Biology subjects

Mordecai, E. A.

Publications and source records attributed to Mordecai, E. A..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Temperature and intraspecific variation affect host-parasite interactions

Parasites play key roles in regulating aquatic ecosystems, yet the impact of climate warming on their ecology and disease transmission remains poorly understood. Isolating the effect of warming is challenging as transmission involves multiple interacting species and potential intraspecific variation in temperature responses of one or more of these species. Here, we leverage a wide-ranging mosquito species and its facultative parasite as a model system to investigate the impact of temperature on host-parasite interactions and disease transmission. We conducted a common garden experiment measuring parasite growth and infection rates at seven temperatures using 12 field-collected parasite populations and a single mosquito population. We find that both free-living growth rates and infection rates varied with temperature, which were highest at 18-24.5{degrees}C and 13{degrees}C, respectively. Further, we find intraspecific variation in peak performance temperature reflecting patterns of local thermal adaptation--parasite populations from warmer source environments typically had higher thermal optima for free-living growth rates. For infection rates, we found a significant interaction between parasite population and nonlinear effects of temperature. These findings underscore the need to consider both host and parasite thermal responses, as well as intraspecific variation in thermal responses, when predicting the impacts of climate change on disease in aquatic ecosystems.

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bioRxiv2023

A mosquito parasite is locally adapted to its host but not temperature

Climate change will alter interactions between parasites and their hosts. Warming may affect patterns of local adaptation, shifting the environment to favor the parasite or host and thus changing the prevalence of disease. We assessed local adaptation in the facultative ciliate parasite Lambornella clarki, which infects the western tree hole mosquito Aedes sierrensis. We conducted laboratory infection experiments with mosquito larvae and parasites collected from across a climate gradient, pairing sympatric or allopatric populations across three temperatures that were either matched or mismatched to the source environment. L. clarki parasites were locally adapted to their hosts, with 2.6x higher infection rates on sympatric compared to allopatric populations, but were not locally adapted to temperature. Infection peaked at the intermediate temperature of 13{degrees}C. Our results highlight the importance of host selective pressure on parasites, despite the impact of temperature on infection success.

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bioRxiv2023

Abnormal weather drives disease outbreaks in wild and agricultural plants

Predicting effects of climate change on plant disease is critical for protecting ecosystems and food production. Climate change could exacerbate plant disease because parasites may be quicker to acclimate and adapt to novel climatic conditions than their hosts due to their smaller body sizes and faster generation times. Here we show how disease pressure responds to the anomalous weather that will increasingly occur with climate change by compiling a global database (5380 plant populations; 437 unique plant-disease combinations; 2,858,795 individual plant-disease samples) of disease incidence in both agricultural and wild plant systems. Because wild plant populations are assumed to be adapted to local climates, we hypothesized that large deviations from historical conditions would increase disease incidence. By contrast, since agricultural plants have been transported globally, we did not expect the historical climate where they are currently grown to be as predictive of disease incidence. Supporting these hypotheses, we found that disease outbreaks tended to occur during periods of warm temperatures in agricultural and cool-climate wild plant systems, but also occurred in warm-adapted wild (but not agricultural) plant systems experiencing anomalously cool weather. Outbreaks were additionally associated with higher rainfall in wild systems, especially those with historically wet climates. Our results suggest that historical climate affects susceptibility to disease for wild plant-disease systems, while warming drives risks for agricultural plant disease outbreaks regardless of historical climate.

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bioRxiv2023

Evidence of thermal adaptation in a widely distributed mosquito species

How mosquitoes may respond to rapid climate warming remains unknown for most species, but will have major consequences for their future distributions, with cascading impacts on human well-being, biodiversity, and ecosystem function. We investigated the adaptive potential of a wide-ranging mosquito species, Aedes sierrensis, across a large climatic gradient by conducting a common garden experiment measuring the thermal limits of mosquito life history traits. Although field-collected populations originated from vastly different thermal environments that spanned over 1,200 km, we found remarkably limited variation in upper thermal tolerance between populations, with the upper thermal limits of fitness varying by <1{degrees}C across the species range. For one life history trait--pupal development rate--we did detect significant variation in upper thermal limits between populations, and this variation was strongly correlated with source temperatures, providing evidence of local thermal adaptation for pupal development. However, we found environmental temperatures already regularly exceed our highest estimated upper thermal limits throughout most of the species range, suggesting limited potential for mosquito thermal tolerance to evolve on pace with warming. Strategies for avoiding high temperatures such as diapause, phenological shifts, and behavioral thermoregulation are likely important for mosquito persistence.

