Looking at how community temperature dependence emerges from simulated species thermal performance curves

toy_community_TPC.R

# Test hypothesis that activation energy of community is determined from activation energies of its determinants

n = 3

community <- data.frame(expand.grid(temp = seq(275, 305,0.5), curve = c(1:3)))
params <- data.frame(ln.c = c(1.5, 1, 0.5), 
                    Ea = c(0.7, 0.5, 0.3), 
                    Eh = rep(rnorm(n, 3, sd = 0.5)), 
                    Th = c(rnorm(n, 300, sd = 2)), 
                    curve = c(1,2,3))

community <- merge(community, params, by = 'curve') %>%
  mutate(., rate = schoolfield.high(ln.c, Ea, Eh, Th, temp, Tc = 15)) %>%
  select(., c(curve, temp, rate))

whole_community <- community %>%
  group_by(temp) %>%
  summarise(rate = sum(rate))

fit_whole_community <- nls(rate ~ schoolfield.high(ln.c, Ea, Eh, Th, temp = temp, Tc = 15),
                           data = whole_community,
                           start = list(ln.c = 1, Ea = 1, Eh = 4, Th = 295))

ggplot(community) +
  geom_line(aes(temp - 273.15, rate, group = curve), alpha = 0.5) +
  geom_line(aes(temp - 273.15, rate), whole_community)