Regulation of Microclimatic Conditions inside Native Beehives and Its Relationship with Climate in Southern Spain

Abstract

In this study, the Wbee Sensor System was used to record data from 10 Iberian beehives for two years in southern Spain. These data were used to identify potential conditioning climatic factors of the internal regulatory behavior of the hive and its weight. Categorical principal components analysis (CATPCA) was used to determine the minimum number of those factors able to capture the maximum percentage of variability in the data recorded. Then, categorical regression (CATREG) was used to select the factors that were linearly related to hive internal humidity, temperature and weight to issue predictive regression equations in Iberian bees. Average relative humidity values of 51.7% ± 10.4 and 54.2% ± 11.7 were reported for humidity in the brood nest and in the food area, while average temperatures were 34.3 °C ± 1.5 in the brood nest and 29.9 °C ± 5.8 in the food area. Average beehive weight was 38.2 kg ± 13.6. Some of our data, especially those related to humidity, contrast with previously published results for other studies about bees from Central and northern Europe. Conclusively, certain combinations of climatic factors may condition within hive humidity, temperature and hive weight. Southern Iberian honeybees’ brood nest humidity regulatory capacity could be lower than brood nest thermoregulatory capacity, maintaining values close to 34 °C, even in dry conditions.

Advances and knowledge gaps on climate change impacts on honey bees and beekeeping: A systematic review

“…An increase in precipitation could exert negative effects mainly on hive homeostasis (e.g., internal temperature and relative humidity of brood and feed area; Gil‐Lebrero et al., 2020), food reserves (e.g., reduced pollen richness and diversity, colony weight gain, and mean annual yield; Montoya‐Pfeiffer et al., 2021; Gajardo‐Rojas et al., 2022; Quinlan et al., 2022), incidence of diseases (European foulbrood Melissococcus plutonius ; Rowland et al., 2021), changes in microbiome (e.g., decreasing symbionts; Castelli et al., 2022), and plant–pollinator interactions (e.g., decreasing abundance on fields; Thomson, 2016), as well affect the distribution pattern of A. mellifera (Cánovas et al., 2014). Contrarily, a decrease in precipitations can affect the behavior (e.g., early appearance dates; Gordo et al., 2010), reduce honey production and exportations (Gajardo‐Rojas et al., 2022; Gounari et al., 2022), and reduce plant–pollinator interactions (e.g., frequency visiting; Jaworski et al., 2022; Maluf et al., 2022).…”

“…; Castelli et al., 2022), positively affect plant–pollinator interactions (Cruz et al., 2022), increase mitochondrial diversity (Cánovas et al., 2014), reduce Varroa spp. abundance (Rowland et al., 2021), positively affect the internal humidity of the feeding area (Gil‐Lebrero et al., 2020), and influence hygienic behavior (Sousa et al., 2016).…”

“…However, despite that net effects were generally negative and neutral for most response variables analyzed, in some cases temperature increase can positively affect food reserves (e.g., increase honey production, advance of honey harvest days, increase hive weight, and increase in nectar volume; Gajardo‐Rojas et al., 2022; Langowska et al., 2017; Pătruică et al., 2019; Bordier et al., 2017; Gil‐Lebrero et al., 2020), reduce diseases (e.g., reduce chalkbrood, decrease deformed wing virus, increase Varroa mite fall; Nürnberger et al., 2019; Rowland et al., 2021; Bordier et al., 2017; Hillayová et al., 2022), affect positively the gene expression (e.g., improve thermo‐tolerance of workers; Al‐Ghzawi et al., 2022), increase the mitochondrial diversity (Cánovas et al., 2014), favor the geographic distribution (Castellanos‐Potenciano et al., 2017), exert positive effect on internal temperature of brood area (Gil‐Lebrero et al., 2020) and brood viability (Cebotari et al., 2019), increase glycogen levels (Bordier et al., 2017), increase the resistance of workers (i.e., daughter of heat‐stressed queens; Al‐Ghzawi et al., 2022), reduce reproduction of some pests (e.g., A. tumida ; Noor‐ul‐Ane & Jung, 2021), affect the plant‐pollinator networks (e.g., increase density, visitation rates, interactive role; Thomson, 2016; Hung et al., 2018; Cruz et al., 2022; Jaboor et al., 2022; Alzate‐Marin et al., 2021), and can increase the critical maximum temperature (Aldea‐Sánchez et al., 2021). Instead, some studies that evaluated the decrease in temperatures suggest an increase in the presence of beneficial bacteria such as Snodgrassella alvi (Castelli et al., 2022), as well as increased levels of protein, glycogen, glycerol, vitellogenin, gene expression, thus increasing the cold tolerance of honey bees (Qin et al., 2019).…”

Modeling Climate Change Effects on Genetic Diversity of an Endangered Horse Breed Using Canonical Correlations

“…The Canonical Correlation Analysis function of the Multiblock Data Analysis menu of XLSTAT 2014 was used to test Pearson’s product–moment correlation. Guidelines to interpret Pearson’s correlation coefficients can be found in the work of Profillidis and Botzoris [ 48 ], as suggested by Gil Lebrero et al [ 49 ].…”

“…Canonical correlations can be interpreted as any other Pearson correlations (see rule of thumb interpretation criteria in the work of Profillidis and Botzoris [48], as suggested by Gil Lebrero et al [49]. Canonical correlations range between -1 and 1: a value near 0 indicates low correlation, and an absolute value near 1 indicates near-perfect correlation [41].…”