Do Growers Using Solo Fungicides Affect the Durability of Disease Control of Growers Using Mixtures and Alternations? The Case of Spot-Form Net Blotch in Western Australia.

Growers often use alternations or mixtures of fungicides to slow down the development of resistance to fungicides. However, within a landscape, some growers will implement such resistance management methods, whereas others do not, and may even apply solo components of the resistance management program. We investigated whether growers using solo components of resistant management programs affect the durability of disease control in fields of those who implement fungicide resistance management. We developed a spatially implicit semidiscrete epidemiological model for the development of fungicide resistance. The model simulates the development of epidemics of spot-form net blotch disease, caused by the pathogen Pyrenophora teres f. maculata. The landscape comprises three types of fields, grouped according to their treatment program, with spore dispersal between fields early in the cropping season. In one field type, a fungicide resistance management method is implemented, whereas in the two others, it is not, with one of these field types using a component of the fungicide resistance management program. The output of the model suggests that the use of component fungicides does affect the durability of disease control for growers using resistance management programs. The magnitude of the effect depends on the characteristics of the pathosystem, the degree of inoculum mixing between fields, and the resistance management program being used. Additionally, although increasing the amount of the solo component in the landscape generally decreases the lifespan within which the resistance management program provides effective control, situations exist where the lifespan may be minimized at intermediate levels of the solo component fungicide. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Optimising risk-based surveillance for early detection of invasive plant pathogens.

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid 'putting all your eggs in one basket'. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

The value of volunteer surveillance for the early detection of biological invaders.

Early detection of invaders requires finding small numbers of individuals across large landscapes. It has been argued that the only feasible way to achieve the sampling effort needed for early detection of an invader is to involve volunteer groups (citizen scientists, passive surveyors, etc.). A key concern is that volunteers may have a considerable false-positive and false-negative rate. The question then becomes whether verification of a report from a volunteer is worth the effort. This question is the topic of this paper. Since we are interested in early detection we calculate the Z% upper limit of the one sided confidence interval of the incidence (fraction infected) and use the term maximum expected plausible incidence for this. We compare the maximum plausible incidence when the expert samples on their own, qE∼, and the maximum plausible incidence when the expert only verifies cases reported by the volunteer surveyor to be infected, qV∼. The maximum plausible incidences qE∼ and qV∼. are related as, qV∼=θfp1-θfnqE∼ where θfp and θfn are the false positive and false negative rate of the volunteer surveyor, respectively. We also show that the optimal monitoring programme consists of verifying only the cases reported by the volunteer surveyor if, TXTN<θfp1-θfn, where TN is the time needed for a sample taken by the expert and TX is the time needed for an expert to verify a case reported by a volunteer surveyor. Our results can be used to calculate the maximum plausible incidence of a plant disease based on reports of passive surveyors that have been verified by experts and data from experts sampling on their own. The results can also be used in the development phase of a surveillance project to assess whether including passive surveyor reports is useful in the early detection of exotic invaders.

Modelling cassava production and pest management under biotic and abiotic constraints.

KEY MESSAGE: We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its' pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

What makes or breaks a campaign to stop an invading plant pathogen?

Diseases in humans, animals and plants remain an important challenge in our society. Effective control of invasive pathogens often requires coordinated concerted action of a large group of stakeholders. Both epidemiological and human behavioural factors influence the outcome of a disease control campaign. In mathematical models that are frequently used to guide such campaigns, human behaviour is often ill-represented, if at all. Existing models of human, animal and plant disease that do incorporate participation or compliance are often driven by pay-offs or direct observations of the disease state. It is however very well known that opinion is an important driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), which is an acute bacterial disease that threatens the sustainability of citrus production across the world. We show how by coupling an epidemiological model of this invasive disease with an opinion dynamics model we are able to answer the question: What makes or breaks the effectiveness of a disease control campaign? Frequent contact between stakeholders and advisors is shown to increase the probability of successful control. More surprisingly, we show that informing stakeholders about the effectiveness of control methods is of much greater importance than prematurely increasing their perceptions of the risk of infection. We discuss the overarching consequences of this finding and the effect on human as well as plant disease epidemics.

