Counting Acorns
Do a quick Google Images search for “wildlife biologist” and you’ll probably get the impression that scientists researching wild animals spend a significant amount of their time in the field, directly handling the creatures that they are studying — catching snakes, measuring the wings of songbirds, and keeping baby bears warm in their jackets while a radio collar is affixed to their mother. All of these activities are important in certain kinds of research, but the frequency of their depiction in news articles, government websites, and TV shows provides a somewhat skewed picture of what an “average” workday looks like for a wildlife professional. Academic biologists have classes to teach, grants to apply for, and papers to write and usually only spend a few weeks every year out in the field. Likewise, fulltime government biologists are kept busy answering questions from citizens, analyzing data, and writing reports; they get out in the field too, but a lot of the data collection that informs management is collected by contractors or seasonally hired field techs, like me.
Even when you are out in the field, you aren’t always doing something as exciting as trapping and handling wild animals. In recent decades, advances in technology and theory have provided more and more opportunities for scientists to study wild animals noninvasively — that is, without putting them through the stress of Bambi’s equivalent to an alien abduction. While there are some limits to the kinds of questions we can answer using these methods, biologists are still able to learn a lot about the behavior and population status of wild animals through noninvasive techniques such as analyzing DNA found in hair or scat, putting up camera traps, and recording animal noises on autonomous microphones.
Wildlife conservation and management also requires a solid understanding of the environmental conditions and resources needed by a target species, so biologists and their field techs will often spend a lot of time collecting data about the habitats in which their study animals live. This data can then be fed it into complex models that help inform policy decisions, such as where to perform habitat management or buy new parcels of land for a protected area. Habitat research in particular is not nearly as photogenic as direct capture, and the methodology can sometimes read a bit like something that a gym-teacher-turned-vice-principle would come up with to punish a chatty student (“Count all the stems in this square!”), but it produces data that is just as important to understanding and protecting wildlife as that which is collected with the animal in hand.
Some of the more esoteric examples of wildlife habitat research are the annual mast surveys conducted by natural resource departments throughout the eastern United States. In the lingo of wildlife biologists, mast refers to any fruit, seed, or nut that is commonly fed upon by wildlife. “Soft mast” refers to softer fruits and berries, while “hard mast” refers to harder seeds and nuts. Mast surveys tend to take place in the late summer or early fall and focus mostly on hard mast such as beach nuts, hickory nuts, and acorns, as well as certain fall berries and fruits. While their methodology varies somewhat depending on the state, it typically involves sending biologists, technicians, and community volunteers to preselected field sites, where they visually observe the crowns of mass-producing tree species for thirty seconds per tree. The observer then estimates the percentage of the crown that has mast growing in it and records that number. Some states may also ask surveyors to take an additional thirty seconds to count as much of the mast growing in the crown as they can and to collect additional information on the size and health of the tree. The data collected is then average for each tree species at a particular site, producing a rough characterization of the mast crop for that year (Gregonis and Williams 2018; Johnson et al. 2022).
Mast surveys may seem a bit tedious, but overall, they’re pretty easy to perform and the data that come out of them are actually really useful — well worth any boredom sustained during the counting part! To understand why, it will be useful to put on our botany hats for moment and talk a little bit about the physiology of seeds. In general, seeds consist of three parts: the actual embryo of the young plant growing inside; a protective outer layer known as the seed coat; and, most important for our purposes here, a mass of starch, oil, and protein called the endosperm. The endosperm functions a bit like the yolk in an egg, providing the embryo inside the seed with food in the time before it sprouts. Indeed, the endosperm is the tasty, nutritive part of the nuts and seeds that we humans consume and the reason why they are often cited as good sources of protein for vegans and vegetarians.
It’s also why mast producing trees are such an important food source for wild animals, especially during the fall when they are trying to bulk up or store food for the winter. Squirrels and chipmunks are probably the best-known for their particular love of acorns, but the truth is that these little packages of nutrients and calories are loved by a wide variety of species, including bears, deer, turkeys, blue jays, and mice. Before they were driven to extinction, massive flocks of passenger pigeons would track the fruiting of mast trees up and down the east coast, gorging themselves and practically blocking out the sun wherever they flew.
