Commit af240982 authored by kunstler's avatar kunstler
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new version of paper and fixe error in rescaling of BA growth

parent fd111d28
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Data set name,Country,Data type,Plot size,Dbh threshold,Number of plots,Traits,Source trait data,References,Contact of person in charge of data formatting,Comments
Panama,Panama,LPP,1 to 50 ha,1 cm,42,"Wood density, SLA, Maximum height, and Seed mass",local,"3,4,25",R. Condit (conditr@gmail.com),The data used include both the 50 ha lot of BCI and the network of 1 ha plots from Condit et al. (2013). The two first census of BCI plot were excluded.
Japan,Japan,LPP,0.35 to 1.05 ha,2.39 cm,16,"Wood density, SLA, Maximum height, and Seed mass",local,5,M. I. Ishihara (moni1000f_networkcenter@fsc.hokudai.ac.jp),
Luquillo,Puerto Rico,LPP,16 ha,1 cm,1,"Wood density, SLA, Maximum height, and Seed mass",local,"6, 23",J. Zimmerman (esskz@ites.upr.edu),
Panama,Panama,LPP,1 to 50 ha,1 cm,42,"Wood density, SLA, Maximum height, and Seed mass",local,"3,4,25","Plot data: R. Condit (conditr@gmail.com), Traits data: J. Wright (wrightj@si.edu)",The data used include both the 50 ha plot of BCI and the network of 1 ha plots from Condit et al. (2013). The two first census of BCI plot were excluded.
Japan,Japan,LPP,0.35 to 1.05 ha,2.39 cm,16,"Wood density, SLA, Maximum height, and Seed mass",local,5,"Plot data: M. I. Ishihara (moni1000f_networkcenter@fsc.hokudai.ac.jp), Traits data: Y Onoda (yusuke.onoda@gmail.com)",
Luquillo,Puerto Rico,LPP,16 ha,1 cm,1,"Wood density, SLA, Maximum height, and Seed mass",local,"6, 23","Plot data: J. Zimmerman (esskz@ites.upr.edu), Traits data: N. Swenson (swensonn@msu.edu )",
M'Baiki,Central African Republic,LPP,4 ha,10 cm,10,"Wood density, SLA, and Seed mass",local,"7,8",G. Vieilledent (ghislain.vieilledent@cirad.fr),
Fushan,Taiwan,LPP,25 ha,1 cm,1,"Wood density, SLA, and Seed mass",local,9,I-F. Sun (ifsun@mail.ndhu.edu.tw),
Paracou,French Guiana,LPP,6.25 ha,10 cm,15,"Wood density, SLA, and Seed mass",local,"10,11,24",B. Herault (bruno.herault@cirad.fr),
Paracou,French Guiana,LPP,6.25 ha,10 cm,15,"Wood density, SLA, and Seed mass",local,"10,11,24","Plot data: B. Herault (bruno.herault@cirad.fr), Traits data: C. Baraloto (Chris.Baraloto@ecofog.gf)",
France,France,NFI,0.017 to 0.07 ha,7.5 cm,41503,"Wood density, SLA, Maximum height, and Seed mass",TRY,"12,13",G. Kunstler (georges.kunstler@gmail.com),"The French NFI is based on temporary plot, but 5 years tree radial growth is estimated with short core. All trees with dbh > 7.5 cm, > 22.5 cm and > 37.5 cm were measured within a radius of 6 m, 9 m and 15 m, respectively. Plots are distributed over forest ecosystems on a 1-km 2 cell grid"
Spain,Spain,NFI,0.0078 to 0.19 ha,7.5 cm,49855,"Wood density, SLA, Maximum height, and Seed mass",TRY,"14,15,16",M. Zavala (madezavala@gmail.com),"Each SFI plot included four concentric circular sub-plots of 5, 10, 15 and 25-m radius. In these sub-plots, adult trees were sampled when diameter at breast height (d.b.h.) was 7.5-12.4 cm, 12.5-22.4 cm, 22.5-42.5 cm and >= 42.5 cm, respectively."
Swiss,Switzerland,NFI,0.02 to 0.05 ha,12 cm,2665,"Wood density, SLA, Maximum height, and Seed mass",TRY,17,N. E. Zimmermann (niklaus.zimmermann@wsl.ch),"All trees with dbh > 12 cm and > 36 cm were measured within a radius of 7.98 m and 12.62 m, respectively."
......
......@@ -5,7 +5,7 @@ We developed the equation of $\alpha_{c,f} = \alpha_{0,f} + \alpha_r \, t_f + \a
\begin{equation} \label{alphaBA}
\sum_{c=1}^{N_p} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f} \, B_{i,tot} + \alpha_r \, t_f \, B_{i,tot} + \alpha_i \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,tot} = \sum_{c=1}^{C_p} {B_{i,c,p,s}}$,
......@@ -27,8 +27,8 @@ and $C_p$ is the number of species on the plot $p$.
- Number of plots: 42
- Traits: Wood density, SLA, Maximum height, and Seed mass
- Source trait data: local
- Contact of person in charge of data formatting: R. Condit (conditr@gmail.com)
- Comments: The data used include both the 50 ha lot of BCI and the network of 1 ha plots from Condit et al. (2013). The two first census of BCI plot were excluded.
- Contact of person in charge of data formatting: Plot data: R. Condit (conditr@gmail.com), Traits data: J. Wright (wrightj@si.edu)
- Comments: The data used include both the 50 ha plot of BCI and the network of 1 ha plots from Condit et al. (2013). The two first census of BCI plot were excluded.
- References:
- Condit, R. (1998). Tropical forest census plots. Springer, Berlin, Germany.
- Condit, R., Engelbrecht, B.M.J., Pino, D., Perez, R., Turner, B.L., (2013). Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110: 5064-5068.
......@@ -44,7 +44,7 @@ and $C_p$ is the number of species on the plot $p$.
