Index
Mario
07/12/2021
All proposed figures and Stats are at THE BOTTOM of the html
“A controlled microclimate contrast of three native and one highly invasive plant species in Southern California”
A controlled microclimate contrast of three native and one highly invasive plant species in Southern California Mario Zuliani1*, Laura Brussa, Jessica Cunsolo, Angela Zuliani, and Christopher J. Lortie1. 1Department of Biological Science, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada Question: How do increasing ambient temperature in arid ecosystems influence both the successive germination and total biomass of 3 natives and 1 exotic annual plant species.
Location: Greenhouse simulation conditions within the Carrizo Plain National Monument, California, USA (35.117985, -119.608762)
Methods: The effects of temperature were tested on 3 native, Salvia columbariae, Layia platyglossa, Phacelia tanacetifolia and 1 exotic plant species, Bromus rubens independently over a 6-week growing period in a temperature controlled greenhouse in 2021. 210 individual replicates were conducted across an increasing temperature gradient meant to simulate the arid ecosystems of Southern California. Hourly temperature was recorded through ambient temperature pendants. Annual biomass was then recorded at the conclusion of each 6-week trial.
Results: The evaluation of temperature on the overall successive germination of native and exotic annual plant species showed an overall negative affect. Increasing temperatures negatively influenced the total germination of one native – Layia platyglossa - and one exotic – Bromus rubens - annual species. Regarding individual biomass, increases in ambient temperature negatively influenced both the overall size and biomass of 2 native – Layia platyglossa & Salvia columbaria – and one exotic – Bromus rubens - annual species.
Conclusion: These findings suggest that increases in ambient temperature due to global warming can have negative impacts on both native and exotic plant establishment and succession. Analyzing the performance and establishment of these annual species is essential to understanding local plant community composition while determining the responses both native and exotic annuals have to increasing abiotic stressors within arid/semi-arid ecosystems. Specifically understanding how increasing ambient temperature influences exotic annual establishment can be vital knowledge for managing the spread and establishment of these species.
Key Words: Intraspecific Association, Desert, Grassland, Greenhouse, Temperature, Bromus rubens, Exotic Species, Restoration
###Packages for future work
library(rmarkdown)
library(ggmap)
library(ggplot2)
library(tidyverse)
library(dplyr)
library(MASS)
library(ggpubr)
library(emmeans)##Prep Temp Data
#Layia <- read.csv("All Temp Data.csv")
#Brome <- read.csv("Brome Temp Sheet.csv")
#Salvia <- read.csv("Salvia Temp Sheet.csv")
#Phacelia <- read.csv("Phacelia Temp Sheet.csv")
###Merge all species temp data
#Temp <- merge(Layia, Brome, all = TRUE)
#Temp <- merge(Temp, Salvia, all = TRUE)
#Temp <- merge(Temp, Phacelia, all = TRUE)
###Set up SD and SE
#se_temp <- sd(Temp$temperature)/sqrt(length(Temp$temperature))
#Temp$se <- se_temp
###Get mean and max temp
#Temp <- Temp %>%
#group_by(as.character(temp), species, pendant_ID, se) %>%
#summarise(mean_temp = mean(temperature), max_temp = max(temperature))
#names(Temp)[1] <- "temp"
###Output file
#write.csv(Temp, "Temp.csv")###Clean up Temperature and determine the mean and max for each pendant.
#Temp2 <- read.csv("Temp_2.csv")#Prep Germination Data and Combine With Temp Data
#Germ <- read.csv("Final Germination.csv")
#final <- merge(Germ, Temp2, all = TRUE)
#write.csv(final, "final.csv")
final <- read.csv("final.csv")###Data Viz
###Temperature vs Number of Germinated Individuals in 6 weeks by species
TempFactor <- ggplot(final, aes(temp, germination),show.legend=FALSE) +
geom_boxplot() +
facet_wrap(~species)+
scale_color_brewer(palette = "Set1") + theme_classic() + labs(tag = "")+
theme(axis.title.x = element_blank()) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Number of Germinated Seeds")
TempFactor
###Plotting the lm
plot(lm(germination ~ mean_temp, data = final), which = 1)
germination_poly <- lm(germination ~ mean_temp + I(mean_temp^2), data = final)
library(car)
library(broom)
Anova(germination_poly)## Anova Table (Type II tests)
##
## Response: germination
## Sum Sq Df F value Pr(>F)
## mean_temp 946 1 7.6086 0.005936 **
## I(mean_temp^2) 916 1 7.3724 0.006760 **
## Residuals 104027 837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###General Figure showing Temp vs total germination in 6 weeks (Not useable)
Temp <- ggplot(final, aes(mean_temp, germination),show.legend=FALSE) +
geom_point() +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Number of Germinated Seeds")
Temp
###Figure showing Temp vs germination by species (Not facetted)
Tempspecies <- ggplot(final, aes(mean_temp, germination, color = species),show.legend=FALSE) +
geom_point() +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Number of Germinated Seeds")
Tempspecies
###This is Temperature vs germination, facet by species. I am not the biggest fan of this figure though
TempspeciesFacet <- ggplot(final, aes(mean_temp, germination, color = species),show.legend=FALSE) +
geom_point() +
facet_wrap(~temp)+
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Number of Germinated Seeds")
TempspeciesFacet <- TempspeciesFacet + theme(legend.title = element_text(size = 3),
legend.text = element_text(size = 3))
TempspeciesFacet
ggplot(shelter.shrub.open, aes((day), temp, color=microsite)) + geom_smooth()+ xlab(“Day”) + ylab (“Temperature (°F)”)+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom=“point”, size=2, aes(shape = microsite))+ labs(color=“Microsite”, shape= “Microsite”)
###Plot for Temperature and germination in 6 weeks by species
TempspeciesFacet <- ggplot(final, aes(mean_temp, germination),show.legend=FALSE) +
geom_smooth(method = lm) +
scale_color_brewer(palette = "Set1") + theme_classic() +
facet_wrap(~species)+
labs(x = "Mean Temperature (°C)", y = "Mean Number of Germinations") +stat_summary(fun.y=mean, geom="point", size=1.5)
TempspeciesFacet
###Figure shows temperature v Germination in 6 weeks, facet by species and filled via temperature
TempspeciesFacetColor <- ggplot(final, aes(mean_temp, germination, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species)+
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Number of Germinated Seeds")
TempspeciesFacetColor
###Statistics For Germination of Each species at varying temperatures
m1 <- glm(germination ~ temp*species+mean_temp, family = "quasipoisson", data = final)
anova(m1, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: germination
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 10186.2
## temp 2 275.3 837 9910.9 < 2.2e-16 ***
## species 3 7283.5 834 2627.4 < 2.2e-16 ***
## mean_temp 1 10.1 833 2617.3 0.06068 .
## temp:species 6 208.3 827 2409.0 1.321e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###Was temp manipulated significantly in the experiment?
e1 <- emmeans(m1, pairwise~temp)
e1## $emmeans
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.62 0.0824 Inf 1.46 1.78
## Low 1.97 0.0614 Inf 1.85 2.09
## Medium 1.83 0.0590 Inf 1.71 1.94
##
## Results are averaged over the levels of: species
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## High - Low -0.351 0.1164 Inf -3.015 0.0072
## High - Medium -0.206 0.1113 Inf -1.851 0.1533
## Low - Medium 0.145 0.0771 Inf 1.879 0.1449
##
## Results are averaged over the levels of: species
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Germination by temp and species
e2 <- emmeans(m1, pairwise~temp|species) ###Low Temp brome germinates the same amount as Layia at all temps
e2## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.2080 0.1355 Inf 0.943 1.474
## Low 2.1947 0.0691 Inf 2.059 2.330
## Medium 1.5953 0.0908 Inf 1.417 1.773
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.9438 0.0836 Inf 1.780 2.108
## Low 2.3695 0.0736 Inf 2.225 2.514
## Medium 2.2819 0.0788 Inf 2.127 2.436
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.3623 0.0817 Inf 3.202 3.522
## Low 3.2934 0.0449 Inf 3.205 3.381
## Medium 3.3526 0.0379 Inf 3.278 3.427
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.0246 0.2208 Inf -0.457 0.408
## Low 0.0355 0.1933 Inf -0.343 0.414
## Medium 0.0837 0.1892 Inf -0.287 0.455
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -0.98667 0.1604 Inf -6.152 <.0001
## High - Medium -0.38724 0.1650 Inf -2.347 0.0496
## Low - Medium 0.59943 0.1131 Inf 5.298 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.42574 0.1214 Inf -3.508 0.0013
## High - Medium -0.33812 0.1257 Inf -2.690 0.0195
## Low - Medium 0.08762 0.0841 Inf 1.042 0.5501
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low 0.06895 0.1100 Inf 0.627 0.8054
## High - Medium 0.00973 0.0925 Inf 0.105 0.9939
## Low - Medium -0.05922 0.0574 Inf -1.031 0.5572
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.06010 0.2976 Inf -0.202 0.9778
## High - Medium -0.10830 0.2945 Inf -0.368 0.9282
## Low - Medium -0.04819 0.2674 Inf -0.180 0.9823
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Difference in germination by species and temp
e3 <- emmeans(m1, pairwise~species|temp) ###Low Temp brome germinates the same amount as Layia at all temps
e3## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.2080 0.1355 Inf 0.943 1.474
## Layia platyglossa 1.9438 0.0836 Inf 1.780 2.108
## Phacelia tanacetifolia 3.3623 0.0817 Inf 3.202 3.522
## Salvia columbariae -0.0246 0.2208 Inf -0.457 0.408
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.1947 0.0691 Inf 2.059 2.330
## Layia platyglossa 2.3695 0.0736 Inf 2.225 2.514
## Phacelia tanacetifolia 3.2934 0.0449 Inf 3.205 3.381
## Salvia columbariae 0.0355 0.1933 Inf -0.343 0.414
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.5953 0.0908 Inf 1.417 1.773
