Modeling landscape-scale pathogen spillover between domesticated and wild hosts: Asian soybean rust and kudzu

Many emerging pathogens infect both domesticated and wild host species, creating the potential for pathogen transmission between domesticated and wild populations. This common situation raises the question of whether managing negative impacts of disease on a focal host population (whether domesticated, endangered, or pest) requires management of only the domesticated host, only the wild host, or both. To evaluate the roles of domesticated and wild hosts in the dynamics of shared pathogens, we developed a spatially implicit model of a pathogen transmitted by airborne spores between two host species restricted to two different landscape patch types. As well as exploring the general dynamics and implications of the model, we fully parameterized our model for Asian soybean rust, a multihost infectious disease that emerged in the United States in 2004. The rust fungus Phakopsora pachyrhizi infects many legume species, including soybeans (Glycine max) and the nonnative invasive species kudzu (Pueraria montana var. lobata). Our model predicts that epidemics are driven by the host species that is more abundant in the landscape. In managed landscapes, this will generally be the domesticated host. However, many pathogens overwinter on a wild host, which acts as the source of initial inoculum at the start of the growing season. Our model predicts that very low local densities of infected wild hosts, surviving in landscape patches separate from the domesticated host, are sufficient to initiate epidemics in the domesticated host, such that managing epidemics by reducing wild host local density may not be feasible. In contrast, managing to reduce pathogen infection of a domesticated host can reduce disease impacts on wild host populations

Modeling landscape-scale pathogen spillover between domesticated and wild hosts: Asian soybean rust and kudzu

Abstract. Many emerging pathogens infect both domesticated and wild host species, creating the potential for pathogen transmission between domesticated and wild populations. This common situation raises the question of whether managing negative impacts of disease on a focal host population (whether domesticated, endangered, or pest) requires management of only the domesticated host, only the wild host, or both. To evaluate the roles of domesticated and wild hosts in the dynamics of shared pathogens, we developed a spatially implicit model of a pathogen transmitted by airborne spores between two host species restricted to two different landscape patch types. As well as exploring the general dynamics and implications of the model, we fully parameterized our model for Asian soybean rust, a multihost infectious disease that emerged in the United States in 2004. The rust fungus Phakopsora pachyrhizi infects many legume species, including soybeans (Glycine max) and the nonnative invasive species kudzu (Pueraria montana var. lobata). Our model predicts that epidemics are driven by the host species that is more abundant in the landscape. In managed landscapes, this will generally be the domesticated host. However, many pathogens overwinter on a wild host, which acts as the source of initial inoculum at the start of the growing season. Our model predicts that very low local densities of infected wild hosts, surviving in landscape patches separate from the domesticated host, are sufficient to initiate epidemics in the domesticated host, such that managing epidemics by reducing wild host local density may not be feasible. In contrast, managing to reduce pathogen infection of a domesticated host can reduce disease impacts on wild host populations

How Development and Survival Combine to Determine the Thermal Sensitivity of Insects

Thermal performance curves (TPCs) depict variation in vital rates in response to temperature and have been an important tool to understand ecological and evolutionary constraints on the thermal sensitivity of ectotherms. TPCs allow for the calculation of indicators of thermal tolerance, such as minimum, optimum, and maximum temperatures that allow for a given metabolic function. However, these indicators are computed using only responses from surviving individuals, which can lead to underestimation of deleterious effects of thermal stress, particularly at high temperatures. Here, we advocate for an integrative frame- work for assessing thermal sensitivity, which combines both vital rates and survival probabilities, and focuses on the temperature interval that allows for population persistence. Using a collated data set of Lepidopteran development rate and survival measured on the same individuals, we show that development rate is generally limiting at low temperatures, while survival is limiting at high temperatures. We also uncover differences between life stages and across latitudes, with extended survival at lower temperatures in temperate regions. Our combined performance metric demonstrates similar thermal breadth in temperate and tropical individuals, an effect that only emerges from integration of both development and survival trends. We discuss the benefits of using this framework in future predictive and management contexts

Twenty years of change in benthic communities across the Belizean Barrier Reef

Disease, storms, ocean warming, and pollution have caused the mass mortality of reef-building corals across the Caribbean over the last four decades. Subsequently, stony corals have been replaced by macroalgae, bacterial mats, and invertebrates including soft corals and sponges, causing changes to the functioning of Caribbean reef ecosystems. Here we describe changes in the absolute cover of benthic reef taxa, including corals, gorgonians, sponges, and algae, at 15 fore-reef sites (12–15m depth) across the Belizean Barrier Reef (BBR) from 1997 to 2016. We also tested whether Marine Protected Areas (MPAs), in which fishing was prohibited but likely still occurred, mitigated these changes. Additionally, we determined whether ocean-temperature anomalies (measured via satellite) or local human impacts (estimated using the Human Influence Index, HII) were related to changes in benthic community structure. We observed a reduction in the cover of reef-building corals, including the long-lived, massive corals Orbicella spp. (from 13 to 2%), and an increase in fleshy and corticated macroalgae across most sites. These and other changes to the benthic communities were unaffected by local protection. The covers of hard-coral taxa, including Acropora spp., Montastraea cavernosa, Orbicella spp., and Porites spp., were negatively related to the frequency of ocean-temperature anomalies. Only gorgonian cover was related, negatively, to our metric of the magnitude of local impacts (HII). Our results suggest that benthic communities along the BBR have experienced disturbances that are beyond the capacity of the current management structure to mitigate. We recommend that managers devote greater resources and capacity to enforcing and expanding existing marine protected areas and to mitigating local stressors, and most importantly, that government, industry, and the public act immediately to reduce global carbon emissions

Beyond the black box: Promoting mathematical collaborations for elucidating interactions in soil ecology

