Prediction of Peromyscus Maniculatus (Deer Mouse) Population Dynamics in Montana, Usa, Using Satellite- Driven Vegetation Productivity and Weather Data

Journal of Wildlife Diseases, 48(2), 2012, pp. 348–360 # Wildlife Disease Association 2012

PREDICTION OF PEROMYSCUS MANICULATUS (DEER MOUSE) POPULATION DYNAMICS IN MONTANA, USA, USING SATELLITE- DRIVEN VEGETATION PRODUCTIVITY AND WEATHER DATA

Rachel A. Loehman,1,6 Joran Elias,2 Richard J. Douglass,3 Amy J. Kuenzi,3 James N. Mills,4 and Kent Wagoner5 1 US Forest Service, Rocky Mountain Research Station Fire Sciences Laboratory, 5775 West US Hwy 10, Missoula, Montana 59808, USA 2 Department of Mathematical Sciences, University of Montana–Missoula, Missoula, Montana 59801, USA 3 Department of Biology, Montana Tech of the University of Montana, Butte, Montana 59701, USA 4 Division of High Consequence Pathogens and Pathogenesis, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, Georgia 30333, USA 5 Office of Institutional Research and Assessment, University of Tennessee, Knoxville, Tennessee 37996, USA 6 Corresponding author (email: [email protected])

ABSTRACT: Deer mice (Peromyscus maniculatus) are the main reservoir host for Sin Nombre virus, the primary etiologic agent of hantavirus pulmonary syndrome in North America. Sequential changes in weather and plant productivity (trophic cascades) have been noted as likely catalysts of deer mouse population irruptions, and monitoring and modeling of these phenomena may allow for development of early-warning systems for disease risk. Relationships among weather variables, satellite-derived vegetation productivity, and deer mouse populations were examined for a grassland site east of the Continental Divide and a sage-steppe site west of the Continental Divide in Montana, USA. We acquired monthly deer mouse population data for mid-1994 through 2007 from long-term study sites maintained for monitoring changes in hantavirus reservoir populations, and we compared these with monthly bioclimatology data from the same period and gross primary productivity data from the Moderate Resolution Imaging Spectroradiometer sensor for 2000–06. We used the Random Forests statistical learning technique to fit a series of predictive models based on temperature, precipitation, and vegetation productivity variables. Although we attempted several iterations of models, including incorporating lag effects and classifying rodent density by seasonal thresholds, our results showed no ability to predict rodent populations using vegetation productivity or weather data. We concluded that trophic cascade connections to rodent population levels may be weaker than originally supposed, may be specific to only certain climatic regions, or may not be detectable using remotely sensed vegetation productivity measures, although weather patterns and vegetation dynamics were positively correlated. Key words: Deer mice, hantavirus, MODIS, Peromyscus, prediction, satellites, trophic cascade.

INTRODUCTION excretions, through direct contact with these substances, or via rodent bite (Peters Hantavirus pulmonary syndrome (HPS) et al., 2006). Rodent-borne hantaviruses was first identified in North America in are well known in Europe and Asia the spring and summer of 1993. Initial (Plyusnin et al., 2001) and have recently cases were clustered in the Four Corners been identified in most countries in South region of the southwestern United States, America (Mattar and Parra, 2004; Pini, where origins of the disease were traced to 2004) as well as Panama (Bayard et al., an unrecognized, directly transmissible 2004). rodent-borne hantavirus (Nichol et al., The 1993 HPS outbreak is attributed to 1993; Childs et al., 1994) later named atypical climatic conditions associated Sin Nombre virus (SNV; genus Hantavi- with the 1991–92 El Nin˜ o Southern rus, family Bunyaviridae). Sin Nombre Oscillation, including heavy spring and virus is believed to be transmitted to summer precipitation in 1991/1992 and a humans from the infected host (the deer mild winter in 1992 (Parmenter et al., mouse, Peromyscus maniculatus) through 1993; Hjelle and Glass, 2000). A bottom- inhalation of aerosols of secretions and up trophic cascade hypothesis is proposed

