Spatial Relationships Among Young Cercocarpus ledifolius (Curlleaf Mountain Ylahogany)

This study analyzed spatial location patterns of Cercocarpus ledifolius Nutt. (curlleaf mountain mahogany) plants, classified as current-year seedling, established seedling, juvenile, and immature individuals, at a central Nevada study site. Most current-year seedlings were located in mahogany stands in which large, mature individuals had the greatest abundance. These stands had greater litter cover and a thicker layer of litter than areas with few current- year seedlings. Most established young Cercocarpus were located in adjacent Artemisia tridentata ssp. vaseyana (mountain big sagebrush) communities, or in infrequent canopy gaps between relatively few large, mature Cercocarpus. We discuss potential roles of plant litter, root growth characteristics, nurse plants, and herbivory in the establishment and renewal of Cercocarpus communities.

Distribution of Dioecious Eastern Red Cedar (Juniperus virginiana) along an Environmental Gradient in Ogallala, NE.

Abstract The purpose of this research was to study the sex distribution and energy allocation of dioecious Eastern Red Cedars (Juniperus virginiana) along an environmental resource gradient. The trees surveyed were growing in a canyon located at the University of Nebraska’s Cedar Point Biological Research Station in Ogallala, Nebraska. Due to the geography of this canyon, environmental factors necessary for plant growth should vary depending on the tree’s location within the canyon. These factors include water availability, sun exposure, ground slope, and soil nitrogen content, all of which are necessary for carbon acquisition. Juniperus virginiana is a dioecious conifer. Dioecious plants maintain male and female reproductive structures on separate individuals. Therefore, proximal spatial location is essential for pollination and successful reproduction. Typically female reproductive structures are more costly and require a greater investment of carbon and nitrogen. For this reason, growth, survival and successful reproduction are more likely to be limited by environmental resources for females than for male individuals. If this is true for Juniperus virginiana, females should be located in more nutrient and water rich areas than males. This also assumes that females can not be reproductively successful in areas of poor environmental quality. Therefore, reproductive males should be more likely to inhabit environments with relatively lower resource availability than females. Whether the environment affects sexual determination or just limits survival of different sexes is still relatively unknown. In order to view distribution trends along the environmental gradient, the position of the tree in the canyon transect was compared to its sex. Any trend in sex should correspond with varying environmental factors in the canyon, ie: sunlight availability, aspect, and ground slope. The individuals’ allocation to growth and reproduction was quantified first by comparing trunk diameter at six inches above ground to sex and location of the tree. The feature of energy allocation was further substantiated by comparing carbon and nitrogen content in tree leaf tissue and soil to location and sex of each individual. Carbon and nitrogen in soil indicate essential nutrient availability to the individual, while C and N in leaf tissue indicate nutrient limitation experienced by the tree. At the conclusion of this experiment, there is modest support that survival and fecundity of females demands environments relatively richer in nutrients, than needed by males to survive and be reproductively active. Side of the canyon appeared to have an influence on diameter of trees, frequency of sex and carbon and nitrogen leaf content. While this information indicated possible trends in the relation of sex to nutrient availability, most of the environmental variables presumed responsible for the sex distribution bias differed minutely and may not have been biologically significant to tree growth.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Distance software: design and analysis of distance sampling surveys for estimating population size

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modeling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modeling analysis engine for spatial and habitat-modeling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of- the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Temporal and Spatial Variations of Ions, Isotopes and Agricultural Contaminants in Surface Waters and Groundwater of Nebraska's Rainwater Basin Wetland Region

The wetlands of south-central Nebraska’s Rainwater Basin region are considered of international importance as a habitat for millions of migratory birds, but are being endangered by agricultural practices. The Rainwater Basin extends across 17 counties and covers 4,000 square miles. The purpose of this study was to assemble baseline chemical data for several representative wetlands across the Rainwater Basin region, and determine the use of these chemical data for investigating groundwater recharge. Eight representative wetlands were chosen across the Rainwater Basin to monitor surface and groundwater chemistry. At each site, a shallow well and deep well were installed and sampled once in the summer of 2009 and again in the spring of 2010. Wetland surface water was sampled monthly from April, 2009 to May, 2010. Waters were analyzed for major ions, nutrients, pesticides and oxygen-18 and deuterium isotopes at the University of Nebraska Water Sciences Laboratory. Geochemical analysis of surface waters presents a range of temporal and spatial variations. Wetlands had variable water volumes, isotopic compositions, ion chemistries and agricultural contaminant levels throughout the year and, except for a few trends, theses variations cannot be predicted with certainty year-to-year or wetland-to-wetland. Isotopic compositions showed evaporation was a contributor to water loss, and thus, did impact water chemistry. Surface water nitrate concentrations ranged from <0.10 to 4.04 mg/L. The nitrate levels are much higher in the groundwater, ranging from <0.10 to 18.4 mg/L, and are of concern because they are found above the maximum contaminant level (MCL) of 10 mg/L. Atrazine concentrations in surface waters ranged from <0.05 to 10.3 ppb. Groundwater atrazine concentrations ranged from <0.05 to 0.28 ppb. The high atrazine concentrations in surface waters are of concern as they are above the MCL of 3 ppb, and the highest levels occur during the spring bird migration. Most sampled groundwaters had detectable tritium indicating a mix of modern (<5 to 10 years old) and submodern (older than 1950s) recharge. The groundwater also had differences in chemical and isotope composition, and in some cases, increased nitrate concentrations, between the two sampling periods. Modern groundwater tritium ages and changes in groundwater chemical and isotopic compositions may indicate connections with surface waters in the Rainwater Basin.

SAMPLING OF CORN TO ASSESS BIRD DAMAGE: A Preliminary Review of a Comprehensive Study

"How large a sample is needed to survey the bird damage to corn in a county in Ohio or New Jersey or South Dakota?" Like those in the Bureau of Sport Fisheries and Wildlife and the U.S.D.A. who have been faced with a question of this sort we found only meager information on which to base an answer, whether the problem related to a county in Ohio or to one in New Jersey, or elsewhere. Many sampling methods and rates of sampling did yield reliable estimates but the judgment was often intuitive or based on the reasonableness of the resulting data. Later, when planning the next study or survey, little additional information was available on whether 40 samples of 5 ears each or 5 samples of 200 ears should be examined, i.e., examination of a large number of small samples or a small number of large samples. What information is needed to make a reliable decision? Those of us involved with the Agricultural Experiment Station regional project concerned with the problems of bird damage to crops, known as NE-49, thought we might supply an ans¬wer if we had a corn field in which all the damage was measured. If all the damage were known, we could then sample this field in various ways and see how the estimates from these samplings compared to the actual damage and pin-point the best and most accurate sampling procedure. Eventually the investigators in four states became involved in this work1 and instead of one field we were able to broaden the geographical base by examining all the corn ears in 2 half-acre sections of fields in each state, 8 sections in all. When the corn had matured well past the dough stage, damage on each corn ear was assessed, without removing the ear from the stalk, by visually estimating the percent of the kernel surface which had been destroyed and rating it in one of 5 damage categories. Measurements (by row-centimeters) of the rows of kernels pecked by birds also were made on selected ears representing all categories and all parts of each field section. These measurements provided conversion factors that, when fed into a computer, were applied to the more than 72,000 visually assessed ears. The machine now had in its memory and could supply on demand a map showing each ear, its location and the intensity of the damage.