Weather Covariates Research Articles

The effects of weather factors on road traffic casualties: Analysis on provincial panel data of China from 2006 to 2021

IntroductionRoad traffic injuries stands as a major concern globally, as they result in significant loss of life, economic impact, and erode trust in government and societal safety. The influence of weather on road traffic safety is undeniable, impacting road conditions, individuals, and vehicles. However, the specific influence of weather on road traffic casualties has seldom been explored. MethodThis study assesses the effect of weather factors on road traffic casualties in China from 2006 to 2021. Vector error correction models (VECM) were utilized to determine the Granger causality between weather factors and covariates. Furthermore, panel autoregressive distribution lag models (ARDL) were applied to quantify the association between weather factors and road traffic casualties. ResultsThe findings indicate that rainfall and temperature exert a short-term negative impact on casualty risk, which intriguingly becomes positive in the long term. A standout discovery is the significant role of health investments, which are shown to reduce casualty numbers in both the short and long-terms. In the long run, the gross domestic product significantly enhances casualties, while expressway mileage notably decreases them. ConclusionsThese results demonstrate the significant influence of weather on road traffic casualties and highlight the critical roles played by factors such as gross domestic product, health investment, and expressway mileage. The evidence presented in the study underscores the urgent need for more effective strategies to mitigate road traffic casualties. Thus, some effective measures are proposed to reduce road traffic casualties. This study is conducive to the improvement of traffic in severe weather in China and provides guidance for traffic management departments.

Time of year and weather influence departure decisions of sandhill cranes at a primary stopover

The Rocky Mountain Population (RMP) of greater sandhill cranes uses a key stopover area, the San Luis Valley (SLV) in Colorado. Parameters of migration phenology can differ between autumn and spring and are affected by weather and environmental factors. We hypothesized that sandhill cranes in the SLV would have a longer stopover duration in autumn than in spring, and that wind assistance, crosswinds, temperature change, barometric air pressure, and surface water area would influence persistence probability. We used data from sandhill cranes fitted with transmitters that spanned autumn and spring, 2015-2022. We used an open robust design mark-recapture model to estimate stopover duration, arrival probability, and persistence probability. We examined the effects of weather and surface water on the persistence probability for 106 sandhill cranes in the SLV. Stopover duration was longer in autumn than in spring and had higher variability across years. Arrival probability to the SLV peaked on 13 October in autumn and 21 February in spring. Persistence probability declined around mid-December in autumn and mid-March in spring. We found that several weather covariates influenced persistence in both seasons. In autumn, sandhill cranes departed the SLV with higher tailwinds, lower crosswinds, and higher surface water availability. In spring, sandhill cranes departed the SLV with lower crosswinds and higher barometric air pressure at the surface and higher wind speeds at altitudes of about 3,000 m. The effect of wind speed was stronger later in the spring. Given the lower variability of arrival and persistence probability and shorter stopover duration in spring compared to autumn, we suspect that RMP sandhill cranes are using a time-minimization strategy during spring. However, given the use of supportive winds and weather conditions ideal for soaring, RMP sandhill cranes appear to be using strategies that save energy in both seasons. Our study identifies the optimal timing of water management and surveys for RMP sandhill cranes and confirms that weather influences their persistence. Understanding differences in migration patterns between seasons and the factors that influence persistence at stopover sites will also be important for anticipating phenological impacts from climate change and land use alterations.

Incorporating weather in counts and trends of migrating Common Nighthawks.

