Spinning a laser web: predicting spider distributions using LiDAR
LiDAR remote sensing has been used to examine relationships between vertebrate diversity and environmental characteristics, but its application to invertebrates has been limited. Our objectives were to determine whether LiDAR-derived variables could be used to accurately describe single-species dist...
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Veröffentlicht in: | Ecological applications 2011-03, Vol.21 (2), p.577-588 |
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description | LiDAR remote sensing has been used to examine relationships between vertebrate diversity and environmental characteristics, but its application to invertebrates has been limited. Our objectives were to determine whether LiDAR-derived variables could be used to accurately describe single-species distributions and community characteristics of spiders in remote forested and mountainous terrain. We collected over 5300 spiders across multiple transects in the Bavarian National Park (Germany) using pitfall traps. We examined spider community characteristics (species richness, the Shannon index, the Simpson index, community composition, mean body size, and abundance) and single-species distribution and abundance with LiDAR variables and ground-based measurements. We used the
R
2
and partial
R
2
provided by variance partitioning to evaluate the predictive power of LiDAR-derived variables compared to ground measurements for each of the community characteristics. The total adjusted
R
2
for species richness, the Shannon index, community species composition, and body size had a range of 25-–57%%. LiDAR variables and ground measurements both contributed >80%% to the total predictive power. For species composition, the explained variance was ∼∼32%%, which was significantly greater than expected by chance. The predictive power of LiDAR-derived variables was comparable or superior to that of the ground-based variables for examinations of single-species distributions, and it explained up to 55%% of the variance. The predictability of species distributions was higher for species that had strong associations with shade in open-forest habitats, and this niche position has been well documented across the European continent for spider species. The similar statistical performance between LiDAR and ground-based measures at our field sites indicated that deriving spider community and species distribution information using LiDAR data can provide not only high predictive power at relatively low cost, but may also allow unprecedented mapping of community- and species-level spider information at scales ranging from stands to landscapes. Therefore, LiDAR is a viable tool to assist species-specific conservation as well as broader biodiversity planning efforts not only for a growing list of vertebrates, but for invertebrates as well. |
doi_str_mv | 10.1890/09-2155.1 |
format | Article |
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R
2
and partial
R
2
provided by variance partitioning to evaluate the predictive power of LiDAR-derived variables compared to ground measurements for each of the community characteristics. The total adjusted
R
2
for species richness, the Shannon index, community species composition, and body size had a range of 25-–57%%. LiDAR variables and ground measurements both contributed >80%% to the total predictive power. For species composition, the explained variance was ∼∼32%%, which was significantly greater than expected by chance. The predictive power of LiDAR-derived variables was comparable or superior to that of the ground-based variables for examinations of single-species distributions, and it explained up to 55%% of the variance. The predictability of species distributions was higher for species that had strong associations with shade in open-forest habitats, and this niche position has been well documented across the European continent for spider species. The similar statistical performance between LiDAR and ground-based measures at our field sites indicated that deriving spider community and species distribution information using LiDAR data can provide not only high predictive power at relatively low cost, but may also allow unprecedented mapping of community- and species-level spider information at scales ranging from stands to landscapes. Therefore, LiDAR is a viable tool to assist species-specific conservation as well as broader biodiversity planning efforts not only for a growing list of vertebrates, but for invertebrates as well.</description><identifier>ISSN: 1051-0761</identifier><identifier>EISSN: 1939-5582</identifier><identifier>DOI: 10.1890/09-2155.1</identifier><identifier>PMID: 21563587</identifier><language>eng</language><publisher>United States: Ecological Society of America</publisher><subject>Animals ; Araneae ; Bavarian Forest National Park ; Body size ; Communities ; conservation planning ; Demography ; Ecosystem ; Forest canopy ; Forest ecology ; Forest habitats ; Germany ; Habitat conservation ; Invertebrates ; Lasers ; LiDAR ; remote sensing ; Remote Sensing Technology - economics ; Remote Sensing Technology - methods ; southeastern Germany ; Species ; species richness ; species-level prediction ; Spiders ; Spiders - physiology ; Statistical variance ; Trees ; variance partitioning</subject><ispartof>Ecological applications, 