A cost-precision model for marine environmental monitoring,based on time-integrated averages

Ongoing marine monitoring programs are seldom designed to detect changes in the environment between different years, mainly due to the high number of samples required for a sufficient statistical precision. We here show that pooling over time (time integration) of seasonal measurements provides an efficient method of reducing variability, thereby improving the precision and power in detecting inter-annual differences. Such data from weekly environmental sensor profiles at 21 stations in the northern Bothnian Sea was used in a cost-precision spatio-temporal allocation model. Time-integrated averages for six different variables over 6 months from a rather heterogeneous area showed low variability between stations (coefficient of variation, CV, range of 0.6–12.4%) compared to variability between stations in a single day (CV range 2.4–88.6%), or variability over time for a single station (CV range 0.4–110.7%). Reduced sampling frequency from weekly to approximately monthly sampling did not change the results markedly, whereas lower frequency differed more from results with weekly sampling. With monthly sampling, high precision and power of estimates could therefore be achieved with a low number of stations. With input of cost factors like ship time, labor, and analyses, the model can predict the cost for a given required precision in the time-integrated average of each variable by optimizing sampling allocation. A following power analysis can provide information on minimum sample size to detect differences between years with a required power. Alternatively, the model can predict the precision of annual means for the included variables when the program has a pre-defined budget. Use of time-integrated results from sampling stations with different areal coverage and environmental heterogeneity can thus be an efficient strategy to detect environmental differences between single years, as well as a long-term temporal trend. Use of the presented allocation model will then help to minimize the cost and effort of a monitoring program.     

Effect of diel period and season on seining effort required to detect changes in Lake Erie beach fish assemblages

Using beach seining data from Lake Erie beaches, we examined the effect of diel period and season on two common study objectives considered sensitive to sampling effort: (1) has fish assemblage composition changed between two sampling time periods?, and (2) how many samples are necessary to provide a representative sample of the fish assemblage? Across a range of effect sizes and power levels, sample sizes were estimated for species richness, total fish abundance, round goby (Neogobius melanostomus) abundance and emerald shiner (Notropis atherinoides) abundance. For most sampling periods and effect sizes, several hundred to several thousand seine hauls were estimated to be required for detecting large-scale (e.g. basin-wide) changes in round goby and emerald shiner abundance. Depending on the indicator of interest, night seining (either spring or fall) was interpreted as the most efficient approach. For both seasons, sampling effort beyond 40 to 50 night seine hauls results in few additional species being detected. Both species accumulation curves and estimated sample sizes indicate that less effort is required to detect species richness changes than fish abundance. For most effect sizes and power levels, estimates of the number of seine hauls required to detect changes in fish abundance are unrealistically large for most monitoring programs. More practical monitoring strategies could be achieved by adopting alternative indicators (e.g. guilds), a more liberal significance level (p=0.1), a paired-site sampling design, or including reference sites.     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

National Inventory of Landscapes in Sweden (NILS)��scope, design, and experiences from establishing a multiscale biodiversity monitoring system

The landscape-level and multiscale biodiversity monitoring program National Inventory of Landscapes in Sweden (NILS) was launched in 2003. NILS is conducted as a sample-based stratified inventory that acquires data across several spatial scales, which is accomplished by combining aerial photo interpretation with field inventory. A total of 631 sample units are distributed across the land base of Sweden, of which 20% are surveyed each year. By 2007 NILS completed the first 5-year inventory phase. As the reinventory in the second 5-year phase (2008?C2012) proceeds, experiences and insights accumulate and reflections are made on the setup and accomplishment of the monitoring scheme. In this article, the emphasis is placed on background, scope, objectives, design, and experiences of the NILS program. The main objective to collect data for and perform analyses of natural landscape changes, degree of anthropogenic impact, prerequisites for natural biological diversity and ecological processes at landscape scale. Different environmental conditions that can have direct or indirect effects on biological diversity are monitored. The program provides data for national and international policy and offers an infrastructure for other monitoring program and research projects. NILS has attracted significant national and international interest during its relatively short time of existence; the number of stakeholders and cooperation partners steadily increases. This is constructive and strengthens the incentive for the multiscale monitoring approach.     

