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Influence of Distance, Environmental Factors, and Native Vegetation on Honeybee ( Apis mellifera ) Foraging in Arid Shrublands and Grasslands.

Alma Delia Baez-GonzalezMario Humberto Royo-MarquezCarlos Alejandro Perez-QuintanaAdrián Isaac Hernández-BernalAlicia Melgoza-CastilloMieke TitulaerJose Humberto Vega-Mares
Published in: Insects (2024)
This study determined the influence of foraging distance, environmental factors, and native vegetation on honeybee ( Apis mellifera ) foraging in arid shrublands and grasslands in Northern Mexico. Apiary distance from inflorescence sites did not have a significant influence on the intensity of foraging. Apiary location and landscape were decisive factors in the response of honeybees to environmental factors. Air temperature, minimum temperature, wind velocity, and relative humidity explained foraging by 87, 80, 68, and 41% (R 2 ), respectively, in shrubland sites in open landscapes but had no significant influence on foraging in the grassland sites in a valley surrounded by hills (1820-2020 amsl). Nights with a minimum temperature of <20 °C increased foraging activity during the day. Minimum temperature, which has the least correlative influence among climate elements, can be used to determine climate change's impact on bees. The quantity of available inflorescence explained the foraging intensity by 78% in shrublands and 84% in grasslands. Moreover, when honeybees depended mainly on native vegetation in grasslands, the quantity of inflorescence explained the intensity of foraging by 95%. High intensity of honeybee foraging was observed in allthorn ( Koeberlinia spinosa ) and wait-a-minute bush ( Mimosa aculeaticarpa ) in shrublands and honey mesquite ( Neltuma glandulosa ) and wait-a-minute bush ( Mimosa aculeaticarpa ) in grasslands. The findings and baseline data contributed by this study may be used to identify suitable environments for increasing apiary productivity and other agricultural and ecological benefits.
Keyphrases
  • climate change
  • high intensity
  • human health
  • minimally invasive
  • body composition
  • big data
  • artificial intelligence