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55 results for “insect abundance”
Supplementary material for "Patterns of high-flying insect abundance are shaped by landscape type and abiotic conditions"
<p><strong>Abstract</strong></p> <p>Insects are of increasing conservation concern as a severe decline of both biomass and biodiversity have been reported. At the same time, data on where and when they occur in the airspace is still sparse, and we currently do not know whether their density is linked to the type of landscape above which they occur. Here, we combine data of high-flying insect abundance from six locations across Switzerland representing rural, urban and mountainous landscapes, which was recorded using vertical-looking radar devices. We analysed the abundance of high-flying insects in relation to meteorological factors, daytime, and type of landscape. Air pressure was positively related to insect abundance, wind speed showed an optimum, and temperature and wind direction did not show a clear relationship. Mountainous landscapes showed a higher insect abundance than the other two landscape types. Insect abundance increased in the morning, decreased in the afternoon, had a peak after sunset, and then declined again, though the extent of this general pattern slightly differed between landscape types. We conclude that the abundance of high-flying insects is not only related to abiotic parameters, but also to the type of landscapes. Thus, conservation measures implemented on the ground should start to also account for the needs of high-flying insects.</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 & Analysis Script for: Phylogenetic relatedness to native congeners drives insect abundance and diversity hosted by non-native trees
<p>The dataset contains all necessary data to reproduce the findings presented in Schweiger et al. 2023 - Phylogenetic relatedness to native congeners drives insect abundance and diversity hosted by non-native trees (submitted).</p> <p>The code necessary to reproduce the findings is included within this repository. The code contains comments. Please note, if you want to reproduce the findings you will have to change file path information matching your personal computer to be able to re-run the code.</p> <p>This data includes the biodiversity raw data collected for the manuscript. It <strong>does not </strong>include data used to calculate geographic, climatic or phylogenetic distances, as these data are freely available and necessary information to reproduce calculations are given within the Material & Methods section.</p> <p>All data is provided within one Excel file. Please, pay attention to the provided ReadMe sheet containing metadata information on the dataset.</p> <p>Please carefully read provided information within ReadMe, Metadata and Code description.</p>
Fig. 1 in Difference in the abundance of scale insect parasitoids among four cardinal directions
Fig. 1. Abundance of Coccophagus lycimnia captured with yellow sticky cards at the cardinal directions on willow oaks in (A) South Carolina and (B) Virginia. E = East, N = north, S = south, W = west.
Figure 3 in Distribution and abundance of insects colonizing cattle dung in South West England
Figure 3. The proportion of the total number of adult Coleoptera (solid squares, solid line), adult Diptera (open circles, solid line), and larval Coleoptera (diamonds, dashed line) insects collected from pats of different ages.
Figure 2 in Distribution and abundance of insects colonizing cattle dung in South West England
Figure 2. The numbers of various taxa recovered from individual artificial cow pats placed out between May and November 2001; day 1 is 1 May. (A) Sylvicola punctata; (B) Chironimidae; (C) Polites lardaria; (D) Scatophaga stercoraria; (E) Chloromyia formosa; (F) Sargus spp.; (G) Cercyon lateralis; (H) Oxytelinae larvae.
Figure 1 in Distribution and abundance of insects colonizing cattle dung in South West England
Figure 1. The median seasonal occurrences (and inter-quartile ranges), and total number recovered for the insect taxa from artificial cow pats placed out between May and November 2001. Day 1 is 1 May.
Fig. 1. Relative abundance, A in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 1. Relative abundance, A* (number of individuals caught. trap-1. month-1) of Diptera and Auchenorrhyncha trapped in six elevation zones over 12 months sampling at Doi Inthanon in 2014. Standard errors indicated. Note log10 scale. Data were fitted to a linear regression model in PAST; Diptera, open circles (r2 = 0.8567, p = 0.0081); Auchenorrhyncha, closed circles (r2 = 0.3182, p = 0.2434). In Kruskal-Wallis H-tests of untransformed data there was a significant difference between the medians for Diptera (H = 29.3, p <0.01) but not for Auchenorrhyncha (H = 3.3, p = 0.657).