## Layia platyglossa 2.2819 0.0788 Inf 2.127 2.436
## Phacelia tanacetifolia 3.3526 0.0379 Inf 3.278 3.427
## Salvia columbariae 0.0837 0.1892 Inf -0.287 0.455
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.736 0.1490 Inf -4.937
## Bromus rubens - Phacelia tanacetifolia -2.154 0.1281 Inf -16.811
## Bromus rubens - Salvia columbariae 1.233 0.2494 Inf 4.942
## Layia platyglossa - Phacelia tanacetifolia -1.419 0.1003 Inf -14.138
## Layia platyglossa - Salvia columbariae 1.968 0.2317 Inf 8.494
## Phacelia tanacetifolia - Salvia columbariae 3.387 0.2232 Inf 15.174
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.175 0.0910 Inf -1.921
## Bromus rubens - Phacelia tanacetifolia -1.099 0.0759 Inf -14.481
## Bromus rubens - Salvia columbariae 2.159 0.2021 Inf 10.686
## Layia platyglossa - Phacelia tanacetifolia -0.924 0.0724 Inf -12.753
## Layia platyglossa - Salvia columbariae 2.334 0.2000 Inf 11.668
## Phacelia tanacetifolia - Salvia columbariae 3.258 0.1946 Inf 16.738
## p.value
## 0.2191
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.687 0.1181 Inf -5.815
## Bromus rubens - Phacelia tanacetifolia -1.757 0.0982 Inf -17.889
## Bromus rubens - Salvia columbariae 1.512 0.2092 Inf 7.224
## Layia platyglossa - Phacelia tanacetifolia -1.071 0.0856 Inf -12.511
## Layia platyglossa - Salvia columbariae 2.198 0.1982 Inf 11.089
## Phacelia tanacetifolia - Salvia columbariae 3.269 0.1926 Inf 16.977
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates###Mass by Temperature
Mass <- ggplot(final, aes(mean_temp, mass),show.legend=FALSE) +
geom_point() +
scale_color_brewer(palette = "Set1") + theme_classic() +
theme() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Mass")
Mass
MassSpecies <- ggplot(final, aes(mean_temp, mass, color = species),show.legend=FALSE) +
geom_smooth(method = lm, se = TRUE) +
scale_color_brewer(palette = "Set1") + theme_classic() +
stat_summary(fun.y=mean, geom="point", size=2)+
labs(x = "Mean Temperature (°C)", y = "Mean Mass (g)")
MassSpecies
MassSpeciesFacet <- ggplot(final, aes(mean_temp, mass),show.legend=FALSE) +
geom_point() +
facet_wrap(~species)+
scale_color_brewer(palette = "Set1") + theme_classic() +
theme(axis.title.x = element_blank()) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Mass (g)")
MassSpeciesFacet
###Temperature v biomass, facet by species and fill by Temp
MassSpeciesFacetColor <- ggplot(final, aes(mean_temp, mass, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species)+
scale_color_brewer(palette = "Set1") + theme_classic() +
theme(axis.title.x = element_blank()) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Biomass (g)")
MassSpeciesFacetColor
#Use guasian
###Biomass by temp and species
m2 <- glm(mass ~ germination*temp*species+mean_temp, family = "quasipoisson", data = final)
anova(m2, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 309.227
## germination 1 175.212 838 134.016 < 2.2e-16 ***
## temp 2 9.921 836 124.094 < 2.2e-16 ***
## species 3 16.556 833 107.538 < 2.2e-16 ***
## mean_temp 1 0.499 832 107.039 0.03672 *
## germination:temp 2 3.034 830 104.005 1.731e-06 ***
## germination:species 3 13.994 827 90.011 < 2.2e-16 ***
## temp:species 6 6.269 821 83.742 5.050e-10 ***
## germination:temp:species 6 0.583 815 83.159 0.53167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e4 <- emmeans(m2, pairwise~temp|species)
e4## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High -2.604 0.294 Inf -3.181 -2.027
## Low -2.170 0.125 Inf -2.415 -1.926
## Medium -1.913 0.158 Inf -2.223 -1.603
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High -2.063 0.135 Inf -2.328 -1.799
## Low -3.619 0.267 Inf -4.141 -3.096
## Medium -2.510 0.161 Inf -2.825 -2.195
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.530 0.132 Inf -0.789 -0.270
## Low -0.391 0.285 Inf -0.949 0.167
## Medium -0.279 0.229 Inf -0.727 0.170
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.378 0.660 Inf 0.085 2.672
## Low -1.155 0.771 Inf -2.666 0.356
## Medium 1.643 0.954 Inf -0.226 3.512
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -0.434 0.325 Inf -1.336 0.3753
## High - Medium -0.691 0.336 Inf -2.058 0.0988
## Low - Medium -0.257 0.200 Inf -1.284 0.4043
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low 1.555 0.305 Inf 5.103 <.0001
## High - Medium 0.447 0.219 Inf 2.040 0.1029
## Low - Medium -1.108 0.299 Inf -3.702 0.0006
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.138 0.319 Inf -0.433 0.9016
## High - Medium -0.251 0.266 Inf -0.944 0.6126
## Low - Medium -0.113 0.365 Inf -0.309 0.9488
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low 2.534 1.020 Inf 2.484 0.0347
## High - Medium -0.265 1.165 Inf -0.227 0.9719
## Low - Medium -2.798 1.224 Inf -2.287 0.0576
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Difference in biomass by species and temp
e5 <- emmeans(m2, pairwise~species|temp)
e5## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.604 0.294 Inf -3.181 -2.027
## Layia platyglossa -2.063 0.135 Inf -2.328 -1.799
## Phacelia tanacetifolia -0.530 0.132 Inf -0.789 -0.270
## Salvia columbariae 1.378 0.660 Inf 0.085 2.672
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.170 0.125 Inf -2.415 -1.926
## Layia platyglossa -3.619 0.267 Inf -4.141 -3.096
## Phacelia tanacetifolia -0.391 0.285 Inf -0.949 0.167
## Salvia columbariae -1.155 0.771 Inf -2.666 0.356
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -1.913 0.158 Inf -2.223 -1.603
## Layia platyglossa -2.510 0.161 Inf -2.825 -2.195
## Phacelia tanacetifolia -0.279 0.229 Inf -0.727 0.170
## Salvia columbariae 1.643 0.954 Inf -0.226 3.512
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.540 0.319 Inf -1.697 0.3254
## Bromus rubens - Phacelia tanacetifolia -2.074 0.308 Inf -6.732 <.0001
## Bromus rubens - Salvia columbariae -3.982 0.715 Inf -5.568 <.0001
## Layia platyglossa - Phacelia tanacetifolia -1.534 0.176 Inf -8.730 <.0001
## Layia platyglossa - Salvia columbariae -3.442 0.669 Inf -5.142 <.0001
## Phacelia tanacetifolia - Salvia columbariae -1.908 0.662 Inf -2.884 0.0205
##
## temp = Low:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa 1.448 0.289 Inf 5.008 <.0001
## Bromus rubens - Phacelia tanacetifolia -1.779 0.309 Inf -5.756 <.0001
## Bromus rubens - Salvia columbariae -1.015 0.779 Inf -1.303 0.5611
## Layia platyglossa - Phacelia tanacetifolia -3.227 0.387 Inf -8.334 <.0001
## Layia platyglossa - Salvia columbariae -2.463 0.812 Inf -3.034 0.0129
## Phacelia tanacetifolia - Salvia columbariae 0.764 0.820 Inf 0.931 0.7881
##
## temp = Medium:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa 0.597 0.223 Inf 2.676 0.0374
## Bromus rubens - Phacelia tanacetifolia -1.634 0.278 Inf -5.874 <.0001
## Bromus rubens - Salvia columbariae -3.556 0.966 Inf -3.680 0.0013
## Layia platyglossa - Phacelia tanacetifolia -2.232 0.279 Inf -8.008 <.0001
## Layia platyglossa - Salvia columbariae -4.153 0.963 Inf -4.312 0.0001
## Phacelia tanacetifolia - Salvia columbariae -1.922 0.981 Inf -1.960 0.2034
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates#Natives Vs Invasives Germination
#Not useful
#locality <- ggplot(final, aes(mean_temp, germination, color = temp),show.legend=FALSE) +
# geom_point() +
#facet_wrap(~locality) +
#scale_color_brewer(palette = "Set1") + theme_classic() +
#geom_smooth(method = lm, se = TRUE) +
#labs(x = "Temperature", y = "Number of Germinated Seeds")
#locality#Not useful
#locality2 <- ggplot(final, aes(mean_temp, germination),show.legend=FALSE) +
# geom_point() +
#facet_wrap(~locality) +
#scale_color_brewer(palette = "Set1") + theme_classic() +
#geom_smooth(method = lm, se = TRUE) +
#labs(x = "Temperature", y = "Number of Germinated Seeds")
#locality2#Native vs Invasive Biomass
###Do not use
#locality3 <- ggplot(final, aes(mean_temp, mass, color = temp),show.legend=FALSE) +
#geom_point() +
# facet_wrap(~locality) +
# scale_color_brewer(palette = "Set1") + theme_classic() +
# geom_smooth(method = lm, se = TRUE) +
# labs(x = "Temperature", y = "Bimass (g)")
#locality3###Do not use
#locality4 <- ggplot(final, aes(mean_temp, mass),show.legend=FALSE) +
# geom_point() +
#facet_wrap(~locality) +
#scale_color_brewer(palette = "Set1") + theme_classic() +
#geom_smooth(method = lm, se = TRUE) +
#labs(x = "Temperature", y = "Biomass (g)")
#locality4#Natives vs Invasives Stats Mass
#Not useful
#m3 <- glm(mass ~ germination*temp*locality, family = "quasipoisson", data = final)
#anova(m3, test = "Chisq")
#e6 <- emmeans(m3, pairwise~locality|temp)
#e6#m4 <- glm(germination ~ temp*locality, family = "quasipoisson", data = final)
#anova(m4, test = "Chisq")
#e7 <- emmeans(m4, pairwise~locality|temp)
#e7###November 24th 2022 (After meeting)
###Boxplots Comparing Mean and Max Temp Blocks
ggplot(final, aes(x=factor (temp, level=c("Low", "Medium", "High")), mean_temp)) +
geom_boxplot() + theme_classic() + labs(x = "Temperature Treatment", y = "Mean Temperature")
ggplot(final, aes(x=factor (temp, level=c("Low", "Medium", "High")), max_temp)) +
geom_boxplot() + theme_classic() + labs(x = "Temperature Treatment", y = "Max Temperature")
###Test Biomass by Established Density with Mean and Max Temp
###Set up Biomass by Established Density
final$average_mass <- final$mass/final$establishment
###By mean Temp
ggplot(final, aes(mean_temp, average_mass),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Bimass per Individual")
###Dont like this one
#ggplot(final, aes(mean_temp, average_mass, color = temp),show.legend=FALSE) +
# geom_point() +
#facet_wrap(~species) +
#scale_color_brewer(palette = "Set1") + theme_classic() +
#geom_smooth(method = lm, se = TRUE) +
#labs(x = "Temperature", y = "Bimass per Individual")
###Max Temp
ggplot(final, aes(max_temp, average_mass),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Bimass per Individual")
ggplot(final, aes(max_temp, average_mass, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Bimass per Individual")
###Ran as Gaussian since temp is blocked
m5 <- glm(average_mass ~ temp*species, family = "gaussian", data = final)
anova(m5, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: average_mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 574 2.3397