Temporal variation in total phosphorus concentrations revealed from a multidecadal monitoring program on Big Platte Lake,Michigan

Effective water quality management depends on enactment of appropriately designed monitoring programs to reveal current and forecasted conditions. Because water quality conditions are influenced by numerous factors, commonly measured attributes such as total phosphorus (TP) can be highly temporally varying. For highly varying processes, monitoring programs should be long-term and periodic quantitative analyses are needed so that temporal trends can be distinguished from stochastic variation, which can yield insights into potential modifications to the program. Using generalized additive mixed modeling, we assessed temporal (yearly and monthly) trends and quantified other sources of variation (daily and subsampling) in TP concentrations from a multidecadal depth-specific monitoring program on Big Platte Lake, Michigan. Yearly TP concentrations decreased from the late 1980s to late 1990s before rebounding through the early 2000s. At depths of 2.29 to 13.72 m, TP concentrations have cycled around stationary points since the early 2000s, while at the surface and depths ≥?18.29 concentrations have continued declining. Summer and fall peaks in TP concentrations were observed at most depths, with the fall peak at deeper depths occurring 1 month earlier than shallower depths. Daily sampling variation (i.e., variation within a given month and year) was greatest at shallowest and deepest depths. Variation in subsamples collected from depth-specific water samples constituted a small fraction of total variation. Based on model results, cost-saving measures to consider for the monitoring program include reducing subsampling of depth-specific concentrations and reducing the number of sampling depths given observed consistencies across the program period.     

Design to monitor trend in abundance and presence of American beaver (Castor canadensis) at the national forest scale

Wildlife conservationists design monitoring programs to assess population dynamics, project future population states, and evaluate the impacts of management actions on populations. Because agency mandates and conservation laws call for monitoring data to elicit management responses, it is imperative to design programs that match the administrative scale for which management decisions are made. We describe a program to monitor population trends in American beaver (Castor canadensis) on the US Department of Agriculture, Black Hills National Forest (BHNF) in southwestern South Dakota and northeastern Wyoming, USA. Beaver have been designated as a management indicator species on the BHNF because of their association with riparian and aquatic habitats and its status as a keystone species. We designed our program to monitor the density of beaver food caches (abundance) within sampling units with beaver and the proportion of sampling units with beavers present at the scale of a national forest. We designated watersheds as sampling units in a stratified random sampling design that we developed based on habitat modeling results. Habitat modeling indicated that the most suitable beaver habitat was near perennial water, near aspen (Populus tremuloides) and willow (Salix spp.), and in low gradient streams at lower elevations. Results from the initial monitoring period in October 2007 allowed us to assess costs and logistical considerations, validate our habitat model, and conduct power analyses to assess whether our sampling design could detect the level of declines in beaver stated in the monitoring objectives. Beaver food caches were located in 20 of 52 sampled watersheds. Monitoring 20 to 25 watersheds with beaver should provide sufficient power to detect 15–40% declines in the beaver food cache index as well as a twofold decline in the odds of beaver being present in watersheds. Indices of abundance, such as the beaver food cache index, provide a practical measure of population status to conduct long-term monitoring across broad landscapes such as national forests.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

Sampling design for long-term regional trends in marine rocky intertidal communities

Probability-based designs reduce bias and allow inference of results to the pool of sites from which they were chosen. We developed and tested probability-based designs for monitoring marine rocky intertidal assemblages at Glacier Bay National Park and Preserve (GLBA), Alaska. A multilevel design was used that varied in scale and inference. The levels included aerial surveys, extensive sampling of 25 sites, and more intensive sampling of 6 sites. Aerial surveys of a subset of intertidal habitat indicated that the original target habitat of bedrock-dominated sites with slope ≤30° was rare. This unexpected finding illustrated one value of probability-based surveys and led to a shift in the target habitat type to include steeper, more mixed rocky habitat. Subsequently, we evaluated the statistical power of different sampling methods and sampling strategies to detect changes in the abundances of the predominant sessile intertidal taxa: barnacles Balanomorpha, the mussel Mytilus trossulus, and the rockweed Fucus distichus subsp. evanescens. There was greatest power to detect trends in Mytilus and lesser power for barnacles and Fucus. Because of its greater power, the extensive, coarse-grained sampling scheme was adopted in subsequent years over the intensive, fine-grained scheme. The sampling attributes that had the largest effects on power included sampling of “vertical” line transects (vs. horizontal line transects or quadrats) and increasing the number of sites. We also evaluated the power of several management-set parameters. Given equal sampling effort, sampling more sites fewer times had greater power. The information gained through intertidal monitoring is likely to be useful in assessing changes due to climate, including ocean acidification; invasive species; trampling effects; and oil spills.     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

