Temperature variability increases Trypanosoma cruzi load but not the extrinsic incubation period in Triatoma infestans

Temperature variability increases Trypanosoma cruzi load but not the extrinsic incubation period in Triatoma infestans | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temperature variability increases Trypanosoma cruzi load but not the extrinsic incubation period in Triatoma infestans Bárbara Álvarez-Duhart, Sabrina Clavijo-Baquet, Lucía Valenzuela-Perez, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7881049/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Parasites & Vectors → Version 1 posted 11 You are reading this latest preprint version Abstract Background Trypanosoma cruzi , the etiologic agent of Chagas disease, is transmitted via the dejections of triatomine insects such as Triatoma infestans . The extrinsic incubation period (EIP), parasite load, and infectivity of the vector are sensitive to environmental temperatures. Global warming is expected to increase both mean temperatures and their variability, potentially altering vector competence. Methods We experimentally infected T. infestans with the T. cruzi Dm28c strain and exposed them to four temperature regimes: two constant (18 °C and 27 °C) and two variables (18 ± 5 °C and 27 ± 5 °C). Over 42 days, we collected dejection samples for parasite quantification via qPCR and recorded the time of the first positive detection to estimate the EIP. Dejection samples were collected every two days throughout the study to quantify the parasite load using qPCR, enabling us to assess infection dynamics over time. Results Higher temperatures significantly shortened the EIP and increased the overall parasite load. However, temperature variability alone did not significantly alter the EIP. In contrast, variability increased the peak parasite load in the cold treatments without affecting the probability of positive dejections. The parasite load exhibited a bell-shaped curve over time, peaking earlier and higher under warmer conditions. A larger volume of ingested blood also reduced the EIP, especially under cold treatments. Conclusions Temperature increases accelerate T. cruzi development in T. infestans , potentially enhancing vector competence under climate change scenarios. Although variability in temperature did not affect EIP, it did influence parasite load, suggesting that both mean temperature and variability must be considered to understand the impact of climate change on Chagas disease transmission. Climate change infectious Chagas disease Vector-borne diseases Figures Figure 1 Figure 2 Figure 3 Background The relationship between climate change and infectious diseases is a pressing ecological [ 1 – 3 ] and global health concern [ 4 ]. Vector-borne diseases are especially susceptible to temperature because their transmission rate and incidence are related to the biological traits of vectors, which are ectothermic arthropods [ 3 ]. Climate change is expected to affect multiple vector-borne diseases [ 5 ], such as dengue fever [ 6 ], chikungunya virus [ 7 ], Zika virus [ 8 ], West Nile virus [ 9 ], Bluetongue virus [ 10 ], leishmaniasis [ 11 ] malaria [ 12 ], and Chagas disease [ 1 ]. The effect of temperature on biting rate, reproduction, development, survival, and probability of becoming infectious after biting an infectious host (i.e., vector competence) has been reported for several disease vectors [ 13 – 15 ]. In summary, temperature influences pathogen transmission by promoting its transmission at optimal constant temperatures and stopping it beyond the lower and upper thermal activity limits [ 3 , 16 ]. Climate change projections include an increase in the average environmental temperature in the long term, as well as more frequent periods of temperature variability, such as daily, monthly, or seasonal variation [ 17 ]. The survival, reproduction, and thermal performance of ectotherms are affected by temperature variability in a nonlinear manner, which can positively affect individuals who are below their thermal optimum and negatively affect individuals when they are above their optimum temperature [ 18 ] [ 19 , 20 ] Therefore, it is difficult to elucidate how global warming change will modify the distribution and incidence of vector-borne diseases [ 3 , 21 ]. Thus, to generate accurate predictions, the precise effects of temperature and mechanisms underlying these effects must be fully understood [ 8 ]. The extrinsic incubation period (EIP, hereafter) is the time required for a pathogen or parasite to develop from its entry into the vector until the first forms capable of infecting another host appear [ 22 ]. EIP influences the incidence of vector-borne diseases [ 23 – 25 ] as a lower EIP generally indicates a faster capacity to transmit the pathogen, which leads to an increase in the incidence of the disease. This parameter determines the basic reproductive number R 0 for vector-borne diseases [ 26 ]. The EIP is also included in the vectorial capacity equation, which is a measure of the transmission potential of a vector-borne pathogen within a susceptible population [ 24 , 25 , 27 ]. Therefore, even small changes in the EIP can have a large impact on the results of mechanistic models based on these equations [ 28 – 30 ]. The practical relevance of this problem has been demonstrated for dengue [ 25 ], malaria [ 31 ], and bluetongue [ 32 ] and the EIP of various species of parasites and viruses has been proven to be susceptible to temperature and its variability [ 23 , 33 , 34 ]. Chagas disease is a neglected tropical disease caused by the flagellate protozoan Trypanosoma cruzi , with 6 million people estimated to be infected worldwide, predominantly in areas endemic to Latin America [ 35 , 36 ]. The primary mode of transmission for this protozoan involves contact with contaminated feces or urine from hematophagous insect vectors belonging to the subfamily Triatominae (Hemiptera: Reduviidae) with mucous membranes or wounds in mammals [ 37 , 38 ]. Additionally, transmission can occur through other routes such as blood transfusions, organ transplants, congenital means, laboratory accidents, and orally in natural reservoirs [ 39 ]. Recent human epidemiological outbreaks have highlighted oral transmission as a significant route of T. cruzi transmission [ 40 – 42 ]. Triatoma infestans is the most important vector in southern South America [ 43 ]. The wild foci of T. infestans have been documented in Bolivia [ 44 ], Argentina [ 45 ], Paraguay [ 46 ], and Chile [ 47 ]. The presence of wild foci hinders disease control initiatives because these foci play a crucial role in the recolonization of peridomestic and domestic habitats [ 47 – 49 ]. In addition, the projected rise in temperature due to climate change could potentially exacerbate the source of individuals from wild foci, as it may lead to an increase in their activity or abundance. Winter temperature has been proposed as the most limiting factor in terms of population growth [ 50 – 52 ]. T. infestans inhabits peridomestic habitats that can buffer temperature fluctuations, and its thermal preference lies between 25°C and 29°C [ 53 ], which is easily reached inside houses, but its activity ranges between 18°C and 42°C [ 54 ]. Currently, the minimum mean winter temperature in the central region of Chile, where wild T. infestans foci have been reported, is 10.7°C but the minimum mean temperature is 4°C in the coldest month, below the T. infestans activity range [ 2 ]. However, climate change projections estimate a temperature increase ranging from 2°C to 5°C in the worst-case scenario [ 2 ]. Considering the buffering effect of peridomestic structures and houses (i.e., up to ± 5°C to ± 8°C), the minimum temperature from the coldest month could fall within the range of T. infestans activity as a consequence of climate change. Since increases in mean temperature due to climate change are expected in southern South America, these shifts in temperatures could potentially expand habitat suitability for T. infestans beyond current expectations. However, it is crucial to consider the complex interplay between the factors that influence the distribution and presence of T. infestans [ 55 ]. Temperature is a major abiotic factor in T. cruzi -triatomine interactions [ 56 , 57 ]. The development of T. cruzi and its relationship with temperature have been studied under field and laboratory conditions [ 57 – 59 ], revealing a positive relationship between environmental temperature and the concentration of infectious forms of Triatoma proctata [ 60 ], T. infestans [ 57 ], and Rhodnius prolixus [ 58 ]. An increase in temperature reduces the incubation time of parasites within the vector [ 57 ]. Field studies in Northwestern Argentina have recorded higher numbers of infectious forms of T. infestans during warm months [ 59 ]. However, the effect of temperature variability on T. infestans parasite development has never been tested under controlled conditions, despite its relevance in the context of climate change. Therefore, in this study, we aimed to evaluate the influence of temperature and its variability on the EIP and parasite load of T. cruzi in T. infestans and the probability of T. cruzi -positive dejections samples over time. Methods A total of 144 fourth-instar T. infestans nymphs were obtained from a colony that had been maintained for two years at the Laboratorio de Ecología, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile. This colony was originally established in 1950 at the Faculty of Medicine at the same university. The fourth instar is easily manageable and can transmit T. cruzi to vertebrate hosts [ 61 ]. The specimens were maintained individually in plastic flasks (3.8 cm x 6.8 cm) inside climatic chambers at 27°C with a photoperiod of 12 L:12D and 50% to 70% humidity. Prior to feeding on infected mice, the triatomines were fasted for 15 days. Triatomine infection with Trypanosoma cruzi Triatomines were infected by feeding on mice previously inoculated with T. cruzi clone Dm28c (DTU-TcI) under controlled laboratory conditions. The Trypomastigote forms of T. cruzi were cultivated in Vero cells (60–70% confluence), 5 ml of RPMI 1640 culture medium (Biological Industries) with 10% fetal bovine serum, 2 ml of trypsin, 1 ml of penicillin, and 100 µg/mL of streptomycin (Biological Industries), following the protocol from Valenzuela et al. [ 62 ]. Fourteen 8-week-old female BALB/c mice were inoculated only once by intraperitoneal injection with 1,000 parasites suspended in 1 mL of RPMI 1640 medium, and the parasite concentration was determined by light microscopy using a Neubauer chamber. After inoculation, mice were housed in cages at 20–25°C and 40–70% relative humidity, with free access to water and food, following the approved bioethics protocol (Nº: 19262-MED-UCH). Mouse parasitemia was evaluated, 0.1 ml blood sample was extracted from the tail vein to measure parasite concentration and viability using light microscopy (40X) with a Neubauer chamber every 3 days from day 7 post-infection until reaching 80,000-500,000 trypomastigotes/ml to ensure triatomine infection. Once reached, the insects were fed on these T. cruzi inoculated mice. Mice were sedated following the bioethics protocol (Nº: 19262-MED-UCH) with a dose of ketamine 100 mg/ml (mouse dose 100 mg/kg) in association with xylazine 5 mg/ml (mouse dose 10 mg/kg) and then placed inside a plastic box (32 × 21 × 14 cm) over a heating pad. Triatomines were fed during the dark phase and placed in the same plastic box as the mouse for 30 minutes in the dark. Each triatomine was weighed before and immediately after feeding using a BOECO Model BAS-31 (precision = 0.0001 g) to check for blood ingestion during the experimental procedure. All individuals were included in the treatments regardless of the volume of blood ingested. Each mouse was fed with a group of 12 T. infestans individuals. Insects were individualized with colored paint according to the protocol [ 63 ]. Temperature treatments After feeding on infected mice, all triatomines were randomly allocated to one of the four thermal treatments (i.e., 18 ± 0°C (n = 36), 18 ± 5°C (n = 36), 27 ± 0°C (n = 36), and 27 ± 5°C (n = 36)). The total maintenance time under these treatments was 42 days post infection (p. i.). After feeding on infected mice, triatomines were randomly assigned to one of four thermal treatments: 18 ± 0°C (n = 36), 18 ± 5°C (n = 36), 27 ± 0°C (n = 36), and 27 ± 5°C (n = 36). Each treatment group comprised 36 individuals, with each insect housed separately in a plastic vial (3.8 × 6.8 cm). Environmental chambers were set to the target temperatures with an accuracy of ± 1°C and a precision of 0.2°C (PITEC), under identical photoperiod and humidity conditions, as previously described. Each vial was labeled with a unique identification code indicating the individual insect, its assigned thermal treatment, and source mouse. The individual identification system was maintained throughout the experiment. Temperature treatments were chosen based on the average temperature of the central zone of Chile [ 2 ], where populations of the species have historically been detected, both in domestic and wild environments [ 47 , 48 ], and the temperature of the insect microsite habitat [ 64 ]. Therefore, low winter temperatures have been proposed as the most limiting factor in T. infestans population growth [ 50 , 51 , 65 ]. The lowest acclimation temperature considered the mean winter temperature (10.7°C), plus a 5°C of microhabitat buffer, and an additional 2°C reflecting projected temperature increases under climate change. Individuals were acclimated to these low temperatures with and without the inclusion of temperature variability (i.e., 18°C ± 5°C and 18°C ± 0°C, respectively). We also acclimated individuals to their optimal temperature, with and without temperature variability (i.e., 27°C ± 5°C and 27°C ± 0°C, respectively), which is the inside-house temperature [ 54 ]. In the variable temperature treatments, the rise in temperature occurred during the light hours and started to increase linearly at 7:00 h, reaching a maximum at 8:00 h (32°C and 23°C for warm and cold treatments, respectively), then remained constant, and began to decrease at 19:00 h, reaching its minimum at 20:00 h (22°C and 13°C for warm and cold treatments, respectively). Additionally, the range of temperatures used in our experiments was well within the thermotolerance range of the species, avoiding individual mortality [ 18 ]. The diurnal variation range selected was conservative with respect to the diurnal daily variation of Chile's Central Region [ 66 ] but was chosen because the animals inside the climatic chambers cannot use behavioral thermoregulation. In addition, these temperatures were previously used what will allow to integrate information for future studies on the effects of climate change on T. infestans [ 18 , 67 ]. Temperature and humidity were monitored daily using climatic chamber sensors. The photoperiod lights were switched on at 7:00 and turned off at 19:00. Dejection sampling Every two days, each individual was transferred to a new, dated flask labeled with its corresponding identification code. Simultaneously, the nymphal state was recorded for all individuals in the experiment. Excretion samples were placed in Eppendorf tubes, labeled, and recorded according to the identification code, date of collection, and number of fecal samples collected in the flask. Each fecal sample was diluted with nuclease-free water (40 µL per excretion). The contents of the solution were subsequently transferred to 1.5 ml Eppendorf tubes labeled with the insect ID and collection date. These tubes were then stored at -20°C until DNA extraction. Kollien & Shaub [ 68 ] observed that a short-term starvation period of 30 days led to the initial appearance of deceased flagellates in the rectum of Triatomines, whereas a starvation period of 90 days resulted in a mortality rate of 99.5% of T. cruzi in the rectum. Thus, triatomines were fed on non-infected laboratory mice ( Mus musculus ) at day 30 post-infection using the same protocol used for triatomine infection. We measured the weight before and after feeding, and the nymphal state. After the experimental period (42 days), the insects were euthanized by freezing for a minimum of 48 h following the bioethical protocol (Nº 19262-MED-UCH). Trypanosoma cruzi detection in dejections We extracted T. cruzi DNA from T. infestans dejections using the innuPREP Blood DNA Mini Kit (Analityk Jena AG), following the manufacturer’s instructions. All samples were co-extracted with 1 pg/µL of a 183 bp synthetic sequence from Arabidopsis thaliana (Brassicales: Brassicaceae), synthesized by gBlocks Gene Fragments (IDT), and used as an exogenous internal amplification control to evaluate the carryover of inhibitors and DNA loss in the extraction process. The qPCR assays amplified the satellite nuclear conserved region of T. cruzi with the primers Cruzi 1 (5 'ASTCGGCTGATCGTTTTCGA 3') and Cruzi 2 (5 'AATTCCTCCAAGCAGCGGATA 3') [ 69 ] in a final volume of 20 µL, containing 5 µL of template DNA, 5X HOT FIREPol EvaGreen qPCR Mix Plus (Solis BioDyne), 0.3 µM of each primer, and nuclease-free water. The cycling conditions were a pre-incubation for 15 min at 95°C, followed by 40 cycles of denaturation step of 95°C for 15 s, a hybridization step of 60°C for 20 s, and an extension step of 72°C for 20 s in a Rotor-Gene Q (QIAGEN) thermal cycler. The emitted fluorescence was recorded at the end of each cycle, and a melting curve was generated at the end of the program, with a ramp from 72 ° C to 95 °, increasing by 1°C in each step, waiting for 90 s of pre-melting conditioning in the first step, and 5 s for each subsequent step. Each reaction was carried out with a negative control (infection-free triatomine dejection’s DNA), a positive control ( T. cruzi DNA quantification standard), and a no-template control (nuclease-free water). All samples were analyzed in duplicate. The DNA standard curve for absolute quantification was obtained from the genomic DNA of T. cruzi strain DM28c. The calculation was performed considering that a parasite cell contains approximately 200 fg of DNA [ 70 , 71 ]. Serial 1/10 dilutions were made with nuclease-free water to cover a range of 106 to 0.1 parasite equivalents/mL. All samples were co-extracted with 1 pg/µl of a sequence of 183 bp from the tonoplast intrinsic protein 5;1 (TIP 5;1) of A. thaliana to normalize the parasite load, as previously described [ 72 ]. Quantification of the parasite equivalents from the DNA samples was performed by considering the amplification of the T. cruzi DNA standard curve, and the results were normalized according to the results of the exogenous IAC. Statistical analysis Extrinsic incubation period (EIP) Only the first positive dejection sample for each individual was used to estimate the effect of temperature on the EIP of T. cruzi in T. infestans (total n = 108; 18 ± 0°C n = 23; 18 ± 5°C n = 28; 27 ± 0°C n = 29; 27 ± 5°C n = 28). Generalized Linear Models (GLM) with a gamma function were fitted with EIP as a response variable and temperature treatment (T), individual body mass before infection (mb), ingested blood during infection (bi), estimated by subtracting the weight before and after feeding, and mouse parasitemia (P m ) as predictors. Model adequacy was evaluated using the likelihood ratio test (LRT) [ 73 ], while model selection was conducted based on the Akaike information criterion (AIC) [ 74 ]. To compare the treatment groups, post hoc analyses were performed using Tukey’s test with Shaffer-adjusted p-values. Parasite load in T. infestans dejections All positive dejection samples were included in this analysis (n = 307), with a total of 50 samples for 18 ± 0°C treatment, 74 for 18 ± 5°C, 97 for 27 ± 0°C, and 86 for the 27 ± 5°C treatment. Generalized Additive Models (GAM) with a lognormal distribution were fitted for the parasite load (Par-eq ml ) as the response variable. In these models, the temperature treatment (T), body mass before infection (mb), ingested blood during infection (bi), time in days of the collected sample post-infection (Days), and mouse parasitemia (P m ) were predictors. Cubic splines were used to capture the nonlinear relationships between each of the following variables and parasite load over time: temperature treatment (T), ingested blood (bi), and body mass before infection (mb). We also included a random effect for triatomine individuals (ID) to correct for the pseudo-replication of data. The model was selected according to the Akaike information criterion (AIC) [ 74 ]. Probability of positive dejections We analyzed the qPCR test results from all dejection samples (n = 438) to determine their positive or negative T. cruzi qPCR test results, generating a binary variable. In this analysis, all insects that fed on infected blood were included regardless of their infection status. To test the probability of positive dejections over time according to the different temperature treatments of individuals, we performed a Generalized Additive Model for Location, Scale, and Shape (GAMLSS) method for statistical analyses [ 75 , 76 ]. This method does not require a priori assumptions regarding the shape of the relationship between the response variable and time (i.e., the probability of positive dejections in time), fitting a cubic spline (cs). Thus, the qPCR test results were the response variable, while temperature treatment (T), mouse parasitemia (P m ), body mass (mb), ingested blood (bi), a cubic spline of the time (Days), and their interactions were the explanatory variables. We also included a random effect for individuals (ID) to correct the pseudo-replication of the data given by the effect of repeated measurements. In GAMLSS, the functions of the variables are unknown smooth functions, providing additional flexibility in the modeling process for a binary response variable. We performed models assuming a binomial distribution with ‘RS’ algorithm [ 76 ]. All analyses were performed in R version 2.13.0 [ 77 ] using RSTUDIO and ggplot2, lmtest, multcomp, mgcv, pROC, and gamlss packages. Results A total of 144 individuals were included in the study: 108 were positive and 36 negative, resulting in 75% positive individuals and 25% negative individuals. A total of 499 dejections were obtained, of which 326 were positive and 173 were negative. Extrinsic incubation period (EIP) A generalized linear model (GLM) with gamma distribution and inverse link function was used to evaluate the effects of thermal treatment (T), ingested blood during infection (bi), body mass before infection (mb), and their interaction on the extrinsic incubation period (EIP) of Trypanosoma cruzi in Triatoma infestans . The analysis included only individuals who tested positive at least once during the 42-day post-infection period (n = 108). The model revealed that the thermal treatment significantly affected the EIP (Table 1 and Fig. 1). Compared with the reference group (18 ± 0 ºC), both the warm constant (27 ± 0 ºC) and warm variable (27 ± 5 °C) treatments were associated with significantly shorter EIP values ( P < 0.005 for both). The generalized linear model showed a substantial improvement in fit compared to the null model (null deviance = 39.26, df = 107; residual deviance = 22.39, df = 101; AIC = 698.34), indicating that the selected predictors significantly contributed to explaining the variation in the extrinsic incubation period of T. cruzi in T. infestans . Tukey test showed a significant difference between the 27 and 18 ºC treatments, either being constant or variable (27 ± 0 ºC - 18 ± 0 ºC, z = 4.525, P < 0.001; 27 ± 5 ºC -18 ± 0 ºC z = 3.819, P < 0.001; 27 ± 0 ºC - 18 ± 5 ºC z = 3.994, P < 0.001; 27 ± 5 ºC - 18 ± 5 ºC z = 3.214, P < 0.005). However, the Tukey test did not show significant differences between the constant and thermally variable treatments within the same mean temperature (18 ± 0 °C–18 ± 5 °C, z = 0.632, P = 0.580 and 27 ± 0 °C–27 ± 5 °C z = -1.05, P = 0.580) (Fig. 1, Supplementary material: Table S2). The predicted values for the EIP were 30 days (± 3.27) for the cold constant treatment, 27 days (± 2.86) for the cold variable treatment, 12 days (± 1.28) for the warm constant treatment, and 14 days (± 1.49) for the warm variable treatment (Table 1). An increase in ingested blood (bi) reduced EIP, and the effect was greater for cold treatments (Supplementary material: Fig. S1). Parasite load in dejection samples of T. infestans The parasite load varied with temperature (T), time post-infection (Days), body mass (mb), and the interaction term between body mass (mb) and ingested blood at the time of infection (bi) (Table 2). The smooth term selected (Days, bs = "cs,” by = T) implies that the parasite load varied among temperature treatments (T) as time went by, and it was significant for all four treatments ( P < 0.005). Thus, the parasite load followed a bell-shaped curve: it started low, peaked between 20 and 30 days, and then declined (Fig. 3). In addition, the effect of temperature treatment on parasite loads varied across groups. Compared to the 18 ± 0 °C group, insects maintained at 27 ± 0 °C and 27 ± 5 °C exhibited significantly higher parasite loads (Estimate = 2.57 and 2.53, respectively; P < 0.001), whereas no significant difference was observed for the 18 ± 5 °C group ( P = 0.964) (Table 2 and Fig. 3). The interaction between body mass and the amount of blood ingested was significant in all treatments ( P < 0.005), except for 27 ± 5 °C ( P = 0.229), and described different bell-shaped curves for each treatment. The interaction between blood ingested (bi) and insect body mass (mb) over parasite load was most evident in insects with low body mass. Hence, smaller individuals who ingested lower blood volumes at the time of infection exhibited lower parasite loads than those who ingested intermediate volumes. This effect gradually diminished as body mass increased and was not marked in larger insects. In fact, the relationship was reversed in the heaviest individuals (Supplementary material: Fig. S2). Altogether, according to the best-competing model (Supplementary Material: Table S3), the parasite load trend changed with time after infection (Day) and was affected by the amount of ingested blood (bi), temperature treatment (T), and body mass (mb) (Table 2). The deviance of this model explained 64.3% of the variance, indicating a good fit for the parasite load in T. infestans . Probability of positive dejections We collected 438 dejection samples from 144 T. infestans individuals; 131 samples were negative and 307 were positive (30% and 70%, respectively). At 18 ± 0 °C, 28 out of 36 insects provided 78 samples; at 18 ± 5 °C, 33 out of 36 insects provided 117 samples; at 27 ± 0 °C, 31 out of 36 individuals provided 117 samples; and finally, at 27 ± 5 °C, all 36 individuals provided samples, with a total of 126 samples. We found significant differences in the probability of a positive result between warm and cold treatments. However, we did not find significant differences between constant and thermally variable treatments within the same temperature range (27 ± 0 °C and 27 ± 5 °C; 18 ± 0 °C and 18 ± 5ºC) (Table 3, Fig. 3). The best model fit for the probability of T. cruzi -positive dejections included a cubic spline on time (Days) with an interaction between temperature treatments (T) (Supplementary material: Table S3), showing differences between warm and cold treatments ( P < 0.05) (Fig. 3 and Table 3). There was no significant difference in the probability of positive dejections between the treatments with the same mean temperature (Table 3 and Fig. 3). The GAMLSS model indicated that time (Days) did not significantly affect the probability of positive results among the cold treatments ( P = 0.967). However, for the warm treatments, the time was significant ( P < 0.05) (Table 3), and as time passed (Days), the probability of positive samples increased (Fig. 3), as represented by the interaction between time (Days) and temperature treatment (T) (Table 3). For these warm treatments, the probability of obtaining negative samples decreased with time and became practically improbable after day 10. In contrast, for individuals acclimated to low temperatures, the probability of finding negative samples in these treatments was maintained throughout the study period (Fig. 3). During the refeeding procedure with uninfected mice (dotted black line), the mean blood ingested by individuals was 0.037 g (± 0.09) at 18 ± 5 °C, 0.046 g (± 0.05) at 18 ± 0 °C, 0.040 g (± 0.04) at 27 ± 0 °C, and 0.068 g (± 0.07) at 27 ± 5 °C. Feeding was considered when > 0.0001 g was consumed. Note that some individuals were removed from the experiment because of mortality; therefore, not all treatments included 36 individuals. Nymphal molts occurred primarily in the warm treatments at 27 ± 0 °C (13 molts) and 27 ± 5 °C (12 molts). All molts reached stage V and occurred 13 days post-infection. In the cold treatment, only one molt was observed in the 18 ± 0 °C treatment. Discussion Our results showed that as the temperature increased, the time at which the first positive sample was detected decreased (Fig. 1 ). Therefore, temperature significantly changed the extrinsic incubation period (EIP) of Trypanosoma cruzi within Triatoma infestans , but temperature variability did not have a significant effect. Furthermore, the probability of positivity of the dejections changed with temperature but not with variability (Fig. 4). The results also showed different patterns in the parasite load over time between the different heat treatments, both constant and variable (Fig. 2 ). A reduction in EIP owing to high environmental temperatures has been observed in three different species of triatomines, including T. infestans [ 57 – 60 , 78 ], which is also supported by our results. Trypanosoma cruzi parasites reared in vitro at four different temperatures (21°C, 24°C, 27°C and 30°C) increased in number in direct relation to temperature [ 79 ]. Unfortunately, in vitro experiments do not include triatomine immune factors or their microbiota among other factors that affect the actual development of T. cruzi in wild vectors [ 80 , 81 ]. We did not observe that daily thermal variation of 10°C affected the extrinsic incubation period of T. cruzi in T. infestans . Populations are expected to evolve physiological adaptations to local climatic conditions in heterogeneous environments [ 64 ], exhibiting plastic strategies that allow them to survive a broad range of temperatures [ 82 ]. This is the case for T. infestans and its parasite that inhabit environments subjected to major temperature variability [ 83 ], such as Chaco in Argentina [ 84 ] and others along its geographic distribution. In these regions, environmental temperature varies in a range that surpasses by more than 10°C those applied in our temperature variable treatments; therefore, both vectors and parasites are probably adapted to thermal variation. Moreover, T. cruzi is continuously exposed to temperature changes throughout its life cycle [ 85 ](Marliére et al., 2015), because insect vectors do not physiologically regulate their body temperature and ingest warm blood periodically [ 86 ]. Thus, the parasite must be adapted to grow at a wide range of temperatures. In addition, the parasite alternates between insect vectors and mammalian endothermic hosts [ 87 ]. EIP was also affected by the amount of blood ingested, with a negative relationship between the amount of infected blood ingested and the time of the first infectious excreta (Supplementary material: Fig. S1 ). Kissing bugs typically consume a large amount of blood in a single meal, resulting in a high consumption of hemoglobin [ 83 ]; a higher intake of this metalloprotein could explain the negative relationship between EIP and ingested blood, since hemoglobin participates in the replication and survival of the parasite [ 83 ]. Previous studies found that blood meal size was positively correlated with parasite concentration in the excreted urine of subsequent nymphal instars of Rhodnius prolixus [ 56 ]. However, at higher intake levels, the volume of ingested blood had a reduced impact on the extrinsic incubation period (EIP) (Supplementary material: Fig. S1 ). This is likely because the parasite has already accessed all the nutrients it requires, reaching its maximum development rate, indicating a threshold beyond which additional blood consumption does not further reduce the EIP. Our study is the first to describe a bell-shaped pattern of parasite population dynamics during the first days of T. infestans infection. However, previous studies have demonstrated a positive linear relationship between parasite load and time [ 57 , 58 ]. Nevertheless, these studies are not directly comparable to ours because one of them used optical microscopy and a different strain [ 57 ], whereas the other used another vector species, R. prolixus , with its tissue samples obtained instead of dejections [ 58 ]. Prolonged fasting can significantly reduce parasite loads in triatomines [ 62 ], which explains the decrease in parasite load across days. However, in our experiments, the reduction in parasite load continued even after the insects were refed uninfected blood on day 30 (Fig. 2 ). Another plausible explanation is the secretion of antibacterial proteins in response to microbial shifts following blood ingestion [ 37 , 88 ]. Kollien and Schaub [ 68 ] proposed that the available surface area of the rectal epithelium is also a limiting factor for T. cruzi multiplication, and once the available space becomes saturated, further parasite development may be restricted. It is more likely that nutrient depletion initiated the decline of parasites within T. infestans , with the immune response subsequently contributing to a further reduction in parasite load after feeding [ 89 ]. However, T. cruzi dynamics within triatomines involves a complex interplay that also includes alterations in the gut microbiota of insects. Azambuja et al. [ 90 ] demonstrated that bacteria can exert a trypanolytic effect and that blood ingestion can modify the bacterial composition in the midgut of triatomines. Consequently, these interactions are highly intricate, necessitating further studies to elucidate these interactions. Remarkably, a bell-shaped curve occurred in all temperature treatments, but with different levels of parasite load. The warm-temperature treatments presented a higher parasite load than the cold treatments during the entire study period (Fig. 2 ). Within the cold treatments, the effect of variable temperatures on parasite populations was evident, showing an increase in the parasite load (Fig. 2 ). This was also seen in the malaria vector Anopheles stephensi infected with Plasmodium chabaudi since temperature variability influenced the number of sporozoites per oocyst and those circulating in the hemocele. [ 31 ]. This is probably due to the activation of insect metabolism [ 91 – 93 ] triggered by the higher temperature from the cold variable treatment (i.e., 23°C), which is close to its preferred range [ 53 , 54 ]. This metabolic activation could lead to Trypanosoma cruzi activation because the number of parasites increases in direct relation to temperature [ 79 ]. Therefore, daily temperature increases near the preferred thermal range might activate both the vector and parasite. Furthermore, the warm-variable treatment showed an earlier appearance of the parasite load peak than the warm-constant temperature treatment (Fig. 3 ). It is likely that the maximum daily temperature within the variable treatment (32°C) exerts a more significant influence on the development and emergence of the parasite than the potential delaying effect of the minimum daily temperature (22°C). Consequently, the fluctuating temperature conditions in this treatment may have resulted in a more rapid onset of the parasite development. High temperatures weaken the immune system of triatomines, decreasing the activity of prophenoloxidase (proPO) and the enzymatic cascade of phenoloxidase (PO), which is involved in the defense mechanism against pathogens [ 83 , 94 ]. This could influence the degree of infection within the vector early, and only in the warm variable treatment, since it reached 32°C during the day, the other thermal treatments did not reach temperature values that could affect the proPO response [ 83 , 94 ]. In summary, environmental variability could potentially influence individual susceptibility to T. cruzi infection because coping with temperature fluctuations probably had a negative impact on the immune system of these insects. Previous studies have shown a decrease in the survival and fecundity of R. prolixus in a thermally variable environment [ 19 ] and a decreased low-temperature performance of T. infestans [ 18 ]. Thus, environmental temperatures play a crucial role in mediating the outcome of host–parasite interactions, and these interactions are multifactorial and complex. Finally, we found a significant effect of temperature on the probability of obtaining positive samples without a temperature variability effect, which was similar to that found on the EIP. We also observed that, for insects maintained at low temperatures, there was greater heterogeneity in the probability of finding positive dejections, showing an individual effect. Beyond day 5 post-infection in the warm treatments, all individuals tested positive, whereas individuals subjected to cold treatments exhibited more heterogeneous distributions over time, suggesting