Amazon river dolphins 589


TABLE 2 Results of model runs. The values in the bottom three rows represent the number of years from the start of the model run when the stated condition was reached. For example, in Simulation 4 the model predicted that 1% of the starting population would remain after 50 years.


Annual survival rate (%)


Simulation 1 Simulation 2 Simulation 3 Simulation 4 Simulation 5 Simulation 6 89


88


Breeding depression at low numbers? No 5% of starting population size 1% of starting population size Probability of extinction = 95%


No


Year 45 Year 68


Year 38 Year 59


Year 165 Year 141


point at which 95%ofmodel iterations predicted extinction. This value is little affected by the estimated initial popula- tion size; in Simulation 1 (Table 2) it increases from year 165 to year 173 of the model run (a 5% difference) when ini- tial population size is doubled to 100,000. Because the current population size is not known, thresholds of low population levels were expressed as percentages of original population size (5 and 1%). Figure 2 illustrates the variation in predicted population


size over 120 years when the model is run 1,000 times. In this scenario, annual survival is set at 89% and no corrections are made for depressed breeding at low population levels (i.e. as represented in Simulation 1). Figure 3 shows the estimated mean and standard devi-


ation of population size over time in Simulations 1 and 3 (Table 2). The two curves illustrate the range of results obtained from the 6 simulation runs. The upper curve (Simulation 1) is based on published data from the Mamirauá study (Mintzer et al., 2013; Martin & da Silva, 2018) and shows the likely population trend if conditions for dolphins do not worsen with time.


Discussion


The annual rate of population decline predicted by the model with default input (Simulation 1) was 5.5%. This is similar to the average 5.48% annual decline of botos observed over 22 years (da Silva et al., 2018a). Our findings demonstrate that three independent data sources (standard- ized counts, estimates of survival of marked animals and estimates of reproductive parameters) lead to the same con- clusion. In this population, fewer botos are being recruited into the breeding population than are being removed from it, and the consequent decline is rapid and persistent (Figs 2 & 3). In each of the six simulation scenarios (Table 2), all of which make the optimistic and perhaps unrealistic assump- tion that environmental conditions will not become less suitable for this species, the initial population is reduced by at least 95%in ,50 years. Although the observed and predicted rates of population


change are consistent, and it is therefore reasonable to assume that the model is working with accurate input


FIG. 2 Predicted population size of the Amazon river dolphin Inia geoffrensis over 120 years when the starting population is 50,000, the survival and reproductive rates are as published by Mintzer et al. (2013) and Martin & da Silva (2018), and no corrections are made for depressed breeding at low population levels (as represented in Simulation 1). This graph illustrates the variation in predicted values generated by the Vortex package over 1,000 model runs.


86 No


Year 30 Year 46


89 Yes


Year 34 Year 50


Year 110 Year 121 88 Yes


Year 29 Year 46


86 Yes


Year 24 Year 38


Year 108 Year 88


FIG. 3 Predicted population size of the Amazon river dolphin over 100 years, with annual survival rates of 89% (Simulation 1, upper curve: Mintzer et al., 2013) and 86% (Simulation 3, lower curve). The mean values and standard deviation of 1,000 model runs are shown. The upper curve represents the same model runs as in Fig. 2. No allowance was made for possible breeding depression at low population numbers. For clarity, only two of the six model runs are shown here, but the remaining four are close to the curves shown.


parameters, the long-term predictions of population size may not be accurate because circumstances are likely to change. Most importantly, the number of people using


Oryx, 2022, 56(4), 587–591 © The Author(s), 2021. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605320001350


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