Na. Slade et al., ALTERNATIVES TO ROBINSON AND REDFORDS METHOD OF ASSESSING OVERHARVESTFROM INCOMPLETE DEMOGRAPHIC-DATA, Conservation biology, 12(1), 1998, pp. 148-155
Conservation biologists often must make decisions about the sustainabi
lity of harvest rates based on minimal demographic information. To ass
ist them Robinson anti Redford (1991) formulated a method to estimate
maximum rates of production which could be used to detect overharvesti
ng based on only age at first reproduction, fecundity, and maximum lon
gevity. By assuming constant adult survival we reduced the Euler equat
ion to a simple form that allows calculation of population growth from
the same minimal demographic data, brit that can incorporate empirica
l prereproductive and adult survival rates if available. With this for
mula, we computed growth rates rising various explicit survival schedu
les, and we compared these rates and those from Robinson and Redford's
(1991) method to rates calculated from 19 relatively complete mammali
an life tables gleaned from the literature. When we applied our method
(assuming 1% survival to maximum longevity) and that of Robinson and
Redford (1991) to the same minimal demographic data, we found that our
growth rates were closer to those from complete life tables. We there
fore reexamined the data of Fa et al (1995) and Fitzgibbon et al. (199
5), who analyzed overharvesting of several populations of commercially
exploited African mammals based on Robinson and Redford's (1991) meth
ods Our reanalysis indicates that several additional populations may b
e overharvested. Our analysis also suggests that data on survival to a
ge at first reproduction improves estimates of population growth rates
more than data on age-specific adult survival. Regardless of the meth
od, approximate growth rates based on incomplete life tables can be us
ed to detect when populations are overharvested, brit one should not c
onclude that harvest rates are sustainable when they are less than app
roximate production rates because simplifying assumptions often lend t
o overestimates.