individual variation. Neither cold nor warm treatments exhibited significant differences when compared to their respective constant counterparts, suggesting a similar impact on the probability of infection. This pattern is consistent with observations from the extrinsic incubation period (EIP). It is possible that the interaction between temperature and parasite load influences the likelihood of obtaining positive samples and producing the observed heterogeneity. Conclusions In this study, we observed that different temperatures (warm and cold), along with their variability, led to an increase in the parasite load on Triatoma infestans , modifying its EIP, which can potentially alter the basic reproductive number (R 0 ) of Chagas and the number of secondary cases [ 24 , 25 , 27 ]. In the context of climate change, this vector may become more effective in transmitting Chagas disease in the near future. Nevertheless, it is important to acknowledge that temperature fluctuations can affect the survival and vital rates of insects, especially when they are outside their optimal thermal thresholds [ 18 , 19 , 32 , 94 , 95 ](Carrington et al., 2013), and that other factors may play a role in climate change, such as changes in precipitation and extreme climatic events, which may alter population parameters. Nonetheless, we hope that these results will contribute to a more complete understanding of the impact of increased temperature and its variability on this Chagas disease vector. Declarations Acknowledgments We thank the Mauricio Canals for providing T. infestans individuals from the Universidad de Médicina colony of Universidad de Chile. Funding Agencia Nacional de Investigación y Desarrollo (ANID) Fondecyt Iniciación 11160839 to SCB; Fondecyt Regular 1180940 to PC and Fondecyt Regular 1210359 to JDM. ANID - Programa Becas-Beca Doctorado Nacional - grant number 21171202 to MS and Doctorado Becas Chile 2019 - grant number 72200391 to AB. Availability of data and materials Authors contributions BA-D: Conceptualization, Formal analysis, Investigation, Methodology and Writing - original draft; SCB: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, and Writing - Review & Editing; LV-P: Methodology, Resources and Review & Editing; JDM: Methodology and Resources; MS: Methodology and Investigation; SO: Investigation; CM-S: Methodology and Investigation; AB: Formal analysis; PEC: Conceptualization and Resources, Validation, Supervision, Funding Acquisition - Review & Editing. Ethics approval and consent to participate All animal procedures were conducted according to the Chilean Animal Protection Law (Ley N° 20.380) and the Guide for the Care and Use of Laboratory Animals (Albus, 2012). The Scientific Ethical Committee for Research Safety of the Universidad de Chile approved all animal protocols used in this study (Protocol Nº: 19262-MED-UCH for Biosecurity and Ethical protocols). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Data access Data will be held on Figshare after acceptance 10.1101/2023.11.08.566164, private link https://figshare.com/s/a0011e5254aab62dfc74 References Ayala S, Alvarado S, Cáceres D, Zulantay I, Canals M. Estimando el efecto del cambio climático sobre el riesgo de la enfermedad de Chagas en Chile por medio del número reproductivo. Rev Médica Chile. 2019;147:683–92. Araya-Osses D, Casanueva A, Román-Figueroa C, Uribe JM, Paneque M. Climate change projections of temperature and precipitation in Chile based on statistical downscaling. Clim Dyn. 2020;54:4309–30. https://doi.org/10.1007/s00382-020-05231-4. Lafferty KD. The ecology of climate change and infectious diseases. Ecology. 2009;90:888–900. https://doi.org/10.1890/08-0079.1. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature. 2005;438:310–7. https://doi.org/10.1038/nature04188. Patz JA, Epstein PR, Burke TA, Balbus JM. Global climate change and emerging infectious diseases. JAMA. 1996;275:217–23. Struchiner CJ, Rocklöv J, Wilder-Smith A, Massad E. Increasing Dengue Incidence in Singapore over the Past 40 Years: Population Growth, Climate and Mobility. PloS One. 2015;10:e0136286. https://doi.org/10.1371/journal.pone.0136286. Tjaden NB, Suk JE, Fischer D, Thomas SM, Beierkuhnlein C, Semenza JC. Modelling the effects of global climate change on Chikungunya transmission in the 21st century. Sci Rep. 2017;7:3813. https://doi.org/10.1038/s41598-017-03566-3. Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, et al. Thermal biology of mosquito-borne disease. Ecol Lett. 2019;22:1690–708. https://doi.org/10.1111/ele.13335. Paz S. Climate change impacts on West Nile virus transmission in a global context. Philos Trans R Soc Lond B Biol Sci. 2015;370. https://doi.org/10.1098/rstb.2013.0561. Guis H, Caminade C, Calvete C, Morse AP, Tran A, Baylis M. Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe. J R Soc Interface. 2012;9:339–50. https://doi.org/10.1098/rsif.2011.0255. Chalghaf B, Chemkhi J, Mayala B, Harrabi M, Benie GB, Michael E, et al. Ecological niche modeling predicting the potential distribution of Leishmania vectors in the Mediterranean basin: impact of climate change. Parasit Vectors. 2018;11:461. https://doi.org/10.1186/s13071-018-3019-x. Kulkarni MA, Desrochers RE, Kajeguka DC, Kaaya RD, Tomayer A, Kweka EJ, et al. 10 Years of Environmental Change on the Slopes of Mount Kilimanjaro and Its Associated Shift in Malaria Vector Distributions. Front Public Health. 2016;4:281. https://doi.org/10.3389/fpubh.2016.00281. Damborsky MP, Bar ME, Gorla D. Life cycle and reproductive patterns of Triatoma rubrovaria (Blanchard, 1843) (Hemiptera: Reduviidae) under constant and fluctuating conditions of temperature and humidity. Rev Soc Bras Med Trop. 2005;38:433–7. https://doi.org/10.1590/s0037-86822005000500014. Shapiro LLM, Whitehead SA, Thomas MB. Quantifying the effects of temperature on mosquito and parasite traits that determine the transmission potential of human malaria. PLoS Biol. 2017;15:e2003489. https://doi.org/10.1371/journal.pbio.2003489. Ciota AT, Matacchiero AC, Kilpatrick AM, Kramer LD. The effect of temperature on life history traits of Culex mosquitoes. J Med Entomol. 2014;51:55–62. https://doi.org/10.1603/me13003. Stillwell RC, Fox CW. Geographic variation in body size, sexual size dimorphism and fitness components of a seed beetle: local adaptation versus phenotypic plasticity. Oikos. 2009;118:703–12. https://doi.org/10.1111/j.1600-0706.2008.17327.x. Masson-Delmotte VP, Zhai P, Pirani SL, Connors C, Péan S, Berger N, et al. IPCC 2021: Summary for policymakers. in: Climate change 2021: The physical science basis. contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. 2021. Clavijo-Baquet S, Cavieres G, González A, Cattan PE, Bozinovic F. Thermal performance of the Chagas disease vector, Triatoma infestans, under thermal variability. PLoS Negl Trop Dis. 2021;15:e0009148. https://doi.org/10.1371/journal.pntd.0009148. Rolandi C, Schilman PE. The costs of living in a thermal fluctuating environment for the tropical haematophagous bug, Rhodnius prolixus. J Therm Biol. 2018;74:92–9. https://doi.org/10.1016/j.jtherbio.2018.03.022. Estay SA, Lima M, Bozinovic F. The role of temperature variability on insect performance and population dynamics in a warming world. Oikos. 2014;123:131–40. https://doi.org/10.1111/j.1600-0706.2013.00607.x. Mills JN, Gage KL, Khan AS. Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan. Environ Health Perspect. 2010;118:1507–14. https://doi.org/10.1289/ehp.0901389. Tjaden NB, Thomas SM, Fischer D, Beierkuhnlein C. Extrinsic Incubation Period of Dengue: Knowledge, Backlog, and Applications of Temperature Dependence. PLoS Negl Trop Dis. 2013;7:e2207. https://doi.org/10.1371/journal.pntd.0002207. Rohani A, Wong YC, Zamre I, Lee HL, Zurainee MN. The effect of extrinsic incubation temperature on development of dengue serotype 2 and 4 viruses in Aedes aegypti (L.). Southeast Asian J Trop Med Public Health. 2009;40:942–50. Christofferson RC, Mores CN. Estimating the magnitude and direction of altered arbovirus transmission due to viral phenotype. PloS One. 2011;6:e16298. https://doi.org/10.1371/journal.pone.0016298. Luz PM, Codeço CT, Massad E, Struchiner CJ. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil. Mem Inst Oswaldo Cruz. 2003;98:871–8. Hartemink N, Cianci D, Reiter P. R0 for vector-borne diseases: impact of the assumption for the duration of the extrinsic incubation period. Vector Borne Zoonotic Dis Larchmt N. 2015;15:215–7. https://doi.org/10.1089/vbz.2014.1684. Dietz K. The estimation of the basic reproduction number for infectious diseases. Stat Methods Med Res. 1993;2:23–41. https://doi.org/10.1177/096228029300200103. Pongsumpun P. Dengue disease model with the effect of extrinsic incubation period. In: Proceedings of the 2006 WSEAS International Conference on Mathematical Biology and Ecology. Miami, Florida; 2006. p. 43–8. Hartemink NA, Purse BV, Meiswinkel R, Brown HE, de Koeijer A, Elbers ARW, et al. Mapping the basic reproduction number (R₀) for vector-borne diseases: a case study on bluetongue virus. Epidemics. 2009;1:153–61. https://doi.org/10.1016/j.epidem.2009.05.004. Putri YE, Rozi S, Tasman H, Aldila D. Assessing the effect of extrinsic incubation period (EIP) prolongation in controlling dengue transmission with wolbachia-infected mosquito intervention. AIP Conf Proc. 2017;1825:020019. https://doi.org/10.1063/1.4978988. Paaijmans KP, Blanford S, Bell AS, Blanford JI, Read AF, Thomas MB. Influence of climate on malaria transmission depends on daily temperature variation. Proc Natl Acad Sci U S A. 2010;107:15135–9. https://doi.org/10.1073/pnas.1006422107. Lambrechts L, Scott TW, Gubler DJ. Consequences of the expanding global distribution of Aedes albopictus for dengue virus transmission. PLoS Negl Trop Dis. 2010;4:e646. https://doi.org/10.1371/journal.pntd.0000646. Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg. 1987;36:143–52. https://doi.org/10.4269/ajtmh.1987.36.143. Paaijmans KP, Read AF, Thomas MB. Understanding the link between malaria risk and climate. Proc Natl Acad Sci U S A. 2009;106:13844–9. https://doi.org/10.1073/pnas.0903423106. PAHO. Factsheet: Chagas Disease in the Americas for Public Health Workers. 2022. Lidani KCF, Andrade FA, Bavia L, Damasceno FS, Beltrame MH, Messias-Reason IJ, et al. Chagas Disease: From Discovery to a Worldwide Health Problem. Front Public Health. 2019;7:166. https://doi.org/10.3389/fpubh.2019.00166. Vieira CB, Praça YR, Bentes KL da S, Santiago PB, Silva SMM, Silva G dos S, et al. Triatomines: Trypanosomatids, Bacteria, and Viruses Potential Vectors? Front Cell Infect Microbiol. 2018;Volume 8-2018. Reisenman CE, Lawrence G, Guerenstein PG, Gregory T, Dotson E, Hildebrand JG. Infection of kissing bugs with Trypanosoma cruzi, Tucson, Arizona, USA. Emerg Infect Dis. 2010;16:400–5. https://doi.org/10.3201/eid1603.090648. Kribs-Zaleta C. Vector Consumption and Contact Process Saturation in Sylvatic Transmission of T. cruzi. Math Popul Stud. 2006;13:135–52. https://doi.org/10.1080/08898480600788576. Noya BA de, Díaz-Bello Z, Colmenares C, Ruiz-Guevara R, Mauriello L, Muñoz-Calderón A, et al. Update on oral Chagas disease outbreaks in Venezuela: epidemiological, clinical and diagnostic approaches. Mem Inst Oswaldo Cruz. 2015;110:377–86. https://doi.org/10.1590/0074-02760140285. Pereira KS, Schmidt FL, Guaraldo AMA, Franco RMB, Dias VL, Passos LAC. Chagas’ disease as a foodborne illness. J Food Prot. 2009;72:441–6. https://doi.org/10.4315/0362-028x-72.2.441. Rassi AJ, Rassi A, Marcondes de Rezende J. American trypanosomiasis (Chagas disease). Infect Dis Clin North Am. 2012;26:275–91. https://doi.org/10.1016/j.idc.2012.03.002. Dias JCP. Southern Cone Initiative for the elimination of domestic populations of Triatoma infestans and the interruption of transfusional Chagas disease. Historical aspects, present situation, and perspectives. Mem Inst Oswaldo Cruz. 2007;102 Suppl 1:11–8. https://doi.org/10.1590/s0074-02762007005000092. Noireau F, Carbajal-de-la-Fuente AL, Lopes CM, Diotaiuti L. Some considerations about the ecology of Triatominae. An Acad Bras Cienc. 2005;77:431–6. https://doi.org/10.1590/s0001-37652005000300006. Barbazan P, Guiserix M, Boonyuan W, Tuntaprasart W, Pontier D, Gonzalez J-P. Modelling the effect of temperature on transmission of dengue. Med Vet Entomol. 2010;24:66–73. https://doi.org/10.1111/j.1365-2915.2009.00848.x. Rolón M, Vega MC, Román F, Gómez A, Rojas de Arias A. First report of colonies of sylvatic Triatoma infestans (Hemiptera: Reduviidae) in the Paraguayan Chaco, using a trained dog. PLoS Negl Trop Dis. 2011;5:e1026. https://doi.org/10.1371/journal.pntd.0001026. Bacigalupo A, Torres-Pérez F, Segovia V, García A, Correa JP, Moreno L, et al. Sylvatic foci of the Chagas disease vector Triatoma infestans in Chile: description of a new focus and challenges for control programs. Mem Inst Oswaldo Cruz. 2010;105:633–41. Bacigalupo B A, Segura M JA, García C A, Hidalgo C J, Galuppo G S, Cattan PE. Primer hallazgo de vectores de la enfermedad de Chagas asociados a matorrales silvestres en la Región Metropolitana, Chile. Rev Médica Chile. 2006;134:1230–6. Brenière SF, Waleckx E, Barnabé C. Over Six Thousand Trypanosoma cruzi Strains Classified into Discrete Typing Units (DTUs): Attempt at an Inventory. PLoS Negl Trop Dis. 2016;10:e0004792. https://doi.org/10.1371/journal.pntd.0004792. Gorla DE. Variables ambientales registradas por sensores remotos como indicadores de la distribución geográfica de Triatoma infestans (Heteroptera: Reduviidae). Ecol Austral. 2002;12:117–27. de la Vega GJ, Medone P, Ceccarelli S, Rabinovich J, Schilman PE. Geographical distribution, climatic variability and thermo-tolerance of Chagas disease vectors. Ecography. 2015;38:851–60. https://doi.org/10.1111/ecog.01028. Gorla DE, Catalá SS, Grilli MP. Efecto de la temperatura sobre la distribución de Triatoma Infestans y el riesgo de transmisión vectorial de la enfermedad de Chagas en Argentina. Acta Toxicológica Argent. 1997;5:39–39. Lazzari CR. Temperature preference in Triatoma infestans (Hemiptera: Reduviidae). Bull Entomol Res. 1991;81:273–6. https://doi.org/10.1017/S0007485300033538. Canals M, Solis R, Valderas J, Ehrenfeld M, Cattan PE. Preliminary studies on temperature selection and activity cycles of Triatoma infestans and T. spinolai (Heteroptera:Reduviidae), Chilean vectors of Chagas’ disease. J Med Entomol. 1997;34:11–7. https://doi.org/10.1093/jmedent/34.1.11. Fouque F, Reeder JC. Impact of past and on-going changes on climate and weather on vector-borne diseases transmission: a look at the evidence. Infect Dis Poverty. 2019;8:51. https://doi.org/10.1186/s40249-019-0565-1. Loshouarn H, Guarneri AA. The interplay between temperature, Trypanosoma cruzi parasite load, and nutrition: Their effects on the development and life-cycle of the Chagas disease vector Rhodnius prolixus. PLoS Negl Trop Dis. 2024;18:e0011937. https://doi.org/10.1371/journal.pntd.0011937. Asin S, Catalá S. Development of Trypanosoma cruzi in Triatoma infestans: Influence of Temperature and Blood Consumption. J Parasitol. 1995;81:1–7. https://doi.org/10.2307/3283997. Tamayo LD, Guhl F, Vallejo GA, Ramírez JD. The effect of temperature increase on the development of Rhodnius prolixus and the course of Trypanosoma cruzi metacyclogenesis. PLoS Negl Trop Dis. 2018;12:e0006735. https://doi.org/10.1371/journal.pntd.0006735. Giojalas LC, Catalá SS, Asin SN, Gorla DE. Seasonal changes in infectivity of domestic populations of Triatoma infestans. Trans R Soc Trop Med Hyg. 1990;84:439–42. https://doi.org/10.1016/0035-9203(90)90355-i. Wood SF. Environmental temperature as a factor in development of Trypanosoma cruzi in Triatoma protracta. Exp Parasitol. 1954;3:227–33. https://doi.org/10.1016/0014-4894(54)90021-1. de Oliveira ABB, Alevi KCC, Imperador CHL, Madeira FF, Azeredo-Oliveira MTV de. Parasite-Vector Interaction of Chagas Disease: A Mini-Review. Am J Trop Med Hyg. 2018;98:653–5. https://doi.org/10.4269/ajtmh.17-0657. Valenzuela L, Sepúlveda S, Ponce I, Galanti N, Cabrera G. The overexpression of TcAP1 endonuclease confers resistance to infective Trypanosoma cruzi trypomastigotes against oxidative DNA damage. J Cell Biochem. 2018;119:5985–95. https://doi.org/10.1002/jcb.26795. Bar ME, Oscherov EB, Damborsky MP, Porcel EA, Alvarez BM. Interacción entre poblaciones de Triatoma infestans y Triatoma sordida. Rev Saúde Pública. 1994;28. Vazquez-Prokopec GM, Ceballos LA, Cecere MC, Gürtler RE. Seasonal variations of microclimatic conditions in domestic and peridomestic habitats of Triatoma infestans in rural northwest Argentina. Acta Trop. 2002;84:229–38. https://doi.org/10.1016/s0001-706x(02)00204-8. Gorla DE. Population dynamics and control of Triatoma infestans. Med Vet Entomol. 1992;6:91–7. https://doi.org/10.1111/j.1365-2915.1992.tb00582.x. DGF. Estudio de la variabilidad climática en Chile para el siglo XXI: Departamento de Geofísica. Técnico. Santiago: Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile; 2007. Álvarez-Duhart B, Cavieres G, González A, Cattan PE, Bozinovic F, Clavijo-Baquet S. Influence of temperature variability on the feeding behavior and blood consumption of Triatoma infestans (Hemiptera: Reduviidae). J Med Entomol. 2024;:tjae034. Kollien AH, Schaub GA. The development of Trypanosoma cruzi in triatominae. Parasitol Today Pers Ed. 2000;16:381–7. https://doi.org/10.1016/s0169-4758(00)01724-5. Piron M, Fisa R, Casamitjana N, López-Chejade P, Puig L, Vergés M, et al. Development of a real-time PCR assay for Trypanosoma cruzi detection in blood samples. Acta Trop. 2007;103:195–200. Duffy T, Cura CI, Ramirez JC, Abate T, Cayo NM, Parrado R, et al. Analytical performance of a multiplex Real-Time PCR assay using TaqMan probes for quantification of Trypanosoma cruzi satellite DNA in blood samples. PLoS Negl Trop Dis. 2013;7:e2000. Kooy RF, Ashall F, Van der Ploeg M, Overdulve JP. On the DNA content of Trypanosoma cruzi. Mol Biochem Parasitol. 1989;36:73–6. Cabe AM, Yañez F, Pinto R, López A, Ortiz S, Martin CM-S, et al. Survivorship of wild caught Mepraia spinolai nymphs: The effect of seasonality and Trypanosoma cruzi infection after feeding and fasting in the laboratory. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis. 2019;71:197–204. https://doi.org/10.1016/j.meegid.2019.04.002. Agresti A. Foundations of linear and generalized linear models. John Wiley & Sons; 2015. Akaike H. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika. 1973;60:255–65. https://doi.org/10.1093/biomet/60.2.255. Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F. Flexible Regression and Smoothing: Using GAMLSS in R. 1st edition. New York: Chapman and Hall/CRC; 2017. Rigby RA, Stasinopoulos MD, Heller GZ, De Bastiani F. Distributions for Modeling Location, Scale, and Shape. Using GAMLSS in R. 1st edition. New York: Chapman and Hall/CRC; 2019. Team RC. R: The R project for statistical computing. No Title. 