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.     

The need for spatially explicit quantification of benefits in invasive‐species management

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive‐species control, and planning processes are needed to identify cost‐effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive‐species management. There is a need to improve understanding of how such assets are considered in invasive‐species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty‐four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty‐five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision‐making processes that guide invasive‐species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge.     

Evaluation of variance approximation techniques for non-linear photosynthesis—irradiance models

Parameters derived from photosynthesis-irradiance (P-I) models, although often empirical in nature, are useful indicators of the photoadaptive state of phytoplankton in culture and in situ. However objective criteria for determining significant changes in P-I curves are rarely provided, because confidence intervals for parameters of non-linear models are not estimated easily. Examination of least-squares residuals in parameter space and Monte Carlo approaches have been used to estimate confidence regions around parameter values, but the computationally intensive nature of these methods has prevented their routine application. We present an alternative method of estimating confidence intervals for parameters of P-I curves that runs quickly on a microcomputer and is easily combined with common parameter-estimation routines. This algorithm was tested using a 3-parameter P-I model and curves describing a wide range of photoadaptive states, with different numbers of observations and different amounts of inherent variability. The method produced results comparable to the Monte Carlo technique. This analysis makes it possible to specify the sample size required to define parameters with acceptable confidence as a function of data variance and photoadaptive state. In most reasonable situations, 25 observations are sufficient.     

Model selection criteria for overdispersed data and their application to the characterization of a host-parasite relationship

In the statistical modeling of a biological or ecological phenomenon, selecting an optimal model among a collection of candidates is a critical issue. To identify an optimal candidate model, a number of model selection criteria have been developed and investigated based on estimating Kullback’s (Information theory and statistics. Dover, Mineola, 1968) directed or symmetric divergence. Criteria that target the directed divergence include the Akaike (2nd international symposium on information theory. Akadémia Kiadó, Budapest, Hungary, pp 267–281, 1973, IEEE Trans Autom Control AC 19:716–723, 1974) information criterion, AIC, and the “corrected” Akaike information criterion (Hurvich and Tsai in Biometrika 76:297–307, 1989), AICc; criteria that target the symmetric divergence include the Kullback information criterion, KIC, and the “corrected” Kullback information criterion, KICc (Cavanaugh in Stat Probab Lett 42:333–343, 1999; Aust N Z J Stat 46:257–274, 2004). For overdispersed count data, simple modifications of AIC and AICc have been increasingly utilized: specifically, the quasi Akaike information criterion, QAIC, and its corrected version, QAICc (Lebreton et al. in Ecol Monogr 62(1):67–118 1992). In this paper, we propose analogues of QAIC and QAICc based on estimating the symmetric as opposed to the directed divergence: QKIC and QKICc. We evaluate the selection performance of AIC, AICc, QAIC, QAICc, KIC, KICc, QKIC, and QKICc in a simulation study, and illustrate their practical utility in an ecological application. In our application, we use the criteria to formulate statistical models of the tick (Dermacentor variabilis) load on a white-footed mouse (Peromyscus leucopus) in northern Missouri.     

Phenological tracking enables positive species responses to climate change

Earlier spring phenology observed in many plant species in recent decades provides compelling evidence that species are already responding to the rising global temperatures associated with anthropogenic climate change. There is great variability among species, however, in their phenological sensitivity to temperature. Species that do not phenologically "track" climate change may be at a disadvantage if their growth becomes limited by missed interactions with mutualists, or a shorter growing season relative to earlier-active competitors. Here, we set out to test the hypothesis that phenological sensitivity could be used to predict species performance in a warming climate, by synthesizing results across terrestrial warming experiments. We assembled data for 57 species across 24 studies where flowering or vegetative phenology was matched with a measure of species performance. Performance metrics included biomass, percent cover, number of flowers, or individual growth. We found that species that advanced their phenology with warming also increased their performance, whereas those that did not advance tended to decline in performance with warming. This indicates that species that cannot phenologically "track" climate may be at increased risk with future climate change, and it suggests that phenological monitoring may provide an important tool for setting future conservation priorities.     

Dispersal Constraints for Stream Invertebrates: Setting Realistic Timescales for Biodiversity Restoration

Biodiversity goals are becoming increasingly important in stream restoration. Typical models of stream restoration are based on the assumption that if habitat is restored then species will return and ecological processes will re-establish. However, a range of constraints at different scales can affect restoration success. Much of the research in stream restoration ecology has focused on habitat constraints, namely the in-stream and riparian conditions required to restore biota. Dispersal constraints are also integral to determining the timescales, trajectory and potential endpoints of a restored ecosystem. Dispersal is both a means of organism recolonization of restored sites and a vital ecological process that maintains viable populations. We review knowledge of dispersal pathways and explore the factors influencing stream invertebrate dispersal. From empirical and modeling studies of restoration in warm-temperate zones of New Zealand, we make predictions about the timescales of stream ecological restoration under differing levels of dispersal constraints. This process of constraints identification and timescale prediction is proposed as a practical step for resource managers to prioritize and appropriately monitor restoration sites and highlights that in some instances, natural recolonization and achievement of biodiversity goals may not occur.     

Can forest management be used to sustain water-based ecosystem services in the face of climate change?

Forested watersheds, an important provider of ecosystems services related to water supply, can have their structure, function, and resulting streamflow substantially altered by land use and land cover. Using a retrospective analysis and synthesis of long-term climate and streamfiow data (75 years) from six watersheds differing in management histories we explored whether streamflow responded differently to variation in annual temperature and extreme precipitation than unmanaged watersheds. We show significant increases in temperature and the frequency of extreme wet and dry years since the 1980s. Response models explained almost all streamflow variability (adjusted R2 > 0.99). In all cases, changing land use altered streamflow. Observed watershed responses differed significantly in wet and dry extreme years in all but a stand managed as a coppice forest. Converting deciduous stands to pine altered the streamflow response to extreme annual precipitation the most; the apparent frequency of observed extreme wet years decreased on average by sevenfold. This increased soil water storage may reduce flood risk in wet years, but create conditions that could exacerbate drought. Forest management can potentially mitigate extreme annual precipitation associated with climate change; however, offsetting effects suggest the need for spatially explicit analyses of risk and vulnerability.