BIO120H1 Lecture Notes - Lecture 5: Infinitesimal, Exponential Growth, Logistic Function
36
BIO120H1 Full Course Notes
Verified Note
36 documents
Document Summary
Collection of plants much more difficult to assess when trying to determine population density: aspen clones one seed, produces many identical, connected stems, dandelion females make copies of self, distributes genotype. 2 different ways to look at how processes change over time: discreet: large jumps from one time period to another. Logistic growth model + allee effects + time lags. Predict trajectory of population growth through time (i. e. n as a function of t) Time advances one step t t + 1. # individuals in population one step later nt + 1: general mode = nt + 1 = f(nt) Challenge choosing simple but realistic parameters for f. E = # who emigrate during one time step. Si(cid:373)plify & co(cid:374)vert cha(cid:374)ges per-capita rates: assume no immigration or emigration. Treat birth & death during one time step as per-capita rates that are fixed constants. Population changes by constant factor each time step: nt+1 = nt.