348 LOEHMAN ET AL.—PREDICTION OF DEER MOUSE POPULATION DYNAMICS 349 as a likely explanation for the timing and density, when data were aggregated across location of the outbreak (Glass et al., 2000; multiyear sampling periods (Calisher et al., Yates et al., 2002; Glass et al., 2007). 2007). Trophic cascades are interactions among Although identifying and quantifying organisms from different levels within a environmental variables associated with food web (e.g., producers, consumers, and increasing reservoir abundance has been predators) that result in changes in identified as a key component of develop- abundance and biomass across trophic ing a predictive model of human risk levels (Pace et al., 1999). In moisture- (Mills et al., 1999), to date no successful limited temperate terrestrial systems pri- generalized predictive model for HPS has mary-level trophic cascades can be initial- been developed. Douglass et al. (2001) ized by precipitation, followed by an suggested that predictive models for SNV- increase in ecosystem productivity (growth related disease may be developed by of grasses, seeds, leaves, nuts, and subse- relating changes in reservoir populations quently insects) stimulated by recharged to fluctuations in plant productivity. Al- soil moisture. Increased primary and though studies have quantified some secondary productivity and mild seasonal relationships between rodent population temperatures can promote high rates of density and landscape and climate vari- reproduction and survival in rodent reser- ables (Lima et al., 1999; Ernest et al., voir populations through extension of the 2000; Langlois et al., 2001; Brown and reproductive period and favorable over- Ernest, 2002), few studies have been wintering conditions (Abbott et al., 1999; conducted in a manner useful for broad- Mills et al., 1999). Thus the trophic scale modeling and prediction. cascade hypothesis may help explain We hypothesized that trophic cascade interannual increases in deer mouse pop- dynamics should be resolvable via a com- ulations. In addition, trophic cascade– bination of weather and vegetation produc- mediated changes in reservoir population tivity data; these data should in turn serve density may influence the prevalence of as predictors of inter- and intra-annual infection within populations through reg- rodent population dynamics. Applying the ulation of rate of pathogen transmission trophic cascade hypothesis as the basis for and probability of infection. When popu- predicting disease risk in reservoir and lations are large and densely distributed human populations suggests that increased (e.g., following irruption events), greater precipitation and biotic productivity trigger frequency of rodent-to-rodent contact establishment of large reservoir popula- may increase the probability of virus tions with high rates of pathogen transmis- transmission to susceptible individuals sion. Such conditions maximize the prob- (Mills et al., 1999; Root et al., 1999; ability of encounters between humans and Douglass et al., 2001; Glass et al., 2007; infected rodents (Fig. 1). We assumed that Wesley et al., 2010). These processes can human risk of hantavirus exposure is result in increasing prevalence of infection strongly related to the probability of in high-density host populations in a contact with infected deer mice, as dem- delayed-density-dependent manner, fol- onstrated by studies that indicated higher lowing seasonal time lags (Madhav et al., rodent densities at likely exposure sites 2007; Kallio et al., 2010). A recent analysis than at nonexposure sites (Childs et al., of long-term trapping data from sites in 1995; Crowcroft et al., 1999), although Arizona, Colorado, Montana, and New other exposure risk factors have been Mexico found a statistically significant identified, including hand plowing, clean- positive relationship between prevalence ing of outbuildings, and other activities that of hantavirus antibodies (a marker of served to aerosolize rodent excreta (Zeitz infection) and deer mouse population et al., 1995; Crowcroft et al., 1999). 350 JOURNAL OF WILDLIFE DISEASES, VOL. 48, NO. 2, APRIL 2012

FIGURE 2. Cascade and Polson, Montana, USA, rodent trapping sites and associated weather stations. MT 5 Montana; ID 5 Idaho; WA 5 Washington; masl 5 meters above sea level.

MATERIALS AND METHODS

Study area We assessed rodent populations, weather trends, and vegetation productivity at two ecologically and climatically distinct sites in Montana, USA (Fig. 2). The eastern site, Cascade, is a grassland ecotype east of the Continental Divide (46u59.39N, 111u35.39W, elevation 1,438 masl), with monthly mean temperatures of 13.3 C in January and 34.3 C in July and annual precipitation of 36.7 cm (average values 1971–2000). The Polson site is a sage-steppe ecotype west of the Continental Divide (47u38.49N, 114u20.79W, elevation 834 masl), with monthly mean temperatures of 14.7 C (January) and 37.4 C (July) and annual precipitation of 34.0 cm.