## temp 2 0.01793 572 2.3218 0.06496 .
## species 3 0.42287 569 1.8989 < 2e-16 ***
## temp:species 6 0.05304 563 1.8459 0.01283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e8 <- emmeans(m5, pairwise~temp|species)
e8## $emmeans
## species = Bromus rubens:
## temp emmean SE df lower.CL upper.CL
## High 0.0207 0.00997 563 0.00108 0.0402
## Low 0.0224 0.00721 563 0.00819 0.0365
## Medium 0.0370 0.00787 563 0.02154 0.0524
##
## species = Layia platyglossa:
## temp emmean SE df lower.CL upper.CL
## High 0.0342 0.00873 563 0.01710 0.0514
## Low 0.0659 0.01063 563 0.04500 0.0868
## Medium 0.0682 0.00772 563 0.05300 0.0833
##
## species = Phacelia tanacetifolia:
## temp emmean SE df lower.CL upper.CL
## High 0.0332 0.00684 563 0.01980 0.0467
## Low 0.0202 0.00684 563 0.00671 0.0336
## Medium 0.0209 0.00684 563 0.00745 0.0343
##
## species = Salvia columbariae:
## temp emmean SE df lower.CL upper.CL
## High 0.1191 0.01194 563 0.09566 0.1426
## Low 0.0864 0.01063 563 0.06548 0.1073
## Medium 0.1025 0.00941 563 0.08401 0.1210
##
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df t.ratio p.value
## High - Low -0.001706 0.01230 563 -0.139 0.9895
## High - Medium -0.016329 0.01270 563 -1.286 0.4036
## Low - Medium -0.014624 0.01067 563 -1.370 0.3573
##
## species = Layia platyglossa:
## contrast estimate SE df t.ratio p.value
## High - Low -0.031635 0.01376 563 -2.299 0.0567
## High - Medium -0.033914 0.01166 563 -2.910 0.0105
## Low - Medium -0.002279 0.01314 563 -0.173 0.9836
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df t.ratio p.value
## High - Low 0.013087 0.00968 563 1.352 0.3670
## High - Medium 0.012349 0.00968 563 1.276 0.4094
## Low - Medium -0.000738 0.00968 563 -0.076 0.9968
##
## species = Salvia columbariae:
## contrast estimate SE df t.ratio p.value
## High - Low 0.032746 0.01599 563 2.048 0.1019
## High - Medium 0.016614 0.01520 563 1.093 0.5190
## Low - Medium -0.016132 0.01420 563 -1.136 0.4923
##
## P value adjustment: tukey method for comparing a family of 3 estimatesm6 <- glm(average_mass ~ mean_temp*species+pendant_ID, family = "quasipoisson", data = final)
anova(m6, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: average_mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 574 32.104
## mean_temp 1 0.6515 573 31.452 0.0003796 ***
## species 3 7.8290 570 23.623 < 2.2e-16 ***
## pendant_ID 1 0.6161 569 23.007 0.0005482 ***
## mean_temp:species 3 0.9895 566 22.017 0.0002507 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e9 <- emmeans(m6, pairwise~species|mean_temp)
e9## $emmeans
## mean_temp = 26.5:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -3.59 0.1134 Inf -3.81 -3.37
## Layia platyglossa -3.23 0.1256 Inf -3.48 -2.99
## Phacelia tanacetifolia -3.74 0.1100 Inf -3.96 -3.53
## Salvia columbariae -2.31 0.0784 Inf -2.46 -2.16
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## mean_temp = 26.5:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.356 0.169 Inf -2.103 0.1520
## Bromus rubens - Phacelia tanacetifolia 0.154 0.158 Inf 0.976 0.7634
## Bromus rubens - Salvia columbariae -1.279 0.138 Inf -9.263 <.0001
## Layia platyglossa - Phacelia tanacetifolia 0.510 0.167 Inf 3.058 0.0120
## Layia platyglossa - Salvia columbariae -0.922 0.145 Inf -6.360 <.0001
## Phacelia tanacetifolia - Salvia columbariae -1.433 0.135 Inf -10.606 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesa <- glm(average_mass ~ max_temp*species+pendant_ID, family = "quasipoisson", data = final)
anova(a, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: average_mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 574 32.104
## max_temp 1 0.3190 573 31.785 0.0137825 *
## species 3 8.1628 570 23.622 < 2.2e-16 ***
## pendant_ID 1 0.6519 569 22.970 0.0004299 ***
## max_temp:species 3 0.8304 566 22.140 0.0012511 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e09 <- emmeans(a, pairwise~species|max_temp)
e09## $emmeans
## max_temp = 28.2:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -3.59 0.1152 Inf -3.82 -3.37
## Layia platyglossa -3.06 0.1042 Inf -3.27 -2.86
## Phacelia tanacetifolia -3.74 0.1058 Inf -3.95 -3.53
## Salvia columbariae -2.30 0.0783 Inf -2.46 -2.15
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## max_temp = 28.2:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.527 0.156 Inf -3.391 0.0039
## Bromus rubens - Phacelia tanacetifolia 0.147 0.157 Inf 0.940 0.7834
## Bromus rubens - Salvia columbariae -1.288 0.140 Inf -9.230 <.0001
## Layia platyglossa - Phacelia tanacetifolia 0.675 0.148 Inf 4.557 <.0001
## Layia platyglossa - Salvia columbariae -0.761 0.128 Inf -5.925 <.0001
## Phacelia tanacetifolia - Salvia columbariae -1.435 0.131 Inf -10.945 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates###Set up percent successful establishment of individuals
final$succession <- final$establishment/final$germination### Treat Temp as Block
### Average Mass
ggplot(final, aes(mean_temp, average_mass, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Bimass per Individual")
model1 <- glm(average_mass ~ temp*species, family = "quasipoisson", data = final)
anova(model1, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: average_mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 574 32.104
## temp 2 0.4039 572 31.700 0.022244 *
## species 3 8.2409 569 23.459 < 2.2e-16 ***
## temp:species 6 1.1449 563 22.314 0.001444 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em1 <- emmeans(model1, pairwise~temp|species)
em1## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.88 0.279 Inf -4.43 -3.33
## Low -3.80 0.194 Inf -4.18 -3.42
## Medium -3.30 0.165 Inf -3.62 -2.97
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.37 0.190 Inf -3.75 -3.00
## Low -2.72 0.167 Inf -3.05 -2.39
## Medium -2.69 0.119 Inf -2.92 -2.45
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.40 0.151 Inf -3.70 -3.11
## Low -3.90 0.194 Inf -4.28 -3.52
## Medium -3.87 0.190 Inf -4.24 -3.49
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -2.13 0.139 Inf -2.40 -1.85
## Low -2.45 0.146 Inf -2.73 -2.16
## Medium -2.28 0.118 Inf -2.51 -2.05
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -0.0793 0.340 Inf -0.233 0.9704
## High - Medium -0.5825 0.324 Inf -1.798 0.1701
## Low - Medium -0.5032 0.254 Inf -1.978 0.1177
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.6543 0.253 Inf -2.590 0.0260
## High - Medium -0.6883 0.224 Inf -3.072 0.0060
## Low - Medium -0.0340 0.205 Inf -0.166 0.9849
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low 0.5003 0.246 Inf 2.036 0.1037
## High - Medium 0.4644 0.243 Inf 1.910 0.1357
## Low - Medium -0.0360 0.272 Inf -0.132 0.9904
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low 0.3215 0.201 Inf 1.596 0.2472
## High - Medium 0.1502 0.183 Inf 0.822 0.6891
## Low - Medium -0.1712 0.188 Inf -0.913 0.6320
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Succession of establishment (Established/germinated)
ggplot(final, aes(mean_temp, succession, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Percent Successfully Established")
model2 <- glm(succession ~ temp*species, family = "quasipoisson", data = final)
anova(model2, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: succession
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 702 383.66
## temp 2 9.895 700 373.77 6.228e-05 ***
## species 3 43.071 697 330.69 < 2.2e-16 ***
## temp:species 6 58.636 691 272.06 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em2 <- emmeans(model2, pairwise~temp|species)
em2## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.55843 0.1458 Inf -0.844 -0.273
## Low -0.31101 0.1020 Inf -0.511 -0.111
## Medium -0.45283 0.1138 Inf -0.676 -0.230
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 0.01987 0.0899 Inf -0.156 0.196
## Low -2.39802 0.2875 Inf -2.962 -1.835
## Medium -1.21443 0.1579 Inf -1.524 -0.905
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.00409 0.0856 Inf -0.172 0.164
## Low -0.00785 0.0858 Inf -0.176 0.160
## Medium -0.00230 0.0855 Inf -0.170 0.165
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.51935 0.1544 Inf -0.822 -0.217
## Low -0.47163 0.1413 Inf -0.749 -0.195
## Medium -0.35771 0.1260 Inf -0.605 -0.111
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -0.24742 0.178 Inf -1.390 0.3459
## High - Medium -0.10559 0.185 Inf -0.571 0.8357
## Low - Medium 0.14182 0.153 Inf 0.928 0.6227
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low 2.41789 0.301 Inf 8.027 <.0001
## High - Medium 1.23429 0.182 Inf 6.793 <.0001
## Low - Medium -1.18360 0.328 Inf -3.608 0.0009
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low 0.00375 0.121 Inf 0.031 0.9995
## High - Medium -0.00179 0.121 Inf -0.015 0.9999
## Low - Medium -0.00554 0.121 Inf -0.046 0.9988
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.04772 0.209 Inf -0.228 0.9718
## High - Medium -0.16164 0.199 Inf -0.811 0.6963
## Low - Medium -0.11392 0.189 Inf -0.602 0.8192
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Overall Establishment
ggplot(final, aes(mean_temp, establishment, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Individual Establishment")
model3 <- glm(establishment ~ temp*species, family = "quasipoisson", data = final)
anova(model3, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: establishment
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 12268.9
## temp 2 71.4 837 12197.6 5.291e-06 ***
## species 3 9552.7 834 2644.9 < 2.2e-16 ***
## temp:species 6 397.5 828 2247.3 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em3 <- emmeans(model3, pairwise~temp|species)
em3## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 0.6035 0.1515 Inf 0.307 0.90042
## Low 2.0263 0.0744 Inf 1.881 2.17203
## Medium 1.2404 0.1102 Inf 1.025 1.45636
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.3791 0.1028 Inf 1.178 1.58058
## Low -0.0144 0.2063 Inf -0.419 0.38997
## Medium 1.0194 0.1230 Inf 0.778 1.26051
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.1588 0.0422 Inf 3.076 3.24156
## Low 3.3539 0.0383 Inf 3.279 3.42896
## Medium 3.3594 0.0382 Inf 3.285 3.43423
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.5108 0.2644 Inf -1.029 0.00745
## Low -0.2412 0.2311 Inf -0.694 0.21174
## Medium -0.1710 0.2231 Inf -0.608 0.26632
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.42273 0.1687 Inf -8.431 <.0001
## High - Medium -0.63691 0.1873 Inf -3.401 0.0019
## Low - Medium 0.78582 0.1329 Inf 5.912 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low 1.39351 0.2305 Inf 6.046 <.0001
## High - Medium 0.35976 0.1603 Inf 2.244 0.0640
## Low - Medium -1.03375 0.2402 Inf -4.304 <.0001
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.19508 0.0570 Inf -3.423 0.0018
## High - Medium -0.20056 0.0569 Inf -3.523 0.0012
## Low - Medium -0.00548 0.0541 Inf -0.101 0.9944
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.26966 0.3512 Inf -0.768 0.7227
## High - Medium -0.33987 0.3460 Inf -0.982 0.5880
## Low - Medium -0.07020 0.3212 Inf -0.219 0.9740
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Overall Germination
ggplot(final, aes(mean_temp, germination, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Individual Germination")
model4 <- glm(germination ~ temp*species, family = "quasipoisson", data = final)
anova(model4, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: germination
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 10186.2
## temp 2 275.3 837 9910.9 < 2.2e-16 ***
## species 3 7283.5 834 2627.4 < 2.2e-16 ***
## temp:species 6 195.9 828 2431.5 1.548e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em4 <- emmeans(model4, pairwise~temp|species)
em4## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.035 0.1217 Inf 0.796 1.273
## Low 2.253 0.0662 Inf 2.123 2.382
## Medium 1.609 0.0913 Inf 1.431 1.788
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.870 0.0802 Inf 1.713 2.027
## Low 2.496 0.0586 Inf 2.381 2.610
## Medium 2.422 0.0608 Inf 2.303 2.541
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.162 0.0420 Inf 3.080 3.244
## Low 3.361 0.0380 Inf 3.286 3.435
## Medium 3.361 0.0380 Inf 3.287 3.436
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.138 0.2187 Inf -0.566 0.291
## Low 0.121 0.1921 Inf -0.256 0.498
## Medium 0.158 0.1886 Inf -0.211 0.528
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.218086 0.1385 Inf -8.795 <.0001
## High - Medium -0.574729 0.1521 Inf -3.779 0.0005
## Low - Medium 0.643356 0.1128 Inf 5.706 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.625962 0.0993 Inf -6.304 <.0001
## High - Medium -0.552669 0.1006 Inf -5.494 <.0001
## Low - Medium 0.073293 0.0845 Inf 0.868 0.6606
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.199019 0.0567 Inf -3.512 0.0013
## High - Medium -0.199515 0.0567 Inf -3.522 0.0012
## Low - Medium -0.000496 0.0538 Inf -0.009 1.0000
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.258574 0.2911 Inf -0.888 0.6477
## High - Medium -0.295845 0.2888 Inf -1.025 0.5613
## Low - Medium -0.037271 0.2692 Inf -0.138 0.9895
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Treating temp as continuous
#average mass by mean_temp
model5 <- glm(average_mass ~ mean_temp*species + pendant_ID, family = "gaussian", data = final)
anova(model5, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: average_mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 574 2.3397
## mean_temp 1 0.02738 573 2.3124 0.0035771 **
## species 3 0.40608 570 1.9063 < 2.2e-16 ***
## pendant_ID 1 0.02722 569 1.8791 0.0036756 **
## mean_temp:species 3 0.05311 566 1.8259 0.0009119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em5 <- emmeans(model5, pairwise~species|mean_temp)
em5## $emmeans
## mean_temp = 26.5:
## species emmean SE df lower.CL upper.CL
## Bromus rubens 0.0285 0.00469 566 0.0193 0.0377
## Layia platyglossa 0.0427 0.00591 566 0.0311 0.0543
## Phacelia tanacetifolia 0.0248 0.00417 566 0.0166 0.0330
## Salvia columbariae 0.1030 0.00626 566 0.0906 0.1153
##
## Confidence level used: 0.95
##
## $contrasts
## mean_temp = 26.5:
## contrast estimate SE df t.ratio
## Bromus rubens - Layia platyglossa -0.0143 0.00761 566 -1.873
## Bromus rubens - Phacelia tanacetifolia 0.0037 0.00624 566 0.594
## Bromus rubens - Salvia columbariae -0.0745 0.00785 566 -9.480
## Layia platyglossa - Phacelia tanacetifolia 0.0180 0.00730 566 2.459
## Layia platyglossa - Salvia columbariae -0.0602 0.00854 566 -7.047
## Phacelia tanacetifolia - Salvia columbariae -0.0782 0.00756 566 -10.342
## p.value
## 0.2409
## 0.9340
## <.0001
## 0.0676
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesmodel6 <- glm(succession ~ mean_temp*species + pendant_ID, family = "gaussian", data = final)
anova(model6, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: succession
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 702 360.37
## mean_temp 1 20.933 701 339.44 1.300e-12 ***
## species 3 18.505 698 320.94 1.187e-09 ***
## pendant_ID 1 0.064 697 320.87 0.6956
## mean_temp:species 3 32.219 694 288.65 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em6 <- emmeans(model6, pairwise~species|mean_temp)
em6## $emmeans
## mean_temp = 26.3:
## species emmean SE df lower.CL upper.CL
## Bromus rubens 0.677 0.0507 694 0.577 0.776
## Layia platyglossa 0.691 0.0534 694 0.586 0.796
## Phacelia tanacetifolia 0.999 0.0482 694 0.904 1.093
## Salvia columbariae 0.642 0.0590 694 0.526 0.758
##
## Confidence level used: 0.95
##
## $contrasts
## mean_temp = 26.3:
## contrast estimate SE df t.ratio
## Bromus rubens - Layia platyglossa -0.0144 0.0746 694 -0.193
## Bromus rubens - Phacelia tanacetifolia -0.3218 0.0694 694 -4.637
## Bromus rubens - Salvia columbariae 0.0348 0.0780 694 0.447
## Layia platyglossa - Phacelia tanacetifolia -0.3074 0.0729 694 -4.219
## Layia platyglossa - Salvia columbariae 0.0492 0.0792 694 0.621
## Phacelia tanacetifolia - Salvia columbariae 0.3567 0.0763 694 4.672
## p.value
## 0.9974
## <.0001
## 0.9703
## 0.0002
## 0.9252
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesmodel7 <- glm(establishment~ mean_temp*species + pendant_ID, family = "gaussian", data = final)
anova(model7, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: establishment
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 110890
## mean_temp 1 1952 838 108938 <2e-16 ***
## species 3 94091 835 14847 <2e-16 ***
## pendant_ID 1 2 834 14845 0.7385
## mean_temp:species 3 1361 831 13484 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em7 <- emmeans(model7, pairwise~species|mean_temp)
em7## $emmeans
## mean_temp = 26.5:
## species emmean SE df lower.CL upper.CL
## Bromus rubens 4.966 0.294 831 4.389 5.54
## Layia platyglossa 3.136 0.325 831 2.497 3.77
## Phacelia tanacetifolia 27.615 0.296 831 27.035 28.20
## Salvia columbariae 0.738 0.282 831 0.185 1.29
##
## Confidence level used: 0.95
##
## $contrasts
## mean_temp = 26.5:
## contrast estimate SE df t.ratio p.value
## Bromus rubens - Layia platyglossa 1.83 0.444 831 4.123 0.0002
## Bromus rubens - Phacelia tanacetifolia -22.65 0.413 831 -54.812 <.0001
## Bromus rubens - Salvia columbariae 4.23 0.409 831 10.349 <.0001
## Layia platyglossa - Phacelia tanacetifolia -24.48 0.445 831 -54.999 <.0001
## Layia platyglossa - Salvia columbariae 2.40 0.428 831 5.605 <.0001
## Phacelia tanacetifolia - Salvia columbariae 26.88 0.410 831 65.582 <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesmodel8 <- glm(germination ~ mean_temp*species + pendant_ID, family = "gaussian", data = final)