2023. Catala SS, Gorla DE, Basombrio MA. Vectorial transmission of Trypanosoma cruzi: an experimental field study with susceptible and immunized hosts. Am J Trop Med Hyg. 1992;47:20–6. https://doi.org/10.4269/ajtmh.1992.47.20. Elliot SL, Rodrigues J de O, Lorenzo MG, Martins-Filho OA, Guarneri AA. Trypanosoma cruzi, etiological agent of Chagas disease, is virulent to its triatomine vector Rhodnius prolixus in a temperature-dependent manner. PLoS Negl Trop Dis. 2015;9:e0003646. https://doi.org/10.1371/journal.pntd.0003646. Díaz S, Villavicencio B, Correia N, Costa J, Haag KL. Triatomine bugs, their microbiota and Trypanosoma cruzi: asymmetric responses of bacteria to an infected blood meal. Parasit Vectors. 2016;9:636. https://doi.org/10.1186/s13071-016-1926-2. Duarte-Silva E, Morais LH, Clarke G, Savino W, Peixoto C. Targeting the Gut Microbiota in Chagas Disease: What Do We Know so Far? Front Microbiol. 2020;11:585857. https://doi.org/10.3389/fmicb.2020.585857. Bozinovic F, Medina NR, Alruiz JM, Cavieres G, Sabat P. Thermal tolerance and survival responses to scenarios of experimental climatic change: changing thermal variability reduces the heat and cold tolerance in a fly. J Comp Physiol [B]. 2016;186:581–7. https://doi.org/10.1007/s00360-016-0980-6. Melo R de FP, Guarneri AA, Silber AM. The Influence of Environmental Cues on the Development of Trypanosoma cruzi in Triatominae Vector. Front Cell Infect Microbiol. 2020;10:27. https://doi.org/10.3389/fcimb.2020.00027. SNS. Servicio Metereológico Argentino. Estadísticas Climáticas. Marliere NP, Latorre-Estivalis JM, Lorenzo MG, Carrasco D, Alves-Silva J, Rodrigues J de O, et al. Trypanosomes modify the behavior of their insect hosts: effects on locomotion and on the expression of a related gene. PLoS Negl Trop Dis. 2015;9:e0003973. Lahondère C, Insausti TC, Paim RM, Luan X, Belev G, Pereira MH, et al. Countercurrent heat exchange and thermoregulation during blood-feeding in kissing bugs. eLife. 2017;6. https://doi.org/10.7554/eLife.26107. Magdaleno A, Ahn I-Y, Paes LS, Silber AM. Actions of a proline analogue, L-thiazolidine-4-carboxylic acid (T4C), on Trypanosoma cruzi. PloS One. 2009;4:e4534. https://doi.org/10.1371/journal.pone.0004534. Vieira CB, Praça YR, Bentes KL da S, Santiago PB, Silva SMM, Silva G dos S, et al. Triatomines: trypanosomatids, bacteria, and viruses potential vectors? Front Cell Infect Microbiol. 2018;8:405. Salcedo-Porras N, Lowenberger C. The innate immune system of kissing bugs, vectors of chagas disease. Dev Comp Immunol. 2019;98:119–28. https://doi.org/10.1016/j.dci.2019.04.007. Azambuja P, Feder D, Garcia ES. Isolation of Serratia marcescens in the midgut of Rhodnius prolixus: impact on the establishment of the parasite Trypanosoma cruzi in the vector. Exp Parasitol. 2004;107:89–96. https://doi.org/10.1016/j.exppara.2004.04.007. Rolandi C, Iglesias MS, Schilman PE. Metabolism and water loss rate of the haematophagous insect Rhodnius prolixus: effect of starvation and temperature. J Exp Biol. 2014;217 Pt 24:4414–22. https://doi.org/10.1242/jeb.109298. González-Tokman D, Córdoba-Aguilar A, Dáttilo W, Lira-Noriega A, Sánchez-Guillén RA, Villalobos F. Insect responses to heat: physiological mechanisms, evolution and ecological implications in a warming world. Biol Rev Camb Philos Soc. 2020;95:802–21. https://doi.org/10.1111/brv.12588. Gillooly JF, Brown JH, West GB, Savage VM, Charnov EL. Effects of size and temperature on metabolic rate. Science. 2001;293:2248–51. https://doi.org/10.1126/science.1061967. González-Rete B, Salazar-Schettino PM, Bucio-Torres MI, Córdoba-Aguilar A, Cabrera-Bravo M. Activity of the prophenoloxidase system and survival of triatomines infected with different Trypanosoma cruzi strains under different temperatures: understanding Chagas disease in the face of climate change. Parasit Vectors. 2019;12:219. https://doi.org/10.1186/s13071-019-3477-9. Carrington LB, Seifert SN, Armijos MV, Lambrechts L, Scott TW. Reduction of Aedes aegypti vector competence for dengue virus under large temperature fluctuations. Am J Trop Med Hyg. 2013;88:689. Tables Table 1 . Summary of the best generalized linear model (GLM) model fitted for T. cruzi EIP in T. infestans . Estimate Size effect (days) Standard Error t value p value Intercept (18 ± 0 ºC) -0.007 30 0.012 -0.534 0.594 18 ± 5 ºC 0.004 27 0.007 0.632 0.529 27 ± 0 ºC 0.050 12 0.011 4.525 < 0.05 27 ± 5 ºC 0.037 14 0.010 3.819 < 0.05 bi 0.786 30 0.189 4.157 < 0.05 mb 0.975 1.28 0.294 3.320 < 0.05 bi * mb -12.918 - 4.044 -3.195 < 0.05 The groups were defined as follows: 18 ± 0 °C, cold constant treatment; 18 ± 5ºC: cold variable treatment; 27 ± 0 °C, warm constant treatment; 27 ± 5ºC: warm variable treatment. Abbreviations are as follows: bi, ingested blood; mb, body mass before infection. The size effect was calculated as 1 / (intercept estimate + estimate of the category). The volume of blood ingested decreased EIP in all treatments. Table 2. GAM estimates and significance from the variables of the best model fitted for parasite load of T. cruzi in T. infestans . Parametric coefficients: Variable Estimate Standard Error t value p value Intercept (18±0 ºC) 5.210 0.582 8.956 < 0.05 18 ± 5 ºC 0.031 0.704 0.044 0.9647 27 ± 0 ºC 2.575 0.770 3.343 < 0.05 27 ± 5 ºC 2.527 0.751 3.365 < 0.05 Approximate significance of smooth terms: Edf Ref.df F p value s(ID) 0.977 1.000 7.173 < 0.05 s(Day): 18 ± 0 ºC 3.002 9.000 2.986 < 0.05 s(Day): 18 ± 5 ºC 6.489 9.000 6.806 < 0.05 s(Day): 27 ± 0 ºC 2.952 9.000 5.969 < 0.05 s(Day): 27 ± 5 ºC 4.049 9 8.036 < 0.05 te(mb,bi): 18 ± 0 ºC 8.157 19 2.755 < 0.05 te(mb,bi): 18 ± 5 ºC 8.711 20 2.082 < 0.05 te(mb,bi): 27 ± 0 ºC 4.575 21 1.041 < 0.05 te(mb,bi): 27 ± 5 ºC 1.945 20 0.138 0.230 R-sq.(adj) = 0.583 Deviance explained = 64.3% n =307 Estimate of the selected model; standard error; t-value; p-value; Edf: effective degrees of freedom; Ref. df: Reference degrees of freedom; F: F-tests on smooth terms; p-value: p-value of smooth terms; R-sq.(adj): Adjusted R-squared value; 18 ± 0 °C: cold constant treatment; 18 ± 5 °C: cold variable treatment; 27 ± 0 °C: warm constant treatment; 27 ± 5 °C warm variable treatment; s: spline; ID: triatomine individuals; Days: time measured in days p.i; m b : body mass before infection; b i: ingested blood. Table 3 . Parameters of the GAMLSS selected model. Estimate Standard Error t value p value Intercept (18 ± 0 ºC) -3.136 0.477 -4.038 < 0.05 18 ± 5 ºC 0.285 0.667 0.293 0.769 27 ± 0 ºC -5.474 2.573 -2.317 < 0.05 27 ± 5 ºC -8.275 2.187 -3.984 < 0.05 cs(Days) 0.151 0.020 4.682 < 0.05 18 ± 5 ºC:cs(Day) -0.001 0.029 -0.040 0.967 27 ± 0 ºC:cs(Day) 1.317 0.335 3.894 < 0.05 27 ± 5 ºC:cs(Day) 1.612 0.272 5.756 < 0.05 Estimate of the selected model; standard error; t value; p value; 27 ± 0 °C: warm constant treatment; 18 ± 0 °C: cold constant treatment; 18 ± 5 °C: cold variable treatment; 27 ± 5 °C warm variable treatment; cs: cubic spline; d: time in days; ID: triatomine individuals. Additional Declarations No competing interests reported. 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09:04:34","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":259313,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7881049/v1/e9831341a15e4350a4ed6a62.html"},{"id":95221220,"identity":"fb91c6a1-b115-4631-8fc7-1357c458a5ed","added_by":"auto","created_at":"2025-11-05 16:18:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":727845,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of extrinsic incubation period (EIP measured as Days p.i) of \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e on \u003cem\u003eTriatoma infestans \u003c/em\u003eexposed to four temperature treatments 18 ± 0 ºC (n = 23); 18 ± 5 ºC (n = 28); 27 ± 0 ºC (n = 29); 27 ± 5 ºC (n = 28). The letters show the results from the posteriori test (Tukey’s test). The boxplot shows no significant differences between the groups at 18 ± 0 ºC and 18 ± 5 ºC, indicated by the same letter 'a' on top, and between the groups 27 ± 0 ºC and 27 ± 5 ºC, indicated by the same letter 'b' on top.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7881049/v1/d4a9eb22c8730b688b1b231b.jpg"},{"id":95002509,"identity":"6f3e418d-8257-49cb-bd9b-bf003ffc46a7","added_by":"auto","created_at":"2025-11-03 09:04:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1198928,"visible":true,"origin":"","legend":"\u003cp\u003eParasite load (in \u003cem\u003eT. infestans\u003c/em\u003e excreta samples over time (Days) by temperature treatment from the best-fitted model (n = 307). Cold constant temperature in blue (18 ± 0 ºC, n = 50), cold variable temperature in light blue (18 ± 5 ºC, n = 74), warm constant temperature in red (27 ± 0 ºC, n = 97), warm variable temperature in dark red (27 ± 5 ºC, n = 86). The dotted vertical lines indicate the time of refeeding with an uninfected blood meal (day 30). Shaded area shows the standard error of mean (s.e.m.) estimated from the best-fit model. Note that parasite load units are parasites equivalent to 200 femtograms of DNA.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7881049/v1/69738ed81204dc02f392abe0.jpg"},{"id":95002495,"identity":"d0a35c7b-e6b0-4ea6-ac76-5592b47e0e64","added_by":"auto","created_at":"2025-11-03 09:04:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1040317,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of positive dejections to \u003cem\u003eT. cruzi\u003c/em\u003e in \u003cem\u003eT. infestans\u003c/em\u003e by temperature treatment. Probability is represented by a gradient palette from turquoise: minimum to orange: maximum probability of positivity. The dotted vertical lines mark the time of refeeding with an uninfected blood meal (day 30). All insects that fed on the infected blood were included, regardless of their infection status, and each point represents a dejection sample obtained from 36 insects by treatment. Sample size per treatment 18 ± 0 ºC (n = 78), 18 ± 5 ºC (n = 117), 27 ± 0 ºC (n = 117) and 27 ± 5 ºC (n = 126).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7881049/v1/6c9275e0cae7e80eaf79e855.jpg"},{"id":102235424,"identity":"c5c503b0-2224-46b6-8f58-c66d6c606d70","added_by":"auto","created_at":"2026-02-09 16:16:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3929452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7881049/v1/f709cd12-b433-4a74-8f91-d8d2c9ab1695.pdf"},{"id":95002496,"identity":"124162fd-c60d-4cc7-98a0-2db8681b2ea2","added_by":"auto","created_at":"2025-11-03 09:04:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1239938,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialPV.docx","url":"https://assets-eu.researchsquare.com/files/rs-7881049/v1/3d532847d7c3d28d6cea0aff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temperature variability increases Trypanosoma cruzi load but not the extrinsic incubation period in Triatoma infestans","fulltext":[{"header":"Background","content":"\u003cp\u003eThe relationship between climate change and infectious diseases is a pressing ecological [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and global health concern [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Vector-borne diseases are especially susceptible to temperature because their transmission rate and incidence are related to the biological traits of vectors, which are ectothermic arthropods [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Climate change is expected to affect multiple vector-borne diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], such as dengue fever [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], chikungunya virus [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], Zika virus [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], West Nile virus [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], Bluetongue virus [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], leishmaniasis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] malaria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and Chagas disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The effect of temperature on biting rate, reproduction, development, survival, and probability of becoming infectious after biting an infectious host (i.e., vector competence) has been reported for several disease vectors [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In summary, temperature influences pathogen transmission by promoting its transmission at optimal constant temperatures and stopping it beyond the lower and upper thermal activity limits [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClimate change projections include an increase in the average environmental temperature in the long term, as well as more frequent periods of temperature variability, such as daily, monthly, or seasonal variation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The survival, reproduction, and thermal performance of ectotherms are affected by temperature variability in a nonlinear manner, which can positively affect individuals who are below their thermal optimum and negatively affect individuals when they are above their optimum temperature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Therefore, it is difficult to elucidate how global warming change will modify the distribution and incidence of vector-borne diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, to generate accurate predictions, the precise effects of temperature and mechanisms underlying these effects must be fully understood [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe extrinsic incubation period (EIP, hereafter) is the time required for a pathogen or parasite to develop from its entry into the vector until the first forms capable of infecting another host appear [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. EIP influences the incidence of vector-borne diseases [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] as a lower EIP generally indicates a faster capacity to transmit the pathogen, which leads to an increase in the incidence of the disease. This parameter determines the basic reproductive number R\u003csub\u003e0\u003c/sub\u003e for vector-borne diseases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The EIP is also included in the vectorial capacity equation, which is a measure of the transmission potential of a vector-borne pathogen within a susceptible population [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, even small changes in the EIP can have a large impact on the results of mechanistic models based on these equations [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The practical relevance of this problem has been demonstrated for dengue [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], malaria [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and bluetongue [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and the EIP of various species of parasites and viruses has been proven to be susceptible to temperature and its variability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChagas disease is a neglected tropical disease caused by the flagellate protozoan \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e, with 6\u0026nbsp;million people estimated to be infected worldwide, predominantly in areas endemic to Latin America [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The primary mode of transmission for this protozoan involves contact with contaminated feces or urine from hematophagous insect vectors belonging to the subfamily Triatominae (Hemiptera: Reduviidae) with mucous membranes or wounds in mammals [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, transmission can occur through other routes such as blood transfusions, organ transplants, congenital means, laboratory accidents, and orally in natural reservoirs [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Recent human epidemiological outbreaks have highlighted oral transmission as a significant route of \u003cem\u003eT. cruzi\u003c/em\u003e transmission [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eTriatoma infestans\u003c/em\u003e is the most important vector in southern South America [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The wild foci of \u003cem\u003eT. infestans\u003c/em\u003e have been documented in Bolivia [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Argentina [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], Paraguay [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and Chile [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The presence of wild foci hinders disease control initiatives because these foci play a crucial role in the recolonization of peridomestic and domestic habitats [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In addition, the projected rise in temperature due to climate change could potentially exacerbate the source of individuals from wild foci, as it may lead to an increase in their activity or abundance. Winter temperature has been proposed as the most limiting factor in terms of population growth [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eT. infestans\u003c/em\u003e inhabits peridomestic habitats that can buffer temperature fluctuations, and its thermal preference lies between 25\u0026deg;C and 29\u0026deg;C [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], which is easily reached inside houses, but its activity ranges between 18\u0026deg;C and 42\u0026deg;C [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Currently, the minimum mean winter temperature in the central region of Chile, where wild \u003cem\u003eT. infestans\u003c/em\u003e foci have been reported, is 10.7\u0026deg;C but the minimum mean temperature is 4\u0026deg;C in the coldest month, below the \u003cem\u003eT. infestans\u003c/em\u003e activity range [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, climate change projections estimate a temperature increase ranging from 2\u0026deg;C to 5\u0026deg;C in the worst-case scenario [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Considering the buffering effect of peridomestic structures and houses (i.e., up to \u0026plusmn;\u0026thinsp;5\u0026deg;C to \u0026plusmn;\u0026thinsp;8\u0026deg;C), the minimum temperature from the coldest month could fall within the range of \u003cem\u003eT. infestans\u003c/em\u003e activity as a consequence of climate change. Since increases in mean temperature due to climate change are expected in southern South America, these shifts in temperatures could potentially expand habitat suitability for \u003cem\u003eT. infestans\u003c/em\u003e beyond current expectations. However, it is crucial to consider the complex interplay between the factors that influence the distribution and presence of \u003cem\u003eT. infestans\u003c/em\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTemperature is a major abiotic factor in \u003cem\u003eT. cruzi\u003c/em\u003e-triatomine interactions [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The development of \u003cem\u003eT. cruzi\u003c/em\u003e and its relationship with temperature have been studied under field and laboratory conditions [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], revealing a positive relationship between environmental temperature and the concentration of infectious forms of \u003cem\u003eTriatoma proctata\u003c/em\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], \u003cem\u003eT. infestans\u003c/em\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and \u003cem\u003eRhodnius prolixus\u003c/em\u003e [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. An increase in temperature reduces the incubation time of parasites within the vector [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Field studies in Northwestern Argentina have recorded higher numbers of infectious forms of \u003cem\u003eT. infestans\u003c/em\u003e during warm months [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, the effect of temperature variability on \u003cem\u003eT. infestans\u003c/em\u003e parasite development has never been tested under controlled conditions, despite its relevance in the context of climate change. Therefore, in this study, we aimed to evaluate the influence of temperature and its variability on the EIP and parasite load of \u003cem\u003eT. cruzi\u003c/em\u003e in \u003cem\u003eT. infestans\u003c/em\u003e and the probability of \u003cem\u003eT. cruzi\u003c/em\u003e-positive dejections samples over time.