Rodent population data We used longitudinal, mark-recapture trapping data for deer mice from live-trapping grids at the Cascade and Polson sites (Fig. 2), 1994–2007, to test whether gross primary FIGURE 1. Flow diagram of the trophic cascade productivity (GPP) derived from the Moderate hypothesis for predicting risk of transmission of Sin Resolution Imaging Spectroradiometer Nombre virus (SNV) in its rodent reservoir (deer (MODIS) and temperature and precipitation mouse, Peromyscus maniculatus) population and from variables were useful predictors of rodent deer mice to humans. Temperature and precipitation population levels. These and other sites are are driving variables that influence rate processes maintained as long-term study areas for (ovals) and system states (boxes). Symbols in paren- monitoring changes in hantavirus reservoir theses represent direction of flow at each step, broken populations and associated environmental arrows indicate time lags in response, and dashed variability (Douglass et al., 2001). We chose arrows indicate probability relationships. The outer to use data from the Cascade and Polson bounding box defines those processes directly related trapping sites because these two have the to rodent population abundance, while rate processes longest period of overlap with the 2000–06 outside of the box are those related to reservoir MODIS GPP data. We selected a single infection levels and transmission to humans. trapping grid per site based on the highest LOEHMAN ET AL.—PREDICTION OF DEER MOUSE POPULATION DYNAMICS 351 deer mouse captures during 2000–06 monthly temperature, and total monthly snow- (n52,651 at Cascade, n53,471 at Polson) fall. and used data from these grids consistently throughout the analyses. We used only one of Remote sensing three available grids in the analysis because The majority of epidemiologic or wildlife grids were co-located within the spatial extent studies that incorporate remotely sensed data of our remote sensing data and did not exhibit for vegetation monitoring have used spectral markedly different trends in rodent abun- vegetation indices such as the Normalized dance. Difference Vegetation Index, a vegetation Rodents were live-trapped for three con- greenness measure based on the contrast secutive nights per month on each 1-ha grid, between red (absorbed by green plants) and for 12 mo annually at Cascade and 6 mo per infrared (reflected by green plants) reflected year (May–October) at Polson. Grids consisted energy (Boone et al., 2000; Viel et al., 2006; 3 of 10 10-trap arrays of 100 live-capture traps Glass et al., 2007; Luis et al., 2009; Cao (H. B. Sherman Live Traps, Tallahassee, et al., 2011). We were interested in testing Florida, USA) spaced at 10-m intervals. A whether rodent dynamics are linked with comprehensive description of trapping meth- variability in vegetation biomass and abun- ods is provided by Douglass et al. (2001). dance, rather than vegetation phenology or Population data were available as minimum greenness, and so chose to use remotely number alive (MNA). The MNA metric sensed GPP to estimate terrestrial vegetation accounts for the current monthly population production and resource availability at our as the sum of all animals captured during that study sites. Primary productivity is the rate at period, plus the number of individuals that which light energy is converted to plant were not captured during the current period biomass, and GPP is the sum total of the but were captured during at least one previous converted energy without accounting for plant and one subsequent trapping period (Chitty respiration costs. The GPP data set, produced and Phipps 1966). from the MODIS sensor onboard the NASA We have no evidence that repeated sam- Earth Observing System Terra and Aqua pling affected our population abundance satellites, is the first continuous satellite-based estimates. Potential trap effects were mini- data-set monitoring global vegetation produc- mized by sampling for only three nights per tivity. Unlike other available vegetation sensors month and removing traps between sampling MODIS incorporates daily bioclimatology and periods. Douglass et al. (2003) compared carbon cycling logic in its vegetation produc- effects of removal of deer mice from trapping tivity estimations, and MODIS GPP data are areas with capture and release methods and biome specific (Hansen et al., 2000). We found that although removal resulted in an obtained MODIS 8-day GPP for 3-km-by-3- increase in recruitment and more overall km matrices centered over the midpoint of each captures over time, population sizes of trapped 1-ha trapping grid. We averaged GPP over the and released mice remained constant. Further, pixels in the matrix and gap-filled across 8-day the monthly sampling interval is ideal for periods to produce a monthly site GPP detecting shifts in abundance. Carver et al. estimate, measured as grams of carbon fixed (2010) found that sampling less frequently by vegetation per square kilometer per month. resulted in an underestimation of monthly MNA by 10–20% and detection of fewer Data analysis annual highs and lows in MNA. In an attempt to develop a model that could Bioclimatology accurately predict deer mouse population levels using weather and plant productivity data, we Monthly weather observations were ob- used the Random Forests (RFs) technique tained from National Climatic Data Center (Breiman, 2001), a model that is known for its weather stations (NCDC, 2008) co-located accuracy, resistance to overfitting, and ability to with trapping sites (Fig. 2). The Cascade 20 handle large numbers of predictor variables. SSE weather station is located 1.6 km north- Random Forests is an ensemble model that east of the Cascade trapping site, and the Kerr forms many simple models, or decision trees, Dam weather station is located approximately and then aggregates (averages) their predictions. 