Cary Institute's 35-year tick record keeps overturning its own authors' predictions

Researchers at the Cary Institute of Ecosystem Studies have published a paper in Proceedings of the National Academy of Sciences setting out nine of the most surprising findings from more than 35 years of monitoring how acorns, rodents, ticks, predators and climate shape Lyme disease risk ^[1][2]. That matters because Lyme is the most commonly reported tick-borne disease in the United States, with nearly half a million people diagnosed each year ^[3], and because several of the study's surprises are cases where the researchers' own reasonable expectations were wrong ^[2].

The clearest example is deer. When Lyme was recognised in the United States in the 1980s, some researchers assumed white-tailed deer mattered a great deal, because deer killed by hunters in autumn often carry large numbers of adult ticks ^[4]. Deer do not infect ticks with Borrelia burgdorferi, the bacterium that causes Lyme ^[5][3], but the logic ran that more deer would feed more adult females, which would lay more eggs and yield more immature ticks ^[5]. On a 2,000-acre forested site in New York's Dutchess County, decades of data show no statistical relationship between deer abundance and the density of nymphal ticks, the life stage most likely to infect people ^[6]. Lead author Richard Ostfeld says a weak positive relationship did appear in short subsets of the record, but "the whole data set shows no such thing" ^[7]. Any control programme whose main dial is deer numbers is, on this evidence, turning a dial that the long record does not connect to nymph density ^[6][7].

What does track nymphs is mice. A strong mouse year raised the following year's nymph count by about 40 percent ^[8]; white-footed mice are good larval food and efficient transmitters of B. burgdorferi ^[9]. Upstream of that, acorn masting feeds a mouse boom the next year and a nymph boom the year after, so a large acorn crop reliably predicts more nymphs two years later ^[10]. That is roughly two seasons of lead time between a countable autumn event and the summer it forecasts ^[11].

The finding that should discipline public messaging is the one that failed. The team expected abundant mice to raise not just nymph numbers but the share of nymphs carrying Borrelia, and early monitoring years showed exactly that, before the relationship disappeared as the record lengthened ^[12]. "It kept me up at night," Ostfeld said, "because it meant that my understanding of how the system works was incomplete or wrong" ^[13]. The data eventually pointed toward other hosts, including skunks, squirrels and opossums, being more abundant in the same good years ^[14]. So abundance and infection prevalence are separate forecasting problems, and a mouse index alone does not deliver the second one ^[15].

Two things to watch. First, the remaining findings: the institute's own summary describes four of the nine surprises, leaving five, including anything on climate, to the full paper ^[16]. Second, whether managers adopt masting counts as an operational two-year indicator, given co-director Shannon LaDeau's point that studies of this depth and length are rare, which is precisely why short-window inference elsewhere keeps producing signals that longer records erase ^[17][7].