Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning

Asian citrus psyllid, Diaphorina citri Kuwayama (Liviidae: Hemiptera) is a menacing and notorious pest of citrus plants. It vectors a phloem vessel-dwelling bacterium Candidatus Liberibacter asiaticus, which is a causative pathogen of the serious citrus disease known as Huanglongbing. Huanglongbing disease is a major bottleneck in the export of citrus fruits from Pakistan. It is being responsible for huge citrus economic losses globally. In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. The pest prediction models can be used for proper applications of pesticides only when needed for reducing the environmental and cost impacts of pesticides. Pearson’s correlation analysis was performed to find the relationship between different predictor (abiotic and biotic) variables and pest infestation rate on citrus plants. Multiple linear regression, random forest regressor, and deep neural network approaches were compared to predict population dynamics of Asian citrus psyllid. In comparison with other regression techniques, a deep neural network-based prediction model resulted in the least root mean squared error values while predicting egg, nymph, and adult populations.

Influence of prevailing weather parameters on population dynamics of spotted stem borer, Chilo partellus (Swinhoe) and its natural enemies on maize in Haryana

The experiment was conducted at CCS Haryana Agricultural University Regional Research Station, Karnal to ascertain the influence of prevailing meteorological parameters on population dynamics of Chilo partellus and its natural enemies on maize during Kharif, 2017. Maximum oviposition (0.75 egg masses per plant) was recorded during 28th standard meteorological week (SMW) whereas larval population was at peak during 31st SMW (3.8 larvae per plant). Cumulative (47.5%) and fresh plant infestation (11.5%) were maximum during 34th and 28th SMW, respectively. Maximum egg parasitisation (6.53%) by Trichogramma sp. and larval parasitisation (31.64%) by Cotesia flavipes was recorded during 28th and 33rd SMW, respectively. Changes in pest population were correlated and regressed with weather parameters. Egg and larval populations of C. partellus and parasitisation by Trichogramma sp. exhibited significant positive correlation with average minimum temperature whereas C. flavipes exhibited significant negative correlation with average maximum temperature (r = -0.741) and highly significant positive correlation with evening relative humidity (r = 0.695). Plant infestation and dead heart formation were significantly correlated with average minimum temperature and non-significantly correlated with all other weather parameters. The multiple linear regression analysis explained the variability due to various weather parameters. This information can be utilised while formulating integrated management tactics against this pest.

Immune function differs among tropical environments but is not downregulated during reproduction in three year-round breeding equatorial lark populations

Seasonal variation in immune function can be attributed to life history trade-offs, and to variation in environmental conditions. However, because phenological stages and environmental conditions co-vary in temperate and arctic zones, their separate contributions have not been determined. We compared immune function and body mass of incubating (female only), chick-feeding (female and male), and non-breeding (female and male) red-capped larks Calandrella cinerea breeding year-round in three tropical equatorial (Kenya) environments with distinct climates. We measured four immune indices: haptoglobin, nitric oxide, agglutination, and lysis. To confirm that variation in immune function between breeding (i.e., incubating or chick-feeding) and non-breeding was not confounded by environmental conditions, we tested if rainfall, average minimum temperature (Tmin), and average maximum temperature (Tmax) differed during sampling times among the three breeding statuses per location. Tmin and Tmax differed between chick-feeding and non-breeding, suggesting that birds utilized environmental conditions differently in different locations for reproduction. Immune indices did not differ between incubating, chick-feeding and non-breeding birds in all three locations. There were two exceptions: nitric oxide was higher during incubation in cool and wet South Kinangop, and it was higher during chick-feeding in the cool and dry North Kinangop compared to non-breeding birds in these locations. For nitric oxide, agglutination, and lysis, we found among-location differences within breeding stage. In equatorial tropical birds, variation in immune function seems to be better explained by among-location climate-induced environmental conditions than by breeding status. Our findings raise questions about how within-location environmental variation relates to and affects immune function.