Smallholder Cassava Planting Material Movement and Grower Behavior in Zambia: Implications for the Management of Cassava Virus Diseases.

Cassava (Manihot esculenta) is an important food crop across sub-Saharan Africa, where production is severely inhibited by two viral diseases, cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), both propagated by a whitefly vector and via human-mediated movement of infected cassava stems. There is limited information on growers' behavior related to movement of planting material, as well as growers' perception and awareness of cassava diseases, despite the importance of these factors for disease control. This study surveyed a total of 96 cassava subsistence growers and their fields across five provinces in Zambia between 2015 and 2017 to address these knowledge gaps. CMD symptoms were observed in 81.6% of the fields, with an average incidence of 52% across the infected fields. No CBSD symptoms were observed. Most growers used planting materials from their own (94%) or nearby (<10 km) fields of family and friends, although several large transactions over longer distances (10 to 350 km) occurred with friends (15 transactions), markets (1), middlemen (5), and nongovernmental organizations (6). Information related to cassava diseases and certified clean (disease-free) seed reached only 48% of growers. The most frequent sources of information related to cassava diseases included nearby friends, family, and neighbors, while extension workers were the most highly preferred source of information. These data provide a benchmark on which to plan management approaches to controlling CMD and CBSD, which should include clean propagation material, increasing growers' awareness of the diseases, and increasing information provided to farmers (specifically disease symptom recognition and disease management options).[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Determinants of optimal insecticide resistance management strategies.

The use of insecticides to control agricultural pests has resulted in resistance developing to most known insecticidal modes of action. Strategies by which resistance can be slowed are necessary to prolong the effectiveness of the remaining modes of action. Here we use a flexible mathematical model of resistance evolution to compare four insecticide application strategies: (i) applying one insecticide until failure, then switching to a second insecticide (sequential application), (ii) mixing two insecticides at their full label doses, (iii) rotating (alternating) two insecticides at full label dose, or (iv) mixing two insecticides at a reduced dose (with each mixture component at half the full label dose). The model represents target-site resistance. Multiple simulations were run representing different insect life-histories and insecticide characteristics. The analysis shows that none of the strategies examined were optimal for all the simulations. The four strategies: reduced dose mixture, label dose mixture, sequential application and label dose rotation, were optimal in 52%, 22%, 20% and 6% of simulations respectively. The most important trait determining the optimal strategy in a single simulation was whether or not the insect pest underwent sexual reproduction. For asexual insects, sequential application was most frequently the optimal strategy, while a label-dose mixture was rarely optimal. Conversely, for sexual insects a mixture was nearly always the optimal strategy, with reduced dose mixture being optimal twice as frequently as label dose mixture. When sequential application of insecticides is not an option, reduced dose mixture is most frequently the optimal strategy whatever an insect's reproduction.

Sampling for disease absence-deriving informed monitoring from epidemic traits.

Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould.

Spatial dynamics and control of a crop pathogen with mixed-mode transmission.

Trade or sharing that moves infectious planting material between farms can, for vertically-transmitted plant diseases, act as a significant force for dispersal of pathogens, particularly where the extent of material movement may be greater than that of infected vectors or inoculum. The network over which trade occurs will then effect dispersal, and is important to consider when attempting to control the disease. We consider the difference that planting material exchange can make to successful control of cassava brown streak disease, an important viral disease affecting one of Africa's staple crops. We use a mathematical model of smallholders' fields to determine the effect of informal trade on both the spread of the pathogen and its control using clean-seed systems, determining aspects that could limit the damage caused by the disease. In particular, we identify the potentially detrimental effects of markets, and the benefits of a community-based approach to disease control.

A method of determining where to target surveillance efforts in heterogeneous epidemiological systems.

The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, and agricultural productivity, worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question-including heterogeneities within the epidemiological system itself. One of the most pronounced realisations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between 'hosts' and 'vectors'-with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled.

Using Epidemiological Principles to Explain Fungicide Resistance Management Tactics: Why do Mixtures Outperform Alternations?

