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133 results for “Insect communities”
Visual survey of insect communities on Iva frutescens in summer 2013 and 2014 on Sapelo Island, Georgia
Visual surveys were conducted in summer 2013 and 2014 to record the insect fauna that occurred on patches of Iva frutescens. Forty-four patches of Iva frutescens at three locations on Sapelo Island, Georgia, were marked for repetitive surveys. Thirty-eight of these patches were sampled in 2014. The patches were visually surveyed every three days from May 29 - June 15, 2013, and from June 1 - July 29, 2014, and the taxon and abundance of insects were recorded. This submission contains three data tables: 1. Insect fauna of Iva patches in 2013. This data set includes observations of 44 patches on 6 sampling dates, and includes Armases crab data. 2. Insect fauna of Iva patches in 2014. This data set includes observations of 38 patches on 20 sampling dates. 3. Structure of Iva patches on Sapelo Island in 2013. This data set contains the dimensions (Length, Width, Height) and surrounding environments of the Iva patches that were surveyed in 2013. The GPS locations of the patches were measured in 2014.
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
Prairie manure application impacts on floral abundance, plant growth, plant community structure, insect and spider community abundance and activity density in experimental plots in Ames, Iowa (2021-2022).
This dataset contains results from a two-year field experiment at Iowa State University’s Horticulture Research Station to evaluate the effects of dairy manure application on native prairie plant and insect communities. We established replicated 4 m² plots across two field types, an established tallgrass prairie and a tilled crop field, and applied four manure treatments (weekly, biweekly, once per season, and control) using liquid slurry from a local dairy farm. Plant responses were monitored through weekly measurements of mortality, ground cover, floral abundance, plant height, and visual obstruction. Insect communities were sampled biweekly using vacuum suction for foliage and flower visitors and pitfall traps for ground-dwelling arthropods. Collected insects were identified to order, with Hymenoptera and Carabidae further resolved to family or genus.
Data for: Soil legacy effects of plants and drought on aboveground insects in native and range-expanding plant communities
<p><span>Soils contain biotic and abiotic legacies of previous conditions that may influence plant community biomass and associated aboveground biodiversity. However, little is known about the relative strengths and interactions of the various belowground legacies on aboveground plant-insect interactions. We used an outdoor mesocosm experiment to investigate the belowground legacy effects of range-expanding versus native plants, extreme drought, and their interactions on plants, aphids, and pollinators. We show that plant biomass was influenced more strongly by the previous plant community than by a previous summer drought. Plant communities consisted of four congeneric pairs of natives and range expanders, and their responses were not unanimous. </span><span>Legacy effects affected the abundance of aphids more strongly than pollinators</span><span>. We conclude that historical climate warming-induced plant latitudinal range expansion and extreme drought contingencies can be contained as soil 'memories' that influence plant performance and aboveground community interactions in the next growing season.</span></p>
Figure 2 in Changes in galling insect community on Caryocar brasiliense trees mediated by soil chemical and physical attributes
Figure 2. Principal components regressions among: (A) Eurytoma sp. galling insect adults (Eur.) with phosphorus-Mehlich 1 (mg dm-3) contents (P.C.) and silt (dag kg-1) (Si.); (B) numbers of Eurytoma sp. glodoid galls (E.G.G.) with pH in water, capacity of cationic exchange (cmol dm-3) (C.C.E.), and P.C.; (C) area (mm2) of Eurytoma sp. glodoid galls (A.E.G.) with pH and clay (dag kg-1) (Cl.); (D) c length of conglomerate of Eurytoma sp. globoid galls (L.G.E.) with pH and Cl.; (E) width of conglomerate of Eurytoma sp. globoid galls (W.G.E.) with pH and Cl.; and (F) number of Hymenopteran discoid galls (H.D.G.) with aluminum (cmol dm-3) contents (A.C.), C.C.E., c and percentage of soil base saturation of the capacity of cationic exchange to pH 7.0 (S.B.S.) on Caryocar brasiliense trees in three years. The symbols represent the averages and the bars the standard errors. n = 111.