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bioRxiv2022

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predicting the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles. Author summaryLeishmaniasis is a zoonotic, vector borne disease of poverty with a high burden throughout the Americas: within Latin America there are an estimated 58,500 new cases per year and 54,050 years of life lost due to disability. Although the World Health Organization has targeted leishmaniasis for elimination and control by 2030, the disease remains a persistent threat. Across the Americas, particularly in Central America, the southeastern United States, and perimeters of the Amazon Basin, risk of infection is increasing in geographic extent and elevation. While it is known that Leishmania parasites, the causative agent of leishmaniasis, are maintained in the environment via a mammalian host, the full suite of wildlife hosts has yet to be documented, which significantly hinders control efforts. Here, we use machine learning and ecological and evolutionary trait profiles of known hosts to identify unrecognized potential wildlife hosts of Leishmania. We identify 136 mammals in the Americas that are likely to be exposed to and infected by zoonotic Leishmania in the wild. The high number of unrecognized potential hosts emphasizes a need to better invest in studying the ecological epidemiology of leishmaniasis. The study provides information and tools to support targeted intervention and management of this important poverty-associated disease.

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bioRxiv2022

Data-Driven Predictions of Potential Leishmania Vectors in Latin America

The incidence of vector-borne diseases is rising as deforestation, climate change, and globalization bring humans in contact with arthropods that can transmit pathogens. In particular, incidence of American Cutaneous Leishmaniasis (ACL), a parasitic disease transmitted by sandflies, is increasing as previously intact habitats are cleared for agriculture and urban areas, potentially bringing people into contact with vectors and reservoir hosts. Previous evidence has identified dozens of sandfly species that have been infected with and/or transmit Leishmania parasites. However, there is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. Here, we apply machine learning models (boosted regression trees) to leverage biological and geographical traits of known sandfly vectors to predict potential vectors. Additionally, we generate trait profiles of confirmed vectors and identify important factors in transmission. Our model performed well with an average out of sample accuracy of 86%. The models predict that synanthropic sandflies living in areas with greater canopy height, less human modification, and within an optimal range of rainfall are more likely to be Leishmania vectors. We also observed that generalist sandflies that are able to inhabit many different ecoregions are more likely to transmit the parasite. Our results suggest that Psychodopygus amazonensis and Nyssomia antunesi are unidentified potential vectors, and should be the focus of sampling and research efforts. Overall, we found that our machine learning approach provides valuable information for Leishmania surveillance and management in an otherwise complex and data sparse system. Author SummaryAmerican Cutaneous Leishmaniasis (ACL) is a neglected parasitic disease transmitted by sandflies in Latin America. There is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. In this study, the authors created a database of sandfly traits, then used predictive models to determine important factors in disease transmission and how different climate and environmental variables affect ACL transmission. The models suggest that transmission occurs at the interface between domestic habitats and well-preserved forests. The authors also generate predictions of which sandflies might be transmitting the parasite that are not known vectors at the time, specifically Psychodopygus amazonensis and Nyssomia antunesi. This new knowledge can lead to a better understanding of the system of transmission, and can point to possible hotspots of the disease. The analysis can also help direct researchers to areas of interest for sampling studies, as well as specific sandflies to focus their effort on.

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bioRxiv2022

Temperature impacts on dengue incidence are nonlinear and mediated by climatic and socioeconomic factors

Temperature can influence mosquito-borne diseases like dengue. These effects are expected to vary geographically and over time in both magnitude and direction and may interact with other environmental variables, making it difficult to anticipate changes in response to climate change. Here, we investigate global variation in temperature-dengue relationship by analyzing published correlations between temperature and dengue and matching them with remotely sensed climatic and socioeconomic data. We found that the correlation between temperature and dengue was most positive at intermediate (near 24{degrees}C) temperatures, as predicted from the thermal biology of the mosquito and virus. Positive temperature-dengue associations were strongest when temperature variation and population density were high and decreased with infection burden and rainfall mean and variation, suggesting alternative limiting factors on transmission. Our results show that while climate effects on diseases are context-dependent they are also predictable from the thermal biology of transmission and its environmental and social mediators.

