diff --git a/docs/analysis.outline/analysis.md b/docs/analysis.outline/analysis.md
index 8aaa08992ea05b5c3ad2d30fbe8eb4b45b932a82..fc944ef794cfae4216416cdea2af3f9b1935c885 100644
--- a/docs/analysis.outline/analysis.md
+++ b/docs/analysis.outline/analysis.md
@@ -34,7 +34,7 @@ Initially, we will apply the framework of Kunstler et al. (2012). A competition
 
 The question is which of these assumptions provides a better prediction, and which traits contribute strongly. 
 
-Our main aim is for the meeting is to actually carry out the proposed analysis and rough out a first-draft paper. A second step that I hope to start during or before the workshop is to include in the analysis additional test of stabilizing frequency dependence that operate at larger spatial scale (see section  Test of stabilizing frequency dependence processes at non local scale) [Section Additional analysis](#add.analysis). In addition  we would be happy for other analyses and questions to also be considered, following group discussion.
+Our main aim is for the meeting is to actually carry out the proposed analysis and rough out a first-draft paper. A second step that I hope to start during or before the workshop is to include in the analysis additional test of stabilizing frequency dependence that operate at larger spatial scale (see section  [Section Additional analysis](#add.analysis) ). In addition  we would be happy for other analyses and questions to also be considered, following group discussion.
 
 
 ## Output
@@ -52,7 +52,7 @@ G_{i,a,p} = \alpha_{a,p} f_1(D) f_2(A_1) f_3(A_2) g(\sum_{b=1}^{K} \lambda_{a,b}
 
 * Where $B_b=\sum_{n=1}^{N_{i,b}} {D_n^2 \pi /4}$ is the sum of the basal area of tree from species $b$ in the neighbourhood of the individual $i$. The neighbourhood will be either the whole plot for small forest inventory plot, or a radius (for instance 15m but we need to discuss that) in large tropical plot with X Y coordinates of all individuals. $\lambda_{a,b}$ is the parameter describing the competitive effect and will be modelled in function of trait.
 
-* The observation error will be assumed to follow a log-normal distribution ($\sigma_o$).  $\alpha_{a,p}$ is a parameter describing growth of species $a$ with  a plot effect $p$ (and individual effect $i$ if multiple measurements) to account to repeated observation on the same plot (or quadrat in large plot).
+* The observation error will be assumed to follow a log-normal distribution ($\sigma_o$).  $\alpha_{a,p}$ is a parameter describing growth of species $a$ with a plot effect $p$ (and individual effect $i$ if multiple measurements) to account to repeated observation on the same plot (or quadrat in large plot).
 
 ## Trait and  $\lambda_{a,b}$
 
@@ -72,6 +72,10 @@ Then we will test the assumption that the competitive effect of species $b$ on s
 \lambda_{a,b} = f(a +b \times (t_{b} - t_{a}))
 \end{equation}
 
+It is also possible to explore an other alternative model in which species maximum competitive ability is reached at intermediate trait value $t_{opt}$ for instance because of trade-off between two traits:
+\begin{equation} 
+\lambda_{a,b} = f(a +b \times (|t_{a} - t_{opt} | - |t_{b} - t_{opt} |))
+\end{equation}
 
 
 ## Potential form for the functions $f_{1 2 3}$ and $g$
@@ -108,30 +112,6 @@ In all case the species specific parameters ($\alpha_{a,p}$, $\beta_{1,a}$, $\be
 
 <!-- In the computation of the competition index traits observations need to cover all species found in the neighbourhood. Rare species, particularly in tropical forest are likely to represent an issue. One solution would be as done in Aubry-Kientz et al. (2013) to assume that because of phylogenetic conservatism species with unknown trait value can be affected trait with a normal distribution of mean, the mean of the genus and $\sigma$, the $\sigma$ of the genus observation (or the mean genus $\sigma$ reported by Kattge et al. (2011) if to few observations in the genus). I'm reluctant to use family level. -->
 