anova(model8, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: germination
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 105034
## mean_temp 1 91 838 104944 0.05185 .
## species 3 84141 835 20802 < 2.2e-16 ***
## pendant_ID 1 36 834 20766 0.22010
## mean_temp:species 3 832 831 19934 1.408e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em8 <- emmeans(model8, pairwise~species|mean_temp)
em8## $emmeans
## mean_temp = 26.5:
## species emmean SE df lower.CL upper.CL
## Bromus rubens 6.54 0.357 831 5.839 7.24
## Layia platyglossa 8.46 0.396 831 7.685 9.24
## Phacelia tanacetifolia 27.69 0.359 831 26.988 28.40
## Salvia columbariae 1.06 0.342 831 0.392 1.74
##
## Confidence level used: 0.95
##
## $contrasts
## mean_temp = 26.5:
## contrast estimate SE df t.ratio p.value
## Bromus rubens - Layia platyglossa -1.92 0.540 831 -3.558 0.0022
## Bromus rubens - Phacelia tanacetifolia -21.15 0.502 831 -42.103 <.0001
## Bromus rubens - Salvia columbariae 5.48 0.497 831 11.025 <.0001
## Layia platyglossa - Phacelia tanacetifolia -19.23 0.541 831 -35.540 <.0001
## Layia platyglossa - Salvia columbariae 7.40 0.520 831 14.221 <.0001
## Phacelia tanacetifolia - Salvia columbariae 26.63 0.498 831 53.443 <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimates### Test everything with Maximum Temp
# Max Temp by Average mass
ggplot(final, aes(max_temp, average_mass, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Bimass per Individual")
model9 <- glm(average_mass ~ max_temp*species, family = "quasipoisson", data = final)
anova(model9, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: average_mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 574 32.104
## max_temp 1 0.3190 573 31.785 0.0153966 *
## species 3 8.1628 570 23.622 < 2.2e-16 ***
## max_temp:species 3 0.9857 567 22.636 0.0004113 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em9 <- emmeans(model9, pairwise~species|max_temp)
em9## $emmeans
## max_temp = 28.2:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -3.61 0.117 Inf -3.84 -3.38
## Layia platyglossa -3.03 0.105 Inf -3.23 -2.82
## Phacelia tanacetifolia -3.74 0.107 Inf -3.95 -3.53
## Salvia columbariae -2.27 0.078 Inf -2.42 -2.11
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## max_temp = 28.2:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.586 0.157 Inf -3.733 0.0011
## Bromus rubens - Phacelia tanacetifolia 0.125 0.159 Inf 0.788 0.8601
## Bromus rubens - Salvia columbariae -1.346 0.141 Inf -9.574 <.0001
## Layia platyglossa - Phacelia tanacetifolia 0.711 0.150 Inf 4.741 <.0001
## Layia platyglossa - Salvia columbariae -0.760 0.131 Inf -5.818 <.0001
## Phacelia tanacetifolia - Salvia columbariae -1.471 0.133 Inf -11.084 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates###Succession of establishment (Established/germinated)
ggplot(final, aes(max_temp, succession, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Percent Successfully Established")
model10 <- glm(succession ~ max_temp*species, family = "quasipoisson", data = final)
anova(model10, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: succession
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 702 383.66
## max_temp 1 22.861 701 360.80 4.069e-13 ***
## species 3 35.296 698 325.50 < 2.2e-16 ***
## max_temp:species 3 55.850 695 269.65 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em10 <- emmeans(model10, pairwise~species|max_temp)
em10## $emmeans
## max_temp = 28.1:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -0.40300 0.0623 Inf -0.5252 -0.2809
## Layia platyglossa -0.95327 0.0823 Inf -1.1146 -0.7919
## Phacelia tanacetifolia -0.00509 0.0466 Inf -0.0964 0.0862
## Salvia columbariae -0.43995 0.0751 Inf -0.5871 -0.2928
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## max_temp = 28.1:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.5503 0.1032 Inf 5.330
## Bromus rubens - Phacelia tanacetifolia -0.3979 0.0778 Inf -5.115
## Bromus rubens - Salvia columbariae 0.0369 0.0976 Inf 0.379
## Layia platyglossa - Phacelia tanacetifolia -0.9482 0.0946 Inf -10.026
## Layia platyglossa - Salvia columbariae -0.5133 0.1114 Inf -4.607
## Phacelia tanacetifolia - Salvia columbariae 0.4349 0.0884 Inf 4.921
## p.value
## <.0001
## <.0001
## 0.9815
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates###Overall Establishment
ggplot(final, aes(max_temp, establishment, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Number Established Individuals")
model11 <- glm(establishment ~ max_temp*species, family = "quasipoisson", data = final)
anova(model11, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: establishment
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 12268.9
## max_temp 1 2.1 838 12266.8 0.4024
## species 3 9621.6 835 2645.3 <2e-16 ***
## max_temp:species 3 319.3 832 2326.0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em11 <- emmeans(model11, pairwise~species|max_temp)
em11## $emmeans
## max_temp = 28.4:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.464 0.0605 Inf 1.346 1.5827
## Layia platyglossa 0.978 0.0749 Inf 0.831 1.1251
## Phacelia tanacetifolia 3.302 0.0231 Inf 3.257 3.3475
## Salvia columbariae -0.324 0.1452 Inf -0.609 -0.0396
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## max_temp = 28.4:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.486 0.0963 Inf 5.045
## Bromus rubens - Phacelia tanacetifolia -1.838 0.0648 Inf -28.376
## Bromus rubens - Salvia columbariae 1.788 0.1573 Inf 11.369
## Layia platyglossa - Phacelia tanacetifolia -2.324 0.0784 Inf -29.632
## Layia platyglossa - Salvia columbariae 1.302 0.1634 Inf 7.971
## Phacelia tanacetifolia - Salvia columbariae 3.626 0.1470 Inf 24.663
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates###Overall Germination
ggplot(final, aes(max_temp, germination, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Number Germinated Individuals")
model12 <- glm(germination ~ temp*species, family = "quasipoisson", data = final)
anova(model12, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: germination
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 10186.2
## temp 2 275.3 837 9910.9 < 2.2e-16 ***
## species 3 7283.5 834 2627.4 < 2.2e-16 ***
## temp:species 6 195.9 828 2431.5 1.548e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em12 <- emmeans(model12, pairwise~temp|species)
em12## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.035 0.1217 Inf 0.796 1.273
## Low 2.253 0.0662 Inf 2.123 2.382
## Medium 1.609 0.0913 Inf 1.431 1.788
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.870 0.0802 Inf 1.713 2.027
## Low 2.496 0.0586 Inf 2.381 2.610
## Medium 2.422 0.0608 Inf 2.303 2.541
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.162 0.0420 Inf 3.080 3.244
## Low 3.361 0.0380 Inf 3.286 3.435
## Medium 3.361 0.0380 Inf 3.287 3.436
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.138 0.2187 Inf -0.566 0.291
## Low 0.121 0.1921 Inf -0.256 0.498
## Medium 0.158 0.1886 Inf -0.211 0.528
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.218086 0.1385 Inf -8.795 <.0001
## High - Medium -0.574729 0.1521 Inf -3.779 0.0005
## Low - Medium 0.643356 0.1128 Inf 5.706 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.625962 0.0993 Inf -6.304 <.0001
## High - Medium -0.552669 0.1006 Inf -5.494 <.0001
## Low - Medium 0.073293 0.0845 Inf 0.868 0.6606
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.199019 0.0567 Inf -3.512 0.0013
## High - Medium -0.199515 0.0567 Inf -3.522 0.0012
## Low - Medium -0.000496 0.0538 Inf -0.009 1.0000
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.258574 0.2911 Inf -0.888 0.6477
## High - Medium -0.295845 0.2888 Inf -1.025 0.5613
## Low - Medium -0.037271 0.2692 Inf -0.138 0.9895
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates###Test establish density by Biomass, Temp Block, and Species
#Based on number of individuals that survived germination
ggplot(final, aes(mean_temp, succession, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Temperature", y = "Successive Individuals")
model13 <- glm(succession ~ mass*temp*species, family = "quasipoisson", data = final)
anova(model13, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: succession
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 702 383.66
## mass 1 27.714 701 355.95 7.811e-14 ***
## temp 2 5.471 699 350.47 0.0040343 **
## species 3 20.344 696 330.13 6.543e-09 ***
## mass:temp 2 8.868 694 321.26 0.0001316 ***
## mass:species 3 11.946 691 309.32 2.410e-05 ***
## temp:species 6 51.170 685 258.15 < 2.2e-16 ***
## mass:temp:species 6 5.959 679 252.19 0.0617671 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em13 <- emmeans(model13, pairwise~temp|species)
em13## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 0.70130 0.589 Inf -0.454 1.8566
## Low -0.20670 0.140 Inf -0.482 0.0684
## Medium -0.20447 0.180 Inf -0.558 0.1488
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.11708 0.137 Inf -0.386 0.1523
## Low -1.90094 0.583 Inf -3.044 -0.7574
## Medium -1.05329 0.191 Inf -1.427 -0.6792
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.00536 0.124 Inf -0.248 0.2369
## Low -0.00089 0.155 Inf -0.305 0.3028
## Medium -0.00290 0.163 Inf -0.323 0.3172
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High 0.23943 0.204 Inf -0.160 0.6393
## Low 0.03293 0.235 Inf -0.428 0.4938
## Medium -0.02247 0.179 Inf -0.374 0.3287
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low 0.90800 0.606 Inf 1.499 0.2916
## High - Medium 0.90577 0.616 Inf 1.470 0.3057
## Low - Medium -0.00223 0.228 Inf -0.010 0.9999
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low 1.78387 0.599 Inf 2.976 0.0082
## High - Medium 0.93621 0.235 Inf 3.981 0.0002
## Low - Medium -0.84765 0.614 Inf -1.381 0.3510
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.00447 0.198 Inf -0.023 0.9997
## High - Medium -0.00246 0.205 Inf -0.012 0.9999
## Low - Medium 0.00201 0.225 Inf 0.009 1.0000
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low 0.20649 0.311 Inf 0.663 0.7848
## High - Medium 0.26190 0.272 Inf 0.965 0.5993
## Low - Medium 0.05541 0.296 Inf 0.187 0.9808
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates### Stats for mass
m20 <- glm(mass ~ species*temp+mean_temp+max_temp, family = "quasipoisson", data = final)
