More importantly, we find that the conditions required for selection to favor elevated PCD in our model are very permissive: elevated PCD can evolve in microbes with a wide range of stochastic switching frequencies, in environments with a wide range of disaster frequencies, and in populations with modest spatial structuring.
Besides analyzing disaster severities in terms of fatalities and extent of affected population, analysis of the disaster frequencies also revealed interesting results in identifying the countries/regions that are more exposed to the various types of disasters.
Should disaster frequency increase, the number of lives saved could rise as high as 50,000.
Data are from simulations with PCD rates of 0.025 and a disaster frequency of 0.01.
The yearly total disaster occurrence is thus "detrended" to estimate the influence of ENSO and climate on disaster frequency.
As a result, the model predicts that disaster frequency decreases with economic growth, but that consequences are larger when a disaster occurs.
Another fundamental problem in Pielke's post is that he looks for a climate change signal in extreme weather by analyzing economic disaster loss data instead of disaster frequency or intensity data.
The upward trend in disaster frequency reflects some combination of the following: the increased reporting of disasters, greater concentrations of people and wealth in high-risk areas, increasing vulnerability, and long-term changes in the frequency or severity of climatic hazard events.
This process of population and development growth that influences disaster frequency and magnitude is known as "expanding the bull's-eye effect". It isn't just the population increase that is important in raising the disaster potential, but also how the population and built environment is distributed across a landscape.
| Updated The final report is out and Roger Pielke, Jr., has a helpful post on the vital fine print.] To my eye the summary accurately reflects the body of science aiming to find a signal of greenhouse-gas influence on climate extremes and disaster frequency and severity (the full report is still being finalized).
The results show that the farmers' understanding of forest insurance, the proportion of forestry revenues in the total household income, forest size, forest disaster frequency, forest insurance liability, insurance amount setup, and the farmers' satisfaction regarding the premium subsidy policy, are the main factors that affect the farmers' demand for forest insurance.
