Analyses of Fish Species Richness with Spatial Covariate

Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American Statistical Association 1997-09, Vol.92 (439), p.846-854
Hauptverfasser: Hobert, James P., Altman, N. S., Schofield, Carl L.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 854
container_issue 439
container_start_page 846
container_title Journal of the American Statistical Association
container_volume 92
creator Hobert, James P.
Altman, N. S.
Schofield, Carl L.
description Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the area, elevation, and acidity of the lake. Data on these and other factors are available for 1,166 Adirondack lakes. The data are analyzed with the goal of quantifying the effects of acid deposition on species richness after controlling for other important factors. A plot of the residuals from a standard multiple regression model against spatial location reveals a strong nonlinear relationship between species richness and lake location. With only one realization of the data, it is difficult, if not impossible, to tell whether this dependence should be modeled as deterministic spatial trend or as a spatial covariance structure. Two models are thus considered. The first is a multiple linear regression model with spatially correlated errors. As is common in geostatistics, it is assumed that the correlation between two errors is a function only of the vector separating the corresponding lakes. This is accomplished by modeling the correlation among the errors with a parametric variogram model. The parameters of the variogram are estimated via restricted maximum likelihood, after which the regression parameters are estimated using generalized least squares. The second model considered is a partial linear (or semiparametric) model containing a nonlinear term in spatial location. Locally weighted quadratic regression is used in conjunction with an estimation technique described by Speckman to estimate simultaneously the regression parameters and the "smooth" function of longitude and latitude. Finally, the ultimate goal of the 1990 legislation is a 50% reduction in sulfate deposition. Both models are used to form prediction intervals for species richness under that scenario. Extrapolation is avoided by using the criteria of Weisberg. The results suggest that a 50% reduction in sulfate deposition may result in a substantial increase in the fish species richness of many Adirondack lakes, particularly those with pH values between 4.5 and 6.5.
doi_str_mv 10.1080/01621459.1997.10474040
format Article
fullrecord <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_839081045</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>2965549</jstor_id><sourcerecordid>2965549</sourcerecordid><originalsourceid>FETCH-LOGICAL-c373t-484d21161ab80398c9aae118318fa3d05a038a6be2ffc97c2ecdf6592e6ade263</originalsourceid><addsrcrecordid>eNqFkFtrGzEQRkVJoG7av1CWptCnTXXdlR6NaZpAIJAL9E1MtBKRWa8czTrB_77aOA6hUKKXQcOZj5lDyFdGTxjV9CdlDWdSmRNmTFtaspVU0g9kxpRoa97KPwdkNkH1RH0knxCXtLxW6xnR8wH6LXqsUqhOI95X12vvYvlfRXc_eMTqKY5TF8YIfbVIj5AjjP4zOQzQo__yUo_I7emvm8VZfXH5-3wxv6idaMVYSy07zljD4E5TYbQzAJ4xLZgOIDqqgAoNzZ3nITjTOu5dFxpluG-g87wRR-THLned08PG42hXEZ3vexh82qDVwlBdjlaF_PYPuUybXM5DWyRoprjmBTr-H8SKOCGoMrJQzY5yOSFmH-w6xxXkrWXUTtLtXrqdpNu99DL4_SUe0EEfMgwu4us0f97iDbbEMeW34VzQ1nLTKCVNweY7LA4h5RU8pdx3doRtn_I-Wryz0V_qOp5Y</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>274815282</pqid></control><display><type>article</type><title>Analyses of Fish Species Richness with Spatial Covariate</title><source>JSTOR Mathematics and Statistics</source><source>Taylor &amp; Francis Journals Complete</source><source>JSTOR</source><source>Periodicals Index Online</source><creator>Hobert, James P. ; Altman, N. S. ; Schofield, Carl L.</creator><creatorcontrib>Hobert, James P. ; Altman, N. S. ; Schofield, Carl L.</creatorcontrib><description>Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the area, elevation, and acidity of the lake. Data on these and other factors are available for 1,166 Adirondack lakes. The data are analyzed with the goal of quantifying the effects of acid deposition on species richness after controlling for other important factors. A plot of the residuals from a standard multiple regression model against spatial location reveals a strong nonlinear relationship between species richness and lake location. With only one realization of the data, it is difficult, if not impossible, to tell whether this dependence should be modeled as deterministic spatial trend or as a spatial covariance structure. Two models are thus considered. The first is a multiple linear regression model with spatially correlated errors. As is common in geostatistics, it is assumed that the correlation between two errors is a function only of the vector separating the corresponding lakes. This is accomplished by modeling the correlation among the errors with a parametric variogram model. The parameters of the variogram are estimated via restricted maximum likelihood, after which the regression parameters are estimated using generalized least squares. The second model considered is a partial linear (or semiparametric) model containing a nonlinear term in spatial location. Locally weighted quadratic regression is used in conjunction with an estimation technique described by Speckman to estimate simultaneously the regression parameters and the "smooth" function of longitude and latitude. Finally, the ultimate goal of the 1990 legislation is a 50% reduction in sulfate deposition. Both models are used to form prediction intervals for species richness under that scenario. Extrapolation is avoided by using the criteria of Weisberg. The results suggest that a 50% reduction in sulfate deposition may result in a substantial increase in the fish species richness of many Adirondack lakes, particularly those with pH values between 4.5 and 6.5.