Whether fungicide resistance management is optimized by spraying chemicals with different modes of action as a mixture (i.e., simultaneously) or in alternation (i.e., sequentially) has been studied by experimenters and modelers for decades. However, results have been inconclusive. We use previously parameterized and validated mathematical models of wheat Septoria leaf blotch and grapevine powdery mildew to test which tactic provides better resistance management, using the total yield before resistance causes disease control to become economically ineffective ("lifetime yield") to measure effectiveness. We focus on tactics involving the combination of a low-risk and a high-risk fungicide, and the case in which resistance to the high-risk chemical is complete (i.e., in which there is no partial resistance). Lifetime yield is then optimized by spraying as much low-risk fungicide as is permitted, combined with slightly more high-risk fungicide than needed for acceptable initial disease control, applying these fungicides as a mixture. That mixture rather than alternation gives better performance is invariant to model parameterization and structure, as well as the pathosystem in question. However, if comparison focuses on other metrics, e.g., lifetime yield at full label dose, either mixture or alternation can be optimal. Our work shows how epidemiological principles can explain the evolution of fungicide resistance, and also highlights a theoretical framework to address the question of whether mixture or alternation provides better resistance management. It also demonstrates that precisely how spray tactics are compared must be given careful consideration. [Formula: see text] Copyright © 2018 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Integrating regulatory surveys and citizen science to map outbreaks of forest diseases: acute oak decline in England and Wales.

The number of emerging tree diseases has increased rapidly in recent times, with severe environmental and economic consequences. Systematic regulatory surveys to detect and establish the distribution of pests are crucial for successful management efforts, but resource-intensive and costly. Volunteers who identify potential invasive species can form an important early warning network in tree health; however, what these data can tell us and how they can be best used to inform and direct official survey effort is not clear. Here, we use an extensive dataset on acute oak decline (AOD) as an opportunity to ask how verified data received from the public can be used. Information on the distribution of AOD was available as (i) systematic regulatory surveys conducted throughout England and Wales, and (ii) ad hoc sightings reported by landowners, land managers and members of the public (i.e. 'self-reported' cases). By using the available self-reported cases at the design stage, the systematic survey could focus on defining the boundaries of the affected area. This maximized the use of available resources and highlights the benefits to be gained by developing strategies to enhance volunteer efforts in future programmes.

Extending the durability of cultivar resistance by limiting epidemic growth rates.

Cultivar resistance is an essential part of disease control programmes in many agricultural systems. The use of resistant cultivars applies a selection pressure on pathogen populations for the evolution of virulence, resulting in loss of disease control. Various techniques for the deployment of host resistance genes have been proposed to reduce the selection for virulence, but these are often difficult to apply in practice. We present a general technique to maintain the effectiveness of cultivar resistance. Derived from classical population genetics theory; any factor that reduces the population growth rates of both the virulent and avirulent strains will reduce selection. We model the specific example of fungicide application to reduce the growth rates of virulent and avirulent strains of a pathogen, demonstrating that appropriate use of fungicides reduces selection for virulence, prolonging cultivar resistance. This specific example of chemical control illustrates a general principle for the development of techniques to manage the evolution of virulence by slowing epidemic growth rates.

Emergence of Resistance to Fungicides: The Role of Fungicide Dose.

Resistance to antimicrobial drugs allows pathogens to survive drug treatment. The time taken for a new resistant mutant to reach a population size that is unlikely to die out by chance is called "emergence time." Prolonging emergence time would delay loss of control. We investigate the effect of fungicide dose on the emergence time in fungal plant pathogens. A population dynamical model is combined with dose-response data for Zymoseptoria tritici, an important wheat pathogen. Fungicides suppress sensitive pathogen population. This has two effects. First, the rate of appearance of resistant mutants is reduced, hence the emergence takes longer. Second, more healthy host tissue becomes available for resistant mutants, increasing their chances to invade and accelerates emergence. In theory, the two competing effects may lead to a non-monotonic dependence of the emergence time on fungicide dose that exhibits a minimum. But according to field data, fungicides are unable to reduce the fungicide-sensitive population strongly enough even at high doses. Hence, for full resistance over realistic ranges of pathogen's life history and fungicide dose-response parameters, emergence time decreases monotonically with increasing dose. For partial resistance, there can be cases within a limited parameter range, when emergence decelerates at higher doses.

Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape.

The global increase in the movement of plant products in recent years has triggered an increase in the number of introduced plant pathogens. Plant nurseries importing material from abroad may play an important role in the introduction and spread of diseases such as ash dieback and sudden oak death which are thought to have been introduced through trade. The economic, environmental and social costs associated with the spread of invasive pathogens become considerably larger as the incidence of the pathogen increases. To control the movement of pathogens across the plant trade network it is crucial to develop monitoring programmes at key points of the network such as plant nurseries. By detecting the introduction of invasive pathogens at low incidence, the control and eradication of an epidemic is more likely to be successful. Equally, knowing the likelihood of having sold infected plants once a disease has been detected in a nursery can help designing tracing plans to control the onward spread of the disease. Here, we develop an epidemiological model to detect and track the movement of an invasive plant pathogen into and from a plant nursery. Using statistical methods, we predict the epidemic incidence given that a detection of the pathogen has occurred for the first time, considering that the epidemic has an asymptomatic period between infection and symptom development. Equally, we calculate the probability of having sold at least one infected plant during the period previous to the first disease detection. This analysis can aid stakeholder decisions to determine, when the pathogen is first discovered in a nursery, the need of tracking the disease to other points in the plant trade network in order to control the epidemic. We apply our method to high profile recent introductions including ash dieback and sudden oak death in the UK and citrus canker and Huanglongbing disease in Florida. These results provide new insight for the design of monitoring strategies at key points of the trade network.

The Effect of Farmers' Decisions on Pest Control with Bt Crops: A Billion Dollar Game of Strategy.

A farmer's decision on whether to control a pest is usually based on the perceived threat of the pest locally and the guidance of commercial advisors. Therefore, farmers in a region are often influenced by similar circumstances, and this can create a coordinated response for pest control that is effective at a landscape scale. This coordinated response is not intentional, but is an emergent property of the system. We propose a framework for understanding the intrinsic feedback mechanisms between the actions of humans and the dynamics of pest populations and demonstrate this framework using the European corn borer, a serious pest in maize crops. We link a model of the European corn borer and a parasite in a landscape with a model that simulates the decisions of individual farmers on what type of maize to grow. Farmers chose whether to grow Bt-maize, which is toxic to the corn borer, or conventional maize for which the seed is cheaper. The problem is akin to the snow-drift problem in game theory; that is to say, if enough farmers choose to grow Bt maize then because the pest is suppressed an individual may benefit from growing conventional maize. We show that the communication network between farmers' and their perceptions of profit and loss affects landscape scale patterns in pest dynamics. We found that although adoption of Bt maize often brings increased financial returns, these rewards oscillate in response to the prevalence of pests.

Durable resistance to crop pathogens: an epidemiological framework to predict risk under uncertainty.

Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management. Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic, their effects on the evolution of the pathogen and durability of resistance has not received attention. We formulated a stochastic epidemiological model, based on the Kramer-Moyal expansion of the Master Equation, to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance. We focused on two hypotheses: firstly, a previous deterministic model has suggested that the effect of cropping ratio (the proportion of land area occupied by the resistant crop) on the durability of crop resistance is negligible. Increasing the cropping ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity, but found that the durability does depend on the cropping ratio. Secondly, we tested whether a superimposed external source of stochasticity (for example due to environmental variation or to intermittent fungicide application) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance. We show that in the pathosystem considered here, in general large stochastic fluctuations in epidemics enhance extinction of the pathogen. This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation (stochastic resonance). The results suggest possible disease control practises by exploiting the natural sources of stochasticity.

Mixtures as a fungicide resistance management tactic.

