Guía para el estudio de macroinvertebrados. II.- Introducción a la metodología de muestreo y análisis de datos.

Sobre la base de los objetivos propuestos, el muestreo tiene que estar cuidadosamente planeado (diseño de protocolo de muestreo) para que se pueda realizar una comprobación estadística de los resultados. Las herramientas de análisis como son los cálculos numéricos y los análisis estadísticos están ligados al estudio en sí, ya que son los que nos permiten demostrar que los resultados de una investigación son el producto de procesos ecológicos y no del azar.

Removing observational noise from fisheries-independent time series data using ARIMA models

Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.