Time-series forecasting models

Statistical and mathematical models of forecasting are of paramount importance for the understanding and study of databases, especially when applied to data of climatological variables, which enables the atmospheric study of a city or region, enabling greater management of the anthropic activities and actions that suffer the direct or indirect influence of meteorological parameters, such as precipitation and temperature. Therefore, this article aimed to analyze the behavior of monthly time series of Average Minimum Temperature, Average Maximum Temperature, Average Compensated Temperature, and Total Precipitation in Belém (Pará, Brazil) on data provided by INMET, for the production and application forecasting models. A 30-year time series was considered for the four variables, from January 1990 to December 2020. The Box and Jenkins methodology was used to determine the statistical models, and during their applications, models of the SARIMA and Holt-Winters class were estimated. For the selection of the models, analyzes of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Autocorrelation Correlogram (ACF), and Partial Autocorrelation (PACF) and tests such as Ljung-Box and Shapiro-Wilk were performed, in addition to Mean Square Error (NDE) and Absolute Percent Error Mean (MPAE) to find the best accuracy in the predictions. It was possible to find three SARIMA models: (0,1,2) (1,1,0) [12], (1,1,1) (0,0,1) [12], (0,1,2) (1,1,0) [12]; and a Holt-Winters model with additive seasonality. Thus, we found forecasts close to the real data for the four-time series worked from the SARIMA and Holt-Winters models, which indicates the feasibility of its applicability in the study of weather forecasting in the city of Belém. However, it is necessary to apply other possible statistical models, which may present more accurate forecasts.

ANNUAL AND SEASONAL VARIATIONS IN TEMPERATURE EXTREMES AND RAINFALL IN BANGLADESH, 1989–2018

Based on temperature and rainfall recorded at 34 meteorological stations in Bangladesh during 1989–2018, the trends of yearly average maximum and minimum temperatures have been found to be increasing at the rates of 0.025∘C and 0.018∘C per year. Analysis of seasonal average maximum temperature showed increasing trend for all seasons except the late autumn season. The increasing trend was particularly significant for summer, rainy and autumn seasons. Seasonal average minimum temperature data also showed increasing trends for all seasons. The trend of yearly average rainfall has been found to be decreasing at a rate of 0.014[Formula: see text]mm per year in the same period; especially, for most of the meteorological stations the rainfall demonstrates an increasing trend for rainy season and a decreasing trend in the winter season. It means that in Bangladesh dry periods became drier and wet periods became wetter.

PHENODYNAMICS OF Solanum mauritianum SCOP. IN A PLANTATION FOR SUBTROPICAL FOREST RESTORATION

We evaluated the phenodynamics of Solanum mauritianum Scop. in a forest plantation to check the ecological behavior of this species in restoration condition. Twelve trees were observed over 24 months, according to regrowth, flowering, fruiting and defoliation. The phenophases were correlated with the photoperiod, climatic variables, maximum, average, minimum temperature and precipitation through Pearson´s correlation. The species exhibited over the year highly synchronic, flowering and fruiting. The flowering occurred from January to December, with floral buds being observed simultaneously with ripe fruits, reaching a maximum dispersion in February. Temperatures below 10°C and frosts inhibited the leaf re-sprouts, promoting a leaf deciduous peak in March and June. The permanent availability of resources as flowers and fruits and the resilience of vegetative phenophases in response to severe frosts make S. mauritianum an adapted species of highly ecological potential to be used in regional restoration projects.

Flock Size Predicts Niche Breadth and Focal Wintering Regions for a Rapidly Declining Boreal-Breeding Passerine, the Rusty Blackbird

Once exceptionally abundant, the Rusty Blackbird (Euphagus carolinus) has declined precipitously over at least the last century. The species breeds across the Boreal forest, where it is so thinly distributed across such remote areas that it is extremely challenging to monitor or research, hindering informed conservation. As such, we employed a targeted citizen science effort on the species’ wintering grounds in the more (human) populated southeast United States: the Rusty Blackbird Winter Blitz. Using a MaxEnt machine learning framework, we modeled patterns of occurrence of small, medium, and large flocks (<20, 20–99, and >99 individuals, respectively) in environmental space using both Blitz and eBird data. Our primary objective was to determine environmental variables that best predict Rusty Blackbird occurrence, with emphasis on (1) examining differences in key environmental predictors across flock sizes, (2) testing whether environmental niche breadth decreased with flock size, and (3) identifying regions with higher predicted occurrence (hotspots). The distribution of flocks varied across environmental predictors, with average minimum temperature (~2 °C for medium and large flocks) and proportional coverage of floodplain forest having the largest influence on occurrence. Environmental niche breadth decreased with increasing flock size, suggesting an increasingly restrictive range of environmental conditions capable of supporting larger flocks. We identified large hotspots in floodplain forests in the Lower Mississippi Alluvial Valley, the South Atlantic Coastal Plain, and the Black Belt Prairie.