Figure 1 in Changes in galling insect community on Caryocar brasiliense trees mediated by soil chemical and physical attributes
Figure 1. Principal components regressions among: (A) abundance of galling insects (A.G.I.) with phosphorus-Mehlich 1 (mg dm-3) contents (P.C.) and sand (dag kg-1) (Sa.); (B) species richness of galling insects (S.R.G.I.) with capacity of cationic exchange (cmol dm-3) c (C.C.E.); (C) diversity of galling insects (D.G.I.) with C.C.E. and Sa.; (D) percentage of galled leaflet by all galls (P.G.L.) with pH in water and clay (dag kg-1) (Cl.); and (E) percentage of leaflet area taken by all galls (P.L.A.G.) with aluminum (cmol dm-3) contents (A.C.) and c C.C.E. on Caryocar brasiliense trees in three years. The symbols represent the averages and the bars the standard errors. n = 111.
Figure 4 in Changes in galling insect community on Caryocar brasiliense trees mediated by soil chemical and physical attributes
Figure 4. Principal components regressions among: (A) abundance of predators (A.Pr.) with phosphorus-Mehlich 1 (mg dm-3) contents (P.C.) and clay (dag kg-1) (Cl.); (B) species richness of predators (S.R.Pr.) with number of Hymenoptera discoid galls (H.D.G.), species richness of parasitoids (S.R.P.), and Cl.; (C) diversity of predators (D.Pr.) with diversity of parasitoids (D.P.) and Cl.; (D) number of Zelus armillatus (Zar.) with P.C., percentage of soil base saturation of the capacity of cationic exchange to pH 7.0 (S.B.S.), silt (dag kg-1) (Si.), numbers of Eurytoma sp. glodoid galls (E.G.G.), and capacity of cationic exchange (cmol dm-3) (C.C.E.); (E) number of Epipolops sp. (Epi.) c with protocooperanting ants (Ants), Cl., and pH in water; and (F) number of spiders (Spi.) with Ants, P.C., and Si. on Caryocar brasiliense trees in three years. The symbols represent the averages and the bars the standard errors. n = 111.
Fig. 2 in Effects of Farming Systems on Insect Communities in the Paddy Fields of a Simplified Landscape During a Pest-control Intervention.
Fig. 2. Two-dimensional NMDS ordination of 40 insect communities sampled under different farming systems in northern Taiwan (stress = 0.18).
Fig. 8 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 8. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from Iguatama county, MG. Bars represent 95% confidence interval.
Fig. 15 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 15. Estimated diversity of secondary pests (S.P.) and natural enemies (N.E.) in tassels of conventional and transgenic maize for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated richness in conventional maize and Bt maize (B), in different counties in Minas Gerais.Bars represent 95% confidence interval.
Fig. 4 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 4. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab and Cry1F proteins) from Varjão de Minas county. Bars represent 95% confidence interval.
Fig. 16 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 16. Relationship between estimated richness of secondary pests and estimated richness of natural enemies in the studied cornfields.
Fig. 11 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 11. Estimated diversity secondary pests (S.P.) and natural enemies (N.E.) in conventional and transgenic maize whorls for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated diversity in conventional maize and Bt maize (B) in different counties in Minas Gerais. Bars represent 95% confidence interval.
Fig. 9 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 9. Estimated richness of insects in the whorl and tassel of conventional (Conv) and transgenic maize (Cry1Ab and Cry1F proteins) from Matozinhos county, MG. Bars represent 95% confidence interval.
Fig. 6 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 6. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from Três Corações county, MG. Bars represent 95% confidence interval.
Fig. 3 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 3. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from Nazareno county, MG. Bars represent 95% confidence interval.
Fig. 1 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 1. Abundance of different sizes of larvae of Spodoptera frugiperda in whorls of conventional and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from cornfields of different counties in Minas Gerais. Bars represent a 95% confidence interval.
Fig. 5 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 5. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab and Cry1F proteins) from Iraí de Minas Gerais county. Bars represent 95% confidence interval.
Fig. 14 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 14. Estimated richness of secondary pests (S.P.) and natural enemies (N.E.) in tassels of conventional and transgenic maize for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated richness in conventional maize and Bt maize (B), in different counties in Minas Gerais.Bars represent 95% confidence interval.
Fig. 13 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?
Fig. 13. Estimated diversity of secondary pests (S.P.) and natural enemies (N.E.) in ears of conventional and transgenic maize for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated diversity in conventional maize and Bt maize (B), in different counties in Minas Gerais. Bars represent 95% confidence interval.
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