Biology subjects

Johnson, L. R.

Publications and source records attributed to Johnson, L. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Humidity shapes the thermal niche of Anopheles stephensi,an invasive malaria vector

Vector-borne pathogens cause 17% of all human infectious diseases, and rising global temperatures are shifting the distribution and abundance of mosquito vectors. Because mosquitoes are ectotherms, temperature strongly governs biological rates and physiology; however, mosquitoes also experience other environmental factors that may interact with temperature to shape the thermal performance of traits driving population dynamics. Here, we use a factorial life-table experiment spanning five relative humidities (30-90%) and seven temperatures (16-38{whitebullet}C) to show that humidity modifies the thermal performance of key fitness traits in adult Anopheles stephensi, an invasive urban malaria vector. When integrated into a demographic model, humidity markedly reshapes projections of population fitness relative to temperatureonly models, suppressing growth and contracting year-round suitability in hot, arid regions while enhancing fitness in more humid or high-elevation climates characteristic of South Asia and Africa. Together, these results highlight the need to integrate multiple environmental drivers into projections of climatic suitability, as temperature-only approaches may mischaracterize both the magnitude and spatial structure of mosquito population fitness. More broadly, our findings demonstrate how moisture availability reshapes thermal niches, population fitness, and climate-driven projections of vector distributions.

ecology↗

bioRxiv2025

Best Practices for Modeling Arthropod Lifetimes in a Bayesian Framework

Most arthropods are ectothermic, with multiple performance traits being constrained by environmental temperature. Through the effects on traits, temperature therefore constrains when, where, and how large are arthropod populations. Because many arthropods are of relevance to humans, for example as pests or disease vectors, biologists have spent substantial time trying to understand the mechanistic relationship between temperature and traits. For example, in mosquito and other vectors species, the lifespan of vectors - the number of days the adult survives - is important in determining both the size of the vector population and whether or not vectors will likely be able to transmit pathogens. Often thermal traits such as lifetime are modeled using Thermal Performance Curves (TPC) - functions that describe the relationship between temperature and traits mathematically. Many functional forms with many possible shapes have been proposed as TPCs. However, the effects of the distribution of data around these shapes and of common data transformations on how well we can infer the TCPs has been relatively ignored. In this paper we use simulated data on vector lifespans, inspired by mosquito data, to explore the reliability of inference under different assumptions about the data on our ability to accurately infer a known TPC. Using a Bayesian approach, we are also able to quantify the effects of data assumptions and transformations on uncertainty in estimates. Our results suggest that mismatches between a true and assumed distribution of data around a TPC can greatly increase uncertainty. Further, some transformations of the data before analysis are more likely to lead to biased results than others. Based on our results, we make suggestions for best practices in the analysis of arthropod thermal trait data such as lifespan and related traits.

ecology↗

bioRxiv2025

Modeling asymmetric Thermal Performance Curves for proportion data

Ectotherms are subject to temperature impacts on a variety of functional traits, such as fecundity and development. Quantifying the shape of the response of traits to temperature involves fitting (usually) non-linear curves to empirically derived data. This study introduces the LINEX Thermal Performance Curve (TPC), a novel non-linear predictor for modeling asymmetric thermal responses in biological traits. Unlike traditional symmetric TPCs, the LINEX function captures a wide range of asymmetric shapes (e.g., left- or right-skewed), making it particularly suitable for traits like survival or fecundity that often exhibit non-normal, bounded distributions. The study implements the LINEX TPC within a Bayesian framework using the bayesTPC R package. We demonstrate the models flexibility and utility through simulations and experimental data from arthropod thermal traits. Results highlight the LINEX TPCs superior fit for asymmetric traits compared to standard quadratic models, as evidenced by improved performance metrics. The approach addresses challenges in estimating uncertainty in bounded data, providing a robust tool for ecological and evolutionary studies that require precise thermal sensitivity modeling.

