pop.estimates package:unknown R Documentation Estimation of population size in wild populations. Description: A function to calculate the estimation of population size in wild populations using the Schumacher and Eschmeyer mark-recapture method for closed populations.This function also calculates the population density based on the size of the sampled area. It is assumed that the assumptions for the application of the method in the field are satisfied Usage: pop.estimates<-function(x,col.t, col.Ct, col.Rt, y, col.lar, col.comp, s, p, df, meas) Arguments: x the input data should be of class data.frame containing the information capture and recapture of animals (see Details). col.t the collumn number of the "day" (t), corresponding to the duration of the field study in each occasion, composed of a specified number of consecutive days (in data.frame x). col.Ct the collumn number of "the total number of individuals captured in the t(th) sampling" (Ct)(in data.frame x). col.Rt the collumn number of "the total number of individuals recaptured in the t(th) sampling" (Rt)(in data.frame x). y the input data should be of class data.frame containing the information of "length" and "width" of the area where the collections were made (see Details). col.len the collumn number of the variable "lenght" of the sampled area (in data.frame y). col.wid the collumn number of the variable "width " of the sampled area (in data.frame y). s number of samples (see Details). p vector of probabilities (see Details). df degrees of freedom (see Details). meas number of times that the trapezoids area calculation should be performed. Details: The assumptions that must be followed in the field for the application of the Schumacher and Eschmeyer mark-recapture method for closed populations are: a) the population should be closed (isolated area); b) the capture of animals is random; c) the mark is not lost during the study period; d) marked and unmarked animals have the same chance of being caught and e) the mark does not affect the chances of capture. The adequacy of the Schumacher and Eschmeyer method for the estimation was tested by applying through-the-origin regression on the scatter-plot of the proportion of marked animals in samples (Rt/Ct ) against the accumulated number of marked animals (Mt ). When the assumptions required to validate the method are met, a positive linear relationship between these variables is expected with significat results (significant line checked by Student's t test, alpha= 0.05). The data contained in data.frame x should be: the day information (t) (this calculation is usually performed with field data from a week in the month), number of captured animals (Ct) that corresponds to how many animals were captured on a given day (and are not marked) and number of recaptured animals (Rt), where every day the animals caught (unmarked) are marked and can be caught again in the next day, therefore it is necessary to measure (count) captured animals with marks and also include this information in the worksheet. The data contained in data.frame y should be: the length and width of the sampled area. The measures should be taken of sessions in order to find the trapezoids area and finally the total area. The results of the total area and the population size estimate will be used to calculate population density. The number of samples (s) corresponds to a short period of consecutive 24-hour interval sampling events (the population size estimate is usually performed with 6 field days in the month). The confidence intervals around the population size estimate were obtained by taking reciprocals of the results obtained from the normal approximation by using the critical value of the Student’s t distribution for s-2 degrees of freedom. The function qt was used to access the critical value of the Student’s t distribution. The probability value (p) is one of the arguments of qt function and should be determined previously. The degrees of freedom (df) is also used in qt function, and should be considered as the result of subtracting 2 from the whole number of samples (Example: 6 minus 2 = 4). Are commonly used the the 95% confidence intervals with the critical value of the Student’s t distribution (α = 0.05). Value: The result of the function will be displayed in R-console (list) and in a new graphic window (with a regression graphic).A list will be displayed, containing: Regression summary: a summary of the regression results (through the origin) will be provided. Regression probability value (The p value): the p-value of the linear model. Anova of Regression: an Anova performed based on the results obtained from regression. Population size estimate: the result from the population size estimate equation of the Schumacher and Eschmeyer mark-recapture method. The Critical Value (Student’s t Distribution): the critical value of the t distribution, which is based on the adopted value of significance and degrees of freedom. The critical value will be used in order to calculate the confidence intervals. Variance: the variance value. The value will be used in order to calculate the standard error Standard Error: the standard error value of the estimation. The standard error will be used in order to calculate the confidence intervals. Confidence Intervals: the confidence intervals values around the population size estimate. Population density: the population density value. Warning: The input data of the mark-recapture of the species and the length and width of the area should be a data.frame. The assumptions for the application of the method in the field should be satisfied. For verify this, the line of regression should be significant. Author(s): Milena Regina Wolf milenarwolf@gmail.com References: Krebs, C. J. 1999. Ecological Methodology. 2nd Edition. Benjamin/Cummings, Menlo Park, CA. Seber, G. A. F. 1982. The Estimation of Animal Abundance and Related Parameters. 2nd Edition. Charles Griffin, London. Zar, J. H. 1996. Biostatistical Analysis. 3rd Edition. Prentice Hall, Upper Saddle River, NJ. See Also: TDist, lm (to access regression through the origin) Examples: # Download the "popul.txt" and "area.txt" to run the example below and save it in the working directory that will be used in R. # Function example pop.estimates(x= popul, col.t= 1, col.Ct= 2, col.Rt= 3, y= area, col.wid= 1, col.len= 2, s= 6, p= 0.975, df= 4, meas= 16)