anova(m20, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 309.23
## species 3 192.303 836 116.92 < 2.2e-16 ***
## temp 2 1.176 834 115.75 0.01571 *
## mean_temp 1 0.410 833 115.34 0.08895 .
## max_temp 1 0.401 832 114.94 0.09236 .
## species:temp 6 8.903 826 106.03 1.177e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e20 <- emmeans(m20, pairwise~temp|species)
e20## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.5946 0.3013 Inf -4.185 -3.004
## Low -2.0866 0.1478 Inf -2.376 -1.797
## Medium -2.4620 0.1535 Inf -2.763 -2.161
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High -2.4554 0.2269 Inf -2.900 -2.011
## Low -3.3742 0.2382 Inf -3.841 -2.907
## Medium -2.2991 0.1527 Inf -2.598 -2.000
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.0583 0.1271 Inf -0.308 0.191
## Low -0.5582 0.0899 Inf -0.734 -0.382
## Medium -0.4589 0.0966 Inf -0.648 -0.270
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -2.8108 0.1985 Inf -3.200 -2.422
## Low -3.2105 0.2252 Inf -3.652 -2.769
## Medium -2.7052 0.1743 Inf -3.047 -2.364
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.5080 0.3587 Inf -4.204 0.0001
## High - Medium -1.1326 0.3334 Inf -3.397 0.0020
## Low - Medium 0.3754 0.2198 Inf 1.708 0.2022
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low 0.9188 0.3267 Inf 2.812 0.0136
## High - Medium -0.1563 0.2881 Inf -0.542 0.8504
## Low - Medium -1.0751 0.2672 Inf -4.024 0.0002
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low 0.4998 0.1547 Inf 3.230 0.0035
## High - Medium 0.4005 0.1310 Inf 3.057 0.0063
## Low - Medium -0.0993 0.0909 Inf -1.092 0.5193
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low 0.3997 0.3093 Inf 1.292 0.3996
## High - Medium -0.1056 0.2726 Inf -0.388 0.9206
## Low - Medium -0.5053 0.2783 Inf -1.816 0.1644
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimatesm21 <- glm(germination ~ temp*establishment*mass*species, family = "quasipoisson", data = final)
anova(m21, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: germination
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 10186.2
## temp 2 275.3 837 9910.9 < 2.2e-16 ***
## establishment 1 6441.5 836 3469.5 < 2.2e-16 ***
## mass 1 52.3 835 3417.2 1.186e-10 ***
## species 3 1540.8 832 1876.4 < 2.2e-16 ***
## temp:establishment 2 151.2 830 1725.2 < 2.2e-16 ***
## temp:mass 2 5.9 828 1719.3 0.0978390 .
## establishment:mass 1 240.7 827 1478.6 < 2.2e-16 ***
## temp:species 6 55.1 821 1423.5 8.380e-08 ***
## establishment:species 3 178.5 818 1245.1 < 2.2e-16 ***
## mass:species 3 19.6 815 1225.4 0.0013822 **
## temp:establishment:mass 2 16.9 813 1208.5 0.0012026 **
## temp:establishment:species 6 42.0 807 1166.5 8.969e-06 ***
## temp:mass:species 6 19.6 801 1146.8 0.0161529 *
## establishment:mass:species 3 69.8 798 1077.0 5.729e-12 ***
## temp:establishment:mass:species 6 33.3 792 1043.7 0.0001841 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e21 <- emmeans(m1, pairwise~temp|species)
e21## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.2080 0.1355 Inf 0.943 1.474
## Low 2.1947 0.0691 Inf 2.059 2.330
## Medium 1.5953 0.0908 Inf 1.417 1.773
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.9438 0.0836 Inf 1.780 2.108
## Low 2.3695 0.0736 Inf 2.225 2.514
## Medium 2.2819 0.0788 Inf 2.127 2.436
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.3623 0.0817 Inf 3.202 3.522
## Low 3.2934 0.0449 Inf 3.205 3.381
## Medium 3.3526 0.0379 Inf 3.278 3.427
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.0246 0.2208 Inf -0.457 0.408
## Low 0.0355 0.1933 Inf -0.343 0.414
## Medium 0.0837 0.1892 Inf -0.287 0.455
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -0.98667 0.1604 Inf -6.152 <.0001
## High - Medium -0.38724 0.1650 Inf -2.347 0.0496
## Low - Medium 0.59943 0.1131 Inf 5.298 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.42574 0.1214 Inf -3.508 0.0013
## High - Medium -0.33812 0.1257 Inf -2.690 0.0195
## Low - Medium 0.08762 0.0841 Inf 1.042 0.5501
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low 0.06895 0.1100 Inf 0.627 0.8054
## High - Medium 0.00973 0.0925 Inf 0.105 0.9939
## Low - Medium -0.05922 0.0574 Inf -1.031 0.5572
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.06010 0.2976 Inf -0.202 0.9778
## High - Medium -0.10830 0.2945 Inf -0.368 0.9282
## Low - Medium -0.04819 0.2674 Inf -0.180 0.9823
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates# Testing Mass, establishment, and germination with temp as a block/category by species (Need pendant ID)
m61 <- glm(mass ~ temp*species+pendant_ID, family = "quasipoisson", data = final)
anova(m61, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: mass
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 309.23
## temp 2 1.176 837 308.05 0.01490 *
## species 3 192.303 834 115.75 < 2.2e-16 ***
## pendant_ID 1 0.841 833 114.91 0.01417 *
## temp:species 6 9.177 827 105.73 3.207e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e91 <- emmeans(m61, pairwise~species|temp)
e91## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -3.714 0.2766 Inf -4.256 -3.172
## Layia platyglossa -2.398 0.1464 Inf -2.685 -2.111
## Phacelia tanacetifolia -0.372 0.0625 Inf -0.494 -0.249
## Salvia columbariae -2.964 0.1918 Inf -3.340 -2.588
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.111 0.1289 Inf -2.364 -1.859
## Layia platyglossa -3.247 0.2274 Inf -3.692 -2.801
## Phacelia tanacetifolia -0.568 0.0596 Inf -0.685 -0.451
## Salvia columbariae -3.166 0.2184 Inf -3.594 -2.738
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.354 0.1528 Inf -2.654 -2.055
## Layia platyglossa -2.130 0.1372 Inf -2.399 -1.861
## Phacelia tanacetifolia -0.473 0.0651 Inf -0.601 -0.346
## Salvia columbariae -2.587 0.1711 Inf -2.922 -2.252
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -1.316 0.309 Inf -4.261 0.0001
## Bromus rubens - Phacelia tanacetifolia -3.342 0.279 Inf -11.973 <.0001
## Bromus rubens - Salvia columbariae -0.750 0.333 Inf -2.254 0.1089
## Layia platyglossa - Phacelia tanacetifolia -2.026 0.151 Inf -13.403 <.0001
## Layia platyglossa - Salvia columbariae 0.566 0.236 Inf 2.397 0.0777
## Phacelia tanacetifolia - Salvia columbariae 2.592 0.195 Inf 13.259 <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa 1.135 0.261 Inf 4.343 0.0001
## Bromus rubens - Phacelia tanacetifolia -1.544 0.142 Inf -10.869 <.0001
## Bromus rubens - Salvia columbariae 1.054 0.254 Inf 4.157 0.0002
## Layia platyglossa - Phacelia tanacetifolia -2.679 0.235 Inf -11.396 <.0001
## Layia platyglossa - Salvia columbariae -0.081 0.315 Inf -0.257 0.9941
## Phacelia tanacetifolia - Salvia columbariae 2.598 0.226 Inf 11.476 <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.225 0.201 Inf -1.116 0.6799
## Bromus rubens - Phacelia tanacetifolia -1.881 0.161 Inf -11.667 <.0001
## Bromus rubens - Salvia columbariae 0.233 0.226 Inf 1.030 0.7318
## Layia platyglossa - Phacelia tanacetifolia -1.656 0.146 Inf -11.307 <.0001
## Layia platyglossa - Salvia columbariae 0.457 0.216 Inf 2.121 0.1462
## Phacelia tanacetifolia - Salvia columbariae 2.114 0.179 Inf 11.834 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesm62 <- glm(establishment ~ temp*species+pendant_ID, family = "quasipoisson", data = final)
anova(m62, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: establishment
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 12268.9
## temp 2 71.4 837 12197.6 5.051e-06 ***
## species 3 9552.7 834 2644.9 < 2.2e-16 ***
## pendant_ID 1 3.3 833 2641.5 0.2878
## temp:species 6 397.5 827 2244.0 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e92 <- emmeans(m62, pairwise~species|temp)
e92## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 0.6270 0.1528 Inf 0.328 0.9265
## Layia platyglossa 1.4026 0.1049 Inf 1.197 1.6083
## Phacelia tanacetifolia 3.1823 0.0475 Inf 3.089 3.2755
## Salvia columbariae -0.4873 0.2648 Inf -1.006 0.0318
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.0219 0.0744 Inf 1.876 2.1677
## Layia platyglossa -0.0187 0.2060 Inf -0.422 0.3850
## Phacelia tanacetifolia 3.3496 0.0385 Inf 3.274 3.4250
## Salvia columbariae -0.2455 0.2307 Inf -0.698 0.2066
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.2206 0.1115 Inf 1.002 1.4392
## Layia platyglossa 0.9996 0.1242 Inf 0.756 1.2430
## Phacelia tanacetifolia 3.3396 0.0424 Inf 3.256 3.4227
## Salvia columbariae -0.1908 0.2235 Inf -0.629 0.2472
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.776 0.1827 Inf -4.245
## Bromus rubens - Phacelia tanacetifolia -2.555 0.1569 Inf -16.281
## Bromus rubens - Salvia columbariae 1.114 0.3042 Inf 3.664
## Layia platyglossa - Phacelia tanacetifolia -1.780 0.1109 Inf -16.047
## Layia platyglossa - Salvia columbariae 1.890 0.2832 Inf 6.674
## Phacelia tanacetifolia - Salvia columbariae 3.670 0.2673 Inf 13.730
## p.value
## 0.0001
## <.0001
## 0.0014
## <.0001
## <.0001
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 2.041 0.2189 Inf 9.323
## Bromus rubens - Phacelia tanacetifolia -1.328 0.0835 Inf -15.902
## Bromus rubens - Salvia columbariae 2.267 0.2423 Inf 9.358
## Layia platyglossa - Phacelia tanacetifolia -3.368 0.2094 Inf -16.083
## Layia platyglossa - Salvia columbariae 0.227 0.3092 Inf 0.733
## Phacelia tanacetifolia - Salvia columbariae 3.595 0.2338 Inf 15.378
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## 0.8837
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.221 0.1648 Inf 1.341
## Bromus rubens - Phacelia tanacetifolia -2.119 0.1164 Inf -18.208
## Bromus rubens - Salvia columbariae 1.411 0.2483 Inf 5.683
## Layia platyglossa - Phacelia tanacetifolia -2.340 0.1286 Inf -18.199
## Layia platyglossa - Salvia columbariae 1.190 0.2543 Inf 4.681
## Phacelia tanacetifolia - Salvia columbariae 3.530 0.2259 Inf 15.626
## p.value
## 0.5366
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesm63 <- glm(germination ~ temp*species+pendant_ID, family = "quasipoisson", data = final)
anova(m63, test = "Chisq")## Analysis of Deviance Table
##
## Model: quasipoisson, link: log
##
## Response: germination
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 839 10186.2
## temp 2 275.3 837 9910.9 < 2.2e-16 ***
## species 3 7283.5 834 2627.4 < 2.2e-16 ***
## pendant_ID 1 22.4 833 2605.0 0.005312 **
## temp:species 6 195.9 827 2409.0 1.125e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e93 <- emmeans(m63, pairwise~species|temp)
e93## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.0872 0.1225 Inf 0.847 1.327
## Layia platyglossa 1.9221 0.0819 Inf 1.762 2.083
## Phacelia tanacetifolia 3.2143 0.0458 Inf 3.125 3.304
## Salvia columbariae -0.0852 0.2184 Inf -0.513 0.343
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.2416 0.0660 Inf 2.112 2.371
## Layia platyglossa 2.4844 0.0585 Inf 2.370 2.599
## Phacelia tanacetifolia 3.3497 0.0382 Inf 3.275 3.425
## Salvia columbariae 0.1098 0.1912 Inf -0.265 0.485
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.5649 0.0922 Inf 1.384 1.746
## Layia platyglossa 2.3778 0.0626 Inf 2.255 2.500
## Phacelia tanacetifolia 3.3169 0.0411 Inf 3.236 3.397
## Salvia columbariae 0.1137 0.1883 Inf -0.255 0.483
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.835 0.1450 Inf -5.759
## Bromus rubens - Phacelia tanacetifolia -2.127 0.1281 Inf -16.610
## Bromus rubens - Salvia columbariae 1.172 0.2490 Inf 4.709
## Layia platyglossa - Phacelia tanacetifolia -1.292 0.0900 Inf -14.352
## Layia platyglossa - Salvia columbariae 2.007 0.2317 Inf 8.662
## Phacelia tanacetifolia - Salvia columbariae 3.299 0.2215 Inf 14.893
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.243 0.0880 Inf -2.760
## Bromus rubens - Phacelia tanacetifolia -1.108 0.0759 Inf -14.591
## Bromus rubens - Salvia columbariae 2.132 0.2022 Inf 10.543
## Layia platyglossa - Phacelia tanacetifolia -0.865 0.0695 Inf -12.447
## Layia platyglossa - Salvia columbariae 2.375 0.1999 Inf 11.880
## Phacelia tanacetifolia - Salvia columbariae 3.240 0.1949 Inf 16.625
## p.value
## 0.0295
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.813 0.1091 Inf -7.448
## Bromus rubens - Phacelia tanacetifolia -1.752 0.0984 Inf -17.805
## Bromus rubens - Salvia columbariae 1.451 0.2085 Inf 6.961
## Layia platyglossa - Phacelia tanacetifolia -0.939 0.0713 Inf -13.162
## Layia platyglossa - Salvia columbariae 2.264 0.1972 Inf 11.483
## Phacelia tanacetifolia - Salvia columbariae 3.203 0.1914 Inf 16.733
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesTemperature Ranges per species from publications:
Salvia ranges from 10C to 25C (Cabrerra-Santos et al. 2022, Dashti et al. 2015) Bromus ranges Layia ranges Phacelia ranges
###GBIF citation GBIF.org (31 January 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.vtkfem
###Ideas from Previous version Purpose: The purpose of this experiment is to determine if cooler temperatures, as seen under shrub canopies, could be used as an indicator for the germination success of both native and exotic plant species in arid ecosystems.