8.4 km northeast of the Polson trapping site. Briefly, a large number (B) of bootstrap samples Meteorologic elements included in the analy- of the data are drawn, and a single binary sis were total monthly precipitation, departure regression tree is fitted to each bootstrap sample from monthly precipitation normals, minimum (De’ath and Fabricius, 2000). The only modifi- and maximum monthly temperatures, mean cation to the standard tree algorithm is that at 352 JOURNAL OF WILDLIFE DISEASES, VOL. 48, NO. 2, APRIL 2012 each node only a subset of the predictor we omitted the first 13 mo of data, since we variables is selected to search for the optimal did not have values for the lagged variables for binary split. Predictions are made by averaging these months. As mentioned previously, the the values of each individual tree. The principal MODIS GPP data set is available for fewer parameters in this model are the number of years than the rodent and weather data sets, models, or trees, that are tested (ntree)andthe minus an additional 13 mo for lag transforma- number of variables selected at each node of tion of variables. We fitted separate models to each tree (mtry). For all models we used ntree 5 consider each case. 1000. All models were run using the random- Given the poor fit of our models to the Forest package in the R software environment continuous data set, we developed a second set (R Development Core Team, 2010). of models based on classified, rather than Bootstrapping provides a convenient meth- continuous, data values. Although classifica- od for independently evaluating the accuracy tion results in a loss of information, thresh- of the fitted model. Although typically a data olding can reduce the amount of ‘‘noise’’ in a partitioning method such as cross-validation variable enough so that a model can find a might be employed, in the case of RFs, due to signal. We transformed the continuous re- the bootstrapping procedure, each observation sponse variable MNA to a binary discrete has been used in the construction of only variable by thresholding at seasonal quantiles. approximately two thirds of the decision tree Each response value yi was converted to a 0 or ‘‘forest.’’ Hence for each model, approximately 1 according to whether it lay below or above one third of the data was not used in its the 75th quantile value for months in the same construction and was considered ‘‘out-of-bag.’’ season. Seasons were assigned as winter (Dec./ We aggregated predictions from only those Jan./Feb.), spring (Mar./Apr./May), summer models for which a particular observation was (June/July/Aug.), and fall (Sept./Oct./Nov.). out-of-bag and used these predictions to Our goal was to see if the RF models could calculate the out-of-bag (OOB) error estimate. accurately identify unusually large MNA Due to the irregular collection of data at the values relative to the season. The 75th quantile Polson site and location of the Cascade and was chosen as the largest quantile possible Polson trapping grids in distinct biomes, we without the binary response variable becoming considered the data for each site separately. too imbalanced in our judgment (far more Because several of the initial suite of model zeros than ones). Then the RF models were weather predictor variables were highly corre- repeated using this binary discrete variable as lated we used a subset: departure from normal the response. temperature (DPNT), departure from normal Finally, we investigated the relationship precipitation (DPNP), total snowfall (TSNW), between MODIS GPP and the locally record- and mean temperature (MNTM), all measured ed weather variables at our sites. The MODIS- monthly (Fig. 3). based vegetation productivity estimates incor- Previous research suggests that rodent porate meteorologic data as a critical input, populations may respond to weather and plant though measured on a much coarser scale than productivity changes in a time-lagged manner the local weather data included as predictors (Ernest et al., 2000; Brown and Ernest, 2002; in our analysis (Zhao et al., 2006). We Thibault et al., 2010; Cao et al., 2011). For expected that if the MODIS GPP data were example, Ernest et al. (2000) found significant successfully measuring vegetative production correlations between rodent density and pre- at our study sites, they should display some vious-season precipitation and plant cover at a correlation with our more locally measured long-term study site in a Chihuahuan desert climatic variables. If not, MODIS GPP was ecosystem in southern Arizona, and a recently likely not capturing vegetative productivity developed model using data from the Cascade, dynamics at our sites and hence ought not to Montana, trapping site identified temperature be related to deer mouse populations using and precipitation 5 mo previous as important trophic cascade logic. To test this relationship factors for deer mouse survival (Luis et al., we fit a RF model using MODIS GPP as a 2009). To account for potential lagging we response variable and local weather variables included predictor variables of 3-, 6-, and 12- as predictors, including the 3-, 6-, and 12-mo mo lags. For instance, at month i we included time lags. the average of each predictor variable for the months (i-1, i-2, i-3), (i-5, i-6, i-7), and (i-11, i-12, i-13), which represent the 3-, 6-, and RESULTS 12-mo lags for each predictor variable. Lag- ging methods reduce the size of the data set We fit two regression models each for somewhat; by including the lagged variables the Cascade and Polson trapping sites, one LOEHMAN ET AL.—PREDICTION OF DEER MOUSE POPULATION DYNAMICS 353