- Number of plots: 16
- Traits: Wood density, SLA, Maximum height, and Seed mass
- Source trait data: local
- Contact of person in charge of data formatting: M. I. Ishihara (moni1000f_networkcenter@fsc.hokudai.ac.jp)
- Contact of person in charge of data formatting: Plot data: M. I. Ishihara (moni1000f_networkcenter@fsc.hokudai.ac.jp), Traits data: Y Onoda (yusuke.onoda@gmail.com)
- Comments:
- References:
- Yakushima Forest Environment Conservation Center, Ishihara, M.I., Suzuki, S.N., Nakamura, M., Enoki, T., Fujiwara, A., Hiura, T., Homma, K., Hoshino, D., Hoshizaki, K., Ida, H., Ishida, K., Itoh, A., Kaneko, T., Kubota, K., Kuraji, K., Kuramoto, S., Makita, A., Masaki, T., Namikawa, K., Niiyama, K., Noguchi, M., Nomiya, H., Ohkubo, T., Saito, S., Sakai, T., Sakimoto, M., Sakio, H., Shibano, H., Sugita, H., Suzuki, M., Takashima, A., Tanaka, N., Tashiro, N., Tokuchi, N., Yoshida, T., Yoshida, Y., (2011). Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecological Research 26: 1007-1008.
......@@ -59,7 +59,7 @@ and $C_p$ is the number of species on the plot $p$.
- Number of plots: 1
- Traits: Wood density, SLA, Maximum height, and Seed mass
- Source trait data: local
- Contact of person in charge of data formatting: J. Zimmerman (esskz@ites.upr.edu)
- Contact of person in charge of data formatting: Plot data: J. Zimmerman (esskz@ites.upr.edu), Traits data: N. Swenson (swensonn@msu.edu )
- Comments:
- References:
- Thompson, J., N. Brokaw, J. K. Zimmerman, R. B. Waide, E. M. Everham III, D. J. Lodge, C. M. Taylor, D. GarciaMontiel, and M. Fluet. (2002). Land use history, environment, and tree composition in a tropical forest. Ecological Applications 12: 1344-1363.
......@@ -106,7 +106,7 @@ and $C_p$ is the number of species on the plot $p$.
- Number of plots: 15
- Traits: Wood density, SLA, and Seed mass
- Source trait data: local
- Contact of person in charge of data formatting: B. Herault (bruno.herault@cirad.fr)
- Contact of person in charge of data formatting: Plot data: B. Herault (bruno.herault@cirad.fr), Traits data: C. Baraloto (Chris.Baraloto@ecofog.gf)
- Comments:
- References:
- Herault, B., Bachelot, B., Poorter, L., Rossi, V., Bongers, F., Chave, J., Paine, C.E., Wagner, F., and Baraloto, C. (2011). Functional traits shape ontogenetic growth trajectories of rain forest tree species. Journal of Ecology 99: 1431-1440.
......@@ -320,11 +320,13 @@ and $C_p$ is the number of species on the plot $p$.
## Trait effects and potential mechanisms
Wood density (WD) was strongly negatively associated with maximum growth, in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@nock_wood_2009; @wright_functional_2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth a low wood density species will have a higher basal area increment than a high wood density species[@enquist_allometric_1999]. Other advantages of light wood may include higher xylem conductivity[@chave_towards_2009], though for angiosperms this is a correlated trait rather than an automatic consequence. A countervailing advantage for high wood density species was their better tolerance to competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@chave_towards_2009; @nock_wood_2009; @wright_functional_2010]. This has generally been related to the higher survival associated with high wood density[@kraft_relationship_2010], via resistance to mechanical damage, herbivores and pathogens[@chave_towards_2009; @kraft_relationship_2010], but may also be connected to a lower maintenance respiration[@larjavaara_perspective_2010]. For growth, the lower respiration may lead to a direct advantage in deep shade, but the correlation might also arise through correlated selection for high survival rate and for high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effect, especially in tropical forest where the confidence interval did not span zero. This might possibly have been mediated by larger crowns (both in depth and radius)[@poorter_architecture_2006; @aiba_architectural_2009], casting a deeper shade.
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key drivers of tree growth[@stephenson_rate_2014; @enquist_allometric_1999]. Then there was a consistent negative effect of the total basal area of neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height > 10cm), agree well with the idea that facilitation processes are generally limited to the regeneration phase rather than at the adult stage [@callaway_competition_1997].
In term of traits effects, Wood density (WD) was strongly negatively associated with maximum growth, in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@nock_wood_2009; @wright_functional_2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth a low wood density species will have a higher basal area increment than a high wood density species[@enquist_allometric_1999]. Other advantages of light wood may include higher xylem conductivity[@chave_towards_2009], though for angiosperms this is a correlated trait rather than an automatic consequence. A countervailing advantage for high wood density species was their better tolerance to competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@chave_towards_2009; @nock_wood_2009; @wright_functional_2010]. This has generally been related to the higher survival associated with high wood density[@kraft_relationship_2010], via resistance to mechanical damage, herbivores and pathogens[@chave_towards_2009; @kraft_relationship_2010], but may also be connected to a lower maintenance respiration[@larjavaara_perspective_2010]. For growth, the lower respiration may lead to a direct advantage in deep shade, but the correlation might also arise through correlated selection for high survival rate and for high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effect, especially in tropical forest where the confidence interval did not span zero. This might possibly have been mediated by larger crowns (both in depth and radius)[@poorter_architecture_2006; @aiba_architectural_2009], casting a deeper shade.
SLA was positively correlated with maximum basal area growth (growth without competition). This agrees well with previous studies that reported a positive correlation between SLA and nitrogen and phosphorus concentration, and gas exchange (the 'leaf economic spectrum'[@wright_worldwide_2004]). As in previous studies[@poorter_are_2008; @wright_functional_2010], this direct effect of SLA was smaller than the effect size of wood density and had wider confidence intervals. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@niinemets_review_2010].
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@poorter_architecture_2006; @poorter_are_2008; @wright_functional_2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might be expected to select for slower growth in long-lived plants[@poorter_are_2008]. Maximum height was negatively correlated with tolerance to competition, in line with the idea that sub-canopy trees are more shade-tolerant[@poorter_architecture_2006]. There was however a tendency for species with tall maximum height to have stronger competitive effect (though with wider confidence intervals intercepting zero), that might be explained by greater light interception from taller trees.