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA total of 144 fourth-instar \u003cem\u003eT. infestans\u003c/em\u003e nymphs were obtained from a colony that had been maintained for two years at the Laboratorio de Ecolog\u0026iacute;a, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile. This colony was originally established in 1950 at the Faculty of Medicine at the same university. The fourth instar is easily manageable and can transmit \u003cem\u003eT. cruzi\u003c/em\u003e to vertebrate hosts [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The specimens were maintained individually in plastic flasks (3.8 cm x 6.8 cm) inside climatic chambers at 27\u0026deg;C with a photoperiod of 12 L:12D and 50% to 70% humidity. Prior to feeding on infected mice, the triatomines were fasted for 15 days.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTriatomine infection with\u003c/b\u003e \u003cb\u003eTrypanosoma cruzi\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTriatomines were infected by feeding on mice previously inoculated with \u003cem\u003eT. cruzi\u003c/em\u003e clone Dm28c (DTU-TcI) under controlled laboratory conditions. The Trypomastigote forms of \u003cem\u003eT. cruzi\u003c/em\u003e were cultivated in Vero cells (60\u0026ndash;70% confluence), 5 ml of RPMI 1640 culture medium (Biological Industries) with 10% fetal bovine serum, 2 ml of trypsin, 1 ml of penicillin, and 100 \u0026micro;g/mL of streptomycin (Biological Industries), following the protocol from Valenzuela et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Fourteen 8-week-old female BALB/c mice were inoculated only once by intraperitoneal injection with 1,000 parasites suspended in 1 mL of RPMI 1640 medium, and the parasite concentration was determined by light microscopy using a Neubauer chamber. After inoculation, mice were housed in cages at 20\u0026ndash;25\u0026deg;C and 40\u0026ndash;70% relative humidity, with free access to water and food, following the approved bioethics protocol (N\u0026ordm;: 19262-MED-UCH). Mouse parasitemia was evaluated, 0.1 ml blood sample was extracted from the tail vein to measure parasite concentration and viability using light microscopy (40X) with a Neubauer chamber every 3 days from day 7 post-infection until reaching 80,000-500,000 trypomastigotes/ml to ensure triatomine infection. Once reached, the insects were fed on these \u003cem\u003eT. cruzi\u003c/em\u003e inoculated mice. Mice were sedated following the bioethics protocol (N\u0026ordm;: 19262-MED-UCH) with a dose of ketamine 100 mg/ml (mouse dose 100 mg/kg) in association with xylazine 5 mg/ml (mouse dose 10 mg/kg) and then placed inside a plastic box (32 \u0026times; 21 \u0026times; 14 cm) over a heating pad. Triatomines were fed during the dark phase and placed in the same plastic box as the mouse for 30 minutes in the dark. Each triatomine was weighed before and immediately after feeding using a BOECO Model BAS-31 (precision\u0026thinsp;=\u0026thinsp;0.0001 g) to check for blood ingestion during the experimental procedure. All individuals were included in the treatments regardless of the volume of blood ingested. Each mouse was fed with a group of 12 \u003cem\u003eT. infestans\u003c/em\u003e individuals. Insects were individualized with colored paint according to the protocol [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eTemperature treatments\u003c/h2\u003e\u003cp\u003eAfter feeding on infected mice, all triatomines were randomly allocated to one of the four thermal treatments (i.e., 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36), 18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36), 27\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36), and 27\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36)). The total maintenance time under these treatments was 42 days post infection (p. i.). After feeding on infected mice, triatomines were randomly assigned to one of four thermal treatments: 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36), 18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36), 27\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36), and 27\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C (n\u0026thinsp;=\u0026thinsp;36). Each treatment group comprised 36 individuals, with each insect housed separately in a plastic vial (3.8 \u0026times; 6.8 cm). Environmental chambers were set to the target temperatures with an accuracy of \u0026plusmn;\u0026thinsp;1\u0026deg;C and a precision of 0.2\u0026deg;C (PITEC), under identical photoperiod and humidity conditions, as previously described. Each vial was labeled with a unique identification code indicating the individual insect, its assigned thermal treatment, and source mouse. The individual identification system was maintained throughout the experiment. Temperature treatments were chosen based on the average temperature of the central zone of Chile [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], where populations of the species have historically been detected, both in domestic and wild environments [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and the temperature of the insect microsite habitat [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Therefore, low winter temperatures have been proposed as the most limiting factor in \u003cem\u003eT. infestans\u003c/em\u003e population growth [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The lowest acclimation temperature considered the mean winter temperature (10.7\u0026deg;C), plus a 5\u0026deg;C of microhabitat buffer, and an additional 2\u0026deg;C reflecting projected temperature increases under climate change. Individuals were acclimated to these low temperatures with and without the inclusion of temperature variability (i.e., 18\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C and 18\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C, respectively). We also acclimated individuals to their optimal temperature, with and without temperature variability (i.e., 27\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C and 27\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C, respectively), which is the inside-house temperature [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In the variable temperature treatments, the rise in temperature occurred during the light hours and started to increase linearly at 7:00 h, reaching a maximum at 8:00 h (32\u0026deg;C and 23\u0026deg;C for warm and cold treatments, respectively), then remained constant, and began to decrease at 19:00 h, reaching its minimum at 20:00 h (22\u0026deg;C and 13\u0026deg;C for warm and cold treatments, respectively). Additionally, the range of temperatures used in our experiments was well within the thermotolerance range of the species, avoiding individual mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The diurnal variation range selected was conservative with respect to the diurnal daily variation of Chile's Central Region [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] but was chosen because the animals inside the climatic chambers cannot use behavioral thermoregulation. In addition, these temperatures were previously used what will allow to integrate information for future studies on the effects of climate change on \u003cem\u003eT. infestans\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Temperature and humidity were monitored daily using climatic chamber sensors. The photoperiod lights were switched on at 7:00 and turned off at 19:00.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDejection sampling\u003c/h3\u003e\n\u003cp\u003eEvery two days, each individual was transferred to a new, dated flask labeled with its corresponding identification code. Simultaneously, the nymphal state was recorded for all individuals in the experiment. Excretion samples were placed in Eppendorf tubes, labeled, and recorded according to the identification code, date of collection, and number of fecal samples collected in the flask. Each fecal sample was diluted with nuclease-free water (40 \u0026micro;L per excretion). The contents of the solution were subsequently transferred to 1.5 ml Eppendorf tubes labeled with the insect ID and collection date. These tubes were then stored at -20\u0026deg;C until DNA extraction. Kollien \u0026amp; Shaub [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] observed that a short-term starvation period of 30 days led to the initial appearance of deceased flagellates in the rectum of Triatomines, whereas a starvation period of 90 days resulted in a mortality rate of 99.5% of \u003cem\u003eT. cruzi\u003c/em\u003e in the rectum. Thus, triatomines were fed on non-infected laboratory mice (\u003cem\u003eMus musculus\u003c/em\u003e) at day 30 post-infection using the same protocol used for triatomine infection. We measured the weight before and after feeding, and the nymphal state. After the experimental period (42 days), the insects were euthanized by freezing for a minimum of 48 h following the bioethical protocol (N\u0026ordm; 19262-MED-UCH).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrypanosoma cruzi\u003c/b\u003e \u003cb\u003edetection in dejections\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe extracted \u003cem\u003eT. cruzi\u003c/em\u003e DNA from \u003cem\u003eT. infestans\u003c/em\u003e dejections using the innuPREP Blood DNA Mini Kit (Analityk Jena AG), following the manufacturer\u0026rsquo;s instructions. All samples were co-extracted with 1 pg/\u0026micro;L of a 183 bp synthetic sequence from \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (Brassicales: Brassicaceae), synthesized by gBlocks Gene Fragments (IDT), and used as an exogenous internal amplification control to evaluate the carryover of inhibitors and DNA loss in the extraction process.\u003c/p\u003e\u003cp\u003eThe qPCR assays amplified the satellite nuclear conserved region of \u003cem\u003eT. cruzi\u003c/em\u003e with the primers Cruzi 1 (5 'ASTCGGCTGATCGTTTTCGA 3') and Cruzi 2 (5 'AATTCCTCCAAGCAGCGGATA 3') [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] in a final volume of 20 \u0026micro;L, containing 5 \u0026micro;L of template DNA, 5X HOT FIREPol EvaGreen qPCR Mix Plus (Solis BioDyne), 0.3 \u0026micro;M of each primer, and nuclease-free water. The cycling conditions were a pre-incubation for 15 min at 95\u0026deg;C, followed by 40 cycles of denaturation step of 95\u0026deg;C for 15 s, a hybridization step of 60\u0026deg;C for 20 s, and an extension step of 72\u0026deg;C for 20 s in a Rotor-Gene Q (QIAGEN) thermal cycler. The emitted fluorescence was recorded at the end of each cycle, and a melting curve was generated at the end of the program, with a ramp from 72 \u0026deg; C to 95 \u0026deg;, increasing by 1\u0026deg;C in each step, waiting for 90 s of pre-melting conditioning in the first step, and 5 s for each subsequent step. Each reaction was carried out with a negative control (infection-free triatomine dejection\u0026rsquo;s DNA), a positive control (\u003cem\u003eT. cruzi\u003c/em\u003e DNA quantification standard), and a no-template control (nuclease-free water). All samples were analyzed in duplicate.\u003c/p\u003e\u003cp\u003eThe DNA standard curve for absolute quantification was obtained from the genomic DNA of \u003cem\u003eT. cruzi\u003c/em\u003e strain DM28c. The calculation was performed considering that a parasite cell contains approximately 200 fg of DNA [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Serial 1/10 dilutions were made with nuclease-free water to cover a range of 106 to 0.1 parasite equivalents/mL. All samples were co-extracted with 1 pg/\u0026micro;l of a sequence of 183 bp from the tonoplast intrinsic protein 5;1 (TIP 5;1) of \u003cem\u003eA. thaliana\u003c/em\u003e to normalize the parasite load, as previously described [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Quantification of the parasite equivalents from the DNA samples was performed by considering the amplification of the \u003cem\u003eT. cruzi\u003c/em\u003e DNA standard curve, and the results were normalized according to the results of the exogenous IAC.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003eExtrinsic incubation period (EIP)\u003c/h2\u003e\u003cp\u003eOnly the first positive dejection sample for each individual was used to estimate the effect of temperature on the EIP of \u003cem\u003eT. cruzi\u003c/em\u003e in \u003cem\u003eT. infestans\u003c/em\u003e (total n\u0026thinsp;=\u0026thinsp;108; 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C n\u0026thinsp;=\u0026thinsp;23; 18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C n\u0026thinsp;=\u0026thinsp;28; 27\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C n\u0026thinsp;=\u0026thinsp;29; 27\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C n\u0026thinsp;=\u0026thinsp;28). Generalized Linear Models (GLM) with a gamma function were fitted with EIP as a response variable and temperature treatment (T), individual body mass before infection (mb), ingested blood during infection (bi), estimated by subtracting the weight before and after feeding, and mouse parasitemia (P\u003csub\u003em\u003c/sub\u003e) as predictors. Model adequacy was evaluated using the likelihood ratio test (LRT) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], while model selection was conducted based on the Akaike information criterion (AIC) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. To compare the treatment groups, post hoc analyses were performed using Tukey\u0026rsquo;s test with Shaffer-adjusted p-values.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eParasite load in T. infestans dejections\u003c/h3\u003e\n\u003cp\u003eAll positive dejection samples were included in this analysis (n\u0026thinsp;=\u0026thinsp;307), with a total of 50 samples for 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C treatment, 74 for 18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C, 97 for 27\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u0026deg;C, and 86 for the 27\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C treatment. Generalized Additive Models (GAM) with a lognormal distribution were fitted for the parasite load (Par-eq\u003csub\u003eml\u003c/sub\u003e) as the response variable. In these models, the temperature treatment (T), body mass before infection (mb), ingested blood during infection (bi), time in days of the collected sample post-infection (Days), and mouse parasitemia (P\u003csub\u003em\u003c/sub\u003e) were predictors. Cubic splines were used to capture the nonlinear relationships between each of the following variables and parasite load over time: temperature treatment (T), ingested blood (bi), and body mass before infection (mb). We also included a random effect for triatomine individuals (ID) to correct for the pseudo-replication of data. The model was selected according to the Akaike information criterion (AIC) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eProbability of positive dejections\u003c/h2\u003e\u003cp\u003eWe analyzed the qPCR test results from all dejection samples (n\u0026thinsp;=\u0026thinsp;438) to determine their positive or negative \u003cem\u003eT. cruzi\u003c/em\u003e qPCR test results, generating a binary variable. In this analysis, all insects that fed on infected blood were included regardless of their infection status. To test the probability of positive dejections over time according to the different temperature treatments of individuals, we performed a Generalized Additive Model for Location, Scale, and Shape (GAMLSS) method for statistical analyses [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. This method does not require a priori assumptions regarding the shape of the relationship between the response variable and time (i.e., the probability of positive dejections in time), fitting a cubic spline (cs). Thus, the qPCR test results were the response variable, while temperature treatment (T), mouse parasitemia (P\u003csub\u003em\u003c/sub\u003e), body mass (mb), ingested blood (bi), a cubic spline of the time (Days), and their interactions were the explanatory variables. We also included a random effect for individuals (ID) to correct the pseudo-replication of the data given by the effect of repeated measurements. In GAMLSS, the functions of the variables are unknown smooth functions, providing additional flexibility in the modeling process for a binary response variable. We performed models assuming a binomial distribution with \u0026lsquo;RS\u0026rsquo; algorithm [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. All analyses were performed in R version 2.13.0 [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] using RSTUDIO and ggplot2, lmtest, multcomp, mgcv, pROC, and gamlss packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 144 individuals were included in the study: 108 were positive and 36 negative, resulting in 75% positive individuals and 25% negative individuals. A total of 499 dejections were obtained, of which 326 were positive and 173 were negative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtrinsic incubation period (EIP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA generalized