Why the South Has So Few Lyme Cases — It’s Not Just Biodiversity

In the northeastern United States, a single hectare of forest can produce more than 1,000 infected nymphal ticks per year — enough to put thousands at risk. In the Southeast, that same hectare yields fewer than 10. This staggering 100-fold difference in Lyme disease risk persists despite the presence of the same tick species, the same pathogens, and many of the same host animals across both regions. For decades, scientists have debated what drives this geographic paradox. The dominant explanation has been biodiversity: the idea that richer, more diverse host communities in the South dilute transmission by offering ticks more dead-end hosts like lizards that don’t pass on the pathogen. But a new modeling study reveals that biodiversity alone doesn’t explain the gap. Instead, it’s the combination of host diversity and a subtle behavioral difference in ticks — how high they climb above the forest floor — that creates this dramatic north-south divide in human disease risk.

The study, by Carli Peterson, Erika Gallo, and Christopher Kribs (2026), builds a detailed mathematical model of Lyme disease transmission dynamics in the eastern U.S., incorporating both ecological and behavioral mechanisms. Their model shows that southern host communities, while less efficient at sustaining Borrelia burgdorferi transmission under typical conditions, are fully capable of supporting outbreaks — but only if ticks behave differently. Specifically, when southern ticks quest higher in the vegetation, they shift from feeding on reptiles to feeding on small mammals, flipping a switch that could ignite sustained transmission. This finding challenges the assumption that the South is inherently low-risk and suggests that climate-driven changes in tick behavior could one day erase the geographic firewall that has long protected the region.

The Science

The researchers developed a discrete-time system of difference equations to simulate the annual life cycle of Ixodes scapularis, the black-legged tick and primary vector of Lyme disease in the eastern U.S. The model tracks three tick stages — larvae, nymphs, and adults — and six host groups, differentiated between northern and southern ecosystems. In the Northeast, hosts include white-footed mice, chipmunks, shrews, squirrels, lizards, and deer. In the Southeast, the host mix is similar but includes additional lizard species like the southeastern five-lined skink and ground skink, reflecting the region’s greater reptile diversity.

Crucially, the model incorporates two key innovations. First, it uses ratio-dependent host-finding success, meaning that a tick’s ability to find a host depends not just on host density, but on the ratio of ticks to hosts. This captures the biological reality that when tick populations are high, competition for hosts increases, and not every tick gets a blood meal. Second, it explicitly models questing behavior through a parameter $c$, representing the proportion of the questing season that ticks spend above the leaf litter. Northern ticks typically have $c \approx 0.8$, meaning they spend most of their time elevated on vegetation, where they encounter small mammals. Southern ticks, adapted to hotter, drier conditions, have $c \approx 0.2$, staying mostly below the litter and encountering more reptiles.

The model also accounts for reservoir competence — the ability of a host to transmit the pathogen to feeding ticks. White-footed mice are highly competent, with up to 90% of ticks becoming infected after feeding on them. Lizards, in contrast, are largely incompetent; most studies show they clear the infection quickly or never transmit it at all. Host group 6 (deer, opossums, raccoons) is treated as incompetent, consistent with field data showing low spirochete loads in these animals.

Using parameter values drawn from published literature — not calibrated to reproduce observed disease patterns — the researchers simulated transmission dynamics over time. They derived the basic reproductive number $\mathcal{R}_0$, the average number of new infections caused by a single infected tick in a fully susceptible population. When $\mathcal{R}_0 > 1$, the pathogen can invade and persist; when $\mathcal{R}_0 < 1$, it fades out.

What They Found

Under baseline conditions — that is, using literature-derived parameters without tuning to match real-world data — the model reproduces the observed north-south divide. In the Northeast, $\mathcal{R}_0$ exceeds 1 across a wide range of tick population sizes, indicating strong potential for pathogen invasion. In the Southeast, $\mathcal{R}_0$ remains below 1, consistent with low nymphal infection prevalence (NIP) and rare human cases.

Figure 1 shows $\mathcal{R}_0$ as a function of the demographic reproductive number $\mathcal{R}_d$, which measures tick population growth potential. The northern curve (solid blue) crosses the threshold $\mathcal{R}_0 = 1$ at low $\mathcal{R}_d$, meaning even small tick populations can sustain transmission. The southern curve (dashed blue) stays below 1 across the same range, indicating transmission failure under typical conditions.

But the real insight comes when the researchers vary the questing parameter $c$.

shows how $\mathcal{R}_0$ changes as $c$ increases from 0 (ticks always below litter) to 1 (always above). In the North, $\mathcal{R}_0$ is high and stable across most values of $c$. In the South, $\mathcal{R}_0$ remains low until $c$ exceeds about 0.6 — then it rises sharply, crossing $\mathcal{R}_0 = 1$ and entering the range where sustained transmission becomes possible.

Basic reproductive number (ℛ₀) vs. questing height (c)

Label	Value
Northern	1.8 dimensionless
Northern	1.75 dimensionless
Northern	1.7 dimensionless
Northern	1.65 dimensionless
Northern	1.6 dimensionless
Northern	1.55 dimensionless
Northern	1.5 dimensionless
Northern	1.45 dimensionless

As the questing parameter c increases, southern ℛ₀ crosses the threshold for sustained transmission (ℛ₀ > 1) around c = 0.6.

