A Risk Map Is a Snapshot, Not a Boundary
Blacklegged tick risk maps are the subject of this series, and the question is why a map that was carefully built can become misleading as the landscape and surveillance evidence change. The practical aim is not to discard maps, but to learn when a projected surface can still direct limited fieldwork—and when it has stopped answering the question being asked.
That distinction matters because public-health teams rarely have the resources to sample everywhere. A map can help them decide where a field crew should drag a cloth, where a health department might focus education, or which places deserve closer observation. But the same map may be read too broadly: as a claim that every point inside a colored region is equally risky, that the color describes today, or that the presence of the tick proves the presence of a particular pathogen. The map is useful precisely because it simplifies a complicated system. Its danger begins when the simplification is mistaken for the system itself.
What a blacklegged tick risk map is for
The blacklegged tick, Ixodes scapularis, is a disease vector whose distribution and abundance can change with climate, habitat, host populations, and movement across the landscape. A risk map does not observe every tick directly. It combines observations from selected places with environmental and host information, then estimates how likely similar conditions are to produce a chosen outcome elsewhere. The outcome might be the probability of finding at least one tick, or a relative estimate of how many ticks a standardized search could encounter. Those are meaningful public-health questions, but they are not the same question as whether a person is infected.
A 2025 Ohio study makes the map’s contract unusually clear. Researchers sampled 161 sites during May through October 2022 and found blacklegged ticks at 112 of them. Their field protocol used a cloth dragged across leaf litter and vegetation for 750 metres, a standardized search roughly comparable to half a mile of walking. They then projected separate occurrence and relative-abundance models across Ohio on an 800-square-metre grid. The occurrence surface represented the probability of encountering a blacklegged tick during that standardized drag; the abundance surface represented relative tick numbers under the same kind of encounter measure.
That wording is more important than the color palette. A high occurrence value means that the modeled conditions resemble places where a standardized search was more likely to find at least one tick. It does not mean that every trail, private yard, season, or person inside the cell has the same exposure. A high relative-abundance value is a ranking of expected encounters, not a promise of an exact count. The study itself limited the interpretation to accessible public land and to the spring-through-fall period represented by its sampling. A map can be precise about its estimate while remaining narrow about what the estimate means.
The map’s value, then, is decision support. It can help a surveillance program focus scarce effort on places where additional information may be valuable. It can identify broad areas for additional sampling, and its results can later be summarized at a township or county scale if that better matches how people and agencies make decisions. But it does not eliminate the need for sampling. It tells a field team where information may be most valuable; it does not supply the information by itself.
This feedback loop is the key to reading any risk map. The map begins with evidence from a particular period and setting. It guides a decision, and the decision produces new observations. Those observations may reinforce the map, expose a boundary, or show that one of its layers needs attention. Without that feedback, a map can retain the appearance of authority while losing contact with the conditions that made it useful.
Why a map can matter before it is perfect
The stakes become clearer when a vector is moving into places where its public-health significance has not yet been fully recognized. In western North Carolina, a CDC-published investigation responded to community concerns about increasing tickborne disease diagnoses in Biltmore Forest, a heavily wooded town where Lyme disease was emerging but was not yet considered endemic. From November 2024 through August 2025, investigators collected 373 ticks from 25 residential properties. Of those, 287—76.9%—were identified as blacklegged ticks.
Among suitable blacklegged ticks tested, 19 of 48 adults, or 39.6%, carried Borrelia burgdorferi sensu stricto, the bacterium that causes Lyme disease. Three of seven nymphs, or 42.9%, also tested positive. Investigators detected Borrelia miyamotoi in two adults and two larval pools, and found both a human-active and a nonhuman-active strain of Anaplasma phagocytophilum in adult blacklegged ticks. These results do not establish a prevalence estimate for western North Carolina, much less for the Southeast. They do establish why a historical label such as “not endemic” cannot be treated as a permanent absence claim.
