By: Joseph Cazier, Dick Rogers, Edgar Hassler, James Wilkes
A Framework for Aggregating Hive Inspection.
One of the most critical tasks that beekeepers do is check on their hives. Knowing how the queen, brood, and other aspects of the hive are doing is critical to good hive management and long term colony performance. However, beekeepers seem to have their own individual lists of what to look for when they open up a hive, and few of them keep systematic records of what they find. Fewer still perform an analysis on which management practices worked for which problems identified in their inspections. And yet, Hive Inspection data has a tremendous amount of potential to improve beekeeping in general, and for the beekeepers that use it in particular.
In this article, we discuss why we need a framework for hive inspections and then present the Healthy Colony Checklist as both an easy-to-use framework and practical inspection checklist to consider. Next month we plan to continue this discussion by showing how we are testing and validating this framework, describing how you can find and use it and sharing how we see this evolving over time.
Why We Need a Framework for Hive Inspections
A framework can be defined as a:
Broad overview, outline, or skeleton of interlinked items which supports a particular approach to a specific objective, and serves as a guide that can be modified as required by adding or deleting items.1
As such, a framework is a way to help us think about things in a systematic, structured way, bringing different ideas together and linking them to each other in ways that can guide us towards a common objective. In this case, the common objective is to record information about hive inspections in a systematic manner that allows us to pool this information and learn from each other’s hives to ultimately manage our bees better.
One example of a framework that is widely used is Game Theory. Originally developed in 1944 by John von Neumann and Oskar Morgenstern; it was extended by John Nash in the 1940s – which was the basis for his Nobel Prize – and has been used by researchers far afield from its economic origins. Today it serves as a framework of analysis in social research, sports (football), biology, and even artificial intelligence and machine learning. Another is Germ Theory which gave us a better understanding of the cause of diseases and led to widespread adoption of different health behaviors.
These frameworks allowed us to look at things in a different way by focusing us on what really mattered to the item studied, giving new insight to address old problems. While a hive inspection framework will not have the same type of scope as the ones listed above, it is a way to organize our thoughts, be more focused and efficient in our observations, and share data and insights in a scalable way.
While there are many things we should record, including hive genetics, treatments, performance, and migration patterns, the hive inspection is the primary way to see inside the hive and understand at a deep level what is happening. This information is critical for good hive management and allows for a proactive approach to giving bees what they need to thrive.
While eventually we will have sensors inside and outside of our hives to share this information with us, we are still figuring out how to make them practical and useful from a business perspective. There are also technical and reliability challenges making it difficult to make sensors affordable and useful in remote areas with limited signal and power availability, and this problem reduces the ability to collect good data. Eventually we will get there, but right now human hive inspections, coupled with good record keeping of management actions and outcomes, can move us a long way towards better beekeeping.
Having a widely adopted common framework for thinking about hive inspections, like the one shown in Figure 1, is one of the first and most important steps to standardizing our beekeeping data and a necessary step to building a Genius Hive as outlined in our article “Peering into the Future: The Path to a Genius Hive” in the April 2018 issue of Bee Culture.
Even before building the genius hive, there are many advantages to using a defined framework for hive inspections. These include:
- Inspection Focus, Efficiency, and Consistency
- Cognitive Complexity Reductions
- Temporal and Spatial Comparisons
- Data Pooling and Aggregation
We discuss each of these below.
Inspection Focus, Efficiency, and Consistency
Having a framework for thinking about a task, such as a hive inspection, increases efficiency in completing that task. By thinking through in advance what things are important to look for and which things are not, it focuses the mind on what really matters.
There are hundreds of things that can be looked for during a hive inspection. However, not all of them are equally important, and most of them are subdomains of a few things that really matter. As part of the process of testing the starting framework we are presenting below, our team collected and analyzed every hive inspection form to which we had access. This was helpful, but at the end of the day, it mostly boiled down to a few things to think about when opening your hive. By focusing on these few things, your inspections become more focused, efficient, and consistent across a variety of beekeeping contexts.
This efficiency lets you inspect more often with the same effort, trading the collection of less relevant details for more timely information about things that matter to make a difference to your bees.
Cognitive Complexity Reductions
Having a framework also lowers the cognitive complexity of doing an inspection, making it simpler for you to record consistent, accurate information in a systematic way. A framework can sharpen your observation skills, drawing your attention to the most important things to look for and shifting your mind to a more focused and receptive state.
Figure 2 shows a typical hive inspection form in electronic format. It has a lot of data and has its uses. However a more focused and efficient form would allow for more frequent and useful inspections, resulting in better bee outcomes by prioritizing the data collected.
Temporal and Spatial Comparisons
The efficiency and consistency advantage previously discussed poses another benefit: allowing for temporal (time) and spatial (distance or location) information. By having the information entered consistently over time, including by different people, that information can be compared to data from your past or that of other beekeepers and associated with hive outcomes based on various management actions or environmental conditions. This can help guide your operations to better performance. We expand on this idea in the next section.
Data Pooling and Aggregation
The diversity of data collected and applied from a broader group of beekeepers, coupled with the increase in data volume, can greatly accelerate our learning about what is best for our bees at different times and locations and in different environments. Properly collected, stored, aggregated, and analyzed, this data holds the key to unlocking the best management practices personalized to each hive given its own history, strengths and weaknesses.
In this next section, we present one candidate for you to think about as a possible common framework for performing hive inspections. This is the Healthy Colony Checklist created by Dick Rogers, Manager of Bee Health Research at Bayer, and released to the public domain for any beekeeper to use and adapt.
