The costs of living in a thermal fluctuating environment for the tropical haematophagous bug, Rhodnius prolixus

Rolandi, Carmen; Schilman, Pablo E.

doi:10.1016/j.jtherbio.2018.03.022

Cited by 16 publications

(8 citation statements)

References 52 publications

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“…Under laboratory conditions daily temperature fluctuations (DTF) did not affect development time and fertility for Rhodnius prolixus [49]. However, fecundity was lower in females reared at DTF than at constant temperature, and males had higher body mass reduction rate and lower survival in the DTF regime, suggesting higher costs associated to fluctuating thermal environments [50]. Importantly, plotting cICE curves (Fig 2B -2E) enhances our ability to detect heterogenous relationships between survival and each feature (the effect of sex was not included).…”

Section: Discussionmentioning

confidence: 96%

Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)

et al. 2021

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Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links between physiology and species persistence. Experimental approaches linking species physiology to micro-climate are complex, time consuming and expensive. E.g., what combination of exposure time and temperature is important for a species thermal tolerance is difficult to judge a priori. We tackled this problem using an active learning approach that utilized machine learning methods to guide thermal tolerance experimental design for three kissing-bug species: Triatoma infestans, Rhodnius prolixus, and Panstrongylus megistus (Hemiptera: Reduviidae: Triatominae), vectors of the parasite causing Chagas disease. As with other pathogen vectors, triatomines are well known to utilize micro-habitats and the associated shift in microclimate to enhance survival. Using a limited literature-collected dataset, our approach showed that temperature followed by exposure time were the strongest predictors of mortality; species played a minor role, and life stage was the least important. Further, we identified complex but biologically plausible nonlinear interactions between temperature and exposure time in shaping mortality, together setting the potential thermal limits of triatomines. The results from this data led to the design of new experiments with laboratory results that produced novel insights of the effects of temperature and exposure for the triatomines. These results, in turn, can be used to better model micro-climatic envelope for the species. Here we demonstrate the power of an active learning approach to explore experimental space to design laboratory studies testing species thermal limits. Our analytical pipeline can be easily adapted to other systems and we provide code to allow practitioners to perform similar analyses. Not only does our approach have the potential to save time and money: it can also increase our understanding of the links between species physiology and climate, a topic of increasing ecological importance.

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Section: Discussionmentioning

confidence: 96%

Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)

et al. 2021

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“…It is well known that there are important physiological effects between regimes of constant and fluctuating temperatures, even with the same mean values; these effects are represented by the so-called Jensen's inequality, that states that for nonlineal processes, such as most biological phenomena dependent upon temperature, the effects of fluctuations in environmental temperature cannot be predicted from the mean of an equivalent constant temperature (Ruel and Ayres, 1999). This has been established in the triatomines by experiments carried out by Luz et al (1999); Damborsky et al (2005), Rabinovich et al (2006), and Rolandi and Schilman (2018). Luz et al (1999) Damborsky et al (2005) compared the life cycle of Triatoma rubrovaria (Blanchard, 1843) in a climatic chamber under constant temperature, and under the natural fluctuating temperature of the laboratory; under controlled temperature the average hatching time was 15.6 days and the average egg to adult development time was 10 months, while under the natural fluctuating temperature hatching time was 19.1 days and egg to adult development time was 14 months; no difference was found in the egg hatching rate nor in the total nymphal mortality, but a small difference was found in reproduction, with a mean number of eggs/female per life of 817.6 eggs under controlled conditions and of 837.1 eggs under fluctuating conditions (2.4%).…”

Section: Temperature Effectsmentioning

confidence: 99%

“…Rabinovich et al (2006) analyzed the effects of fluctuating temperatures on the development time of the eggs of the triatomine T. guasayana using three nonlinear models (Devar, Lactin, and Rueda); these authors found a lower egg development threshold between 15 and 17.5 • C, and a thermal death point of eggs at 34.4 • C; using a generalized development rate response of T. guasayana eggs to increasing temperature was proposed, with three ranges: between 10 and 14.8 • C development rate increases in an accelerating way; between 14.8 and 29.2 • C development rate increases more or less linearly, and between 29.2 and 34 • C development rate decreases. Rolandi and Schilman (2018) studied the effect of daily temperature fluctuation (DTF) of 17-32 • C (mean = 24 • C) on R. prolixus, and found that development time and fertility were not affected by DTF, but that fecundity was lower in females reared at DTF than at constant temperature; considering Jensen's inequality and the species' tropical distribution these authors predicted that living in a variable thermal environment would have associated higher energetic costs. Cabello (1999) is one of the few authors to construct a complete life table of a triatomine (in R. neivai), and to evaluate the demographic parameters at three constant temperatures (22, 27, and 32 • C).…”

