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BIO 356 / BEE 587 Lab 3 - Life History Analysis Sara Asher 2/20/2024 Q1 The balance of evolutionary trade-offs is shifted during the domestication process from perennials to annuals. In order to accommodate human preferences and agricultural goals, domestication places a higher value on features that maximize short-term reproductive success than on long-term survival tactics. Q2. Another example of domesticated organism where selective breeding for desirable life history trait was obtained is the domestication of dogs. In the part dogs were domesticated from wolves when they were puppies for various human purposes such as companionship,herding etc.Due to this their life history traits have increased with reduction in overall body size and an increase in reproductive output.whereas in smaller dogs we have also observed faster growth but the down side to this is that because of domestication these dogs have lost their ferociousness and aggression which could help them survive in the wild. Since they are now tamed their ability to survive in the wild has reduced greatly. Q3. It can be seen that based on the table CO population evolved a later breeding schedule since the last day of laying is later than the CB population. Q4. The CO population evolved a longer lifespan with the longevity being 30.25 Based on the previous question later breeding schedule results in a longer lifespan. Q5. Assuming the longevity and late breeding schedules co evolve as seen in the experiment we predict that in an uncertain environment where individuals are prone to unpredictable mortality early breeding would be more suitable in that environment Q6. The authors specifically compare the growth of the fastest-growing saplings against survival of the slowest- growing saplings, as opposed to comparing average growth to average survival across all trees of each species because trade offs don’t just depend on the organisms but also on the environment as an organism exists 1

within its environment and it is important to consider that as a major factor. Showing the average stifles the factor of environment impact since it is not a very specific analysis that doesn’t take into consideration the environment. Q7. We expect to see a positive association between wood density and survival and a negative association between wood density and relative growth rate. Denser wood may improve survivability in storm-prone places and other conditions with high mechanical stress because of its enhanced resilience. On the other hand, species with lower wood density but faster growth rates may out compete denser species in resource-rich environments, suggesting that there may be a negative relationship between wood density and growth. Q8. Yes the empirical observations match my prediction for wood density since we can observe a positive associ- ation between the two quantities. The correlation coefficient is 0.48 which is not a very strong correlation. Q9. With a correlation of 0.00 and a p-value of 0.983, which indicates no meaningful relationship, height has the least correlation with sapling survival. In comparison to other features like leaf mass per area, log- transformed seed mass, and wood specific gravity, this shows that sapling height may not be a major factor impacting survival. Q10. For LMA, log seed mass and WSG we can clearly see a negative relationship between growth and vs func- tional trait which supports the hypothesis that if a sapling invests more in growth then the survival will decrease.Comparing the two graphs we can see that in Q8 traits are positively correlated with survival and Q10 the traits are negatively correlated with growth which hence further supports the hypothesis. 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 ' ) %>% 2

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()‘). LOGSEEDMASS WSG HEIGHT LMA -4 -2 0 0.3 0.4 0.5 0.6 0.7 0.8 10 20 30 40 25 50 75 100 0.10 0.15 0.20 0.25 0.30 0.35 0.10 0.15 0.20 0.25 0.30 0.35 0.10 0.15 0.20 0.25 0.30 0.35 0.10 0.15 0.20 0.25 0.30 0.35 trait value tive growth rate of fastest-growing saplings (cm per cm dbh per yea Sapling growth rate vs functional traits on BCI Q11. It can be observed that species A is the competitor and Species B is the colonizer where species A out competes species B but species B occupies more space by decreasing mortality for species A 3

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Q12. If adult mortality is decreased species A the competitor should benefit as that would lead to less vacant spots available to species B as represented in the graph. ## 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 ) ) 4

} # 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 = . 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 ) ## 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 5

## generated. plot_timeseries 0.0 0.2 0.4 0.6 0.8 0 5 10 15 20 25 time occupancy species empty species A species B # 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) + 6

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scale_y_sqrt () plot_stemplot + xlab ( ' competition rank ' ) 0.000 0.025 0.050 0.075 0 25 50 75 100 competition rank occupancy Q13. Between species 10 and species 90 we see that species 90 has a higher fecundity while species 10 has the higher competitive rank. There is a trade off between competition and fecundity where those species who have higher competitive rank and survive longer tend to have a lower fecundity. meanwhile those who have lower competitive rank tend to have a higher fecundity since their goal is to reproduce faster and in larger numbers. Q14. If we gave all the species the same fecundity we can observe that the most competitive species takes over all the occupancy to increase their own fecundity and the other species do not have any fecundity or occupancy. # Parameters number_of_species = 100 fecundity = rep ( 80 , length = number_of_species) p0 = rep ( 1 / number_of_species, number_of_species) mortality = 80 7

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 ' ) 0e+00 5e-07 1e-06 0 25 50 75 100 competition rank occupancy Q15. It can be seen that the theoretical scenario in Q14 relates very well to the concept of Darwinian demon which talks about one species that can maximize all elements of its fitness simultaneously.In scenario Q14. we see 8

that when the fecundity is the same for all organisms the most competitive organism will take over all the occupancy and maximize all its elements as much as it can.In both the scenarios we can also observe that there is no trade off or constraints holding the species back.Hence as seen in scenario above the competitor has no trade off therefore they will have no constraints in reproduction. Q16. The results of England et Al support the IDH. Some additional evidence regarding the species examined would further corroborate the IDH would be the ratio between competitors and colonizers in terms of which one is dominating and if they are able to coexist 9

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