Characteristics of animal-related motor vehicle crashes in select National Park Service units—United States, 1990–2013

Objectives: Nationally, animal–motor vehicle crashes (AVCs) account for 4.4% of all types of motor vehicle crashes (MVCs). AVCs are a safety risk for drivers and animals and many National Park Service (NPS) units (e.g., national park, national monument, or national parkway) have known AVC risk factors, including rural locations and substantial animal densities. We sought to describe conditions and circumstances involving AVCs to guide traffic and wildlife management for prevention of AVCs in select NPS units.

Methods: We conducted an analysis using NPS law enforcement MVC data. An MVC is a collision involving an in-transit motor vehicle that occurred or began on a public roadway. An AVC is characterized as a collision between a motor vehicle and an animal. A non-AVC is a crash between a motor vehicle and any object other than an animal or noncollision event (e.g., rollover crash). The final data for analysis included 54,068 records from 51 NPS units during 1990–2013. Counts and proportions were calculated for categorical variables and medians and ranges were calculated for continuous variables. We used Pearson’s chi-square to compare circumstances of AVCs and non-AVCs. Data were compiled at the park regional level; NPS parks are assigned to 1 of 7 regions based on the park’s location.

Results: AVCs accounted for 10.4% (5,643 of 54,068) of all MVCs from 51 NPS units. The Northeast (2,021 of 5,643; 35.8%) and Intermountain (1,180 of 5,643; 20.9%) regions had the largest percentage of the total AVC burden. November was the peak month for AVCs across all regions (881 of 5,643; 15.6%); however, seasonality varied by park geographic regions. The highest counts of AVCs were reported during fall for the National Capital, Northeast/Southeast, and Northeast regions; winter for the Southeast region; and summer for Intermountain and Pacific West regions.

Conclusions: AVCs represent a public health and wildlife safety concern for NPS units. AVCs in select NPS units were approximately 2-fold higher than the national percentage for AVCs. The peak season for AVCs varied by NPS region. Knowledge of region-specific seasonality patterns for AVCs can help NPS staff develop mitigation strategies for use primarily during peak AVC months. Improving AVC data collection might provide NPS with a more complete understanding of risk factors and seasonal trends for specific NPS units. By collecting information concerning the animal species hit, park managers can better understand the impacts of AVC to wildlife population health.     

How and why is Aquatic Quality Changing at Nahanni National Park Reserve, nwt, Canada?

Nahanni National Park Reserve is located at southwestern NWT-Yukon border. One of the first UNESCO World Heritage sites, Nahanni lies within Taiga Cordillera and Taiga Shield Ecozones. Base and precious metal mining occurred upstream of Nahanni prior to park establishment. Nahanni waters, sediments, fish, and caribou have naturally elevated metals levels. Baseline water, sediment and fish tissue quality data were collected and analyzed throughout Nahanni during 1988–91 and 1992–97. These two programs characterized how aquatic quality variables are naturally varying in space and time, affected by geology, stream flow, seasonality, and extreme meteorological and geological events. Possible anthropogenic causes of aquatic quality change were examined. Measured values were compared to existing Guidelines and site-specific objectives were established.     

NONPARAMETRIC TESTS FOR TREND DETECTION IN WATER QUALITY TIME SERIES1

ABSTRACT: A review of nonparametric tests for trend leads to the conclusion that Mann-Whitney, Spearman, and Kendall tests are the best choice for trend detection in water quality time series. Recently these tests have been adapted to account for dependence and seasonality in such series (Lettenmaier, 1976; Hirsch, et al., 1972; Hirsch and Slack, 1984). For monotonic trends, a procedure allowing to select the pertinent tests considering the characteristics of time series is proposed and the practical limitations of the tests are also brought out. This procedure has been applied to identify the appropriate trend detection test for the time series of nine water quality parameters at Lake Laflamme (Québec). When a time series can be tested with the Mann-Whitney, Kendall, Spearman, or Lettenmaier (1976) test, the number of observations required to detect trends of a given magnitude, for selected significance and power levels can be calculated with the power function of the t test. When the test proposed by Hirsch, et al. (1984), Hirsch and Slack (1984), or Farrell (1980) need to be used, the number of observations can only be estimated approximately from the results of empirical power studies.     

Incorporation of Seasonal Nitrogen Dynamics within a Long-Term Acidification Model

This paper describes an application of the long termdynamic model, MAGIC, on a monthly timestep, enablingincorporation of the seasonal dynamics associated with abroad understanding of the ecosystem N cycle. The modelhas been applied to the Dargall Lane catchment in theGalloway region of Scotland where marked seasonal Ndynamics are apparent. Mean monthly proportions ofrainfall, runoff, deposition fluxes and net retention ofN are utilised to drive the model on a monthly timestep.Calibration of the model has successfully reproduced thepresent day observed seasonal variation in streamNO₃ and ANC. Prediction of recovery at the siteunder the second sulphur protocol indicates that,although mean annual ANC increases, mean monthly ANC doesnot rise above zero for all months of the year until2010.     

