Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies.

Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.

Interactive range-limit theory (iRLT): An extension for predicting range shifts.

A central theme of range-limit theory (RLT) posits that abiotic factors form high-latitude/altitude limits, whereas biotic interactions create lower limits. This hypothesis, often credited to Charles Darwin, is a pattern widely assumed to occur in nature. However, abiotic factors can impose constraints on both limits and there is scant evidence to support the latter prediction. Deviations from these predictions may arise from correlations between abiotic factors and biotic interactions, as a lack of data to evaluate the hypothesis, or be an artifact of scale. Combining two tenets of ecology-niche theory and predator-prey theory-provides an opportunity to understand how biotic interactions influence range limits and how this varies by trophic level. We propose an expansion of RLT, interactive RLT (iRLT), to understand the causes of range limits and predict range shifts. Incorporating the main predictions of Darwin's hypothesis, iRLT hypothesizes that abiotic and biotic factors can interact to impact both limits of a species' range. We summarize current thinking on range limits and perform an integrative review to evaluate support for iRLT and trophic differences along range margins, surveying the mammal community along the boreal-temperate and forest-tundra ecotones of North America. Our review suggests that range-limit dynamics are more nuanced and interactive than classically predicted by RLT. Many (57 of 70) studies indicate that biotic factors can ameliorate harsh climatic conditions along high-latitude/altitude limits. Conversely, abiotic factors can also mediate biotic interactions along low-latitude/altitude limits (44 of 68 studies). Both scenarios facilitate range expansion, contraction or stability depending on the strength and the direction of the abiotic or biotic factors. As predicted, biotic interactions most often occurred along lower limits, yet there were trophic differences. Carnivores were only limited by competitive interactions (n = 25), whereas herbivores were more influenced by predation and parasitism (77%; 55 of 71 studies). We highlight how these differences may create divergent range patterns along lower limits. We conclude by (a) summarizing iRLT; (b) contrasting how our model system and others fit this hypothesis and (c) suggesting future directions for evaluating iRLT.