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The effect on dispersal from complex correlations in small-scale movement
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Ecology .
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology .ORCID iD: 0000-0001-7856-2925
Linköping University, The Institute of Technology. Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology .
2008 (English)In: Ecological Modelling, ISSN 0304-3800, Vol. 213, no 2, 263-272 p.Article in journal (Refereed) Published
Abstract [en]

Calculations of large-scale displacement distances were made to evaluate the combined effect of small-scale movement pattern of a Collembola, Protaphorura armata. The effect of presence of food and conspecific density on turning angle, step length and activity/motility was investigated. Calculations of net square displacement were made both by assuming correlated random walk (CRW) and by resampling data to account for correlation structures in movement patterns that violate the assumptions of CRW. In presence of food, individuals spent less time moving (decreased activity), but when they moved they showed larger turning angles than individuals moving in areas without food. Increased conspecific density did not affect time spent moving by individuals, but when step length decreased and turning angle increased. P. armata showed negative density-dependent dispersal and exhibited area-restricted search as a response to both food and increased conspecific density. The CRW was relatively robust to some violations of its underlying assumptions. However, the expected displacement increased substantially, as much as 50%, when accounting for observed auto-correlation in step length and correlation between step length and turning angle. Hence, an explanation for increased displacement and dispersal of a species can also be the result of a more complex correlation of its behaviour rather than solely altering specific movement parameters, for example increasing step length or decreasing turning angle. The results emphasise the importance of careful analysis of small-scale movement before using them as predictors of population distribution and invasion speed in heterogeneous landscapes. © 2007 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
2008. Vol. 213, no 2, 263-272 p.
National Category
Natural Sciences
URN: urn:nbn:se:liu:diva-44411DOI: 10.1016/j.ecolmodel.2007.12.011Local ID: 76579OAI: diva2:265273
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-08-31
In thesis
1. Spatial Spread of Organisms: Modeling ecological and epidemiological processes
Open this publication in new window or tab >>Spatial Spread of Organisms: Modeling ecological and epidemiological processes
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on the spread of organisms in both ecological and epidemiological contexts. In most of the studies presented, displacement is modeled with a spatial kernel function, which is characterized by scale and shape. These are measured by the net squared displacement (or kernel variance) and kurtosis, respectively. If organisms disperse by the assumptions of a random walk or correlated random walk, a Gaussian shaped kernel is expected. Empirical studies often report deviations from this, and commonly leptokurtic distributions are found, often as a result of heterogeneity in the dispersal process.

In the studies presented in two of the included papers, the importance of the kernel shape is tested, by using a family of kernels where the shape and scale can be separated effectively. Both studies utilize spectral density approaches for modeling the spatial environment. It is concluded that the shape is not important when studying the population distribution in a habitat/matrix context. The shape is however important when looking at the invasion of organisms in a patchy environment, when the arrangement of patches deviates from randomly distributed. The introduced method for generating patch distribution is also compared to empirical distributions of patches (farms and old trees). Here it is concluded that the assumptions used for modeling of the spatial environment are consistent with the observed patterns. These assumptions include fractal properties such that the same aggregational patterns are found at different scales.

In a series of papers, movements of animals are considered as vectors for between-herd disease spread. The studies are based on data found in databases held by the Swedish Board of Agricultural (SJV), consisting of reported movements, as well as farm location and characteristics. The first study focuses on the distance related probability of contacts between herds. In the following papers, the analysis is expanded to include production type and herd size. Movement data of pigs (and cattle in Paper I) are analyzed with Bayesian models, implemented with Markov Chain Monte Carlo (MCMC). This is a flexible approach that allows for parameter estimations of complex models, and at the same time includes parameter uncertainty.

In Paper IV, the effects of the included factors are investigated. It is shown that all three factors (herd size, production type structure and distance related probability of contacts) are expected to influence disease spread dynamics, however the production type structure is found to be the most important factor. This emphasizes the value of keeping such information in central databases. The models presented can be used as support for risk analysis and disease tracing. However, data reliability is always a problem, and implementation may be improved with better quality data.

The thesis also shows that utilizing spatial kernels for description of the spatial spread of organisms is an appropriate approach. However, these kernels must be flexible and flawed assumptions about the shape may lead to erroneous conclusions. Hence, the joint distribution of kernel shape and scale should be estimated. The flexibility of Bayesian analysis, implemented with MCMC techniques, is a good approach for this, and further allows for implementation of more complex models where other factors may be included.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 54 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1311
Spatial kernel, Spatially explicit modeling, Disease transmission, Animal movements
National Category
Natural Sciences
urn:nbn:se:liu:diva-54839 (URN)978-91-7393-401-5 (ISBN)
Public defence
2010-05-07, Planck, Hus F, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Available from: 2010-04-15 Created: 2010-04-15 Last updated: 2016-08-31Bibliographically approved

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