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@poorter_architecture_2006; @poorter_are_2008; @wright_functional_2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might be expected to select for slower growth in long-lived plants[@poorter_are_2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@poorter_architecture_2006]. There was however a tendency for species with tall maximum height to have stronger competitive effect (though with wider confidence intervals intercepting zero), that might be explained by greater light interception from taller trees.
Our results raised the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and response are the two central elements of the species competitive ability[@goldberg_competitive_1991]. One may expect that because of intra-specific competition, species with strong competitive effect should have evolved a high tolerance to competition. However, in agreement with previous studies[@goldberg_components_1990; @goldberg_competitive_1991; @wang_are_2010], we found little evidence for such coordination. It was present only for wood density, where high density conferred better competitive response and also stronger competitive effect (but with wide confidence intervals). For SLA there was no clear coordinations. For maximum height as explained above there was a tendency for short maximum height to lead to high tolerance to competition but to low competitive effect. This interesting because a trade-off between competitive tolerance and maximum height has been proposed as fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@kohyama_stratification_2009]. Finally the lack of support for coordination between response and effect is important because it means that competitive interaction is not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_r$ as in @kunstler_competitive_2012; @kraft_functional_2014; @lasky_trait-mediated_2014). Traits of competitors alone or of focal plants alone may convey more information. If traits are strongly linked to either competitive effect or competitive response, this still means that some trait values will have an advantage in competitive interactions.
......@@ -334,7 +336,6 @@ Given that the effect sizes we report for effects of traits on competitive inter
## Variations between biomes
Overall most results were rather consistent across biomes (Fig 3 main text), but some exceptions deserve comment.
Only for SLA, the sign of the effect size parameters were changing a lot between biomes (Fig. 3 main text). High SLA species tended to be more competition-tolerant (competitive response parameter $\alpha_r$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical forests. These different outcomes may trace to the prevalence of deciduous species in temperate forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@lusk_why_2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive impact was positive, versus negative in the other biomes (Fig 3 main text). We do not have a mechanistic explanation to suggest for this discrepancy, but can observe that taiga has relatively few species many of which are conifers where the range of wood density is narrower than for angiosperms (see Extended data Table 1).
......
......@@ -5,7 +5,7 @@ We developed the equation of $\alpha_{c,f} = \alpha_{0,f} + \alpha_r \, t_f + \a
\begin{equation} \label{alphaBA}
\sum_{c=1}^{N_p} {\alpha_{c,f} B_{i,c,p,s}} = \alpha_{0,f} \, B_{i,tot} + \alpha_r \, t_f \, B_{i,tot} + \alpha_i \, B_{i,t_c} + \alpha_s \, B_{i,\vert t_c - t_f \vert}
\end{equation}
Where:
$B_{i,tot} = \sum_{c=1}^{C_p} {B_{i,c,p,s}}$,
......@@ -62,11 +62,13 @@ writeLines(unlist(list.t))
## Trait effects and potential mechanisms
Wood density (WD) was strongly negatively associated with maximum growth, in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@nock_wood_2009; @wright_functional_2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth a low wood density species will have a higher basal area increment than a high wood density species[@enquist_allometric_1999]. Other advantages of light wood may include higher xylem conductivity[@chave_towards_2009], though for angiosperms this is a correlated trait rather than an automatic consequence. A countervailing advantage for high wood density species was their better tolerance to competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@chave_towards_2009; @nock_wood_2009; @wright_functional_2010]. This has generally been related to the higher survival associated with high wood density[@kraft_relationship_2010], via resistance to mechanical damage, herbivores and pathogens[@chave_towards_2009; @kraft_relationship_2010], but may also be connected to a lower maintenance respiration[@larjavaara_perspective_2010]. For growth, the lower respiration may lead to a direct advantage in deep shade, but the correlation might also arise through correlated selection for high survival rate and for high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effect, especially in tropical forest where the confidence interval did not span zero. This might possibly have been mediated by larger crowns (both in depth and radius)[@poorter_architecture_2006; @aiba_architectural_2009], casting a deeper shade.
The most important driver of individual growth was individual tree size with a positive effect on basal area growth (see Extended data Table 3). This is unsurprising as tree size is known to be a key drivers of tree growth[@stephenson_rate_2014; @enquist_allometric_1999]. Then there was a consistent negative effect of the total basal area of neighbouring competitors across all biomes. The dominance of a competitive effect for the growth of adult trees (diameter at breast height > 10cm), agree well with the idea that facilitation processes are generally limited to the regeneration phase rather than at the adult stage [@callaway_competition_1997].
In term of traits effects, Wood density (WD) was strongly negatively associated with maximum growth, in agreement with the idea that shade-intolerant species with low wood density have faster growth in absence of competition (in full light conditions) than shade tolerant species[@nock_wood_2009; @wright_functional_2010]. One advantage of low wood density is clearly that it is cheaper to build light than dense wood, thus for the same biomass growth a low wood density species will have a higher basal area increment than a high wood density species[@enquist_allometric_1999]. Other advantages of light wood may include higher xylem conductivity[@chave_towards_2009], though for angiosperms this is a correlated trait rather than an automatic consequence. A countervailing advantage for high wood density species was their better tolerance to competition (less growth reduction per unit of basal area of competitors), which is in line with the idea that these species are more shade tolerant[@chave_towards_2009; @nock_wood_2009; @wright_functional_2010]. This has generally been related to the higher survival associated with high wood density[@kraft_relationship_2010], via resistance to mechanical damage, herbivores and pathogens[@chave_towards_2009; @kraft_relationship_2010], but may also be connected to a lower maintenance respiration[@larjavaara_perspective_2010]. For growth, the lower respiration may lead to a direct advantage in deep shade, but the correlation might also arise through correlated selection for high survival rate and for high growth in shade. Finally, high wood density was also weakly correlated with stronger competitive effect, especially in tropical forest where the confidence interval did not span zero. This might possibly have been mediated by larger crowns (both in depth and radius)[@poorter_architecture_2006; @aiba_architectural_2009], casting a deeper shade.