FIGURE 3. Time series plots for data from Cascade and Polson, Montana trapping sites. Quantities displayed are MNA (minimum number of mice alive), GPP (vegetation gross primary productivity), DPNT (departure from normal temperature), MNTM (mean temperature), DPNP (departure from normal precipitation), and TSNW (total snowfall). Missing data values are indicated by broken lines. The MODIS GPP data set was available only for a portion of the time series. 354 JOURNAL OF WILDLIFE DISEASES, VOL. 48, NO. 2, APRIL 2012

TABLE 1. Results for all random forest (RF) models. Mean squared error (MSE) and percentage variation explained values apply to RF models based on continuous minimum number of mice alive (MNA) response variables. Out-of-bag (OOB) error and baseline error values represent results from RF models based on discrete binary MNA response variables. Where OOB error values are less than baseline error values the model performed more poorly than naı¨ve baseline predictions.

RF model MSE % variation explained OOB error Baseline error

Cascade: weather only 401.90 18.93 0.220 0.250 Polson: weather only 1593.10 6.74 0.276 0.252 Cascade: weather and GPP 686.00 11.69 0.333 0.252 Polson: weather and GPP 1,518.20 0.46 0.289 0.311 with the MODIS GPP data and one MNA, and the worst (Polson, using weather without. For models that included and GPP) explained practically none of the MODIS GPP we had 84 observations variation. and 15 predictor variables (including lags), Our classified models, for which we and for models using only weather pre- employed a thresholding technique to dictors we had 163 observations and 12 identify unusually large MNA values predictor variables. The out-of-bag mean relative to season, performed poorly as squared error and percentage of the well. The out-of-bag error rates and the variability explained for each model are baseline error for each model are shown in shown in Table 1. As illustrated in Figure Table 1. The baseline error is the error we 4, all of these models fit poorly; the best would observe in the case of a null model model (Cascade, using weather data only) that predicts every observation to be the explained around 18% of the variation in majority class. In all cases, the RF models’ ability to distinguish between months with high or low rodent reservoir population levels was poor. For example, for the Cascade model, using weather data only, a naive baseline prediction that says that every month will have relatively high rodent reservoir populations will have an error rate of around 25%. The actual OOB error rate for this RF model was 22%. Two of the models (Cascade, using weather only; Polson, using weather and GPP) performed worse than the baseline model. In addition, there was no consistent set of predictor variables across the models tested with regard to either variable type (e.g., temperature or precipitation) or time lag, regardless of study site or type of model (Table 2). FIGURE 4. Fitted versus actual minimum number alive (MNA) values for each random forest (RF) In contrast, our RF model using model, using both weather predictors and MODIS MODIS GPP as a response variable and gross primary productivity (GPP). Plots are (a) local weather variables as predictors, Cascade, Montana, USA, with weather only; (b) including the 3-, 6-, and 12-mo time lags, Polson, Montana, with weather only; (c) Cascade % with weather and GPP; (d) Polson with weather and fit well, explaining 86 of the variation in GPP. If a model fits well, points should cluster near MODIS GPP (Fig. 5). In addition the the line. model identified MNTM (and its 6- and LOEHMAN ET AL.—PREDICTION OF DEER MOUSE POPULATION DYNAMICS 355

