The acceptance of eco-friendly black soldier fly larvae meal (BSFLM) as sustainable alternative protein ingredient in poultry feeds continues to gain momentum worldwide. This study evaluates the impact of BSFLM in layer chick and grower diets on the growth, carcass quality and economic returns. Mean weekly weight gain and total live weight per chick and grower varied significantly. The highest final weight gain was achieved when birds were provided diet with 25.6% BSFLM. Average daily feed intake (ADFI), average daily weight gain (ADG) and overall weight gain of the chick varied significantly, except for the feed conversion ratio (FCR). For grower birds, ADFI, ADG, FCR and overall weight gain did not vary significantly across the various feeding regimes. The weight of the wings and drumsticks had a quadratic response with a maximum weight obtained at 33% inclusion of BSFLM. The weight of the internal organs were not significantly affected by dietary types. Positive cost–benefit ratio and return on investment was recorded for diet types with higher BSFLM inclusion levels (>75%). Diets with 25% and 100% BSFLM inclusion were the most suitable and cost-effective, respectively. Thus, BSFLM represents a promising alternative source of protein that could be sustainably used in the poultry industries.

The Lake Baringo and Lake Victoria regions of Kenya are associated with high seroprevalence of mosquito-transmitted arboviruses. However, molecular identification of potential mosquito vector species, including morphologically identified ones, remains scarce. To estimate the diversity, abundance, and distribution of mosquito vectors on the mainland shores and adjacent inhabited islands in these regions, we collected and morphologically identified adult and immature mosquitoes and obtained the corresponding sequence variation at cytochrome c oxidase 1 (COI) and internal transcribed spacer region 2 (ITS2) gene regions. A total of 63 species (including five subspecies) were collected from both study areas, 47 of which have previously been implicated as disease vectors. Fourteen species were found only on island sites, which are rarely included in mosquito diversity surveys. We collected more mosquitoes, yet with lower species composition, at Lake Baringo (40,229 mosquitoes, 32 species) than at Lake Victoria (22,393 mosquitoes, 54 species). Phylogenetic analysis of COI gene sequences revealed Culex perexiguus and Cx . tenagius that could not be distinguished morphologically. Most Culex species clustered into a heterogeneous clade with closely related sequences, while Culex pipiens clustered into two distinct COI and ITS2 clades. These data suggest limitations in current morphological identification keys. This is the first DNA barcode report of Kenyan mosquitoes. To improve mosquito species identification, morphological identifications should be supported by their molecular data, while diversity surveys should target both adults and immatures. The diversity of native mosquito disease vectors identified in this study impacts disease transmission risks to humans and livestock.

Liriomyza leafminers (Diptera: Agromyzidae) are severe pests of vegetables and ornamentals worldwide. Previous studies revealed low leafminer parasitism across different agroecological zones in Kenya. The present paper reports on the composition of leafminer parasitoids at different elevations, in different seasons, and on different host crops. Surveys were conducted monthly from January to November 2012, and nine parasitoid species were recovered. Total mean parasitism in the study sites was 31.23 ± 1.03% from a total of 20 different vegetable Liriomyza-infested crops belonging to seven families. Diglyphus isaea (Walker) (Hymenoptera: Eulophidae), Phaedrotoma scabriventris, a newly released parasitoid, and Opius dissitus Muesebeck (Hymenoptera: Braconidae) were the most abundant at all elevations, accounting for 67.3, 18.6, and 9.2% of total parasitoids, respectively. Elevation, season, and host crop significantly affected the parasitoid species present and their abundance. Diglyphus isaea was more abundant at the high- and mid-elevations at all seasons compared with the low-elevation, whereas the lower-elevation favored higher abundance of P. scabriventris and O. dissitus during the long rainy season compared with the high- and mid-elevations at all seasons. Of all the host crops surveyed, parasitoids were more abundant on tomato, local kidney bean, snow pea and French bean than other crops. The total parasitism rate observed in this study suggests a considerable improvement in leafminer parasitism compared with previous surveys in Kenya. The implications of these findings for leafminer management in vegetable and ornamental production in Kenya are discussed.