-----
-
-## ADDITIONAL ANALYSIS: Test of stabilizing frequency dependence processes at non local scale {#add.analysis} 
-
-Showing that competition at local scal is driven by hierarchical traits distance and not traits absolute distance is an important results because it shows that the classical assumption that trait differences reflect local resource partitioning and thus promotes lower comptition is not supported by field data for forest ecosystems. However this leaves open the question of what process is promoting the diversity of traits observed. Any trait is probably related to both competitive exclusion at local scale but also stabilizing effect at larger scale (see Adler et al. 2013[^Adler]). One of the most classical coexistence mechanism in forest community is successionnal niche segregation. 
-<!-- In the framework of Alder et al. 2013, however the successional process related to species shade-tolerance is presented within the spatial heterogeneity effect that promotes coexistence (spatial heterogeneity). According to this paper the key test would show that *"the covariance between the Environmental effect E and the competition effect C increase with increasing traits difference between the focal species and the rest of the community"*. This however unclear for me. Indeed as explained by Gravel et al. 2010, the spatial storage effect can not directly be related to the mosaic of gap in a forest because the variable light availability is in fact related to competition. In Chesson theory the spatial variability has to be related to environmental variable different than competition (see Gravel et al. 2010 Oikos). --> <!-- Gravel et al. propose that the shade tolerance (low light survival $vs.$ high light growth  can promotes species coexistence through three different mechanisms: 'relative non linearity' (relative non linearity in the response of the species to light results in one species dominating in constant environment and an other in a variable environment through the Jensen' inequality), the 'successional niche' (disturbance may promotes an inferior competitors at late successional stage because of its advantage in early successional stage) and the 'storage effect'  (in this case either through spatial variability in disturbance intensity or frequency or spatial variability of soil fertility). --> Gravel et al. 2010[^Gravel] provides an interessting theorethical framework to understand how this process can promotes coexistence of species with a trade off between shade tolerance and high light growth. In addition, Daniel Faltser evolutionnary model provides complementary results in a model in which species strategies is directly related to trait value trough physiological processes. Both model show that this successional niche can promotes coexistence, but for a relatively limited numner of species/strategies (2 to 4). <!-- shows that the low-light survival $vs.$ high light growth can support a relatively limited number of coexisting species (2 and sometime 3 - more than 3 have not been explored). This agree roughly with the number of SLA type supported in Daniel Falster model. --> An other stabilzing process is the spatial storage effect, in which the spatial heterogeinity of environmetal  conditions (E for instance either soil, climate or disturbance regime) interact with competition to promotes coexistence.
-
-[^Adler]:In the framework of Alder et al. 2013, however the successional process related to species shade-tolerance is presented within the spatial storage effect. In my view, the spatial storage effect can not directly be related to the mosaic of gap in a forest because the variable light availability is in fact related to competition. In Chesson theory the spatial variability has to be related to environmental variable different than competition (see Gravel et al. 2010 Oikos)
-[^Gravel]:Gravel et al. propose that low light survival $vs.$ high light growth trade off  can promotes species coexistence through three different mechanisms: 'relative non linearity' (relative non linearity in the response of the species to light results in one species dominating in constant environment and an other in a variable environment through the Jensen' inequality), the 'successional niche' (disturbance may promotes an inferior competitors at late successional stage because of its advantage in early successional stage) and the 'storage effect'  (in this case either through spatial variability in disturbance intensity or frequency or spatial variability of soil fertility)
-
-***I'm planning to add  additional analysis to test what is promoting the diversity of traits even if competitive ability is  related to species trait hierarchy (hierarchical trait distance) at local scale.***
-
-
-Rough description of analysis to add:
-
-* **Successional niche**: Low competition (i.e. high light) growth rate of small tree is inversely related to traits that promote competitive ability in late successional stage. Under this analysis high light growth rate determine the competitive ability in early successionnal stage.  <!-- We know that temporal variability of light/competition created by disturbance can only promotes the coexistence of two extremes type of species in a single isolated patch (Chesson 1994 and Gravel et al. 2010 this may promote only the two extremes strategies). In meta-community of patches connected by dispersal with disturbance this can promotes 3 species according to Gravel et al. 2010 (but in the special case of perfectly asynchronous disturbances), but we don't know if this can promotes a large traits diversity. Daniel Falster model predict the coexistence of up to 4 SLA type via this process (but more likely 3). --> ANALYSIS: Does trait related to competitive ability in high competition is inversely related to juvenile growth in low competition?
-
-* **Spatial storage effect** (see Sears & Chesson 2007 Ecology for a test with annual plant): *2 points to test.* 
-     1. the covariance between competition $C$ and the environment effect $E$ is higher for species with trait value related to a high competitive ability than for opposed trait value (the environment effect can be estimated by the random plot effect or by the abiotic variables or both). *Analysis*: from estimated growth model compute $cov(E,C)$ (for each plot $E$= random plot effect + abiotic effect; and $C$ average competition experienced by the species on the plot)  and test how this is related to the trait linked to competitive ability.
-	 2. species with low competitive ability traits have a different response to the environment (E) than species with high competitive ability traits. *Analysis*: Does dissimilarity in environmental response between species increase with traits distance between species (cov($E_1$,$E_2$) ~ abs($E_1$,$E_2$)).
-	
-----
-
-
 
