Approach
Approach
Lasting influence of the Bever model
Code
# Dataset downloaded from Figshare:
# curl::curl_download("https://figshare.com/ndownloader/files/14874749", destfile = "../crawford-supplement.xlsx")
read_excel("../data/crawford-supplement.xlsx", sheet = "Data") |>
mutate(stab = -0.5*rrIs) |> # show in terms of stabilization instead of I_s
filter(stab > -3) |> # Remove the pair with Stab approx. 6 -- according to McCarthy Neuman (original author), this is likely due to bad seed survival
ggplot(aes(x = stab)) +
geom_histogram(color = "black") +
geom_histogram(fill = "#56A0D3") +
geom_rug(color = "grey25") +
geom_vline(xintercept = 0) +
annotate("text", x = Inf, y = Inf, label = "N = 1037 pairwise comparisons\n
57% experience positive stabilization",
vjust=1, hjust = 1, size = 6) +
xlab("Stabilization") + ylab("Count")
Data from Crawford et al. (2019)
A puzzle: same stabilization, different model dynamics
Code
# Define a function used for making the PSF framework
# schematic, given a set of parameter values
make_params_plot <- function(params, scale = 1.5) {
color_func <- function(x) {
ifelse(x < 0, "#EE6677", "#4477AA")
}
df <- data.frame(x = c(0,0,1,1),
y = c(0,1,0,1),
type = c("M", "P", "M", "P"))
params_plot <-
ggplot(df) +
annotate("text", x = 0, y = 1.1, size = 3.25, label = "Plant 1", color = "black", fill = "#9970ab", fontface = "bold") +
annotate("text", x = 1, y = 1.1, size = 3.25, label = "Plant 2", color = "black", fill = "#5aae61", fontface = "bold") +
annotate("text", x = 0, y = -0.15, size = 3.25, label = "Soil\nmicrobes A", label.size = 0, fill = "transparent") +
annotate("text", x = 1, y = -0.15, size = 3.25, label = "Soil\nmicrobes B", label.size = 0, fill = "transparent") +
annotate("text", x = 0.45, y = -0.4, size = 3.5, fill = "transparent",
label.size = 0.05,
label = (paste0("Stabilization = ",
0.5*(params["m1B"] + params["m2A"] - params["m1A"] - params["m2B"])))) +
geom_segment(aes(x = 0, xend = 0, y = 0.1, yend = 0.9),
arrow = arrow(length = unit(0.03, "npc")),
linewidth = abs(params["m1A"])*scale,
color = alpha(color_func(params["m1A"]), 1)) +
geom_segment(aes(x = 0.05, xend = 0.95, y = 0.1, yend = 0.9),
arrow = arrow(length = unit(0.03, "npc")),
linewidth = abs(params["m2A"])*scale,
color = alpha(color_func(params["m1B"]),1)) +
geom_segment(aes(x = 0.95, xend = 0.05, y = 0.1, yend = 0.9),
arrow = arrow(length = unit(0.03, "npc")),
linewidth = abs(params["m1B"])*scale,
color = alpha(color_func(params["m2A"]), 1)) +
geom_segment(aes(x = 1, xend = 1, y = 0.1, yend = 0.9),
arrow = arrow(length = unit(0.03, "npc"),),
linewidth = abs(params["m2B"])*scale,
color = alpha(color_func(params["m2B"]), 1)) +
# Plant cultivation of microbes
geom_segment(aes(x = -0.25, xend = -0.25, y = 0.9, yend = 0.1), linewidth = 0.15, linetype = 1,
arrow = arrow(length = unit(0.03, "npc"))) +
geom_segment(aes(x = 1.25, xend = 1.25, y = 0.9, yend = 0.1), linewidth = 0.15, linetype = 1,
arrow = arrow(length = unit(0.03, "npc"))) +
annotate("text", x = 0, y = 0.5,
label = (paste0("m1A = ", params["m1A"])),
angle = 90, vjust = -0.25, size = 3) +
annotate("text", x = 1.15, y = 0.5,
label = (paste0("m2B = ", params["m2B"])),
angle = -90, vjust = 1.5, size = 3) +
annotate("text", x = 0.75, y = 0.75,
label = (paste0("m2A = ", params["m2A"])),
angle = 45, vjust = -0.25, size = 3) +
annotate("text", x = 0.25, y = 0.75,
label = (paste0("m1B = ", params["m1B"])),
angle = -45, vjust = -0.25, size = 3) +