All this attention from seed predators causes a bit of the problem for mast producing trees — while some animals, such as squirrels and blue jays, beneficially spread seeds to new locations when they bury and forget about them, they also feed on the seeds directly and too much of that can have a series impact on tree reproduction. As a result, many trees have evolved strategies for limiting seed predation while still taking advantage of the dispersal services that certain animals provide. Trees in the white oak family, for example, tend to germinate quickly after being dropped by their parent trees, making it more likely that they will use up their nutrient rich endosperm before they are found by predators. Red oak acorns, on the other hand, don’t germinate until the following spring and therefore “keep” better underground. Because of these differences, animals foraging for acorns during the fall will usually bury red oak acorns for leaner times and either consume any white oak acorns on the spot or remove the embryo before burying them. Each year, an acceptable number of white oak acorns avoid predation by sprouting quickly before any animals are able to find them; meanwhile, the red oak acorns that are forgotten about by the squirrels and birds that buried them survive to germinate in the spring (Fox 1982; Smallwood et al. 2001).
Another reason why seed predators don’t typically have too much of a negative impact on tree reproduction has to do with the trees’ ability to coordinate when they produce bumper crops of mast across large areas. The exact mechanism by which trees are able to do this is still being studied (Kelly and Sork 2002; Pearse et al. 2016), but the advantages of coordination are pretty obvious. Because mast is such an important resource for animals preparing for the winter, its availability can have a major impact on short term population trends. A series of good, consecutive mast years will mean that more seed predators are able to survive the winter and reproduce, which will result in their populations increasing and putting more pressure on mast producing trees. If the trees coordinate, however, and produce less mast for a few years, the number of seed predators will decrease as more die over the winter and their pressure on the trees during future bumper crop years will ease, while still allowing them to take advantage of the seed dispersal services of the survivors.
This is where the mast surveys come back in. One of the most important things that wildlife biologists have to do is monitor and predict the short and long-term population trends of the animals they are studying so that informed decisions can be made about how to manage them. There are a ton of different factors that influence the ups and downs of wild animal populations, but at least here in the eastern United States, mast crops are a particularly important one. There appears to be a strong positive coloration, for example, between variations in the annual acorn crop and variations in the population of white-footed mice, an important host for the ticks that carry Lyme disease (Elkinto et al. 19916; Jones et al. 1998; Stein 2019). When oak trees produce large acorn crops, the number of mice that survive the winter to reproduce increases, resulting in a jump in the mouse population the following summer. With more mice around, ticks have a better chance of finding a host and reproducing, so that they’re population also increases significantly by the next spring. Understanding this pattern, along with other environmental factors that influence the prevalence of ticks, allows epidemiologists to predict when Lyme disease is going to be especially prevalent in a particular region and respond accordingly.
Mast surveys are also very helpful for game managers trying to understand harvest rates and give hunters an idea of what to expect during a coming season. In general, fall harvest rates for deer and wild turkeys seem to be lower during years with above average mast crops, probably because the animals don’t need to move around as much to find food and so can stay hidden in forest cover (Norman et al. 2003; Ryan et al. 2004). Conversely, harvest rates are much higher during years with low mast crops because animals are more likely to come out into the open looking for food. Many state wildlife agencies publish the results of their mast surveys as part of hunting outlook or forecast reports so that hunters can adjust their strategies and expectations accordingly (Carpentor et al. 2022; Johnson et al. 2022). The results can also provide important context for harvest reports — if data on the mast crop and other factors that influence short term variations in deer and turkey harvests predict a good season, but harvest rates are still unusually low, it may indicate a more significant problem in the population. Likewise, lower harvest rates predicted by high acorn crops or other similar factors are probably nothing to worry about.