Hypothesis: We hypothesize that variations in fine scale temperature can act as a direct proxy for successful desert plant establishment and germination.
Predictions: 1) Fine scale temperature can be experimentally manipulated via heat lamps in an enclosed setting. 2) Plants will respond to varying fine scale temperatures. 3) The response to temperature will be species specific. 4) Responses to temperature will vary between native and exotic plant species.
Data: All data can be access on KNB. https://knb.ecoinformatics.org/view/doi:10.5063/F1GQ6W6R
Rough Ideas: 1)To connect this to shrub density I want to show that the cooler temperatures, as experienced under shrubs, can provide a benefit for species germination. The law table is meant to simulate these lower temperatures while the medium and high tables are meant to act as open areas at a moderate temperature and at extreme temperature. 2)There should be a visible difference between overall germination and mass between native and invasive species. If we can determine if invasives can germinate better under shrubs than in open areas then we can connect this to competition between natives and invasive. 3)Species should vary in their response to fine scale temperature since the range that they germinate vary from species to species. We should see that some natives germinate better at specific temperature while others do not. 4) We can connect this possibly to climate change. If we find that higher temperatures means a lower germination success then that can be the take-home message, that increasing temperatures have negative effects on desert plant species.
### Test if table temp significant
Layia <- read.csv("All Temp Data.csv")
Brome <- read.csv("Brome Temp Sheet.csv")
Salvia <- read.csv("Salvia Temp Sheet.csv")
Phacelia <- read.csv("Phacelia Temp Sheet.csv")
### Merge all species temp data
Temp <- merge(Layia, Brome, all = TRUE)
Temp <- merge(Temp, Salvia, all = TRUE)
Temp <- merge(Temp, Phacelia, all = TRUE)
final_temp <- Temp %>%
group_by(temp, species) %>%
summarise(mean_table_temp = mean(temperature))
final2 <- merge(final, final_temp, all = TRUE) %>%
na.omit()### Test to see if raw temp significant
library(glmmTMB)
temp <- glmmTMB(temperature ~ temp*species, data = Temp, family = "gaussian")
temp## Formula: temperature ~ temp * species
## Data: Temp
## AIC BIC logLik df.resid
## 1001590.4 1001770.2 -500778.2 288852
##
## Number of obs: 288869
##
## Dispersion estimate for gaussian family (sigma^2): 1.88
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 20.54555
## tempHigh
## 9.78197
## tempLow
## 4.65034
## tempMedium
## 5.63550
## speciesLayia platyglossa
## -2.37064
## speciesPhacelia tanacetifolia
## 0.15460
## speciesSalvia columbariae
## -0.56503
## tempHigh:speciesLayia platyglossa
## 0.28082
## tempLow:speciesLayia platyglossa
## 0.80825
## tempMedium:speciesLayia platyglossa
## -0.48833
## tempHigh:speciesPhacelia tanacetifolia
## 0.55616
## tempLow:speciesPhacelia tanacetifolia
## -0.38300
## tempMedium:speciesPhacelia tanacetifolia
## -0.02612
## tempHigh:speciesSalvia columbariae
## -0.63153
## tempLow:speciesSalvia columbariae
## -0.06691
## tempMedium:speciesSalvia columbariae
## -0.80514Proposed Boxplot figures and stats
### Average Mass by mean temp
Mass_plot <- ggplot(final2, aes(mean_table_temp, average_mass, color = temp)) +
geom_boxplot(width = 5, length = 10) +
scale_fill_discrete(breaks=c("High", "Medium", "Low")) +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() + ylim(0,0.4)
Mass_plot
Germination_plot <- ggplot(final2, aes(mean_table_temp, germination, color = temp)) +
geom_boxplot(width = 5) +
scale_fill_discrete(breaks=c("High", "Medium", "Low")) +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic()
Germination_plot
Establishment_plot <- ggplot(final2, aes(mean_table_temp, establishment, color = temp)) +
geom_boxplot(width = 5) +
scale_fill_discrete(breaks=c("High", "Medium", "Low")) +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic()
Establishment_plot
### Boxplot models
# Mass
model1 <- glm(mass ~ mean_table_temp*species+temp, family = "quasipoisson", data = final2)
broom::tidy(anova(model1, test = "Chisq"))## # A tibble: 5 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 574 174. NA
## 2 mean_table_temp 1 18.4 573 156. 6.22e- 37
## 3 species 3 89.7 570 66.0 2.76e-170
## 4 temp 2 0.0249 568 66.0 8.97e- 1
## 5 mean_table_temp:species 3 2.90 565 63.1 1.25e- 5estimate1 <- emmeans(model1, pairwise~species|temp)
estimate1 # Broom::tidy() does not work on emmeans## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -0.969 0.6044 Inf -2.154 0.216
## Layia platyglossa -1.396 0.2670 Inf -1.919 -0.872
## Phacelia tanacetifolia 0.750 0.6090 Inf -0.443 1.944
## Salvia columbariae -1.102 0.4339 Inf -1.953 -0.252
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.637 0.2159 Inf -3.060 -2.213
## Layia platyglossa -3.063 0.5519 Inf -4.145 -1.982
## Phacelia tanacetifolia -0.917 0.1918 Inf -1.293 -0.541
## Salvia columbariae -2.770 0.3871 Inf -3.529 -2.011
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.298 0.1033 Inf -2.500 -2.095
## Layia platyglossa -2.724 0.4050 Inf -3.518 -1.931
## Phacelia tanacetifolia -0.578 0.0605 Inf -0.697 -0.460
## Salvia columbariae -2.431 0.2455 Inf -2.912 -1.950
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.427 0.3685 Inf 1.158
## Bromus rubens - Phacelia tanacetifolia -1.719 0.0915 Inf -18.782
## Bromus rubens - Salvia columbariae 0.134 0.2194 Inf 0.609
## Layia platyglossa - Phacelia tanacetifolia -2.146 0.3694 Inf -5.809
## Layia platyglossa - Salvia columbariae -0.293 0.2172 Inf -1.350
## Phacelia tanacetifolia - Salvia columbariae 1.853 0.2132 Inf 8.689
## p.value
## 0.6533
## <.0001
## 0.9293
## <.0001
## 0.5309
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.427 0.3685 Inf 1.158
## Bromus rubens - Phacelia tanacetifolia -1.719 0.0915 Inf -18.782
## Bromus rubens - Salvia columbariae 0.134 0.2194 Inf 0.609
## Layia platyglossa - Phacelia tanacetifolia -2.146 0.3694 Inf -5.809
## Layia platyglossa - Salvia columbariae -0.293 0.2172 Inf -1.350
## Phacelia tanacetifolia - Salvia columbariae 1.853 0.2132 Inf 8.689
## p.value
## 0.6533
## <.0001
## 0.9293
## <.0001
## 0.5309
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.427 0.3685 Inf 1.158
## Bromus rubens - Phacelia tanacetifolia -1.719 0.0915 Inf -18.782
## Bromus rubens - Salvia columbariae 0.134 0.2194 Inf 0.609
## Layia platyglossa - Phacelia tanacetifolia -2.146 0.3694 Inf -5.809
## Layia platyglossa - Salvia columbariae -0.293 0.2172 Inf -1.350
## Phacelia tanacetifolia - Salvia columbariae 1.853 0.2132 Inf 8.689
## p.value
## 0.6533
## <.0001
## 0.9293
## <.0001
## 0.5309
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates# Establishment
model2 <- glm(establishment ~ mean_table_temp*species+temp, family = "quasipoisson", data = final2)
broom::tidy(anova(model2, test = "Chisq"))## # A tibble: 5 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 574 6763. NA
## 2 mean_table_temp 1 271. 573 6492. 1.38e-29
## 3 species 3 5243. 570 1249. 0
## 4 temp 2 3.77 568 1245. 4.11e- 1
## 5 mean_table_temp:species 3 137. 565 1109. 7.14e-14estimate2 <- emmeans(model2, pairwise~species|mean_table_temp)
estimate2 # Broom::tidy() does not work on emmeans## $emmeans
## mean_table_temp = 26.5:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.897 0.110 Inf 1.682 2.112
## Layia platyglossa 1.399 0.197 Inf 1.013 1.785
## Phacelia tanacetifolia 3.438 0.108 Inf 3.227 3.649
## Salvia columbariae 0.519 0.130 Inf 0.264 0.774
##
## Results are averaged over the levels of: temp
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## mean_table_temp = 26.5:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.498 0.2955 Inf 1.686
## Bromus rubens - Phacelia tanacetifolia -1.541 0.0538 Inf -28.635
## Bromus rubens - Salvia columbariae 1.378 0.1950 Inf 7.068
## Layia platyglossa - Phacelia tanacetifolia -2.039 0.2995 Inf -6.809
## Layia platyglossa - Salvia columbariae 0.880 0.1964 Inf 4.480
## Phacelia tanacetifolia - Salvia columbariae 2.919 0.1957 Inf 14.920
## p.value
## 0.3308
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are averaged over the levels of: temp
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates# Germination
model3 <- glm(germination ~ mean_table_temp*species+temp, family = "quasipoisson", data = final2)
broom::tidy(anova(model3, test = "Chisq"))## # A tibble: 5 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 574 5353. NA
## 2 mean_table_temp 1 33.8 573 5320. 0.0000900
## 3 species 3 4018. 570 1302. 0
## 4 temp 2 24.2 568 1278. 0.00409
## 5 mean_table_temp:species 3 15.5 565 1262. 0.0705estimate3 <- emmeans(model3, pairwise~species|temp)
estimate3 # Broom::tidy() does not work on emmeans## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.561 0.2781 Inf 1.0158 2.106
## Layia platyglossa 2.119 0.1311 Inf 1.8616 2.376
## Phacelia tanacetifolia 2.820 0.2798 Inf 2.2713 3.368
## Salvia columbariae 0.364 0.2284 Inf -0.0842 0.811
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.249 0.0987 Inf 2.0551 2.442
## Layia platyglossa 2.806 0.2463 Inf 2.3238 3.289
## Phacelia tanacetifolia 3.507 0.0868 Inf 3.3374 3.678
## Salvia columbariae 1.051 0.2018 Inf 0.6558 1.447
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.074 0.0550 Inf 1.9665 2.182
## Layia platyglossa 2.632 0.1870 Inf 2.2658 2.999
## Phacelia tanacetifolia 3.333 0.0363 Inf 3.2620 3.404
## Salvia columbariae 0.877 0.1560 Inf 0.5714 1.183
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.558 0.1672 Inf -3.336
## Bromus rubens - Phacelia tanacetifolia -1.259 0.0484 Inf -26.017
## Bromus rubens - Salvia columbariae 1.197 0.1458 Inf 8.214
## Layia platyglossa - Phacelia tanacetifolia -0.701 0.1669 Inf -4.200
## Layia platyglossa - Salvia columbariae 1.755 0.1470 Inf 11.939
## Phacelia tanacetifolia - Salvia columbariae 2.456 0.1430 Inf 17.171
## p.value
## 0.0047
## <.0001
## <.0001
## 0.0002
## <.0001
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.558 0.1672 Inf -3.336