xlim(c(-0.4, 1.4)) +
coord_cartesian(ylim = c(-0.25, 1.15), clip = "off") +
theme_void() +
theme(legend.position = "none",
plot.caption = element_text(hjust = 0.5, size = 10))
return(params_plot)
}
# Define a function for simulating the dynamics of the
# PSF model with deSolve
params_coex <- c(m10 = 0.16, m20 = 0.16,
m1A = 0.11, m1B = 0.26,
m2A = 0.27, m2B = 0.13, v = 1)
time <- seq(0,50,0.1)
init_pA_05 <- c(N1 = 3, N2 = 7, p1 = 0.3, pA = 0.3)
out_pA_05 <- ode(y = init_pA_05, times = time, func = psf_model, parms = params_coex) |> data.frame()
params_to_plot_coex <- c(m1A = unname(params_coex["m1A"]-params_coex["m10"]),
m1B = unname(params_coex["m1B"]-params_coex["m10"]),
m2A = unname(params_coex["m2A"]-params_coex["m20"]),
m2B = unname(params_coex["m2B"]-params_coex["m20"]), v=1)
param_plot_coex <-
make_params_plot(params_to_plot_coex, scale = 6) +
annotate("text", x = 1.375, y = 0.5,
label = paste0("v = ", params_coex["v"]),
angle = -90, vjust = 1.5, size = 3)
panel_abund_coex <-
out_pA_05 |>
as_tibble() |>
select(time, N1, N2) |>
pivot_longer(N1:N2) |>
ggplot(aes(x = time, y = value, color = name)) +
geom_line(linewidth = 0.9) +
scale_color_manual(values = c("#9970ab", "#5aae61"),
name = "Plant species", label = c("Plant 1", "Plant 2"), guide = "none") +
scale_y_continuous(breaks = c(2e4, 4e4, 6e4, 8e4), labels = scales::scientific) +
ylab("Abundance") +
theme(axis.title = element_text(size = 10))
# Panel D: plot for frequencies of both plants when growing
# in dynamic soils
panel_freq_coex <-
out_pA_05 |>
as_tibble() |>
mutate(p2 = 1-p1) |>
select(time, p1, p2) |>
pivot_longer(p1:p2) |>
ggplot(aes(x = time, y = value, color = name)) +
geom_line(linewidth = 1.2) +
scale_color_manual(values = c("#9970ab", "#5aae61"),
name = "Plant species", label = c("Plant 1", "Plant 2")) +
scale_y_continuous(limits = c(0,1), breaks = c(0, 0.5, 1)) +
ylab("Frequency") +
theme(axis.title = element_text(size = 10))
param_plot_coex + {panel_abund_coex+panel_freq_coex &
theme(axis.text = element_text(size = 8), legend.position = 'none')} +
plot_layout(widths = c(1/3, 2/3))
Code
# Define a function for simulating the dynamics of the
# PSF model with deSolve
params <- c(m10 = 0.16, m20 = 0.16,
m1A = 0.02, m2A = 0.33,
m1B = 0.18, m2B = 0.20, v = 1)
time <- seq(0,200,0.1)
init_pA_05 <- c(N1 = 3, N2 = 7, p1 = 0.3, pA = 0.3)
out_pA_05 <- ode(y = init_pA_05, times = time, func = psf_model, parms = params) |> data.frame()
params_to_plot <- c(m1A = unname(params["m1A"]-params["m10"]),
m1B = unname(params["m1B"]-params["m10"]),
m2A = unname(params["m2A"]-params["m20"]),
m2B = unname(params["m2B"]-params["m20"]), v=1)
param_plot <-
make_params_plot(params_to_plot, scale = 6) +
annotate("text", x = 1.375, y = 0.5,
label = paste0("v = ", params["v"]),
angle = -90, vjust = 1.5, size = 3)
panel_abund <-
out_pA_05 |>
as_tibble() |>
select(time, N1, N2) |>
pivot_longer(N1:N2) |>
ggplot(aes(x = time, y = value, color = name)) +
geom_line(linewidth = 0.9) +
scale_color_manual(values = c("#9970ab", "#5aae61"),
name = "Plant species", label = c("Plant 1", "Plant 2"), guide = "none") +
scale_y_continuous(breaks = c(0,8e17,1.6e18), labels = c("0", "7e17","1.4e18")) +
ylab("Abundance")
# Panel D: plot for frequencies of both plants when growing
# in dynamic soils
panel_freq <-
out_pA_05 |>
as_tibble() |>
mutate(p2 = 1-p1) |>
select(time, p1, p2) |>
pivot_longer(p1:p2) |>
ggplot(aes(x = time, y = value, color = name)) +