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bioRxiv2021

Land-use planning for health: Tradeoffs and nonlinearities govern how land-use change impacts vector-borne disease risk

Patterns of land-use can affect the transmission of many infectious diseases with human health implications; yet, applied ecosystem service models have rarely accounted for disease transmission risk. A mechanistic understanding of how land-use changes alter infectious disease transmission would help to target public health interventions and to minimize human risk of disease with either ecosystem degradation or restoration. Here, we present a spatially explicit model of disease transmission on heterogeneous landscapes that is designed to serve as a road map for modeling the multifaceted impacts of land-use on disease transmission. We model the transmission of three vector-borne diseases with distinct transmission dynamics (parameterized using published literature to represent dengue, yellow fever, and malaria) on simulated landscapes of varying spatial heterogeneity in tree cover and urban area. Overall, we find that these three diseases depend on the biophysical landscape in different nonlinear ways, leading to tradeoffs in disease risk across the landscape; rarely do we predict disease risk to be high for all three diseases in a local setting. We predict that dengue risk peaks in areas of high urban intensity and human population density, yellow fever risk peaks in areas with low to moderate human population density and high tree cover, and malaria risk peaks where patchy tree cover abuts urban area. To examine how this approach can inform land use planning, we applied the model to a small landscape to the northwest of Bogota, Colombia under multiple restoration scenarios. We predict that in an area inhabited by both Aedes aegypti and Ae. albopictus, any increase in overall tree cover would increase dengue and yellow fever risk, but that risk can be minimized by pursuing a large contiguous reforestation project as opposed to many small, patchy projects. A large contiguous reforestation project is also able to both reduce overall malaria risk and the number of malaria hotspots. As sustainable development goals make ecosystem restoration and biodiversity conservation top priorities, it is imperative that land use planning account for potential impacts on both disease transmission and other ecosystem services. Open Research statementAll data and code used in this study are available in the online supplemental material. Code and data are also hosted at: https://github.com/morgankain/Land-Use_Disease_Model.

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bioRxiv2021

Physiology and ecology combine to determine host and vector importance for Ross River virus and other vector-borne diseases

Identifying the key vector and host species driving transmission is notoriously difficult for vector-borne zoonoses, but critical for disease control. Here, we present a general approach for quantifying the role hosts and vectors play in transmission that integrates species physiological competence with their ecological traits. We apply this model to the medically important arbovirus Ross River virus (RRV), in Brisbane, Australia. We found that vertebrate species with high physiological competence werent the most important for community transmission. Instead, we estimated that humans (previously overlooked as epidemiologically important hosts) potentially play an important role in RRV transmission, in part, because highly competent vectors readily feed on them and are highly abundant. By contrast, vectors with high physiological competence were also important for community transmission. Finally, we uncovered two potential transmission cycles: an enzootic cycle involving birds and an urban cycle involving humans. This modelling approach has direct application to other zoonotic arboviruses.

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bioRxiv2020

Temperature effects on individual-level parasitism translate into predictable effects on parasitism in populations

Parasitism is expected to change in a warmer future, but whether warming leads to substantial increases in parasitism remains unclear. Understanding how warming effects on parasitism in individual hosts (e.g., parasite load) translate to effects on population-level parasitism (e.g., prevalence, R0) remains a major knowledge gap. We analyzed the temperature dependence of parasitism at both host and population levels in thirteen empirical vector-borne host-parasite systems and found a strong, significant positive correlation between the thermal optima of individual- and population-level parasitism. We also found a significant, positive correlation in eleven environmentally-transmitted parasite systems, though several of these systems exhibited thermal optima >5{o}C apart between individual and population levels. Similarly, parasitism thermal optima were close to host performance thermal optima in vector-borne systems but not in environmentally-transmitted systems. We then adapted and simulated simple models for both transmission modes and found a similar pattern to the empirical systems: thermal optima in vector-borne systems were more strongly correlated across scales compared to environmentally-transmitted systems. Generally, our results suggest that information on the temperature-dependence, and specifically the thermal optimum, at either the individual- or population-level should provide a useful--though not quantitatively exact--baseline for predicting temperature dependence at the other level, especially in vector-borne parasite systems. Environmentally-transmitted parasitism may operate by a different set of rules, in which temperature-dependence is decoupled in some systems, requiring the need for trait-based studies of temperature-dependence at individual and population levels.

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bioRxiv2020

Understanding the emergence of contingent and deterministic exclusion in multispecies communities

Competitive exclusion can be classified as deterministic or as historically contingent. While competitive exclusion is common in nature, it has remained unclear when multispecies communities should be dominated by deterministic or contingent exclusion. Here, we provide a general theoretical approach to explain both the emergence and sources of competitive exclusion in multispecies communities. We illustrate our approach on an empirical competition system between annual and perennial plant species. First, we find that the life-history of perennial species increases the probability of observing contingent exclusion by increasing their effective intrinsic growth rates. Second, we find that the probability of observing contingent exclusion increases with weaker intraspecific competition, and not with the level of hierarchical competition. Third, we find a shift from contingent exclusion to dominance with increasing numbers of competing species. Our work provides a heuristic framework to increase our understanding about the predictability of species persistence within multispecies communities.