Section: Temperature Effectsmentioning

confidence: 99%

Morphology, Life Cycle, Environmental Factors and Fitness – a Machine Learning Analysis in Kissing Bugs (Hemiptera, Reduviidae, Triatominae)

Rabinovich

2021

Front. Ecol. Evol.

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Populations are permanently evolving and their evolution will influence their survival and reproduction, which will then alter demographic parameters. Several phenotypic, life history and environmental variables are known to be related to fitness measures. The goal of this article was to look into the possible types of those relationships in insects of the subfamily Triatominae, vectors of Trypanosoma cruzi, the causative agent of Chagas disease. After an exhaustive literature review of 7,207 records of publications referring exclusively to all possible features of the triatomines, using 15 keywords those records were reduced to 2,968 publications, that were analyzed individually; after deleting those publications that did not have the data in quantitative form as needed for the objective of this article, I found that 171 papers were adequate for the present analysis. From them I compiled a dataset of 11 variables and 90 cases from 36 triatomine species. Those variables included four environmental, two life cycle, and four morphological variables, and one demographic parameter: a fitness measure (the population intrinsic rate of natural increase, r0), used as dependent variable. However, the relationship between T. cruzi and its vector host was not included in this analysis despite triatomine-T. cruzi interactions constitute an important factor in the evolution of triatomine’s life history. I resorted to the Random Forest method as a machine learning approach for the analysis of this dataset, and found that –in addition to the triatomine species themselves– only the two life cycle variables (mean development time from egg to adult, and mean fecundity, expressed as the average number of female eggs laid per female per day) were statistically significant in determining fitness (r0). The machine learning approach used in the analysis provided a similar but deeper insight into these relationships than classical regression. Except for an analysis on senescence, this is the first study in triatomines addressing these questions. These results will be useful for other theoretical optimization approaches (frequency-dependence, density-dependence, evolutionary game theory, and adaptive dynamics), thus contributing to the theoretical framework for interpreting the succession of stages in insect adaptations, a framework yet to be constructed.

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“…It is well-established that changes in temperature affect biological processes. The impact of such temperature changes on ectothermic animals, such as insects is greater because of their inability to control their body temperature (Rolandi and Schilman, 2018).…”

Section: Temperature Variationmentioning

confidence: 99%

The Influence of Environmental Cues on the Development of Trypanosoma cruzi in Triatominae Vector

Melo

Guarneri

Silber

2020

Front. Cell. Infect. Microbiol.

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Trypanosoma cruzi, a hemoflagellate parasite, is the etiological agent of Chagas disease that affects about 6-7 million people worldwide, mostly in Latin America. The parasite life cycle is complex and alternates between an invertebrate host-Triatominae vector-and a mammalian host. The parasite adaptation to the several microenvironments through which it transits is critical to success in establishing infection. Moreover, environmental cues also play an important role on the parasite development, and it can modulate the infection. In the present study, we discussed how the temperature oscillations and the nutritional state of the invertebrate host can affect the parasite development, multiplication, and the differentiation process of epimastigote forms into metacyclic trypomastigotes, called metacyclogenesis. The impact of oxidative imbalance and osmotic stresses on the parasite-vector relationship are also discussed.

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The costs of living in a thermal fluctuating environment for the tropical haematophagous bug, Rhodnius prolixus

Cited by 16 publications

References 52 publications

Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)

Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae)

Morphology, Life Cycle, Environmental Factors and Fitness – a Machine Learning Analysis in Kissing Bugs (Hemiptera, Reduviidae, Triatominae)

The Influence of Environmental Cues on the Development of Trypanosoma cruzi in Triatominae Vector

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