geom_line(linewidth = 1.2) +
scale_color_manual(values = c("#9970ab", "#5aae61"),
name = "Plant species", label = c("Plant 1", "Plant 2")) +
scale_y_continuous(limits = c(0,1), breaks = c(0, 0.5, 1)) +
ylab("Frequency")
param_plot + {panel_abund + panel_freq &
theme(axis.text = element_text(size = 8), legend.position = 'none')} +
plot_layout(widths = c(1/3, 2/3))
How can we more accurately infer predicted plant dynamics under plant–soil feedback?
Build on insights from coexistence theory (Chesson 2018):
- Stablization is necessary for coexistence but doesn’t guarantee coexistence
- Stable coexistence is possible only when stabilization overcomes competitive imbalances (fitness differences)
How can we more accurately infer predicted plant dynamics under plant–soil feedback?
How can we more accurately infer predicted plant dynamics under plant–soil feedback?
How can we more accurately infer predicted plant dynamics under plant–soil feedback?
How can we more accurately infer predicted plant dynamics under plant–soil feedback?
Code
base <-
tibble(sd = seq(-1,1,0.1),
fd = c(seq(1,0,-0.1), seq(0.1,1,0.1))) |>
ggplot(aes(x = sd, y = fd)) +
geom_line(linetype = "dashed") +
geom_hline(yintercept = 0, linewidth = 1) +
geom_vline(xintercept = 0, linewidth = 1) +
xlab("Stabilization") +
ylab("Fitness difference") +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.title = element_text(size = 25)) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0.04,0))
base
Code
base <-
base +
annotate("text", x = Inf, y = 0, hjust = 1, vjust = -1, label = "Coexistence", size = 7, family = "italic") +
annotate("text", x = -Inf, y = 0, hjust = 0, vjust = -1, label = "Priority effects", size = 7, family = "italic") +
annotate("label", x = 0, y = Inf, vjust = 1.5, label = "Species exclusion", size = 7, fill = "#F0F1EB",family = "italic")
base
Code
metrics <- function(params) {
IS <- with(as.list(params), {m1A - m2A - m1B + m2B})
FD <- with(as.list(params), {(1/2)*(m1A+m2A) - (1/2)*(m1B+m2B)})
SD <- (-1/2)*IS
return(c(IS = IS, SD = SD, FD = FD))
}
coex_metrics <- metrics(params_coex)
base_coex <-
base +
inset_element({panel_freq_coex + theme(legend.position = 'none',
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
plot.background = element_rect(color = "grey"))},
0.75,0.15,1,0.5)
base_coex
Code
# Example point, using UR_FE values
base +
annotate("pointrange", x = 0.275, y = 0.0591, ymin = 0.0591-0.307,ymax = 0.0591+0.307, shape = 21, stroke = 1.5, size = 1, linewidth = 0.1, fill = "#4477aa") +
annotate("pointrange", x = 0.275, y = 0.0591, xmin = 0.275-0.152,xmax = 0.275+0.152, shape = 21, fill = "#4477aa", linewidth = 0.1, size = 1, stroke = 1.5) +
annotate("text", x = 0.275 + 0.3, y = 0.0591 + 0.15, color = "#4477aa", label = "Festuca - Plantago", size = 8, fontface = "italic") +
theme(axis.title = element_blank())
Code
# Dataset downloaded from Figshare:
# curl::curl_download("https://datadryad.org/downloads/file_stream/367188", destfile = "../data/kandlikar2021-supplement.csv")
biomass_wide <-
read_csv("../data/kandlikar2021-supplement.csv") |>
mutate(log_agb = log(abg_dry_g)) |>
mutate(source_soil = ifelse(source_soil == "aastr", "str", source_soil),
source_soil = ifelse(source_soil == "abfld", "fld", source_soil),
pair = paste0(source_soil, "_", focal_species)) |>
select(replicate, pair, log_agb) |>
pivot_wider(names_from = pair, values_from = log_agb) |>
filter(!is.na(replicate))