An Introduction to the Healthy Colony Checklist
What It Is
In addition to being a framework for thinking about hive health, the Healthy Colony Checklist is a one-page document, listing the six conditions that need to be satisfied for a honey bee colony to be considered healthy. The form is very simple to use, requires very little writing to complete, and is still able to capture the specific details needed to answer the three most important questions when you open a hive: 1) Is the colony healthy?; 2) If not, why?; and 3) What needs to be done to fix the problem?
Using knowledge and experience, beekeepers assess each condition and put a quick checkmark or X next to each of the six conditions to indicate if it is satisfactory or deficient. If a condition is deficient, then the beekeeper should determine what the deficiency is and what action is needed to fix the problem and then record this information in the box beside the condition. A colony can be considered apparently healthy if all six conditions are checked; however, if one or more conditions have deficiencies, follow up assessments or corrective actions are required.
That is all there is to it. Once the form is completed, there is no post-inspection processing, except to simply schedule when you are going to do the fixes or follow-up.
How It Came to Be
Dick Rogers is an entomologist who has been keeping and studying honey bees for 45 years. In the early years, he experienced the frustration of being overwhelmed by the complexity of the inner workings of a colony in a hive, and then later by the abundance of data that can quickly accumulate when collecting qualitative and quantitative data during bee health studies. Within the past decade, he realized that more frequent colony assessments were needed to detect problems early so they could be addressed quickly. Early detection is the best strategy for saving hives of honey bees from causes that might otherwise go undetected when inspections are infrequent. However, early attempts to come up with a fast, simple tool for capturing inspection observations were not effective or efficient in capturing the needed data for frequent inspections.
The practical determination of the health status of a honey bee colony can be achieved through careful inspection by a knowledgeable and experienced beekeeper. The path to a desirable system of colony assessment first required developing a definition of a healthy honey bee colony. The following simple definition is one Dick has used for many years.
A healthy honey bee colony has below threshold levels of parasites, pathogens, and predators; no deficiency of, or out of balance, beneficial microbes; and strength and health is sustainable with a reasonable amount of management by the beekeeper to provide food, shelter, and safety as needed, as for any livestock operation.
For practical use, the above definition was further expanded to identify six key assessable conditions that contribute to optimal colony growth potential and overall health. To be considered “apparently” healthy, for practical purposes, a colony must have, as seasonably appropriate, 1) all stages and instars of brood; 2) sufficient adult bees and age structure to care for brood and perform all the tasks of the colony; 3) a young productive laying queen; 4) sufficient nutritious forage and food stores; 5) no stressors that would lead to reduced colony survival and growth potential (including environmental conditions inside and outside hive, and issues with biosecurity and mingling); and 6) suitable hive space for current and near-term colony needs that is sanitary and defendable, and has room for egg laying and food storage.
Beekeepers have numerous ways of keeping records, from putting twigs or stones on the top of their hives to writing on hive covers and boxes, to making notes on their hands or on a piece of paper. On the opposite end of the spectrum are the records that researchers make to track colony health by looking at both sides of every frame, counting cells, and recording so much data that it takes hours of data entry and number-crunching to understand what the data are indicating – a process entirely too time-consuming for regular, routine monitoring.
The Healthy Colony Checklist, as shown in Figure 3, is a simple tool for those of you who are responsible for maintaining healthy honey bee colonies, so you can do more frequent hive inspections more quickly (<15 min per hive) with minimal writing. Also, the tool helps capture what specific management tasks are needed, and can be used to improve the assignment and scheduling of those tasks.
Any framework that is created and adopted will likely need to evolve over time and adapt to changing circumstances. Also, as more data comes in, we will be able to test the relative importance of various data elements to see what works well from a management standpoint. However, from our experience so far, this framework seems to be a good start at building something that can work for most beekeepers in most situations. No framework is perfect, but some are useful. Based on our early tests and evaluation, this framework for hive inspections seems to be a useful place to start for building a standard hive inspection process.
At the end of the day, what really matters is that we all do it the same way: we record key information and share the data and outcomes so we all learn how to be better beekeepers. Just as one could speak in English, Spanish or Chinese and understand each other as long as you were all speaking the same language, by having a common inspection process we can more quickly assess the health of a hive and share that information with others. This will lead to greater learning and understanding for all. Even though each language may have a few quirks, the fact that they are the same allows for efficient communication of information that can be pooled and analyzed to help us all be better beekeepers.
Next month we plan to explore this idea in more detail by discussing some of the ways we are testing and using this framework, and explaining how you can help or adopt it in your own operations.
Finally, special thanks to Project Apis m. for supporting a portion of this work with a Healthy Hives 2020 grant and to Bee Culture for providing a venue for sharing these ideas with an interested audience.
Joseph Cazier is the Chief Analytics Officer for HiveTracks and the Director of the Center for Analytics Research and Education at Appalachian State University. He spends his days thinking about ways to use analytics for good and then finding ways to do them. You can reach him at firstname.lastname@example.org.
Richard (Dick) Rogers is the manager of bee health research at the Bayer Bee Care Center.
Ed Hassler is an Assistant Professor in Information Systems and Associate Director of CARE for Technology at Appalachian State University.
James Wilkes is the Founder and CEO of Hivetracks. and a Computer Science Professor at Appalachian State University. His lifelong passion for bees keeps fueling the development and mission of HiveTracks software. You can reach him at email@example.com.