Distance models as a tool for modelling detection probability and density of native bumblebees
Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect de...
Gespeichert in:
Veröffentlicht in: | Journal of applied entomology (1986) 2019-04, Vol.143 (3), p.225-235 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 235 |
---|---|
container_issue | 3 |
container_start_page | 225 |
container_title | Journal of applied entomology (1986) |
container_volume | 143 |
creator | McNeil, Darin J. Otto, Clint R. V. Moser, Erin L. Urban‐Mead, Katherine R. King, David E. Rodewald, Amanda D. Larkin, Jeffery L. |
description | Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators. |
doi_str_mv | 10.1111/jen.12583 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2237543148</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2189904151</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3653-74fdc125a19ff52d59104f6078b52516addf89075e5ce75a35485573800de9833</originalsourceid><addsrcrecordid>eNp1kE1PwzAMhiMEEmNw4B9E4gKHbkmTtOkRjfGlCS5wJUobB2Vqk9F0oP17MsoJCcuSLfux9epF6JySGU0xX4Of0VxIdoAmlLMqI5zKQzQhFaNZTrg8RicxrgmhBRdkgt5uXBy0bwB3wUAbsU6JhxBabEM_Dlvn37GBAZrBBY83fah17Vo37LD2Jm183PfBYq8H9wm43nZ1CzVAPEVHVrcRzn7rFL3eLl8W99nq-e5hcb3KGlYIlpXcmibJ1rSyVuRGVJRwW5BS1iIXtNDGWFmRUoBooBSaCS6FKJkkxEAlGZuiy_FvEvexhTiozsUmSdcewjaqPGel4IxymdCLP-g6bHuf1KmcyqpKhgmaqKuRavoQYw9WbXrX6X6nKFF7p1VyWv04ndj5yH65Fnb_g-px-TRefAN5JX56</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2189904151</pqid></control><display><type>article</type><title>Distance models as a tool for modelling detection probability and density of native bumblebees</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>McNeil, Darin J. ; Otto, Clint R. V. ; Moser, Erin L. ; Urban‐Mead, Katherine R. ; King, David E. ; Rodewald, Amanda D. ; Larkin, Jeffery L.</creator><creatorcontrib>McNeil, Darin J. ; Otto, Clint R. V. ; Moser, Erin L. ; Urban‐Mead, Katherine R. ; King, David E. ; Rodewald, Amanda D. ; Larkin, Jeffery L.</creatorcontrib><description>Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.</description><identifier>ISSN: 0931-2048</identifier><identifier>EISSN: 1439-0418</identifier><identifier>DOI: 10.1111/jen.12583</identifier><language>eng</language><publisher>Berlin: Wiley Subscription Services, Inc</publisher><subject>Abundance ; Bees ; Bombus ; Bumblebees ; data collection ; Density ; detection probability ; distance modelling ; Estimates ; forests ; Grasses ; Habitats ; Modelling ; Monitoring ; netting ; Netting (materials/structures) ; Pennsylvania ; Pollinators ; Polls & surveys ; Population number ; population size ; Population statistics ; probability ; Relative abundance ; Sampling ; shrubs ; Small mammals ; surveys ; transects ; trapping ; Vegetation ; vegetation structure</subject><ispartof>Journal of applied entomology (1986), 2019-04, Vol.143 (3), p.225-235</ispartof><rights>2018 Blackwell Verlag GmbH</rights><rights>Copyright © 2019 Blackwell Verlag GmbH</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3653-74fdc125a19ff52d59104f6078b52516addf89075e5ce75a35485573800de9833</citedby><cites>FETCH-LOGICAL-c3653-74fdc125a19ff52d59104f6078b52516addf89075e5ce75a35485573800de9833</cites><orcidid>0000-0003-4595-8354 ; 0000-0002-6719-6306 ; 0000-0002-7582-3525</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fjen.12583$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjen.12583$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>McNeil, Darin J.</creatorcontrib><creatorcontrib>Otto, Clint R. V.</creatorcontrib><creatorcontrib>Moser, Erin L.</creatorcontrib><creatorcontrib>Urban‐Mead, Katherine R.</creatorcontrib><creatorcontrib>King, David E.</creatorcontrib><creatorcontrib>Rodewald, Amanda D.</creatorcontrib><creatorcontrib>Larkin, Jeffery L.</creatorcontrib><title>Distance models as a tool for modelling detection probability and density of native bumblebees</title><title>Journal of applied entomology (1986)</title><description>Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.</description><subject>Abundance</subject><subject>Bees</subject><subject>Bombus</subject><subject>Bumblebees</subject><subject>data collection</subject><subject>Density</subject><subject>detection probability</subject><subject>distance modelling</subject><subject>Estimates</subject><subject>forests</subject><subject>Grasses</subject><subject>Habitats</subject><subject>Modelling</subject><subject>Monitoring</subject><subject>netting</subject><subject>Netting (materials/structures)</subject><subject>Pennsylvania</subject><subject>Pollinators</subject><subject>Polls & surveys</subject><subject>Population number</subject><subject>population size</subject><subject>Population statistics</subject><subject>probability</subject><subject>Relative abundance</subject><subject>Sampling</subject><subject>shrubs</subject><subject>Small mammals</subject><subject>surveys</subject><subject>transects</subject><subject>trapping</subject><subject>Vegetation</subject><subject>vegetation structure</subject><issn>0931-2048</issn><issn>1439-0418</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kE1PwzAMhiMEEmNw4B9E4gKHbkmTtOkRjfGlCS5wJUobB2Vqk9F0oP17MsoJCcuSLfux9epF6JySGU0xX4Of0VxIdoAmlLMqI5zKQzQhFaNZTrg8RicxrgmhBRdkgt5uXBy0bwB3wUAbsU6JhxBabEM_Dlvn37GBAZrBBY83fah17Vo37LD2Jm183PfBYq8H9wm43nZ1CzVAPEVHVrcRzn7rFL3eLl8W99nq-e5hcb3KGlYIlpXcmibJ1rSyVuRGVJRwW5BS1iIXtNDGWFmRUoBooBSaCS6FKJkkxEAlGZuiy_FvEvexhTiozsUmSdcewjaqPGel4IxymdCLP-g6bHuf1KmcyqpKhgmaqKuRavoQYw9WbXrX6X6nKFF7p1VyWv04ndj5yH65Fnb_g-px-TRefAN5JX56</recordid><startdate>201904</startdate><enddate>201904</enddate><creator>McNeil, Darin J.