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<result><vid>114567</vid><uid>0</uid><title>Development of a Model for Predicting Large Scale Spatio-Temporal Variability in Juvenile Fish Abundance from Electrofishing Data</title><log>Updated by FeedsNodeProcessor</log><status>1</status><comment>1</comment><promote>0</promote><sticky>0</sticky><ds_switch></ds_switch><nid>4172</nid><type>data_source</type><language>und</language><created>1446113969</created><changed>1550786877</changed><tnid>0</tnid><translate>0</translate><revision_timestamp>1550786877</revision_timestamp><revision_uid>0</revision_uid><body><und is_array="true"><item><value>&lt;p&gt;Scottish Marine and Freshwater Science Vol 6 No 6&lt;/p&gt;
&lt;p&gt;Models of juvenile salmonid abundance are required to inform electrofishing based assessment approaches and potentially as an intermediate step in scaling conservation limits from data rich to data poor catchments. This report describes an approach for modelling large-scale spatio-temporal variability in fish densities using GIS derived covariates. The technical challenges, modelling approaches and software developed during the project are described. The utility of the approach was illustrated by fitting models to data on Atlantic salmon fry.&lt;/p&gt;
</value><summary/><format>full_html</format><safe_value>&lt;p&gt;Scottish Marine and Freshwater Science Vol 6 No 6&lt;/p&gt;
&lt;p&gt;Models of juvenile salmonid abundance are required to inform electrofishing based assessment approaches and potentially as an intermediate step in scaling conservation limits from data rich to data poor catchments. This report describes an approach for modelling large-scale spatio-temporal variability in fish densities using GIS derived covariates. The technical challenges, modelling approaches and software developed during the project are described. The utility of the approach was illustrated by fitting models to data on Atlantic salmon fry.&lt;/p&gt;
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