SLA was positively correlated with maximum basal area growth (growth without competition). This agrees well with previous studies that reported a positive correlation between SLA and nitrogen and phosphorus concentration, and gas exchange (the 'leaf economic spectrum'[@wright_worldwide_2004]). As in previous studies[@poorter_are_2008; @wright_functional_2010], this direct effect of SLA was smaller than the effect size of wood density and had wider confidence intervals. Low SLA was also correlated with a stronger competitive effect. This may be related to a longer leaf life span characteristic of low SLA species because leaf longevity leads to a higher accumulation of leaf in the canopy and thus a higher light interception[@niinemets_review_2010].
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@poorter_architecture_2006; @poorter_are_2008; @wright_functional_2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might be expected to select for slower growth in long-lived plants[@poorter_are_2008]. Maximum height was negatively correlated with tolerance to competition, in line with the idea that sub-canopy trees are more shade-tolerant[@poorter_architecture_2006]. There was however a tendency for species with tall maximum height to have stronger competitive effect (though with wider confidence intervals intercepting zero), that might be explained by greater light interception from taller trees.
Maximum height was weakly positively correlated with maximum growth rate (confidence intervals spanned zero except for temperate rain forest). Previous studies[@poorter_architecture_2006; @poorter_are_2008; @wright_functional_2010] found mixed support for this relationship. Possible mechanisms are contradictory: maximum height may be associated with greater access to light and thus faster growth, but at the same time life history strategies might be expected to select for slower growth in long-lived plants[@poorter_are_2008]. Maximum height was negatively correlated with tolerance to competition (confidence intervals spanned zero except for temperate rain forest and taiga), in line with the idea that sub-canopy trees are more shade-tolerant[@poorter_architecture_2006]. There was however a tendency for species with tall maximum height to have stronger competitive effect (though with wider confidence intervals intercepting zero), that might be explained by greater light interception from taller trees.
Our results raised the question whether there is a coordination between trait values conferring strong competitive effect and trait values conferring high competitive tolerance. Competitive effect and response are the two central elements of the species competitive ability[@goldberg_competitive_1991]. One may expect that because of intra-specific competition, species with strong competitive effect should have evolved a high tolerance to competition. However, in agreement with previous studies[@goldberg_components_1990; @goldberg_competitive_1991; @wang_are_2010], we found little evidence for such coordination. It was present only for wood density, where high density conferred better competitive response and also stronger competitive effect (but with wide confidence intervals). For SLA there was no clear coordinations. For maximum height as explained above there was a tendency for short maximum height to lead to high tolerance to competition but to low competitive effect. This interesting because a trade-off between competitive tolerance and maximum height has been proposed as fundamental mechanisms of coexistence of species in size-structured population in the stratification theory of species coexistence[@kohyama_stratification_2009]. Finally the lack of support for coordination between response and effect is important because it means that competitive interaction is not well described as a trait hierarchy relating a focal species to its competitors (measured as $t_c -t_f$ and thus assuming $\alpha_e = \alpha_r$ as in @kunstler_competitive_2012; @kraft_functional_2014; @lasky_trait-mediated_2014). Traits of competitors alone or of focal plants alone may convey more information. If traits are strongly linked to either competitive effect or competitive response, this still means that some trait values will have an advantage in competitive interactions.
......@@ -76,7 +78,6 @@ Given that the effect sizes we report for effects of traits on competitive inter
## Variations between biomes
Overall most results were rather consistent across biomes (Fig 3 main text), but some exceptions deserve comment.
Only for SLA, the sign of the effect size parameters were changing a lot between biomes (Fig. 3 main text). High SLA species tended to be more competition-tolerant (competitive response parameter $\alpha_r$) in temperate forests (confidence interval only marginally intercepted zero) while low SLA species were more competition-tolerant in tropical forests. These different outcomes may trace to the prevalence of deciduous species in temperate forests (see Extended data Table 1), because the link between shade-tolerance and SLA is different for deciduous and evergreen species[@lusk_why_2008]. In tropical forests shade-tolerant species often have long leaf lifespans, associated with low SLA. On the other hand in temperate deciduous forests the length of the growing season is fixed by temperature. Shade tolerant species cannot increase leaf longevity and instead reduce the cost of leaf production (high SLA) to offset the reduced income due to low light availability. The other noticeable difference between biomes was for taiga where the parameter relating wood density to competitive impact was positive, versus negative in the other biomes (Fig 3 main text). We do not have a mechanistic explanation to suggest for this discrepancy, but can observe that taiga has relatively few species many of which are conifers where the range of wood density is narrower than for angiosperms (see Extended data Table 1).
......
......@@ -84,7 +84,7 @@ diameter growth rates, following criteria developed at the BCI site
(ii) that were a palm or a tree fern species, or (iii) that were
measured at different height in two consecutive censuses.
For each individual tree, we estimated the local abundance of competitor species as the sum of basal area for all individuals > 10cm diameter within a specified neighbourhood. For LPPs, we defined the neighbourhood as being a circle with 15m radius. This value was selected based on previous studies showing the maximum radius of interaction to lie in the range 10-20m[@uriarte_neighborhood_2004; @lamanna_functional_2014]. To avoid edge effects, we also excluded trees less than 15m from the edge of a plot. To account for variation of abiotic conditions within the LPPs, we divided plots into regularly spaced 20x20m quadrats.
For each individual tree, we estimated the local abundance of competitor species as the sum of basal area for all individuals > 10cm diameter within a specified neighbourhood. For LPPs, we defined the neighbourhood as being a circle with 15m radius. This value was selected based on previous studies showing the maximum radius of interaction to lie in the range 10-20m[@uriarte_neighborhood_2004; @uriarte_trait_2010]. To avoid edge effects, we also excluded trees less than 15m from the edge of a plot. To account for variation of abiotic conditions within the LPPs, we divided plots into regularly spaced 20x20m quadrats.