## Bromus rubens - Phacelia tanacetifolia -1.259 0.0484 Inf -26.017
## Bromus rubens - Salvia columbariae 1.197 0.1458 Inf 8.214
## Layia platyglossa - Phacelia tanacetifolia -0.701 0.1669 Inf -4.200
## Layia platyglossa - Salvia columbariae 1.755 0.1470 Inf 11.939
## Phacelia tanacetifolia - Salvia columbariae 2.456 0.1430 Inf 17.171
## p.value
## 0.0047
## <.0001
## <.0001
## 0.0002
## <.0001
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.558 0.1672 Inf -3.336
## Bromus rubens - Phacelia tanacetifolia -1.259 0.0484 Inf -26.017
## Bromus rubens - Salvia columbariae 1.197 0.1458 Inf 8.214
## Layia platyglossa - Phacelia tanacetifolia -0.701 0.1669 Inf -4.200
## Layia platyglossa - Salvia columbariae 1.755 0.1470 Inf 11.939
## Phacelia tanacetifolia - Salvia columbariae 2.456 0.1430 Inf 17.171
## p.value
## 0.0047
## <.0001
## <.0001
## 0.0002
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesProposed figures and stats
### Treat Temp as Block
# Testing Mass, establishment, and germination with temp as a block/category by species (Need pendant ID)
m61 <- glm(mass ~ temp*species+pendant_ID, family = "quasipoisson", data = final)
broom::tidy(anova(m61, test = "Chisq"))## # A tibble: 5 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 309. NA
## 2 temp 2 1.18 837 308. 1.49e- 2
## 3 species 3 192. 834 116. 6.88e-298
## 4 pendant_ID 1 0.841 833 115. 1.42e- 2
## 5 temp:species 6 9.18 827 106. 3.21e- 12e91 <- emmeans(m61, pairwise~species|temp)
e91 # Broom::tidy() does not work on emmeans## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -3.714 0.2766 Inf -4.256 -3.172
## Layia platyglossa -2.398 0.1464 Inf -2.685 -2.111
## Phacelia tanacetifolia -0.372 0.0625 Inf -0.494 -0.249
## Salvia columbariae -2.964 0.1918 Inf -3.340 -2.588
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.111 0.1289 Inf -2.364 -1.859
## Layia platyglossa -3.247 0.2274 Inf -3.692 -2.801
## Phacelia tanacetifolia -0.568 0.0596 Inf -0.685 -0.451
## Salvia columbariae -3.166 0.2184 Inf -3.594 -2.738
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -2.354 0.1528 Inf -2.654 -2.055
## Layia platyglossa -2.130 0.1372 Inf -2.399 -1.861
## Phacelia tanacetifolia -0.473 0.0651 Inf -0.601 -0.346
## Salvia columbariae -2.587 0.1711 Inf -2.922 -2.252
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -1.316 0.309 Inf -4.261 0.0001
## Bromus rubens - Phacelia tanacetifolia -3.342 0.279 Inf -11.973 <.0001
## Bromus rubens - Salvia columbariae -0.750 0.333 Inf -2.254 0.1089
## Layia platyglossa - Phacelia tanacetifolia -2.026 0.151 Inf -13.403 <.0001
## Layia platyglossa - Salvia columbariae 0.566 0.236 Inf 2.397 0.0777
## Phacelia tanacetifolia - Salvia columbariae 2.592 0.195 Inf 13.259 <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa 1.135 0.261 Inf 4.343 0.0001
## Bromus rubens - Phacelia tanacetifolia -1.544 0.142 Inf -10.869 <.0001
## Bromus rubens - Salvia columbariae 1.054 0.254 Inf 4.157 0.0002
## Layia platyglossa - Phacelia tanacetifolia -2.679 0.235 Inf -11.396 <.0001
## Layia platyglossa - Salvia columbariae -0.081 0.315 Inf -0.257 0.9941
## Phacelia tanacetifolia - Salvia columbariae 2.598 0.226 Inf 11.476 <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.225 0.201 Inf -1.116 0.6799
## Bromus rubens - Phacelia tanacetifolia -1.881 0.161 Inf -11.667 <.0001
## Bromus rubens - Salvia columbariae 0.233 0.226 Inf 1.030 0.7318
## Layia platyglossa - Phacelia tanacetifolia -1.656 0.146 Inf -11.307 <.0001
## Layia platyglossa - Salvia columbariae 0.457 0.216 Inf 2.121 0.1462
## Phacelia tanacetifolia - Salvia columbariae 2.114 0.179 Inf 11.834 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesm62 <- glm(establishment ~ temp*species+pendant_ID, family = "quasipoisson", data = final)
broom::tidy(anova(m62, test = "Chisq"))## # A tibble: 5 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 12269. NA
## 2 temp 2 71.4 837 12198. 5.05e- 6
## 3 species 3 9553. 834 2645. 0
## 4 pendant_ID 1 3.31 833 2642. 2.88e- 1
## 5 temp:species 6 398. 827 2244. 7.45e-27e92 <- emmeans(m62, pairwise~species|temp)
e92## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 0.6270 0.1528 Inf 0.328 0.9265
## Layia platyglossa 1.4026 0.1049 Inf 1.197 1.6083
## Phacelia tanacetifolia 3.1823 0.0475 Inf 3.089 3.2755
## Salvia columbariae -0.4873 0.2648 Inf -1.006 0.0318
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.0219 0.0744 Inf 1.876 2.1677
## Layia platyglossa -0.0187 0.2060 Inf -0.422 0.3850
## Phacelia tanacetifolia 3.3496 0.0385 Inf 3.274 3.4250
## Salvia columbariae -0.2455 0.2307 Inf -0.698 0.2066
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.2206 0.1115 Inf 1.002 1.4392
## Layia platyglossa 0.9996 0.1242 Inf 0.756 1.2430
## Phacelia tanacetifolia 3.3396 0.0424 Inf 3.256 3.4227
## Salvia columbariae -0.1908 0.2235 Inf -0.629 0.2472
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.776 0.1827 Inf -4.245
## Bromus rubens - Phacelia tanacetifolia -2.555 0.1569 Inf -16.281
## Bromus rubens - Salvia columbariae 1.114 0.3042 Inf 3.664
## Layia platyglossa - Phacelia tanacetifolia -1.780 0.1109 Inf -16.047
## Layia platyglossa - Salvia columbariae 1.890 0.2832 Inf 6.674
## Phacelia tanacetifolia - Salvia columbariae 3.670 0.2673 Inf 13.730
## p.value
## 0.0001
## <.0001
## 0.0014
## <.0001
## <.0001
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 2.041 0.2189 Inf 9.323
## Bromus rubens - Phacelia tanacetifolia -1.328 0.0835 Inf -15.902
## Bromus rubens - Salvia columbariae 2.267 0.2423 Inf 9.358
## Layia platyglossa - Phacelia tanacetifolia -3.368 0.2094 Inf -16.083
## Layia platyglossa - Salvia columbariae 0.227 0.3092 Inf 0.733
## Phacelia tanacetifolia - Salvia columbariae 3.595 0.2338 Inf 15.378
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## 0.8837
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.221 0.1648 Inf 1.341
## Bromus rubens - Phacelia tanacetifolia -2.119 0.1164 Inf -18.208
## Bromus rubens - Salvia columbariae 1.411 0.2483 Inf 5.683
## Layia platyglossa - Phacelia tanacetifolia -2.340 0.1286 Inf -18.199
## Layia platyglossa - Salvia columbariae 1.190 0.2543 Inf 4.681
## Phacelia tanacetifolia - Salvia columbariae 3.530 0.2259 Inf 15.626
## p.value
## 0.5366
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimatesm63 <- glm(germination ~ temp*species+pendant_ID, family = "quasipoisson", data = final)
broom::tidy(anova(m63, test = "Chisq"))## # A tibble: 5 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 10186. NA
## 2 temp 2 275. 837 9911. 2.00e-21
## 3 species 3 7284. 834 2627. 0
## 4 pendant_ID 1 22.4 833 2605. 5.31e- 3
## 5 temp:species 6 196. 827 2409. 1.13e-12e93 <- emmeans(m63, pairwise~species|temp)
e93## $emmeans
## temp = High:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.0872 0.1225 Inf 0.847 1.327
## Layia platyglossa 1.9221 0.0819 Inf 1.762 2.083
## Phacelia tanacetifolia 3.2143 0.0458 Inf 3.125 3.304
## Salvia columbariae -0.0852 0.2184 Inf -0.513 0.343
##
## temp = Low:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 2.2416 0.0660 Inf 2.112 2.371
## Layia platyglossa 2.4844 0.0585 Inf 2.370 2.599
## Phacelia tanacetifolia 3.3497 0.0382 Inf 3.275 3.425
## Salvia columbariae 0.1098 0.1912 Inf -0.265 0.485
##
## temp = Medium:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.5649 0.0922 Inf 1.384 1.746
## Layia platyglossa 2.3778 0.0626 Inf 2.255 2.500
## Phacelia tanacetifolia 3.3169 0.0411 Inf 3.236 3.397
## Salvia columbariae 0.1137 0.1883 Inf -0.255 0.483
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## temp = High:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.835 0.1450 Inf -5.759
## Bromus rubens - Phacelia tanacetifolia -2.127 0.1281 Inf -16.610
## Bromus rubens - Salvia columbariae 1.172 0.2490 Inf 4.709
## Layia platyglossa - Phacelia tanacetifolia -1.292 0.0900 Inf -14.352
## Layia platyglossa - Salvia columbariae 2.007 0.2317 Inf 8.662
## Phacelia tanacetifolia - Salvia columbariae 3.299 0.2215 Inf 14.893
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## temp = Low:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.243 0.0880 Inf -2.760
## Bromus rubens - Phacelia tanacetifolia -1.108 0.0759 Inf -14.591
## Bromus rubens - Salvia columbariae 2.132 0.2022 Inf 10.543
## Layia platyglossa - Phacelia tanacetifolia -0.865 0.0695 Inf -12.447
## Layia platyglossa - Salvia columbariae 2.375 0.1999 Inf 11.880
## Phacelia tanacetifolia - Salvia columbariae 3.240 0.1949 Inf 16.625
## p.value
## 0.0295
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## temp = Medium:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa -0.813 0.1091 Inf -7.448
## Bromus rubens - Phacelia tanacetifolia -1.752 0.0984 Inf -17.805
## Bromus rubens - Salvia columbariae 1.451 0.2085 Inf 6.961
## Layia platyglossa - Phacelia tanacetifolia -0.939 0.0713 Inf -13.162
## Layia platyglossa - Salvia columbariae 2.264 0.1972 Inf 11.483
## Phacelia tanacetifolia - Salvia columbariae 3.203 0.1914 Inf 16.733
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates### Average Mass by mean temp
ggplot(final, aes(mean_temp, average_mass, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Bimass per pot (g)")
model1 <- glm(average_mass ~ temp*species, family = "quasipoisson", data = final)
broom::tidy(anova(model1, test = "Chisq"))## # A tibble: 4 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 574 32.1 NA
## 2 temp 2 0.404 572 31.7 2.22e- 2
## 3 species 3 8.24 569 23.5 1.88e-33
## 4 temp:species 6 1.14 563 22.3 1.44e- 3em1 <- emmeans(model1, pairwise~temp|species)
em1## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.88 0.279 Inf -4.43 -3.33
## Low -3.80 0.194 Inf -4.18 -3.42
## Medium -3.30 0.165 Inf -3.62 -2.97
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.37 0.190 Inf -3.75 -3.00
## Low -2.72 0.167 Inf -3.05 -2.39
## Medium -2.69 0.119 Inf -2.92 -2.45
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High -3.40 0.151 Inf -3.70 -3.11
## Low -3.90 0.194 Inf -4.28 -3.52
## Medium -3.87 0.190 Inf -4.24 -3.49
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -2.13 0.139 Inf -2.40 -1.85
