Leveraging a Landscape-Level Monitoring and Assessment Program for Developing Resilient Shorelines throughout the Laurentian Great Lakes.

Traditionally, ecosystem monitoring, conservation, and restoration have been conducted in a piecemeal manner at the local scale without regional landscape context. However, scientifically driven conservation and restoration decisions benefit greatly when they are based on regionally determined benchmarks and goals. Unfortunately, required data sets rarely exist for regionally important ecosystems. Because of early recognition of the extreme ecological importance of Laurentian Great Lakes coastal wetlands, and the extensive degradation that had already occurred, significant investments in coastal wetland research, protection, and restoration have been made in recent decades and continue today. Continued and refined assessment of wetland condition and trends, and the evaluation of restoration practices are all essential to ensuring the success of these investments. To provide wetland managers and decision makers throughout the Laurentian Great Lakes basin with the optimal tools and data needed to make scientifically-based decisions, our regional team of Great Lakes wetland scientists developed standardized methods and indicators used for assessing wetland condition. From a landscape perspective, at the Laurentian Great Lakes ecosystem scale, we established a stratified random-site-selection process to monitor birds, anurans, fish, macroinvertebrates, vegetation, and physicochemical conditions of coastal wetlands in the US and Canada. Monitoring of approximately 200 wetlands per year began in 2011 as the Great Lakes Coastal Wetland Monitoring Program. In this paper, we describe the development, delivery, and expected results of this ongoing international, multi-disciplinary, multi-stakeholder, landscape-scale monitoring program as a case example of successful application of landscape conservation design.

Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts.

Point counts are a common method for sampling avian distribution and abundance. Although methods for estimating detection probabilities are available, many analyses use raw counts and do not correct for detectability. We use a removal model of detection within an N-mixture approach to estimate abundance trends corrected for imperfect detection. We compare the corrected trend estimates to those estimated from raw counts for 16 species using 15 years of monitoring data on three national forests in the western Great Lakes, USA. We also tested the effects of overdispersion by modeling both counts and removal mixtures under three statistical distributions: Poisson, zero-inflated Poisson, and negative binomial. For most species, the removal model produced estimates of detection probability that conformed to expectations. For many species, but not all, estimates of trends were similar regardless of statistical distribution or method of analysis. Within a given combination of likelihood (counts vs. mixtures) and statistical distribution, trends usually differed by both stand type and national forest, with species showing declines in some stand types and increases in others. For three species, Brown Creeper, Yellow-rumped Warbler, and Black-throated Green Warbler, temporal patterns in detectability resulted in substantial differences in estimated trends under the removal mixtures compared to the analysis of raw counts. Overall, we found that the zero-inflated Poisson was the best distribution for our data, although the Poisson or negative binomial performed better for a few species. The similarity in estimated trends that we observed among counts and removal mixtures was probably a result of both experimental design and sampling effort. First, the study was originally designed to avoid confounding observer effects with habitats or time. Second, our time series is relatively long and our sample sizes within years are large.

Human influences on water quality in Great Lakes coastal wetlands.

A better understanding of relationships between human activities and water chemistry is needed to identify and manage sources of anthropogenic stress in Great Lakes coastal wetlands. The objective of the study described in this article was to characterize relationships between water chemistry and multiple classes of human activity (agriculture, population and development, point source pollution, and atmospheric deposition). We also evaluated the influence of geomorphology and biogeographic factors on stressor-water quality relationships. We collected water chemistry data from 98 coastal wetlands distributed along the United States shoreline of the Laurentian Great Lakes and GIS-based stressor data from the associated drainage basin to examine stressor-water quality relationships. The sampling captured broad ranges (1.5-2 orders of magnitude) in total phosphorus (TP), total nitrogen (TN), dissolved inorganic nitrogen (DIN), total suspended solids (TSS), chlorophyll a (Chl a), and chloride; concentrations were strongly correlated with stressor metrics. Hierarchical partitioning and all-subsets regression analyses were used to evaluate the independent influence of different stressor classes on water quality and to identify best predictive models. Results showed that all categories of stress influenced water quality and that the relative influence of different classes of disturbance varied among water quality parameters. Chloride exhibited the strongest relationships with stressors followed in order by TN, Chl a, TP, TSS, and DIN. In general, coarse scale classification of wetlands by morphology (three wetland classes: riverine, protected, open coastal) and biogeography (two ecoprovinces: Eastern Broadleaf Forest [EBF] and Laurentian Mixed Forest [LMF]) did not improve predictive models. This study provides strong evidence of the link between water chemistry and human stress in Great Lakes coastal wetlands and can be used to inform management efforts to improve water quality in Great Lakes coastal ecosystems.