We have reviewed the experimental and modeling evidence on the use of mixtures of fungicides of differing modes of action as a resistance management tactic. The evidence supports the following conclusions. 1. Adding a mixing partner to a fungicide that is at-risk of resistance (without lowering the dose of the at-risk fungicide) reduces the rate of selection for fungicide resistance. This holds for the use of mixing partner fungicides that have either multi-site or single-site modes of action. The resulting predicted increase in the effective life of the at-risk fungicide can be large enough to be of practical relevance. The more effective the mixing partner (due to inherent activity and/or dose), the larger the reduction in selection and the larger the increase in effective life of the at-risk fungicide. 2. Adding a mixing partner while lowering the dose of the at-risk fungicide reduces the selection for fungicide resistance, without compromising effective disease control. The very few studies existing suggest that the reduction in selection is more sensitive to lowering the dose of the at-risk fungicide than to increasing the dose of the mixing partner. 3. Although there are very few studies, the existing evidence suggests that mixing two at-risk fungicides is also a useful resistance management tactic. The aspects that have received too little attention to draw generic conclusions about the effectiveness of fungicide mixtures as resistance management strategies are as follows: (i) the relative effect of the dose of the two mixing partners on selection for fungicide resistance, (ii) the effect of mixing on the effective life of a fungicide (the time from introduction of the fungicide mode of action to the time point where the fungicide can no longer maintain effective disease control), (iii) polygenically determined resistance, (iv) mixtures of two at-risk fungicides, (v) the emergence phase of resistance evolution and the effects of mixtures during this phase, and (vi) monocyclic diseases and nonfoliar diseases. The lack of studies on these aspects of mixture use of fungicides should be a warning against overinterpreting the findings in this review.

The Effect of Horsfall-Barratt Category Size on the Accuracy and Reliability of Estimates of Pecan Scab Severity.

Pecan scab (Fusicladium effusum) is a destructive pecan disease. Disease assessments may be made using interval-scale-based methods or estimates of severity to the nearest percent area diseased. To explore the effects of rating method-Horsfall-Barratt (H-B) scale estimates versus nearest percent estimates (NPEs)-on the accuracy and reliability of severity estimates over different actual pecan scab severity ranges on fruit valves, raters assessed two cohorts of images with actual area (0 to 6, 6+ to 25%, and 25+ to 75%) diseased. Mean estimated disease within each actual disease severity range varied substantially. Means estimated by NPE within each actual disease severity range were not necessarily good predictors of the H-B scale estimate at <25% severity. H-B estimates by raters most often placed severity in the wrong category compared with actual disease. Measures of bias, accuracy, precision, and agreement using Lin's concordance correlation depended on the range of actual severity, with improvements increasing with actual disease severity category (from 0 to 6 through 25+ to 75%); however, the improvement was unaffected by the H-B assessments. Bootstrap analysis indicated that NPEs provided either equally good or more accurate and precise estimate of disease compared with the H-B scale at severities of 25+ to 75%. Inter-rater reliability using NPEs was greater at 25+ to 75% actual disease severity compared with using the H-B scale. Using NPEs compared with the H-B scale will more often result in more precise and accurate estimates of pecan scab severity, particularly when estimating actual disease severities of 25+ to 75%.

The evolution of plant pathogens in response to host resistance: factors affecting the gain from deployment of qualitative and quantitative resistance.

Disease resistance genes are valuable natural resources which should be deployed in a way which maximises the gain to crop productivity before they lose efficacy. Here we present a general epidemiological model for plant diseases, formulated to study the evolution of phenotypic traits of plant pathogens in response to host resistance. The model was used to analyse how the characteristics of the disease resistance, and the method of deployment, affect the size and duration of the gain. The gain obtained from growing a resistant cultivar, compared to a susceptible cultivar, was quantified as the increase in green canopy area resulting from control of foliar disease, integrated over many years-termed 'Healthy Area Duration (HAD) Gain'. Previous work has suggested that the effect of crop ratio (the proportion of land area occupied by the resistant crop) on the gain from qualitative (gene-for-gene) resistance is negligible. Increasing the crop ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur for quantitative, multi-genic resistance, but found that the HAD Gain increased at higher crop ratios. Then we tested the hypothesis that the gain from quantitative host resistance could differ depending on the life-cycle component (sporulation rate or infection efficiency) constrained by the resistance. For the patho-system considered, a quantitative resistant cultivar that reduced the infection efficiency gave a greater HAD Gain than a cultivar that reduced sporulation rate, despite having equivalent transmission rates.