Publications by authors named "Devin Kirk"

Predicting the effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate and weather anomalies by compiling a global database (4339 plant-disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesised that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported.

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Article Synopsis
  • The study investigates how temperature affects schistosomiasis, a disease caused by schistosome parasites and their host snails, particularly in sub-Saharan Africa where the disease is common.
  • Previous models underestimated the effective temperature range for schistosomiasis transmission, prompting this research to analyze how temperature influences the parasites and snails involved.
  • The findings indicate that optimal transmission temperatures are higher than previously thought, suggesting that climate change may increase schistosomiasis risk in regions currently suitable for the disease.
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The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis' thermal optimum at 21.7 °C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic.

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Climate change has profound effects on infectious disease dynamics, yet the impacts of increased short-term temperature fluctuations on disease spread remain poorly understood. We empirically tested the theoretical prediction that short-term thermal fluctuations suppress endemic infection prevalence at the pathogen's thermal optimum. This prediction follows from a mechanistic disease transmission model analyzed using stochastic simulations of the model parameterized with thermal performance curves (TPCs) from metabolic scaling theory and using nonlinear averaging, which predicts ecological outcomes consistent with Jensen's inequality (i.

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Background: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale.

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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.

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Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March-June, 2020) in Santa Clara County, California.

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The potential for adaptive evolution to enable species persistence under a changing climate is one of the most important questions for understanding impacts of future climate change. Climate adaptation may be particularly likely for short-lived ectotherms, including many pest, pathogen, and vector species. For these taxa, estimating climate adaptive potential is critical for accurate predictive modeling and public health preparedness.

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Vector-borne diseases (VBDs) are embedded within complex socio-ecological systems. While research has traditionally focused on the direct effects of VBDs on human morbidity and mortality, it is increasingly clear that their impacts are much more pervasive. VBDs are dynamically linked to feedbacks between environmental conditions, vector ecology, disease burden, and societal responses that drive transmission.

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Predicting the effects of seasonality and climate change on the emergence and spread of infectious disease remains difficult, in part because of poorly understood connections between warming and the mechanisms driving disease. Trait-based mechanistic models combined with thermal performance curves arising from the metabolic theory of ecology (MTE) have been highlighted as a promising approach going forward; however, this framework has not been tested under controlled experimental conditions that isolate the role of gradual temporal warming on disease dynamics and emergence. Here, we provide experimental evidence that a slowly warming host-parasite system can be pushed through a critical transition into an epidemic state.

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Ecological communities are partly structured by indirect interactions, where one species can indirectly affect another by altering its interactions with a third species. In the absence of direct predation, nonconsumptive effects of predators on prey have important implications for subsequent community interactions. To better understand these interactions, we used a -parasite-predator cue system to evaluate if predation risk affects responses to a parasite.

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Non-pharmaceutical interventions to combat COVID-19 transmission have worked to slow the spread of the epidemic but can have high socio-economic costs. It is critical we understand the efficacy of non-pharmaceutical interventions to choose a safe exit strategy. Many current models are not suitable for assessing exit strategies because they do not account for epidemic resurgence when social distancing ends prematurely (e.

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Parasitism and competition are both ubiquitous interactions in ecological communities. The ability of host species to interact directly via competition and indirectly through shared parasites suggests that host traits related to competition and parasitism are likely important in structuring communities and disease dynamics. Specifically, those host traits affecting competition and those mediating parasitism are often correlated either because of trade-offs (in resource acquisition or resource allocation) or condition dependence, yet the consequences of these trait relationships for community and epidemiological dynamics are poorly understood.

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Article Synopsis
  • The metabolic theory of ecology (MTE) helps predict how climate change impacts disease spread by examining the relationship between temperature, host size, and parasite transmission.
  • Experiments with a gut parasite revealed that contact rates increase with temperature and host size, while the infection rate has a specific response to temperature and decreases with host size.
  • A 3.5 times higher infection probability was observed in smaller hosts compared to larger ones, and small temperature changes can lead to significant variations in transmission, confirming that MTE can effectively forecast these dynamics.
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The complexity of host-parasite interactions makes it difficult to predict how host-parasite systems will respond to climate change. In particular, host and parasite traits such as survival and virulence may have distinct temperature dependencies that must be integrated into models of disease dynamics. Using experimental data from Daphnia magna and a microsporidian parasite, we fitted a mechanistic model of the within-host parasite population dynamics.

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Trophic interactions are likely to change under climate warming. These interactions can be altered directly by changing consumption rates, or indirectly by altering growth rates and size asymmetries among individuals that in turn affect feeding. Understanding these processes is particularly important for intraspecific interactions, as direct and indirect changes may exacerbate antagonistic interactions.

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