# Calculate the Stabilization between each species pair ----
# Recall that following the definition of the m terms in Bever 1997,
# and the analysis of this model in Kandlikar 2019,
# stablization = -0.5*(log(m1A) + log(m2B) - log(m1B) - log(m2A))
# Here is a function that does this calculation
# (recall that after the data reshaping above,
# each column represents a given value of log(m1A))
calculate_stabilization <- function(df) {
df |>
# In df, each row is one repilcate/rack, and each column
# represents the growth of one species in one soil type.
# e.g. "FE_ACWR" is the growth of FE in ACWR-cultivated soil.
mutate(AC_FE = -0.5*(AC_ACWR - AC_FEMI - FE_ACWR + FE_FEMI),
AC_HO = -0.5*(AC_ACWR - AC_HOMU - HO_ACWR + HO_HOMU),
AC_SA = -0.5*(AC_ACWR - AC_SACO - SA_ACWR + SA_SACO),
AC_PL = -0.5*(AC_ACWR - AC_PLER - PL_ACWR + PL_PLER),
AC_UR = -0.5*(AC_ACWR - AC_URLI - UR_ACWR + UR_URLI),
FE_HO = -0.5*(FE_FEMI - FE_HOMU - HO_FEMI + HO_HOMU),
FE_SA = -0.5*(FE_FEMI - FE_SACO - SA_FEMI + SA_SACO),
FE_PL = -0.5*(FE_FEMI - FE_PLER - PL_FEMI + PL_PLER),
FE_UR = -0.5*(FE_FEMI - FE_URLI - UR_FEMI + UR_URLI),
HO_PL = -0.5*(HO_HOMU - HO_PLER - PL_HOMU + PL_PLER),
HO_SA = -0.5*(HO_HOMU - HO_SACO - SA_HOMU + SA_SACO),
HO_UR = -0.5*(HO_HOMU - HO_URLI - UR_HOMU + UR_URLI),
SA_PL = -0.5*(SA_SACO - SA_PLER - PL_SACO + PL_PLER),
SA_UR = -0.5*(SA_SACO - SA_URLI - UR_SACO + UR_URLI),
PL_UR = -0.5*(PL_PLER - PL_URLI - UR_PLER + UR_URLI)) |>
select(replicate, AC_FE:PL_UR) |>
gather(pair, stabilization, AC_FE:PL_UR)
}
stabilization_values <- calculate_stabilization(biomass_wide)
# Seven values are NA; we can omit these
stabilization_values <- stabilization_values |>
filter(!(is.na(stabilization)))