</description><identifier>ISSN: 0162-1459</identifier><identifier>EISSN: 1537-274X</identifier><identifier>DOI: 10.1080/01621459.1997.10474040</identifier><identifier>CODEN: JSTNAL</identifier><language>eng</language><publisher>Alexandria, VA: Taylor &amp; Francis Group</publisher><subject>Applications ; Applications and Case Studies ; Biology, psychology, social sciences ; Distribution ; Emissions ; Exact sciences and technology ; Fish ; Freshwater fishes ; Geodetic position ; Geometric anisotropy ; Insurance, economics, finance ; Lakes ; Linear inference, regression ; Linear models ; Linear regression ; Locally weighted regression ; Mathematics ; Medical sciences ; Modeling ; Multivariate analysis ; Musical intervals ; Parametric models ; Partial linear model ; Probability and statistics ; Regression analysis ; Reliability, life testing, quality control ; Restricted maximum likelihood ; Sciences and techniques of general use ; Semi-parametric model ; Semiparametric modeling ; Spatial models ; Statistical analysis ; Statistics ; Sulfates ; Variogram ; Water pollution</subject><ispartof>Journal of the American Statistical Association, 1997-09, Vol.92 (439), p.846-854</ispartof><rights>Copyright Taylor &amp; Francis Group, LLC 1997</rights><rights>Copyright 1997 American Statistical Association</rights><rights>1997 INIST-CNRS</rights><rights>Copyright American Statistical Association Sep 1997</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c373t-484d21161ab80398c9aae118318fa3d05a038a6be2ffc97c2ecdf6592e6ade263</citedby><cites>FETCH-LOGICAL-c373t-484d21161ab80398c9aae118318fa3d05a038a6be2ffc97c2ecdf6592e6ade263</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2965549$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2965549$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,27869,27924,27925,58017,58021,58250,58254,59647,60436</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=2815280$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Hobert, James P.</creatorcontrib><creatorcontrib>Altman, N. S.</creatorcontrib><creatorcontrib>Schofield, Carl L.</creatorcontrib><title>Analyses of Fish Species Richness with Spatial Covariate</title><title>Journal of the American Statistical Association</title><description>Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the area, elevation, and acidity of the lake. Data on these and other factors are available for 1,166 Adirondack lakes. The data are analyzed with the goal of quantifying the effects of acid deposition on species richness after controlling for other important factors. A plot of the residuals from a standard multiple regression model against spatial location reveals a strong nonlinear relationship between species richness and lake location. With only one realization of the data, it is difficult, if not impossible, to tell whether this dependence should be modeled as deterministic spatial trend or as a spatial covariance structure. Two models are thus considered. The first is a multiple linear regression model with spatially correlated errors. As is common in geostatistics, it is assumed that the correlation between two errors is a function only of the vector separating the corresponding lakes. This is accomplished by modeling the correlation among the errors with a parametric variogram model. The parameters of the variogram are estimated via restricted maximum likelihood, after which the regression parameters are estimated using generalized least squares. The second model considered is a partial linear (or semiparametric) model containing a nonlinear term in spatial location. Locally weighted quadratic regression is used in conjunction with an estimation technique described by Speckman to estimate simultaneously the regression parameters and the "smooth" function of longitude and latitude. Finally, the ultimate goal of the 1990 legislation is a 50% reduction in sulfate deposition. Both models are used to form prediction intervals for species richness under that scenario. Extrapolation is avoided by using the criteria of Weisberg. The results suggest that a 50% reduction in sulfate deposition may result in a substantial increase in the fish species richness of many Adirondack lakes, particularly those with pH values between 4.5 and 6.5.