Relationship between Euschistus conspersus (Hem., Pentatomidae) pheromone trap catch and canopy samples in processing tomatoes

Abstract:  This study identified statistically significant, and practically important, seasonal pheromone trap response dynamics of Euschistus conspersus (Uhler) in processing tomatoes in 12 fields over 5 years in California, USA. Commercial fields were monitored weekly using traps baited with Euschistus spp. pheromone and direct canopy shake samples. Regression analysis determined no significant relationship between the two sampling methods at any given time during the growing season. This result indicates that the number of E. conspersus recovered in pheromone traps cannot be expected to serve as a direct estimate of the surrounding field population density from week to week during the processing tomato growing season. However, E. conspersus response to pheromone traps was characterized by differential attraction across the sample dates and elevated response early in the growing season relative to the associated field population. Among the most important findings of this work are differential responses of female and male E. conspersus to pheromone traps by crop phenology stage, and the fact that female-biased trap catch coincided with elevated trap response relative to the surrounding field population early in the growing season during tomato vegetative growth through flowering phenology stages. These results, when coupled with further E. conspersus reproductive biology studies, suggest that growers and crop consultants will be able to rely on E. conspersus pheromone trap catch patterns identified in this study to implement pheromone trap monitoring and phenology-based timing of insecticide treatment targeting E. conspersus nymphal stages in processing tomatoes.     

A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees

Woodlands provide valuable ecosystem services, and it is important to understand their dynamics. To predict the way in which these might change, we need process‐based predictive ecological models, but these are necessarily very data intensive. We tested the ability of existing datasets to provide the parameters necessary to instantiate a well‐used forest model (SORTIE) for a well‐studied woodland (Wytham Woods). Only five of SORTIE's 16 equations describing different aspects of the life history and behavior of individual trees could be parameterized without additional data collection. One age class – seedlings – was completely missed as they are shorter than the height at which Diameter at Breast Height (DBH) is measured. The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time. This results in there being insufficient data to parameterize process‐based models in order to meet the societal demand for ecological prediction. We do not advocate ceasing the measurement of DBH, but we do recommend that those concerned with tree mensuration consider whether additional measures of trees could be added to their data collection protocols. We also see advantages in integrating techniques such as ground‐based LIDAR or remote sensing techniques with long‐term datasets to both preserve continuity with what has been performed in the past and to expand the range of measurements made.     

Predicting the phenology of codling moth,Cydia pomonella,for sustainable pest management in Swiss apple orchards

The effects of temperature on post‐diapause development, female lifespan, and reproduction of Cydia pomonella L. (Lepidoptera: Tortricidae) were studied under controlled conditions to determine the respective parameters for a temperature‐driven phenology model. Lower thermal thresholds of 10.0 and 9.7 °C and thermal constants of 417.2 and 427.7 degree days were established for post‐diapause development of females and males, respectively. Female lifespan was found to be 202.6 degree days on average, with a lower thermal threshold of 11.2 °C. Total fecundity was highest at 27.7 °C with an average of 241.9 eggs per female. Literature data were used to quantify the lower thermal thresholds and the thermal constants for eggs, larvae, and pupae. For validation, the model was run with weather data from 13 consecutive years and the output was compared with pheromone trap catches from 39 to 63 different orchards, depending on the year. The model proved to be a reliable and useful tool to forecast codling moth phenology under Swiss conditions not only for a whole growing season but also during several years with very variable weather conditions. Hence, it can be used to support apple growers in pest management decisions on the one hand and to predict changes in codling moth phenology with respect to climate change on the other.     

The ability of synthetic sex pheromone traps to forecast Plutella xylostella infestations depends on survival of immature stages

The potential for using synthetic sex pheromone traps as a simple and practical method of monitoring population densities of insect pests has been investigated in many crop systems. Yet, factors enabling the forecast of infestations based on pheromone trap catches are not fully understood. This study tested the prediction that high survival of immature stages of the target pest is a pre‐requisite for trap catches to correlate well with future infestations on the crop. The influence of parasitoids, as an important natural mortality factor of diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae), larvae and pupae in South Africa, on the ability of pheromone traps to forecast infestations was investigated continuously at weekly intervals over 6 years on unsprayed cabbage. During late October–May, when parasitism levels were high (≥50%), infestations and trap catches were significantly lower than during low parasitism (<50%) observed during June–early October. Because infestations were negatively related to parasitism level, trap catches correlated with infestations better when observations were made in the same week during periods of high parasitism. Conversely, when survival of P. xylostella immature stages was high due to low parasitism levels, trap catches correlated with future infestations well for up to 2 weeks. Thus, trap catches can be used to forecast infestations during September–October, a period that coincides with high P. xylostella infestations as a consequence of low natural control by parasitoids. This is the first study to show that the ability of pheromone trap catches to forecast infestations depends on survival of the immature stages of the target pest.     

The predictability of a lake phytoplankton community,over time‐scales of hours to years

Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time‐scales. Communities were highly predictable over hours to months: model R² decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long‐term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model‐inferred environmental growth dependencies matched laboratory studies, and suggested novel trade‐offs governing their competition. High‐frequency monitoring and machine learning can set prediction targets for process‐based models and help elucidate the mechanisms underlying ecological dynamics.     

Population dynamics of noctuid moths and damage forecasting in sugar beet

相似文献   

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

This paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale-dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Akaike's Information Criterion (AIC) are used to analyse the forecast's accuracy. The best model has been selected based on the predictions with the lowest value, according to the three-performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security.     

The influence of bat ecology on viral diversity and reservoir status

Repeated emergence of zoonotic viruses from bat reservoirs into human populations demands predictive approaches to preemptively identify virus‐carrying bat species. Here, we use machine learning to examine drivers of viral diversity in bats, determine whether those drivers depend on viral genome type, and predict undetected viral carriers. Our results indicate that bat species with longer life spans, broad geographic distributions in the eastern hemisphere, and large group sizes carry more viruses overall. Life span was a stronger predictor of deoxyribonucleic acid viral diversity, while group size and family were more important for predicting ribonucleic acid viruses, potentially reflecting broad differences in infection duration. Importantly, our models predict 54 bat species as likely carriers of zoonotic viruses, despite not currently being considered reservoirs. Mapping these predictions as a proportion of local bat diversity, we identify global regions where efforts to reduce disease spillover into humans by identifying viral carriers may be most productive.     

Forecasting olive (Olea europaea) crop production by monitoring airborne pollen

Forecasting harvests of olives destined for the production of olive oil can be based on counts of airborne olive pollen, and meteorological and agronomic observations. This study was carried out during six consecutive years (1990–1995) in the Campiña Alta (an olive-producing region in the province of Córdoba, south-west Spain). Olive pollen totals are the annual sum of the concentrations recorded for the periods that the filters of a Cour trap were exposed. The meteorological data are the values of accumulated rainfall between 1 September and the following 15 April (a date prior to the beginning of olive flowering). The agronomic data are the forecast and actual productions for the province of Córdoba, supplied by the Board of Agriculture of the Andalusian government, and the actual production of the Campiña Alta, supplied at the end of harvest by private olive-growing co-operatives. The data were combined, and four mathematical equations were obtained to forecast the crop 6 months in advance, with varying degrees of reliability. The reliability was very high for an appropriate agricultural area. The most accurate equation isY=?1.90×10⁴+2.35X+53.94 (which forecasts the production of the Campiña Alta), whereY is the olive production (MT),X the olive pollen count,Z the rainfall prior to flowering, anda, b andc are constants. The least accurate equation is that relating olive pollen concentrations with olive production in the province of Córdoba.