COMPARISON OF SIMPLE AND MULTIMETRIC DIATOM-BASED INDICES FOR GREAT LAKES COASTLINE DISTURBANCE(1).

Because diatom communities are subject to the prevailing water quality in the Great Lakes coastal environment, diatom-based indices can be used to support coastal-monitoring programs and paleoecological studies. Diatom samples were collected from Great Lakes coastal wetlands, embayments, and high-energy sites (155 sites), and assemblages were characterized to the species level. We defined 42 metrics on the basis of autecological and functional properties of species assemblages, including species diversity, motile species, planktonic species, proportion dominant taxon, taxonomic metrics (e.g., proportion Stephanodiscoid taxa), and diatom-inferred (DI) water quality (e.g., DI chloride [Cl]). Redundant metrics were eliminated, and a diatom-based multimetric index (MMDI) to infer coastline disturbance was developed. Anthropogenic stresses in adjacent coastal watersheds were characterized using geographic information system (GIS) data related to agricultural and urban land cover and atmospheric deposition. Fourteen independent diatom metrics had significant regressions with watershed stressor data; these metrics were selected for inclusion in the MMDI. The final MMDI was developed as the weighted sum of the selected metric scores with weights based on a metric's ability to reflect anthropogenic stressors in the adjacent watersheds. Despite careful development of the multimetric approach, verification using a test set of sites indicated that the MMDI was not able to predict watershed stressors better than some of the component metrics. From this investigation, it was determined that simpler, more traditional diatom-based metrics (e.g., DI Cl, proportion Cl-tolerant species, and DI total phosphorus [TP]) provide superior prediction of overall stressor influence at coastal locales.

Integrated measures of anthropogenic stress in the U.S. Great lakes basin.

Integrated, quantitative expressions of anthropogenic stress over large geographic regions can be valuable tools in environmental research and management. Despite the fundamental appeal of a regional approach, development of regional stress measures remains one of the most important current challenges in environmental science. Using publicly available, pre-existing spatial datasets, we developed a geographic information system database of 86 variables related to five classes of anthropogenic stress in the U.S. Great Lakes basin: agriculture, atmospheric deposition, human population, land cover, and point source pollution. The original variables were quantified by a variety of data types over a broad range of spatial and classification resolutions. We summarized the original data for 762 watershed-based units that comprise the U.S. portion of the basin and then used principal components analysis to develop overall stress measures within each stress category. We developed a cumulative stress index by combining the first principal component from each of the five stress categories. Maps of the stress measures illustrate strong spatial patterns across the basin, with the greatest amount of stress occurring on the western shore of Lake Michigan, southwest Lake Erie, and southeastern Lake Ontario. We found strong relationships between the stress measures and characteristics of bird communities, fish communities, and water chemistry measurements from the coastal region. The stress measures are taken to represent the major threats to coastal ecosystems in the U.S. Great Lakes. Such regional-scale efforts are critical for understanding relationships between human disturbance and ecosystem response, and can be used to guide environmental decision-making at both regional and local scales.

Environmentally stratified sampling design for the development of Great Lakes environmental indicators.

Understanding the relationship between human disturbance and ecological response is essential to the process of indicator development. For large-scale observational studies, sites should be selected across gradients of anthropogenic stress, but such gradients are often unknown for apopulation of sites prior to site selection. Stress data available from public sources can be used in a geographic information system (GIS) to partially characterize environmental conditions for large geographic areas without visiting the sites. We divided the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then summarized over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute sites across the range of environmental variation in our GIS data. This design has broad applicability when the goal is to develop ecological indicators using observational data from large-scale surveys.