linear model (GLM) with gamma distribution and inverse link function was used to evaluate the effects of thermal treatment (T), ingested blood during infection (bi), body mass before infection (mb), and their interaction on the extrinsic incubation period (EIP) of \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e in \u003cem\u003eTriatoma infestans\u003c/em\u003e. The analysis included only individuals who tested positive at least once during the 42-day post-infection period (n = 108). The model revealed that the thermal treatment significantly affected the EIP (Table 1 and Fig. 1). Compared with the reference group (18 ± 0 ºC), both the warm constant (27 ± 0 ºC) and warm variable (27 ± 5 °C) treatments were associated with significantly shorter EIP values (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005 for both). The generalized linear model showed a substantial improvement in fit compared to the null model (null deviance = 39.26, df = 107; residual deviance = 22.39, df = 101; AIC = 698.34), indicating that the selected predictors significantly contributed to explaining the variation in the extrinsic incubation period of \u003cem\u003eT. cruzi\u003c/em\u003e in \u003cem\u003eT. infestans\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTukey test showed a significant difference between the 27 and 18 ºC treatments, either being constant or variable (27 ± 0 ºC - 18 ± 0 ºC, \u003cem\u003ez\u0026nbsp;\u003c/em\u003e= 4.525, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; 27 ± 5 ºC -18 ± 0 ºC \u003cem\u003ez\u0026nbsp;\u003c/em\u003e= 3.819, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; 27 ± 0 ºC - 18 ± 5 ºC\u003cem\u003e\u0026nbsp;z\u003c/em\u003e = 3.994, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; 27 ± 5 ºC - 18 ± 5 ºC \u003cem\u003ez\u0026nbsp;\u003c/em\u003e= 3.214, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.005). However, the Tukey test did not show significant differences between the constant and thermally variable treatments within the same mean temperature (18 ± 0 °C–18 ± 5 °C, \u003cem\u003ez\u003c/em\u003e =\u0026nbsp;0.632, \u003cem\u003eP\u003c/em\u003e = 0.580 and 27 ± 0 °C–27 ± 5 °C \u003cem\u003ez\u0026nbsp;\u003c/em\u003e=\u0026nbsp;-1.05, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.580) (Fig. 1, Supplementary material: Table S2). The predicted values for the EIP were 30 days (±\u0026nbsp;3.27)\u0026nbsp;for the cold constant treatment, 27 days (±\u0026nbsp;2.86)\u0026nbsp;for the cold variable treatment, 12 days (± 1.28)\u0026nbsp;for the warm constant treatment, and 14 days (± 1.49)\u0026nbsp;for the warm variable treatment (Table 1). An increase in ingested blood (bi) reduced EIP, and the effect was greater for cold treatments (Supplementary material: Fig. S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParasite load in dejection samples of \u003cem\u003eT. infestans\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe parasite load varied with temperature (T), time post-infection (Days), body mass (mb), and the interaction term between body mass (mb) and ingested blood at the time of infection (bi) (Table 2). The smooth term selected (Days, bs = \"cs,” by = T) implies that the parasite load varied among temperature treatments (T) as time went by, and it was significant for all four treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005). Thus, the parasite load followed a bell-shaped curve: it started low, peaked between 20 and 30 days, and then declined (Fig. 3). In addition, the effect of temperature treatment on parasite loads varied across groups. Compared to the 18 ± 0 °C group, insects maintained at 27 ± 0 °C and 27 ± 5 °C exhibited significantly higher parasite loads (Estimate = 2.57 and 2.53, respectively; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), whereas no significant difference was observed for the 18 ± 5 °C group (\u003cem\u003eP\u003c/em\u003e = 0.964) (Table 2 and Fig. 3). The interaction between body mass and the amount of blood ingested was significant in all treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.005), except for 27 ± 5 °C (\u003cem\u003eP\u003c/em\u003e = 0.229), and described different bell-shaped curves for each treatment. The interaction between blood ingested (bi) and insect body mass (mb) over parasite load was most evident in insects with low body mass. Hence, smaller individuals who ingested lower blood volumes at the time of infection exhibited lower parasite loads than those who ingested intermediate volumes. This effect gradually diminished as body mass increased and was not marked in larger insects. In fact, the relationship was reversed in the heaviest individuals (Supplementary material: Fig. S2). Altogether, according to the best-competing model (Supplementary Material: Table S3), the parasite load trend changed with time after infection (Day) and was affected by the amount of ingested blood (bi), temperature treatment (T), and body mass (mb) (Table 2). The deviance of this model explained 64.3% of the variance, indicating a good fit for the parasite load in \u003cem\u003eT. infestans\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProbability\u003c/strong\u003e \u003cstrong\u003eof positive dejections\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected 438 dejection samples from 144 \u003cem\u003eT. infestans\u003c/em\u003e individuals; 131 samples were negative and 307 were positive (30% and 70%, respectively). At 18 ± 0 °C, 28 out of 36 insects provided 78 samples; at 18 ± 5 °C, 33 out of 36 insects provided 117 samples; at 27 ± 0 °C, 31 out of 36 individuals provided 117 samples; and finally, at 27 ± 5 °C, all 36 individuals provided samples, with a total of 126 samples. We found significant differences in the probability of a positive result between warm and cold treatments. However, we did not find significant differences between constant and thermally variable treatments within the same temperature range (27 ± 0 °C and 27 ± 5 °C; 18 ± 0 °C and 18 ± 5ºC) (Table 3, Fig. 3).\u003c/p\u003e\n\u003cp\u003eThe best model fit for the probability of \u003cem\u003eT. cruzi\u003c/em\u003e-positive dejections included a cubic spline on time (Days) with an interaction between temperature treatments (T) (Supplementary material: Table S3), showing differences between warm and cold treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Fig. 3 and Table 3). There was no significant difference in the probability of positive dejections between the treatments with the same mean temperature (Table 3 and Fig. 3). The GAMLSS model indicated that time (Days) did not significantly affect the probability of positive results among the cold treatments (\u003cem\u003eP\u003c/em\u003e = 0.967). However, for the warm treatments, the time was significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 3), and as time passed (Days), the probability of positive samples increased (Fig. 3), as represented by the interaction between time (Days) and temperature treatment (T) (Table 3). For these warm treatments, the probability of obtaining negative samples decreased with time and became practically improbable after day 10. In contrast, for individuals acclimated to low temperatures, the probability of finding negative samples in these treatments was maintained throughout the study period (Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the refeeding procedure with uninfected mice (dotted black line), the mean blood ingested by individuals was 0.037 g (± 0.09) at 18 ± 5 °C, 0.046 g (± 0.05) at 18 ± 0 °C, 0.040 g (± 0.04) at 27 ± 0 °C, and 0.068 g (± 0.07) at 27 ± 5 °C. Feeding was considered when \u0026gt; 0.0001 g was consumed. Note that some individuals were removed from the experiment because of mortality; therefore, not all treatments included 36 individuals. Nymphal molts occurred primarily in the warm treatments at 27 ± 0 °C (13 molts) and 27 ± 5 °C (12 molts). All molts reached stage V and occurred 13 days post-infection. In the cold treatment, only one molt was observed in the 18 ± 0 °C treatment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results showed that as the temperature increased, the time at which the first positive sample was detected decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, temperature significantly changed the extrinsic incubation period (EIP) of \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e within \u003cem\u003eTriatoma infestans\u003c/em\u003e, but temperature variability did not have a significant effect. Furthermore, the probability of positivity of the dejections changed with temperature but not with variability (Fig.\u0026nbsp;4). The results also showed different patterns in the parasite load over time between the different heat treatments, both constant and variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A reduction in EIP owing to high environmental temperatures has been observed in three different species of triatomines, including \u003cem\u003eT. infestans\u003c/em\u003e [\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], which is also supported by our results. \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e parasites reared \u003cem\u003ein vitro\u003c/em\u003e at four different temperatures (21\u0026deg;C, 24\u0026deg;C, 27\u0026deg;C and 30\u0026deg;C) increased in number in direct relation to temperature [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Unfortunately, \u003cem\u003ein vitro\u003c/em\u003e experiments do not include triatomine immune factors or their microbiota among other factors that affect the actual development of \u003cem\u003eT. cruzi\u003c/em\u003e in wild vectors [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe did not observe that daily thermal variation of 10\u0026deg;C affected the extrinsic incubation period of \u003cem\u003eT. cruzi\u003c/em\u003e in \u003cem\u003eT. infestans\u003c/em\u003e. Populations are expected to evolve physiological adaptations to local climatic conditions in heterogeneous environments [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], exhibiting plastic strategies that allow them to survive a broad range of temperatures [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. This is the case for \u003cem\u003eT. infestans\u003c/em\u003e and its parasite that inhabit environments subjected to major temperature variability [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], such as Chaco in Argentina [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] and others along its geographic distribution. In these regions, environmental temperature varies in a range that surpasses by more than 10\u0026deg;C those applied in our temperature variable treatments; therefore, both vectors and parasites are probably adapted to thermal variation. Moreover, \u003cem\u003eT. cruzi\u003c/em\u003e is continuously exposed to temperature changes throughout its life cycle [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e](Marli\u0026eacute;re et al., 2015), because insect vectors do not physiologically regulate their body temperature and ingest warm blood periodically [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Thus, the parasite must be adapted to grow at a wide range of temperatures. In addition, the parasite alternates between insect vectors and mammalian endothermic hosts [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEIP was also affected by the amount of blood ingested, with a negative relationship between the amount of infected blood ingested and the time of the first infectious excreta (Supplementary material: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Kissing bugs typically consume a large amount of blood in a single meal, resulting in a high consumption of hemoglobin [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]; a higher intake of this metalloprotein could explain the negative relationship between EIP and ingested blood, since hemoglobin participates in the replication and survival of the parasite [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Previous studies found that blood meal size was positively correlated with parasite concentration in the excreted urine of subsequent nymphal instars of \u003cem\u003eRhodnius prolixus\u003c/em\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, at higher intake levels, the volume of ingested blood had a reduced impact on the extrinsic incubation period (EIP) (Supplementary material: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This is likely because the parasite has already accessed all the nutrients it requires, reaching its maximum development rate, indicating a threshold beyond which additional blood consumption does not further reduce the EIP.\u003c/p\u003e\u003cp\u003eOur study is the first to describe a bell-shaped pattern of parasite population dynamics during the first days of \u003cem\u003eT. infestans\u003c/em\u003e infection. However, previous studies have demonstrated a positive linear relationship between parasite load and time [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Nevertheless, these studies are not directly comparable to ours because one of them used optical microscopy and a different strain [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], whereas the other used another vector species, \u003cem\u003eR. prolixus\u003c/em\u003e, with its tissue samples obtained instead of dejections [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Prolonged fasting can significantly reduce parasite loads in triatomines [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], which explains the decrease in parasite load across days. However, in our experiments, the reduction in parasite load continued even after the insects were refed uninfected blood on day 30 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Another plausible explanation is the secretion of antibacterial proteins in response to microbial shifts following blood ingestion [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Kollien and Schaub [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] proposed that the available surface area of the rectal epithelium is also a limiting factor for \u003cem\u003eT. cruzi\u003c/em\u003e multiplication, and once the available space becomes saturated, further parasite development may be restricted. It is more likely that nutrient depletion initiated the decline of parasites within \u003cem\u003eT. infestans\u003c/em\u003e, with the immune response subsequently contributing to a further reduction in parasite load after feeding [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. However, \u003cem\u003eT. cruzi\u003c/em\u003e dynamics within triatomines involves a complex interplay that also includes alterations in the gut microbiota of insects. Azambuja et al. [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] demonstrated that bacteria can exert a trypanolytic effect and that blood ingestion can modify the bacterial composition in the midgut of triatomines. Consequently, these interactions are highly intricate, necessitating further studies to elucidate these interactions.\u003c/p\u003e\u003cp\u003eRemarkably, a bell-shaped curve occurred in all temperature treatments, but with different levels of parasite load. The warm-temperature treatments presented a higher parasite load than the cold treatments during the entire study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Within the cold treatments, the effect of variable temperatures on parasite populations was evident, showing an increase in the parasite load (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This was also seen in the malaria vector \u003cem\u003eAnopheles stephensi\u003c/em\u003e infected with \u003cem\u003ePlasmodium chabaudi\u003c/em\u003e since temperature variability influenced the number of sporozoites per oocyst and those circulating in the hemocele. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This is probably due to the activation of insect metabolism [\u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e] triggered by the higher temperature from the cold variable treatment (i.e., 23\u0026deg;C), which is close to its preferred range [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This metabolic activation could lead to \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e activation because the number of parasites increases in direct relation to temperature [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Therefore, daily temperature increases near the preferred thermal range might activate both the vector and parasite. Furthermore, the warm-variable treatment showed an earlier appearance of the parasite load peak than the warm-constant temperature treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is likely that the maximum daily temperature within the variable treatment (32\u0026deg;C) exerts a more significant influence on the development and emergence of the parasite than the potential delaying effect of the minimum daily temperature (22\u0026deg;C). Consequently, the fluctuating temperature conditions in this treatment may have resulted in a more rapid onset of the parasite development. High temperatures weaken the immune system of triatomines, decreasing the activity of prophenoloxidase (proPO) and the enzymatic cascade of phenoloxidase (PO), which is involved in the defense mechanism against pathogens [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. This could influence the degree of infection within the vector early, and only in the warm variable treatment, since it reached 32\u0026deg;C during the day, the other thermal treatments did not reach temperature values that could affect the proPO response [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. In summary, environmental variability could potentially influence individual susceptibility to \u003cem\u003eT. cruzi\u003c/em\u003e infection because coping with temperature fluctuations probably had a negative impact on the immune system of these insects. Previous studies have shown a decrease in the survival and fecundity of \u003cem\u003eR. prolixus\u003c/em\u003e in a thermally variable environment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and a decreased low-temperature performance of \u003cem\u003eT. infestans\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, environmental temperatures play a crucial role in mediating the outcome of host\u0026ndash;parasite interactions, and these interactions are multifactorial and complex.