TABLE 2. Top five predictor variables for continuous and classified random forest (RF) models that included gross primary productivity (GPP). Variable names are departure from normal temperature (DPNT) and precipitation (DPNP), total snowfall (TSNW), and mean temperature (MNTM); numbers are time lags in months. Ranking is based on decrease in predictive accuracy (continuous) and decrease in Gini index (classified). In each case the measure of importance is calculated from a random permutation of the values for each predictor variable, and the degree to which this degrades the predictive accuracy of the model is the measure of importance.

Cascade Polson

Continuous Classified Continuous Classified

DPNT6 DPNT12 DPNP12 DPNP3 TSNW6 MNTM3 DPNT3 DPNT3 DPNT12 TSNW6 DPNP3 DPNT DPNT DPNT6 DPNT6 DPNP6 MNTM12 DPNT3 DPNP MNTM

12-mo lags) as the most important predic- in a variety of fashions (regression vs. tors of GPP; and the TSNW variable also classification, with and without time lags), held a fair degree of predictive power. none of the models we fit displayed a Scatter plots of GPP versus the six most significant predictive ability. In general important variables in the RF model are our findings suggest that MODIS GPP shown in Fig. 6. and weather data, at least over the time period of our analysis, can provide limited DISCUSSION predictive information regarding deer mouse population levels at Cascade and Despite our use of RFs, a powerful and Polson. These results, in turn, suggested flexible modeling technique that has a that using these same predictor variables strong history of predictive accuracy, and despite attempting to model MNA levels

