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Population modeling for a captive squirrel monkey colony
Author(s) -
Akkoç Can C.,
Williams Lawrence E.
Publication year - 2005
Publication title -
american journal of primatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.988
H-Index - 81
eISSN - 1098-2345
pISSN - 0275-2565
DOI - 10.1002/ajp.20112
Subject(s) - population , statistics , smoothing , probability distribution , monte carlo method , stochastic modelling , range (aeronautics) , computer science , mathematics , engineering , demography , sociology , aerospace engineering
Population modeling for a squirrel monkey colony breeding in a captive laboratory environment was approached with the use of two different mathematical modeling techniques. Deterministic modeling was used initially on a spreadsheet to estimate future census figures for animals in various age/sex classes. Historical data were taken as input parameters for the model, combined with harvesting policies to calculate future population figures in the colony. This was followed by a more sophisticated stochastic model that is capable of accommodating random variations in biological phenomena, as well as smoothing out measurement errors. Point estimates (means) for input parameters used in the deterministic model are replaced by probability distributions fitted into historical data from colony records. With the use of Crystal Ball ® (Decisioneering, Inc., Denver, CO) software, user‐selected distributions are embedded in appropriate cells in the spreadsheet model. A Monte Carlo simulation scheme running within the spreadsheet draws (on each cycle) random values for input parameters from the distribution embedded in each relevant cell, and thus generates output values for forecast variables. After several thousand runs, a distribution is formed at the output end representing estimates for population figures (forecast variables) in the form of probability distributions. Such distributions provide the decision‐maker with a mathematical habitat for statistical analysis in a stochastic setting. In addition to providing standard statistical measures (e.g., mean, variance, and range) that describe the location and shape of the distribution, this approach offers the potential for investigating crucial issues such as conditions surrounding the plausibility of extinction. Am. J. Primatol. 65:239–254, 2005. © 2005 Wiley‐Liss, Inc.