The paper by Jensen et al. (1996) reports an attempt to test the predictive abilities of a population model based on the use of reconstructive techniques (Duarte et al. 1994). The population model that was tested utilizes the age distributions of individual seagrass shortshoots collected at least once per year, from which annual gross shoot recruitment rates (R,,,,, yr-' = In NI InN,,,, where N, is the total number of shoots and N,,, is the number of shoots older than a year) and instantaneous mortality rates [M, derived from the number of shoots in consecutive cohorts and estimated by fitting the equation N(t) = N(0)e-"l to observed data, where N(t) is the number of shoots at time t , N(0) represents the number of shoots at t = 0, if t = 1 yr then the units for M are yr-'] a re calculated (Duarte et al. 1994). Shoot ages are determined by counting the number of leaves and leaf scars for each shoot. The interval between the formation of consecutive leaf scars is termed the plastochron interval (PI; Erikson & Michelini 1957). Conversion of leaf-scar PIS to absolute time can be accomplished directly using the leafpunch productivity method (Patriquin 1973, Brouns 1985), and indirectly by reconstructive methods such as determining the differences in numbers of leaf scars between successive annual, cohorts (large numbers of shoots of simllar age) divided by 365 (Duarte et al. 1994). Jensen et al. (1996) correctly point out that the calculation of mortality using the exponential decline of numbers of living shoots with age assumes constant age-specific shoot mortality and recruitment rates. However, they make no attempt to test if these assumptions are satisfied. An exponential model with age-dependent mortality rates can also be fitted to the population age data, but, in the absence of evidence that shoot mortality is age dependent, the simple exponential model provides the most parsimonious and robust model (Cox & Oates 1984). In addition, recruitment and mortality in plants are typically more correlated with plant size, rather independently of age (Sarukhan et al. 1985). Plots of residuals from mortality curves versus shoot-cohort ages and total-sample shoot ages (the approach used by Jensen e t al. 1996) for Thalassia testudinum collected from Rabbit Key Basin during 1989 and 1990 from Durako (1994) are shown in Fig. 1. Regression slopes for all of the plots are not significantly different from 0, suggesting no age-dependent blas in the annual mortality estimates derived from the simple exponential model. Alternatively, Duarte et al. (1994) recommend that mortality estimates should be derived from the distribution of age at death (i.e. that of ages of dead shoots) to avoid the assumption of constant mortality. This alternative was not considered in the model test by Jensen et al. (1996). Differences between gross recruitment and mortality estimates (R,,, = R,,,,, M) can be used to assess whether a population is increasing (R,,,,, M ) , or in steady state (R ,,,,, = M ) , at the time of sampling (Duarte et al. 1994). Net recruitment may change from year to year because of annual changes in recruitment and mortality rates. Restricting inferences derived from demographic statistics to within a year-to-year (i.e. annual) time frame is a fundamental concept on the use of these population models (Ricker 1975, Duarte et al. 1994). In this regard, Durako (1994) reported significant annual (1989 vs 1990) fluctuations in both Ryross and M in 3 Thalassia testudinum populations in Florida Bay resulting in dramatic year-to-year differences in the magnitude and direction of calculated R,,, (Table 1). Population half-life ITll2 = ln(2)/M] can also be calculated from age-distribution data. Jensen e t al. (1996) state that 'the mortality estimate is calculated from an exponential decay equation and as such the half-life
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Jan 1, 2010
S Saglam 