These are just some of the more common uses of mast survey data — other studies have noted the impacts of variations in seed production on populations dynamics in invasive spongy moths, songbirds, and predators of small mammals such as fishers, martins, and various birds of prey (Elkinson et al. 1996, Jones et al. 1998; Clotfelter et al. 2009; Jenson et al. 2012; Gregonis and Williams 2018). Going into detail on all of these would require a much longer essay, however, and I want to keep this one relatively short. In fact, if I’m being completely honest, my main reason for telling you about mast surveys in the first place (besides trying to instill an appreciation for the importance of “boring” science and encouraging you to volunteer for these kinds of project, yada yada) is so you can understand this cartoon I drew a couple years ago at work:
Go ahead, you can laugh…its funny! Seriously, I expect some lols in the comments — I don’t care if they’re out of pity.
Sources:
Carpentor C., Morris H., Richmond E., Ryan C., & Barton, E. (2022). 2022 West Virginia hunting outlook and mast survey. West Division of Natural Resources, Wildlife Resources Section. https://wvdnr.gov/wp-content/uploads/2023/09/2023-Hunting-Outlook-and-Mast-Survey-092223.pdf
Clotfelter, E. D., Pedersen, A. B., Cranford, J. A., Ram, N., Snajdr, E. A., Nolan, V., & Ketterson, E. D. (2007). Acorn mast drives long-term dynamics of rodent and songbird populations. Oecologia, 154, 493–503.
Elkinton, J. S., Healy, W. M., Buonaccorsi, J. P., Boettner, G. H., Hazzard, A. M., & Smith, H. R. (1996). Interactions among gypsy moths, white‐footed mice, and acorns. Ecology, 77(8), 2332–2342.
Fox, J. F. (1982). Adaptation of gray squirrel behavior to autumn germination by white oak acorns. Evolution, 800–809.
Gregonis M. & Williams S. (2018). Masting drives bird of prey abundances. Connecticut Department of Energy and Environmental Protection/Connecticut Agricultural Experimental Station. https://portal.ct.gov/-/media/CAES/DOCUMENTS/Publications/Forest_Health_Monitoring_Workshops/2020/7-Williams-Forest-Health-Workshop-2020.pdf
Jensen, P. G., Demers, C. L., Mcnulty, S. A., Jakubas, W. J., & Humphries, M. M. (2012). Marten and fisher responses to fluctuations in prey populations and mast crops in the northern hardwood forest. The Journal of wildlife management, 76(3), 489–502.
Johnson E., Danks Z., Moser E., & Rhoden C. (2022). 2022 mast survey report. Kentuckey Department of Fish and Wildlife Resources. https://fw.ky.gov/Hunt/Documents/Mast_Report_2022.pdf
Jones, C. G., Ostfeld, R. S., Richard, M. P., Schauber, E. M., & Wolff, J. O. (1998). Chain reactions linking acorns to gypsy moth outbreaks and Lyme disease risk. Science, 279(5353), 1023–1026.
Kelly, D., & Sork, V. L. (2002). Mast seeding in perennial plants: why, how, where?. Annual review of ecology and systematics, 33(1), 427–447.
Norman, G. W., & Steffen, D. E. (2003). Effects of recruitment, oak mast, and fall-season format on wild turkey harvest rates in Virginia. Wildlife Society Bulletin, 553–559.
Pearse, I. S., Koenig, W. D., & Kelly, D. (2016). Mechanisms of mast seeding: resources, weather, cues, and selection. New Phytologist, 212(3), 546–562.
Ryan, C. W., Pack, J. C., Igo, W. K., Rieffenberger, J. C., & Billings, A. B. (2004). Relationship of mast production to big-game harvests in West Virginia. Wildlife Society Bulletin, 32(3), 786–794.
Smallwood, P. D., Steele, M. A., & Faeth, S. H. (2001). The ultimate basis of the caching preferences of rodents, and the oak-dispersal syndrome: tannins, insects, and seed germination. American Zoologist, 41(4), 840–851.
Stein, R. A. (2019). Lyme disease. In J. O. Nriagu (Ed), Encyclopedia of environmental health (2nd ed.). Elsevier.
Photo Credits:
(1) "GAMBEL'S OAK ACORN" - Bryant Olsen (CC BY-NC 2.0)
(2) "Blue Jay (185317371)" - by Jocelyn Anderson (CC BY 3.0 Deed)
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