Fig. 3. Relative abundance, A in Spatiotemporal dynamics of insect diversity in tropical seasonal forests is linked to season and elevation, a case from northern Thailand
Fig. 3. Relative abundance, A* (number of individuals caught. trap-1. month-1) of Diptera and Auchenorrhyncha over 12 months sampling at Doi Inthanon in 2014. Standard errors indicated. Note log10 scale. In Kruskal-Wallis H-tests of untransformed data there was a significant difference between the medians for Diptera (H = 24.5, p <0.05) and Auchenorrhyncha (H = 34.3, p <0.01).
No net insect abundance and diversity declines across US Long Term Ecological Research sites
<p>Recent reports of dramatic declines in insect abundance suggest grave consequences for global ecosystems and human society. Most evidence comes from Europe, however, leaving uncertainty about insect population trends worldwide. We used > 5,300 time series for insects and other arthropods, collected over 4-36 years at monitoring sites representing 68 different natural and managed areas, to search for evidence of declines across the United States. Some taxa and sites showed decreases in abundance and diversity while others increased or were unchanged, yielding net abundance and biodiversity trends generally indistinguishable from zero. This lack of overall increase or decline was consistent across arthropod feeding groups, and was similar for heavily disturbed versus relatively natural sites. The apparent robustness of U.S. arthropod populations is reassuring. Yet, this result does not diminish the need for continued monitoring and could mask subtler changes in species composition that nonetheless endanger insect-provided ecosystem services. </p>
Light and malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance
<p><span>1. Conclusions reached in meta-analyses of changes in insect communities may be influenced by method-specific sampling biases, which may lead to inappropriate conservation measures.</span></p> <p><span>2. </span><span>We argue that the contradictory conclusions regarding terrestrial insect biomass, abundance and richness patterns are, at least partly, due to methodological limitations that reflect taxon-specific responses to environmental changes.</span></p> <p><span>3. </span><span>In this study, light and Malaise traps were simultaneously deployed to sample insects at 52 plots in a temperate forest in Germany along gradients of elevation (> 1000 m) and canopy openness (3 - 100 %). These gradients were used as predictors in models of total arthropod biomass according to the two trapping methods, and in models of abundance and richness of three commonly targeted groups: nocturnal moths, sampled using light traps, and hoverflies and bees, collected with Malaise traps.</span></p> <p><span>4. </span><span>A comparison of the total arthropod biomass obtained with the two methods revealed contrary results along the canopy openness gradient. Biomass in light traps showed a decreasing trend with increasing canopy openness while biomass in Malaise traps increased. The same opposing pattern was found for the abundance of selected taxa.</span></p> <p><span>5. </span><span>The different patterns describing spatial variation of arthropod communities obtained using light and Malaise traps can be explained by differences in the taxa predominantly collected. Regarding the ongoing debate on insect decline, our results demonstrate that comparing different taxa from different taxon-specific traps is inappropriate. Thus, we recommend that future meta-analyses take into account the sampling methods and taxon-specific responses to environmental changes.</span></p>
Data from: What makes a good pollinator? Abundant and specialized insects with long flight periods transport the most strawberry pollen
<ol> <li>Despite the importance of insect pollination to produce marketable fruits, insect pollination management is limited by insufficient knowledge about key crop pollinator species. This lack of knowledge is due in part to: 1) the extensive labour involved in collecting direct observations of pollen-transport, 2) the variability of insect assemblages over space and time, and 3) the possibility that pollinators may need access to wild plants as well as crop floral resources.</li> <li>We address these problems using strawberry in the UK as a case study. First, we compare two proxies for estimating pollinator importance: flower visits and pollen transport. Pollen-transport data might provide a closer approximation of pollination service, but visitation data are less time-consuming to collect. Second, we identify insect parameters that are associated with high importance as pollinators, estimated using each of the proxies above. Third, we estimated insects' use of wild plants as well as the strawberry crop.</li> <li>Overall, pollinator importances estimated based on easier-to-collect visitation data were strongly correlated with importances estimated based on pollen loads. Both frameworks suggest that bees <em>Apis</em> and <em>Bombus</em> and hoverflies <em>Eristalis</em> are likely to be key pollinators of strawberries, although visitation data underestimate the importance of bees.</li> <li>Moving beyond species identities, abundant, relatively specialised insects with long active periods are likely to provide more pollination service. </li> <li>Most insects visiting strawberry plants also carried pollen from wild plants, suggesting that pollinators need diverse floral resources.</li> <li>Identifying essential pollinators or pollinator parameters based on visitation data will reach the same general conclusions as those using pollen transport data, at least in monoculture crop systems. Managers may be able to enhance pollination service by preserving habitats surrounding crop fields to complement pollinators' diets and provide habitats for diverse life stages of wild pollinators.</li> </ol>