Analysis of landmark-based morphometric measurements taken on body parts of insects have been a useful taxonomic approach alongside DNA barcoding in insect identification. Statistical analysis of morphometrics have largely been dominated by traditional methods and approaches such as principal component analysis (PCA), canonical variate analysis (CVA) and discriminant analysis (DA). However, advancement in computing power creates a paradigm shift to apply modern tools such as machine learning. Herein, we assess the predictive performance of four machine learning classifiers; K-nearest neighbor (KNN), random forest (RF), support vector machine (the linear, polynomial and radial kernel SVMs) and artificial neural network (ANNs) on fruit fly morphometrics that were previously analysed using PCA and CVA. KNN and RF performed poorly with overall model accuracy lower than “no-information rate” (NIR) (p value > 0.1). The SVM models had a predictive accuracy of > 95%, significantly higher than NIR (p < 0.001), Kappa > 0.78 and area under curve (AUC) of the receiver operating characteristics was > 0.91; while ANN model had a predictive accuracy of 96%, significantly higher than NIR, Kappa of 0.83 and AUC was 0.98. Wing veins 2, 3, 8, 10, 14 and tibia length were of higher importance than other variables based on both SVM and ANN models. We conclude that SVM and ANN models could be used to discriminate fruit fly species based on wing vein and tibia length measurements or any other morphologically similar pest taxa. These algorithms could be used as candidates for developing an integrated and smart application software for insect discrimination and identification. Variable importance analysis results in this study would be useful for future studies for deciding what must be measured.

Following its recent invasion of African countries, fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), now co-exists with resident stemborers such as Busseola fusca (Lepidoptera: Noctuidae) and Chilo partellus (Lepidoptera: Crambidae) causing severe damage to maize crops. Due to niche overlap, interspecific interactions occur among the three species, but the mechanisms and degree remain unclear. In this study, we assessed plant-mediated intraspecific and interspecific interactions, predation in laboratory and semi-field settings, and larval field occurrence of S. frugiperda and the two stemborer species. Larval feeding assays to evaluate competitive plant-mediated interactions demonstrated that initial S. frugiperda feeding negatively affected subsequent stemborer larval feeding and survival, suggesting induction of herbivore-induced mechanisms by S. frugiperda, which deters establishment and survival of competing species. Predation assays showed that, at different developmental larval stages, second–sixth instars of S. frugiperda preyed on larvae of both B. fusca and C. partellus. Predation rates of S. frugiperda on stemborers was significantly higher than cannibalism of S. frugiperda and its conspecifics (p < 0.001). Cannibalism of S. frugiperda in the presence of stemborers was significantly lower than in the presence of conspecifics (p = 0.04). Field surveys showed a significantly higher number of S. frugiperda larvae than stemborers across three altitudinally different agroecological zones (p < 0.001). In conclusion, this study showed that the invasive S. frugiperda exhibited a clear competitive advantage over resident stemborers within maize cropping systems in Kenya. Our findings reveal some of the possible mechanisms employed by S. frugiperda to outcompete resident stemborers and provide crucial information for developing pest management strategies for these lepidopteran pests.

The study focusses on handling of multiclass imbalanced data on classification of stingless bee samples by employing data balancing techniques, namely Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) approach. These techniques are applied in combination with machine learning (ML) algorithms; specifically Random Forest (RF), and Support Vector Machine (SVM), to assess the models' predictive performance to infer stingless bee samples identities. We studied ML classifier models: RF, RF + SMOTE, RF + ADASYN, SVM, SVM + SMOTE and SVM + ADASYN on the six-class imbalanced dataset of stingless bees morphometrics. Multi-class area under curve (AUC), F1-score, G-mean, balanced accuracy, sensitivity and "No information rate" were used to assess model performance. SMOTE and ADASYN marginally improved the performance of RF and SVM classifiers. SVM outperformed RF, with SVM using SMOTE performing better than with ADASYN. SVM with ADASYN had a lower multi-class AUC (0.9898) and sensitivity (0.956) but a higher F1-score (0.939) compared to SVM with SMOTE (AUC = 0.9918, sensitivity = 0.959, F1-score = 0.934). Overall, SVM with SMOTE was superior to RF with SMOTE. All models except SVM with ADASYN, correctly classified four of the six species, M. (Meliponula) bocandei, M. (Meliplebeia) lendliana, D. schmidti and P. armata but not the two morphs, Meliponula (Axestotrigona) togoensis and Meliponula (Axestotrigona) ferruginea. This study therefore confirms that the impact of imbalanced learning is minimal when classes are separable. Random forest recursive feature elimination technique was used to assess variable importance, guiding future studies on key morphometric measurements to save time and cost while maintaining high classification performance. Our results pave the way for the development of smart and automated machine learning applications to complement the existing methods for the identification of stingless bee species.