FIGURE 6. Scatterplots of MODIS gross primary productivity GPP versus the six most important FIGURE 5. Actual (true) versus predicted gross weather predictor variables from a random forest primary productivity (GPP) from a random forest (RF) model. The model predicts MODIS GPP using (RF) model in which local weather station variables the locally measured climate variables MNTM (mean explained about 86% of the variability in GPP at two temperature) and TSNW (total snowfall), with 6- and rodent trapping sites in Montana, USA. 12-mo time lags. 356 JOURNAL OF WILDLIFE DISEASES, VOL. 48, NO. 2, APRIL 2012 to develop a generalized model for HPS tion. If, as generally hypothesized, tem- risk in humans, as called for in previous perature and precipitation are key drivers studies, may not be feasible. An interna- of rodent population fluctuations via tional team of forecasters attempting to trophic cascades (Glass et al., 2000), we use weather variables to predict a longer would expect that both the MODIS GPP but overlapping MNA time series at the data and our local weather data would Cascade site came to a similar conclusion have successfully predicted patterns in (Yaffee et al., 2008). Although tempera- rodent abundance. Thus we suggest that ture, precipitation, and snowfall were the application of a rainfall-based trophic predictors in our models, they explained cascade hypothesis as a broad-scale expla- so little of the variance that we had no nation for increases in deer mouse popu- confidence that the relative importance of lation density and subsequent HPS risk the variables reflected signal rather than may not be universally appropriate, espe- noise. cially in temperate ecosystems. We posit several potential explanations Second, MODIS GPP may have insuf- for the results of our modeling experi- ficiently characterized resource availability ment. First, resource-consumer relation- at our study sites either because of ships can exhibit nonlinear or chaotic inaccuracies in the data product or differ- behavior that may confound simple bot- ences in scale between GPP and deer tom-up regulation, as defined by the mouse habitat characteristics. Compari- trophic cascade hypothesis (Brown and sons of MODIS GPP with ground-based Ernest, 2002). In addition, the trophic productivity estimates across multiple cascade hypothesis, although proposed as biomes have demonstrated that MODIS an explanatory mechanism for rodent data tended to be underestimates at low- population irruptions in the southwestern productivity sites and overestimates at United States (Parmenter et al., 1999; high-productivity sites, although they cap- Glass et al., 2000; Glass et al., 2002) may tured seasonal variation in a manner apply only within arid ecosystems where consistent with ground-based measures water is a clear limiting factor in plant (Turner et al., 2005, 2006). Our MODIS growth, and monsoonal precipitation pat- GPP predictors are averages of a 9 km2 terns, common across the southwestern (900 ha) area as compared with the 1-ha United States, have been found to be rodent trapping grids. Thus, the remotely strong drivers of biotic cascades. sensed data may not have resolved vege- Thibault et al. (2010) note that the tative productivity at a spatial scale that consequences of precipitation-driven in- influences deer mouse populations. Fur- creases in primary productivity on con- ther, MODIS GPP does not capture sumers have not been well explored in elements of landscape structure such as nonarid environments. Water is less lim- fragmentation, dispersal corridors, shrub iting at our Montana study sites than in cover, and vegetation community compo- desert ecosystems (Nemani et al., 2003), sition that have been cited as important and precipitation patterns are not mon- habitat characteristics for deer mice (Root soon driven. Thus, vegetation communi- et al., 1999; Langlois et al., 2001). The ties and rodent populations may exhibit MODIS GPP also does not measure less sensitivity to changes in timing and nonvegetative food sources such as arthro- amount of rainfall. Our local, fine-scale pods, which have been noted to provide an weather variables were poor predictors of important dietary component for deer mice MNA at both sites but successfully pre- and potentially alter their habitat selection dicted GPP, suggesting that coarse-scale (Jameson, 1952; Pearson et al., 2000). GPP weather inputs are appropriately As noted by Turchin (2003) and others, tracking site temperature and precipita- density-dependent processes may influence LOEHMAN ET AL.—PREDICTION OF DEER MOUSE POPULATION DYNAMICS 357 rodent demography. Population fluctuations context of projected climate changes that that result from a combination of exogenous are likely to significantly alter continental (density-independent, such as weather and patterns of precipitation, temperature, resource availability) and endogenous (den- andvegetationdistribution(Parmesan sity-dependent, such as predation and and Yohe, 2003; Parmesan, 2006; Chris- competition) factors may exhibit nonlinear tensen, 2007; Iverson et al., 2010). behavior and confound attempts to develop generalized prediction models. For exam- ACKNOWLEDGMENTS ple, although Luis et al. (2009) modeled Funding for this project was from the U.S. deer mouse population survival and recruit- Centers for Disease Control through Coop- ment at the Cascade site using climate erative Agreement US3/CCU813599-07. Ad- predictors, the authors noted that other ditional support for manuscript preparation was provided by NIH Grant P20 RR16455- important exogenous or endogenous factors 04 from the INBRE-BRIN Program of the likely influenced temporal population vari- National Center for Research Resources. ation, including resource-based factors such Further support for this research was pro- as primary productivity. vided by NASA Headquarters under Earth Many researchers have recognized the System Science Fellowship Grant NGT5. We thank Bill Semmens, Kevin Hughes, and need for generalized models capable of Brent Lonner for making this work possible predicting temporal and spatial risks of through their longitudinal data collection epidemics (Epstein, 1999; Patz and Lind- and processing; Faith Ann Heinsch and say, 1999). Whereas vegetation produc- Qiaozhen Mu for assistance with acquiring tivity is hypothesized to be an important and processing MODIS data; and the stu- dents and staff of the Numerical Terradynamic driver for increases in population density Simulation Group, University of Montana, for of hantavirus reservoir species, our study their support. and others’ suggest that additional processes regulate reservoir populations (Ab- LITERATURE CITED bott et al., 1999; Calisher et al., 1999; ABBOTT, K. D., T. G. KSIAZEK, AND J. N. MILLS. 1999. Mills et al., 1999). Although deer mouse Long-term hantavirus persistence in rodent population dynamics are likely, in part, populations in central Arizona. Emerging Infec- influenced by changes in resource abun- tious Diseases 5: 102–112. dance, there seems to be no single, easily BAYARD, V. S., P. D. KITSUTANI,E.O.BARRIA,L.A. 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