# Calculating the Fitness difference between each species pair ----
# Similarly, we can now calculate the fitness difference between
# each pair. Recall that FD = 0.5*(log(m1A)+log(m1B)-log(m2A)-log(m2B))
# But recall that here, IT IS IMPORTANT THAT
# m1A = (m1_soilA - m1_fieldSoil)!
# The following function does this calculation:
calculate_fitdiffs <- function(df) {
df |>
mutate(AC_FE = 0.5*((AC_ACWR-fld_ACWR) + (FE_ACWR-fld_ACWR) - (AC_FEMI-fld_FEMI) - (FE_FEMI-fld_FEMI)),
AC_HO = 0.5*((AC_ACWR-fld_ACWR) + (HO_ACWR-fld_ACWR) - (AC_HOMU-fld_HOMU) - (HO_HOMU-fld_HOMU)),
AC_SA = 0.5*((AC_ACWR-fld_ACWR) + (SA_ACWR-fld_ACWR) - (AC_SACO-fld_SACO) - (SA_SACO-fld_SACO)),
AC_PL = 0.5*((AC_ACWR-fld_ACWR) + (PL_ACWR-fld_ACWR) - (AC_PLER-fld_PLER) - (PL_PLER-fld_PLER)),
AC_UR = 0.5*((AC_ACWR-fld_ACWR) + (UR_ACWR-fld_ACWR) - (AC_URLI-fld_URLI) - (UR_URLI-fld_URLI)),
FE_HO = 0.5*((FE_FEMI-fld_FEMI) + (HO_FEMI-fld_FEMI) - (FE_HOMU-fld_HOMU) - (HO_HOMU-fld_HOMU)),
FE_SA = 0.5*((FE_FEMI-fld_FEMI) + (SA_FEMI-fld_FEMI) - (FE_SACO-fld_SACO) - (SA_SACO-fld_SACO)),
FE_PL = 0.5*((FE_FEMI-fld_FEMI) + (PL_FEMI-fld_FEMI) - (FE_PLER-fld_PLER) - (PL_PLER-fld_PLER)),
FE_UR = 0.5*((FE_FEMI-fld_FEMI) + (UR_FEMI-fld_FEMI) - (FE_URLI-fld_URLI) - (UR_URLI-fld_URLI)),
HO_PL = 0.5*((HO_HOMU-fld_HOMU) + (PL_HOMU-fld_HOMU) - (HO_PLER-fld_PLER) - (PL_PLER-fld_PLER)),
HO_SA = 0.5*((HO_HOMU-fld_HOMU) + (SA_HOMU-fld_HOMU) - (HO_SACO-fld_SACO) - (SA_SACO-fld_SACO)),
HO_UR = 0.5*((HO_HOMU-fld_HOMU) + (UR_HOMU-fld_HOMU) - (HO_URLI-fld_URLI) - (UR_URLI-fld_URLI)),
SA_PL = 0.5*((SA_SACO-fld_SACO) + (PL_SACO-fld_SACO) - (SA_PLER-fld_PLER) - (PL_PLER-fld_PLER)),
SA_UR = 0.5*((SA_SACO-fld_SACO) + (UR_SACO-fld_SACO) - (SA_URLI-fld_URLI) - (UR_URLI-fld_URLI)),
PL_UR = 0.5*((PL_PLER-fld_PLER) + (UR_PLER-fld_PLER) - (PL_URLI-fld_URLI) - (UR_URLI-fld_URLI))) |>
select(replicate, AC_FE:PL_UR) |>
gather(pair, fitdiff_fld, AC_FE:PL_UR)
}
fd_values <- calculate_fitdiffs(biomass_wide)
# Twenty-two values are NA; let's omit these.
fd_values <- fd_values |> filter(!(is.na(fitdiff_fld)))
# Now, generate statistical summaries of SD and FD
stabiliation_summary <- stabilization_values |> group_by(pair) |>
summarize(mean_sd = mean(stabilization),
sem_sd = sd(stabilization)/sqrt(n()),
n_sd = n())
fitdiff_summary <- fd_values |> group_by(pair) |>
summarize(mean_fd = mean(fitdiff_fld),
sem_fd = sd(fitdiff_fld)/sqrt(n()),
n_fd = n())
# Combine the two separate data frames.
sd_fd_summary <- left_join(stabiliation_summary, fitdiff_summary)
