Characteristics and Drivers of High-Altitude Ladybird Flight: Insights from Vertical-Looking Entomological Radar
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{"title"=>"Characteristics and drivers of high-altitude ladybird flight: Insights from vertical-looking entomological radar", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel L.", "last_name"=>"Jeffries", "scopus_author_id"=>"50761106100"}, {"first_name"=>"Jason", "last_name"=>"Chapman", "scopus_author_id"=>"7402661080"}, {"first_name"=>"Helen E.", "last_name"=>"Roy", "scopus_author_id"=>"7004925547"}, {"first_name"=>"Stuart", "last_name"=>"Humphries", "scopus_author_id"=>"56020363700"}, {"first_name"=>"Richard", "last_name"=>"Harrington", "scopus_author_id"=>"35229049400"}, {"first_name"=>"Peter M.J.", "last_name"=>"Brown", "scopus_author_id"=>"55454865800"}, {"first_name"=>"Lori J.Lawson", "last_name"=>"Handley", "scopus_author_id"=>"12800937600"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"372231396", "sgr"=>"84893188134", "scopus"=>"2-s2.0-84893188134", "pmid"=>"24367512", "doi"=>"10.1371/journal.pone.0082278"}, "id"=>"b0f61e57-46b3-391c-891c-9a40b1381abe", "abstract"=>"Understanding the characteristics and drivers of dispersal is crucial for predicting population dynamics, particularly in range-shifting species. Studying long-distance dispersal in insects is challenging, but recent advances in entomological radar offer unique insights. We analysed 10 years of radar data collected at Rothamsted Research, U.K., to investigate characteristics (altitude, speed, seasonal and annual trends) and drivers (aphid abundance, air temperature, wind speed and rainfall) of high-altitude flight of the two most abundant U.K. ladybird species (native Coccinella septempunctata and invasive Harmonia axyridis). These species cannot be distinguished in the radar data since their reflectivity signals overlap, and they were therefore analysed together. However, their signals do not overlap with other, abundant insects so we are confident they constitute the overwhelming majority of the analysed data. The target species were detected up to ∼1100 m above ground level, where displacement speeds of up to ∼60 km/h were recorded, however most ladybirds were found between ∼150 and 500 m, and had a mean displacement of 30 km/h. Average flight time was estimated, using tethered flight experiments, to be 36.5 minutes, but flights of up to two hours were observed. Ladybirds are therefore potentially able to travel 18 km in a \"typical\" high-altitude flight, but up to 120 km if flying at higher altitudes, indicating a high capacity for long-distance dispersal. There were strong seasonal trends in ladybird abundance, with peaks corresponding to the highest temperatures of mid-summer, and warm air temperature was the key driver of ladybird flight. Climatic warming may therefore increase the potential for long-distance dispersal in these species. Low aphid abundance was a second significant factor, highlighting the important role of aphid population dynamics in ladybird dispersal. This research illustrates the utility of radar for studying high-altitude insect flight and has important implications for predicting long-distance dispersal.", "link"=>"http://www.mendeley.com/research/characteristics-drivers-highaltitude-ladybird-flight-insights-verticallooking-entomological-radar", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>3, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Environmental Science"=>3, "Agricultural and Biological Sciences"=>14, "Medicine and Dentistry"=>1, "Biochemistry, Genetics and Molecular Biology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/1321906"], "description"=>"<p><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082278#pone.0082278.s012\" target=\"_blank\">Tables S6</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082278#pone.0082278.s013\" target=\"_blank\">S7</a> for results of full model including wind speed and partial model excluding wind speed, respectively). For the qpGLMs, both <i>t</i> statistic, and <i>F</i> statistic with corresponding and <i>P</i> values are given. The qpGLM dispersion parameter, ρ = 0.374, and residual deviance  = 22.932 on 57 degrees of freedom (df). Residual df  = 57 for each model. Note that the GLM deviance, <i>F</i> and <i>P</i> values correspond to the full model, where residual deviance is 21.777 on 55 df. GLS without autocorrelation is equivalent to standard multiple linear regression.<sup></sup> Results for minimal qpGLMs and GLS models (See </p><p><i>P</i> value codes: <sup>***</sup><i>P</i><0.000; <sup>**</sup><i>P</i><0.001; <sup>*</sup><i>P</i><0.01; <sup>+</sup><i>P</i><0.05.