Joel H. Reynolds

A review of statistical methods for the meteorological adjustment of tropospheric ozone

Abstract A variety of statistical methods for meteorological adjustment of ozone have been proposed in the literature over the last decade for purposes of forecasting, estimating ozone time trends, or investigating underlying mechanisms from an empirical perspective. The methods can be broadly classified into regression, extreme value, and space–time methods. We present a critical review of these methods, beginning with a summary of what meteorological and ozone monitoring data have been considered and how they have been used for statistical analysis. We give particular attention to the question of trend estimation, and compare selected methods in an application to ozone time series from the Chicago area. We conclude that a number of approaches make useful contributions to the field, but that no one method is most appropriate for all purposes and all meteorological scenarios. Methodological issues such as the need for regional-scale analysis, the nonlinear dependence of ozone on meteorology, and extreme value analysis for trends are addressed. A comprehensive and reliable methodology for space–time extreme value analysis is attractive but lacking.

Evaluating the ability of the differenced Normalized Burn Ratio (dNBR) to predict ecologically significant burn severity in Alaskan boreal forests

During the 2004 fire season ~6.6 million acres (~2.7 million ha) burned across Alaska. Nearly 2 million of these were on National Wildlife Refuge System lands inaccessible from the state’s limited road system. Many fires burned through September, driven by unusually warm and dry temperatures throughout the summer. Using several fires from this season, we assessed the national burn severity methodology’s performance on refuge lands. Six fires, spanning 814 489 acres (329 613 ha), were sampled on five boreal forest refuges. In total, 347 sites were sampled for vegetation composition and ground-based burn severity estimates following the national protocols. The relationship between the differenced Normalized Burn Ratio (dNBR) and composite burn index (CBI) was unexpectedly weak (R2adjusted, 0.11–0.64). The weak relationship was not a result of data or image processing errors, nor of any biotic or abiotic confounding variable. The inconsistent results, and dNBR’s limited ability to discern the ecologically significant differences within moderate and high severity burn sites, indicate that the current methodology does not satisfy key Alaskan boreal forest management objectives.

MULTI-CRITERIA ASSESSMENT OF ECOLOGICAL PROCESS MODELS

The Pareto Optimal Model Assessment Cycle (POMAC), a multiple-criteria model assessment methodology, is described for exploring uncertainty in the relationships between ecological theory, model structure, and assessment data. Model performance is optimized to satisfy, simultaneously, each component of a vector of assessment criteria (model outputs), rather than the usual procedure of optimizing performance with respect to a single criterion. Pareto Optimality is used to define the vector optimization. The Pareto Optimal Set reveals which combinations of assessment criteria the model can satisfy si- multaneously. Binary interval error measures, which classify whether a parameterization result is within an acceptable range of values, are defined for each criterion. Their use masks small differences in the performance of different parameterizations, allowing the Pareto Optimal Set to reveal conflicts in ability to achieve simultaneously different col- lections of criteria. POMAC improves the researchers ability to detect deficiencies and locate their sources. It is more stringent and informative than traditional model assessment procedures because it uses multiple criteria without weighting and aggregating them. The Pareto Optimal Set reveals the presence of deficiencies through the models inability to satisfy all the criteria simultaneously. POMAC then guides the researcher in locating deficiencies in: inadequate selection of component ecological hypotheses underlying the model, inadequate mathe- matical representations of these hypotheses, inadequate parameterization, poor selection and formulation of the assessment criteria, or combinations of these. In an example, POMAC is applied to the spatially explicit canopy competition model WHORL using ten assessment criteria. Each criterion was selected to provide information on different aspects of WHORLs functioning: three stand height distribution criteria, three crown morphology criteria, and four criteria focusing on stand competitions characteristic differentiation of growth rates. The Pareto Optimal Set was generated using simulated evolution optimization. POMAC revealed deficiencies in both the model structure and its assessment criteria, leading to an improved model and better understanding of its effective domain.

Moderately and highly polymorphic microsatellites provide discordant estimates of population divergence in sockeye salmon, Oncorhynchus nerka

Mutation rate can vary widely among microsatellite loci. This variation may cause discordant single-locus and multi-locus estimates of FST, the commonly used measure of population divergence. We use 16 microsatellite and five allozyme loci from 14 sockeye salmon populations to address two questions about the affect of mutation rate on estimates of FST: (1) does mutation rate influence FST estimates from all microsatellites to a similar degree relative to allozymes?; (2) does the influence of mutation rate on FST estimates from microsatellites vary with geographic scale in spatially structured populations? For question one we find that discordant estimates of FST among microsatellites as well as between the two marker classes are correlated with mean within-population heterozygosity (HS ) and thus are likely due to differences in mutation rate. Highly polymorphic microsatellites (HS > 0.84) provide significantly lower estimates of FST than moderately polymorphic microsatellites and allozymes (HS < 0.60). Estimates of FST from binned allele frequency data and RST provide more accurate measures of population divergence for highly polymorphic but not for moderately polymorphic microsatellites. We conclude it is more important to pool loci of like HS rather than marker class when estimating FST. For question two we find the FST values for moderately and highly polymorphic loci, while significantly different, are positively correlated for geographically proximate but not geographically distant population pairs. These results are consistent with expectations from the equilibrium approximation of Wright’s infinite island model and confirm that the influence of mutation on estimates of FST can vary in spatially structured populations presumably because the rate of migration varies inversely with geographic scale.

