A Spatial Approach to Analyzing the Effects of Weather Patterns on Honey Bee (Apis mellifera) Colony Loss Across the United States
Type of DegreeMaster's Thesis
Restriction TypeAuburn University Users
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Western honey bees (Apis mellifera) have experienced high rates of colony loss over the past decade. Honey bees are critical to agricultural production, providing 15 billion to the United States (U.S.) economy each year via their pollination services. Due to their importance, studies have attempted to determine what factors are behind these observed losses. However, relatively few studies have considered the effects of weather in their analyses despite weather being a key driver in ecological systems. Those studies that have considered the effects of weather have not utilized spatial analysis despite studies in other fields finding that issues such as non-stationarity, where relationships between variables differ across space, and the modifiable areal unit problem (MAUP) may affect analysis results. The objectives of Chapter 2 were to determine which weather variables best predict winter colony loss rates at a national scale in the U.S. and then compare the results from a traditional non-linear approach, using a generalized linear regression (GLR), to results from a spatial approach, using a geographically weighted regression (GWR) which takes into consideration data non-stationarity, thus helping to elucidate how changes may occur across space. The best supported variables at the national scale were mean maximum temperature during the month of November, mean precipitation during the month of February, mean windspeed, and mean elevation. The GWR had an AIC score that was 328.80 points lower than the GLR and had an R-squared value of 20% versus 13.5% for the GLR. Thus, these results show that a spatial approach is more statistically robust than the traditional GLR and indicate that the effects of weather on colony loss are non stationary. iii The objective of Chapter 3 was to determine how MAUP would change model results when a linear regression was run at six different scales - zip code, county, state, and level one, two, and three ecoregions. MAUP is the phenomenon of different data aggregation methods resulting in different results. Results indicated that the effects of the two variables analyzed – mean temperature and mean precipitation – differed substantially between the various scales of analysis. Additionally, the R-squared values changed drastically, with of low of 8% at the zip code level and a high of 73% for level two ecoregions. These results are consistent with findings from other fields and indicate that MAUP should be considered when analyzing an aggregated honey bee colony loss dataset. The results of this thesis show that spatial phenomena such as non-stationarity and MAUP can alter the results of honey bee colony loss analyses, sometimes to a great degree. This highlights the need for more localized management strategies, as the effects of weather, and potentially other variables, on colony loss vary by location. Additionally, this thesis shows the need for more research involving spatial analysis in this field. Future studies may seek to analyze other variables, such as varroa mite levels or pesticide presence, alongside weather variables to create a fuller model that may further aid local management decisions.