When a Map Starts Misleading You

The blacklegged tick risk map becomes a problem when its output is asked to answer a different question from the one its evidence supports. This chapter treats a map-versus-field mismatch as something to diagnose, not as automatic proof that the map has failed. The reader’s task is to separate an ecological change from a changed sampling process, a seasonal difference, or a newly important pathogen question before deciding whether to annotate, test, recalibrate, or rebuild the map.

A stale map is not necessarily an old map. An older map may still rank places well for the surveillance task for which it was designed. A relatively new map may already be misleading if it is applied to a new land type, season, outcome, or geographic setting. The first diagnostic question is therefore not “How many years has this map existed?” It is “What decision is being made now, and what evidence did this map actually learn from?”

Start with the mismatch, not the calendar

A review should begin with a concrete symptom. Perhaps current field crews find ticks in places the map ranks low, or find few ticks in places it ranks high. Perhaps the map is being used to estimate present abundance even though it was built to rank relative encounters. Perhaps a health department wants a pathogen-specific alert from a surface that models only vector occurrence. Each symptom points to a different possible failure, and none can be interpreted without the map’s original contract.

The Ohio study offers a useful example of what validation can and cannot establish. Its occurrence model was tested against 110 observations from 86 unique sites collected by the Ohio Department of Health during October through May in 2019–2023. The model assigned lower mean occurrence probabilities to sites where ticks were absent than to sites where they were present: 0.652 compared with 0.895. The predicted probability also had a positive mean correlation of 0.57 with the independent observations, with a reported p-value below 0.001. These results support directional information. The model tended to place higher values where independent sampling found ticks.

That is meaningful for prioritization, but it is not the same as exact calibration. A probability that ranks one site above another does not promise that a crew will find a tick on every visit to the higher-ranked site. Nor does a significant correlation show that the model captures every mechanism or performs equally across Ohio. The model-development data and the independent data came from different seasonal windows and different life-stage mixes. The independent collection contained mostly adults, while the training data were dominated by larval encounters and nymphs. A mismatch in results may therefore be testing seasonal portability rather than revealing a broken spatial relationship.

The abundance model makes the distinction sharper. Predicted relative abundance was also positively correlated with independent observations, with a mean correlation of 0.30 and a p-value of 0.003. Yet the mean number of ticks collected rarely fell within the model’s predicted credible intervals. The model could still help identify places that tend to be higher or lower relative to one another, while failing to provide a dependable count for the next visit.