ecology↗

bioRxiv2025

Gaussian process forecasting of sparse ecological time series

Ecological time series are often unevenly sampled in time. That is, because the sampling processes used are resource intensive, data may be collected infrequently, or with adaptive frequencies triggered by presence of a target variable. When the data are irregularly spaced, standard time series methods may not be directly applicable. Instead, approaches that take inspiration from linear regression (LR) may be appropriate. In this paper, we explore flexible, nonparametric Gaussian process (GP) models as tools for producing forecasts of unevenly sampled observations. Our example is data on abundances of nymphal Amblyomma americanum from nine locations spread across the eastern United States, collected by NEON. The data exhibit highly variable sampling regimes and abundance levels across locations and time. We implement two versions of GPs to forecast tick abundance and benchmark models against LR approaches. Both GPs are able to capture population patterns without the need for forecasting additional drivers, such as temperature, or specifying a specific relationship between the response and predictors. We find that GP models provide an effective method to forecast irregularly sampled populations at short to intermediate time scales, outperforming LR and other comparators. Open Research statementThis paper uses data that are already published and publicly available together with novel code. Both are provided together, to allow for reproduction of results, at a public git repository linked here: https://bitbucket.org/parulpatil22/tickgp-code/.

ecology↗

bioRxiv2025

Beyond temperature: Relative humidity systematically shifts the temperature dependence of population growth in a malariavector

Understanding ectotherm responses to environmental change is central to coping with many of humanitys current and future challenges in public health, biodiversity conservation, and food security. Complex relationships between abiotic and biotic factors can influence ectotherm abundance and distribution patterns by introducing stage-specific variation in fitness trait responses. Variation in temperature, rainfall, competition, and habitat have all been considered in previous attempts to understand how environmental factors can interact and vary in their relative influence on species maximal population growth rates, rm. However, the combined effects of temperature and humidity on this fundamental metric are poorly understood. We show that variation in relative humidity can influence juvenile trait responses and rms temperature dependence in Anopheles stephensi, an important malaria vector. Our climate suitability maps show that the interactive effects of temperature x humidity on juvenile traits have important implications for predicting how environmental change will influence arthropod-mediated systems.

ecology↗

bioRxiv2025

Local adaptation of the mosquito vector, Aedes aegypti, and implications for predicting the effects of temperature and climate change on dengue transmission.

There is concern that increases in temperature due to climate change could lead to shifts in the dynamics and distribution of mosquito vectors. Many current models assume there are average thermal performance curves for a given vector species transmission. However, this one-size-fits-all assumption ignores the potential for local adaptation to create population-specific differences in thermal performance. In this study, we explored thermal performance of five independent field populations of Ae. aegypti from Mexico, together with a standard laboratory strain. We reared these six populations at temperatures between 13{degrees}C-37{degrees}C to generate thermal performance curves for a suite of life-history traits. Composite models integrating these traits revealed the effects of temperature on population growth rates and dengue virus transmission potential. The results provide strong evidence for the potential for local adaptation in Ae. aegypti populations, challenging applicability of one-size-fits-all thermal performance models to assess climate impact on mosquito-borne diseases.

ecology↗

bioRxiv2025

MIReVTD, a Minimum Information Standard for Reporting Vector Trait Data

Vector-borne diseases pose a persistent and increasing challenge to human, animal, and agricultural systems globally. Mathematical modeling frameworks incorporating vector trait responses are powerful tools to assess risk and predict vector-borne disease impacts. Developing these frameworks and the reliability of their predictions hinge on the availability of experimentally derived vector trait data for model parameterization and inference of the biological mechanisms underpinning transmission. Trait experiments have generated data for many known and potential vector species, but the terminology used across studies is inconsistent, and accompanying publications may share data with insufficient detail for reuse or synthesis. The lack of data standardization can lead to information loss and prohibits analytical comprehensiveness. Here, we present MIReVTD, a Minimum Information standard for Reporting Vector Trait Data. Our reporting checklist balances completeness and labor- intensiveness with the goal of making these important experimental data easier to find and reuse, without onerous effort for scientists generating the data. To illustrate the standard, we provide an example reproducing results from an Aedes aegypti mosquito study.

ecology↗

bioRxiv2024

Thermal biology of aphids and implications for agriculture and food security

Climate change poses significant challenges to agriculture and food security, particularly through its effects on insect vector populations and the pathogens they transmit. Aphids are one of the biggest group of ectotherms that transmit viruses to plants; more than 200 species have been identified as pathogen vectors. These aphids are responsible for transmitting over 300 viruses. The life-history traits of aphids such as fecundity and survival respond strongly and non-linearly to temperature and therefore to global warming. In this study, we elaborate the thermal responses for the main life-history traits (i.e, development and mortality rate) for aphid species for which data were available or generated. Also, thermal responses for virus transmission rates were elaborated to describe plant host to vector and vector to plant host dynamics. With these data, we elaborated thermal suitability models which were used to map current and projected scenarios for the transmission of viruses by aphids. Data was only available/generated for 19 aphid species, many of which only have data either at the lower or upper thermal limits. For virus transmission rates, from host plant to vector and from vector to plant data was only available/generated for potato virus Y (PVY), potato virus A (PVA), and potato leaf roll virus (PLRV) transmitted by the aphid Myzus persicae. Projections show that virus transmission by aphids will shift to northern latitudes. Understanding the thermal biology of aphids is crucial for developing effective measures to safeguard agriculture, especially staple crops, and food security in the context of climate change.