Temporal Variation in Phenotypic and Genotypic Traits in Two Sockeye Salmon Populations, Tustumena Lake, Alaska

Abstract Sockeye salmon Oncorhynchus nerka in two tributary streams (about 20 km apart) of the same lake were compared for temporal variation in phenotypic (length, depth adjusted for length) and genotypic (six microsatellite loci) traits. Peak run time (July 16 versus 11 August) and run duration (43 versus 26 d) differed between streams. Populations were sampled twice, including an overlapping point in time. Divergence at microsatellite loci followed a temporal cline: Population sample groups collected at the same time were not different (F ST = 0), whereas those most separated in time were different (F ST = 0.011, P = 0.001). Although contemporaneous sample groups did not differ significantly in microsatellite genotypes (F ST = 0), phenotypic traits did differ significantly (MANOVA, P < 0.001). Fish from the larger stream were larger; fish from the smaller stream were smaller, suggesting differential fitness related to size. Results indicate run time differences among and within sockeye salmon populatio...

Twenty-Five Year Record of Changes in Plant Cover on Tundra of Northeastern Alaska

Abstract Northern Alaska has warmed over recent decades and satellite data indicate that vegetation productivity has increased. To document vegetation changes in the Arctic National Wildlife Refuge, we monitored plant cover at 27 plots between 1984 and 2009. These are among the oldest permanently marked and continuously monitored vegetation plots in the Arctic. We quantified percent cover of all plant species by line-point intercept sampling and assessed change over time for seven plant growth forms. Cover of bryophytes and deciduous shrubs showed slight decreasing trends. Evergreen shrubs, horsetails, and depth of thawed soil above permafrost had no trends. For lichens, graminoids, and forbs, trends varied by plant community type. Overall, vegetation in the plots changed little over the study period, in contrast to results from other studies in northern Alaska. A few plots had dramatic changes, however, which we attributed to subsidence from melting ground ice or to floodplain dynamics. Our results demonstrate that vegetation change on the Arctic Refuge coastal plain over the past quarter century has been spatially heterogeneous and facilitated by disturbance. The findings highlight the need for greater work linking plotlevel and regional remote sensing measurements of change.

Application of a Double-Observer Aerial Line-Transect Method to Estimate Brown Bear Population Density in Southwestern Alaska

Abstract Brown bear Ursus arctos population density was estimated for a 21,178-km2 study area in southwest Alaska. Estimates were obtained using an aerial line-transect method that allows for peak ...

COLONY MAPPING: A NEW TECHNIQUE FOR MONITORING CREVICE-NESTING SEABIRDS

Abstract Monitoring populations of auklets and other crevice-nesting seabirds remains problematic, although numerous methods have been attempted since the mid-1960s. Anecdotal evidence suggests several large auklet colonies have recently decreased in both abundance and extent, concurrently with vegetation encroachment and succession. Quantifying changes in the geographical extent of auklet colonies may be a useful alternative to monitoring population size directly. We propose a standardized method for colony mapping using a randomized systematic grid survey with two components: a simple presence/absence survey and an auklet evidence density survey. A quantitative auklet evidence density index was derived from the frequency of droppings and feathers. This new method was used to map the colony on St. George Island in the southeastern Bering Sea and results were compared to previous colony mapping efforts. Auklet presence was detected in 62 of 201 grid cells (each grid cell  =  2500 m2) by sampling a randomly placed 16 m2 plot in each cell; estimated colony area  =  155 000 m2. The auklet evidence density index varied by two orders of magnitude across the colony and was strongly correlated with means of replicated counts of birds socializing on the colony surface. Quantitatively mapping all large auklet colonies is logistically feasible using this method and would provide an important baseline for monitoring colony status. Regularly monitoring select colonies using this method may be the best means of detecting changes in distribution and population size of crevice-nesting seabirds.

Comparing mixture estimates by parametric bootstrapping likelihood ratios

Wildlife managers and researchers often need to estimate the relative contributions of distinct populations in a miture of organisms. Increasingly, there is interest in comparing these mixture contributions across space or time. Comparisons usually just check for overlap in the interval estimates for each population contribution from each mixture. This method inflates Type I error rates, has limited power due to its focus on marginal comparisons, and employs a fundamentally inappropriate measure of mixture difference. Given the difficulty of defining an appropriate measure of mixture difference, a powerful alternative is to compare mixtures using a likelihood ratio test. In applications where the standard asymptotic theory does not hold, the null reference distribution can be obtained through parametric bootstrapping. In addition to testing simple hypotheses, a likelihood ratio framework encourages modeling the change in mixture contributions as a function of covariates. The method is demonstrated with an analysis of potential sampling bias in the estimation of population contributions to the commercial sockeye, salmon (Oncorhynchus nerka) fishery in Upper Cook Inlet, Alaska.

A road map for designing and implementing a biological monitoring program.

Designing and implementing natural resource monitoring is a challenging endeavor undertaken by many agencies, NGOs, and citizen groups worldwide. Yet many monitoring programs fail to deliver useful information for a variety of administrative (staffing, documentation, and funding) or technical (sampling design and data analysis) reasons. Programs risk failure if they lack a clear motivating problem or question, explicit objectives linked to this problem or question, and a comprehensive conceptual model of the system under study. Designers must consider what “success” looks like from a resource management perspective, how desired outcomes translate to appropriate attributes to monitor, and how they will be measured. All such efforts should be filtered through the question “Why is this important?” Failing to address these considerations will produce a program that fails to deliver the desired information. We addressed these issues through creation of a “road map” for designing and implementing a monitoring program, synthesizing multiple aspects of a monitoring program into a single, overarching framework. The road map emphasizes linkages among core decisions to ensure alignment of all components, from problem framing through technical details of data collection and analysis, to program administration. Following this framework will help avoid common pitfalls, keep projects on track and budgets realistic, and aid in program evaluations. The road map has proved useful for monitoring by individuals and teams, those planning new monitoring, and those reviewing existing monitoring and for staff with a wide range of technical and scientific skills.