A Framework for the Design of Ecological Monitoring Programs as a Tool for Environmental and Nature Management

Environmental and nature management can not operate effectivelywithout reliable information on changes in the environment andon the causes of those changes. Ecological monitoring canrepresent an important source of information. However, manyoperational monitoring programs are not very effective, i.e., not very useful for decision-making. We present a conceptualframework for the development and maintenance of effectiveecological monitoring programs. In the decision-making process,two main functions for monitoring can be recognized: an earlywarning and an early control function. Both these functionsrequire a high diagnostic power. This is used as a guideline forthe design process. The design consists of choices concerningmonitoring objectives, objects and variables to be monitored,sampling strategy and design, data collection, data handling, maintenance and organization. Arguments commonly put forward inliterature and in practice to support the various choices aresubjected to a critical analysis. The framework will be helpfulin the design of effective monitoring systems as it avoidsimportant components to be overlooked, clarifies the relationbetween the different components, maximizes the exploitation ofexisting possibilities and opportunities and identifiesshortcomings in advance. This will result in monitoring programsthat should be able to live up to their expectations.     

Assessing urban runoff program progress through a dry weather hybrid reconnaissance monitoring design

Characterizing dry weather conditions in urban Municipal Separate Storm Sewer Systems (MS4s), and then prioritizing and addressing problems due to urban pollutants, is a daunting challenge. The size and complexity of most MS4s and the ephemeral nature of many dry weather problems hamper efforts to identify and eliminate pollutant sources, and to track trends in condition. As a result, assessing overall program progress has proven difficult. We describe a hybrid dry weather urban monitoring design from southern California that combines probabilistic and targeted sampling to rigorously identify and prioritize problems and track program progress. Data from probabilistic sites define the urban background and establish tolerance intervals, which identify sites that persistently exceed the overall urban background. Targeted sites focus on locations where nearby activities and/or past history suggest that pollutant levels will be elevated. Embedding targeted monitoring within a probabilistic design enables data from targeted sites to be interpreted in a more meaningful regional context. Data from all sites are also used to construct site- and pollutant-specific control charts. These charts quickly identify instances where a site's behavior significantly changes, compared to its past behavior, suggesting an active source in the upstream drainage area. The hybrid design, and the use of formal statistical tools (tolerance intervals and control charts), permit the program to systematically prioritize problematic sites, compare conditions to the regional urban background, and track trends over time. In addition, the program's design allows several measures of program progress to be defined and thus consistently followed over time. Such hybrid designs can provide substantial advantages compared to more traditional monitoring approaches.     

Prioritizing organic chemicals for long-term air monitoring by using empirical monitoring data—application to data from the Swedish screening program

This paper illustrates a step-by-step approach for evaluating chemical monitoring data in air and deposition and for prioritizing chemicals to be included in long-term air monitoring programs. The usability of the method is shown by application to data generated within the Swedish screening program. The suggested prioritization approach uses a novel methodology by combining empirical data on occurrence in air and deposition with publicly available quantitative structure activity relationship estimation tools that predict atmospheric persistence and bioaccumulation. A selection tree is presented, which may be used by regulatory bodies to prioritize chemicals for long-term air monitoring. A final ranking list is presented proposing a prioritization order for inclusion in monitoring programs. Based on the suggested strategy, the chemicals identified as most relevant to include in Swedish long-term monitoring programs were short-chain chlorinated paraffins(C10-C13), perfluorooctane sulfonate, octachlorostyrene, hexabromocyclododecane, hexachlorobenzene, pentachloroanisole, decamethylcyclopentasiloxane, octamethylcyclotetrasiloxane, pentachlorobenzene, 1,2,3,4-tetrachlorobenzene, hexachlorobutadiene, dodecamethylcyclohexasiloxane, perfluorodecane sulfonate, 1,2,4,5-tetrachlorobenzene, and pentabromophenol.     

