Honey bees have actually seen increases in some states.
The graph below shows 5 states with the largest honey bee populations in the US.
While California’s honey bee population has been decreasing for the last few decades, North Dakota has actually seen an increase in colonies, while Florida, South Dakota, and Minnesota has been relatively unchanged.
Bee survivability & thrive-ability really depends on a variety of factors including geography and varies state to state.
How do colony losses differ by state?Just from eyeing the maps, it’s apparent that 2011 and 2013 winters weren’t too bad, but 2012 and 2014–2016 were much more fatal for bees.
Geographically, states in the Midwest and Mid-Atlantic experience the worse bee die-offs.
Which states are the best and worst for bees?States with the highest hive loss % over the winter tend to be located in the Mid-Atlantic and Midwest while states with lowest hive loss % tend to be located in the Western US.
States with the Highest Hive Loss %States with the Lowest Hive Loss %How do colonies die?The two main reasons for colony loss are deadouts and colony collapse disorder.
Deadouts are when all the bees in a colonies die.
Bees’ immune systems weaken in the winter and they are more susceptible to infections and parasites (like varroa mites).
Colony Collapse Disorder, or CCD, is when the majority of bees in a colony permanently fly away from the hive leaving just the queen and a few worker bees.
CCD is still being studied to determine what factors contribute to it.
Unfortunately the USDA has only mandated data collection of CCD and Deadout losses starting in 2015, so there isn’t much data.
Based on the graph, we can see deadouts are far more common; in addition, there are seasonal trends among both types of colony loss.
Hives that perish over the winter are almost 4 times as likely to experience deadouts than colony collapse disorder.
Is there correlation between cold temperature and colony loss?In order to answer the question, I used a dataset of temperature anomalies by year (dating back to the late 1800s).
I then took the minimum temperature anomaly by year and wanted to see if this value had any correlation by the percentage change in bee hives year-to-year.
First I wanted to look at the data to see if there was a general trend.
On the left is the minimum temperature anomaly for the year by year and below that is the percentage change in colonies from year-to-year.
There doesn’t appear to be any shared pattern in the data.
To quantify the relation, I found the correlation between the % change in colonies and temperature anomaly.
I was expecting to find a high positive correlation — my intuition being that more negative temperatures = harsher winter = more negative percentage change in colonies.
I found that the correlation was 0.
23There is slight positive correlation between minimum temperature anomaly and percentage change in colonies from the previous yearHow are colonies correlated with prices of consumer goods?I scraped CSV files of different Consumer Price Indices from FRED (Federal Reserve Bank of St.
Louis) for an assortment of food products.
I hypothesized that melons, fruits, and almonds would have strong negative correlation with colonies.
Because melons, fruits, and almonds are mainly pollinated by honey bees, I assumed that as the number of colonies decreased, the prices of these goods would increase while the remaining goods would have ~0 correlation.
I looked at how the % change of colonies is correlated with % change of price indices for a variety of consumer goods (as opposed to absolute numbers).
Correlation Table between % Change in Colonies and % Change in Price Indices of Various Consumer Goods.
*Last row is the most importantI also tried lagging the colony % change data by 1 year to see if prices would be affected after-the-fact and actually saw correlation significantly closer to 0.
While prices of goods are correlated with honey bee populations, there are likely a myriad of other factors that influence them.
Can we predict honey prices?Average honey price is strongly correlated with production of honey [-0.
8] and is moderately correlated with pounds of honey per colony [-0.
59] and number of colonies [-0.
I built a linear model to try predicting the price of honey based on the other three variables.
Although the overall model is statistically significant (p-value = 3.
972e-07) and the r-squared is moderate (0.
6818), none of the variables are statistically significant predictors — I don't have too much faith in the quality of the model, however, I thought it'd be interesting to look at nonetheless.
Coefficients: Estimate Std.
Error t value Pr(>|t|) (Intercept) 1474.
00895 **`Colonies (Thousands)` -0.
`Yield (lbs per colony)` -15.
`Production (Millions)` 4.
codes: 0 ‘***’ 0.
001 ‘**’ 0.
01 ‘*’ 0.
1 ‘ ’ 1Residual standard error: 36.
04 on 28 degrees of freedomMultiple R-squared: 0.
6818, Adjusted R-squared: 0.
6477 F-statistic: 19.
99 on 3 and 28 DF, p-value: 3.
972e-07The datasets I used had insufficient data to try predicting honey prices.
DataKaggle: Bee Colony StatisticsFRED: CPI dataNASA: Temperature anomalies from 1880-present.. More details