\u003c/p\u003e\u003cp\u003eFinally, we found a significant effect of temperature on the probability of obtaining positive samples without a temperature variability effect, which was similar to that found on the EIP. We also observed that, for insects maintained at low temperatures, there was greater heterogeneity in the probability of finding positive dejections, showing an individual effect. Beyond day 5 post-infection in the warm treatments, all individuals tested positive, whereas individuals subjected to cold treatments exhibited more heterogeneous distributions over time, suggesting individual variation. Neither cold nor warm treatments exhibited significant differences when compared to their respective constant counterparts, suggesting a similar impact on the probability of infection. This pattern is consistent with observations from the extrinsic incubation period (EIP). It is possible that the interaction between temperature and parasite load influences the likelihood of obtaining positive samples and producing the observed heterogeneity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we observed that different temperatures (warm and cold), along with their variability, led to an increase in the parasite load on \u003cem\u003eTriatoma infestans\u003c/em\u003e, modifying its EIP, which can potentially alter the basic reproductive number (R\u003csub\u003e0\u003c/sub\u003e) of Chagas and the number of secondary cases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the context of climate change, this vector may become more effective in transmitting Chagas disease in the near future. Nevertheless, it is important to acknowledge that temperature fluctuations can affect the survival and vital rates of insects, especially when they are outside their optimal thermal thresholds [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e](Carrington et al., 2013), and that other factors may play a role in climate change, such as changes in precipitation and extreme climatic events, which may alter population parameters. Nonetheless, we hope that these results will contribute to a more complete understanding of the impact of increased temperature and its variability on this Chagas disease vector.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Mauricio Canals for providing \u003cem\u003eT. infestans\u0026nbsp;\u003c/em\u003eindividuals from the Universidad de M\u0026eacute;dicina colony of Universidad de Chile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgencia Nacional de Investigaci\u0026oacute;n y Desarrollo (ANID) Fondecyt Iniciaci\u0026oacute;n 11160839 to SCB; Fondecyt Regular 1180940 to PC and Fondecyt Regular 1210359 to JDM. ANID - Programa Becas-Beca Doctorado Nacional - grant number 21171202 to MS and Doctorado Becas Chile 2019 - grant number 72200391 to AB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBA-D: Conceptualization, Formal analysis, Investigation, Methodology and Writing - original draft; SCB: Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, and Writing - Review \u0026amp; Editing; LV-P: Methodology, Resources and Review \u0026amp; Editing; JDM: Methodology and Resources; MS: Methodology and Investigation; SO: Investigation; CM-S: Methodology and Investigation; AB: Formal analysis; PEC: Conceptualization and Resources, Validation, Supervision, Funding Acquisition - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal procedures were conducted according to the Chilean Animal Protection Law (Ley N\u0026deg; 20.380) and the Guide for the Care and Use of Laboratory Animals (Albus, 2012). The Scientific Ethical Committee for Research Safety of the Universidad de Chile approved all animal protocols used in this study (Protocol N\u0026ordm;: 19262-MED-UCH for Biosecurity and Ethical protocols).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData access\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be held on Figshare after acceptance 10.1101/2023.11.08.566164, private link https://figshare.com/s/a0011e5254aab62dfc74\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAyala S, Alvarado S, C\u0026aacute;ceres D, Zulantay I, Canals M. Estimando el efecto del cambio clim\u0026aacute;tico sobre el riesgo de la enfermedad de Chagas en Chile por medio del n\u0026uacute;mero reproductivo. Rev M\u0026eacute;dica Chile. 2019;147:683\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eAraya-Osses D, Casanueva A, Rom\u0026aacute;n-Figueroa C, Uribe JM, Paneque M. Climate change projections of temperature and precipitation in Chile based on statistical downscaling. Clim Dyn. 2020;54:4309\u0026ndash;30. https://doi.org/10.1007/s00382-020-05231-4.\u003c/li\u003e\n\u003cli\u003eLafferty KD. The ecology of climate change and infectious diseases. Ecology. 2009;90:888\u0026ndash;900. https://doi.org/10.1890/08-0079.1.\u003c/li\u003e\n\u003cli\u003ePatz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature. 2005;438:310\u0026ndash;7. https://doi.org/10.1038/nature04188.\u003c/li\u003e\n\u003cli\u003ePatz JA, Epstein PR, Burke TA, Balbus JM. Global climate change and emerging infectious diseases. JAMA. 1996;275:217\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eStruchiner CJ, Rockl\u0026ouml;v J, Wilder-Smith A, Massad E. Increasing Dengue Incidence in Singapore over the Past 40 Years: Population Growth, Climate and Mobility. PloS One. 2015;10:e0136286. https://doi.org/10.1371/journal.pone.0136286.\u003c/li\u003e\n\u003cli\u003eTjaden NB, Suk JE, Fischer D, Thomas SM, Beierkuhnlein C, Semenza JC. Modelling the effects of global climate change on Chikungunya transmission in the 21st century. Sci Rep. 2017;7:3813. https://doi.org/10.1038/s41598-017-03566-3.\u003c/li\u003e\n\u003cli\u003eMordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, et al. Thermal biology of mosquito-borne disease. Ecol Lett. 2019;22:1690\u0026ndash;708. https://doi.org/10.1111/ele.13335.\u003c/li\u003e\n\u003cli\u003ePaz S. Climate change impacts on West Nile virus transmission in a global context. Philos Trans R Soc Lond B Biol Sci. 2015;370. https://doi.org/10.1098/rstb.2013.0561.\u003c/li\u003e\n\u003cli\u003eGuis H, Caminade C, Calvete C, Morse AP, Tran A, Baylis M. Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe. J R Soc Interface. 2012;9:339\u0026ndash;50. https://doi.org/10.1098/rsif.2011.0255.\u003c/li\u003e\n\u003cli\u003eChalghaf B, Chemkhi J, Mayala B, Harrabi M, Benie GB, Michael E, et al. Ecological niche modeling predicting the potential distribution of Leishmania vectors in the Mediterranean basin: impact of climate change. Parasit Vectors. 2018;11:461. https://doi.org/10.1186/s13071-018-3019-x.\u003c/li\u003e\n\u003cli\u003eKulkarni MA, Desrochers RE, Kajeguka DC, Kaaya RD, Tomayer A, Kweka EJ, et al. 10 Years of Environmental Change on the Slopes of Mount Kilimanjaro and Its Associated Shift in Malaria Vector Distributions. Front Public Health. 2016;4:281. https://doi.org/10.3389/fpubh.2016.00281.\u003c/li\u003e\n\u003cli\u003eDamborsky MP, Bar ME, Gorla D. Life cycle and reproductive patterns of Triatoma rubrovaria (Blanchard, 1843) (Hemiptera: Reduviidae) under constant and fluctuating conditions of temperature and humidity. Rev Soc Bras Med Trop. 2005;38:433\u0026ndash;7. https://doi.org/10.1590/s0037-86822005000500014.\u003c/li\u003e\n\u003cli\u003eShapiro LLM, Whitehead SA, Thomas MB. Quantifying the effects of temperature on mosquito and parasite traits that determine the transmission potential of human malaria. PLoS Biol. 2017;15:e2003489. https://doi.org/10.1371/journal.pbio.2003489.\u003c/li\u003e\n\u003cli\u003eCiota AT, Matacchiero AC, Kilpatrick AM, Kramer LD. The effect of temperature on life history traits of Culex mosquitoes. J Med Entomol. 2014;51:55\u0026ndash;62. https://doi.org/10.1603/me13003.\u003c/li\u003e\n\u003cli\u003eStillwell RC, Fox CW. Geographic variation in body size, sexual size dimorphism and fitness components of a seed beetle: local adaptation versus phenotypic plasticity. Oikos. 2009;118:703\u0026ndash;12. https://doi.org/10.1111/j.1600-0706.2008.17327.x.\u003c/li\u003e\n\u003cli\u003eMasson-Delmotte VP, Zhai P, Pirani SL, Connors C, P\u0026eacute;an S, Berger N, et al. IPCC 2021: Summary for policymakers. in: Climate change 2021: The physical science basis. contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. 2021.\u003c/li\u003e\n\u003cli\u003eClavijo-Baquet S, Cavieres G, Gonz\u0026aacute;lez A, Cattan PE, Bozinovic F. Thermal performance of the Chagas disease vector, Triatoma infestans, under thermal variability. PLoS Negl Trop Dis. 2021;15:e0009148. https://doi.org/10.1371/journal.pntd.0009148.\u003c/li\u003e\n\u003cli\u003eRolandi C, Schilman PE. The costs of living in a thermal fluctuating environment for the tropical haematophagous bug, Rhodnius prolixus. J Therm Biol. 2018;74:92\u0026ndash;9. https://doi.org/10.1016/j.jtherbio.2018.03.022.\u003c/li\u003e\n\u003cli\u003eEstay SA, Lima M, Bozinovic F. The role of temperature variability on insect performance and population dynamics in a warming world. Oikos. 2014;123:131\u0026ndash;40. https://doi.org/10.1111/j.1600-0706.2013.00607.x.\u003c/li\u003e\n\u003cli\u003eMills JN, Gage KL, Khan AS. Potential influence of climate change on vector-borne and zoonotic diseases: a review and proposed research plan. Environ Health Perspect. 2010;118:1507\u0026ndash;14. https://doi.org/10.1289/ehp.0901389.\u003c/li\u003e\n\u003cli\u003eTjaden NB, Thomas SM, Fischer D, Beierkuhnlein C. Extrinsic Incubation Period of Dengue: Knowledge, Backlog, and Applications of Temperature Dependence. PLoS Negl Trop Dis. 2013;7:e2207. https://doi.org/10.1371/journal.pntd.0002207.\u003c/li\u003e\n\u003cli\u003eRohani A, Wong YC, Zamre I, Lee HL, Zurainee MN. The effect of extrinsic incubation temperature on development of dengue serotype 2 and 4 viruses in Aedes aegypti (L.). Southeast Asian J Trop Med Public Health. 2009;40:942\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eChristofferson RC, Mores CN. Estimating the magnitude and direction of altered arbovirus transmission due to viral phenotype. PloS One. 2011;6:e16298. https://doi.org/10.1371/journal.pone.0016298.\u003c/li\u003e\n\u003cli\u003eLuz PM, Code\u0026ccedil;o CT, Massad E, Struchiner CJ. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil. Mem Inst Oswaldo Cruz. 2003;98:871\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHartemink N, Cianci D, Reiter P. R0 for vector-borne diseases: impact of the assumption for the duration of the extrinsic incubation period. Vector Borne Zoonotic Dis Larchmt N. 2015;15:215\u0026ndash;7. https://doi.org/10.1089/vbz.2014.1684.\u003c/li\u003e\n\u003cli\u003eDietz K. The estimation of the basic reproduction number for infectious diseases. Stat Methods Med Res. 1993;2:23\u0026ndash;41. https://doi.org/10.1177/096228029300200103.\u003c/li\u003e\n\u003cli\u003ePongsumpun P. Dengue disease model with the effect of extrinsic incubation period. In: Proceedings of the 2006 WSEAS International Conference on Mathematical Biology and Ecology. Miami, Florida; 2006. p. 43\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHartemink NA, Purse BV, Meiswinkel R, Brown HE, de Koeijer A, Elbers ARW, et al. Mapping the basic reproduction number (R₀) for vector-borne diseases: a case study on bluetongue virus. Epidemics. 2009;1:153\u0026ndash;61. https://doi.org/10.1016/j.epidem.2009.05.004.\u003c/li\u003e\n\u003cli\u003ePutri YE, Rozi S, Tasman H, Aldila D. Assessing the effect of extrinsic incubation period (EIP) prolongation in controlling dengue transmission with wolbachia-infected mosquito intervention. AIP Conf Proc. 2017;1825:020019. https://doi.org/10.1063/1.4978988.\u003c/li\u003e\n\u003cli\u003ePaaijmans KP, Blanford S, Bell AS, Blanford JI, Read AF, Thomas MB. Influence of climate on malaria transmission depends on daily temperature variation. Proc Natl Acad Sci U S A. 2010;107:15135\u0026ndash;9. https://doi.org/10.1073/pnas.1006422107.\u003c/li\u003e\n\u003cli\u003eLambrechts L, Scott TW, Gubler DJ. Consequences of the expanding global distribution of Aedes albopictus for dengue virus transmission. PLoS Negl Trop Dis. 2010;4:e646. https://doi.org/10.1371/journal.pntd.0000646.\u003c/li\u003e\n\u003cli\u003eWatts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg. 1987;36:143\u0026ndash;52. https://doi.org/10.4269/ajtmh.1987.36.143.\u003c/li\u003e\n\u003cli\u003ePaaijmans KP, Read AF, Thomas MB. Understanding the link between malaria risk and climate. Proc Natl Acad Sci U S A. 2009;106:13844\u0026ndash;9. https://doi.org/10.1073/pnas.0903423106.\u003c/li\u003e\n\u003cli\u003ePAHO. Factsheet: Chagas Disease in the Americas for Public Health Workers. 2022.\u003c/li\u003e\n\u003cli\u003eLidani KCF, Andrade FA, Bavia L, Damasceno FS, Beltrame MH, Messias-Reason IJ, et al. Chagas Disease: From Discovery to a Worldwide Health Problem. Front Public Health. 2019;7:166. https://doi.org/10.3389/fpubh.2019.00166.\u003c/li\u003e\n\u003cli\u003eVieira CB, Pra\u0026ccedil;a YR, Bentes KL da S, Santiago PB, Silva SMM, Silva G dos S, et al. Triatomines: Trypanosomatids, Bacteria, and Viruses Potential Vectors? Front Cell Infect Microbiol. 2018;Volume 8-2018.\u003c/li\u003e\n\u003cli\u003eReisenman CE, Lawrence G, Guerenstein PG, Gregory T, Dotson E, Hildebrand JG. Infection of kissing bugs with Trypanosoma cruzi, Tucson, Arizona, USA. Emerg Infect Dis. 2010;16:400\u0026ndash;5. https://doi.org/10.3201/eid1603.090648.\u003c/li\u003e\n\u003cli\u003eKribs-Zaleta C. Vector Consumption and Contact Process Saturation in Sylvatic Transmission of T. cruzi. Math Popul Stud. 2006;13:135\u0026ndash;52. https://doi.org/10.1080/08898480600788576.\u003c/li\u003e\n\u003cli\u003eNoya BA de, D\u0026iacute;az-Bello Z, Colmenares C, Ruiz-Guevara R, Mauriello L, Mu\u0026ntilde;oz-Calder\u0026oacute;n A, et al. Update on oral Chagas disease outbreaks in Venezuela: epidemiological, clinical and diagnostic approaches. Mem Inst Oswaldo Cruz. 2015;110:377\u0026ndash;86. https://doi.org/10.1590/0074-02760140285.\u003c/li\u003e\n\u003cli\u003ePereira KS, Schmidt FL, Guaraldo AMA, Franco RMB, Dias VL, Passos LAC. Chagas\u0026rsquo; disease as a foodborne illness. J Food Prot. 2009;72:441\u0026ndash;6. https://doi.org/10.4315/0362-028x-72.2.441.\u003c/li\u003e\n\u003cli\u003eRassi AJ, Rassi A, Marcondes de Rezende J. American trypanosomiasis (Chagas disease). Infect Dis Clin North Am. 2012;26:275\u0026ndash;91. https://doi.org/10.1016/j.idc.2012.03.002.\u003c/li\u003e\n\u003cli\u003eDias JCP. Southern Cone Initiative for the elimination of domestic populations of Triatoma infestans and the interruption of transfusional Chagas disease. Historical aspects, present situation, and perspectives. Mem Inst Oswaldo Cruz. 2007;102 Suppl 1:11\u0026ndash;8. https://doi.org/10.1590/s0074-02762007005000092.\u003c/li\u003e\n\u003cli\u003eNoireau F, Carbajal-de-la-Fuente AL, Lopes CM, Diotaiuti L. Some considerations about the ecology of Triatominae. An Acad Bras Cienc. 2005;77:431\u0026ndash;6. https://doi.org/10.1590/s0001-37652005000300006.\u003c/li\u003e\n\u003cli\u003eBarbazan P, Guiserix M, Boonyuan W, Tuntaprasart W, Pontier D, Gonzalez J-P. Modelling the effect of temperature on transmission of dengue. Med Vet Entomol. 2010;24:66\u0026ndash;73. https://doi.org/10.1111/j.1365-2915.2009.00848.x.\u003c/li\u003e\n\u003cli\u003eRol\u0026oacute;n M, Vega MC, Rom\u0026aacute;n F, G\u0026oacute;mez A, Rojas de Arias A. First report of colonies of sylvatic Triatoma infestans (Hemiptera: Reduviidae) in the Paraguayan Chaco, using a trained dog. PLoS Negl Trop Dis. 2011;5:e1026. https://doi.org/10.1371/journal.pntd.0001026.\u003c/li\u003e\n\u003cli\u003eBacigalupo A, Torres-P\u0026eacute;rez F, Segovia V, Garc\u0026iacute;a A, Correa JP, Moreno L, et al. Sylvatic foci of the Chagas disease vector Triatoma infestans in Chile: description of a new focus and challenges for control programs. Mem Inst Oswaldo Cruz. 2010;105:633\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eBacigalupo B A, Segura M JA, Garc\u0026iacute;a C A, Hidalgo C J, Galuppo G S, Cattan PE. Primer hallazgo de vectores de la enfermedad de Chagas asociados a matorrales silvestres en la Regi\u0026oacute;n Metropolitana, Chile. Rev M\u0026eacute;dica Chile. 2006;134:1230\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eBreni\u0026egrave;re SF, Waleckx E, Barnab\u0026eacute; C. Over Six Thousand Trypanosoma cruzi Strains Classified into Discrete Typing Units (DTUs): Attempt at an Inventory. PLoS Negl Trop Dis. 2016;10:e0004792. https://doi.org/10.1371/journal.pntd.0004792.\u003c/li\u003e\n\u003cli\u003eGorla DE. Variables ambientales registradas por sensores remotos como indicadores de la distribuci\u0026oacute;n geogr\u0026aacute;fica de Triatoma infestans (Heteroptera: Reduviidae). Ecol Austral. 2002;12:117\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003ede la Vega GJ, Medone P, Ceccarelli S, Rabinovich J, Schilman PE. Geographical distribution, climatic variability and thermo-tolerance of Chagas disease vectors. Ecography. 2015;38:851\u0026ndash;60. https://doi.org/10.1111/ecog.01028.\u003c/li\u003e\n\u003cli\u003eGorla DE, Catal\u0026aacute; SS, Grilli MP. Efecto de la temperatura sobre la distribuci\u0026oacute;n de Triatoma Infestans y el riesgo de transmisi\u0026oacute;n vectorial de la enfermedad de Chagas en Argentina. Acta Toxicol\u0026oacute;gica Argent. 1997;5:39\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eLazzari CR. Temperature preference in Triatoma infestans (Hemiptera: Reduviidae). Bull Entomol Res. 1991;81:273\u0026ndash;6. https://doi.org/10.1017/S0007485300033538.\u003c/li\u003e\n\u003cli\u003eCanals M, Solis R, Valderas J, Ehrenfeld M, Cattan PE. Preliminary studies on temperature selection and activity cycles of Triatoma infestans and T. spinolai (Heteroptera:Reduviidae), Chilean vectors of Chagas\u0026rsquo; disease. J Med Entomol. 1997;34:11\u0026ndash;7. https://doi.org/10.1093/jmedent/34.1.11.\u003c/li\u003e\n\u003cli\u003eFouque F, Reeder JC. Impact of past and on-going changes on climate and weather on vector-borne diseases transmission: a look at the evidence. Infect Dis Poverty. 2019;8:51. https://doi.org/10.1186/s40249-019-0565-1.\u003c/li\u003e\n\u003cli\u003eLoshouarn H, Guarneri AA. The interplay between temperature, Trypanosoma cruzi parasite load, and nutrition: Their effects on the development and life-cycle of the Chagas disease vector Rhodnius prolixus. PLoS Negl Trop Dis. 2024;18:e0011937. https://doi.org/10.1371/journal.pntd.0011937.\u003c/li\u003e\n\u003cli\u003eAsin S, Catal\u0026aacute; S. Development of Trypanosoma cruzi in Triatoma infestans: Influence of Temperature and Blood Consumption. J Parasitol. 1995;81:1\u0026ndash;7. https://doi.org/10.2307/3283997.\u003c/li\u003e\n\u003cli\u003eTamayo LD, Guhl F, Vallejo GA, Ram\u0026iacute;rez JD. The effect of temperature increase on the development of Rhodnius prolixus and the course of Trypanosoma cruzi metacyclogenesis. PLoS Negl Trop Dis. 2018;12:e0006735. https://doi.org/10.1371/journal.pntd.0006735.\u003c/li\u003e\n\u003cli\u003eGiojalas LC, Catal\u0026aacute; SS, Asin SN, Gorla DE. Seasonal changes in infectivity of domestic populations of Triatoma infestans. Trans R Soc Trop Med Hyg. 1990;84:439\u0026ndash;42. https://doi.org/10.1016/0035-9203(90)90355-i.