Optimising the sampling effort in riparian surveys

Riparian condition is commonly measured as part of stream health monitoring programs as riparian vegetation provides an intricate linkage between the terrestrial and aquatic ecosystems. Field surveys of a riparian zone provide comprehensive riparian attribute data but can be considerably intensive and onerous on resources and workers. Our objective was to assess the impact of reducing the sampling effort on the variation in key riparian health indicators. Subsequently, we developed a non-parametric approach to calculate an information retained (IR) statistic for comparing several constrained systematic sampling schemes to the original survey. The IR statistic is used to select a scheme that reduces the time taken to undertake riparian surveys (and thus potentially the costs) whilst maximising the IR from the original survey. Approximate bootstrap confidence intervals were calculated to improve the inferential capability of the IR statistic. The approach is demonstrated using riparian vegetation indicators collected as part of an aquatic ecosystem health monitoring program in Queensland, Australia. Of the nine alternative sampling designs considered, the sampling design that reduced the sampling intensity per site by sixfold without significantly comprising the quality of the IR, results in halving the time taken to complete a riparian survey at a site. This approach could also be applied to reducing sampling effort involved in monitoring other ecosystem health indicators, where an intensive systematic sampling scheme was initially employed.     

Attributes of reliable long-term landscape-scale studies: Malpractice insurance for landscape ecologists

Monitoring long-term change in forested landscapes is an intimidating challenge with considerable practical, methodological, and theoretical limitations. Current field approaches used to assess vegetation change at the plot-to-stand scales and nationwide forest monitoring programs may not be appropriate at landscape scales. We emphasize that few vegetation monitoring programs (and, thus, study design models) are designed to detect spatial and temporal trends at landscape scales. Based primarily on advice from many sources, and trial and error, we identify 14 attributes of a reliable long-term landscape monitoring program: malpractice insurance for landscape ecologists. The attributes are to: secure long-term funding and commitment; develop flexible goals; refine objectives; pay adequate attention to information management; take an experimental approach to sampling design; obtain peer-review and statistical review of research proposals and publications; avoid bias in selection of long-term plot locations; insure adequate spatial replication; insure adequate temporal replication; synthesize retrospective, experimental, and related studies; blend theoretical and empirical models with the means to validate both; obtain periodic research program evaluation; integrate and synthesize with larger and smaller scale research, inventory, and monitoring programs; and develop an extensive outreach program. Using these 14 attributes as a guide, we describe one approach to assess the potential effect of global change on the vegetation of the Front Range of the Colorado Rockies. This self-evaluation helps identify strengthes and weaknesses in our program, and may serve the same role for other landscape ecologists in other programs.     

Optimizing water quality monitoring networks using continuous longitudinal monitoring data: a case study of Wen-Rui Tang River, Wenzhou, China

Identification of representative sampling sites is a critical issue in establishing an effective water quality monitoring program. This is especially important at the urban-agriculture interface where water quality conditions can change rapidly over short distances. The objective of this research was to optimize the spatial allocation of discrete monitoring sites for synoptic water quality monitoring through analysis of continuous longitudinal monitoring data collected by attaching a water quality sonde and GPS to a boat. Sampling was conducted six times from March to October 2009 along a 6.5 km segment of the Wen-Rui Tang River in eastern China that represented an urban-agricultural interface. When travelling at a velocity of ～2.4 km h(-1), this resulted in water quality measurements at ～20 m interval. Ammonia nitrogen (NH(4)(+)-N), electrical conductivity (EC), dissolved oxygen (DO), and turbidity data were collected and analyzed using Cluster Analysis (CA) to identify optimal locations for establishment of long-term monitoring sites. The analysis identified two distinct water quality segments for NH(4)(+)-N and EC and three distinct segments for DO and turbidity. According to our research results, the current fixed-location sampling sites should be adjusted to more effectively capture the distinct differences in the spatial distribution of water quality conditions. In addition, this methodology identified river reaches that require more comprehensive study of the factors leading to the changes in water quality within the identified river segment. The study demonstrates that continuous longitudinal monitoring can be a highly effective method for optimizing monitoring site locations for water quality studies.     