2011-03, Vol.21 (2), p.577-588</ispartof><rights>Ecological Society of America</rights><rights>Copyright © 2011 The Ecological Society of America</rights><rights>2011 by the Ecological Society of America</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4077-159453c9a36c018563c19d2ffac081fbbe351376fca6a70138fad96e3df0e4003</citedby><cites>FETCH-LOGICAL-a4077-159453c9a36c018563c19d2ffac081fbbe351376fca6a70138fad96e3df0e4003</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/29779684$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/29779684$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,1411,27901,27902,45550,45551,57992,58225</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21563587$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Radeloff, VC</contributor><creatorcontrib>Vierling, K. T</creatorcontrib><creatorcontrib>Vierling, L. A</creatorcontrib><creatorcontrib>Bäässler, C</creatorcontrib><creatorcontrib>Brandl, R</creatorcontrib><creatorcontrib>Weißß, I</creatorcontrib><creatorcontrib>Müüller, J</creatorcontrib><title>Spinning a laser web: predicting spider distributions using LiDAR</title><title>Ecological applications</title><addtitle>Ecol Appl</addtitle><description>LiDAR remote sensing has been used to examine relationships between vertebrate diversity and environmental characteristics, but its application to invertebrates has been limited. Our objectives were to determine whether LiDAR-derived variables could be used to accurately describe single-species distributions and community characteristics of spiders in remote forested and mountainous terrain. We collected over 5300 spiders across multiple transects in the Bavarian National Park (Germany) using pitfall traps. We examined spider community characteristics (species richness, the Shannon index, the Simpson index, community composition, mean body size, and abundance) and single-species distribution and abundance with LiDAR variables and ground-based measurements. We used the
R
2
and partial
R
2
provided by variance partitioning to evaluate the predictive power of LiDAR-derived variables compared to ground measurements for each of the community characteristics. The total adjusted
R
2
for species richness, the Shannon index, community species composition, and body size had a range of 25-–57%%. LiDAR variables and ground measurements both contributed >80%% to the total predictive power. For species composition, the explained variance was ∼∼32%%, which was significantly greater than expected by chance. The predictive power of LiDAR-derived variables was comparable or superior to that of the ground-based variables for examinations of single-species distributions, and it explained up to 55%% of the variance. The predictability of species distributions was higher for species that had strong associations with shade in open-forest habitats, and this niche position has been well documented across the European continent for spider species. The similar statistical performance between LiDAR and ground-based measures at our field sites indicated that deriving spider community and species distribution information using LiDAR data can provide not only high predictive power at relatively low cost, but may also allow unprecedented mapping of community- and species-level spider information at scales ranging from stands to landscapes. Therefore, LiDAR is a viable tool to assist species-specific conservation as well as broader biodiversity planning efforts not only for a growing list of vertebrates, but for invertebrates as well.</description><subject>Animals</subject><subject>Araneae</subject><subject>Bavarian Forest National Park</subject><subject>Body size</subject><subject>Communities</subject><subject>conservation planning</subject><subject>Demography</subject><subject>Ecosystem</subject><subject>Forest canopy</subject><subject>Forest ecology</subject><subject>Forest habitats</subject><subject>Germany</subject><subject>Habitat conservation</subject><subject>Invertebrates</subject><subject>Lasers</subject><subject>LiDAR</subject><subject>remote sensing</subject><subject>Remote Sensing Technology - economics</subject><subject>Remote Sensing Technology - methods</subject><subject>southeastern Germany</subject><subject>Species</subject><subject>species richness</subject><subject>species-level prediction</subject><subject>Spiders</subject><subject>Spiders - physiology</subject><subject>Statistical variance</subject><subject>Trees</subject><subject>variance partitioning</subject><issn>1051-0761</issn><issn>1939-5582</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kEtLxDAUhYMoPkYX_gCl4EJcVHOTpmncDb5hQPGxDmmbSKTT1qRF59-b0vEBajYJ93znXHIQ2gV8DJnAJ1jEBBg7hhW0CYKKmLGMrIY3ZhBjnsIG2vL-BYdDCFlHG4FOKcv4Jpo-tLaubf0cqahSXrvoTeenUet0aYtumPvWlmFcWt85m_edbWof9X6QZvZ8er-N1oyqvN5Z3hP0dHnxeHYdz26vbs6ms1glmPMYmEgYLYSiaYEhC_sLECUxRhU4A5PnmjKgPDWFShXHQDOjSpFqWhqsE4zpBB2Oua1rXnvtOzm3vtBVpWrd9F5mnAQT8IE8GsnCNd47bWTr7Fy5hQQsh8IkFnIoTEJg95epfT7X5Rf52VAAkhF4s5Ve_J8kL6Z3BAMQIIwPtr3R9uK7xn3HCs5FmiVBPxh11S3appbaqx9ZbWlk9979Sf3-wwfDMZNw</recordid><startdate>201103</startdate><enddate>201103</enddate><creator>Vierling, K. T</creator><creator>Vierling, L. A</creator><creator>Bäässler, C</creator><creator>Brandl, R</creator><creator>Weißß, I</creator><creator>Müüller, J</creator><general>Ecological Society of