Data from: What makes a good pollinator? Abundant and specialized insects with long flight periods transport the most strawberry pollen
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Data from: The quantity of deposited environmental DNA in plant-insect interactions depends on taxon, abundance, and interaction time
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No net insect abundance and diversity declines across US Long Term Ecological Research sites
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Light and malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance
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Arachnida species (spiders) abundance: Trophic Structure: Insect Species Diversity, Abundance and Body Size
The goal of this study was to examine the populations of insects in prairies and savannas. Most of the prairies had developed after being abandoned from agriculture, but none of the savannas had been cultivated. The history of burning varied between sites. The main sampling for this study was conducted in 1992 by the lead investigators: John Haarstad, Evan Siemann, and David Tilman. Insects were sampled via sweep-net sampling, pitfalls and ant scent plates throughout the growing season in each of 49 grassland fields and savannas. In total, 89,596 individuals of 1,167 species were captured and enumerated. Body size was measured for a subset of grasshoppers collected. In 2004, John Haarstad conducted two similar studies by identifying and enumerating all insects collected in sweepnet samples taken in old fields (prairies) as part of E014 grasshopper studies and from sweepnet samples taken in savannas.
Coleoptera species abundance: Trophic Structure: Insect Species Diversity, Abundance and Body Size
The goal of this study was to examine the populations of insects in prairies and savannas. Most of the prairies had developed after being abandoned from agriculture, but none of the savannas had been cultivated. The history of burning varied between sites. The main sampling for this study was conducted in 1992 by the lead investigators: John Haarstad, Evan Siemann, and David Tilman. Insects were sampled via sweep-net sampling, pitfalls and ant scent plates throughout the growing season in each of 49 grassland fields and savannas. In total, 89,596 individuals of 1,167 species were captured and enumerated. Body size was measured for a subset of grasshoppers collected. In 2004, John Haarstad conducted two similar studies by identifying and enumerating all insects collected in sweepnet samples taken in old fields (prairies) as part of E014 grasshopper studies and from sweepnet samples taken in savannas.
Diptera species abundance: Trophic Structure: Insect Species Diversity, Abundance and Body Size
The goal of this study was to examine the populations of insects in prairies and savannas. Most of the prairies had developed after being abandoned from agriculture, but none of the savannas had been cultivated. The history of burning varied between sites. The main sampling for this study was conducted in 1992 by the lead investigators: John Haarstad, Evan Siemann, and David Tilman. Insects were sampled via sweep-net sampling, pitfalls and ant scent plates throughout the growing season in each of 49 grassland fields and savannas. In total, 89,596 individuals of 1,167 species were captured and enumerated. Body size was measured for a subset of grasshoppers collected. In 2004, John Haarstad conducted two similar studies by identifying and enumerating all insects collected in sweepnet samples taken in old fields (prairies) as part of E014 grasshopper studies and from sweepnet samples taken in savannas.
Hemiptera species abundance: Trophic Structure: Insect Species Diversity, Abundance and Body Size
The goal of this study was to examine the populations of insects in prairies and savannas. Most of the prairies had developed after being abandoned from agriculture, but none of the savannas had been cultivated. The history of burning varied between sites. The main sampling for this study was conducted in 1992 by the lead investigators: John Haarstad, Evan Siemann, and David Tilman. Insects were sampled via sweep-net sampling, pitfalls and ant scent plates throughout the growing season in each of 49 grassland fields and savannas. In total, 89,596 individuals of 1,167 species were captured and enumerated. Body size was measured for a subset of grasshoppers collected. In 2004, John Haarstad conducted two similar studies by identifying and enumerating all insects collected in sweepnet samples taken in old fields (prairies) as part of E014 grasshopper studies and from sweepnet samples taken in savannas.
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International Brain Laboratory public data
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OpenNeuro
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