 ## Ecoregion for NFI data
 For the NFI data we will divide the data set by regions with similar ecological conditions. This will allow to estimate the link between competitive interactions and traits within regions of similar conditions and see how the results vary (for instance in the US there is a large variability between the north and the south). This will allow to make comparison with large tropical plot more easy. Then this will help to have smaller data set to speed up the estimation. Please could you either provides a source of ecoregion with a GIS layer that we can use or better directly includes this variable in the data (at the plot level). Similarly in term of climatic variables I was planning to use the best variables available for each data rather than a global data base of lower quality. Could you either give the link of such a data set or better directly do get the variables for each plot.  If some ecoregion have too few observation I will merge them with the most similar other ecoregion. 
@@ -147,13 +127,13 @@ For the NFI data we will divide the data set by regions with similar ecological
 
           Fushan                 Large plot            Available with data                  ok                Topography soil  
 
-         Luquillo                Large plot            Available with data        Wait Uriarte email          Topography soil 
+         Luquillo                Large plot            Available with data                 Wait               Topography soil 
 
-        La Chonta                Large plot           Available by Lourens      Wait plot data availability   Topography soil 
+        La Chonta                Large plot           Available by Lourens                 Wait               Topography soil 
 
-         Paracou                 Large plot            Available with data                  ok                Topography soil 
+         Paracou                 Large plot            Available with data                 data               Topography soil 
 
-          Mbaiki                 Large plot            Available with data                  ok                Topography soil
+          Mbaiki                 Large plot            Available with data                 Wait               Topography soil
 
            FIA             Forest inventory plots              TRY                         data                   climate      
 
@@ -163,9 +143,9 @@ For the NFI data we will divide the data set by regions with similar ecological
 
           Spain            Forest inventory plots              TRY                         data                   climate      
                                                                                                                 
-          Sweden           Forest inventory plots        Same as France                     Ok                    climate      
+          Sweden           Forest inventory plots        Same as France                    data                   climate      
 
-       Switzerland         Forest inventory plots        Same as France                     ok                    climate      
+       Switzerland         Forest inventory plots        Same as France                    data                   climate      
 
        New Zealand         Forest inventory plots Landcare but TRY data request            data                   climate      
                                                              as well                                                           
@@ -173,10 +153,38 @@ For the NFI data we will divide the data set by regions with similar ecological
 Autralia NSW Kooyman plots   Medium size plots        Available but no LMA                 data                   climate      
                                                        (requested to TRY)                                                      
 
