The variable density model gives predictions which compare well with commercial data; ignoring density variations leads to significant underprediction.
While diffusion-induced convection can be neglected for sufficiently dilute solutions, ignoring density differences even in this limit can lead to erroneous results.
Ignoring density effects also leads to models placing too much emphasis on the environmental conditions prevailing during the naïve pup's first year at sea.
Although this system is more complex than the one-dimensional idealization of the Bartonian model (even ignoring density dependence), numerical results suggest that it may be possible to approximate the dynamics of EU by a one-dimensional model.
However, the notion of the importance of adult survival to population growth based on this analysis has to be treated somewhat cautiously as the technique of basic elasticity analysis has been criticised for ignoring density dependence, stochasticity and correlations between the transition matrix entries [ 35, 36].
Critics argue that such studies are unproductive and flawed because they often combine unrelated diseases, apply arbitrary or even "gerrymandered" boundaries, contain insufficient numbers of cases, and ignore population density, latency, and known risk factors [ 1].
Ignoring density dependence, this range of outcomes is equivalent to a population that could decline by 10% over 7 years, to one that could grow by 100% in just 5 years.
Ignoring density dependence in growth may thus result in erroneous predictions about the strength of selection and the predicted evolutionary endpoints.
In addition to having different descriptions of counterion distribution, MH and GC ignore low-density regions (and other structural features) related to the solvent.
