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Statistical modelling of wildwood mushroom abundance
This study analysed the relationship between weather conditions and the amount of wildwood mushrooms. A model of this relationship may be used to predict the amount of mushrooms with an elevated level of radionuclides that are available to domestic animals feeding outside during the summer and autum...
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Published in: | Scandinavian journal of forest research 2008-06, Vol.23 (3), p.244-249 |
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container_title | Scandinavian journal of forest research |
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creator | Dahl, Fredrik A Galteland, Torborg Gjelsvik, Runhild |
description | This study analysed the relationship between weather conditions and the amount of wildwood mushrooms. A model of this relationship may be used to predict the amount of mushrooms with an elevated level of radionuclides that are available to domestic animals feeding outside during the summer and autumn months in Norway. Data on mushroom abundance were collected through a web application run by the Norwegian Mushroom Association. The data were coupled with meteorological data. Through linear regression analysis, it was found that average local cloud cover 5–3 weeks before the observation was the most important variable for explaining mushroom abundance at a given time and place. When controlling for cloud cover, weather variables covering rainfall, temperature, air humidity and wind speed had only marginal explanatory value. Of these, only rainfall and wind speed led to a significant (but small) improvement in the model. There was substantial unexplained residual variance in the model (R2=0.23), but data analysis from identical locations and periods indicated that this was due mainly to variability in the response variable. |
doi_str_mv | 10.1080/02827580802036184 |
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A model of this relationship may be used to predict the amount of mushrooms with an elevated level of radionuclides that are available to domestic animals feeding outside during the summer and autumn months in Norway. Data on mushroom abundance were collected through a web application run by the Norwegian Mushroom Association. The data were coupled with meteorological data. Through linear regression analysis, it was found that average local cloud cover 5–3 weeks before the observation was the most important variable for explaining mushroom abundance at a given time and place. When controlling for cloud cover, weather variables covering rainfall, temperature, air humidity and wind speed had only marginal explanatory value. Of these, only rainfall and wind speed led to a significant (but small) improvement in the model. 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A model of this relationship may be used to predict the amount of mushrooms with an elevated level of radionuclides that are available to domestic animals feeding outside during the summer and autumn months in Norway. Data on mushroom abundance were collected through a web application run by the Norwegian Mushroom Association. The data were coupled with meteorological data. Through linear regression analysis, it was found that average local cloud cover 5–3 weeks before the observation was the most important variable for explaining mushroom abundance at a given time and place. When controlling for cloud cover, weather variables covering rainfall, temperature, air humidity and wind speed had only marginal explanatory value. Of these, only rainfall and wind speed led to a significant (but small) improvement in the model. There was substantial unexplained residual variance in the model (R2=0.23), but data analysis from identical locations and periods indicated that this was due mainly to variability in the response variable.</description><subject>air</subject><subject>animal feeding</subject><subject>autumn</subject><subject>cloud cover</subject><subject>domestic animals</subject><subject>Ecology</subject><subject>humidity</subject><subject>Internet</subject><subject>linear models</subject><subject>meteorological data</subject><subject>meteorology</subject><subject>mushrooms</subject><subject>radionuclides</subject><subject>rain</subject><subject>regression analysis</subject><subject>statistical models</subject><subject>summer</subject><subject>temperature</subject><subject>variance</subject><subject>wind speed</subject><issn>0282-7581</issn><issn>1651-1891</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqFkctKAzEUQIMoWKsf4MpZuRvNTSbzADdSfIHgonYdMnnUyMykJhlq_96UcVfQVS7knPtE6BLwDeAa32JSk4rVKSSYllAXR2gGJYMc6gaO0Wz_nycATtFZCJ8YYwaUzlCxjCLaEK0UXdY7pbvODuvMmWxrO7V1TmX9GD68c30m2nFQYpD6HJ0Y0QV98fvO0erx4X3xnL--Pb0s7l9zWWCIuTGmVrqhhpWFbJgmBSlaME0q3GhqCMbGVEVFiFailkwZ0yQEoG2lYDIhc3Q95d149zXqEHlvg0wtikG7MfCUgWECTQJhAqV3IXht-MbbXvgdB8z3--EH-0lONTl2MM73Yut8p3gUu85549OYNhxaPH7HZN79a9K_Cl9NuhGOi7VP9GpJMLD9UUrCKP0BMmeGiw</recordid><startdate>200806</startdate><enddate>200806</enddate><creator>Dahl, Fredrik A</creator><creator>Galteland, Torborg</creator><creator>Gjelsvik, Runhild</creator><general>Almqvist & Wiksell Periodical Co</general><general>Taylor & Francis Group</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>M7N</scope></search><sort><creationdate>200806</creationdate><title>Statistical modelling of wildwood mushroom abundance</title><author>Dahl, Fredrik A ; Galteland, Torborg ; Gjelsvik, Runhild</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-fff8de93f564c95e2424b1f91339e3f200ff74722eda8c5dff924211bbca5c9e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>air</topic><topic>animal feeding</topic><topic>autumn</topic><topic>cloud cover</topic><topic>domestic animals</topic><topic>Ecology</topic><topic>humidity</topic><topic>Internet</topic><topic>linear models</topic><topic>meteorological data</topic><topic>meteorology</topic><topic>mushrooms</topic><topic>radionuclides</topic><topic>rain</topic><topic>regression analysis</topic><topic>statistical models</topic><topic>summer</topic><topic>temperature</topic><topic>variance</topic><topic>wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dahl, Fredrik A</creatorcontrib><creatorcontrib>Galteland, Torborg</creatorcontrib><creatorcontrib>Gjelsvik, Runhild</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><jtitle>Scandinavian journal of forest research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dahl, Fredrik A</au><au>Galteland, Torborg</au><au>Gjelsvik, Runhild</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Statistical modelling of wildwood mushroom abundance</atitle><jtitle>Scandinavian journal of forest research</jtitle><date>2008-06</date><risdate>2008</risdate><volume>23</volume><issue>3</issue><spage>244</spage><epage>249</epage><pages>244-249</pages><issn>0282-7581</issn><eissn>1651-1891</eissn><abstract>This study analysed the relationship between weather conditions and the amount of wildwood mushrooms. A model of this relationship may be used to predict the amount of mushrooms with an elevated level of radionuclides that are available to domestic animals feeding outside during the summer and autumn months in Norway. Data on mushroom abundance were collected through a web application run by the Norwegian Mushroom Association. The data were coupled with meteorological data. Through linear regression analysis, it was found that average local cloud cover 5–3 weeks before the observation was the most important variable for explaining mushroom abundance at a given time and place. When controlling for cloud cover, weather variables covering rainfall, temperature, air humidity and wind speed had only marginal explanatory value. Of these, only rainfall and wind speed led to a significant (but small) improvement in the model. There was substantial unexplained residual variance in the model (R2=0.23), but data analysis from identical locations and periods indicated that this was due mainly to variability in the response variable.</abstract><pub>Almqvist & Wiksell Periodical Co</pub><doi>10.1080/02827580802036184</doi><tpages>6</tpages></addata></record> |
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subjects | air animal feeding autumn cloud cover domestic animals Ecology humidity Internet linear models meteorological data meteorology mushrooms radionuclides rain regression analysis statistical models summer temperature variance wind speed |
title | Statistical modelling of wildwood mushroom abundance |
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