Geology topics

Andrew S. Norton

Publications and source records attributed to Andrew S. Norton.

9 recordsLinked to original sources

USGS2025

Development of high-throughput genomic resources to inform white-tailed deer population and disease management

White-tailed deer ( Odocoileus virginianus ) are the most abundant and widespread cervid in North America. Genetic data are used as a tool to monitor populations and make management decisions for this game species. However, the development and use of genomic tools that can generate a set of markers suitable for longitudinal genomic data collection, whether for management purposes or to study the demographic and evolutionary processes of widely distributed species, have been challenging. This is mainly due to the cost required to fully implement and interpret the data produced. Here, we generated whole genome resequencing data for 44 free-ranging deer from three regions in their central and eastern North American range and identified over 89 million single nucleotide polymorphisms (SNPs). We used a subset of these SNPs to develop two nested SNP tools, a high-density array (702,183 SNPs) and a medium-density array (72,723 SNPs) to support deer and chronic wasting disease (CWD) management and research. SNPs were selected to ensure an even distribution across scaffolds of the reference genome and include SNPs associated with CWD susceptibility. Using genotyping results for 469 deer from 15 states in the US and Mexico generated by the high-density array and 1335 deer from 18 states generated by the medium-density array, we assessed genotyping success across different populations and explored some insights into population structure. These genomic tools offer a standard set of markers that will enable researchers and managers to address important questions related to white-tailed deer and CWD management. Our SNP arrays also offer the opportunity to examine aspects of white-tailed deer ecology and evolutionary history that were previously difficult to address.

Molecular Ecology Resources↗

USGS2023

No evidence for cottonwood forest decline along a flow-augmented western U.S. river

In contrast to many other arid region rivers, streamflow in the South Platte River is heavily augmented by trans-basin water imports and irrigation return flows. Hydrological changes began in the 1880s, resulting in channel narrowing and the development of a continuous Populus-Salix forest by the mid-twentieth century. We assessed the composition, structure and regeneration status of the riparian forest and identified environmental variables affecting annual Populus deltoides tree growth. We sampled forest structure at four sites in 2015, and conducted dendroecological analysis at seven additional sites in 2019. The riparian forest was dominated by P. deltoides , which occurred at all sites, comprising 79% of total tree basal area and 62% of total tree density. Age structure data indicated ongoing though episodic recruitment of P. deltoides , at least over the past ~130 years. We tested 14 linear mixed effects models to describe the effect of climate and streamflow on individual tree growth (modeled as the log of BAI, n = 237 trees). The most parsimonious model selected with AICc explained 28.6% of BAI variability, and included hydrology and climate factors during the growing season (i.e., June–August streamflow, June–July PDSI), some aspects of off-season (i.e., previous November and March) streamflow, along with tree age and study site effects. The riparian forest developed in response to, and has been maintained by, current climate conditions and water management regimes. It may be negatively affected by future climate change and increased urban water demand in the basin.

Colorado↗

USGS2022

Invasion of Siberian elm (Ulmus pumila) along the South Platte River: The roles of seed source, human influence, and river geomorphology

Riparian ecosystems in the western USA have been invaded by non-native woody species deliberately introduced for stream bank stabilization, agricultural windbreaks, and urban shade. Recent work suggests that the non-native tree Ulmus pumila (Siberian elm) is capable of significant spread in western riparian ecosystems, that range infilling is still incomplete, and that the invasion is dispersal-limited. Our objective was to understand the interacting roles of propagule pressure from upland U. pumila , human influences, and river geomorphology in promoting riparian U. pumila invasion along the South Platte River, Colorado, USA. We used linear regression and information-theoretic model selection to evaluate the relative importance of these factors to riparian U. pumila stem density. U. pumila stem density increased with increasing channel and floodplain restriction and increasing human influence from both urban and rural development. Model selection indicated that local upland U. pumila seed sources were relatively unimportant to riparian U. pumila stem density, suggesting that upland propagule pressure is currently contributing less than other human influences to U. pumila spread along the South Platte River. In particular, higher road density was the most important predictor for the proportional abundance of smaller U. pumila individuals (DBH<5-cm and 5-15-cm), suggesting that human influence in densely populated areas has been the primary driver of recent U. pumila population expansion. U. pumila stem density was only weakly associated with abundance of other common riparian tree species. Land managers and other entities concerned with non-native tree invasion into important riparian habitat may be able to reduce U. pumila spread most effectively by focusing U. pumila control efforts where human influences are greatest.

Colorado↗

USGS2019

A collaborative approach to bridging the gap between wildlife managers and researchers

Although most wildlife professionals agree that science should inform wildlife management decisions, disconnect still exists between researchers and managers. If researchers are not striving to incorporate their findings into management decisions, support for research programs by managers can wane. If managers are not using research findings to inform management decisions, those decisions may be less effective or more vulnerable to legal challenges. Both of these situations can have negative consequences for wildlife conservation. We outline a collaborative research‐management approach to bridging the gap between wildlife managers and researchers. We describe differences in perspectives, perceptions, and priorities between managers and researchers; outline how and why the divide between researchers and managers has likely occurred and continues to grow; and present specific strategies and recommendations to foster stronger collaborations between managers and researchers. We advocate increased synergy between managers and researchers based on a shared vision of conservation and a collaborative structure that rewards researchers and managers. Most importantly, we suggest that relationships and communication between managers and researchers must be established early in research development and decision‐making processes, fostering the trust needed for collaboration. Institutions and agencies can facilitate these relationships by creating opportunities and incentives for integrating collaborative research into management decisions. We suggest this approach will strengthen ties between researchers and managers, increase relevance of research to management decisions, promote effectiveness of management decisions, reduce legal challenges, and ultimately produce positive, tangible, and lasting effects on wildlife conservation.