plant biology↗

bioRxiv2024

Effects of temperature on the life-history traits of Myzus persicae and its efficiency in transmitting potato virus Y (PVY) in potato crops

Aphids are highly sensitive to temperature changes and play a crucial role in transmitting plant viruses, accounting for the transmission of more than 50% of viruses that cause disease in crops. Among them, Myzus persicae is a major global pest, affecting over 400 plant species and transmitting more than 100 plant viruses, including potato virus Y (PVY), which poses a severe threat to potato crops. This study examines how temperature influences the life-history traits of M. persicae and its efficiency in transmitting PVY. Our research revealed that temperature significantly affects developmental duration, survival, and fecundity of M. persicae. The aphids exhibit the longest lifespan at 10{degrees}C and the shortest at 30{degrees}C. Similarly, fecundity declined from 29.81 offspring per female at 10{degrees}C to 14.25 at 30{degrees}C. PVY transmission efficiency was highest at 20{degrees}C. We also mapped potential PVY transmission regions and identified tropical and subtropical areas as high-risk due to their favourable temperatures and aphids abundance. Understanding these geographical variations is crucial for effective pest/disease management. Our findings emphasize the importance of integrating climatological, ecological, and epidemiological data to develop robust pest/disease management strategies to mitigate the impact of M. persicae and PVY on potato production and enhancing global food security.

plant biology↗

bioRxiv2024

Divergence of discrete- versus continuous-time calculations of the temperature dependence of maximum population growth rate in a disease vector

O_LIThe temperature dependence of maximal population growth rate (rm) is key to predicting how organisms respond and adapt to natural and anthropogenic changes in climate. For organisms with complex life histories, discrete-time matrix projection models (MPMs) can be used to calculate temperature-dependent rm because they directly capture variation in empirically-observed life-history trait values as well as the time delays inherent in those traits. C_LIO_LIHowever, MPM calculations can be laborious and do not capture the continuous nature of time. Temperature-dependent rm calculated from more complex approaches based on delay-differential equation and integral projection models are more accurate but are notoriously difficult to parameterise. Ordinary differential equation-based models (ODEMs) offer a relatively tractable alternative of intermediate complexity but it is largely unknown whether ODEM-based calculations and MPMs broadly agree when the effects of time delays and altered juvenile survival trajectories on temperature-dependent rm are introduced by environmental variation. C_LIO_LIHere we investigate differences in the predicted temperature dependence of rm obtained from an ODE-based model with those calculated from MPMs using high-resolution temperature- and resource dependent life-history trait data for the globally-distributed disease vector, Aedes aegypti. C_LIO_LIWe show that the level of agreement between discrete- and continuous-time representations of temperature-dependent rm can vary with resource availability, and is extremely sensitive to how juvenile survival is characterised. This finding suggests that analytic rm models can consistently provide comparable rm predictions to standard methods except for under severe resource constraints. C_LIO_LIOur study also suggests that all formulations of the intrinsic growth rate of a population may not be equally accurate for all types of organisms in all situations. Furthermore, this studys findings raise questions relating to whether existing mathematical models can be used to predict and understand population-level effects of environmental change. C_LI

ecology↗

bioRxiv2024

bayesTPC: Bayesian inference for Thermal Performance Curves in R

1O_LIReliable predictions of arthropod responses to climatic warming are important because many of these species perform important roles that can directly impact human society. C_LIO_LIThermal performance curves (TPCs) provide useful information on the physiological constraints that limit the capacity of temperature-sensitive organisms (like arthropods) to exist and grow. C_LIO_LINLS pipelines for fitting TPCs are widely available, but these approaches rely on assumptions that can yield unreliable parameter estimates. C_LIO_LIWe present bayesTPC, an R package for fitting TPCs to trait responses using the nimble language and machinery as the underlying engine for Markov Chain Monte Carlo. bayesTPC aims to support the adoption of Bayesian approaches in thermal physiology, and promote TPC fitting that adequately quantifies uncertainty. C_LI

ecology↗

bioRxiv2023

Variation in temperature of peak trait performance will constrain adaptation of arthropod populations to climatic warming.

