Ecological interactions: Patterns of host utilization by tropical butterflies

Structural complexity of ecological networks facilitate the functional robustness of natural ecosystems. Threatened by the human actions such as habitat destruction and climate change, species may be more or less prone to ecological perturbations depending on the nature of their interactions. We examined the host network of tropical butterflies from the Indian region to see their level of interconnectedness. We manually curated larval host utilization data for 1053 butterflies of India. About 98.8% of species that occur pan-India and 90.6% of species exclusive to the Western Ghats had known hosts whereas it was only 25.9% for species exclusive to north-east India. There were 2589 unique butterfly-host interactions comprising 519 butterfly species and their 1091 known hosts. However, nearly 30% of the species had only single hosts. The Fabaceae and Poaceae were the key host families that accounted for 32.8% of the interactions. There were clear host preferences and monocots hosted disproportionately more butterfly species and interactions. Vanessa cardui had at least 39 known hosts while Ochlandra travancorica supported 19 butterfly species. There were 2693 species-pairs and 4226 interactions among 469 butterflies due to shared hosts. Many butterfly species that have relatively few/unique hosts might be vulnerable in the context of habitat destruction and climate change. This work has great relevance to the ecology and conservation of butterflies in India.

Retrotransposon-Based Blood Meal Analysis of Nymphal Deer Ticks Demonstrates Spatiotemporal Diversity of Borrelia burgdorferi and Babesia microti Reservoirs

ABSTRACT Deer tick-transmitted Borrelia burgdorferi sensu stricto (Lyme disease) and Babesia microti (babesiosis) increasingly burden public health across eastern North America. The white-footed mouse is considered the primary host for subadult deer ticks and the most important reservoir host for these and other disease agents. Local transmission is thought to be modulated by less reservoir-competent hosts, such as deer, diverting ticks from feeding on mice. We measured the proportion of mouse-fed or deer-fed host-seeking nymphs from 4 sites during 2 transmission seasons by blood meal remnant analysis using a new retrotransposon-based quantitative PCR (qPCR) assay. We then determined the host that was associated with the infection status of the tick. During the first year, the proportion of mouse-fed ticks ranged from 17% on mainland sites to 100% on an island, while deer-fed ticks ranged from 4% to 24%. The proportion of ticks feeding on mice and deer was greater from island sites than mainland sites (on average, 92% versus 43%). Mouse-fed ticks decreased significantly during year 2 in 3 of 4 sites (most were <20%), while deer-fed ticks increased for all sites (75% at one site). Overall, ticks were more likely to be infected when they had fed on mice (odds ratio [OR] of 2.4 and 1.6 for Borrelia and Babesia, respectively) and were less likely to be infected if they had fed on deer (OR, 0.8 and 0.4). We conclude that host utilization by deer ticks is characterized by significant spatiotemporal diversity, which may confound efficacy tests of interventions targeting reservoir hosts. IMPORTANCE White-footed mice are thought to be the most important reservoir host for the deer tick-transmitted pathogens that cause Lyme disease and human babesiosis because they are the primary host for immature ticks. Transmission would be reduced, however, if ticks feed on deer, which are not capable of infecting ticks with either pathogen. By directly measuring whether ticks had fed on either mice or deer using a new quantitative PCR (qPCR) assay to detect remnants of host DNA leftover from the larval blood meal, we demonstrate that host utilization by ticks varies significantly over time and space and that mice often feed fewer ticks than expected. This finding has implications for our understanding of the ecology of these diseases and for the efficacy of control measures.

Feeding Habits of Vector Mosquitoes in Harris County, TX, 2018

Mosquito-borne pathogens contribute significantly to the global burden of infectious diseases and are a continuing public health concern in the United States. Blood feeding by vector mosquitoes is a critical step in the transmission of human pathogens. Continuous surveillance of mosquito feeding patterns, especially in major population centers, is necessary for sustainable, effective control strategies. To better understand female feeding habits in Harris County, TX, we trapped mosquitoes from various locations, distributed among urban and semi-urban environments. Bloodmeal hosts were determined using a cytochrome C oxidase I DNA barcoding strategy. We identified a diverse array of vertebrate hosts with a high degree of avian host utilization, most surprisingly from anthropophilic species like Aedes aegypti (L.). We also detected sequences from two different vertebrate hosts in about half of specimens examined, suggesting that multiple bloodmeals had been acquired in the same feeding cycle by a sizable fraction of females in both urban and semi-urban locations. The high proportion of feeding on domestic chickens may indicate that a significant number of homeowners are rearing chickens within close proximity to study trap sites. As non-amplifying hosts, chickens may have a diluting effect on West Nile virus, as well as a zooprophylactic effect in their immediate vicinities. Ultimately, spatial and temporal host utilization patterns add insight into potential disease transmission dynamics, thereby informing vector control strategies in Harris County and other metropolitan areas.

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.