Puddling Behavior of Temperate Butterflies: Preference for Urine of Specific Mammals?

Bodri, Michael S.

doi:10.18473/lepi.v72i2.a3

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Previous studies have reported that butterfly communities are influenced by site level (Hanula and Horn 2011b, Mathis et al 2021) landscape-level habitat characteristics (Davis et 2007, Moranz et al 2012); although we did not observe all of these patterns as our models detected butterfly density to not be driven by within-site factors but were so by landscape variables. Our finding of weak or few within-site associations with butterfly density and morphospecies richness is likely because butterflies are noncentral place foragers, use nonnectar resources (Bodri 2018), and are also driven by host plant availability, which we did not measure (Fartmann et al 2013). The one exception with woody vegetation relationships we observed was a positive association with basal area and butterfly density in June.…”

Section: Discussionmentioning

confidence: 92%

Factors driving bumble bee (Hymenoptera: Apidae: Bombus) and butterfly (Lepidoptera: Rhopalocera) use of sheared shrubland and young forest communities of the western Great Lakes

Keele,

McNeil,

Duchamp

et al. 2023

Environmental Entomology

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In the northern Great Lakes region, the creation and maintenance of early-successional woody communities as wildlife habitat have increasingly become a conservation priority. The extent to which insect pollinators use these systems remains largely anecdotal. In summer (June–August) of 2021, we surveyed 49 early-successional sites in the western Great Lakes region treated with either shrub-shearing or silviculture (young forest) for bumble bees, butterflies, and habitat components (i.e., structural vegetation and floral resources). Hierarchical distance models predicted pollinator densities (λ^) to be, on average, λ^ = 84 bumble bees/ha and λ^ = 102 butterflies/ha. Although sheared shrubland and young forest communities supported comparable densities of bumble bees and butterflies, density was not equal across all sites. At the microhabitat scale, butterfly density and morphospecies richness were negatively associated with tall shrub cover and butterfly morphospecies richness (but not density) was driven by floral richness. Similarly, bumble bee density was positively associated with metrics of floral resources, underscoring the importance of blooming plants within these woody systems. Landscape covariates explained variation in butterfly density/richness but not bumble bee density. Ultimately, our results demonstrate that blooming plant abundance is an important driver of bumble bee and butterfly densities within these managed early-successional communities. Because early-successional woody communities are dynamic and their herbaceous openings are ephemeral, routine management would ensure that a variety of successional conditions exist on the landscape to meet the needs of bumble bees, butterflies, and potentially other insect pollinators.

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Section: Discussionmentioning

confidence: 92%

Factors driving bumble bee (Hymenoptera: Apidae: Bombus) and butterfly (Lepidoptera: Rhopalocera) use of sheared shrubland and young forest communities of the western Great Lakes

Keele,

McNeil,

Duchamp

et al. 2023

Environmental Entomology

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“…Malaria mosquitoes acquire and allocate nitrogenous compounds through compensatory feeding on cattle urine, i.e., puddling, to enhance life-history traits, similar to that of other insects (Bodri, 2018;Honda et al, 2012;Molleman, 2010;Petit et al, 2019;Shen et al, 2009). Female mosquitoes locate this resource through olfaction, and are able to regulate the uptake of nitrogenous compounds in urine, including the main nitrogenous constituent of urine, urea (Dijkstra et al, 2013;Kilande et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Malaria mosquitoes acquire and allocate cattle urine to enhance life history traits

Dawit

Hill

Birgersson

et al. 2020

Preprint

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Nutrient acquisition and allocation integrate foraging and life-history traits in insects. To compensate for the lack of a particular nutrient at different life stages, insects may acquire these through supplementary feeding on e.g., vertebrate secretions, in a process known as puddling. The mosquito Anopheles arabiensis emerges undernourished, and as such, requires nutrients for both metabolism and reproduction. Host-seeking and blood-fed An. arabiensis are attracted to the natural and synthetic odour of cattle urine, which signals a source of nutrients, but not the presence of a host or oviposition site. Females actively imbibe cattle urine, and its main nitrogenous compound, urea, and allocate these resources according to life history trade-offs to flight, survival or reproduction, as a function of physiological state. As a consequence, this behaviour affects vectorial capacity by increasing daily survival and vector density, and thus should be considered in future models. Future vector management strategies are discussed.

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“…In BOA, the fragrance possesses two kinds of power exponent such as stimulus intensity (I) and sensory modality [13]. The phrase power is the exponent to choose important intensity which performed in a linear response, compression response, and regular.…”

Section: Fragrancementioning

confidence: 99%

Crowd Behaviour Recognition using Enhanced Butterfly Optimization Algorithm based Recurrent Neural Network

Chen¹

2020

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The crowd emotion recognition is a motivating research area that helps the security personals by means of the public emotions to interpret the crowd activity in a region. Approximately several conventional techniques exploit the lowlevel visual features to comprehend the behaviors of a crowd which widen the gap between the high as well as the low-level features. The objective model is used to expand the automatic algorithm for emotion recognition; hence this work uses the Recurrent Neural Network (RNN). The Bhattacharya distance is used for effectual emotion recognition, which is necessary to choose video keyframes. The keyframes are subjected to the Space-Time Interest Points (STI) descriptor which extracts features that structure input vector to the classifier. RNN is trained by exploiting the enhanced Butterfly Optimization Algorithm (Enhanced-BOA). The developed classifier identifies the crowd emotions, like Escape, Angry, Happy, Fight, Running/Walking, Normal, as well as Violence. The experimentation of the developed technique revealed that developed technique obtained a maximum accuracy, sensitivity as well as specificity, correspondingly.

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Puddling Behavior of Temperate Butterflies: Preference for Urine of Specific Mammals?

Cited by 4 publications

References 23 publications

Factors driving bumble bee (Hymenoptera: Apidae: Bombus) and butterfly (Lepidoptera: Rhopalocera) use of sheared shrubland and young forest communities of the western Great Lakes

Factors driving bumble bee (Hymenoptera: Apidae: Bombus) and butterfly (Lepidoptera: Rhopalocera) use of sheared shrubland and young forest communities of the western Great Lakes

Malaria mosquitoes acquire and allocate cattle urine to enhance life history traits

Crowd Behaviour Recognition using Enhanced Butterfly Optimization Algorithm based Recurrent Neural Network

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