The North Carolina investigation also shows why current surveillance has to be more than a search for the vector’s name. A map may indicate suitable habitat or a growing probability of encountering I. scapularis. Pathogen testing asks a different question: what, if anything, do the sampled ticks carry? Even that answer depends on the life stage, the tested denominator, the assay, and the particular pathogens included. The report distinguished a human-active strain of A. phagocytophilum from a strain that occurs in blacklegged ticks but does not cause disease in humans. Collapsing both results into a single “infected tick” category would remove information that changes the public-health meaning.
The two studies are not a before-and-after experiment. Ohio and Biltmore Forest differ in geography, sampling setting, season, field design, and outcome. Their numbers cannot be combined into one national risk estimate. Their relationship is more useful than that: the Ohio study shows how a map can organize surveillance at a practical scale, while the North Carolina report shows what a local surveillance signal can reveal that a vector model alone cannot. One is a structured projection; the other is direct, limited observation of ticks and pathogens in a changing region.
This is why a map may be worth using even when it is not perfect. A model that can rank places for a particular surveillance task can save effort and reveal patterns that would be difficult to see from scattered observations. Its imperfections become dangerous only when its intended task is silently expanded. A map designed to prioritize public-trail dragging is not automatically a map of infection probability, private-property exposure, or clinical diagnosis.
The contract hidden behind the color
Every map claim carries a set of conditions, whether or not the legend shows them. The first is place: Ohio statewide projections are not automatically portable to a residential community in North Carolina. The second is time: the study may use observations from one season, land-cover information from another year, and climate averages covering several decades. The third is outcome: occurrence, relative abundance, pathogen detection, and human disease are related but distinct. The fourth is setting and sampling: public trails, residential properties, and randomly selected habitat do not expose the same observation process. The fifth is scale: a grid cell is a prediction unit, not a guarantee that conditions are uniform inside it.
The dates behind the Ohio map illustrate the problem. Its climate variables came from 30-year monthly normals covering 1990–2020. Its forest layer came from 2019 land-cover data. Deer density was estimated from 2019 harvest data, while robin density came from an earlier atlas-based estimate. The ticks used to fit the models were collected in 2022. Calling the final product simply a “2025 map” would hide these different clocks. It would also suggest a kind of currentness that the study never claimed.
A map becomes stale when one of these conditions no longer fits the decision—not merely when a certain number of years has passed. A new field observation may show that the vector is present beyond a historical boundary. A change in forest configuration or host density may alter abundance without immediately changing the broad occurrence pattern. A hotter or drier local environment may differ from the coarse climate layer used in the model. A new pathogen finding may change the health significance of an area even if the vector layer still looks reasonable. Or the apparent change may come from a different season, collection method, or detection opportunity.
That last possibility is essential. The Ohio researchers did not model detection explicitly and sampled each site once because they prioritized broad spatial coverage. A site where no tick was collected could therefore be a genuinely low-occurrence site, or a site where the standardized search did not detect a tick that was present. The independent Ohio testing data also came mainly from October through May, outside the May–October period used for model development, and contained a different mix of life stages. These are not flaws that make the map worthless. They are conditions that define how its evidence should be used.
So the first question about an old map should not be “Is it still true?” That question is too vague to answer. Ask instead: What was this map built to decide, what evidence does its color summarize, and have those conditions changed enough to make the decision different? A map that remains fit for prioritizing broad surveillance can still be too weak for pathogen-specific warnings. A current local pathogen result can justify targeted follow-up without replacing a statewide ecological model. And a mismatch between a map and a field observation may call for better sampling before it calls for a new model.
The map is not a boundary around reality. It is a dated argument about where a particular observation is more likely under particular conditions. To understand why that argument ages, we need to look closely at the ecology it compresses: a mostly immobile tick, mobile hosts, changing habitat, and climate conditions operating at different spatial scales. That is where the apparent stability of a map meets the movement of the system it represents.