Effective conservation planning for species of concern requires long-term monitoring data that can accurately estimate population trends. Supplemental or alternative methods for estimating population trends are necessary for species that are poorly sampled by traditional breeding bird survey methods. Counts of migrating birds are commonly used to assess raptor population trends and could be useful for additional taxa that migrate diurnally and are difficult to monitor during the breeding season. In North America, the Common Nighthawk (Chordeiles minor) is challenging to detect during comprehensive dawn surveys like the North American Breeding Bird Survey and is considered a species of conservation concern because of steep population declines across its range. We conducted standardized evening counts of migrating Common Nighthawks at a fixed survey location along western Lake Superior each autumn from 2008 to 2022. To document peak migration activity, counts spanned ~3 hours each evening from mid-August to early September for a mean of 19.4 ± 2.4 days. These count data were then used to assess the effects of weather on daily counts and high-count days and to calculate population trends over this 15-year period. We used generalized linear mixed effects models to determine the relationship between daily counts and high-count days (i.e., ≥1000 migrating nighthawks) and weather variables. Additionally, using our 15-year dataset, we calculated a geometric mean passage rate that accounted for annual differences in weather to estimate count trends. Annual counts averaged ~18,000 (min = 2514, max = 32,837) individuals and high-count days occurred 56 times throughout the course of the study. Model results indicated lighter, westerly winds and warmer temperatures were associated with higher daily counts and greater probability of a large migratory flight. Results from the trend analyses suggest stable or non-significantly increasing trends for Common Nighthawks during this monitoring period; however, the trend models explained a relatively low percentage of the variation in the counts. Results from a power analysis suggest that continued monitoring efforts and adjustments with weather covariates will be necessary to effectively use visible migration count data to estimate Common Nighthawk trends. Establishing annual monitoring programs that use standardized visual counts to document Common Nighthawk migration at key sites across North America may provide supplemental information useful for population trend estimates of this species. Therefore, we advocate for the use of visible migration counts to monitor Common Nighthawks in North America and emphasize the value of long-term monitoring efforts.

Snow depth influences harvest of a boreal ungulate more than socio-economic factors: Implications for food security in a changing climate

Climate change may affect the ability of hunters to harvest wildlife and, hence, threaten food security of local people. However, few studies have investigated the relative influence of environmental conditions on wildlife harvest rates. We harnessed a 24-year dataset of harvest dates for a boreal ungulate in a region where climate change is having pronounced impacts on snow depth, precipitation, and temperatures to investigate the effect of weather on harvest rates. We used generalized linear models and a model selection framework to examine the influence of weather covariates (snow depth, mean daily temperature, precipitation) and socio-economic factors (gasoline and red meat prices, employment rates, and moose [Alces americanus] harvest) on harvest rates of bison (Bison bison) in Yukon, Canada, at two temporal scales: annual and daily. At an annual scale, snow depth was the only covariate that was important in explaining bison harvest. No socioeconomic variables improved our model beyond the null. At the daily scale, snow depth and mean daily temperature influenced bison harvest rates, with a 1 SD increase resulting in a 14 % and 9 % increase in daily harvest rates, respectively. Increased snow depth facilitates ease of travel in remote, roadless areas by snowmobile to locate bison and truncates movements of bison, resulting in increased harvest rates. Decreased snow depth due to climate change will impact hunter access to boreal ungulates and food security for northern people. More broadly, our data suggests that in some socioecological systems, environmental covariates have a greater influence on wildlife harvest rates than socioeconomic factors and need to be considered in future studies to better understand and predict harvest rates.

Spatial and temporal variability of corn response to nitrogen and seed rates

AbstractThe quantification of the spatial and temporal variability of crop response to controllable inputs and the relationship with their controlling factors is important for making input prescriptions. In this study, we (1) assessed the within‐field spatial variability of corn (Zea mays L.) response to nitrogen (NR) and seed (SR) rates using on‐farm precision experiments (OFPE) data and geographically weighted regression (GWR) models; (2) determined the spatial agreement between NR and SR response classes, and their temporal stability; and (3) modeled the effect of weather and site‐specific features on the spatial distribution of the responses. Response maps were estimated by fitting GWR models to yields and as‐applied NR and SR from 14 OFPE. Responses classified into positive and non‐positive response classes were spatially joined to weather, soil, and landscape covariates to train a random forest model. GWR models explained 30%–60% of yield variation. Positive responses to NR and SR represented about 46% and 33% of fields’ area, respectively, with large variation across field‐year combinations. Temporal agreement was about 50%–60%, for NR and SR, respectively. The spatial agreement between NR and SR response classes suggests that factors controlling the crop response to these inputs are similar. In both cases, weather variables were the most important predictors, followed by landscape and soil attributes. Although weather variables acted at a field level, that is, field‐year, the latter reflected the effect of within‐field variability on crop response. To reduce this weather uncertainty, future research should consider conducting larger numbers of OFPE across multiple seasons.

Uncertainty Reduction and Environmental Justice in Air Pollution Epidemiology: The Importance of Minority Representation.

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities worldwide. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality data set and high-resolution gridded data sets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that a 10% increase in mortality information reduces model uncertainty three times more than increased resolution of the air pollution model from 12 to 1km. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution.

