DOI: 10.6084/m9.figshare.29543156
title: Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas

description: This dataset results from a stingless bee colony loss survey from 2016 to 2021 in Latin America. A total of 472 meliponiculturists participated in the survey. On average, meliponiculturists lost 43.4 % of their stingless bee colonies annually, 33.3 % during the rainy season, and 22.0 % during the dry season. We found that colony losses during the rainy season decreased with higher abundance of forested areas and increased with higher abundance of agricultural area around meliponaries. Moreover, annual colony losses also decreased with higher abundance of surrounding forested area. Interestingly, the positive effects of the surrounding forested area on colony survival were higher than the negative effects from the surrounding agricultural area. Our results highlight the importance of forest conservation while pinpointing the challenges faced by Latin American meliponiculturists to preserve colony health.<br>The data are related to the scientific paper “Sibaja Leyton M. et al., (under review) Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas”.Data are available as a csv file titled:"Sibaja_Leyton_et_al_Dataset_Stingless_Bee_Colony_Losses_in_Latin_America.csv".<br>#METADATA#'data.frame': 472 obs. of 28 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 #$ 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_dry: Numeric variable; representing the number of colonies lost during the dry season#$ N_alive_dry: Numeric variable; representing the number of colonies alive during the dry season #$ N_dead_rainy: Numeric variable; representing the number of colonies lost during the rainy season#$ N_alive_rainy: Numeric variable; representing the number of colonies alive during the rainy season #$ Education: Factor variable; four factors are available ("Self-taught","Learned from another melip","Intro training","Formal tech training"), representing the training level in meliponiculture #$ Operation_Size: Numeric variable; representing the number of colonies managed by the participant (in n) #$ propAgri: Numeric variable; representing the percentage of agricultural area surrounding the meliponary (in %) #$ propForest: Numeric variable; representing the percentage of forested area surrounding the meliponary (in %)#$ temp.avg_annual: Numeric variable; representing the average annual temperature (in ºC)#$ precip_annual_sum: Numeric variable; representing the total accumulated precipitation (in mm)#$ precip_Oct_March_sum: Numeric variable; representing the total accumulated precipitation between October to March (in mm)#$ precip_Apri_Sept_sum: Numeric variable; representing the total accumulated precipitation between April to September (in mm)#$ temp.avg_Oct_March: Numeric variable; representing the total accumulated precipitation between October to March (in ºC)#$ temp.avg_Apri_Sept: Numeric variable; representing the total accumulated precipitation between April to September (in ºC) #$ Importance_dead: Factor variable; three factors are available 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<br>