For NFI data coordinates of individual trees within plots were generally not available, thus neighbourhoods were defined based on plot size. In the NFI from the United States, four sub-plots of 7.35m located within 20m of one another were measured. We grouped these sub-plots to give a single estimate of the local competitor abundance. Thus, the neighbourhoods used in the competition analysis ranged in size from 10-25 m radius, with most plots 10-15 m radius.
......
# Summary paragraph outline (199 / ideally of about 200 words, but certainly no more than 300 words)
Competition is very important to understand and predict the dynamics
# Summary paragraph outline (204 / ideally of about 200 words, but certainly no more than 300 words)
Competition is key to understand and predict the dynamics
of plant community composition. In terrestrial
vegetation, where plants strongly modify the environment in their
immediate neighbourhood, competition is conspusious but our ability to predict its consequences on plant performances is extremely limited. Predicting competition via phenotypic traits may be
......@@ -22,7 +22,7 @@ of competition. This is an important trade-off to demonstrate at
global scale because it is a classical hypothesis for successional
coexistence of species in forest ecosystems.
# Main text (MAX 1500 words till the end of Main text plus summary paragraph = 1241)
# Main text (MAX 1500 words till the end of Main text plus summary paragraph = 1458)
Individuals interact in a myriad of different ways in ecological
communities. These interactions are crucial to understand and predict species composition and its
......@@ -38,7 +38,7 @@ $N$. Also this species-pair approach does not lead naturally to
generalization across different vegetation types and different
continents. Modeling
competition via phenotypic traits rather than via species might overcome these limitations and allow general
relationships to be established at at biome to global scale. However,
relationships to be established at biome to global scale. However,
available
studies[@uriarte_trait_2010; @kunstler_competitive_2012; @hillerislambers_rethinking_2012; @lasky_trait-mediated_2014; @kraft_plant_2015] are too few and too local to allow broad generalization about how traits influence competition. Notably there is continuing debate about the relative importance of mechanisms whereby particular trait values confer competitive advantage, vs. mechanisms whereby competition is weaker when two species have dissimilar traits[@mayfield_opposing_2010]. This distinction is fundamental because if competition is driven mainly by trait similarity, this will favour coexistence of a wide spread of traits values.
......@@ -66,10 +66,8 @@ $m^2/ha$), accounting for traits of both the focal tree and its
competitors. This analysis allowed effect sizes to be estimated for
each of the four pathways (Fig. \ref{ilustr}c).
Across all biomes the strongest drivers of individual growth was, as
expected, a
positive effect of tree size (stem diameter). A negative effect of
the local abundance of competitors independent of their traits was the second strongest effect indicating that competition was dominant rather than facilitation. Then among trait
Across all biomes the strongest drivers of individual growth was a negative effect of
the local abundance of competitors independent of their traits, indicating that competition was dominant rather than facilitation. Then among trait
influences the most important were processes giving a competitive
advantage for some trait values compared to others. Strongest were
direct influences of traits on the focal plant’s growth in absence of
......@@ -112,7 +110,8 @@ interception[@niinemets_review_2010]. Tall maximum height was
positively related to maximum growth in most biomes, as previously
reported[@wright_functional_2010] (though with wide confidence
interval in all biomes expect temperate rainforest), but
with a lower tolerance to competition than shorter species, in line
with a lower tolerance to competition than shorter species (though with wide confidence
interval in all biomes expect temperate rainforest and taiga), in line
with the idea that sub-canopy trees are more
shade-tolerant[@poorter_architecture_2006]. Coordination between trait values conferring high competitive effect and trait values conferring high competitive tolerance has been widely expected[@goldberg_competitive_1996; @kunstler_competitive_2012]. However, in agreement with previous studies[@goldberg_components_1990; @goldberg_competitive_1991; @wang_are_2010], we found little evidence for such coordination. It was present only for wood density, where high density conferred better competitive response and also stronger competitive effect (but with wide confidence interval, Fig. \ref{res2}). Finally, the underlying mechanisms that may explain the trait similarity effects are unknown for these traits, but could include neighbouring species with similar traits supporting heavier loads of specialised pathogens[@bagchi_pathogens_2014], capturing light less efficiently[@sapijanskas_tropical_2014] or recycling litter less efficiently[@sapijanskas_beyond_2013].
......@@ -134,7 +133,18 @@ between potential maximum growth and performance in condition of high competitio
**Supplementary Information** is available in the online version of the paper.
**Acknowledgements**
We are grateful to people whose long term commitment established and maintained the forest plots and their associated databases, and who granted us access - New Zealand, Japan, MAGRAMA for the Spanish Forest Inventory, France, Switzerland, Sweden, US, Canada (for the following provinces: Quebec, Ontario, Saskatchewan, Manitoba, New Brunswick, and Newfoundland and Labrador), CTFS plots (BCI, Fushan and Luquillo) and Cirad permanent plots (Paracou, M’Baïki). GK was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Program (Demo-traits project, no. 299340). The working group that initiated this synthesis was supported by Macquarie U and by Australian Research Council through a fellowship to MW.
We are grateful to people whose long term commitment established and
maintained the forest plots and their associated databases, and who
granted us access - forest inventories of New Zealand, Spain
(MAGRAMA), France, Switzerland, Sweden, US, Canada (for the following
provinces: Quebec, Ontario, Saskatchewan, Manitoba, New Brunswick, and
Newfoundland and Labrador), CTFS plots (BCI, Fushan and Luquillo),
Cirad permanent plots (Paracou, M’Baïki) and Japan permament plots. GK
was supported by a Marie Curie International Outgoing Fellowship
within the 7th European Community Framework Program (Demo-traits
project, no. 299340). The working group that initiated this synthesis
was supported by Macquarie U and by Australian Research Council
through a fellowship to MW.