Combining Field Observations and Remote Sensing to Forecast Fine Fuel Loads

Effective wildfire management requires accurate information about the spatial distribution of fuels. While static maps work well for coarse forest fuels with slow turnover, fine fuels—the driver of rangeland fires—often vary dramatically from year to year. Our goal was to develop a fine fuel forecast to help managers optimally allocate fire suppression resources and funding before the start of the fire season and to identify critical gaps in understanding or data that limit forecast skill. We compiled a historical record of fine fuel loads collected in the Great Basin and combined it with remotely sensed data on herbaceous productivity. Using a Bayesian State-Space approach to account for both process and observation error, we built a “Fuels Model” in which the predicted fuel load at a location depends on the fuel load in the previous year and productivity of the current year. Next, we built a “productivity model” to predict current year production based on remotely sensed data available in early spring. Finally, we combined these two models to generate early spring forecasts for summer fuel loads from 1987 to 2020 and quantified the associated uncertainty. We found that current year productivity contributed twice as much as the previous year's fuel load to the current fuel load. Our productivity predictions reduced mean absolute predictive error by 11% compared with a strong null model without early-season weather covariates. However, when we fed the productivity predictions into our fuel model, the resulting fuel load forecasts had too much uncertainty to inform management decisions, with most uncertainty coming from the process error of the Fuels Model. Reducing this uncertainty will require higher-quality observations of fuel loads. Until those are available, our results suggest that managers could rely on productivity forecasts as a reasonable proxy for fuel load forecasts.

Adaptive LASSO estimation for functional hidden dynamic geostatistical models

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models.

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates are largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields. Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000–2020 across a 12-state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015 to 2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forest model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008–2014. We also evaluated the full 2000–2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000–2020, each standard deviation increase in early-season rainfall delayed planting by ∼2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall or persisting early-season frost risks may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

Effects of climate on fall migration phenology of monarch butterflies departing the northeastern breeding grounds in Canada.

Monarch butterflies (Danaus plexippus) undergo an iconic multi-generational migration, traveling thousands of kilometers from the summer breeding grounds in southern Canada to overwintering sites in central Mexico. This migration phenomena can be affected by climate change, which may have important implications on fitness and ultimately populations status. We investigated the long-term trends in fall migration phenology of monarchs using a 25-year dataset collected along the coast of Lake Erie in Ontario, Canada. We also investigated local long-term trends in weather covariates that have the potential to influence migration phenology at this site. Patterns in standardized daily counts of monarchs were compared with local weather covariates using two methods (i.e., monthly averages and moving windows) to assess difference in outputs between analytical approaches. Our results suggest that monarch migration timing (migration midpoint, average peak, first peak, and late passage) and weather covariates have been consistent over time, in direct contrast to a similar study in Cape May, New Jersey, which showed a significant increase in both fall temperature and a 16- to 19-day shift in monarch migration timing. Furthermore, our results differed between analytical approaches. With respect to annual variability in air temperature, our monthly average analysis suggested that for each degree increase in September air temperature, late season passage would advance 4.71 days (±1.59 SE, p=.01). However, the moving window analysis suggested that this result is likely spurious and found no significant correlations between migration timing and any weather covariates. Importantly, our results caution against extrapolating the effects of climate change on the migration phenology of the monarch across study regions and the need for more long-term monitoring efforts to better understand regional drivers of variability in migration timing.

Contrasted impacts of weather conditions in species sensitive to both survival and fecundity: A montane bird case study.

There is growing evidence that the Earth's climate is undergoing profound changes that are affecting biodiversity worldwide. This gives rise to the pressing need to develop robust predictions on how species will respond in order to inform conservation strategies and allow managers to adapt mitigation measures accordingly. While predictions have begun to emerge on how species at the extremes of the so-called slow-fast continuum might respond to climate change, empirical studies for species for which all demographic traits contribute relatively equally to population dynamics are lacking. Yet, climate change is expected to strongly affect them throughout their entire lifecycle. We built a 21-year integrated population model to characterize the population dynamics of the rock partridge (Alectoris graeca) in France, and tested the influence of nine weather covariates on demographic parameters. As predicted, both annual survival and breeding success were affected by weather covariates. Thick snow cover during winter was associated with low survival and small brood size the following breeding season. Brood size was higher with intermediate winter temperatures and snowmelt timing, positively correlated to breeding period temperature, but negatively correlated to temperature during the coldest fortnight and precipitation during the breeding period. Survival was positively correlated to winter temperature, but negatively to breeding period precipitation. Large-scale indices indicated that cold and wet winters were associated with small brood size the following breeding season but with high survival. Expected changes of weather conditions due to climate change are likely to impact demographic traits of the rock partridge both positively and negatively depending on the traits and on the affected weather variables. Future population dynamics will thus depend on the magnitude of these different impacts. Our study illustrates the difficulty to make strong predictions about how species with a population dynamic influenced by both survival and fecundity will respond to climate change.

