Eco-efficiency as a strategy for optimizing the sustainability of pest management.

Agricultural industrialization and the subsequent reliance on pesticides has resulted in numerous unintended consequences, such as impacts upon the environment and by extension human health. Eco-efficiency is a strategy for sustainably increasing production, while simultaneously decreasing these externalities on ecological systems. Eco-efficiency is defined as the ratio of production to environmental impacts. It has been widely adopted to improve chemical production, but we investigate the challenges of applying eco-efficiency to pesticide use. Eco-efficiency strategies include technological innovation, investment in research and development, improvement of business processes, and accounting for market forces. These components are often part of integrated pest management (IPM) systems that include alternatives to pesticides, but its implementation is often thwarted by commercial realities and technical challenges. We propose the creation and adoption of an eco-efficiency index for pesticide use so that the broad benefits of eco-efficient strategies such as IPM can be more readily quantified. We propose an index based upon the ratio of crop yield to a risk quotient (RQ) calculated from pesticide toxicity. Eco-efficiency is an operational basis for optimizing pest management for sustainability. It naturally favors adoption of IPM and should be considered by regulators, researchers, and practitioners involved in pest management. © 2019 Society of Chemical Industry.

Perspective: service-based business models to incentivize the efficient use of pesticides in crop protection.

Several problems limit the productivity and acceptance of crop protection, including pesticide overuse, pesticide resistance, poor adoption of integrated pest management (IPM), declining funding for research and extension, and inefficiencies of scale. We discuss the proposition that alternative business models for crop protection can address these problems by incentivizing and benefiting from efficiency of pesticide use. Currently, business models are not linked to the adoption of IPM and are sometimes at odds with IPM practices. We explore a business model based on the provision of pest management adequacy through services rather than the sale of pesticide products. Specifically, we advocate for establishment of crop protection adequacy standards that would allow a market system to maximize efficiency. Changing some of the relationships between agricultural companies and producers from one based on products to one based on services is an idea worthy of debate and evaluation for improving the efficiency of pest management. Contemporary information technology enhancing monitoring and coordination warrants attention in this debate. © 2019 Society of Chemical Industry.

Weather-Based Models for Assessing the Risk of Sclerotinia sclerotiorum Apothecial Presence in Soybean (Glycine max) Fields.

Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable. Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).

Focus expansion and stability of the spread parameter estimate of the power law model for dispersal gradients.

Empirical and mechanistic modeling indicate that pathogens transmitted via aerially dispersed inoculum follow a power law, resulting in dispersive epidemic waves. The spread parameter (b) of the power law model, which is an indicator of the distance of the epidemic wave front from an initial focus per unit time, has been found to be approximately 2 for several animal and plant diseases over a wide range of spatial scales under conditions favorable for disease spread. Although disease spread and epidemic expansion can be influenced by several factors, the stability of the parameter b over multiple epidemic years has not been determined. Additionally, the size of the initial epidemic area is expected to be strongly related to the final epidemic extent for epidemics, but the stability of this relationship is also not well established. Here, empirical data of cucurbit downy mildew epidemics collected from 2008 to 2014 were analyzed using a spatio-temporal model of disease spread that incorporates logistic growth in time with a power law function for dispersal. Final epidemic extent ranged from 4.16 ×108 km2 in 2012 to 6.44 ×108 km2 in 2009. Current epidemic extent became significantly associated (P < 0.0332; 0.56 < R2 < 0.99) with final epidemic area beginning near the end of April, with the association increasing monotonically to 1.0 by the end of the epidemic season in July. The position of the epidemic wave-front became exponentially more distant with time, and epidemic velocity increased linearly with distance. Slopes from the temporal and spatial regression models varied with about a 2.5-fold range across epidemic years. Estimates of b varied substantially ranging from 1.51 to 4.16 across epidemic years. We observed a significant b ×time (or distance) interaction (P < 0.05) for epidemic years where data were well described by the power law model. These results suggest that the spread parameter b may not be stable over multiple epidemic years. However, b ≈ 2 may be considered the lower limit of the distance traveled by epidemic wave-fronts for aerially transmitted pathogens that follow a power law dispersal function.

Climate Suitability for Magnaporthe oryzae Triticum Pathotype in the United States.

Wheat blast, caused by the Triticum pathotype of Magnaporthe oryzae, is an emerging disease considered to be a limiting factor to wheat production in various countries. Given the importance of wheat blast as a high-consequence plant disease, weather-based infection models were used to estimate the probabilities of M. oryzae Triticum establishment and wheat blast outbreaks in the United States. The models identified significant disease risk in some areas. With the threshold levels used, the models predicted that the climate was adequate for maintaining M. oryzae Triticum populations in 40% of winter wheat production areas of the United States. Disease outbreak threshold levels were only reached in 25% of the country. In Louisiana, Mississippi, and Florida, the probability of years suitable for outbreaks was greater than 70%. The models generated in this study should provide the foundation for more advanced models in the future, and the results reported could be used to prioritize research efforts regarding the biology of M. oryzae Triticum and the epidemiology of the wheat blast disease.