......@@ -153,12 +163,12 @@ GK conceived the study and lead a workshop to develop this analysis with the par
\newpage
![**Trait effects and trait-independent effects on maximum growth and competition.** Standardized regression coefficients for growth modeled, fitted separately for each trait (points: mean estimates and lines: 95% confidence intervals). The parameter estimate represents: maximum growth variation with trait of the focal tree $m_1$, the competition effect independent of traits $\alpha_0$, the effect of the focal tree’s traits on its competitive response $\alpha_r$ (positive $\alpha_r$ indicates that high trait values of the focal tree limits its growth reduction via competition), the effect of competitor traits on their competitive effect $\alpha_e$ (negative $\alpha_e$ indicates that high trait values of the competitors increase their competitive reduction of the growth of the focal tree), and the effect on competition of trait similarity between the focal tree and its competitors $\alpha_s$ (negative $\alpha_s$ indicates that high trait similarity worsen the growth reduction). \label{res1}](../../figs/figres1.pdf)
![**Trait effects and trait-independent effects on maximum growth and competition.** Standardized regression coefficients for growth modeled, fitted separately for each trait (points: mean estimates and lines: 95% confidence intervals). The parameter estimate represents: maximum growth variation with trait of the focal tree $m_1$, the competition effect independent of traits $\alpha_0$, the effect of the focal tree’s traits on its competitive response $\alpha_r$ (positive $\alpha_r$ indicates that high trait values of the focal tree limits its growth reduction via competition), the effect of competitor traits on their competitive effect $\alpha_e$ (negative $\alpha_e$ indicates that high trait values of the competitors increase their competitive reduction of the growth of the focal tree), and the effect on competition of trait similarity between the focal tree and its competitors $\alpha_s$ (positive $\alpha_s$ indicates that high trait similarity worsen the growth reduction). \label{res1}](../../figs/figres1.pdf)
\newpage
![**Variation between biomes of trait effects on maximum growth and competition.** Standardized regression coefficients of the growth models fitted separately for each trait as in Fig. 2, but with separate estimates for each biome (see Fig 1a. for definitions of the biomes). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few data were available. \label{res2}](../../figs/figres2.pdf)
![**Variation between biomes of trait effects on maximum growth and competition.** Standardized regression coefficients of the growth models fitted separately for each trait as in Fig. 2, but with separate estimates for each biome (see Fig 1a. for definitions of the biomes). Tropical rainforest and tropical seasonal forest were merged together as tropical forest, tundra was merged with taiga, and desert was not included as too few data were available. \label{res2}](../../figs/figres2b.pdf)
\newpage
......
......@@ -1265,3 +1265,20 @@ Information about variation in growth between individuals and between census per
langid = {english},
file = {Kraft et al_2014_Functional trait differences and the outcome of community assembly.pdf:/Users/gkunstler/Dropbox/biblio/pdfNEW/Kraft et al_2014_Functional trait differences and the outcome of community assembly2.pdf:application/pdf}
}
@article{callaway_competition_1997,
title = {Competition and {Facilitation}: {A} {Synthetic} {Approach} to {Interactions} in {Plant} {Communities}},
volume = {78},
issn = {00129658},
shorttitle = {Competition and {Facilitation}},
url = {http://www.jstor.org/stable/2265936?origin=crossref},
doi = {10.2307/2265936},
number = {7},
urldate = {2015-06-09},
journal = {Ecology},
author = {Callaway, Ragan M. and Walker, Lawrence R.},
month = oct,
year = {1997},
pages = {1958},
file = {2265936.pdf:/home/georges/Dropbox/biblio/zoterofiles/storage/JI6KIMJ6/2265936.pdf:application/pdf}
}
......@@ -109,7 +109,7 @@ list.all.results.set <-
readRDS('output/list.lmer.out.all.NA.simple.set.rds')
list.all.results.set.TP <-
readRDS('output/list.lmer.out.all.NA.simple.set.TF.rds')
readRDS('output/list.lmer.out.all.NA.simple.set.TP.rds')
list.all.results.set.0 <-
readRDS('output/list.lmer.out.all.NA.simple.set.0.rds')
......@@ -125,9 +125,112 @@ vec.rel.grad.set.TP <- sapply(list.all.results.set.TP,
function(list.t) { max(abs(list.t[['relgrad']]))})
vec.rel.grad.set.TP < 0.001
fun.get.conv(list.all.results.set.0)
vec.rel.grad.set.0 <- sapply(list.all.results.set.0,
function(list.t) { max(abs(list.t[['relgrad']]))})
vec.rel.grad.set.0 < 0.001
# Figmean
pdf('figs/figres1.pdf', height = 7, width = 14)
plot.param(list.all.results.set , data.type = "simple",
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.species',
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn", "Tf"),
param.print = 1:5,
param.names = c(expression(paste('Trait sim ',