America</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope></search><sort><creationdate>201103</creationdate><title>Spinning a laser web: predicting spider distributions using LiDAR</title><author>Vierling, K. T ; Vierling, L. A ; Bäässler, C ; Brandl, R ; Weißß, I ; Müüller, J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4077-159453c9a36c018563c19d2ffac081fbbe351376fca6a70138fad96e3df0e4003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Animals</topic><topic>Araneae</topic><topic>Bavarian Forest National Park</topic><topic>Body size</topic><topic>Communities</topic><topic>conservation planning</topic><topic>Demography</topic><topic>Ecosystem</topic><topic>Forest canopy</topic><topic>Forest ecology</topic><topic>Forest habitats</topic><topic>Germany</topic><topic>Habitat conservation</topic><topic>Invertebrates</topic><topic>Lasers</topic><topic>LiDAR</topic><topic>remote sensing</topic><topic>Remote Sensing Technology - economics</topic><topic>Remote Sensing Technology - methods</topic><topic>southeastern Germany</topic><topic>Species</topic><topic>species richness</topic><topic>species-level prediction</topic><topic>Spiders</topic><topic>Spiders - physiology</topic><topic>Statistical variance</topic><topic>Trees</topic><topic>variance partitioning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vierling, K. T</creatorcontrib><creatorcontrib>Vierling, L. A</creatorcontrib><creatorcontrib>Bäässler, C</creatorcontrib><creatorcontrib>Brandl, R</creatorcontrib><creatorcontrib>Weißß, I</creatorcontrib><creatorcontrib>Müüller, J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Ecological applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vierling, K. T</au><au>Vierling, L. A</au><au>Bäässler, C</au><au>Brandl, R</au><au>Weißß, I</au><au>Müüller, J</au><au>Radeloff, VC</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spinning a laser web: predicting spider distributions using LiDAR</atitle><jtitle>Ecological applications</jtitle><addtitle>Ecol Appl</addtitle><date>2011-03</date><risdate>2011</risdate><volume>21</volume><issue>2</issue><spage>577</spage><epage>588</epage><pages>577-588</pages><issn>1051-0761</issn><eissn>1939-5582</eissn><abstract>LiDAR remote sensing has been used to examine relationships between vertebrate diversity and environmental characteristics, but its application to invertebrates has been limited. Our objectives were to determine whether LiDAR-derived variables could be used to accurately describe single-species distributions and community characteristics of spiders in remote forested and mountainous terrain. We collected over 5300 spiders across multiple transects in the Bavarian National Park (Germany) using pitfall traps. We examined spider community characteristics (species richness, the Shannon index, the Simpson index, community composition, mean body size, and abundance) and single-species distribution and abundance with LiDAR variables and ground-based measurements. We used the
R
2
and partial
R
2
provided by variance partitioning to evaluate the predictive power of LiDAR-derived variables compared to ground measurements for each of the community characteristics. The total adjusted
R
2
for species richness, the Shannon index, community species composition, and body size had a range of 25-–57%%. LiDAR variables and ground measurements both contributed >80%% to the total predictive power. For species composition, the explained variance was ∼∼32%%, which was significantly greater than expected by chance. The predictive power of LiDAR-derived variables was comparable or superior to that of the ground-based variables for examinations of single-species distributions, and it explained up to 55%% of the variance. The predictability of species distributions was higher for species that had strong associations with shade in open-forest habitats, and this niche position has been well documented across the European continent for spider species. The similar statistical performance between LiDAR and ground-based measures at our field sites indicated that deriving spider community and species distribution information using LiDAR data can provide not only high predictive power at relatively low cost, but may also allow unprecedented mapping of community- and species-level spider information at scales ranging from stands to landscapes. Therefore, LiDAR is a viable tool to assist species-specific conservation as well as broader biodiversity planning efforts not only for a growing list of vertebrates, but for invertebrates as well.</abstract><cop>United States</cop><pub>Ecological Society of America</pub><pmid>21563587</pmid><doi>10.1890/09-2155.1</doi><tpages>12</tpages></addata></record> |
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source | Jstor Complete Legacy; MEDLINE; Wiley Online Library Journals Frontfile Complete |
subjects | Animals Araneae Bavarian Forest National Park Body size Communities conservation planning Demography Ecosystem Forest canopy Forest ecology Forest habitats Germany Habitat conservation Invertebrates Lasers LiDAR remote sensing Remote Sensing Technology - economics Remote Sensing Technology - methods southeastern Germany Species species richness species-level prediction Spiders Spiders - physiology Statistical variance Trees variance partitioning |
title | Spinning a laser web: predicting spider distributions using LiDAR |
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