The capacity of arthropod populations to adapt to long-term climatic warming is uncertain. Here, we combine theory and extensive data on diverse arthropod taxa to show that their rate of thermal adaptation to climatic warming will be constrained in two fundamental ways. First, the rate of thermal adaptation is predicted to be limited by the rate of shift in the temperature of peak performance of four life-history traits in a specific order: juvenile development, adult fecundity, juvenile mortality, and adult mortality. Second, thermal adaptation will be constrained due to differences in the temperature of peak performance among these four traits, which are expected to persist because of trade-offs. By compiling a new global dataset of 61 diverse arthropod species, we find strong evidence that contemporary populations have indeed evolved under these constraints. Our results provide a basis for using relatively feasible trait measurements to predict the adaptive capacity of diverse arthropod populations to climatic warming.

ecology↗

bioRxiv2022

Mapping current and future thermal limits to suitability for malaria transmission by the invasive mosquito Anopheles stephensi

BackgroundAnopheles stephensi is a malaria-transmitting mosquito that has recently expanded from its primary range in Asia and the Middle East, to locations in Africa. This species is a competent vector of both P. falciparum (PF) and P. vivax (PV) malaria. Perhaps most alarming, the characteristics of An. stephensi, such as container breeding and anthropophily, make it particularly adept at exploiting built environments in areas with no prior history of malaria risk. MethodsIn this paper we created global maps of thermal transmission suitability and people at risk (PAR) for malaria transmission by An. stephensi, under current and future climate. Temperature-dependent transmission suitability thresholds derived from recently published species-specific thermal curves were used to threshold gridded, monthly mean temperatures under current and future climatic conditions. These temperature driven transmission models were coupled with gridded population data for 2020 and 2050, under climate-matched scenarios for future outcomes, to compare with baseline predictions for 2020 populations. ResultsUsing the Global Burden of Disease regions approach, we found that heterogenous regional increases and decreases in risk did not mask the overall pattern of massive increases of PAR for malaria transmission suitability with An. stephensi presence. General patterns of poleward expansion for thermal suitability were seen for both PF and PV transmission potential. ConclusionsUnderstanding the potential suitability for An. stephensi transmission in a changing climate provides a key tool for planning, given an ongoing invasion and expansion of the vector. Anticipating the potential impact of onward expansion to transmission suitable areas, and the size of population at risk under future climate scenarios, and where they occur, can serve as a large-scale call for attention, planning, and monitoring.

ecology↗

bioRxiv2019

Understanding the effect of temperature on Bluetongue disease risk in livestock

The transmission of vector-borne diseases is governed by complex factors including pathogen characteristics, vector-host interactions, and environmental conditions. Temperature is a major driver for many vector-borne diseases including Bluetongue viral (BTV) disease, a midge-borne febrile disease of ruminants, notably livestock, whose etiology ranges from mild or asymptomatic to rapidly fatal, thus threatening animal agriculture and the economy of affected countries. Using modeling tools, we seek to predict where transmission can occur based on suitable temperatures for BTV. We fit thermal performance curves to temperature sensitive midge life history traits, using a Bayesian approach. Then, we incorporated these into a new formula for the disease basic reproductive number, R0, to include trait responses, for two species of key midge vectors, Culicoides sonorensis and Culicoides variipennis. Our results show that outbreaks of BTV are more likely between 15{degrees}C and 33{degrees}C with predicted peak transmission at 26{degrees}C. The greatest uncertainty in R0 is associated with the uncertainty in: mortality and fecundity of midges near optimal temperature for transmission; midges probability of becoming infectious post infection at the lower edge of the thermal range; and the biting rate together with vector competence at the higher edge of the thermal range. We compare our R0 to two other R0 formulations and show that incorporating thermal curves into all three leads to similar BTV risk predictions. To demonstrate the utility of this model approach, we created global suitability maps indicating the areas at high and long-term risk of BTV transmission, to assess risk, and anticipate potential locations of establishment.

ecology↗

bioRxiv2019

Transmission of West Nile virus and other temperate mosquito-borne viruses occurs at lower environmental temperatures than tropical diseases