(alpha[s] %*% abs(t[c] - t[f])))),
expression(paste('Response ',
(alpha[r] %*% t[f]))),
expression(paste('Effect ',
(alpha[e] %*% t[c]))),
expression(paste('Trait indep ',
(alpha[0]))),
expression(paste("Direct trait ",
(m[1] %*% t[f]))),
expression(paste("Size ",
(gamma %*% log(D))) )) ,
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.3, 0.3))
dev.off()
pdf('figs/figres12.pdf', height = 14, width = 16)
plot.param.mean.and.biomes.fixed(list.all.results.set , data.type = "simple",
models = c('lmer.LOGLIN.ER.AD.Tf.r.set.species',
'lmer.LOGLIN.ER.AD.Tf.r.set.fixed.biomes.species'),
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn", "Tf"),
param.print = 1:5,
param.names = c(expression(paste('Trait sim ',
(alpha[s] %*% abs(t[c] - t[f])))),
expression(paste('Response ',
(alpha[r] %*% t[f]))),
expression(paste('Effect ',
(alpha[e] %*% t[c]))),
expression(paste('Trait indep ',
(alpha[0]))),
expression(paste("Direct trait ",
(m[1] %*% t[f]))),
expression(paste("Size ",
(gamma %*% log(D))) )) ,
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.28, 0.27),
add.param.descrip.TF =2)
dev.off()
pdf('figs/figres2b.pdf', height = 7, width = 16)
plot.param.biomes.fixed(list.all.results.set , data.type = "simple",
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.fixed.biomes.species',
traits = c('Wood.density' , 'SLA', 'Max.height'),
param.vec = c("sumTnTfBn.abs", "sumTfBn","sumTnBn",
"sumBn", "Tf"),
param.print = 1:5,
param.names = c(expression(paste('Trait sim ',
(alpha[s] %*% abs(t[c] - t[f])))),
expression(paste('Response ',
(alpha[r] %*% t[f]))),
expression(paste('Effect ',
(alpha[e] %*% t[c]))),
expression(paste('Trait indep ',
(alpha[0]))),
expression(paste("Direct trait ",
(m[1] %*% t[f]))),
expression(paste("Size ",
(gamma %*% log(D))) )) ,
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.3, 0.3),
add.param.descrip.TF =2)
dev.off()
pdf('figs/figres2.pdf', height = 7, width = 16)
plot.param.biomes.fixed(list.all.results.set, data.type = 'simple',
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.fixed.biomes.species',
param.vec = c("sumTnTfBn.abs", "sumTfBn", "sumTnBn",
"Tf"),
param.names = c(expression(paste('Trait sim ',
(alpha[s] %*% abs(t[c] - t[f])))),
expression(paste('Response ',
(alpha[r] %*% t[f]))),
expression(paste('Effect ',
(alpha[e] %*% t[c]))),
expression(paste("Direct trait ",
(m[1] %*% t[f])))) ,
param.print = 1:4,
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
xlim = c(-0.3, 0.3))
dev.off()
plot.param(list.all.results.set.TP , data.type = "simple",
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species',
traits = c('Wood.density' , 'SLA', 'Max.height'),
......@@ -150,9 +253,7 @@ plot.param(list.all.results.set.TP , data.type = "simple",
pch.vec = fun.col.pch.biomes()$pch.vec,
names.bio = names.biomes ,
xlim = c(-0.45, 0.45))
dev.off()
pdf('figs/figres2.pdf', height = 7, width = 16)
plot.param.biomes.fixed(list.all.results.set.TP, data.type = 'simple',
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.fixed.biomes.species',
param.vec = c("sumTnTfBn.abs", "sumTfBn", "sumTnBn",
......@@ -169,7 +270,6 @@ plot.param.biomes.fixed(list.all.results.set.TP, data.type = 'simple',
col.vec = fun.col.pch.biomes()$col.vec,
pch.vec = fun.col.pch.biomes()$pch.vec,
xlim = c(-0.27, 0.298))
dev.off()
......@@ -184,6 +284,13 @@ plot.growth.ba(traits = c('Wood.density', 'SLA', 'Max.height'),
labels.leg = c('High trait value', 'Low trait value'))
dev.off()
plot.growth.ba(traits = c('Wood.density', 'SLA', 'Max.height'),
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.species',
type = 'Tabs', data.type = 'simple',
dir.root = '.',
list.res = list.all.results.set,
labels.leg = c('High trait value', 'Low trait value'))
mat.R2c <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.R2c, list.res = list.all.results.set,
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.species'))
......@@ -192,6 +299,15 @@ mat.R2m <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
model = 'lmer.LOGLIN.ER.AD.Tf.r.set.species'))
mat.R2c.TP <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.R2c, list.res = list.all.results.set.TP,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species'))
mat.R2m.TP <- do.call('cbind', lapply(c('Wood.density', 'SLA', 'Max.height'),
extract.R2m, list.res = list.all.results.set.TP,
model = 'lmer.LOGLIN.ER.AD.Tf.MAT.MAP.r.set.species'))
##### PLOTS FOR SLIDES
pdf('figs/fig_res_fix_slide1.pdf', height = 7, width = 14)
plot.param(list.all.results.set , data.type = "simple",
......
##########################
## EXPLORE POTENTIAL BIAS IN ESTIM
slope <- 0.3
x <- rnorm(100)
x <- rep(x, each =20)
plot.id <- rep(1:100, each = 20)
y <- 2 - x*0.3 +rnorm(100*20,sd = 0.5)
z <- 2+slope*y+0.8*x + rnorm(100*20, sd = 0.4)
par(mfrow = c(2,2))
plot(x, y)
plot(x, z)
plot(y, z)
df <- data.frame(x = x, y = y, z = z, plot.id = factor(plot.id))
library(lme4)
library(lmerTest)
m0 <- lm(z ~ y, data = df)
ci0 <- confint(m0)
fix0 <- coefficients(m0)
std0 <- sqrt(diag(vcov(m0)))
