at different scales, as well.     Besides our measures can be related with the temporal and organizational scales and fluctuating environments, they can be related with other ecological aspects like occupancy, movement patterns and numbers of species as show in Fernández et al (2013). This way, proposed measures can be good complements in status and trends study, in ecological communities.     From the case of study, we show the useful of this measure in the ecological field which is     \subsection{Complexity and others Measures of Information.}     Complexity has been correlated with other measures of information like Fisher Information (Prokopenko et al., 2011) and Tsallis Information (Tsallis, 2002; Gell-Mann and Tsallis, 2004). Contrasting C with Fisher Information we observed that C is smoother, so it can represent dynamical change in a more gradual fashion. Fisher information has a much higher steepness in comparison with C. On the other hand, test of Tsallis information for Ar lakes shows that it follows self-organization patterns in one cases and in others emergence patterns. This results generate a difficult to establish a significant correlation with C for Tsallis measure , is increased with its variable scale and determination for the optimal q choice. It seems that q=2 is the value in which some correlations can be appear.     In the context of the physics, an interesting point is that different measures of entropy are used for describe different probability distribution. Shanon entropy is logarithmic and it is appropriated for the phenomena with exponential distribution. Tsallis entropy has a power model and it is appropriated for phenomena with power distribution. It has been find that critical phenomena considered by someones as complex, usually have power law distribution referred as self-organized criticality (Per Bak, **). Consequently, Tsallis information has been recommended as complexity measure (**). However, we consider that in itself Tsallis entropy might not be a complexity measure, in particular for its q parameter dependence and sensitivity. Tsallis information could be a more a general description applicable to several phenomenon, previous their distribution inspection and knowing.  In spite of the popularity of Shannon’s entropy as the base for a measure of community diversity, its interpretation in terms of unpredicitability related with high diversity and its relations with community stability have been a difficult point of understanding and agreement among ecology scientifics. According with Anad and Orlici (1996) measuring the complexity of a biological community from its randomness and unpredictability fails to adequately capture it (Ricota, **).