Critical technical elements of state bioassessment programs: a process to evaluate program rigor and comparability

We developed a systematic process to evaluate state/tribal bioassessment programs to provide information about the rigor of the technical approach. This is accomplished via on-site interviews to produce an evaluation that assigns one of four levels of rigor as an outcome. Level 4 is the most rigorous and reflects a technical capacity to accurately determine incremental condition and support management programs. The remaining three levels are less able to assess incremental condition and are appropriate for only some management support needs. Accurately determining impairment and diagnosing pollution-specific stressors are fundamental tasks that states/tribes must accomplish to provide management support. This goal is fulfilled to varying degrees by most states/tribes. The evaluation employs a checklist and a sliding scale of rigor for 13 technical elements. Feedback is provided to each state/tribe via a technical memorandum that describes the technical components of the monitoring program, highlights strengths, and recommends improvements for specific technical issues. This can be used to refine the bioassessment and monitoring programs to better support management programs. The results of 14 state/tribal evaluations are included here. The majority (nine states, one tribe) revealed that most operate at level 2 with developmental activities that will elevate the level of program rigor already underway. Two states operate level 4 programs and each have numeric biocriteria and refined designated uses in their water quality standards. This is the ultimate goal of the process of engaging states in the development of bioassessment programs in the U.S.     

Addressing statistical and operational challenges in designing large-scale stream condition surveys

Implementing a statistically valid and practical monitoring design for large-scale stream condition monitoring and assessment programs can be difficult due to factors including the likely existence of a diversity of ecosystem types such as ephemeral streams over the sampling domain; limited resources to undertake detailed monitoring surveys and address knowledge gaps; and operational constraints on effective sampling at monitoring sites. In statistical speak, these issues translate to defining appropriate target populations and sampling units; designing appropriate spatial and temporal sample site selection methods; selection and use of appropriate indicators; and setting effect sizes with limited ecological and statistical information about the indicators of interest. We identify the statistical and operational challenges in designing large-scale stream condition surveys and discuss general approaches for addressing them. The ultimate aim in drawing attention to these challenges is to ensure operational practicality in carrying out future monitoring programs and that the resulting inferences about stream condition are statistically valid and relevant.     

Seagrass (Posidonia oceanica) monitoring in western Mediterranean: implications for management and conservation

The seagrass Posidonia oceanica is extensively monitored in Mediterranean coastal waters and is an ideal candidate for an eco-regional assessment of the coastal ecosystem. The aim of this study is to evaluate the potential of P. oceanica as eco-regional indicator for its assessment at the scale of Mediterranean basin. For this purpose, regional and national P. oceanica monitoring programmes are identified, and their data and metadata are collected and compared in terms of objectives, strategies, sampling designs and sampling methods. The analysis identifies a number of issues concerning data quality, reliability and comparability. In particular, the adoption of different sampling designs and methods may introduce relevant errors when comparing data. The results of this study stress the necessity of carefully planning monitoring programmes. Moreover, it highlights that the adoption of a number of common tools would facilitate all Mediterranean monitoring activities and allows an optimisation of management efforts at an eco-regional scale.     