## Low -2.45 0.146 Inf -2.73 -2.16
## Medium -2.28 0.118 Inf -2.51 -2.05
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -0.0793 0.340 Inf -0.233 0.9704
## High - Medium -0.5825 0.324 Inf -1.798 0.1701
## Low - Medium -0.5032 0.254 Inf -1.978 0.1177
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.6543 0.253 Inf -2.590 0.0260
## High - Medium -0.6883 0.224 Inf -3.072 0.0060
## Low - Medium -0.0340 0.205 Inf -0.166 0.9849
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low 0.5003 0.246 Inf 2.036 0.1037
## High - Medium 0.4644 0.243 Inf 1.910 0.1357
## Low - Medium -0.0360 0.272 Inf -0.132 0.9904
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low 0.3215 0.201 Inf 1.596 0.2472
## High - Medium 0.1502 0.183 Inf 0.822 0.6891
## Low - Medium -0.1712 0.188 Inf -0.913 0.6320
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates### Overall Establishment by mean temp
ggplot(final, aes(mean_temp, establishment, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Establishment per pot")
model3 <- glm(establishment ~ temp*species, family = "quasipoisson", data = final)
broom::tidy(anova(model3, test = "Chisq"))## # A tibble: 4 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 12269. NA
## 2 temp 2 71.4 837 12198. 5.29e- 6
## 3 species 3 9553. 834 2645. 0
## 4 temp:species 6 398. 828 2247. 9.57e-27em3 <- emmeans(model3, pairwise~temp|species)
em3## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 0.6035 0.1515 Inf 0.307 0.90042
## Low 2.0263 0.0744 Inf 1.881 2.17203
## Medium 1.2404 0.1102 Inf 1.025 1.45636
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.3791 0.1028 Inf 1.178 1.58058
## Low -0.0144 0.2063 Inf -0.419 0.38997
## Medium 1.0194 0.1230 Inf 0.778 1.26051
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.1588 0.0422 Inf 3.076 3.24156
## Low 3.3539 0.0383 Inf 3.279 3.42896
## Medium 3.3594 0.0382 Inf 3.285 3.43423
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.5108 0.2644 Inf -1.029 0.00745
## Low -0.2412 0.2311 Inf -0.694 0.21174
## Medium -0.1710 0.2231 Inf -0.608 0.26632
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.42273 0.1687 Inf -8.431 <.0001
## High - Medium -0.63691 0.1873 Inf -3.401 0.0019
## Low - Medium 0.78582 0.1329 Inf 5.912 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low 1.39351 0.2305 Inf 6.046 <.0001
## High - Medium 0.35976 0.1603 Inf 2.244 0.0640
## Low - Medium -1.03375 0.2402 Inf -4.304 <.0001
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.19508 0.0570 Inf -3.423 0.0018
## High - Medium -0.20056 0.0569 Inf -3.523 0.0012
## Low - Medium -0.00548 0.0541 Inf -0.101 0.9944
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.26966 0.3512 Inf -0.768 0.7227
## High - Medium -0.33987 0.3460 Inf -0.982 0.5880
## Low - Medium -0.07020 0.3212 Inf -0.219 0.9740
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates### Overall Germination by mean temp
ggplot(final, aes(mean_temp, germination, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Mean Temperature", y = "Germination per pot")
model4 <- glm(germination ~ temp*species, family = "quasipoisson", data = final)
broom::tidy(anova(model4, test = "Chisq"))## # A tibble: 4 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 10186. NA
## 2 temp 2 275. 837 9911. 3.21e-21
## 3 species 3 7284. 834 2627. 0
## 4 temp:species 6 196. 828 2431. 1.55e-12em4 <- emmeans(model4, pairwise~temp|species)
em4## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.035 0.1217 Inf 0.796 1.273
## Low 2.253 0.0662 Inf 2.123 2.382
## Medium 1.609 0.0913 Inf 1.431 1.788
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.870 0.0802 Inf 1.713 2.027
## Low 2.496 0.0586 Inf 2.381 2.610
## Medium 2.422 0.0608 Inf 2.303 2.541
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.162 0.0420 Inf 3.080 3.244
## Low 3.361 0.0380 Inf 3.286 3.435
## Medium 3.361 0.0380 Inf 3.287 3.436
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.138 0.2187 Inf -0.566 0.291
## Low 0.121 0.1921 Inf -0.256 0.498
## Medium 0.158 0.1886 Inf -0.211 0.528
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.218086 0.1385 Inf -8.795 <.0001
## High - Medium -0.574729 0.1521 Inf -3.779 0.0005
## Low - Medium 0.643356 0.1128 Inf 5.706 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.625962 0.0993 Inf -6.304 <.0001
## High - Medium -0.552669 0.1006 Inf -5.494 <.0001
## Low - Medium 0.073293 0.0845 Inf 0.868 0.6606
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.199019 0.0567 Inf -3.512 0.0013
## High - Medium -0.199515 0.0567 Inf -3.522 0.0012
## Low - Medium -0.000496 0.0538 Inf -0.009 1.0000
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.258574 0.2911 Inf -0.888 0.6477
## High - Medium -0.295845 0.2888 Inf -1.025 0.5613
## Low - Medium -0.037271 0.2692 Inf -0.138 0.9895
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates### Max Temp by Average mass by max temp
ggplot(final, aes(max_temp, average_mass, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Bimass per pot (g)")
model9 <- glm(average_mass ~ max_temp*species, family = "quasipoisson", data = final)
broom::tidy(anova(model9, test = "Chisq"))## # A tibble: 4 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 574 32.1 NA
## 2 max_temp 1 0.319 573 31.8 1.54e- 2
## 3 species 3 8.16 570 23.6 2.35e-32
## 4 max_temp:species 3 0.986 567 22.6 4.11e- 4em9 <- emmeans(model9, pairwise~species|max_temp)
em9## $emmeans
## max_temp = 28.2:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens -3.61 0.117 Inf -3.84 -3.38
## Layia platyglossa -3.03 0.105 Inf -3.23 -2.82
## Phacelia tanacetifolia -3.74 0.107 Inf -3.95 -3.53
## Salvia columbariae -2.27 0.078 Inf -2.42 -2.11
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## max_temp = 28.2:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.586 0.157 Inf -3.733 0.0011
## Bromus rubens - Phacelia tanacetifolia 0.125 0.159 Inf 0.788 0.8601
## Bromus rubens - Salvia columbariae -1.346 0.141 Inf -9.574 <.0001
## Layia platyglossa - Phacelia tanacetifolia 0.711 0.150 Inf 4.741 <.0001
## Layia platyglossa - Salvia columbariae -0.760 0.131 Inf -5.818 <.0001
## Phacelia tanacetifolia - Salvia columbariae -1.471 0.133 Inf -11.084 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates### Overall Establishment by max temp
ggplot(final, aes(max_temp, establishment, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Establishment per pot")
model11 <- glm(establishment ~ max_temp*species, family = "quasipoisson", data = final)
broom::tidy(anova(model11, test = "Chisq"))## # A tibble: 4 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 12269. NA
## 2 max_temp 1 2.12 838 12267. 4.02e- 1
## 3 species 3 9622. 835 2645. 0
## 4 max_temp:species 3 319. 832 2326. 9.77e-23em11 <- emmeans(model11, pairwise~species|max_temp)
em11## $emmeans
## max_temp = 28.4:
## species emmean SE df asymp.LCL asymp.UCL
## Bromus rubens 1.464 0.0605 Inf 1.346 1.5827
## Layia platyglossa 0.978 0.0749 Inf 0.831 1.1251
## Phacelia tanacetifolia 3.302 0.0231 Inf 3.257 3.3475
## Salvia columbariae -0.324 0.1452 Inf -0.609 -0.0396
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## max_temp = 28.4:
## contrast estimate SE df z.ratio
## Bromus rubens - Layia platyglossa 0.486 0.0963 Inf 5.045
## Bromus rubens - Phacelia tanacetifolia -1.838 0.0648 Inf -28.376
## Bromus rubens - Salvia columbariae 1.788 0.1573 Inf 11.369
## Layia platyglossa - Phacelia tanacetifolia -2.324 0.0784 Inf -29.632
## Layia platyglossa - Salvia columbariae 1.302 0.1634 Inf 7.971
## Phacelia tanacetifolia - Salvia columbariae 3.626 0.1470 Inf 24.663
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates## #Overall Germination by max temp
ggplot(final, aes(max_temp, germination, color = temp),show.legend=FALSE) +
geom_point() +
facet_wrap(~species) +
scale_color_brewer(palette = "Set1") + theme_classic() +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Maximum Temperature", y = "Germination per pot")
model12 <- glm(germination ~ temp*species, family = "quasipoisson", data = final)
broom::tidy(anova(model12, test = "Chisq"))## # A tibble: 4 × 6
## term df Deviance Resid..Df Resid..Dev p.value
## <chr> <int> <dbl> <int> <dbl> <dbl>
## 1 NULL NA NA 839 10186. NA
## 2 temp 2 275. 837 9911. 3.21e-21
## 3 species 3 7284. 834 2627. 0
## 4 temp:species 6 196. 828 2431. 1.55e-12em12 <- emmeans(model12, pairwise~temp|species)
em12## $emmeans
## species = Bromus rubens:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.035 0.1217 Inf 0.796 1.273
## Low 2.253 0.0662 Inf 2.123 2.382
## Medium 1.609 0.0913 Inf 1.431 1.788
##
## species = Layia platyglossa:
## temp emmean SE df asymp.LCL asymp.UCL
## High 1.870 0.0802 Inf 1.713 2.027
## Low 2.496 0.0586 Inf 2.381 2.610
## Medium 2.422 0.0608 Inf 2.303 2.541
##
## species = Phacelia tanacetifolia:
## temp emmean SE df asymp.LCL asymp.UCL
## High 3.162 0.0420 Inf 3.080 3.244
## Low 3.361 0.0380 Inf 3.286 3.435
## Medium 3.361 0.0380 Inf 3.287 3.436
##
## species = Salvia columbariae:
## temp emmean SE df asymp.LCL asymp.UCL
## High -0.138 0.2187 Inf -0.566 0.291
## Low 0.121 0.1921 Inf -0.256 0.498
## Medium 0.158 0.1886 Inf -0.211 0.528
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## species = Bromus rubens:
## contrast estimate SE df z.ratio p.value
## High - Low -1.218086 0.1385 Inf -8.795 <.0001
## High - Medium -0.574729 0.1521 Inf -3.779 0.0005
## Low - Medium 0.643356 0.1128 Inf 5.706 <.0001
##
## species = Layia platyglossa:
## contrast estimate SE df z.ratio p.value
## High - Low -0.625962 0.0993 Inf -6.304 <.0001
## High - Medium -0.552669 0.1006 Inf -5.494 <.0001
## Low - Medium 0.073293 0.0845 Inf 0.868 0.6606
##
## species = Phacelia tanacetifolia:
## contrast estimate SE df z.ratio p.value
## High - Low -0.199019 0.0567 Inf -3.512 0.0013
## High - Medium -0.199515 0.0567 Inf -3.522 0.0012
## Low - Medium -0.000496 0.0538 Inf -0.009 1.0000
##
## species = Salvia columbariae:
## contrast estimate SE df z.ratio p.value
## High - Low -0.258574 0.2911 Inf -0.888 0.6477
## High - Medium -0.295845 0.2888 Inf -1.025 0.5613
## Low - Medium -0.037271 0.2692 Inf -0.138 0.9895
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 3 estimates