  IBMs are particularly useful because they can also be spatially explicit (see  Section 5.2),  that is, they can incorporate locational relationships between the individuals being modeled. These models have even been used to assess change on a global scale. For instance, forest models (which are among the most successful IBMs) have been used to assess the result of climate change on the atmosphere because of a potential breakdown of the presumed balance between production and decomposition of carbon-containing compounds. Such an extrapolation of scale relies on sampling each of the terrestrial lifezones and constructing some IBMs for all of them, and subsequently integrating the  results.[89]  The future will show how reliable this strategy is. IBMs have also recently begun to be used for population viability analysis, tracking the trajectory of each individual during its  lifetime.[90]  This use is likely to grow. In both the situations discussed here, the main problem with the use of IBMs is the immense quantity of reliable data that they require.