</creator><creator>Otto, Clint R. V.</creator><creator>Moser, Erin L.</creator><creator>Urban‐Mead, Katherine R.</creator><creator>King, David E.</creator><creator>Rodewald, Amanda D.</creator><creator>Larkin, Jeffery L.</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0003-4595-8354</orcidid><orcidid>https://orcid.org/0000-0002-6719-6306</orcidid><orcidid>https://orcid.org/0000-0002-7582-3525</orcidid></search><sort><creationdate>201904</creationdate><title>Distance models as a tool for modelling detection probability and density of native bumblebees</title><author>McNeil, Darin J. ; Otto, Clint R. V. ; Moser, Erin L. ; Urban‐Mead, Katherine R. ; King, David E. ; Rodewald, Amanda D. ; Larkin, Jeffery L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3653-74fdc125a19ff52d59104f6078b52516addf89075e5ce75a35485573800de9833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Abundance</topic><topic>Bees</topic><topic>Bombus</topic><topic>Bumblebees</topic><topic>data collection</topic><topic>Density</topic><topic>detection probability</topic><topic>distance modelling</topic><topic>Estimates</topic><topic>forests</topic><topic>Grasses</topic><topic>Habitats</topic><topic>Modelling</topic><topic>Monitoring</topic><topic>netting</topic><topic>Netting (materials/structures)</topic><topic>Pennsylvania</topic><topic>Pollinators</topic><topic>Polls & surveys</topic><topic>Population number</topic><topic>population size</topic><topic>Population statistics</topic><topic>probability</topic><topic>Relative abundance</topic><topic>Sampling</topic><topic>shrubs</topic><topic>Small mammals</topic><topic>surveys</topic><topic>transects</topic><topic>trapping</topic><topic>Vegetation</topic><topic>vegetation structure</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McNeil, Darin J.</creatorcontrib><creatorcontrib>Otto, Clint R. V.</creatorcontrib><creatorcontrib>Moser, Erin L.</creatorcontrib><creatorcontrib>Urban‐Mead, Katherine R.</creatorcontrib><creatorcontrib>King, David E.</creatorcontrib><creatorcontrib>Rodewald, Amanda D.</creatorcontrib><creatorcontrib>Larkin, Jeffery L.</creatorcontrib><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Journal of applied entomology (1986)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McNeil, Darin J.</au><au>Otto, Clint R. V.</au><au>Moser, Erin L.</au><au>Urban‐Mead, Katherine R.</au><au>King, David E.</au><au>Rodewald, Amanda D.</au><au>Larkin, Jeffery L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distance models as a tool for modelling detection probability and density of native bumblebees</atitle><jtitle>Journal of applied entomology (1986)</jtitle><date>2019-04</date><risdate>2019</risdate><volume>143</volume><issue>3</issue><spage>225</spage><epage>235</epage><pages>225-235</pages><issn>0931-2048</issn><eissn>1439-0418</eissn><abstract>Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees (Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.</abstract><cop>Berlin</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1111/jen.12583</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-4595-8354</orcidid><orcidid>https://orcid.org/0000-0002-6719-6306</orcidid><orcidid>https://orcid.org/0000-0002-7582-3525</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0931-2048 |
ispartof | Journal of applied entomology (1986), 2019-04, Vol.143 (3), p.225-235 |
issn | 0931-2048 1439-0418 |
language | eng |
recordid | cdi_proquest_miscellaneous_2237543148 |
source | Wiley Online Library Journals Frontfile Complete |
subjects | Abundance Bees Bombus Bumblebees data collection Density detection probability distance modelling Estimates forests Grasses Habitats Modelling Monitoring netting Netting (materials/structures) Pennsylvania Pollinators Polls & surveys Population number population size Population statistics probability Relative abundance Sampling shrubs Small mammals surveys transects trapping Vegetation vegetation structure |
title | Distance models as a tool for modelling detection probability and density of native bumblebees |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T17%3A14%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Distance%20models%20as%20a%20tool%20for%20modelling%20detection%20probability%20and%20density%20of%20native%20bumblebees&rft.jtitle=Journal%20of%20applied%20entomology%20(1986)&rft.au=McNeil,%20Darin%20J.&rft.date=2019-04&rft.volume=143&rft.issue=3&rft.spage=225&rft.epage=235&rft.pages=225-235&rft.issn=0931-2048&rft.eissn=1439-0418&rft_id=info:doi/10.1111/jen.12583&rft_dat=%3Cproquest_cross%3E2189904151%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2189904151&rft_id=info:pmid/&rfr_iscdi=true |