Plant viruses, often spread by arthropod vectors, impact natural and agricultural ecosystems worldwide. Intuitively, the movement behavior and life history of vectors influence pathogen spread, but the relative contribution of each factor has not been examined. Recent research has highlighted the influence of host infection status on vector behavior and life history. Here, we developed a model to explore how vector traits influence the spread of vector‐borne plant viruses. We allowed vector life history (growth rate, carrying capacity) and movement behavior (departure and settlement rates) parameters to be conditional on whether the plant host is infected or healthy and whether the vector is viruliferous (carrying the virus) or not. We ran simulations under a wide range of parameter combinations and quantified the fraction of hosts infected over time. We also ran case studies of the model for Barley yellow dwarf virus, a persistently transmitted virus, and for Potato virus Y, a non‐persistently transmitted virus. We quantified the relative importance of each parameter on pathogen spread using Latin hypercube sampling with the statistical partial rank correlation coefficient technique. We found two general types of mechanisms in our model that increased the rate of pathogen spread. First, increasing factors such as vector intrinsic growth rate, carrying capacity, and departure rate from hosts (independent of whether these factors were condition‐dependent) led to more vectors moving between hosts, which increased pathogen spread. Second, changing condition‐dependent factors such as a vector's preference for settling on a host with a different infection status than itself, and vector tendency to leave a host of the same infection status, led to increased contact between hosts and vectors with different infection statuses, which also increased pathogen spread. Overall, our findings suggest that vector population growth rates had the greatest influence on rates of virus spread, but rates of vector dispersal from infected hosts and from hosts of the same infection status were also very important. Our model highlights the importance of simultaneously considering vector life history and behavior to better understand pathogen spread. Although developed for plant viruses, our model could readily be utilized with other vector‐borne pathogen systems.

Recently, data have emerged indicating that not only high food carbon:phosphorus (C:P) ratio but also low food C:P (P-rich food) can have negative effects on the growth of consumers. The shape of this “stoichiometric knife edge,” however, is not yet well-documented, and the mechanisms underpinning it are not understood. Here we report the results of experiments using 3 species of Daphnia (D. magna, D. pulicaria, D. pulex) consuming the green alga Scenedesmus acutus with widely varying C:P ratios (from 1500 by atoms). The experiments were designed to (1) characterize the potential stoichiometric knife edge for each species, and (2) evaluate potential changes in feeding and respiration rates that may underpin the unimodal response to food C:P. All 3 Daphnia species grew more slowly when food C:P (atomic) exceeded ~250–300 but also when C:P was <120. Both high and low C:P foods were associated with increased respiration rates, indicating that the negative effects of food C:P imbalance at leas...

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations (FDEs) as approximations of some type of fractional nonlinear birth and death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While ordinary differential equations (ODEs) are commonly used to model epidemics, FDEs are more flexible in fitting empirical data and theoretically offer improved model predictions. The question arises whether, in practice, the benefits of using FDEs over ODEs outweigh the added computational complexities. While important differences in transient dynamics were observed, the FDE only outperformed the ODE in one of out three data sets. In general, FDE modeling approaches may be worth it in situations with large refined data sets and good numerical algorithms.