MORTALITY FUNCTIONS FOR NORTH QUEENSLAND RAIN FORESTS

Abstract

Subjective a priori grouping of tropical rain forest species for growth prediction may be unreliable because 1) there may be hundreds of species, many comparatively uncommon, the ecology of which may not be well known, 2) species within the same genus may have significantly different growth patterns, and 3) growth rate may not provide a reliable indication of mortality. Growth models can retain the species identity of each simulated tree, but some aggregation is necessary to enable estimation of increment and mortality functions. An objective approach aggregated 100 rain forest tree species into ten groups to enable efficient estimation of mortality functions. This strategy provided better predictions than a previous subjective grouping. Annual survival probabilities were predicted from tree size, stand density and site quality using a logistic equation fitted by maximum likelihood estimation. Additional species with insufficient data for analysis were subjectively assigned to these ten equations. Several strategies were investigated; the best approach for these species seemed to be to employ the equation which served the greatest number of species. The increment pattern did not provide a good basis for assigning such species to equations, and this suggests that different groupings may be necessary to model the various components of tree growth.