<sup></sup></p>", "links"=>[], "tags"=>["aerial"], "article_id"=>881769, "categories"=>["Biological Sciences", "Ecology", "Earth and Environmental Sciences"], "users"=>["Daniel L. Jeffries", "Jason Chapman", "Helen E. Roy", "Stuart Humphries", "Richard Harrington", "Peter M. J. Brown", "Lori-J. Lawson Handley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0082278.t001", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictors_of_aerial_density_Minimal_optimal_model_/881769", "title"=>"Predictors of aerial density: Minimal (optimal) model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-18 04:02:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1321905"], "description"=>"<p>Graphs show the relationship between monthly mean aerial density and each of the explanatory variables (also monthly means). Units for the explanatory variables are: temperature: °C, wind speed: m/s, rainfall: mm, aphids: absolute number counted in suction trap. Note aerial density, rainfall and number of aphids are not normally distributed and are therefore log transformed (see main text). “Rsq”  =  adjusted <i>R<sup>2</sup></i> (<i>R<sup>2</sup><sub>adj</sub></i>). Temperature and aphids are both significant predictors of aerial density (see main text).</p>", "links"=>[], "tags"=>["regression", "aerial", "explanatory"], "article_id"=>881768, "categories"=>["Biological Sciences", "Ecology", "Earth and Environmental Sciences"], "users"=>["Daniel L. Jeffries", "Jason Chapman", "Helen E. Roy", "Stuart Humphries", "Richard Harrington", "Peter M. J. Brown", "Lori-J. Lawson Handley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0082278.g004", "stats"=>{"downloads"=>0, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_regression_of_aerial_density_against_all_explanatory_variables_/881768", "title"=>"Linear regression of aerial density against all explanatory variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-18 04:02:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1321900"], "description"=>"<p>Results of time series analysis for aerial density and each explanatory variable for May to October 2000–2010. Note the correspondence between peaks in temperature and aerial density, and the lag between peaks in aerial density and aphid abundance.</p>", "links"=>[], "tags"=>[], "article_id"=>881763, "categories"=>["Biological Sciences", "Ecology", "Earth and Environmental Sciences"], "users"=>["Daniel L. Jeffries", "Jason Chapman", "Helen E. Roy", "Stuart Humphries", "Richard Harrington", "Peter M. J. Brown", "Lori-J. Lawson Handley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0082278.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_series_plots_for_each_variable_/881763", "title"=>"Time series plots for each variable.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-18 04:02:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1321908", "https://ndownloader.figshare.com/files/1321909", "https://ndownloader.figshare.com/files/1321910", "https://ndownloader.figshare.com/files/1321911", "https://ndownloader.figshare.com/files/1321912", "https://ndownloader.figshare.com/files/1321913", "https://ndownloader.figshare.com/files/1321914", "https://ndownloader.figshare.com/files/1321915", "https://ndownloader.figshare.com/files/1321916", "https://ndownloader.figshare.com/files/1321917", "https://ndownloader.figshare.com/files/1321918", "https://ndownloader.figshare.com/files/1321919", "https://ndownloader.figshare.com/files/1321920", "https://ndownloader.figshare.com/files/1321921", "https://ndownloader.figshare.com/files/1321922"], "description"=>"<div><p>Understanding the characteristics and drivers of dispersal is crucial for predicting population dynamics, particularly in range-shifting species. Studying long-distance dispersal in insects is challenging, but recent advances in entomological radar offer unique insights. We analysed 10 years of radar data collected at Rothamsted Research, U.K., to investigate characteristics (altitude, speed, seasonal and annual trends) and drivers (aphid abundance, air temperature, wind speed and rainfall) of high-altitude flight of the two most abundant U.K. ladybird species (native <i>Coccinella septempunctata</i> and invasive <i>Harmonia axyridis</i>). These species cannot be distinguished in the radar data since their reflectivity signals overlap, and they were therefore analysed together. However, their signals do not overlap with other, abundant insects so we are confident they constitute the overwhelming majority of the analysed data. The target species were detected up to ∼1100 m above ground level, where displacement speeds of up to ∼60 km/h were recorded, however most ladybirds were found between ∼150 and 500 m, and had a mean displacement of 30 km/h. Average flight time was estimated, using tethered flight experiments, to be 36.5 minutes, but flights of up to two hours were observed. Ladybirds are therefore potentially able to travel 18 km in a “typical” high-altitude flight, but up to 120 km if flying at higher altitudes, indicating a high capacity for long-distance dispersal. There were strong seasonal trends in ladybird abundance, with peaks corresponding to the highest temperatures of mid-summer, and warm air temperature was the key driver of ladybird flight. Climatic warming may therefore increase the potential for long-distance dispersal in these species. Low aphid abundance was a second significant factor, highlighting the important role of aphid population dynamics in ladybird dispersal. This research illustrates the utility of radar for studying high-altitude insect flight and has important implications for predicting long-distance dispersal.