Multivariate Demand Forecasting for Rental Bike Systems Based on an Unobserved Component Model

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model.

Modelling of tea mosquito bug (Helopeltis theivora) incidence on neem tree: A zero inflated count data analysis

Neem (Azadirachta indica) is an evergreen tree belonging to the Meliaceae family and is highly infected by the seasonal pest called Helopeltis theivora, the tea mosquito bug. The study monitors the pest infection between May 2019 and April 2021 by the direct counting method. Weekly counts of insect pest population were found to be correlated with weather parameters viz., maximum temperature (Tmax.), minimum temperature (Tmin), relative humidity [morning (07.22hrs) and afternoon (14.22hrs) (RH)], rainfall (mm/day) and wind speed (km/h). Zero inflated count data techniques were opted for modelling the pest dynamics of tea mosquito bug as the data was featured by excess zeroes and heteroscedasticity nature. Poisson, Negative Binomial (NB), zero-inflated Negative Binomial (ZINB) and Negative Binomial Hurdle (NBH) models were fitted for the collected data and compared. The results of different count data models show that the negative binomial hurdle model is a good fit for given data, followed by the zero-inflated negative model. The fitted models show the weather covariates, which highly influencing the pest infestation on neem tree.

Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta).

The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016–2017, 2017–2018, 2018–2019, and 2019–2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance–covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.

Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions

We propose a novel method to model daily peak electricity demand using temperature and additional hourly and daily weather covariates, such as humidity and wind speed. Rather than enter into the temperature response function additively, the additional covariates may flexibly impact the demand response to temperature. Such flexibility allows differential responses to the actual temperature based on the heat index and wind chill factor, for example. Most notably, we find that ignoring humidity substantially underestimates the effect of high temperatures, while ignoring the effect of cloud cover overestimates the effect of low temperatures. Time of day also matters: a demand response to the same temperature may be different at different times of day. Moreover, accounting for weather-related covariates improves the model’s ability to explain daily peak demand.

Seasonal trends in adult apparent survival and reproductive trade-offs reveal potential constraints to earlier nesting in a migratory bird.

Birds aim to optimize resources for feeding young and self-maintenance by timing reproduction to coincide with peak food availability. When reproduction is mistimed, birds could incur costs that affect their survival. We studied whether nesting phenology correlated with the apparent survival of American kestrels (Falco sparverius) from two distinct populations and examined trends in clutch-initiation dates. We estimated apparent survival using multi-state mark-recapture models with nesting timing, nesting success, sex, age, and weather covariates. Nesting timing predicted the apparent survival of successful adults; however, the effect differed between populations. Early nesting kestrels had higher apparent survival than later nesters in the western population, where kestrels have a relatively long nesting season. At the eastern site, where kestrels have a relatively short nesting season, the pattern was reversed-later nesters had higher apparent survival than earlier nesters. Nesting timing did not affect the apparent survival of adults with failed nests suggesting that the energetic cost of producing fledglings contributed to the timing effect. Finally, clutch-initiation dates advanced in the western population and remained static in the eastern population. Given that both populations have seasonal declines in productivity, population-specific survival patterns provide insight into seasonal trade-offs. Specifically, nesting timing effects on survival paralleled productivity declines in the western population and inverse patterns of survival and reproduction in the eastern population suggest a condition-dependent trade-off. Concomitant seasonal declines in reproduction and survival may facilitate population-level responses to earlier springs, whereas seasonal trade-offs may constrain phenology shifts and increase vulnerability to mismatch.

Predicting daily initial attack aircraft targets in British Columbia

We developed spatially explicit models of the daily probability of aircraft use in initial attack (IA) on a fire (hereafter the conditional models), and estimates of the unconditional probability of daily aircraft IA targets to support preparedness planning in the province of British Columbia, Canada, using a grid cell × day lasso-logistic framework. Novel aspects of our work include: (1) inclusion of an historical aircraft baseline covariate to account for missing or poorly estimated factors in our models; and of 2 day lead weather and Forest Fire Weather Index (FWI) covariates as proxies for fire potential trend, and (2) linking the conditional models of aircraft use in IA to models of daily fire occurrence to estimate the daily number of aircraft IA targets. The baseline risk of using an aircraft, population and road density were highly influential spatial covariates in both aircraft conditional models. The probability of sustained flaming, temperature, and FWI lead, and Sheltered Duff Moisture Code, temperature, and the Showalter Index were the three most influential meteorological variables in the conditional airtanker and helicopter IA models, respectively. We demonstrate the application of the models to portray the distribution of the expected number of daily aircraft IA targets.

