BIO 356 Lab 3 Life History Analysis (1)

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BIO 356 / BEE 587 Lab Number 3 - Life History Analysis Michael Chiarello 2/20/24 Q1. One plausible explanation could be due to the fact that domesticated species don’t have to rely on sustained year over year survival in order to maintain the continuity of the species, because humans will replant yearly. Comparatively, wild species need to sustain life year over year in order to survive. A more compelling explanation could be that humans selectively bred plants that focused their energy on fruit production as opposed to structural growth, this could lend itself to plants that die after fruiting vs producing small fruit and living year over year. In the second scenario, plants are selectively bred for a specific trait, which in turn forces an evolutionary trade o ff for another, with the other being perenniality. Q2. The selective breeding of chickens to have faster growth rates and significantly more robust bodies. This practice is performed with the intent of decreasing the time, resources, and money it takes to raise chickens to slaughter size. The evolutionary trade o ff in this scenario would be in the survivorship of the chickens if not slaughtered, since they often develop health problems due to their irregular and accelerated growth. Q3. The CO, because the last day of egg laying is later. In addition, the medians of the data from “Five-day egg laying” and “Cumulative egg laying” from CO are both situated later in the selection stage data. Q4. The CO evolved longer life spans, because their longevity was higher in comparison to CB. It is likely that the longer life spans enabled a later data of maturity, which led to the CO group to produce more o ff spring later in life, pushing the median of eggs laid later into the study. Q5. When a species lives in an unpredictable environment with low survivorship, it is likely that the early breeders within that population will be able to have more o ff spring. The o ff spring from random early breeders could have greater survivorship if they are also early breeders and have accelerated maturation. Over time a shift in breeding age and maturation will occur within the population in order to compensate for the increased and unpredictable environmental mortality rate. Q6. The author of the study may have compared the two extremes in order to draw more meaning from the data, with the hope of gaining greater significance. Representing the averages may not statistically or visually show the desired trends, because averages by definition are average. Q7. I would expect to see a positive relationship between wood density and survivorship. According to the data, slow growing trees have increased survivorship compared to fast growing. In general, slow growing trees have denser wood, which helps to back the hypothesis. Q8. In general, the empirical observations did match my prediction for wood density and survivorship. According to the data, a positive relationship existed between survivorship and wood specific gravity(WSG) which is a homologous indicator of wood density. These findings indicate that trees with denser wood survived longer. Q9. Height seems to have the weakest correlation, which can be seen in the number of data outliers Q10. The correlation of the new data does not match the growth-survival hypothesis from the last question. In this data, height and Logseedmass are inconclusive, but WSG and LMA have a negative relationship with survivorship. This relationship indicates that saplings with higher wood density and higher leaf mass per area have lower survivorship. dat = read.table( 'https://raw.githubusercontent.com/rafaeldandrea/BIO-356/master/Supplement_20100505.txt' , skip = 25 , header = TRUE ) %>% as_tibble ## Original data set records missing data as -99. This line replaces it with NA dat[dat == - 99 ] = NA dtf = dat %>% select(GENUS., SPECIES., WSG, SEEDMASS, HEIGHT, LMA, RGR95SAP, MRT25SAP) %>% mutate(LOGSEEDMASS = log10(SEEDMASS)) %>% pivot_longer(-c(GENUS., SPECIES., RGR95SAP, MRT25SAP), names_to = 'trait' ) %>% mutate(growth = RGR95SAP, survival = 100 - MRT25SAP) %>% select(-c(RGR95SAP, MRT25SAP)) %>% pivot_longer(c(growth, survival), names_to = 'demographic' ,values_to = 'rate' ) %>% filter(trait != 'SEEDMASS' ) plot_growth_vs_traits = dtf %>% filter(demographic == 'growth' ) %>% ggplot(aes(value, rate)) + geom_point() + theme(aspect.ratio = 1 ) + facet_wrap(~ trait, ncol = 2 , scales = 'free' ) + labs( x = 'trait value' , y = 'relative growth rate of fastest-growing saplings (cm per cm dbh per year)' ) + ggtitle( 'Sapling growth rate vs functional traits on BCI' ) plot_growth_vs_traits ## Warning: Removed 154 rows containing missing values (`geom_point()`). Q11. Species B is the colonizer and species A is the competitor. This is supported by the fact that species B has a higher initial and sustained population that is balanced by species A. ## Model CCT_Model = function ( initial_p, fecundity, mortality, displacement_matrix, final_time, time_step ){ CCT = function (t, state, parameters){ with(as.list(parameters), { p = state p[p < 1e-200 ] = 0 dpdt = (( 1 - sum(p)) * f - m + as.numeric( T %*% p)) * p list(dpdt) }) } times = seq( 0 , final_time, by = time_step) parameters = list( f = fecundity, m = mortality, h = displacement_matrix, T = fecundity * displacement_matrix - t(fecundity * displacement_matrix) ) state = initial_p out = ode(y = state, times = times, func = CCT, parms = parameters) out[out < 0 ] = 0 return ( list( parameters = list( initial_p, fecundity, mortality, displacement_matrix ), initial_conditions = initial_p, state = out ) ) } # Plotting the model outcome Plot_CCT_timeseries = function (model){ as.data.frame(model$state) %>% pivot_longer(-time, names_to = 'species' , values_to = 'occupancy' ) %>% ggplot(aes(time, occupancy, group = species, color = species)) + geom_line(size = 1 ) + expand_limits(y = 0 ) } Plot_CCT_stemplot = function (trait, model){ tibble(trait = trait, occupancy = model$state[nrow(model$state), - 1 ]) %>% ggplot() + geom_segment(aes(trait, occupancy, xend = trait, yend = occupancy - occupancy)) + geom_point(aes(trait, occupancy)) } # Parameters number_of_species = 2 fecundity = c( 1 , 5 ) p0 = c( .1 , .1 ) mortality = .7 final_year = 25 # Call the model model = CCT_Model( initial_p = p0, fecundity = fecundity, mortality = mortality, displacement_matrix = 1 * upper.tri(diag(number_of_species)), final_time = final_year, time_step = .1 ) # Plot results plot_timeseries = as.data.frame(model$state) %>% as_tibble %>% rename(`species A` = `1`, `species B` = `2`) %>% mutate(empty = 1 - `species A` - `species B`) %>% pivot_longer(-time, names_to = 'species' , values_to = 'occupancy' ) %>% ggplot(aes(time, occupancy, group = species, color = species)) + geom_line(size = 1 ) + expand_limits(y = 0 ) + theme(aspect.ratio = 1 ) ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. ## ℹ Please use `linewidth` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. plot_timeseries Q12. When the mortality is decreased to .5, species A is to benefit. When mortality is decreased, it results in there being slightly less empty space. The decrease in empty space curbs the colonizers ability to colonize and allows the competitors to thrive, as the colonizers are not good competitors. Graphically this leads to a population crossover that leaves species A above species B in terms of occupancy. # Parameters number_of_species = 2 fecundity = c( 1 , 5 ) p0 = c( .1 , .1 ) mortality = .5 final_year = 25 # Call the model model = CCT_Model( initial_p = p0, fecundity = fecundity, mortality = mortality, displacement_matrix = 1 * upper.tri(diag(number_of_species)), final_time = final_year, time_step = .1 ) # Plot results plot_timeseries = as.data.frame(model$state) %>% as_tibble %>% rename(`species A` = `1`, `species B` = `2`) %>% mutate(empty = 1 - `species A` - `species B`) %>% pivot_longer(-time, names_to = 'species' , values_to = 'occupancy' ) %>% ggplot(aes(time, occupancy, group = species, color = species)) + geom_line(size = 1 ) + expand_limits(y = 0 ) + theme(aspect.ratio = 1 ) plot_timeseries Q13. Since fecundity increases as competitive ability decreases due to evolutionary trade o ff s, species 90 would have higher fecundity because they have lower occupancy and a lower competition rank, as compared to species 10 which has a high competition rank and higher occupancy. # Parameters number_of_species = 100 fecundity = seq( 81 , 1000 , length = number_of_species) p0 = rep( 1 /number_of_species, number_of_species) mortality = 80 final_year = 10000 # Call the model model = CCT_Model( initial_p = p0, fecundity = fecundity, mortality = mortality, displacement_matrix = 1 * upper.tri(diag(number_of_species)), final_time = final_year, time_step = 1 ) # Plot results plot_stemplot = Plot_CCT_stemplot(trait = seq(number_of_species), model = model) + scale_y_sqrt() plot_stemplot + xlab( 'competition rank' ) Q14. If you give all of the species the same fecundity, then all of the species will have the same occupancy regardless of competition rank, with the exception of the species at competition rank 1. This phenomena occurs because when fecundity is constant the species that is most competitive will be able to overwhelm the rest of the remaining species, which relates to evolutionary trade o ff s. This is confirmed by the model that shows a heavily skewed distribution, with the species at competition rank 1 having the highest occupancy, and all other species having equally low occupancy in comparison. # Parameters number_of_species = 100 fecundity = rep( 100 , length = number_of_species) p0 = rep( 1 /number_of_species, number_of_species) mortality = 80 final_year = 10000 # Call the model model = CCT_Model( initial_p = p0, fecundity = fecundity, mortality = mortality, displacement_matrix = 1 * upper.tri(diag(number_of_species)), final_time = final_year, time_step = 1 ) # Plot results plot_stemplot = Plot_CCT_stemplot(trait = seq(number_of_species), model = model) + scale_y_sqrt() plot_stemplot + xlab( 'competition rank' ) Q15. The species at competition rank 1 would likely exhibit signs of a Darwinian Demon, “which is an unrealistic product of evolution” -Marisa, because that species seems to maximize fitness and maintain high occupancy, allowing them to overwhelm all of the other species. Q16. The results from England et al. somewhat support the IDH, because species count does increase with environmental disturbances, but the data plateaus. At lower levels of disturbance, the species count remains low, but as the wave count increases the species count increases substantially, only to decrease again when wave count reaches unfavorable amounts to sustain diverse life. The wave count relates to mortality, as high mortality level at extreme wave counts is unfavorable for both colonizers and competitors. Additional evidence regarding mortality levels and the breakdown of species counts into competitors and colonizers could give more context for corroborating the IDH.

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