Understanding the ecological response to anthropogenic environmental changes, including changes in climate, land use, land cover and other factors, requires quantitative tools to characterize, analyze and visualize dynamic changes in the populations of key organisms of interest. Developing such tools is made difficult by the fact that population responses to environmental change are spatially and temporally complex, particularly for organisms with multiple environmental life stages, such as those that participate in the transmission of vector-borne diseases (VBD). Disease vector populations may exhibit variations in seasonal timing and duration, and their generally non-linear response to environmental signals makes prediction of the risk posed by VBD under altered environmental conditions challenging [1
]. When exposed to changing climatic conditions, vector distribution and the risks of VBD may shift substantially across time and space [1
]. Yet, a great deal of uncertainty remains for many VBD systems [4
], and little is known regarding the dynamic nature of the population response to climate change, particularly vector phenology (timing of life stages), seasonality and the duration of key population events.
While some ecological analyses have characterized the dynamic population response of various plant and arthropod species to external forcings in a spatially explicit fashion (e.g., [9
]), much analogous work on VBD has neglected the spatial domain [13
]. Still, other work forgoes system dynamics, instead investigating the spatial patterns of static population measures, such as presence/absence or mean abundance (see, for instance, [15
] for Lyme disease and [16
] for hantavirus). Such analyses make use of statistical relationships between climate and habitat suitability to estimate, for instance, the potential changes in the distribution of habitat suitability for, or nymphal density of, Ixodes scapularis
, the vector of Lyme disease [17
]. This approach offers little insight into the nature of the population’s response over time, such as shifts in peak population timing or variability in population density during key exposure periods (e.g., high season for recreational activities). Given the substantial and continuing disagreement regarding how climate may change the distribution of VBD (e.g., [4
]), analyses capable of assessing the relationship between exogenous forcings and population dynamics in space and time may provide such insights.
What is more, geovisualization of the dynamic VBD response to environmental change could provide key information (e.g., maps summarizing complex spatio-temporal phenomena) for developing policies to respond to shifting risk. Thus, geospatial tools for characterizing, analyzing and predicting the response of VBD to future changes are desirable, and these should emphasize dynamic phenomena known to be important for understanding risk, such as vector phenology and seasonality. Phenology—the timing of life stages—is known to be sensitive to climatic change and is an important determinant of the spatial distribution of arthropods [19
]. Current models investigating arthropod distribution under future climates generally ignore phenology, instead, establishing a relationship between a vector’s current abundance and key habitat characteristics and, then, applying that model to projected future conditions [21
]. An examination of an organism’s phenological response can reveal important, but subtle, impacts of changing climate. For instance, the date of flowering and fruiting have been shown to be important determinants of aspen distribution [23
], and the date of first oviposition has been shown to be important for gypsy moth distribution [24
]. Characterization of life stage-specific dynamic responses can highlight such subtle determinants of the distribution of vectors under the future climate.
The seasonality of events may also shift under future conditions, with important consequences for VBD risk. For instance, vector populations may peak at certain times of the year, with peak incidence of disease occurring at other times (e.g., see [25
] for Lyme disease). Some models of VBD response to climate change attempt to roughly characterize changes in seasonality (e.g., [26
]); some integrate seasonal elements, such as temperature, humidity and daytime hours, through degree-day models (e.g., [27
]), and still, others do not explicitly account for seasonality (e.g., [31
]). A more detailed spatial representation of seasonal shifts would make it possible to characterize the potentially profound effect that environmental change may have on the length and timing of VBD transmission seasons.
Here, we develop a spatially-explicit modeling approach for investigating the dynamic population responses of a disease vector of interest, with the goal of enhancing our understanding of future VBD risk. We introduce the concept of dynamic population features (DPFs), which provide information on population cycling, seasonal timing and phenological events across vector life stages. Importantly, we describe how analysis of such features—such as number and timing of population peaks (Table 1
)—may be used to predict disease risk.
To demonstrate the utility of this modeling approach, we examine the responses of the black-legged deer tick (Ixodes scapularis
), the vector for Lyme disease, to changes in temperature across the eastern United States. I. scapularis
is an excellent model organism with which to examine the influence of climate change on phenological and seasonal characteristics: it is known to be highly sensitive to environmental conditions, including temperature [6
]. Furthermore, the three I. scapularis
life stages (larva, nymph and adult) require different temperature conditions to support host finding or progress to the next life stage [28
Dynamic population features (DPFs) of population response.