</p></div>", "links"=>[], "tags"=>["drivers", "high-altitude", "ladybird", "insights", "vertical-looking", "entomological"], "article_id"=>881771, "categories"=>["Biological Sciences", "Ecology", "Earth and Environmental Sciences"], "users"=>["Daniel L. Jeffries", "Jason Chapman", "Helen E. Roy", "Stuart Humphries", "Richard Harrington", "Peter M. J. Brown", "Lori-J. Lawson Handley"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0082278.s001", "https://dx.doi.org/10.1371/journal.pone.0082278.s002", "https://dx.doi.org/10.1371/journal.pone.0082278.s003", "https://dx.doi.org/10.1371/journal.pone.0082278.s004", "https://dx.doi.org/10.1371/journal.pone.0082278.s005", "https://dx.doi.org/10.1371/journal.pone.0082278.s006", "https://dx.doi.org/10.1371/journal.pone.0082278.s007", "https://dx.doi.org/10.1371/journal.pone.0082278.s008", "https://dx.doi.org/10.1371/journal.pone.0082278.s009", "https://dx.doi.org/10.1371/journal.pone.0082278.s010", "https://dx.doi.org/10.1371/journal.pone.0082278.s011", "https://dx.doi.org/10.1371/journal.pone.0082278.s012", "https://dx.doi.org/10.1371/journal.pone.0082278.s013", "https://dx.doi.org/10.1371/journal.pone.0082278.s014", "https://dx.doi.org/10.1371/journal.pone.0082278.s015"], "stats"=>{"downloads"=>50, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_and_Drivers_of_High_Altitude_Ladybird_Flight_Insights_from_Vertical_Looking_Entomological_Radar_/881771", "title"=>"Characteristics and Drivers of High-Altitude Ladybird Flight: Insights from Vertical-Looking Entomological Radar", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-12-18 04:02:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1321902"], "description"=>"<p>“ACF” is the auto-correlation function, and “AD” Aerial density. Peaks that cross the dotted blue lines are considered significant at the 5% level. All explanatory variables show at least some significant peaks suggesting some influence on aerial density, however patterns for temperature, wind speed and aphids are particularly strong (Figure 3 a, b and d).</p>", "links"=>[], "tags"=>["auto-correlation", "aerial", "explanatory"], "article_id"=>881765, "categories"=>["Biological Sciences", "Ecology", "Earth and Environmental Sciences"], "users"=>["Daniel L. Jeffries", "Jason Chapman", "Helen E. Roy", "Stuart Humphries", "Richard Harrington", "Peter M. J. Brown", "Lori-J. Lawson Handley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0082278.g003", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Partial_auto_correlation_plots_for_aerial_density_against_the_four_explanatory_variables_/881765", "title"=>"Partial auto-correlation plots for aerial density against the four explanatory variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-18 04:02:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1321898"], "description"=>"<p>Figure 1a Box plot for ladybird aerial density summarized by month. Boxes correspond to the 25<sup>th</sup> and 75<sup>th</sup> percentile, horizontal bars within boxes to means, and whiskers to maximum values or 1.5 times the interquartile range (when there are outliers present, represented by open circles). For boxplots of aerial density by year, or number of target species records in the VLR database, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082278#pone.0082278.s001\" target=\"_blank\">Figure S1</a>. Figure 1b Barplot of percentage total aerial density by altitude. The majority (roughly 85%) of ladybirds were detected in the first 5 range gates. Gate numbers correspond to the following altitudes (AGL): 1: 150–195; 2: 221–266; 3: 292–337; 4: 363–408; 5: 434–479; 6: 505–550; 7: 576–621; 8: 647–692; 9: 718–763; 10: 789–834; 11: 860–905; 12: 931–976; 13: 1002–1047; 14: 1073–1118; 15: 1144–1189.</p>", "links"=>[], "tags"=>["summarized", "altitude"], "article_id"=>881761, "categories"=>["Biological Sciences", "Ecology", "Earth and Environmental Sciences"], "users"=>["Daniel L. Jeffries", "Jason Chapman", "Helen E. Roy", "Stuart Humphries", "Richard Harrington", "Peter M. J. Brown", "Lori-J. Lawson Handley"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0082278.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Aerial_density_summarized_by_a_month_and_b_altitude_for_2000_8211_2010_/881761", "title"=>"Aerial density summarized by a) month and b) altitude for 2000–2010.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-18 04:02:38"}

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Relative Metric

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