A database for a multi-institutional environment monitoring program

A several year program is underway in Israel for monitoring a wastewater reclamation and storage complex for agriculture irrigation. The program covers wastewater treatment and storage, irrigation, aquifers, air pollution, crops, soil, geography and meteorology, and is operated by one Principal Institution and several Participant Institutions. The institutions feed their data to a central database. Multi-institutional databases are fed by research teams whose areas of activity are traditionally unrelated. The main problem of the Database Administrator consists in combining data created by different methods under different basic assumptions. And to make them available to investigators from several disciplines and institutions who are used to different data analysis procedures. Database Administrator's background should cover both computer and environmental sciences fields. The selection of a flexible multipurpose software is recommended, but there are several decisions on database design that depend not on the software but on the characteristics and requirements of the Monitoring Program. Entering the data from different sources in independent files facilitates input, debugging, administrative control of the contribution of each institution, and changes in units and parameters. This multi-file design also overcomes difficulties due to excessive file size, and better matchs the actual usage of the database in a multi-institutional program. To enter the data with their original characteristics and units also matchs better the database usage. Ready-to-use routines for file merging and unit conversion, and debugging and documentation needs are also discussed. The Database-User interactions are the mechanism that maintains an evolving dynamic Database.     

Aerial Monitoring of Marine Waterfowl in the Alaskan Beaufort Sea

The purpose of this study was to design and test a monitoring protocol for marine waterfowl in the central Alaskan Beaufort Sea. The study provides an important case-study of how a long-term monitoring program may be affected by unanticipated human disturbances.Because of its overwhelming and widespread abundance, relatively sedentary behavior, ease in counting, and the extensive historical database, the long-tailed duck (Clangula hyemalis) was selected as the focal species. Two null hypotheses were formulated concerning potential changes in the numbers and distribution of long-tailed ducks in relation to disturbance in an industrial study area, compared to a reference study area located about 50 km to the east.A 9-year historical database (1977–1984, 1989) of long-tailed duck densities and other important data recorded during systematic aerial surveys was analyzed retrospectively using multiple regression techniques. The retrospective analyses determined which of several predictor variables recorded were significantly related to long-tailed duck density. Separate analyses were conducted for two periods: (1) the overall period when long-tailed ducks were present in the lagoon study areas, and (2) the shorter adult male molt period. The results of the two analyses indicated that 57% and 68%, respectively, of the total variation in long-tailed duck density during the two periods could be explained by variables recorded during the surveys. Predictor variables representing habitat, day of the year, time of day, amount of ice, and wave height recorded on-transect during surveys were most closely associated with long-tailed duck density. Measurement error during the surveys, and influences outside the study area such as nesting success in tundra habitats and mortality during migration and in over-wintering areas likely also had strong influences on the results, but these factors were not measurable in our study.Based on results of the retrospective analyses, a long-term monitoring protocol consisting of a program of systematic aerial surveys and an analyses of variance and covariance (ANOVA and ANCOVA) statistical procedure was designed and initially tested in 1990 and 1991. This 2-year testing phase resulted in several revisions to the monitoring protocol. Refinements were made to the original sampling procedures, to the survey schedule, and to the recommended statistical analysis procedures. Results of the ANOVA and ANCOVA indicated that there was no evidence of a change in long-tailed duck densities that could be attributable to disturbance (from any source) in the industrial study area relative to a reference area with no industrial development. Other analyses indicated that the sampling and analysis procedures would be adequate to detect long-term trends in long-tailed duck density and localized disturbance effects, but that the monitoring program should be continued well beyond two years to detect statistically significant changes. As a result, additional aerial surveys of both study areas were conducted again during 1999–2001.Results of the revised ANOVA and ANCOVA of the 1990–1991 and 1999–2001 survey data indicated that the density of long-tailed ducks had significantly declined in coastal lagoons along the central Alaskan Beaufort Sea coast during the study period. In addition, disturbances throughout the barrier island-lagoon systems used by these ducks, including both the industrial and the reference study areas, had significantly increased over the same period. However, because unanticipated disturbances from a variety of anthropogenic sources, and not just industry sources, increased in both study areas, the reference study area was not an effective statistical control. As a result, the decline in long-tailed duck density in both study areas was not attributable to industry-related activities. Although the monitoring protocol described here is an effective method to detect statistically significant changes in long-tailed duck distribution and abundance in the nearshore Alaskan Beaufort Sea, many more years of sampling would be necessary to attribute observed changes to industry-related disturbances.