Dynamic population features (DPFs) of population response.
|Absolute Population Features|
|Mean &Median||Avg. and median population (3yr)|| |
|Peak Pop.||Avg. of maximum yearly population|
|Peaks per Year||Avg. no. of peaks per year|
|Timing Population Features|
|Peak Month||Month of the yearly peak|| |
|Peak to Trough||No. of days between yearly peak and yearly trough|
|IP to IP||Time between inflection points (IP) on either side of yearly max. pop.|
|UQ/IQR||Avg. of month during which the inter-quartile range (IQR) of the upper quartile (UQ) occur|
|Wave Angle||Wave angle for period = 90.5 days, from continuous wavelet analysis using a complex Morlet waveform (after ). |
|Exposure Population Features|
|IP Pop||The summation of tick population for all days included in the IP to IP calculation|| |
We explore the Lyme disease system as a case study, simulating I. scapularis population dynamics over the eastern United States using modeled climate data, and spatially characterize, analyze and visualize key DPFs for each tick life stage. We examine DPFs from simulated dynamics under current climate conditions and compare these to observed data to ascertain which features best predict current levels of disease risk. We then project DPFs under two future climate scenarios and provide key geovisualizations of projected vector dynamics over the spatial range. We show, by characterizing and visualizing DPFs, how we can determine which population features best predict disease risk under current conditions and can then explore how future conditions may lead to shifts in these same DPFs in the future. We analyze DPFs in the context of I. scapularis and Lyme disease risk, but note that the approach shows promise for other organisms and disease systems.
4. Discussion and Conclusions
When examining the response of vector populations to climate change, shifts in phenology, seasonality and other dynamic characteristics can be anticipated across the spatial range and life stages of the organism of interest. Risk of VBD is dependent on both timing and probability of exposure to the vector, and thus, characterizing the dynamic population response over space is crucial in order to anticipate and manage potential future risks. Here, we provide a framework for evaluating both static and dynamic effects of climate change on populations over large geographic areas, using spatially explicit simulation of a climate-driven, stage-structured population model.
Our findings with respect to Ixodes scapularis illustrate both the methodology and its utility. The derivation and analysis of dynamic population features are key to the analytical approach. DPFs provide quantitative information about a range of population characteristics and allow for comparison between dynamic simulation output and observed disease data, as well as between baseline and projected climates. Absolute DPFs, such as Mean, Median and Peak Population can be interpreted as indicators of survivorship, while timing DPFs, such as number of days from the yearly maximum population to the yearly minimum population (Peak to Trough) and month in which peak population occurs (Peak Month) characterize the timing and length of a given life stage’s season.
In the case of Ixodes, DPFs associated with the peak of the simulated population curve, Peak Population and Peak Month, proved to be the most important in predicting high risk of Lyme disease, though all DPFs showed some level of discriminatory ability. AUC analyses showed that dichotomizations isolating high risk improved discriminatory ability across all DPFs and life stages. Aggregation of medium and high risk also showed improved discriminatory ability across life stages and DPFs as compared to the minimal vs. low/medium/high dichotomization. This trend of improvement, as high disease risk is progressively isolated into a single category, suggests strongly that these DPFs are useful in predicting the timing and location of higher Lyme disease risks.
When DPFs are examined for two projected climate scenarios, we show that the dynamic population response of I. scapularis
is not uniform across life stages and varies over space. Spatial shifts in temporal features include geographic shifts in season, and these shifts are not consistently northward as one might intuitively hypothesize. While the month in which the greatest number of ticks are questing (Peak Month
) is delayed for the adult life stage (Figure 1
), QN and QL peaks do not show geographically uniform shifts to earlier questing season. Also, Peak to Trough
and IP to IP
indicate potential changes in season length in projected scenarios. Spatial shifts in absolute DPFs, such as Peak Population
, vary by region. For instance, the peak populations in the Midwest and the Northeast regions are both expected to rise far more as compared to the Appalachian mountain range or the Gulf Coast, where these populations are expected to remain more stable.
Although the finding that QL Peak Month and Peak Population show high predictive ability for Lyme disease risk is significant, the causal implications of this finding, and others like it, must be interpreted cautiously. Disease risk is not directly related to the questing larval stage, which takes the first blood meal in the lifecycle, and thus, is responsible for Lyme transmission only under the rare circumstance that larvae are infected transovarially. Likewise, QN Peak Month and Peak Population have similar AUC values for all dichotomizations of Lyme disease risk, an effect driven largely by the similarity of tick response to temperature in these two life stages, rather than mutual causal relationships with disease. Complex temporal relationships are inherent in these populations: questing nymphs and questing larvae, for instance, peak at approximately the same time of year, and their populations in a given location are ostensibly correlated, though the QN population does not result from the QL population in the same year, but rather previous years’ QL.
