More realistic statistical models for stage-structured time-series data

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Biologists often need to make predictions about how quickly animal or plant populations will grow or decline. For example, predictions of insect populations in agriculture are needed for pest management, and predictions of small fish and plankton species that are vital for marine food webs are important for marine conservation and management. Predicting population change is notoriously difficult because biologists know relatively little about the life cycle of most organisms and because they are subject to many influences of weather, predators, food resources, and habitat conditions. One important approach is to use past records of populations to estimate how quickly organisms develop, reproduce, and die. Accomplishing this is particularly difficult for the many kinds of organisms that can be counted only by their life stages, such as the eggs, larvae, or adults of insects. This project will improve methodology for estimating patterns of population change when only data on organism stages is available. The approach will be to adapt state-of-the-art computer algorithms to the context of such data. These algorithms will determine the range of plausible population growth patterns from the kind of rough data that can typically be collected. An important step in validating new algorithms for data analysis is to evaluate their performance in a controlled setting. Laboratory experiments with Pacific spider mites, an important agricultural pest, will be used for this purpose. The new analytical methodology to be developed in this project will be made available to the public as open-source software. In addition, training workshops will be conducted at major national conferences to facilitate the broad dissemination and application of this software. This project will result in the training of undergraduate and graduate students and a post-doctoral researcher in mathematical and statistical methods for population ecology.