DOI: 10.6084/m9.figshare.29590985.v1
title: Data from: Climate change may increase honey bee colony losses among hobbyist beekeepers in Latin America

description: This dataset results from a honey bee colony loss survey from 2016 to 2021 in Latin America. A total of 3093 beekeepers participated in the survey. Beekeepers encountered average annual losses of 32.3 %, average winter losses of 20.3 %, and average summer losses of 22.0 %. We found that hobbyist beekeepers lose more colonies than professionals for both seasonal and annual periods. Moreover, this difference in colony losses between beekeeping professionalization profiles increased with higher temperatures (warmer climates) and higher temperature seasonality. Neither seasonal nor annual colony losses were influenced by landscape composition, more specifically by the abundance of surrounding semi-natural habitats. These results highlight the increased challenges Latin American hobbyist beekeepers will face in the near future compared with professional and semi-professional beekeepers and emphasize the need for targeted strategies that account for both climate and beekeeper professionalization profile to mitigate honey bee colony losses. The data are related to the scientific paper “Sibaja Leyton M. et al., (under review) Climate change may increase honey bee colony losses among hobbyist beekeepers in Latin America”. Data are available as a csv file titled: "Sibaja_Leyton_et_al_Dataset_Honey_Bee_Colony_Losses_patterns_in_Latin_America.csv".#METADATA #'data.frame': 3093 obs. of 29 variables: #$ ID: Factor variable; a unique identity for the response to the survey#$ Year: Factor variable; six factors available (2016, 2017, 2018, 2019, 2020, 2021) representing the year for the response to the survey#$ Beekeeper_profile: Factor variable; three factors available (“Hobbyist”, “Semi-professional”, and “Professional”) representing the beekeeper professionalization profile (determined by the economic dependency on beekeeping)#$ N_dead_annual: Numeric variable; representing the number of colonies annually lost#$ N_alive_annual: Numeric variable; representing the number of colonies annually alive#$ N_dead_winter: Numeric variable; representing the number of colonies lost during winter#$ N_alive_winter: Numeric variable; representing the number of colonies alive during winter#$ N_dead_summer: Numeric variable; representing the number of colonies lost during summer#$ N_alive_summer: Numeric variable; representing the number of colonies alive during summer#$ propSemiNat: Numeric variable; representing the percentage of semi-natural areas area surrounding the apiary (in %)#$ temp.avg_annual: Numeric variable; representing the average annual temperature (in ºC)#$ temp.sd_annual: Numeric variable; temperature seasonality as the standard deviation of the 12 mean monthly temperature values (in ºC)#$ Importance_dead: Factor variable; five factors are available (“Very low”, “Low”, “Normal", "High", "Very high"), representing the perception of the significance of annual colony losses #$ Climatic_environmental: Binary variable; representing if the participant considered climatic and environmental problems as a potential driver (1) or not (0) of their annual colony losses #$ Contamination: Binary variable; representing if the participant considered contamination problems as a potential driver (1) or not (0) of their annual colony losses #$ Nutritional: Binary variable; representing if the participant considered nutritional problems as a potential driver (1) or not (0) of their annual colony losses #$ Sanitary: Binary variable; representing if the participant considered sanitary problems as a potential driver (1) or not (0) of their annual colony losses #$ Queen: Binary variable; representing if the participant considered queen problems as a potential driver (1) or not (0) of their annual colony losses #$ Time: Binary variable; representing if the participant considered time problems as a potential driver (1) or not (0) of their annual colony losses #$ Economic: Binary variable; representing if the participant considered economic problems as a potential driver (1) or not (0) of their annual colony losses #$ Attacks: Binary variable; representing if the participant considered time attacks as a potential driver (1) or not (0) of their annual colony losses #$ Swarming: Binary variable; representing if the participant considered swarming problems as a potential driver (1) or not (0) of their annual colony losses #$ precip_annual_sum: Numeric variable; representing the total accumulated precipitation (in mm)#$ Log_operation_Size: Numeric variable; representing the log-transformed number of colonies managed by the participant#$ Freq_visit: Factor variable; four factors are available (“&gt;8weeks”, “4-8weeks”, “2-4weeks", "&lt;2weeks”) representing the frequency of apiary visits #$ Sup_feeding: Binary variable; representing if the participant used supplementary feeding (1) or not (0)#$ Varroa_control: Binary variable; representing if the participant used any Varroa mite control method (1) or not (0)#$ Transhumance: Binary variable; representing if the participant practiced transhumance (1) or not (0)#$ Queen_change: Binary variable; representing if the participant changed the queen (1) or not (0)