Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance

Precipitation change accentuates or reverses temperature effects on aphid dispersal

Global temperatures are generally increasing, and this is leading to a well documented advancement and extension of seasonal activity of many pest insects. Effects of changing precipitation have received less attention, but might be complex because rain and snow are increasing in some places but decreasing in others. This raises the possibility that altered precipitation could accentuate, or even reverse, the effects of rising temperatures on pest outbreaks. We used >592 K aphid suction-trap captures over 15 years, in the heavily farmed central USA, to examine how the activity of Aphis glycines (soybean aphid), Rhopalosiphum maidis (corn aphid), and Rhopalosiphum padi (bird cherry-oat aphid) changed with variation in both temperature and precipitation. Increasing precipitation caused late-season flight activity of A. glycines and early-season activity of R. padi to shift earlier, while increasing temperature did the same for early-season activity of A. glycines and R. maidis. In these cases, precipitation and temperature exhibited directionally similar, but independent, effects. However, precipitation sometimes mediated temperature effects in complex ways. At relatively low temperatures, greater precipitation generally caused late-season flights of R. maidis to occur earlier. However, this pattern was reversed at higher temperatures with precipitation delaying late-season activity. In contrast, greater precipitation delayed peak flights of R. padi at lower temperatures, but caused them to occur earlier at higher temperatures. So, in these two cases the interactive effects of precipitation on temperature were mirror images of one another. When projecting future aphid flight phenology, models that excluded precipitation covariates consistently underpredicted the degree of phenological advance for A. glycines and R. padi, and underpredicted the degree of phenological delay for R. maidis under expected future climates. Overall, we found broad evidence that changing patterns of aphid flight phenology could only be understood by considering both temperature and precipitation changes. In our study region, temperature and precipitation are expected to increase in tandem, but these correlations will be reversed elsewhere. This reinforces the need to include both main and interactive effects of precipitation and temperature when seeking to accurately predict how pest pressure will change with a changing climate.

Towards Predictions of Interaction Dynamics between Cereal Aphids and Their Natural Enemies: A Review

Simple Summary

Understanding how pests and their natural enemies interact dynamically during the growing season and what drivers act on those interactions will help to develop efficient pest control strategies. We reviewed empirical and modeling publications on the drivers influencing the aphids–natural enemy dynamics. We found disparities between what is known empirically and what is used as main drivers in the models. Predation and parasitism are rarely measured empirically but are often represented in models, while plant phenology is supposed to be a strong driver of aphids’ dynamics while it is rarely used in models. Since modelers and empirical scientists do not share a lot of publications, we incite more crossover works between both communities to elaborate (i) new empirical settings based on simulation results and (ii) build more accurate and robust models integrating more key drivers of the aphid dynamics. These models could be integrated into decision support systems to help advisors and farmers to design more effective integrated pest management systems.

Abstract

(1) Although most past studies are based on static analyses of the pest regulation drivers, evidence shows that a greater focus on the temporal dynamics of these interactions is urgently required to develop more efficient strategies. (2) Focusing on aphids, we systematically reviewed (i) empirical knowledge on the drivers influencing the dynamics of aphid–natural enemy interactions and (ii) models developed to simulate temporal or spatio-temporal aphid dynamics. (3) Reviewed studies mainly focus on the abundance dynamics of aphids and their natural enemies, and on aphid population growth rates. The dynamics of parasitism and predation are rarely measured empirically, although it is often represented in models. Temperature is mostly positively correlated with aphid population growth rates. Plant phenology and landscape effects are poorly represented in models. (4) We propose a research agenda to progress towards models and empirical knowledge usable to design effective CBC strategies. We claim that crossover works between empirical and modeling community will help design new empirical settings based on simulation results and build more accurate and robust models integrating more key drivers of aphid dynamics. Such models, turned into decision support systems, are urgently needed by farmers and advisors in order to design effective integrated pest management.

Sampling Optimization and Crop Interface Effects on Lygus lineolaris Populations in Southeastern USA Cotton

Tarnished plant bug, Lygus lineolaris (Hemiptera: Miridae), is an economically damaging pest in cotton production systems across the southern United States. We systematically scouted 120 commercial cotton fields across five southeastern states during susceptible growth stages in 2019 and 2020 to investigate sampling optimization and the effect of interface crop and landscape composition on L. lineolaris abundance. Variance component analysis determined field and within-field spatial scales, compared with agricultural district and state, accounted for more variation in L. lineolaris density using sweep net and drop cloth sampling. This result highlights the importance of field-level scouting efforts. Using within-field samples, a fixed-precision sampling plan determined 8 and 23 sampling units were needed to determine L. lineolaris population estimates with 0.25 precision for sweep net (100 sweeps per unit) and drop cloth (1.5 row-m per unit) sampling, respectively. A spatial Bayesian hierarchical model was developed to determine local landscape (<0.5 km from field edges) effects on L. lineolaris in cotton. The proportion of agricultural area and double-crop wheat and soybeans were positively associated with L. lineolaris density, and fields with more contiguous cotton areas negatively predicted L. lineolaris populations. These results will improve L. lineolaris monitoring programs and treatment management decisions in southeastern USA cotton.