Phylogenetic structure and host abundance drive disease pressure in communities.

Pathogens play an important part in shaping the structure and dynamics of natural communities, because species are not affected by them equally. A shared goal of ecology and epidemiology is to predict when a species is most vulnerable to disease. A leading hypothesis asserts that the impact of disease should increase with host abundance, producing a 'rare-species advantage'. However, the impact of a pathogen may be decoupled from host abundance, because most pathogens infect more than one species, leading to pathogen spillover onto closely related species. Here we show that the phylogenetic and ecological structure of the surrounding community can be important predictors of disease pressure. We found that the amount of tissue lost to disease increased with the relative abundance of a species across a grassland plant community, and that this rare-species advantage had an additional phylogenetic component: disease pressure was stronger on species with many close relatives. We used a global model of pathogen sharing as a function of relatedness between hosts, which provided a robust predictor of relative disease pressure at the local scale. In our grassland, the total amount of disease was most accurately explained not by the abundance of the focal host alone, but by the abundance of all species in the community weighted by their phylogenetic distance to the host. Furthermore, the model strongly predicted observed disease pressure for 44 novel host species we introduced experimentally to our study site, providing evidence for a mechanism to explain why phylogenetically rare species are more likely to become invasive when introduced. Our results demonstrate how the phylogenetic and ecological structure of communities can have a key role in disease dynamics, with implications for the maintenance of biodiversity, biotic resistance against introduced weeds, and the success of managed plants in agriculture and forestry.

The impact of plant enemies shows a phylogenetic signal.

The host ranges of plant pathogens and herbivores are phylogenetically constrained, so that closely related plant species are more likely to share pests and pathogens. Here we conducted a reanalysis of data from published experimental studies to test whether the severity of host-enemy interactions follows a similar phylogenetic signal. The impact of herbivores and pathogens on their host plants declined steadily with phylogenetic distance from the most severely affected focal hosts. The steepness of this phylogenetic signal was similar to that previously measured for binary-response host ranges. Enemy behavior and development showed similar, but weaker phylogenetic signal, with oviposition and growth rates declining with evolutionary distance from optimal hosts. Phylogenetic distance is an informative surrogate for estimating the likely impacts of a pest or pathogen on potential plant hosts, and may be particularly useful in early assessing risk from emergent plant pests, where critical decisions must be made with incomplete host records.

Using a network model to assess risk of forest pest spread via recreational travel.

Long-distance dispersal pathways, which frequently relate to human activities, facilitate the spread of alien species. One pathway of concern in North America is the possible spread of forest pests in firewood carried by visitors to campgrounds or recreational facilities. We present a network model depicting the movement of campers and, by extension, potentially infested firewood. We constructed the model from US National Recreation Reservation Service data documenting more than seven million visitor reservations (including visitors from Canada) at campgrounds nationwide. This bi-directional model can be used to identify likely origin and destination locations for a camper-transported pest. To support broad-scale decision making, we used the model to generate summary maps for 48 US states and seven Canadian provinces that depict the most likely origins of campers traveling from outside the target state or province. The maps generally showed one of two basic spatial patterns of out-of-state (or out-of-province) origin risk. In the eastern United States, the riskiest out-of-state origin locations were usually found in a localized region restricted to portions of adjacent states. In the western United States, the riskiest out-of-state origin locations were typically associated with major urban areas located far from the state of interest. A few states and the Canadian provinces showed characteristics of both patterns. These model outputs can guide deployment of resources for surveillance, firewood inspections, or other activities. Significantly, the contrasting map patterns indicate that no single response strategy is appropriate for all states and provinces. If most out-of-state campers are traveling from distant areas, it may be effective to deploy resources at key points along major roads (e.g., interstate highways), since these locations could effectively represent bottlenecks of camper movement. If most campers are from nearby areas, they may have many feasible travel routes, so a more widely distributed deployment may be necessary.

Dispersal of invasive forest insects via recreational firewood: a quantitative analysis.

Recreational travel is a recognized vector for the spread of invasive species in North America. However, there has been little quantitative analysis of the risks posed by such travel and the associated transport of firewood. In this study, we analyzed the risk of forest insect spread with firewood and estimated related dispersal parameters for application in geographically explicit invasion models. Our primary data source was the U.S. National Recreation Reservation Service database, which records camper reservations at > 2,500 locations nationwide. For > 7 million individual reservations made between 2004 and 2009 (including visits from Canada), we calculated the distance between visitor home address and campground location. We constructed an empirical dispersal kernel (i.e., the probability distribution of the travel distances) from these "origin-destination" data, and then fitted the data with various theoretical distributions. We found the data to be strongly leptokurtic (fat-tailed) and fairly well fit by the unbounded Johnson and lognormal distributions. Most campers ( approximately 53%) traveled <100 km, but approximately 10% traveled > 500 km (and as far as 5,500 km). Additionally, we examined the impact of geographic region, specific destinations (major national parks), and specific origin locations (major cities) on the shape of the dispersal kernel, and found that mixture distributions (i.e., theoretical distribution functions composed of multiple univariate distributions) may fit better in some circumstances. Although only a limited amount of all transported firewood is likely to be infested by forest insects, this still represents a considerable increase in dispersal potential beyond the insects' natural spread capabilities.

