liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
The shape of the spatial kernel and its implications for biological invasions in patchy environments
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.ORCID iD: 0000-0001-7856-2925
Skövde University.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology . Linköping University, The Institute of Technology.
2011 (English)In: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 278, no 1711, 1564-1571 p.Article in journal (Refereed) Published
Abstract [en]

Ecological and epidemiological invasions occur in a spatial context. In the study presented we tested how these processes relate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or infective units. The distance dependence was described by a spatial kernel which can be characterized by its shape, quantified by kurtosis, and width, quantified by the kernel variance. We also introduced a method to analyze or generate non randomly distributed infective units or patches as point pattern landscapes. The method is based on Fourier transform and consists of two measures in the spectral representation; Continuity that relates to autocorrelation and Contrast that refers to difference in density of patches, or infective units, in different areas of the landscape. The method was also used to analyze some relevant empirical data where our results are expected to have implications for ecological or epidemiological studies. We analyzed distributions of large old trees (Quercus and Ulmus) as well as the distributions of farms (both cattle and pig) in Sweden. We tested the invasion speed in generated landscapes with different amount of Continuity and Contrast. The results showed that kurtosis, i.e. the kernel shape, was not important for predicting the invasion speed in randomly distributed patches or infective units. However, depending on the assumptions of dispersal, it may be highly important when the distribution of patches or infective units deviates from randomness, in particular when the Contrast is high. We conclude that speed of invasions and spread of diseases depends on its spatial context through the spatial kernel intertwined to the spatial structure. This implies high demands on the empirical data; it requires knowledge of both shape and width of the spatial kernel as well as spatial structure of patches or infective units.

Place, publisher, year, edition, pages
Royal Society , 2011. Vol. 278, no 1711, 1564-1571 p.
Keyword [en]
Kurtosis, Spread of disease, Point patterns, Spectral density, Dispersal, Invasion
National Category
Natural Sciences
URN: urn:nbn:se:liu:diva-54838DOI: 10.1098/rspb.2010.1902ISI: 000289719100016PubMedID: 20356640OAI: diva2:310646
Available from: 2010-04-15 Created: 2010-04-15 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

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Lindström, TomHåkansson, NinaWennergren, Uno
By organisation
Theoretical Biology The Institute of Technology
In the same journal
Proceedings of the Royal Society of London. Biological Sciences
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 80 hits
ReferencesLink to record
Permanent link

Direct link