Mechanistic movement models identify continuously updated autumn migration cues in Arctic caribou

BackgroundMigrations in temperate systems typically have two migratory phases, spring and autumn, and many migratory ungulates track the pulse of spring vegetation growth during a synchronized spring migration. In contrast, autumn migrations are generally less synchronous and the cues driving them remain understudied. Our goal was to identify the cues that migrants use in deciding when to initiate migration and how this is updated while en route.MethodsWe analyzed autumn migrations of Arctic barren-ground caribou (Rangifer tarandus) as a series of persistent and directional movements and assessed the influence of a suite of environmental factors. We fitted a dynamic-parameter movement model at the individual-level and estimated annual population-level parameters for weather covariates on 389 individual-seasons across 9 years.ResultsOur results revealed strong, consistent effects of decreasing temperature and increasing snow depth on migratory movements, indicating that caribou continuously update their migratory decision based on dynamic environmental conditions. This suggests that individuals pace migration along gradients of these environmental variables. Whereas temperature and snow appeared to be the most consistent cues for migration, we also found interannual variability in the effect of wind, NDVI, and barometric pressure. The dispersed distribution of individuals in autumn resulted in diverse environmental conditions experienced by individual caribou and thus pronounced variability in migratory patterns.ConclusionsBy analyzing autumn migration as a continuous process across the entire migration period, we found that caribou migration was largely related to temperature and snow conditions experienced throughout the journey. This mechanism of pacing autumn migration based on indicators of the approaching winter is analogous to the more widely researched mechanism of spring migration, when many migrants pace migration with a resource wave. Such a similarity in mechanisms highlights the different environmental stimuli to which migrants have adapted their movements throughout their annual cycle. These insights have implications for how long-distance migratory patterns may change as the Arctic climate continues to warm.

QTL x environment modeling of malting barley preharvest sprouting.

HvMKK3 alleles are temperature sensitive and are major contributors to environmental stability of preharvest sprouting in barley. Preharvest sprouting (PHS) can severely damage barley (Hordeum vulgare L.) malting quality, but PHS resistance is often negatively correlated with malting quality. Seed dormancy is closely related to PHS. Increased temperature during grain fill can decrease seed dormancy in barley, but genetic components of seed dormancy temperature sensitivity are poorly understood. Six years of PHS data were used to fit quantitative trait locus (QTL) x environment mixed models incorporating marker data from seed dormancy genes HvAlaAT1, HvGA20ox1, and HvMKK3 and weather covariates in spring and winter two-row malting barley. Variation in winter barley PHS was best modeled by average temperature range during grain fill and spring barley PHS by total precipitation during grain fill. Average high temperature during grain fill also accurately modeled PHS for both datasets. A highly non-dormant HvMKK3 allele determined baseline PHS susceptibility and HvAlaAT1 interactions with multiple HvMKK3 alleles conferred environmental sensitivity. Polygenic variation for PHS within haplotype was detected. Residual genotype and QTL by environment interaction variance indicated additional environmental and genetic factors involved in PHS. These models provide insight into genotype and environmental regulation of barley seed dormancy, a method for PHS forecasting, and a tool for breeders to improve PHS resistance.

Ordinal Time Series Forecasting of the Air Quality Index.

This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast classification, and measures the forecast accuracy rates. We employ statistical modeling and machine learning with three weather covariates of daily accumulated precipitation, temperature, and wind direction and also include seasonal dummy variables. The study utilizes four models to forecast air quality levels: (1) an autoregressive model with exogenous variables and GARCH (generalized autoregressive conditional heteroskedasticity) errors; (2) an autoregressive multinomial logistic regression; (3) multi-class classification by support vector machine (SVM); (4) neural network autoregression with exogenous variable (NNARX). These models relate to lag-1 AQI values and the previous day’s weather covariates (precipitation and temperature), while wind direction serves as an hour-lag effect based on the idea of nowcasting. The results demonstrate that autoregressive multinomial logistic regression and the SVM method are the best choices for AQI-level predictions regarding the high average and low variation accuracy rates.