Evolutionary tools for phytosanitary risk analysis: phylogenetic signal as a predictor of host range of plant pests and pathogens.

Assessing risk from a novel pest or pathogen requires knowing which local plant species are susceptible. Empirical data on the local host range of novel pests are usually lacking, but we know that some pests are more likely to attack closely related plant species than species separated by greater evolutionary distance. We use the Global Pest and Disease Database, an internal database maintained by the United States Department of Agriculture Animal and Plant Health Inspection Service - Plant Protection and Quarantine Division (USDA APHIS-PPQ), to evaluate the strength of the phylogenetic signal in host range for nine major groups of plant pests and pathogens. Eight of nine groups showed significant phylogenetic signal in host range. Additionally, pests and pathogens with more known hosts attacked a phylogenetically broader range of hosts. This suggests that easily obtained data - the number of known hosts and the phylogenetic distance between known hosts and other species of interest - can be used to predict which plant species are likely to be susceptible to a particular pest. This can facilitate rapid assessment of risk from novel pests and pathogens when empirical host range data are not yet available and guide efficient collection of empirical data for risk evaluation.

Developmental database for phenology models: related insect and mite species have similar thermal requirements.

Two values of thermal requirements, the lower developmental threshold (LDT), that is, the temperature at which development ceases, and the sum of effective temperatures, that is, day degrees above the LDT control the development of ectotherms and are used in phenology models to predict time at which the development of individual stages of a species will be completed. To assist in the rapid development of phenology models, we merged a previously published database of thermal requirements for insects, gathered by online search in CAB Abstracts, with independently collected data for insects and mites from original studies. The merged database comprises developmental times at various constant temperatures on 1,054 insect and mite species, many of them in several populations, mostly pests and their natural enemies, from all over the world. We show that closely related species share similar thermal requirements and therefore, for a species with unknown thermal requirements, the value of LDT and sum of effective temperatures of its most related species from the database can be used.

Enhancing early detection of exotic pests in agricultural and forest ecosystems using an urban-gradient framework.

Urban areas are hubs of international transport and therefore are major gateways for exotic pests. Applying an urban gradient to analyze this pathway could provide insight into the ecological processes involved in human-mediated invasions. We defined an urban gradient for agricultural and forest ecosystems in the contiguous United States to (1) assess whether ecosystems nearer more urbanized areas were at greater risk of invasion, and (2) apply this knowledge to enhance early detection of exotic pests. We defined the gradient using the tonnage of imported products in adjacent urban areas and their distance to nearby agricultural or forest land. County-level detection reports for 39 exotic agricultural and forest pests of major economic importance were used to characterize invasions along the gradient. We found that counties with more exotic pests were nearer the urban end of the gradient. Assuming that the exotic species we analyzed represent typical invaders, then early detection efforts directed at 21-26% of U.S. agricultural and forest land would likely be able to detect 70% of invaded counties and 90% of the selected species. Applying an urban-gradient framework to current monitoring strategies should enhance early detection efforts of exotic pests, facilitating optimization in allocating resources to areas at greater risk of future invasions.

Modeling spatial establishment patterns of exotic forest insects in urban areas in relation to tree cover and propagule pressure.

As international trade increases so does the prominence of urban areas as gateways for exotic forest insects (EFI). Delimiting hot spots for invasions (i.e., areas where establishment is likely) within urban areas would facilitate monitoring efforts. We used a propagule-pressure framework to delimit establishment hot spots of a hypothetical generalist EFI in six U.S. urban areas: Chicago, Detroit, Houston, Los Angeles-Long Beach-Santa Ana, New York-Newark, and Seattle. We assessed how urban tree cover and propagule pressure interact to delimit establishment hot spots and compared the location of these hot spots with actual recent U.S. detections of two EFI: the Asian strain of the gypsy moth, Lymantria dispar (L.) (Lepidoptera: Lymantriidae), and Asian longhorned beetle, Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae). Using a lattice of 5-km-diameter cells for each urban area, we used the input data (urban tree cover and propagule pressure) to model establishment and Moran's I to delimit hot spots. We used urban population size and the area of commercial-industrial land use as indicators of propagule pressure in the model. Relative establishment of EFI was influenced more by the two propagule pressure indicators than by tree cover. The delimited land use-based hot spots for Los Angeles-Long Beach-Santa Ana and New York-Newark encompassed more of the actual detections of L. dispar and A. glabripennis, respectively, than the population-based hot spots. No significant difference occurred between hot spot types for A. glabripennis detections in the Chicago urban area. Implications of these findings for management and design of monitoring programs in urban areas are discussed.