“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)
## Loading required package: ggplot2
## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.
library(ggplot2)
library(tidyverse)
## ── Attaching packages
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## tidyverse 1.3.2 ──
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ✔ purrr 0.3.5
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(dplyr)
library(MASS)
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
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
## `geom_smooth()` using formula = 'y ~ x'
###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)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
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
## `geom_smooth()` using formula = 'y ~ x'
###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
## `geom_smooth()` using formula = 'y ~ x'
###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
## `geom_smooth()` using formula = 'y ~ x'
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)
## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
TempspeciesFacet
## `geom_smooth()` using formula = 'y ~ x'
###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
## `geom_smooth()` using formula = 'y ~ x'
###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)
## NOTE: Results may be misleading due to involvement in interactions
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
## `geom_smooth()` using formula = 'y ~ x'
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
## `geom_smooth()` using formula = 'y ~ x'
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
## `geom_smooth()` using formula = 'y ~ x'
###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
## `geom_smooth()` using formula = 'y ~ x'
#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 ' ' 1
e4 <- emmeans(m2, pairwise~temp|species)
## NOTE: Results may be misleading due to involvement in interactions
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)
## NOTE: Results may be misleading due to involvement in interactions
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")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 265 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 265 rows containing missing values (`geom_point()`).
###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")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 265 rows containing non-finite values (`stat_smooth()`).
## Removed 265 rows containing missing values (`geom_point()`).
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")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 265 rows containing non-finite values (`stat_smooth()`).
## Removed 265 rows containing missing values (`geom_point()`).
###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 ' ' 1
e8 <- 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 estimates
m6 <- 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 ' ' 1
e9 <- 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
##
## $contrasts
## mean_temp = 26.5:
## contrast estimate SE df z.ratio p.value
## Bromus rubens - Layia platyglossa -0.356 0.169 Inf -2.103 0.1520
## Bromus rubens - Phacelia tanacetifolia 0.154 0.158 Inf 0.976 0.7634
## Bromus rubens - Salvia columbariae -1.279 0.138 Inf -9.263 <.0001
## Layia platyglossa - Phacelia tanacetifolia 0.510 0.167 Inf 3.058 0.0120
## Layia platyglossa - Salvia columbariae -0.922 0.145 Inf -6.360 <.0001
## Phacelia tanacetifolia - Salvia columbariae -1.433 0.135 Inf -10.606 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates
###Set up percent successful establishment of individuals
final$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 = "Temperature", y = "Bimass per Individual")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 265 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 265 rows containing missing values (`geom_point()`).
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 ' ' 1
em1 <- 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 = "Temperature", y = "Percent Successfully Established")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 137 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 137 rows containing missing values (`geom_point()`).
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 ' ' 1
em2 <- 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 = "Temperature", y = "Number Established Individuals")
## `geom_smooth()` using formula = 'y ~ x'
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 ' ' 1
em3 <- 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 = "Temperature", y = "Number Germinated Individuals")
## `geom_smooth()` using formula = 'y ~ x'
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 ' ' 1
em4 <- 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 ' ' 1
em5 <- 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 estimates
model6 <- 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 ' ' 1
em6 <- 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 estimates
model7 <- 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 ' ' 1
em7 <- 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 estimates
model8 <- 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 ' ' 1
em8 <- 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")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 265 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 265 rows containing missing values (`geom_point()`).
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 ' ' 1
em9 <- 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")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 137 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 137 rows containing missing values (`geom_point()`).
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 ' ' 1
em10 <- 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")
## `geom_smooth()` using formula = 'y ~ x'
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 ' ' 1
em11 <- 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")
## `geom_smooth()` using formula = 'y ~ x'
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 ' ' 1
em12 <- 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 = "Number Germinated Individuals")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 137 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 137 rows containing missing values (`geom_point()`).
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 ' ' 1
em13 <- emmeans(model13, pairwise~temp|species)
## NOTE: Results may be misleading due to involvement in interactions
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
###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.