Combining culturomic sources to uncover trends in popularity and seasonal interest in plants

Culturomic tools enable the exploration of trends in human–nature interactions, although they entail inherent biases and necessitate careful validation. Furthermore, people may engage with nature across different culturomic data sets differently. We evaluated people's digital interest and engagement with plant species based on Wikipedia and Google data and explored the conservation implications of these temporal interest patterns. As a case study, we explored the digital footprints of the most popular plant species in Israel. We analyzed 4 years of daily page views from Hebrew Wikipedia and 10 years of daily Google search volume in Israel. We modeled popularity of plant species in these 2 data sets based on a suite of plant attributes. We further explored the seasonal trends of people's interest in each species. We found differences in how people interacted digitally with plants in Wikipedia and Google. Overall, in Google, searches for species that have utility to humans were more common, whereas in Wikipedia, plants that serve as cultural emblems received more attention. Furthermore, in Google, popular species attracted more attention over time, opposite to the trend in Wikipedia. In Google, interest in species with short bloom duration exhibited more pronounced seasonal patterns, whereas in Wikipedia, seasonality of interest increased as bloom duration increased. Together, our results suggest that people's digital interactions with nature may be inherently different depending on the sources explored, which may affect use of this information for conservation. Although culturomics holds much promise, better understanding of its underpinnings is important when translating insights into conservation actions.     

Cyclo-stationary maximum cross-covariance analysis for exogenously forced environmental systems

The response of an ecological system to external forcings shows marked seasonality and complex time delays. In order to take seasonality into account, the assumption of stationarity inherent in most statistical models for time series is replaced here by more flexible cyclo-stationarity. To describe the dominant modes of the cross-covariance structure of the system and the forcing, temporal (orthonormal) patterns are derived. They are estimated by a singular value decomposition of the cyclo-stationary sample cross-covariance matrix. These patterns allow to formulate a prediction scheme for the system once the forcing is known. The method has comparatively little tendency for multi-collinearity, so that it is well applicable also for multivariate settings and shorter data records. The method is illustrated for an aquatic ecosystem with distinct phases of a biological succession.     

降水与农业种植变化对黄河流域径流影响研究

黄河是我国西北、华北地区重要水源,了解其径流变化特征及成因有着重要意义。论文基于趋势性分析、广义可加模型（GAMLSS）等方法,采用黄河流域近60 a来流量、降水、主要农业种植面积及大型水库资料等数据,通过细化农业小麦生长期与玉米和大豆生长期以及假定不同降水情景,在季节尺度上深入分析了1960—2005年黄河流域气候、农业种植面积等变化对径流的影响。研究结果表明：1）黄河流域5个水文站点小麦生长期与玉米和大豆生长期径流变异点均发生于20世纪80年代中后期和90年代初期,且除花园口站小麦生长期间径流有少部分呈上升趋势外,黄河流域径流整体呈下降趋势,其中,唐乃亥、兰州的小麦生长期、玉米和大豆生长期及龙门、花园口的玉米和大豆生长期趋势均达到了0.05的显著性水平,呈显著下降趋势;2）对比两种降水假设状态,暴雨年的径流始终高于小雨年的径流,因此,降水仍是影响径流的主要因子之一,而农业种植面积变化对径流的影响程度,不仅与降水量的多少有关,还与流量分位数的大小有关,对于唐乃亥站小麦生长期的暴雨年时期,增加农作物种植面积,在低流量分位数时可以增加径流,而在高于0.75分位数时会减小径流,小雨年时期规律相反。该研究结果对变化环境下黄河流域水资源管理与优化配置具有一定理论与现实意义。     

官厅水库上游河流营养物参照状态的制定

采用参照河流百分位法、一般河流百分位法和多元线性回归法,制定了官厅水库上游流域河流雨季和旱季的营养物参照状态,探讨季风气候对河流营养物参照状态的季节性影响,并识别影响官厅水库上游河流营养物浓度的主要人类活动.结果表明：参照河流点和一般河流点总氮（TN）和总磷（TP）浓度在雨季和旱季均表现出明显的季节性差异.参照河流百分位法、一般河流百分位法和多元线性回归法制定的营养物参照状态在雨季和旱季也均存在季节性差异,取3种方法的平均值作为官厅水库上游河流的营养物参照状态：TN在雨季和旱季参照状态的建议值分别为1.580和1.672mg/L; TP在雨季和旱季参照状态的建议值分别为0.075和0.055mg/L.影响河流TN浓度的人类活动按贡献大小依次为：畜禽养殖污染负荷强度、耕地（坡度0~8°）比例和城市用地比例.影响河流TP浓度的人类活动按贡献大小依次为：耕地（坡度0~8°）比例和畜禽养殖污染负荷强度.     

Time series analysis of elemental and isotopic data from biomineralized whale tissue

A temporal record of environmental conditions is often contained within accretionary biological tissue. These records can provide knowledge of the environmental conditions that existed at the time the tissue was formed. In this study, we look at trace element concentrations and isotopic ratios of carbon and nitrogen as contained in baleen from bowhead whales in the eastern and western Arctic Ocean. Time series techniques, including maximum likelihood method and likelihood ratio tests, are applied to analysis of data and inference about their mean structures.     

ESTIMATION OF COASTAL WATER CONTAMINATION LONG-TERM TREND USING SEASONAL LINEAR MODELS1

ABSTRACT: Bivalves are used as bioindicators to assess trends of the chemical quality of coastal and marine environments due to their ability to concentrate chemicals. These shellfish are subject to seasonal physiological changes influencing the chemical concentration. Using quarterly data, we model concentration via linear regression with a biologically based seasonal component. This was applied to cadmium concentration measured in the blue mussel (Mytilus edulis) at three sites in the Seine estuary (Normandy, France). In this case we have a high concentration season from January to June and a “low concentration” season from July to December. This season definition was checked a posteriori, using box-and-whisker plots and a statistical test of comparison of pair-wise adjusted least-squares mean differences, and it appears to be very reasonable. We averaged data by season and across sites. Our final model (R²= 0.846 with N= 27 observations) includes highly significant terms: a season effect, which accounts for 45% of the total variability, a linear and a quadratic time term. Outliers were identified by high Studentized residual values and attributed to bias in the temporal sampling schemes. The methodology developed will further be used with other shellfish and/or other trace elements and organic chemicals.