</description><subject>Applications</subject><subject>Applications and Case Studies</subject><subject>Biology, psychology, social sciences</subject><subject>Distribution</subject><subject>Emissions</subject><subject>Exact sciences and technology</subject><subject>Fish</subject><subject>Freshwater fishes</subject><subject>Geodetic position</subject><subject>Geometric anisotropy</subject><subject>Insurance, economics, finance</subject><subject>Lakes</subject><subject>Linear inference, regression</subject><subject>Linear models</subject><subject>Linear regression</subject><subject>Locally weighted regression</subject><subject>Mathematics</subject><subject>Medical sciences</subject><subject>Modeling</subject><subject>Multivariate analysis</subject><subject>Musical intervals</subject><subject>Parametric models</subject><subject>Partial linear model</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Reliability, life testing, quality control</subject><subject>Restricted maximum likelihood</subject><subject>Sciences and techniques of general use</subject><subject>Semi-parametric model</subject><subject>Semiparametric modeling</subject><subject>Spatial models</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Sulfates</subject><subject>Variogram</subject><subject>Water pollution</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><sourceid>K30</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkFtrGzEQRkVJoG7av1CWptCnTXXdlR6NaZpAIJAL9E1MtBKRWa8czTrB_77aOA6hUKKXQcOZj5lDyFdGTxjV9CdlDWdSmRNmTFtaspVU0g9kxpRoa97KPwdkNkH1RH0knxCXtLxW6xnR8wH6LXqsUqhOI95X12vvYvlfRXc_eMTqKY5TF8YIfbVIj5AjjP4zOQzQo__yUo_I7emvm8VZfXH5-3wxv6idaMVYSy07zljD4E5TYbQzAJ4xLZgOIDqqgAoNzZ3nITjTOu5dFxpluG-g87wRR-THLned08PG42hXEZ3vexh82qDVwlBdjlaF_PYPuUybXM5DWyRoprjmBTr-H8SKOCGoMrJQzY5yOSFmH-w6xxXkrWXUTtLtXrqdpNu99DL4_SUe0EEfMgwu4us0f97iDbbEMeW34VzQ1nLTKCVNweY7LA4h5RU8pdx3doRtn_I-Wryz0V_qOp5Y</recordid><startdate>19970901</startdate><enddate>19970901</enddate><creator>Hobert, James P.</creator><creator>Altman, N. S.</creator><creator>Schofield, Carl L.</creator><general>Taylor &amp; Francis Group</general><general>American Statistical Association</general><general>Taylor &amp; Francis Ltd</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JRZRW</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AF</scope><scope>8BJ</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>K9-</scope><scope>K9.</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M0R</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>19970901</creationdate><title>Analyses of Fish Species Richness with Spatial Covariate</title><author>Hobert, James P. ; Altman, N. S. ; Schofield, Carl L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-484d21161ab80398c9aae118318fa3d05a038a6be2ffc97c2ecdf6592e6ade263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Applications</topic><topic>Applications and Case Studies</topic><topic>Biology, psychology, social sciences</topic><topic>Distribution</topic><topic>Emissions</topic><topic>Exact sciences and technology</topic><topic>Fish</topic><topic>Freshwater fishes</topic><topic>Geodetic position</topic><topic>Geometric anisotropy</topic><topic>Insurance, economics, finance</topic><topic>Lakes</topic><topic>Linear inference, regression</topic><topic>Linear models</topic><topic>Linear regression</topic><topic>Locally weighted regression</topic><topic>Mathematics</topic><topic>Medical sciences</topic><topic>Modeling</topic><topic>Multivariate analysis</topic><topic>Musical intervals</topic><topic>Parametric models</topic><topic>Partial linear model</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Reliability, life testing, quality control</topic><topic>Restricted maximum likelihood</topic><topic>Sciences and techniques of general use</topic><topic>Semi-parametric model</topic><topic>Semiparametric modeling</topic><topic>Spatial models</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Sulfates</topic><topic>Variogram</topic><topic>Water pollution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hobert, James P.</creatorcontrib><creatorcontrib>Altman, N. S.</creatorcontrib><creatorcontrib>Schofield, Carl L.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Periodicals Index Online Segment 35</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - West</collection><collection>Primary Sources Access (Plan D) - International</collection><collection>Primary Sources Access &amp; Build (Plan A) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Midwest</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Northeast</collection><collection>Primary Sources Access (Plan D) - Southeast</collection><collection>Primary Sources Access (Plan D) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Southeast</collection><collection>Primary Sources Access (Plan D) - South Central</collection><collection>Primary Sources Access &amp; Build (Plan A) - UK / I</collection><collection>Primary Sources Access (Plan D) - Canada</collection><collection>Primary Sources Access (Plan D) - EMEALA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - North Central</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - South Central</collection><collection>Primary Sources Access &amp; Build (Plan A) - International</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - International</collection><collection>Primary Sources Access (Plan D) - West</collection><collection>Periodicals Index Online Segments 1-50</collection><collection>Primary Sources Access (Plan D) - APAC</collection><collection>Primary Sources Access (Plan D) - Midwest</collection><collection>Primary Sources Access (Plan D) - MEA</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - Canada</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - EMEALA</collection><collection>Primary Sources Access &amp; Build (Plan A) - APAC</collection><collection>Primary Sources Access &amp; Build (Plan A) - Canada</collection><collection>Primary Sources Access &amp; Build (Plan A) - West</collection><collection>Primary Sources Access &amp; Build (Plan A) - EMEALA</collection><collection>Primary Sources Access (Plan D) - Northeast</collection><collection>Primary Sources Access &amp; Build (Plan A) - Midwest</collection><collection>Primary Sources Access &amp; Build (Plan A) - North Central</collection><collection>Primary Sources Access &amp; Build (Plan A) - Northeast</collection><collection>Primary Sources Access &amp; Build (Plan A) - South Central</collection><collection>Primary Sources Access &amp; Build (Plan A) - Southeast</collection><collection>Primary Sources Access (Plan D) - UK / I</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - APAC</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - MEA</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>ProQuest Family Health</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>ProQuest Health Management</collection><collection>Medical Database</collection><collection>ProQuest research library</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of the American Statistical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hobert, James P.</au><au>Altman, N. S.</au><au>Schofield, Carl L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analyses of Fish Species Richness with Spatial Covariate</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>1997-09-01</date><risdate>1997</risdate><volume>92</volume><issue>439</issue><spage>846</spage><epage>854</epage><pages>846-854</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><coden>JSTNAL</coden><abstract>Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the area, elevation, and acidity of the lake. Data on these and other factors are available for 1,166 Adirondack lakes. The data are analyzed with the goal of quantifying the effects of acid deposition on species richness after controlling for other important factors. A plot of the residuals from a standard multiple regression model against spatial location reveals a strong nonlinear relationship between species richness and lake location. With only one realization of the data, it is difficult, if not impossible, to tell whether this dependence should be modeled as deterministic spatial trend or as a spatial covariance structure. Two models are thus considered. The first is a multiple linear regression model with spatially correlated errors. As is common in geostatistics, it is assumed that the correlation between two errors is a function only of the vector separating the corresponding lakes. This is accomplished by modeling the correlation among the errors with a parametric variogram model. The parameters of the variogram are estimated via restricted maximum likelihood, after which the regression parameters are estimated using generalized least squares. The second model considered is a partial linear (or semiparametric) model containing a nonlinear term in spatial location. Locally weighted quadratic regression is used in conjunction with an estimation technique described by Speckman to estimate simultaneously the regression parameters and the "smooth" function of longitude and latitude. Finally, the ultimate goal of the 1990 legislation is a 50% reduction in sulfate deposition. Both models are used to form prediction intervals for species richness under that scenario. Extrapolation is avoided by using the criteria of Weisberg. The results suggest that a 50% reduction in sulfate deposition may result in a substantial increase in the fish species richness of many Adirondack lakes, particularly those with pH values between 4.5 and 6.5.</abstract><cop>Alexandria, VA</cop><pub>Taylor &amp; Francis Group</pub><doi>10.1080/01621459.1997.10474040</doi><tpages>9</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0162-1459
ispartof Journal of the American Statistical Association, 1997-09, Vol.92 (439), p.846-854
issn 0162-1459
1537-274X
language eng
recordid cdi_proquest_miscellaneous_839081045
source JSTOR Mathematics and Statistics; Taylor & Francis Journals Complete; JSTOR; Periodicals Index Online
subjects Applications
Applications and Case Studies
Biology, psychology, social sciences
Distribution
Emissions
Exact sciences and technology
Fish
Freshwater fishes
Geodetic position
Geometric anisotropy
Insurance, economics, finance
Lakes
Linear inference, regression
Linear models
Linear regression
Locally weighted regression
Mathematics
Medical sciences
Modeling
Multivariate analysis
Musical intervals
Parametric models
Partial linear model
Probability and statistics
Regression analysis
Reliability, life testing, quality control
Restricted maximum likelihood
Sciences and techniques of general use
Semi-parametric model
Semiparametric modeling
Spatial models
Statistical analysis
Statistics
Sulfates
Variogram
Water pollution
title Analyses of Fish Species Richness with Spatial Covariate
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T06%3A19%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analyses%20of%20Fish%20Species%20Richness%20with%20Spatial%20Covariate&rft.jtitle=Journal%20of%20the%20American%20Statistical%20Association&rft.au=Hobert,%20James%20P.&rft.date=1997-09-01&rft.volume=92&rft.issue=439&rft.spage=846&rft.epage=854&rft.pages=846-854&rft.issn=0162-1459&rft.eissn=1537-274X&rft.coden=JSTNAL&rft_id=info:doi/10.1080/01621459.1997.10474040&rft_dat=%3Cjstor_proqu%3E2965549%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=274815282&rft_id=info:pmid/&rft_jstor_id=2965549&rfr_iscdi=true