© 2019 The Authors. Understanding soil systems is critical because they form the structural and nutritional foundation for plants and thus every terrestrial habitat and agricultural system. In this paper, we encourage increased use of mathematical models to drive forward understanding of interactions in soil ecological systems. We discuss several distinctive features of soil ecosystems and empirical studies of them. We explore some perceptions that have previously deterred more extensive use of models in soil ecology and some advances that have already been made using models to elucidate soil ecological interactions. We provide examples where mathematical models have been used to test the plausibility of hypothesized mechanisms, to explore systems where experimental manipulations are currently impossible, or to determine the most important variables to measure in experimental and natural systems. To aid in the development of theory in this field, we present a table describing major soil ecology topics, the theory previously used, and providing key terms for theoretical approaches that could potentially address them. We then provide examples from the table that may either contribute to important incremental developments in soil science or potentially revolutionize our understanding of plant-soil systems. We challenge scientists and mathematicians to push theoretical explorations in soil systems further and highlight three major areas for the development of mathematical models in soil ecology: Theory spanning scales and ecological hierarchies, processes, and evolution

Beyond the black box: promoting mathematical collaborations for elucidating interactions in soil ecology

This work is licensed under a Creative Commons Attribution 4.0 International License.Understanding soil systems is critical because they form the structural and nutritional foundation for plants and thus every terrestrial habitat and agricultural system. In this paper, we encourage increased use of mathematical models to drive forward understanding of interactions in soil ecological systems. We discuss several distinctive features of soil ecosystems and empirical studies of them. We explore some perceptions that have previously deterred more extensive use of models in soil ecology and some advances that have already been made using models to elucidate soil ecological interactions. We provide examples where mathematical models have been used to test the plausibility of hypothesized mechanisms, to explore systems where experimental manipulations are currently impossible, or to determine the most important variables to measure in experimental and natural systems. To aid in the development of theory in this field, we present a table describing major soil ecology topics, the theory previously used, and providing key terms for theoretical approaches that could potentially address them. We then provide examples from the table that may either contribute to important incremental developments in soil science or potentially revolutionize our understanding of plant–soil systems. We challenge scientists and mathematicians to push theoretical explorations in soil systems further and highlight three major areas for the development of mathematical models in soil ecology: theory spanning scales and ecological hierarchies, processes, and evolution

Resource Availability Determines Stability for Mutualist–pathogen–host Interactions

Traditional explorations of interspecific interactions have generated extensive bodies of theory on mutualism and disease independently, but few studies have considered the interaction between them. We developed a model exploring the interactions among a fungal mutualist, a viral pathogen, and their shared plant host. Both microbes were assumed to alter the uptake and use of nutrients by the plant. We found that the productivity of the system and the strength of the plant–fungal mutualism influenced community dynamics. In particular, at low productivity, the pathogen may depend on the presence of the fungal mutualist for persistence. Furthermore, under some conditions, both the productivity of the system and the strength of the plant–fungal mutualism may simultaneously cause the mutualist to go extinct. We note the presence of cyclic plant–pathogen population dynamics only in the presence of the mutualist. As found in other models of consumer–resource interactions, cyclic dynamics were driven by high productivity, but, in contrast to simpler systems, high pathogen effectiveness did not consistently lead to cyclic dynamics. In total, association with mutualists can alter host–pathogen interactions, and the reverse is also true in that pathogens may alter host–mutualist interactions

Resource Availability Determines Stability for Mutualist–pathogen–host Interactions

Traditional explorations of interspecific interactions have generated extensive bodies of theory on mutualism and disease independently, but few studies have considered the interaction between them. We developed a model exploring the interactions among a fungal mutualist, a viral pathogen, and their shared plant host. Both microbes were assumed to alter the uptake and use of nutrients by the plant. We found that the productivity of the system and the strength of the plant–fungal mutualism influenced community dynamics. In particular, at low productivity, the pathogen may depend on the presence of the fungal mutualist for persistence. Furthermore, under some conditions, both the productivity of the system and the strength of the plant–fungal mutualism may simultaneously cause the mutualist to go extinct. We note the presence of cyclic plant–pathogen population dynamics only in the presence of the mutualist. As found in other models of consumer–resource interactions, cyclic dynamics were driven by high productivity, but, in contrast to simpler systems, high pathogen effectiveness did not consistently lead to cyclic dynamics. In total, association with mutualists can alter host–pathogen interactions, and the reverse is also true in that pathogens may alter host–mutualist interactions

Data from: The analysis and interpretation of critical temperatures

Critical temperatures are widely used to quantify the upper and lower thermal limits of organisms. But measured critical temperatures often vary with methodological details, leading to spirited discussions about the potential consequences of stress and acclimation during the experiments. We review a model based on the simple assumption that failure rate increases with increasing temperature, independent of previous temperature exposure, water loss or metabolism during the experiment. The model predicts that mean critical thermal maximal temperatures (CTmax) increases nonlinearly with starting temperature and ramping rate, a pattern frequently observed in empirical studies. We then develop a statistical model that estimates a failure rate function (the relationship between failure rate and current temperature) using maximum likelihood; the best model accounts for 58% of the variation in CTmax in an exemplary dataset for tsetse flies. We then extend the model to incorporate potential effects of stress and acclimation on the failure rate function; the results show how stress accumulation at low ramping rate may increase the failure rate and reduce observed values of CTmax. We also applied the model to an acclimation experiment with hornworm larvae that used a single starting temperature and ramping rate; the analyses show that increasing acclimation temperature significantly reduced the slope of the failure rate function, increasing the temperature at which failure occurred. The model directly applies to critical thermal minima, and can utilize data from both ramping and constant temperature assays. Our model provides a new approach to analyzing and interpreting critical temperatures