Abstract
Large herbivores are monitored for various reasons in many areas around the world. Data from such monitoring can be of varying degree of detail and extent. Several studies have shown that we need extensive amounts of data with a high degree of detail to be able to understand anything from it. Nevertheless, data in the less detailed form of annual censuses of large herbivore densities and weather is still very common. In addition, it is collected for relatively short time spans and with large observation errors. In this thesis I explore some different aspects of the problems a researcher or manager has when trying to interpret the complex processes from such simple data. To handle these problems, a monitoring program should be reviewed in all its parts (modeling, experiments, statistical analysis and data collection) and refined accordingly.
In the first paper I refine the statistical tools used for time series analysis of large herbivore data in two ways. First, I take observation errors into account when estimating statistical parameters by using state space modeling. Second, I use mechanistic modeling of the pathway from precipitation to herbivore dynamics to interpret the statistical parameters. In the second paper I extended the work started in the first by comparing a number of different models of the pathway from precipitation to herbivore dynamics. The results tell us that the statistical parameters are not as easily interpreted as previously thought. The third paper investigates the consequences of mismatch between the scales at which a large herbivore population is monitored and the scales of the processes that govern the dynamics. The results have implications both for data collection and statistical analysis. In the final paper I incorporate large herbivore population dynamics into the LundPotsdamJena Dynamic Global Vegetation Model to investigate the relationship between weather and herbivore dynamics. I use the full model to analyze if indirect weather effects can be understood without information on vegetation dynamics.
To summarize, understanding weather effects on large herbivore population dynamics does not necessarily require highly detailed data. In this thesis I present several ways to improve our ability to extract reliable information from data with low degree of detail.
In the first paper I refine the statistical tools used for time series analysis of large herbivore data in two ways. First, I take observation errors into account when estimating statistical parameters by using state space modeling. Second, I use mechanistic modeling of the pathway from precipitation to herbivore dynamics to interpret the statistical parameters. In the second paper I extended the work started in the first by comparing a number of different models of the pathway from precipitation to herbivore dynamics. The results tell us that the statistical parameters are not as easily interpreted as previously thought. The third paper investigates the consequences of mismatch between the scales at which a large herbivore population is monitored and the scales of the processes that govern the dynamics. The results have implications both for data collection and statistical analysis. In the final paper I incorporate large herbivore population dynamics into the LundPotsdamJena Dynamic Global Vegetation Model to investigate the relationship between weather and herbivore dynamics. I use the full model to analyze if indirect weather effects can be understood without information on vegetation dynamics.
To summarize, understanding weather effects on large herbivore population dynamics does not necessarily require highly detailed data. In this thesis I present several ways to improve our ability to extract reliable information from data with low degree of detail.
Original language  English 

Qualification  Doctor 
Awarding Institution 

Supervisors/Advisors 

Award date  2006 Oct 13 
Publisher  
ISBN (Print)  9171052488 
Publication status  Published  2006 
Bibliographical note
Defence detailsDate: 20061013
Time: 13:00
Place: Blå Hallen, Ekologihuset, Sölvegatan 37, Lund
External reviewer(s)
Name: Fryxell, John M
Title: Professor
Affiliation: Department of Integrative Biology, University of Guelph, Guelph, Canada

The information about affiliations in this record was updated in December 2015.
The record was previously connected to the following departments: Theoretical ecology (Closed 2011) (011006011)
Subject classification (UKÄ)
 Ecology
Keywords
 monitoring
 management
 data collection
 Ecology
 Ekologi
 indirect effects
 vegetation
 modeling
 time series analysis
 precipitation
 weather
 population dynamics
 Large herbivores