The temperature-dependence of many important mosquito-borne diseases has never been quantified. These relationships are critical for understanding current distributions and predicting future shifts from climate change. We used trait-based models to characterize temperature-dependent transmission of 10 vector-pathogen pairs of mosquitoes (Culex pipiens, Cx. quinquefascsiatus, Cx. tarsalis, and others) and viruses (West Nile, Eastern and Western Equine Encephalitis, St. Louis Encephalitis, Sindbis, and Rift Valley Fever viruses), most with substantial transmission in temperate regions. Transmission is optimized at intermediate temperatures (23-26{degrees}C) and often has wider thermal breadths (due to cooler lower thermal limits) compared to pathogens with predominately tropical distributions (in previous studies). The incidence of human West Nile virus cases across US counties responded unimodally to average summer temperature and peaked at 24{degrees}C, matching model-predicted optima (24-25{degrees}C). Climate warming will likely shift transmission of these diseases, increasing it in cooler locations while decreasing it in warmer locations.

ecology↗

bioRxiv2018

More than a flying syringe: Using functional traits in vector borne disease research

Many important endemic and emerging diseases are vector-borne. The functional traits of vectors affect not just pathogen transmission rates, but also the fitness and population dynamics of these animals themselves. Increasing empirical evidence suggests that vector traits vary significantly at time scales relevant to transmission dynamics. Currently, an understanding of how this variation in key traits impacts transmission is hindered by a lack of both empirical data and theoretical methods for mechanistically incorporating traits into transmission models. Here, we present a framework for incorporating both intrinsic and environment-driven variation in vector traits into empirical and theoretical vector-borne disease research. This framework mechanistically captures the effect of trait variation on vector fitness, the correlation between vector traits, and how these together determine transmission dynamics. We illustrate how trait-based vector-borne disease modelling can make novel predictions, and identify key steps and challenges in the construction, empirical parameterisation and validation of such models, as well as the organization and prioritization of data collection efforts.

ecology↗

bioRxiv2018

Predicting the fundamental thermal niche of crop pests and diseases in a changing world: a case study on citrus greening

O_LIPredicting where crop pests and diseases can occur, both now and in the future under different climate change scenarios, is a major challenge for crop management. One solution is to estimate the fundamental thermal niche of the pest/disease to indicate where establishment is possible. Here we develop methods for estimating and displaying the fundamental thermal niche of pests and pathogens and apply these methods to Huanglongbing (HLB), a vector-borne disease that is currently threatening the citrus industry worldwide.\nC_LIO_LIWe derive a suitability metric based on a mathematical model of HLB transmission between tree hosts and its vector Diaphorina citri, and incorporate the effect of temperature on vector traits using data from laboratory experiments performed at different temperatures. We validate the model using data on the historical range of HLB.\nC_LIO_LIOur model predicts that transmission of HLB is possible between 16{degrees}C and 33{degrees}C with peak transmission at ~25{degrees}C. The greatest uncertainty in our suitability metric is associated with the mortality of the vectors at peak transmission, and fecundity at the edges of the thermal range, indicating that these parameters need further experimental work.\nC_LIO_LIWe produce global thermal niche maps by plotting how many months each location is suitable for establishment of the pest/disease. This analysis reveals that the highest suitability for HLB occurs near the equator in large citrus-producing regions, such as Brazil and South-East Asia. Within the northern hemisphere, the Iberian peninsula and California are HLB suitable for up to 7 months of the year and are free of HLB currently.\nC_LIO_LIPolicy implications. The thermal niche map indicates the places at greatest risk of HLB establishment should the disease enter these regions. This indicates where surveillance should be focused to prevent establishment. Our mechanistic method can be used to predict new areas for HLB transmission under different climate change scenarios and is easily adapted to other vector-borne diseases.\nC_LI

epidemiology↗

bioRxiv2018

A tutorial on Bayesian parameter inference for dynamic energy budget models

Mechanistic representations of individual life-history trajectories are powerful tools for the prediction of organismal growth, reproduction and survival under novel environmental conditions. Dynamic energy budget (DEB) theory provides compact models to describe the acquisition and allocation of energy by organisms over their full life cycle. However, estimating DEB model parameters, and their associated uncertainties and covariances, is not trivial. Bayesian inference provides a coherent way to estimate parameter uncertainty, and propagate it through the model, while also making use of prior information to constrain the parameter space. We outline a Bayesian inference approach for energy budget models and provide two case studies - based on a simplified DEBkiss model, and the standard DEB model - detailing the implementation of such inference procedures using the open-source software package deBInfer. We demonstrate how DEB and DEBkiss parameters can be estimated in a Bayesian framework, but our results also highlight the difficulty of identifying DEB model parameters which serves as a reminder that fitting these models requires statistical caution.

physiology↗