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bioRxiv2020

Household and climate factors influence Aedes aegypti risk in the arid city of Huaquillas, Ecuador

Arboviruses transmitted by Aedes aegypti (e.g., dengue, chikungunya, Zika) are of major public health concern on the arid coastal border of Ecuador and Peru. This high transit border is a critical disease surveillance site due to human movement-associated risk of transmission. Local level studies are thus integral to capturing the dynamics and distribution of vector populations and social-ecological drivers of risk, to inform targeted public health interventions. Our study examines factors associated with household-level Ae. aegypti presence in Huaquillas, Ecuador, while accounting for spatial and temporal effects. From January to May of 2017, adult mosquitoes were collected from a cohort of households (n = 63) in clusters (n = 10), across the city of Huaquillas, using aspirator backpacks. Household surveys describing housing conditions, demographics, economics, travel, disease prevention, and city services were conducted by local enumerators. This study was conducted during the normal arbovirus transmission season (January - May), but during an exceptionally dry year. Household level Ae. aegypti presence peaked in February, and counts were highest in weeks with high temperatures and a week after increased rainfall. Presence of Ae. aegypti was highly variable between clusters. Hierarchical generalized linear models were used to explore household social-ecological variables and female Ae. aegypti presence. Houses with Ae. aegypti used larvicide in water tanks and had high awareness of dengue transmission. We found that homes were more likely to have Ae. aegypti when heads of household had lived in the neighborhoods for longer than average (>22 years), when households had more occupants than average (>4.5), had a female head of household, and received more frequent garbage collection. Ae. aegypti presence was less likely in households with reliable water supply and septic systems. Based on our findings, infrastructure access, urban occupancy patterns, and seasonal climate are important considerations for vector control in this city, and even in dry years, this arid environment supports Ae. aegypti breeding habitat. Author summaryMosquito transmitted infectious diseases are a growing concern around the world. The yellow fever mosquito (Aedes aegypti) has been responsible for recent major outbreaks of disease, including dengue fever and Zika. This mosquito prefers to bite humans and lay its eggs in artificial containers such as water tanks and planters. This makes Ae. aegypti well suited to become established in growing urban areas. Controlling these mosquitoes has been an important way to reduce the risk of disease transmission. Studies that are undertaken to understand local factors that contribute to the continued survival of the mosquito can be used to inform control practices. We conducted a study in the largest city on the border of Ecuador and Peru where we collected adult mosquitoes from houses and surveyed household members about their behaviors, perceptions, and housing infrastructure associated with the mosquito vector. Mosquitoes were most numerous in weeks with high temperatures and a week after increased rainfall. Larvicide was a commonly used control strategy in homes where Ae. aegypti was present. We found that houses that had more people, female heads of household, heads of household that had lived in the neighborhood for a long time, and had unreliable water service, were more likely have mosquitoes present, while houses that used septic systems were less likely to have mosquitoes present.

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bioRxiv2020

Native perennial and non-native annual grasses shape pathogen community composition and disease severity in a California grassland

O_LIThe densities of highly competent plant hosts may shape pathogen community composition and disease severity, altering disease risk and impacts. Life history and evolutionary history influence host competence: longer-lived species tend to be better defended than shorter-lived species and pathogens adapt to infect species with which they have longer evolutionary histories. It is unclear, however, how the densities of species that differ in competence due to life and evolutionary histories affect plant pathogen community composition and disease severity. C_LIO_LIWe examined foliar fungal pathogens on two host groups in a California grassland: native perennial and non-native annual grasses. We first characterized pathogen community composition and disease severity on the two host groups to approximate differences in competence. We then used observational and manipulated gradients of native perennial and non-native annual grass densities to assess the effects of each host group on pathogen community composition and disease severity in 1-m2 plots. C_LIO_LINative perennial and non-native annual grasses hosted distinct pathogen communities but shared generalist pathogens. Native perennial grasses hosted pathogens with larger host ranges and experienced greater disease severity (75% higher proportions of leaves with lesions) than non-native annuals. While the relative abundances of three common pathogens tended to shift, and disease severity of native perennial grasses tended to increase with increasing densities of both host groups, these changes were not statistically significant. C_LIO_LISynthesis. The life and evolutionary histories of grasses likely influence their competence for different pathogen species, leading to distinct pathogen communities and differences in disease severity. However, there was no evidence that the density of either host group significantly affected pathogen community composition or disease severity. Therefore, variation in competence for different pathogens likely shapes pathogen community composition and disease severity but may not interact with host density to alter disease risk and impacts at small scales. C_LI

