Spatial Variation in Mortality Rates

Understanding and modeling of mortality processes is incomplete without considering their variability in space. A set of semi-isolated local populations is called a metapopulation. Analysis of meta-population dynamics leads to more realistic models than the analysis of a single local population. Several groups of species cannot survive without continuous migration. The first group contains species that colonize disturbed environments. These species usually have little tolerance to resource limitation; thus, as time progresses and more species arrive to the previously disturbed area, their mortality increases due to resource limitation, and the population declines till extinction. However during this short period of existence of a local population, it produces migrating organisms that colonize new disturbed habitats. Thus, even the persistence of local populations is limited, the meta-population persists indefinitely if disturbances continue to happen. The second group of species destroy their environment and hence are forced to migrate somewhere else. An example is the southern pine beetle (Dendroctonus frontalis) which kills infested trees. A single beetle cannot infest a healthy tree if only few individuals are engaged in the attack. Thus, the mortality rate of bark beetles is high at low population density. However, these beetles emit the aggregation pheromone which attracts other beetles to the same tree. Thus, the local number of beetles may exceed the threshold required for a successful attack of a healthy tree (inverse density dependence). Then local mortality rate decreases and the population numbers start growing rapidly. The infestation spreads to neighboring trees and eventually large areas of forest become destroyed. After destruction of the habitat, beetles disperse. Obviously, analysis of this kind of populations requires a meta-population approach.

According to mathematical models, spatial heterogeneity together with movement ofindividuals between local populations has a strong stabilizing effect on the meta-population dynamics. For example, a locally unstable host-parasite system becomes stable if hosts are partially protected from parasite in some local habitats or if parasites are attracted to aggregations of hosts (aggregational response).

Meta-population analysis requires more extensive data collection and more complex modeling strategies. Instead of a single mortality rate for each process, we need to consider a frequency distribution of mortality rates in various habitats. Life tables need to be collected in multiple geographic locations and the differences in mortality rates have to be related to local conditions. However, the total time of the study can be reduced by observing multiple local populations simultaneously. For example, if studying density dependence within one local population may take 30 years, the same study can be completed in 2-3 years by a simultaneous analysis of 30 local populations with various densities.

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