As in other ecological modeling analyses, data quality determines the utility of this analysis framework for a given system. In our analysis, CDC data quality may account for the lack of significant AUCs of DPFs in comparison to the observed tick data. Tick presence/absence data are collected using a variety of methods, such as dragging and deer surveys, often under serious resource constraints [33
]. Rather than providing consistent, systematic information about tick presence and absence, the national tick dataset offers a coarse categorization derived from disparate information. This is in contrast to the national Lyme disease dataset, which is based on a consistent reporting standard. Given the higher quality of data collected, this dataset is more useful in substantiating the results of our model.
Other climate factors besides temperature, such as humidity, have been shown to affect Ixodes
] and correlate with human Lyme disease risk [43
]. The population model used here did not incorporate Ixodes
’ response to humidity, and although our simulated population data demonstrated good correspondence with Lyme incidence, it is possible that including other key environmental variables may yield yet greater correspondence. Likewise, host and pathogen populations were not considered in our analysis, which was limited to vector dynamics. Relatively little research has been done on the potential population responses of Borrelia
under altered climate conditions. However, it has been suggested that changes in Ixodes
phenology in response to climatic changes may affect the evolution of various tick-borne pathogens, so as to modify their lifespan, transmission and pathogenicity [44
]. Host dynamics can also greatly impact infected vector density and consequent human risk in a variety of VBD systems [45
]. In the case of Lyme disease, the abundance of key hosts, such as mice and chipmunks, has been shown to predict the density of infected nymphs in eastern deciduous forests [48
]; in other areas, such as the southern United States, lizards are believed to exert a dampening effect on the spread of Lyme disease, due to poor host competence or zooprophylactic effects [49
]. Including host, vector and pathogen dynamics in a combined model would pose significant methodological and computational challenges, but is also likely to add greatly to our mechanistic understanding of shifting VBD risk under future environmental conditions. We note that a similar simulation, summarization (e.g., DPFs) and analysis approach can be pursued with such a combined model; yet, other summarizations (e.g., R0
) become available for geovisualization in that context (e.g., [51
The methodological contributions made by the modeling analysis described here are considerable. We provide a quantitative assessment of population dynamics—with potential consequences for disease risk—under future climates, which is made possible by use of a spatially-explicit, mechanistic model [53
]. Our spatial characterization of DPFs allows a detailed visual assessment (e.g., Figure 1
), alongside a quantitative analysis, of the dynamic population response to future climate, revealing potential changes that are non-intuitive. For instance, across the eastern United States, under projected temperatures as compared to the baseline scenario, nymphs and larvae are projected to arrive at their peak population earlier in the season, while adults are projected to reach peak population later in the season (Figure 1
). The approach taken here also highlights the value of modeling abundance, which, unlike habitat suitability or other static measures, allows for the examination of phenology and seasonality among life stages and the potential implications for (and correlation with) disease risk. For instance, IP to IP
indicates that the length of larval “season” is stable across the three temperature scenarios, while the adult, and, to a lesser extent, the nymphal stages exhibit “seasons” that are strongly sensitive to the projected increasing temperatures. Such life-stage-specific responses in time and space would be unapparent using traditional methods that examine, for instance, aggregate, annual effects.
We caution above against a causal interpretation of a DPF’s predictive power. A strong correlation between a DPF and observed disease incidence may not represent a causal relationship, but such a finding can raise hypotheses that ultimately lead to greater mechanistic understanding of the relationship between vector populations and disease risk in space and time and, thus, an improved causal understanding. Finally, population models, such as the one examined here, can also be used to evaluate the efficacy and economy of potential public health interventions [53
], such as vector or host control (e.g., [54
] for Lyme disease). A coupled analysis of the effect of temperature in the presence of a vector control program would be an obvious extension of the approach, and such an application of this model is possible for many different vectors, interventions and diseases.
We have demonstrated the ability of a spatially-explicit dynamic population model to discriminate between dynamic population features most strongly associated with disease risk, as well as to characterize the geographically varied response of I. scapularis life stages to climate dynamics. Use of such an approach to describe shifts in dynamics is not limited to Lyme disease. The technique may provide new insights into the dynamic responses of a range of disease vectors to environmental changes, particularly shifts in their seasonal and phenological features. Such analyses may provide helpful information about the consequent risk of vector-borne disease under future conditions.