# Some of the FDs are negative, let's flip these to be positive
# and also flip the label so that the first species in the name
# is always the fitness superior.
sd_fd_summary <- sd_fd_summary |>
# if mean_fd is < 0, the following command gets the absolute
# value and also flips around the species code so that
# the fitness superior is always the first species in the code
mutate(pair = ifelse(mean_fd < 0,
paste0(str_extract(pair, "..$"),
"_",
str_extract(pair, "^..")),
pair),
mean_fd = abs(mean_fd))
sd_fd_summary <-
sd_fd_summary |>
mutate(
# outcome = ifelse(mean_fd - 2*sem_fd >
# mean_sd + 2*sem_sd, "exclusion", "neutral"),
outcome2 = ifelse(mean_fd > mean_sd, "exclusion", "coexistence"),
outcome2 = ifelse(mean_sd < 0, "exclusion or priority effect", outcome2)
)
base +
geom_pointrange(data = sd_fd_summary,
aes(x = mean_sd, y = mean_fd,
ymin = mean_fd-sem_fd,
ymax = mean_fd+sem_fd, fill = outcome2), size = 1, linewidth = 0.1,
shape = 21, stroke = 1.5) +
geom_pointrange(data = sd_fd_summary,
aes(x = mean_sd, y = mean_fd,
xmin = mean_sd-sem_sd,
xmax = mean_sd+sem_sd, fill = outcome2), size = 1, linewidth = 0.1, shape = 21, stroke = 1.5) +
scale_fill_manual(values = c("#4477aa", "#ee6677", "#ccbb44")) +
theme(legend.position = "none")+
theme(axis.title = element_blank())
Detailed in Kandlikar et al. (2021)
Do microbes generate fitness differences in nature?
In California grasslands, microbes tend to drive stronger fitness differences than stabilization.
Can we answer this question more generally?
Results in Yan, Levine, and Kandlikar (2022); Analyses available on Zenodo
Microbially-mediated intransitivity in pine-oak forests in Spain, Pajares-Murgó et al. (2024)
Historical range of the longleaf pine ecosystem
Code
library(sf)
library(tidyverse)
little <- sf::st_read("../img/pinupalu.shp")
## Reading layer `pinupalu' from data source
## `C:\Users\gkandlikar\Desktop\gklab\talks\img\pinupalu.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 38 features and 5 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -95.21806 ymin: 26.61921 xmax: -75.79999 ymax: 36.84856
## CRS: NA
# States we want to show
llp_states <- c("texas", "louisiana","mississippi", "alabama", "florida", "georgia", "south carolina", "north carolina", "virginia", "tennessee")
states <- map_data("state") |>
filter(region %in% llp_states)
ggplot() +
geom_polygon(data = states,
aes(x = long, y = lat, group = group), fill = "transparent", color = 'darkgrey') +
geom_sf(data = little, fill = alpha('#228833', 0.5)) +
theme_void()
Fire helps maintain the open understory
Understory plant communities are among the most species–rich in North America!
Our question: Do microbes play a role in “shrubification” of unburned understories?
Across ecosystems, grasses tend to experience negative feedback
Code
# curl::curl_download("https://datadryad.org/downloads/file_stream/2789266", destfile = "../data/jiang2024-dataset.csv")
jiang_dat <- read_csv("../data/jiang2024-dataset.csv")
# jiang_metamod <- metafor::rma.mv(rr, var, data = jiang_dat, mods = ~ Life.form-1)
# saveRDS(jiang_metamod, "../data/jiang-metamod.rds")
metamod <- readRDS("../data/jiang-metamod.rds")
metamod_s <- broom::tidy(metamod)
jiang_dat |>
ggplot(aes(x = rr, y = Life.form, color = Life.form)) +
ggbeeswarm::geom_quasirandom(shape = 21) +
scale_color_manual(values = c("#ccbb44","#228833")) +
annotate("point",
x = metamod_s$estimate[1], y = 1, size = 3, shape = 21, stroke = 1.5) +
annotate("point",
x = metamod_s$estimate[2], y = 2, size = 3, shape = 21, stroke = 1.5) +
ylab("") +
xlab("Growth in self vs. growth in non-self") +
xlim(-2.5,2.5)+
geom_vline(xintercept = 0, linetype = 'dashed') +
theme(legend.position = "none",
axis.text.y = element_text(size = 12, color = 'black'))
Data from Jiang et al. (2024)
Common shrubs in longleaf pine understories
(Quercus marilandica)
(Vaccinium arboreum)
Common shrubs in longleaf pine understories
(Quercus marilandica)
Associates with ectomycorrhial fungi
(Vaccinium arboreum)
Associates with ericoid mycorrhizal fungi
Most herbaceous plants here associate with arbuscular mycorrhizae
Soil feedbacks vary by mycorrhizal type
Code
# download.file("https://www.science.org/doi/suppl/10.1126/science.aai8212/suppl_file/bennett_aai8212_database-s1.xlsx", destfile = "../data/bennett2017-dataset.xlsx")
readxl::read_xlsx("../data/bennett-2017.xlsx", sheet = 2) |>
mutate(`Type of Mycorrhiza` = factor(`Type of Mycorrhiza`, c("EM", "AM"))) |>
mutate(logratio = log(`Average of Biomass in Conspecific Soil`/`Average of Biomass in Heterospecific Soil`)) |>
arrange(logratio) |>
mutate(rown = row_number()) |>
ggplot(aes(x = logratio, color = `Type of Mycorrhiza`, y = rown, shape = `Type of Mycorrhiza`)) +
geom_point(size = 2) +
geom_segment(aes(x = 0, xend = logratio), linewidth = 0.125) +
xlab("Growth in self-conditioned microbes vs.\n nonself-conditioned microbes") +
geom_vline(xintercept = 0) +
scale_color_manual(values = c("#0077bb", "#cc3311")) +
theme(axis.line.y = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "inside",
legend.position.inside = c(0.9, 0.1),
legend.text = element_text(size = 12),
legend.title = element_blank())
Data from Bennett et al. (2017)
Do soil microbes play a role in driving shrubification under fire suppression?