Modeling Overdispersion, Autocorrelation, and Zero-Inflated Count Data Via Generalized Additive Models and Bayesian Statistics in an Aphid Population Study

Count variables are often positively skewed and may include many zero observations, requiring specific statistical approaches. Interpreting abiotic factor changes in insect populations of crop pests, under this condition, can be difficult. The analysis becomes even more complicated because of possible temporal or spatial correlation, irregularly spaced data, heterogeneity over time, and zero inflation. Generalized additive models (GAM) are important tools to evaluate abiotic factors. Moreover, Markov chain Monte Carlo (MCMC) techniques can be used to fit a model that contains a temporal correlation structure, based on Bayesian statistics (BGAM). We compared methods of modeling the effects of temperature, precipitation, and time for the Brevicoryne brassicae (L.) population in Uberlândia, Brasil. We applied the proposed BGAM to the data, comparing this to the GAM model with and without autocorrelation for time, using the statistical programming language R. Analysis of deviance identified significant effects of the smoothers for precipitation and time on the frequentist models. With BGAM, the problem in variance estimations for precipitation and temperature from the previous models was solved. Furthermore, trace and density plots for population-level effects for all parameters converged well. The estimated smoothing curves showed a linear effect with an increase of precipitation, where lower precipitation indicated no presence of the aphid. The average temperature did not affect the aphid incidence. Autocorrelation was solved with ARMA structures, and the excess of zero was solved with zero-inflation models. The example of B. brassicae incidence showed how well abiotic (and biotic) factors can be modeled and analyzed using BGAM.

A matrix model describing host-parasitoid population dynamics: The case of Aphelinus certus and soybean aphid

Integrating elements from life tables into population models within a matrix framework has been an underutilized method of describing host-parasitoid population dynamics. This type of modeling is useful in describing demographically-structured populations and in identifying points in the host developmental timeline susceptible to parasitic attack. We apply this approach to investigate the effect of parasitism by the Asian parasitoid Aphelinus certus on its host, the soybean aphid (Aphis glycines). We present a matrix population model with coupled equations that are analogous to a Nicholson-Bailey model. To parameterize the model, we conducted several bioassays outlining host and parasitoid life history and supplemented these studies with data obtained from the literature. Analysis of the model suggests that, at a parasitism rate of 0.21 d-1, A. certus is capable of maintaining aphid densities below economically damaging levels in 31.0% of simulations. Several parameters-parasitoid lifespan, colonization timeline, host developmental stage, and mean daily temperature-were also shown to markedly influence the overall dynamics of the system. These results suggest that A. certus might provide a valuable service in agroecosystems by suppressing soybean aphid populations at relatively low levels of parasitism. Our results also support the use of A. certus within a dynamic action threshold framework in order to maximize the value of biological control in pest management programs.

Current and future suitability of wintering grounds for a long-distance migratory raptor

Conservation of migratory species faces the challenge of understanding the ecological requirements of individuals living in two geographically separated regions. In some cases, the entire population of widely distributed species congregates at relatively small wintering areas and hence, these areas become a priority for the species’ conservation. Satellite telemetry allows fine tracking of animal movements and distribution in those less known, often remote areas. Through integrating satellite and GPS data from five separated populations comprising most of the breeding range, we created a wide habitat suitability model for the Eleonora’s falcon on its wintering grounds in Madagascar. On this basis, we further investigated, for the first time, the impact of climate change on the future suitability of the species’ wintering areas. Eleonora’s falcons are mainly distributed in the north and along the east of Madagascar, exhibiting strong site fidelity over years. The current species’ distribution pattern is associated with climatic factors, which are likely related to food availability. The extent of suitable areas for Eleonora’s falcon is expected to increase in the future. The integration of habitat use information and climatic projections may provide insights on the consequences of global environmental changes for the long-term persistence of migratory species populations.

From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability

Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance-based risk maps using multi-year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R²  = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance-based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance-based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.

Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise

Ecoinformatics, as defined in this review, is the use of preexisting data sets to address questions in ecology. We provide the first review of ecoinformatics methods in agricultural entomology. Ecoinformatics methods have been used to address the full range of questions studied by agricultural entomologists, enabled by the special opportunities associated with data sets, nearly all of which have been observational, that are larger and more diverse and that embrace larger spatial and temporal scales than most experimental studies do. We argue that ecoinformatics research methods and traditional, experimental research methods have strengths and weaknesses that are largely complementary. We address the important interpretational challenges associated with observational data sets, highlight common pitfalls, and propose some best practices for researchers using these methods. Ecoinformatics methods hold great promise as a vehicle for capitalizing on the explosion of data emanating from farmers, researchers, and the public, as novel sampling and sensing techniques are developed and digital data sharing becomes more widespread.