This threshold effect means that southern ecosystems are not inherently resistant to Lyme disease. They are conditionally resistant — their low risk depends on ticks staying close to the ground. Shift their behavior, and the system flips.

The model also reveals that ratio-dependent host-finding fundamentally alters transmission dynamics. Traditional models assume that host-finding success is either density-dependent (scales linearly with host density) or frequency-dependent (scales with host proportion). But in reality, when tick density is very high, not all ticks find hosts — there’s a saturation effect. The authors show that ratio-dependent contact rates produce a saturating effect on $\mathcal{R}_0$ (

), meaning that beyond a certain point, adding more ticks doesn’t increase transmission risk proportionally. This contrasts with density-independent models, where $\mathcal{R}_0$ grows without bound, potentially overestimating risk in high-tick areas.

Nymphal infection prevalence (NIP) under different questing behaviors

Label	Value
Northeast (c = 0.8)	24 %
Southeast (c = 0.2)	0.5 %
Southeast (c = 0.6)	8 %
Southeast (c = 0.8)	22 %

NIP in the Southeast rises sharply as questing height increases, reaching near-northern levels at c = 0.8.

Finally, the model tracks two key public health metrics: nymphal infection prevalence (NIP) and density of infected nymphs (DIN). Under baseline southern conditions, NIP is near 0%, matching field observations. But when $c$ increases, both NIP and DIN rise rapidly. At $c = 0.8$, southern DIN reaches levels comparable to the Northeast — enough to pose a serious human health threat.

Why This Changes Things

For years, the dilution effect — the idea that biodiversity reduces disease risk — has dominated ecological thinking about Lyme disease. The logic is elegant: more host species mean more incompetent hosts, which “dilute” the pool of infections. And in the South, high lizard diversity seemed to explain low transmission. But this new study shows that dilution alone isn’t enough. Southern host communities, when exposed to northern-style questing behavior, can sustain transmission just as well as northern ones. That means the South’s low risk isn’t due to its hosts being poor at spreading the pathogen — it’s due to ticks not feeding on the right hosts in the first place.

This reframes the problem. It’s not that southern ecosystems are safer. They’re just currently mismatched: ticks are behaviorally adapted to avoid desiccation, which incidentally reduces their contact with competent mammalian hosts. But that mismatch could vanish with climate change. As temperatures rise and humidity patterns shift, southern ticks may no longer need to stay buried in moist leaf litter. They could begin questing higher — and in doing so, unlock a latent transmission potential.

The implications extend beyond Lyme disease. Many vector-borne diseases — from tick-borne encephalitis to malaria — depend on the spatial overlap between vectors and hosts. This study shows that small behavioral shifts, driven by environmental change, can flip entire systems from low- to high-risk states. It’s a warning that current disease maps may not reflect future reality.

It also challenges how we assess risk. Public health officials often rely on NIP and DIN as static indicators. But this model shows they’re dynamic — sensitive to behavioral parameters that aren’t routinely monitored. A forest that looks safe today could become dangerous tomorrow if tick behavior changes, even if host populations stay the same.

And it underscores the limits of traditional modeling assumptions. The saturating effect of ratio-dependent host-finding means that in dense tick populations, adding more ticks doesn’t linearly increase risk. This has practical implications for control strategies: reducing tick numbers below a certain threshold may be more effective than chasing incremental reductions in already high-density areas.

What’s Next

The model’s predictions are theoretical, but they point to urgent questions for field research. Do southern I. scapularis populations already show variation in questing height? Are there microclimates or forest types where ticks already quest higher — and if so, do those areas have higher NIP?

Genetic studies suggest northern and southern ticks may be locally adapted, with heritable differences in questing behavior (Arsnoe et al., 2019). If so, evolution could either slow or accelerate a northward shift in behavior. But if behavior is plastic — shaped by immediate environmental conditions — then climate change could drive rapid change.

Another open question is the role of co-feeding transmission, which the model excludes. In some systems, infected and uninfected ticks feeding close together on the same host can transmit the pathogen directly, bypassing the need for systemic host infection. If this occurs in southern populations, it could lower the threshold for $\mathcal{R}_0 > 1$, making outbreaks even more likely under changing conditions.

The model also assumes stable host populations. But land use change, urbanization, and wildlife management — such as deer culling — could alter host community composition. A decline in lizards or an increase in white-footed mice could further erode the South’s current protection.

Finally, the study highlights the need for behavioral surveillance in tick ecology. We monitor tick abundance and infection rates, but rarely their microhabitat use. Integrating questing behavior into routine surveillance could provide early warning of shifting risk.

The big picture is clear: the South’s low Lyme disease burden is not a permanent feature of its ecology. It’s a fragile equilibrium, maintained by the interplay of biodiversity and tick behavior. Disrupt that balance, and the region could face a new era of tick-borne disease. The firewall is not in the genes of the hosts or the pathogens — it’s in the leaves on the forest floor. And as the climate changes, that firewall may not hold.