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bioRxiv2020

Climate explains geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28 - 85% for vectors, 44 - 88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

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bioRxiv2020

Impact of prior and projected climate change on US Lyme disease incidence

Lyme disease is the most common vector-borne disease in temperate zones and a growing public health threat in the United States (US). The life cycles of the tick vectors and spirochete pathogen are highly sensitive to climate, but determining the impact of climate change on Lyme disease burden has been challenging due to the complex ecology of the disease and the presence of multiple, interacting drivers of transmission. Here we incorporated 18 years of annual, county-level Lyme disease case data in a panel data statistical model to investigate prior effects of climate variation on disease incidence while controlling for other putative drivers. We then used these climate-disease relationships to project Lyme disease cases using CMIP5 global climate models and two potential climate scenarios (RCP4.5 and RCP8.5). We find that interannual variation in Lyme disease incidence is associated with climate variation in all US regions encompassing the range of the primary vector species. In all regions, the climate predictors explained less of the variation in Lyme disease incidence than unobserved county-level heterogeneity, but the strongest climate-disease association detected was between warming annual temperatures and increasing incidence in the Northeast. Lyme disease projections indicate that cases in the Northeast will increase significantly by 2050 (23,619 {+/-} 21,607 additional cases), but only under RCP8.5, and with large uncertainty around this projected increase. Significant case changes are not projected for any other region under either climate scenario. The results demonstrate a regionally variable and nuanced relationship between climate change and Lyme disease, indicating possible nonlinear responses of vector ticks and transmission dynamics to projected climate change. Moreover, our results highlight the need for improved preparedness and public health interventions in endemic regions to minimize the impact of further climate change-induced increases in Lyme disease burden.

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bioRxiv2019

Empirical dynamic modeling reveals ecological drivers of dengue dynamics

Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analyzing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modeling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects, respectively. By capturing mechanistic, nonlinear, and context-dependent effects of population susceptibility, temperature, and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drive dengue dynamics in a nonlinear and complex, yet predictable way.

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bioRxiv2019

Effects of temperature on Zika dynamics and control

ABSTRACTWhen a formerly rare pathogen emerges to cause a pandemic, it is critical to understand the ecology of the disease dynamics and its potential effects on disease control. Here, we take advantage of newly available experimental data to parameterize a temperature-dependent dynamical model of Zika virus (ZIKV) transmission, and analyze the effects of temperature variability and the parameters related to control strategies on ZIKV R0 and the final epidemic size (i.e., total number of human cases). Sensitivity analyses identified that R0 and the final epidemic size were largely driven by different parameters, with the exception of temperature, which is the dominant driver of epidemic dynamics in the models. Our estimate of R0 had a single optimum temperature (≈ 30° C), comparable to recently published results (≈ 29°)1. However, the total number of human cases (“final epidemic size”) is maximized across a wider temperature range, from 24 to 36°C. The models indicate that the disease is highly sensitive to seasonal temperature variation. For example, although the model predicts that Zika transmission cannot occur at a constant temperature of 22°C, with seasonal variation of 5°C around a mean of 22°C, the model predicts a larger epidemic than what would occur at a constant 30°C, the temperature predicted to maximize R0. This suggests that the potential geographic range of Zika is wider than indicated from static R0 models, underscoring the importance of climate dynamics and variation on emerging infectious diseases.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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bioRxiv2019

Mosquito species and age influence thermal performance of traits relevant to malaria transmission

Models predicting disease transmission are a vital tool in the control of mosquito populations and malaria reduction as they can target intervention efforts. We compared the performance of temperature-dependent transmission models when mosquito life history traits were allowed to change across the lifespan of Anopheles stephensi, the urban malaria mosquito, to models parameterized with commonly derived estimates of lifetime trait values. We conducted an experiment on adult female An. stephensi to generate daily per capita values for lifespan, egg production, and biting rate at six constant temperatures. Both temperature and age significantly affected trait values. Further, we found quantitative and qualitative differences between temperature-trait relationships estimated based on daily rates versus directly observed lifetime values. Incorporating these temperature-trait relationships into an expression governing transmission suitability, relative R0(T), model resulted in minor differences in the breadth of suitable temperatures for Plasmodium falciparum transmission between the two models constructed from only An. stephensi trait data, but a substantial increase in breadth compared to a previously published model consisting of trait data from multiple mosquito species. Overall this work highlights the importance of considering how mosquito trait values vary with mosquito age and mosquito species when generating temperature-based environmental suitability predictions of transmission.

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