ycistd0 <- c(fix0['y'] - 1.96*std0['y'],
fix0['y'] + 1.96*std0['y'])
m1 <- lmer(z ~ y+(1|plot.id), data = df)
ci1 <- confint(m1)
fix1 <- fixef(m1)
std1 <- sqrt(diag(vcov(m1)))
ycistd1 <- c(fix1['y'] - 1.96*std1[2],
fix1['y'] + 1.96*std1[2])
m2 <- lmer(z ~ y+x+(1|plot.id), data = df)
ci2 <- confint(m2)
fix2 <- fixef(m2)
std2 <- sqrt(diag(vcov(m2)))
ycistd2 <- c(fix2['y'] - 1.96*std2[2],
fix2['y'] + 1.96*std2[2])
df <- data.frame(
rbind(c(1, 0,fix0['y'], ci0['y', ]),
c(1, 1,fix1['y'], ci1['y', ]),
c(1, 2,fix2['y'], ci2['y', ]),
c(2, 0,fix0['y'], ycistd0[]),
c(2, 1,fix1['y'], ycistd1[]),
c(2, 2,fix2['y'], ycistd2[])))
names(df) <- c('ci.type', 'method', 'mean', 'cil', 'cih')
df$ci.type <- factor(df$ci.type)
df$method <- factor(df$method)
library(ggplot2)
ggplot() +
geom_errorbar(data=df, mapping=aes(x=method, ymin=cil, ymax=cih, colour = ci.type),
width=0.2, size=1, position=position_dodge(width=0.8)) +
geom_point(data=df, mapping=aes(x=method, y=mean, colour = ci.type), size=4,
shape=21, position=position_dodge(width=0.8)) +theme_bw() +
geom_abline(intercept = slope, slope = 0, col ='red')
......@@ -37,19 +37,19 @@ saveRDS(data.all, 'output/processed/data.all.global.t.rds')
library(dplyr)
data.quant.traits <- summarise(data.all,
ql.SLA = quantile(SLA.focal, probs = 0.025, na.rm = TRUE),
qh.SLA = quantile(SLA.focal, probs = 0.975, na.rm = TRUE),
ql.Leaf.N = quantile(Leaf.N.focal, probs = 0.025, na.rm = TRUE),
qh.Leaf.N = quantile(Leaf.N.focal, probs = 0.975, na.rm = TRUE),
ql.Wood.density = quantile(Wood.density.focal, probs = 0.025, na.rm = TRUE),
qh.Wood.density = quantile(Wood.density.focal, probs = 0.975, na.rm = TRUE),
ql.Max.height = quantile(Max.height.focal, probs = 0.025, na.rm = TRUE),
qh.Max.height = quantile(Max.height.focal, probs = 0.975, na.rm = TRUE),
ql.Seed.mass = quantile(Seed.mass.focal, probs = 0.025, na.rm = TRUE),
qh.Seed.mass = quantile(Seed.mass.focal, probs = 0.975, na.rm = TRUE),
ql.BATOT = quantile(BATOT, probs = 0.025, na.rm = TRUE),
qh.BATOT = quantile(BATOT, probs = 0.975, na.rm = TRUE),
qm.D = quantile(D, probs = 0.5, na.rm = TRUE)
ql.SLA = quantile(SLA.focal, probs = 0.05, na.rm = TRUE),
qh.SLA = quantile(SLA.focal, probs = 0.95, na.rm = TRUE),
ql.Leaf.N = quantile(Leaf.N.focal, probs = 0.05, na.rm = TRUE),
qh.Leaf.N = quantile(Leaf.N.focal, probs = 0.95, na.rm = TRUE),
ql.Wood.density = quantile(Wood.density.focal, probs = 0.05, na.rm = TRUE),
qh.Wood.density = quantile(Wood.density.focal, probs = 0.95, na.rm = TRUE),
ql.Max.height = quantile(Max.height.focal, probs = 0.05, na.rm = TRUE),
qh.Max.height = quantile(Max.height.focal, probs = 0.95, na.rm = TRUE),
ql.Seed.mass = quantile(Seed.mass.focal, probs = 0.05, na.rm = TRUE),
qh.Seed.mass = quantile(Seed.mass.focal, probs = 0.95, na.rm = TRUE),
ql.BATOT = quantile(BATOT, probs = 0.05, na.rm = TRUE),
qh.BATOT = quantile(BATOT, probs = 0.95, na.rm = TRUE),
qm.D = quantile(D, probs = 0.5, na.rm = TRUE)
)
## in global
saveRDS(data.quant.traits, 'output/processed/data.quant.traits.global.rds')
......@@ -78,19 +78,19 @@ rm( data.all.I, data.all.B)
gc()
saveRDS(data.all, 'output/processed/data.all.no.log.t.rds')
data.quant.traits <- summarise(data.all,
ql.SLA = quantile(SLA.focal, probs = 0.025, na.rm = TRUE),
qh.SLA = quantile(SLA.focal, probs = 0.975, na.rm = TRUE),
ql.Leaf.N = quantile(Leaf.N.focal, probs = 0.025, na.rm = TRUE),
qh.Leaf.N = quantile(Leaf.N.focal, probs = 0.975, na.rm = TRUE),
ql.Wood.density = quantile(Wood.density.focal, probs = 0.025, na.rm = TRUE),
qh.Wood.density = quantile(Wood.density.focal, probs = 0.975, na.rm = TRUE),
ql.Max.height = quantile(Max.height.focal, probs = 0.025, na.rm = TRUE),
qh.Max.height = quantile(Max.height.focal, probs = 0.975, na.rm = TRUE),
ql.Seed.mass = quantile(Seed.mass.focal, probs = 0.025, na.rm = TRUE),
qh.Seed.mass = quantile(Seed.mass.focal, probs = 0.975, na.rm = TRUE),
ql.BATOT = quantile(BATOT, probs = 0.025, na.rm = TRUE),
qh.BATOT = quantile(BATOT, probs = 0.975, na.rm = TRUE),
qm.D = quantile(D, probs = 0.5, na.rm = TRUE)
ql.SLA = quantile(SLA.focal, probs = 0.05, na.rm = TRUE),
qh.SLA = quantile(SLA.focal, probs = 0.95, na.rm = TRUE),
ql.Leaf.N = quantile(Leaf.N.focal, probs = 0.05, na.rm = TRUE),
qh.Leaf.N = quantile(Leaf.N.focal, probs = 0.95, na.rm = TRUE),
ql.Wood.density = quantile(Wood.density.focal, probs = 0.05, na.rm = TRUE),
qh.Wood.density = quantile(Wood.density.focal, probs = 0.95, na.rm = TRUE),
ql.Max.height = quantile(Max.height.focal, probs = 0.05, na.rm = TRUE),
qh.Max.height = quantile(Max.height.focal, probs = 0.95, na.rm = TRUE),
ql.Seed.mass = quantile(Seed.mass.focal, probs = 0.05, na.rm = TRUE),
qh.Seed.mass = quantile(Seed.mass.focal, probs = 0.95, na.rm = TRUE),
ql.BATOT = quantile(BATOT, probs = 0.05, na.rm = TRUE),
qh.BATOT = quantile(BATOT, probs = 0.95, na.rm = TRUE),
qm.D = quantile(D, probs = 0.5, na.rm = TRUE)
)
## in log10
saveRDS(data.quant.traits, 'output/processed/data.quant.traits.no.log.rds')
......
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