Modelling and predicting biogeographical patterns in river networks
Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range) in which the abundance o...
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Online Access: | http://escholarship.org/uc/item/34v1f10s |
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doaj-462cd28f4e8540e68fcef4b721e6bfb12020-11-25T00:37:37ZengInternational Biogeography SocietyFrontiers of Biogeography1948-65962016-04-0181ark:13030/qt34v1f10sModelling and predicting biogeographical patterns in river networksSabela Lois0University of Santiago de CompostelaStatistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range) in which the abundance of the parasitic freshwater pearl mussel (<em>Margaritifera margaritifera</em> L.) is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.<br />http://escholarship.org/uc/item/34v1f10sabundance, anisotropy, dispersal, distribution, geostatistics, host-parasite, river-networks, spatial autocorrelation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sabela Lois |
spellingShingle |
Sabela Lois Modelling and predicting biogeographical patterns in river networks Frontiers of Biogeography abundance, anisotropy, dispersal, distribution, geostatistics, host-parasite, river-networks, spatial autocorrelation |
author_facet |
Sabela Lois |
author_sort |
Sabela Lois |
title |
Modelling and predicting biogeographical patterns in river networks |
title_short |
Modelling and predicting biogeographical patterns in river networks |
title_full |
Modelling and predicting biogeographical patterns in river networks |
title_fullStr |
Modelling and predicting biogeographical patterns in river networks |
title_full_unstemmed |
Modelling and predicting biogeographical patterns in river networks |
title_sort |
modelling and predicting biogeographical patterns in river networks |
publisher |
International Biogeography Society |
series |
Frontiers of Biogeography |
issn |
1948-6596 |
publishDate |
2016-04-01 |
description |
Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range) in which the abundance of the parasitic freshwater pearl mussel (<em>Margaritifera margaritifera</em> L.) is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.<br /> |
topic |
abundance, anisotropy, dispersal, distribution, geostatistics, host-parasite, river-networks, spatial autocorrelation |
url |
http://escholarship.org/uc/item/34v1f10s |
work_keys_str_mv |
AT sabelalois modellingandpredictingbiogeographicalpatternsinrivernetworks |
_version_ |
1725300444187590656 |