Multidimensional discriminant analysis of species, strains and culture age of closely related entomopathogenic fungi using reflectance spectroscopy.

The diversity of fungal strains is influenced by genetic and environmental factors, growth conditions and mycelium age, and the spectral features of fungal mycelia are associated with their biochemical, physiological, and structural traits. This study investigates whether intraspecific differences can be detected in two closely related entomopathogenic species, namely Cordyceps farinosa and Cordyceps fumosorosea, using ultraviolet A to shortwave infrared (UVA-SWIR) reflectance spectra. Phylogenetic analysis of all strains revealed a high degree of uniformity among the populations of both species. The characteristics resulting from variation in the species, as well as those resulting from the age of the cultures were determined. We cultured fungi on PDA medium and measured the reflectance of mycelia in the 350-2500 nm range after 10 and 17 days. We subjected the measurements to quadratic discriminant analysis (QDA) to identify the minimum number of bands containing meaningful information. We found that when the age of the fungal culture was known, species represented by a group of different strains could be distinguished with no more than 3-4 wavelengths, compared to 7-8 wavelengths when the age of the culture was unknown. At least 6-8 bands were required to distinguish cultures of a known species among different age groups. Distinguishing all strains within a species was more demanding: at least 10 bands were required for C. fumosorosea and 21 bands for C. farinosa. In conclusion, fungal differentiation using point reflectance spectroscopy gives reliable results when intraspecific and age variations are taken into account.

Relative impact of environmental variables on the lake trophic state highlights the complexity of eutrophication controls.

For the effective management of lakes apart from defining and monitoring their current state it is crucial to identify environmental variables that are mostly responsible for the nutrient input. We used interpretative machine learning to investigate the environmental parameters that influence the lake's trophic state and recognize their patterns. We analysed the influence of the 25 environmental variables on the commonly used trophic state indicators values: total phosphorus (TP), Chlorophyll-a (Chl-a) and Secchi depth (SD) of 60 lakes located in the Central European Lowlands. We attempted to delineate the lakes into groups due to the influence of common prevailing environment variable/variables on the water trophic state reflected by each indicator. The results indicated that the relative impact of environmental variables on the lake trophic state has an individual hierarchy unique for each indicator. The most important are variables related to catchment impact on the lake, Ohle ratio (L. catchment area/L. area) for TP and Schindler ratio (L. area + L. catchment area)/L. volume for Chl-a and SD. There are also few variables strongly influential only for small sub-groups of lakes that stand out: lake maximum depth, catchment slope steepness expressed by the height standard deviation. The methods used in the study enabled the assessment of the character of the influence of the environmental variables on the indicator value and revealed that most essential variables (Ohle ratio for TP and Schindler ratio for Chl-a and SD) have bimodal distribution with a clear threshold value. These findings contribute to a better understanding of the drivers shaping the lake trophic status and have implication for planning effective management strategies.

Reflectance spectroscopy and machine learning as a tool for the categorization of twin species based on the example of the Diachrysia genus.

In our work we used noninvasive point reflectance spectroscopy in the range from 400 to 2100 nm coupled with machine learning to study scales on the brown and golden iridescent areas on the dorsal side of the forewing of Diachrysia chrysitis and D. stenochrysis. We used our approach to distinguish between these species of moths. The basis for the study was a statistically significant collection of 95 specimens identified based on morphological feature and gathered during 23 years in Poland. The numerical part of an experiment included two independent discriminant analyses: stochastic and deterministic. The more sensitive stochastic approach achieved average compliance with the species identification made by entomologists at the level of 99-100%. It demonstrated high stability against the different configurations of training and validation sets, hence strong predictors of Diachrysia siblings distinctiveness. Both methods resulted in the same small set of relevant features, where minimal fully discriminating subsets of wavelengths were three for glass scales on the golden area and four for the brown. The differences between species in scales primarily concern their major components and ultrastructure. In melanin-absent glass scales, this is mainly chitin configuration, while in melanin-present brown scales, melanin reveals as an additional factor.

An application of reflectance spectroscopy to differentiate of entomopathogenic fungi species.

Fourier Transform Infrared Spectroscopy (FTIR) methods are the most commonly used spectroscopic techniques for differentiation of fungi species, however reflectance spectroscopy as a non-invasive technique can also be used. The aim of the study was to develop a method to rapidly differentiate fungi by means of reflectance spectroscopy using visible-infrared spectrum. Spectral measurements were conducted on six entomopathogenic fungi: Beauveria bassiana, Isaria fumosorosea, I. farinosa, I. tenuipes, Lecanicillium lecanii, L. muscarium cultured on Petri-dishes. The FieldSpec3 ASD spectroradiometer. Recording reflected radiance in the range 350-2500 nm was used. Measurements were performed in two modes: contact and proximal and obtained spectra were transformed using two methods: Savitzky-Golay (SG) and baseline alignment (BA) smoothing and derivative. The success rate of 100% in differentiate between fungi species was achieved with spectra recorded in visible-near infrared range with contact and proximal measurement and after SG transformation. Two wavelengths (411 nm and 520 nm) were needed to differentiate fungi using SG and proximal measurement while seven wavelengths were necessary to get full separation with contact measurement. BA spectra transformation method gave separation accuracy of 84, and 90% with four to five wavelengths for contact and proximal measurements, respectively, however, BA do not require full spectrum of wavelengths to fungi discrimination. Proposed reflectance spectroscopy method could discriminate between fungi species very similar macroscopically e.g. L. lecanii and L. muscarium until recently recognized as one species.