Journal of Wildlife Management↗

USGS2017

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

Ecology and Evolution↗

USGS2012

Incorporating harvest rates into the sex-age-kill model for white-tailed deer

Although monitoring population trends is an essential component of game species management, wildlife managers rarely have complete counts of abundance. Often, they rely on population models to monitor population trends. As imperfect representations of real-world populations, models must be rigorously evaluated to be applied appropriately. Previous research has evaluated population models for white-tailed deer ( Odocoileus virginianus ); however, the precision and reliability of these models when tested against empirical measures of variability and bias largely is untested. We were able to statistically evaluate the Pennsylvania sex-age-kill (PASAK) population model using realistic error measured using data from 1,131 radiocollared white-tailed deer in Pennsylvania from 2002 to 2008. We used these data and harvest data (number killed, age-sex structure, etc.) to estimate precision of abundance estimates, identify the most efficient harvest data collection with respect to precision of parameter estimates, and evaluate PASAK model robustness to violation of assumptions. Median coefficient of variation (CV) estimates by Wildlife Management Unit, 13.2% in the most recent year, were slightly above benchmarks recommended for managing game species populations. Doubling reporting rates by hunters or doubling the number of deer checked by personnel in the field reduced median CVs to recommended levels. The PASAK model was robust to errors in estimates for adult male harvest rates but was sensitive to errors in subadult male harvest rates, especially in populations with lower harvest rates. In particular, an error in subadult (1.5-yr-old) male harvest rates resulted in the opposite error in subadult male, adult female, and juvenile population estimates. Also, evidence of a greater harvest probability for subadult female deer when compared with adult (≥2.5-yr-old) female deer resulted in a 9.5% underestimate of the population using the PASAK model. Because obtaining appropriate sample sizes, by management unit, to estimate harvest rate parameters each year may be too expensive, assumptions of constant annual harvest rates may be necessary. However, if changes in harvest regulations or hunter behavior influence subadult male harvest rates, the PASAK model could provide an unreliable index to population changes.

Journal of Wildlife Management↗

USGS2011

White-tailed deer age ratios as herd management and predator impact measures in Pennsylvania

A review of the Pennsylvania Game Commission's (PGC) deer management program and public concern about predator impacts on deer ( Odocoileus virginianus ) populations compelled the PGC to investigate the role of age ratios in developing management recommendations. Age ratios, such as proportion of juveniles in the antlerless harvest, may provide an index to population productivity and predator impacts. We estimated proportion of juveniles in the antlerless harvest from hunter-killed deer, population trends using the Pennsylvania (USA) sex–age–kill model, and reproduction from road-killed females. Using these estimates and a simulation model, we concluded that no single age-ratio value would serve as a reliable measure of population status. Wildlife Management Unit-specific trends in proportion of juveniles in the antlerless harvest and population trends provided the most relevant management information. We also provide an example decision chart to guide management actions in response to declining age ratios in the harvest. Although predator management activities and juvenile survival studies are often desired by the public, our decision-chart example indicated a number of deer management options exist before investing resources in predator management activities and juvenile survival studies.

Pennsylvannia↗

USGS2011

Spatio-temporal variation in male white-tailed deer harvest rates in Pennsylvania: Implications for estimating abundance

The performance of 2 popular methods that use age-at-harvest data to estimate abundance of white-tailed deer is contingent on assumptions about variation in estimates of subadult (1.5 yr old) and adult (≥2.5 yr old) male harvest rates. Auxiliary data (e.g., estimates of survival or harvest rates from radiocollared animals) can be used to relax some assumptions, but unless these population parameters exhibit limited temporal or spatial variation, these auxiliary data may not improve accuracy. Unfortunately maintaining sufficient sample sizes of radiocollared deer for parameter estimation in every wildlife management unit (WMU) is not feasible for most state agencies. We monitored the fates of 397 subadult and 225 adult male white-tailed deer across 4 WMUs from 2002 to 2008 using radio telemetry. We investigated spatial and temporal variation in harvest rates and investigated covariates related to the patterns observed. We found that most variation in harvest rates was explained spatially and that adult harvest rates (0.36–0.69) were more variable among study areas than subadult harvest rates (0.26–0.42). We found that hunter effort during the archery and firearms season best explained variation in harvest rates of adult males among WMUs, whereas hunter effort during only the firearms season best explained harvest rates for subadult males. From a population estimation perspective, it is advantageous that most variation was spatial and explained by a readily obtained covariate (hunter effort). However, harvest rates may vary if hunting regulations or hunter behavior change, requiring additional field studies to obtain accurate estimates of harvest rates.

Pennsylvania↗

USGS2011

Using multilevel models to quantify heterogeneity in resource selection

Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer ( Odocoileus virginianus ) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer ( Cervus nippon ) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection.

Journal of Wildlife Management↗