-       CSIRO plots           Medium size plots            NO LEAF TRAITS                    OK                    climate         
+       CSIRO plots           Medium size plots            NO LEAF TRAITS                   Wait                   climate         
 -------------------------------------------------------------------------------------------------------------------------------
 : Data description
-<!-- I have the data for Spain, US, France, New Zealand (old data from David), NSW. -->
+
+\pagebreak
+
+----
+
+## ADDITIONAL ANALYSIS: Test of stabilizing frequency dependence processes at non local scale {#add.analysis} 
+
+Showing that competition at local scal is driven by hierarchical traits distance and not traits absolute distance is an important results because it shows that the classical assumption that trait differences reflect local resource partitioning and thus promotes lower comptition is not supported by field data for forest ecosystems. However this leaves open the question of what process is promoting the diversity of traits observed. Any trait is probably related to both competitive exclusion at local scale but also stabilizing effect at larger scale (see Adler et al. 2013[^Adler]). One of the most classical coexistence mechanism in forest community is successionnal niche segregation. 
+<!-- In the framework of Alder et al. 2013, however the successional process related to species shade-tolerance is presented within the spatial heterogeneity effect that promotes coexistence (spatial heterogeneity). According to this paper the key test would show that *"the covariance between the Environmental effect E and the competition effect C increase with increasing traits difference between the focal species and the rest of the community"*. This however unclear for me. Indeed as explained by Gravel et al. 2010, the spatial storage effect can not directly be related to the mosaic of gap in a forest because the variable light availability is in fact related to competition. In Chesson theory the spatial variability has to be related to environmental variable different than competition (see Gravel et al. 2010 Oikos). --> <!-- Gravel et al. propose that the shade tolerance (low light survival $vs.$ high light growth  can promotes species coexistence through three different mechanisms: 'relative non linearity' (relative non linearity in the response of the species to light results in one species dominating in constant environment and an other in a variable environment through the Jensen' inequality), the 'successional niche' (disturbance may promotes an inferior competitors at late successional stage because of its advantage in early successional stage) and the 'storage effect'  (in this case either through spatial variability in disturbance intensity or frequency or spatial variability of soil fertility). --> Gravel et al. 2010[^Gravel] provides an interessting theorethical framework to understand how this process can promotes coexistence of species with a trade off between shade tolerance and high light growth. In addition, Daniel Faltser evolutionnary model provides complementary results in a model in which species strategies is directly related to trait value trough physiological processes. Both model show that this successional niche can promotes coexistence, but for a relatively limited numner of species/strategies (2 to 4). <!-- shows that the low-light survival $vs.$ high light growth can support a relatively limited number of coexisting species (2 and sometime 3 - more than 3 have not been explored). This agree roughly with the number of SLA type supported in Daniel Falster model. --> An other stabilzing process is the spatial storage effect, in which the spatial heterogeinity of environmetal  conditions (E for instance either soil, climate or disturbance regime) interact with competition to promotes coexistence.
+
+[^Adler]:In the framework of Alder et al. 2013, however the successional process related to species shade-tolerance is presented within the spatial storage effect. In my view, the spatial storage effect can not directly be related to the mosaic of gap in a forest because the variable light availability is in fact related to competition. In Chesson theory the spatial variability has to be related to environmental variable different than competition (see Gravel et al. 2010 Oikos)
+[^Gravel]:Gravel et al. propose that low light survival $vs.$ high light growth trade off  can promotes species coexistence through three different mechanisms: 'relative non linearity' (relative non linearity in the response of the species to light results in one species dominating in constant environment and an other in a variable environment through the Jensen' inequality), the 'successional niche' (disturbance may promotes an inferior competitors at late successional stage because of its advantage in early successional stage) and the 'storage effect'  (in this case either through spatial variability in disturbance intensity or frequency or spatial variability of soil fertility)
+
+***I'm planning to add  additional analysis to test what is promoting the diversity of traits even if competitive ability is  related to species trait hierarchy (hierarchical trait distance) at local scale.***
+
+
+Rough description of analysis to add:
+
+* **Successional niche**: Low competition (i.e. high light) growth rate of small tree is inversely related to traits that promote competitive ability in late successional stage. Under this analysis high light growth rate determine the competitive ability in early successionnal stage.  <!-- We know that temporal variability of light/competition created by disturbance can only promotes the coexistence of two extremes type of species in a single isolated patch (Chesson 1994 and Gravel et al. 2010 this may promote only the two extremes strategies). In meta-community of patches connected by dispersal with disturbance this can promotes 3 species according to Gravel et al. 2010 (but in the special case of perfectly asynchronous disturbances), but we don't know if this can promotes a large traits diversity. Daniel Falster model predict the coexistence of up to 4 SLA type via this process (but more likely 3). --> ANALYSIS: Does trait related to competitive ability in high competition is inversely related to juvenile growth in low competition?
+
+* **Spatial storage effect** (see Sears & Chesson 2007 Ecology for a test with annual plant): *2 points to test.* 
+     1. the covariance between competition $C$ and the environment effect $E$ is higher for species with trait value related to a high competitive ability than for opposed trait value (the environment effect can be estimated by the random plot effect or by the abiotic variables or both). *Analysis*: from estimated growth model compute $cov(E,C)$ (for each plot $E$= random plot effect + abiotic effect; and $C$ average competition experienced by the species on the plot)  and test how this is related to the trait linked to competitive ability.
+	 2. species with low competitive ability traits have a different response to the environment (E) than species with high competitive ability traits. *Analysis*: Does dissimilarity in environmental response between species increase with traits distance between species (cov($E_1$,$E_2$) ~ abs($E_1$,$E_2$)).
+	
+----
+
+\pagebreak
+
+
 
 # Indication on data formatting
 
diff --git a/docs/meeting.agenda/agenda.md b/docs/meeting.agenda/agenda.md
index a307c3912ddc62f0ec6a1fd391aabe3ca19dc740..61b1bd92eef04303b668c4d3317f08d628909c22 100644
--- a/docs/meeting.agenda/agenda.md
+++ b/docs/meeting.agenda/agenda.md
@@ -90,6 +90,8 @@ Participants arrive in Sydney. Personal site seeing in Sydney region.
     * Work on model including variability of traits (mean drawn from normal with given sd)
     * Explore first results and start to work a paper outline
 
+* 16:00-17:00 Skype meeting with Paloma Ruiz Madrid
+
 * 17:00-17:30   Wrap up. Planning 'final' analysis to be sent and code to be written for that on Wednesday.
 
 * 22:00-23:00   (Georges) Skype meeting with Maria Uriarte (and Nathan Swenson?). Skype meeting with Miguel Zavala ?
@@ -122,7 +124,9 @@ Participants arrive in Sydney. Personal site seeing in Sydney region.
 * 11:00-13:00   Work to finish this analysis and organization for that (additional data set not include so far?)
 * 13:00-14:00  *Lunch*
 * 14:00-15:30   Group discussion about future work that may rise from the workshop (*max 10 min per person*)
-* 16:00-17:00   Skype meeting with Madrid (or later in the evening), Switzerland and Sweden.
+* 16:00-17:00   Skype meeting with  Switzerland and Sweden.
+
+* 21:00-22:00  Skype meeting with Miguel Zavala and Paloma Ruiz
 
 \pagebreak