\u003c/li\u003e\n\u003cli\u003eWood SF. Environmental temperature as a factor in development of Trypanosoma cruzi in Triatoma protracta. Exp Parasitol. 1954;3:227\u0026ndash;33. https://doi.org/10.1016/0014-4894(54)90021-1.\u003c/li\u003e\n\u003cli\u003ede Oliveira ABB, Alevi KCC, Imperador CHL, Madeira FF, Azeredo-Oliveira MTV de. Parasite-Vector Interaction of Chagas Disease: A Mini-Review. Am J Trop Med Hyg. 2018;98:653\u0026ndash;5. https://doi.org/10.4269/ajtmh.17-0657.\u003c/li\u003e\n\u003cli\u003eValenzuela L, Sep\u0026uacute;lveda S, Ponce I, Galanti N, Cabrera G. The overexpression of TcAP1 endonuclease confers resistance to infective Trypanosoma cruzi trypomastigotes against oxidative DNA damage. J Cell Biochem. 2018;119:5985\u0026ndash;95. https://doi.org/10.1002/jcb.26795.\u003c/li\u003e\n\u003cli\u003eBar ME, Oscherov EB, Damborsky MP, Porcel EA, Alvarez BM. Interacci\u0026oacute;n entre poblaciones de Triatoma infestans y Triatoma sordida. Rev Sa\u0026uacute;de P\u0026uacute;blica. 1994;28.\u003c/li\u003e\n\u003cli\u003eVazquez-Prokopec GM, Ceballos LA, Cecere MC, G\u0026uuml;rtler RE. Seasonal variations of microclimatic conditions in domestic and peridomestic habitats of Triatoma infestans in rural northwest Argentina. Acta Trop. 2002;84:229\u0026ndash;38. https://doi.org/10.1016/s0001-706x(02)00204-8.\u003c/li\u003e\n\u003cli\u003eGorla DE. Population dynamics and control of Triatoma infestans. Med Vet Entomol. 1992;6:91\u0026ndash;7. https://doi.org/10.1111/j.1365-2915.1992.tb00582.x.\u003c/li\u003e\n\u003cli\u003eDGF. Estudio de la variabilidad clim\u0026aacute;tica en Chile para el siglo XXI: Departamento de Geof\u0026iacute;sica. T\u0026eacute;cnico. Santiago: Facultad de Ciencias F\u0026iacute;sicas y Matem\u0026aacute;ticas, Universidad de Chile; 2007.\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-Duhart B, Cavieres G, Gonz\u0026aacute;lez A, Cattan PE, Bozinovic F, Clavijo-Baquet S. Influence of temperature variability on the feeding behavior and blood consumption of Triatoma infestans (Hemiptera: Reduviidae). J Med Entomol. 2024;:tjae034.\u003c/li\u003e\n\u003cli\u003eKollien AH, Schaub GA. The development of Trypanosoma cruzi in triatominae. Parasitol Today Pers Ed. 2000;16:381\u0026ndash;7. https://doi.org/10.1016/s0169-4758(00)01724-5.\u003c/li\u003e\n\u003cli\u003ePiron M, Fisa R, Casamitjana N, L\u0026oacute;pez-Chejade P, Puig L, Verg\u0026eacute;s M, et al. Development of a real-time PCR assay for Trypanosoma cruzi detection in blood samples. Acta Trop. 2007;103:195\u0026ndash;200.\u003c/li\u003e\n\u003cli\u003eDuffy T, Cura CI, Ramirez JC, Abate T, Cayo NM, Parrado R, et al. Analytical performance of a multiplex Real-Time PCR assay using TaqMan probes for quantification of Trypanosoma cruzi satellite DNA in blood samples. PLoS Negl Trop Dis. 2013;7:e2000.\u003c/li\u003e\n\u003cli\u003eKooy RF, Ashall F, Van der Ploeg M, Overdulve JP. On the DNA content of Trypanosoma cruzi. Mol Biochem Parasitol. 1989;36:73\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eCabe AM, Ya\u0026ntilde;ez F, Pinto R, L\u0026oacute;pez A, Ortiz S, Martin CM-S, et al. Survivorship of wild caught Mepraia spinolai nymphs: The effect of seasonality and Trypanosoma cruzi infection after feeding and fasting in the laboratory. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis. 2019;71:197\u0026ndash;204. https://doi.org/10.1016/j.meegid.2019.04.002.\u003c/li\u003e\n\u003cli\u003eAgresti A. Foundations of linear and generalized linear models. John Wiley \u0026amp; Sons; 2015.\u003c/li\u003e\n\u003cli\u003eAkaike H. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika. 1973;60:255\u0026ndash;65. https://doi.org/10.1093/biomet/60.2.255.\u003c/li\u003e\n\u003cli\u003eStasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F. Flexible Regression and Smoothing: Using GAMLSS in R. 1st edition. New York: Chapman and Hall/CRC; 2017.\u003c/li\u003e\n\u003cli\u003eRigby RA, Stasinopoulos MD, Heller GZ, De Bastiani F. Distributions for Modeling Location, Scale, and Shape. Using GAMLSS in R. 1st edition. New York: Chapman and Hall/CRC; 2019.\u003c/li\u003e\n\u003cli\u003eTeam RC. R: The R project for statistical computing. No Title. 2023.\u003c/li\u003e\n\u003cli\u003eCatala SS, Gorla DE, Basombrio MA. Vectorial transmission of Trypanosoma cruzi: an experimental field study with susceptible and immunized hosts. Am J Trop Med Hyg. 1992;47:20\u0026ndash;6. https://doi.org/10.4269/ajtmh.1992.47.20.\u003c/li\u003e\n\u003cli\u003eElliot SL, Rodrigues J de O, Lorenzo MG, Martins-Filho OA, Guarneri AA. Trypanosoma cruzi, etiological agent of Chagas disease, is virulent to its triatomine vector Rhodnius prolixus in a temperature-dependent manner. PLoS Negl Trop Dis. 2015;9:e0003646. https://doi.org/10.1371/journal.pntd.0003646.\u003c/li\u003e\n\u003cli\u003eD\u0026iacute;az S, Villavicencio B, Correia N, Costa J, Haag KL. Triatomine bugs, their microbiota and Trypanosoma cruzi: asymmetric responses of bacteria to an infected blood meal. Parasit Vectors. 2016;9:636. https://doi.org/10.1186/s13071-016-1926-2.\u003c/li\u003e\n\u003cli\u003eDuarte-Silva E, Morais LH, Clarke G, Savino W, Peixoto C. Targeting the Gut Microbiota in Chagas Disease: What Do We Know so Far? Front Microbiol. 2020;11:585857. https://doi.org/10.3389/fmicb.2020.585857.\u003c/li\u003e\n\u003cli\u003eBozinovic F, Medina NR, Alruiz JM, Cavieres G, Sabat P. Thermal tolerance and survival responses to scenarios of experimental climatic change: changing thermal variability reduces the heat and cold tolerance in a fly. J Comp Physiol [B]. 2016;186:581\u0026ndash;7. https://doi.org/10.1007/s00360-016-0980-6.\u003c/li\u003e\n\u003cli\u003eMelo R de FP, Guarneri AA, Silber AM. The Influence of Environmental Cues on the Development of Trypanosoma cruzi in Triatominae Vector. Front Cell Infect Microbiol. 2020;10:27. https://doi.org/10.3389/fcimb.2020.00027.\u003c/li\u003e\n\u003cli\u003eSNS. Servicio Metereol\u0026oacute;gico Argentino. Estad\u0026iacute;sticas Clim\u0026aacute;ticas.\u003c/li\u003e\n\u003cli\u003eMarliere NP, Latorre-Estivalis JM, Lorenzo MG, Carrasco D, Alves-Silva J, Rodrigues J de O, et al. Trypanosomes modify the behavior of their insect hosts: effects on locomotion and on the expression of a related gene. PLoS Negl Trop Dis. 2015;9:e0003973.\u003c/li\u003e\n\u003cli\u003eLahond\u0026egrave;re C, Insausti TC, Paim RM, Luan X, Belev G, Pereira MH, et al. Countercurrent heat exchange and thermoregulation during blood-feeding in kissing bugs. eLife. 2017;6. https://doi.org/10.7554/eLife.26107.\u003c/li\u003e\n\u003cli\u003eMagdaleno A, Ahn I-Y, Paes LS, Silber AM. Actions of a proline analogue, L-thiazolidine-4-carboxylic acid (T4C), on Trypanosoma cruzi. PloS One. 2009;4:e4534. https://doi.org/10.1371/journal.pone.0004534.\u003c/li\u003e\n\u003cli\u003eVieira CB, Pra\u0026ccedil;a YR, Bentes KL da S, Santiago PB, Silva SMM, Silva G dos S, et al. Triatomines: trypanosomatids, bacteria, and viruses potential vectors? Front Cell Infect Microbiol. 2018;8:405.\u003c/li\u003e\n\u003cli\u003eSalcedo-Porras N, Lowenberger C. The innate immune system of kissing bugs, vectors of chagas disease. Dev Comp Immunol. 2019;98:119\u0026ndash;28. https://doi.org/10.1016/j.dci.2019.04.007.\u003c/li\u003e\n\u003cli\u003eAzambuja P, Feder D, Garcia ES. Isolation of Serratia marcescens in the midgut of Rhodnius prolixus: impact on the establishment of the parasite Trypanosoma cruzi in the vector. Exp Parasitol. 2004;107:89\u0026ndash;96. https://doi.org/10.1016/j.exppara.2004.04.007.\u003c/li\u003e\n\u003cli\u003eRolandi C, Iglesias MS, Schilman PE. Metabolism and water loss rate of the haematophagous insect Rhodnius prolixus: effect of starvation and temperature. J Exp Biol. 2014;217 Pt 24:4414\u0026ndash;22. https://doi.org/10.1242/jeb.109298.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Tokman D, C\u0026oacute;rdoba-Aguilar A, D\u0026aacute;ttilo W, Lira-Noriega A, S\u0026aacute;nchez-Guill\u0026eacute;n RA, Villalobos F. Insect responses to heat: physiological mechanisms, evolution and ecological implications in a warming world. Biol Rev Camb Philos Soc. 2020;95:802\u0026ndash;21. https://doi.org/10.1111/brv.12588.\u003c/li\u003e\n\u003cli\u003eGillooly JF, Brown JH, West GB, Savage VM, Charnov EL. Effects of size and temperature on metabolic rate. Science. 2001;293:2248\u0026ndash;51. https://doi.org/10.1126/science.1061967.\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Rete B, Salazar-Schettino PM, Bucio-Torres MI, C\u0026oacute;rdoba-Aguilar A, Cabrera-Bravo M. Activity of the prophenoloxidase system and survival of triatomines infected with different Trypanosoma cruzi strains under different temperatures: understanding Chagas disease in the face of climate change. Parasit Vectors. 2019;12:219. https://doi.org/10.1186/s13071-019-3477-9.\u003c/li\u003e\n\u003cli\u003eCarrington LB, Seifert SN, Armijos MV, Lambrechts L, Scott TW. Reduction of Aedes aegypti vector competence for dengue virus under large temperature fluctuations. Am J Trop Med Hyg. 2013;88:689.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Summary of the best generalized linear model (GLM) model fitted for \u003cem\u003eT. cruzi\u003c/em\u003e EIP in \u003cem\u003eT. infestans\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSize effect (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eIntercept (18 \u0026plusmn; 0 \u0026ordm;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e18 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 0 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ebi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003emb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ebi * mb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-12.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-3.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe groups were defined as follows: 18 \u0026plusmn; 0 \u0026deg;C, cold constant treatment; 18 \u0026plusmn; 5\u0026ordm;C: cold variable treatment; 27 \u0026plusmn; 0 \u0026deg;C, warm constant treatment; 27 \u0026plusmn; 5\u0026ordm;C: warm variable treatment. Abbreviations are as follows: bi, ingested blood; mb, body mass before infection. The size effect was calculated as 1 / (intercept estimate + estimate of the category). The volume of blood ingested decreased EIP in all treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eGAM estimates and significance from the variables of the best model fitted for parasite load of \u003cem\u003eT. cruzi\u003c/em\u003e in \u003cem\u003eT. infestans\u003c/em\u003e.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 491px;\"\u003e\n \u003cp\u003eParametric coefficients:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eIntercept (18\u0026plusmn;0 \u0026ordm;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e18 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 0 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e3.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 491px;\"\u003e\n \u003cp\u003eApproximate significance of smooth terms:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEdf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef.df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003es(ID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e7.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003es(Day): 18 \u0026plusmn; 0 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003es(Day): 18 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e6.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e6.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003es(Day): 27 \u0026plusmn; 0 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e5.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003es(Day): 27 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e8.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ete(mb,bi): 18 \u0026plusmn; 0 \u0026ordm;C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ete(mb,bi): 18 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e2.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ete(mb,bi): 27 \u0026plusmn; 0 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003ete(mb,bi): 27 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eR-sq.(adj) =\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eDeviance explained =\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e64.3%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003en =307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eEstimate of the selected model; standard error; t-value; p-value; Edf: effective degrees of freedom; Ref. df: Reference degrees of freedom; F: F-tests on smooth terms; p-value: p-value of smooth terms; R-sq.(adj): Adjusted R-squared value; 18 \u0026plusmn; 0 \u0026deg;C: cold constant treatment; 18 \u0026plusmn; 5 \u0026deg;C: cold variable treatment; 27 \u0026plusmn; 0 \u0026deg;C: warm constant treatment; 27 \u0026plusmn; 5 \u0026deg;C warm variable treatment; s: spline; ID: triatomine individuals; Days: time measured in days p.i; m\u003csub\u003eb\u003c/sub\u003e: body mass before infection; b\u003csub\u003ei:\u0026nbsp;\u003c/sub\u003eingested blood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Parameters of the GAMLSS selected model.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eIntercept (18 \u0026plusmn; 0 \u0026ordm;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-3.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-4.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 0 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-5.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-2.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 5 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-8.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-3.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ecs(Days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18 \u0026plusmn; 5 \u0026ordm;C:cs(Day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 0 \u0026ordm;C:cs(Day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 \u0026plusmn; 5 \u0026ordm;C:cs(Day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEstimate of the selected model; standard error; t value; p value; 27 \u0026plusmn; 0 \u0026deg;C: warm constant treatment; 18 \u0026plusmn; 0 \u0026deg;C: cold constant treatment; 18 \u0026plusmn; 5 \u0026deg;C: cold variable treatment; 27 \u0026plusmn; 5 \u0026deg;C warm variable treatment; cs: cubic spline; d: time in days; ID: triatomine individuals.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, infectious, Chagas disease, Vector-borne diseases","lastPublishedDoi":"10.21203/rs.3.rs-7881049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7881049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\n \u003cem\u003eTrypanosoma cruzi\u003c/em\u003e, the etiologic agent of Chagas disease, is transmitted via the dejections of triatomine insects such as \u003cem\u003eTriatoma infestans\u003c/em\u003e. The extrinsic incubation period (EIP), parasite load, and infectivity of the vector are sensitive to environmental temperatures. Global warming is expected to increase both mean temperatures and their variability, potentially altering vector competence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe experimentally infected \u003cem\u003eT. infestans\u003c/em\u003e with the \u003cem\u003eT. cruzi\u003c/em\u003e Dm28c strain and exposed them to four temperature regimes: two constant (18 °C and 27 °C) and two variables (18 ± 5 °C and 27 ± 5 °C). Over 42 days, we collected dejection samples for parasite quantification via qPCR and recorded the time of the first positive detection to estimate the EIP. Dejection samples were collected every two days throughout the study to quantify the parasite load using qPCR, enabling us to assess infection dynamics over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nHigher temperatures significantly shortened the EIP and increased the overall parasite load. However, temperature variability alone did not significantly alter the EIP. In contrast, variability increased the peak parasite load in the cold treatments without affecting the probability of positive dejections. The parasite load exhibited a bell-shaped curve over time, peaking earlier and higher under warmer conditions. A larger volume of ingested blood also reduced the EIP, especially under cold treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nTemperature increases accelerate \u003cem\u003eT. cruzi\u003c/em\u003e development in \u003cem\u003eT. infestans\u003c/em\u003e, potentially enhancing vector competence under climate change scenarios. Although variability in temperature did not affect EIP, it did influence parasite load, suggesting that both mean temperature and variability must be considered to understand the impact of climate change on Chagas disease transmission.\u003c/p\u003e","manuscriptTitle":"Temperature variability increases Trypanosoma cruzi load but not the extrinsic incubation period in Triatoma infestans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 09:01:49","doi":"10.21203/rs.3.rs-7881049/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T00:37:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T17:30:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T19:03:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"919701891210216471021630770234889566","date":"2025-10-24T18:44:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128132828663236418850959213843884562668","date":"2025-10-23T16:06:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58884368629262653850051110239896162485","date":"2025-10-22T23:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191783129553523433149244685248696364540","date":"2025-10-22T23:47:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T23:45:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-18T14:33:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T14:38:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2025-10-16T21:48:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef342309-190f-4d80-a1d4-3bd97ef29c4c","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:12:06+00:00","versionOfRecord":{"articleIdentity":"rs-7881049","link":"https://doi.org/10.1186/s13071-026-07270-y","journal":{"identity":"parasites-and-vectors","isVorOnly":false,"title":"Parasites \u0026 Vectors"},"publishedOn":"2026-02-02 15:58:54","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-11-03 09:01:49","video":"","vorDoi":"10.1186/s13071-026-07270-y","vorDoiUrl":"https://doi.org/10.1186/s13071-026-07270-y","workflowStages":[]},"version":"v1","identity":"rs-7881049","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7881049","identity":"rs-7881049","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}