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Bee venom (BV) is the most valuable product harvested from honeybees ($30 - $300 USD per gram) but marginally produced in apiculture. Though widely studied and used in alternative medicine, recent efforts in BV research have focused on its therapeutic and cosmetic applications, for the treatment of degenerative and infectious diseases. The protein and peptide composition of BV is integral to its bioactivity, yet little research has investigated the ecological factors influencing the qualitative and quantitative variations in the BV composition. Bee venom from Apis mellifera ligustica (Apidae), collected over one flowering season of Corymbia calophylla (Myrtaceae; marri) was characterized to test if the protein composition and amount of BV variation between sites is influenced by i) ecological factors (temperature, relative humidity, flowering index and stage, nectar production); ii) management (nutritional supply and movement of hives); and/or iii) behavioural factors. BV samples from 25 hives across a 200 km-latitudinal range in Southwestern Australia were collected using stimulatory devices. We studied the protein composition of BV by mass spectrometry, using a bottom-up proteomics approach. Peptide identification utilised sequence homology to the A. mellifera reference genome, assembling a BV peptide profile representative of 99 proteins, including a number of previously uncharacterised BV proteins. Among ecological factors, BV weight and protein diversity varied by temperature and marri flowering stage but not by index, this latter suggesting that inter and intra-year flowering index should be further explored to better appreciate this influence. Site influenced BV protein diversity and weight difference in two sites. Bee behavioural response to the stimulator device impacted both the protein profile and weight, whereas management factors did not. Continued research using a combination of proteomics, and bio-ecological approaches is recommended to further understand causes of BV variation in order to standardise and improve the harvest practice and product quality attributes.

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<p><strong>Abstract.</strong> Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (<i>Corymbia calophylla</i>) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92<span class="thinspace"></span>% of the wavelengths between 350<span class="thinspace"></span>nm and 1800<span class="thinspace"></span>nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10<span class="thinspace"></span>% of the wavelengths, with peak separation between 518<span class="thinspace"></span>nm and 557<span class="thinspace"></span>nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520<span class="thinspace"></span>nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80<span class="thinspace"></span>% accuracy.</p>

Honey yield from apiary sites varies significantly between years. This affects the beekeeper’s ability to manage hive health, as well as honey production. This also has implications for ecosystem services, such as forage availability for nectarivores or seed sets. This study investigates whether machine learning methods can develop predictive harvest models of a key nectar source for honeybees, Corymbia calophylla (marri) trees from South West Australia, using data from weather stations and remotely sensed datasets. Honey harvest data, weather and vegetation-related datasets from satellite sensors were input features for machine learning algorithms. Regression trees were able to predict the marri honey harvested per hive to a Mean Average Error (MAE) of 10.3 kg. Reducing input features based on their relative model importance achieved a MAE of 11.7 kg using the November temperature as the sole input feature, two months before marri trees typically start to produce nectar. Combining weather and satellite data and machine learning has delivered a model that quantitatively predicts harvest potential per hive. This can be used by beekeepers to adaptively manage their apiary. This approach may be readily applied to other regions or forage species, or used for the assessment of some ecosystem services.

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1. Sampling nectar from forest canopies is logistically challenging as it requires physical access to the canopy to a height greater than that can be achieved by hand. The most common solutions comprise the use of cherry pickers, cranes or tree climbers. These techniques are generally expensive, logistically complex, and often involve additional safety risks and specialized technicians to use the equipment/machinery. In addition, access is required up to the tree for cherry pickers and cranes, and tree climbers are often unable to reach the outermost branches.