Background/Question/Methods "Forests appear stable because the ecologists who study them die." The chronosequence, with its space-for-time substitution, is a widely-employed workaround for a difficult problem: many interesting ecosystem processes occur at much longer time scales than researchers can afford to spend studying them. But their use is problematic, particularly for vegetation succession but also for biogeochemical cycling: having sites of different ages is only a necessary, and not a sufficient, condition. How do we test the validity and representativeness of a chronosequence, particularly given ecosystem variability in time and space? Results/Conclusions Here we use data from an intensively-studied group of stands in northern Manitoba, Canada, to assess the spatial and temporal variability of carbon fluxes in this boreal forest, and examine the suitability of a chronosequence study design for making larger-scale (in space and time) generalizations. This ecosystem is well suited for examining this question, being floristically simple, frequently disturbed by wildfire, and thus generally composed of even-aged forests of known origin. A number of techniques can be brought to bear: re-visiting chronosequence sites after a significant period converts single-point measurements into data vectors; measurements that integrate fluxes over longer time periods (e.g., tree ring cores) provide a similar capability, extending our observation window; replication of the chronosequence stands extends the spatial domain; process modeling may indicate site selection errors. We can also examine data at local, regional and global scales to ask, for any particular pool or flux, if replication in time or space is more useful. For example, global soil respiration studies indicate that spatial and interannual variability are of roughly equal magnitude; in contrast, boreal tree ring data suggest that carbon sequestration is more variable year-to-year than it is site-to-site, after controlling for forest age and soil drainage. Different sampling strategies may thus be appropriate for each flux; historical records such as databases of wildfire occurrence also help constrain this problem. We conclude that while unreplicated chronosequences provide anecdotes, not data, extending measurements in space and time lets us quantifiably assess their performance.