What are the direct impacts of fire on soil microbial dynamics?
Does the history and severity of fire mediate the impact of soil microbes on plant performance and coexistence?
Postdoc
Mathematical modeling of plant dynamics
Fire disrupts plant–soil feedback
Model predictions
Interpretation: Fire suppression drives microbially driven shrub encroachment – especially if shrubs are insensitive to fire (as they generally are).
Manuscript in prep
Field studies of microbially mediated plant dynamics
Questions:
- How do soil abiotic properties recover post-burn?
- How does fire affect soil microbial composition and function (litter decomposition)?
Plant growth experiment
Question: How do soil communities shape interactions between grasses and shrubs in longleaf pine understories?
Focal species: Big bluestem, Little bluestem, Beaked panicgrass, Beautyberry, Winged sumac, Sweetgum
We are eager to accept any advice on seed germination!
Gradients in soil moisture from forest edge to interior
Gradients in soil moisture from forest edge to interior
Gradients in soil moisture from forest edge to interior
Detailed in Krishnadas et al. (2018)
Can variable plant–soil feedback explain this result?
Quantify pairwise plant–soil feedback under high and low water conditions in a shadehouse
Saini, Kandlikar, and Krishnadas in review
Saini, Kandlikar, and Krishnadas in review
Implication: Exaggerated microbially mediated fitness differences in dry soils could contribute to erosion of diversity at fragment edges.
Applications of basic ecology to habitat management
Does a soil-centric perspective help manage coffee invasions and restoration?
Do soil fungi shape tree abundance in tropical forests?
Postbac Scholar
Code
library(tidyverse)
read_csv("brapa-data.csv") |>
select(-x) |>
mutate(Seed = ifelse(Seed == "HW", "High water", "Low water"),
Soil = ifelse(Soil == "HW", "High water", "Low water"),
Current = ifelse(Current == "HW", "High water", "Low water")) |>
pivot_wider(names_from = Type, values_from = y) |>
ggplot(aes(x = Current, y = mean, ymin = sdlo,
ymax = sdhi, fill = Soil, shape = Seed)) +
geom_pointrange(position = position_dodge(width = 0.5),
size = .75) +
scale_shape_manual(values = c(21,23)) +
scale_fill_manual(values = c("#004488", "#bb5566")) +
guides(shape = guide_legend(position = "inside", title =
"Seed history",
override.aes = list(fill = "grey")),
fill = guide_legend(position = "inside",
title = "Soil history",
override.aes = list(shape = 21))) +
ylab("Per-capita fecundity") +
xlab("Contemporary watering") +
labs(caption = "Estimates from a Bayesian GLMM (Poisson family)") +
theme(axis.title = element_text(size = 12),
legend.position = c(0.8, 0.7),
legend.box.background = element_rect(color = "grey", linewidth = 0.5))
Plant history (maternal effects) and soil history both shape plant response to drought
Other projects in the lab
Plant–mycorrhizal symbiosis under nutrient enrichment
Citizen science for invasion ecology
Other projects in the lab
Fungal drivers of monodominance in tropical forests
Metabolic modeling of symbioses under abiotic stress