Mapping the Vector Abundance of Endemic Mosquito-Borne Diseases in Southern Quebec

Background: Climate change is increasing the dispersion of mosquitoes and the spread of viruses of which some mosquitoes are the main vectors. This increases the risk of humans coming into contact with infected mosquitoes and developing diseases with sometimes fatal consequences. In Quebec, the surveillance and management of endemic mosquito-borne diseases, such as West Nile virus or Eastern equine encephalitis, could be improved by mapping the areas of risk supporting vector populations. However, there is currently no active tool tailored to Quebec that can predict annual mosquito population abundances. Methods: Our modelling approach is designed to meet this need. Four species of mosquitos were studied in this project for the period from 2003 to 2014 for the southern part of the province: Aedes vexans (VEX), Coquillettidia perturbans (CQP), Culex pipiens-restuans group (CPRg) and Ochlerotatus stimulans group (STMg) species. We used a mixed linear regression approach to model the abundances of each species or species groups as a function of meteorological and land cover variables. Results: The best models incorporate, for CPRg, the agricultural land, grassland and woodland classes and the average minimum temperature in September of the previous year; for STMg, the urban and woodland classes and the mean precipitation in June; for CQP, urban areas and the mean precipitation in January and August; and finally, for VEX, the agricultural land class and the mean precipitation in January, February and September. Conclusions: The models proved to be robust and precise over almost the entire study area, and the presence of significant climate variables for each of the species or species groups makes it possible to consider their use in predicting long-term spatial variations, based on climate and landscape change, in the abundance of mosquitoes potentially harmful to public health in southern Quebec.

Seasonal Dynamics and Spatial Distribution of Aedes albopictus (Diptera: Culicidae) in a Temperate Region in Europe, Southern Portugal

Aedes albopictus is an invasive mosquito that has colonized several European countries as well as Portugal, where it was detected for the first time in 2017. To increase the knowledge of Ae. albopictus population dynamics, a survey was carried out in the municipality of Loulé, Algarve, a Southern temperate region of Portugal, throughout 2019, with Biogents Sentinel traps (BGS traps) and ovitraps. More than 19,000 eggs and 400 adults were identified from May 9 (week 19) and December 16 (week 50). A positive correlation between the number of females captured in the BGS traps and the number of eggs collected in ovitraps was found. The start of activity of A. albopictus in May corresponded to an average minimum temperature above 13.0 °C and an average maximum temperature of 26.2 °C. The abundance peak of this A. albopictus population was identified from September to November. The positive effect of temperature on the seasonal activity of the adult population observed highlight the importance of climate change in affecting the occurrence, abundance, and distribution patterns of this species. The continuously monitoring activities currently ongoing point to an established population of A. albopictus in Loulé, Algarve, in a dispersion process to other regions of Portugal and raises concern for future outbreaks of mosquito-borne diseases associated with this invasive mosquito species.

Mapping the Vector Abundance of Endemic Mosquito-Borne Diseases in Southern Quebec

Background: Climate change is increasing the dispersion of mosquitoes and the spread of viruses of which some mosquitoes are the main vectors. This increases the risk of humans coming into contact with infected mosquitoes and developing diseases with sometimes fatal consequences. In Quebec, the surveillance and management of endemic mosquito-borne diseases, such as West Nile virus or Eastern equine encephalitis, could be improved by mapping the areas of risk supporting vector populations. However, there is currently no active tool tailored to Quebec that can predict annual mosquito population abundances.Methods: Our modelling approach is designed to meet this need. Four species of mosquitos were studied in this project for the period from 2003 to 2014 for the southern part of the province: Aedes vexans (VEX), Coquillettidia perturbans (CQP), Culex pipiens-restuans group (CPRg) and Ochlerotatus stimulans group (STMg) species. We used a mixed linear regression approach to model the abundances of each species or species groups as a function of meteorological and land cover variables.Results: The best models incorporate, for CPRg, the agricultural land, grassland and woodland classes and the average minimum temperature in September of the previous year; for STMg, the urban and woodland classes and the mean precipitation in June; for CQP, urban areas and the mean precipitation in January and August; and finally, for VEX, the agricultural land class and the mean precipitation in January, February and September.Conclusions: The models proved to be robust and precise over almost the entire study area, and the presence of significant climate variables for each of the species or species groups makes it possible to consider their use in predicting long-term spatial variations, based on climate and landscape change, in the abundance of mosquitoes potentially harmful to public health in southern Quebec.Manuscript intended for publication in International J. of Health Geographics