MODELING THE IMPACTS OF WATER LEVEL CHANGES ON A GREAT LAKES COMMUNITY1

ABSTRACT: Recent research that couples climate change scenarios based on general circulation models (GCM) with Great Lakes hydrologic models has indicated that average water levels are projected to decline in the future. This paper outlines a methodology to assess the potential impact of declining water levels on Great Lakes waterfront communities, using the Lake Huron shoreline at Goderich, Ontario, as an example. The methodology utilizes a geographic information system (GIS) to combine topographic and bathymetric datasets. A digital elevation surface is used to model projected shoreline change for 2050 using water level scenarios. An arbitrary scenario, based on a 1 m decline from February 2001 lake levels, is also modeled. By creating a series of shoreline scenarios, a range of impact and cost scenarios are generated for the Goderich Harbor and adjacent marinas. Additional harbor and marina dredging could cost as much as CDN $7.6 million. Lake freighters may experience a 30 percent loss in vessel capacity. The methodology is used to provide initial estimates of the potential impacts of climate change that can be readily updated as more robust climate change scenarios become available and is adaptable for use in other Great Lakes coastal communities.     

DRY AND WET WEATHER FLOW NUTRIENT LOADS FROM A LOS ANGELES WATERSHED1

Effective watershed management requires an accurate assessment of the pollutant loads from the associated point and nonpoint sources. The importance of wet weather flow (WWF) pollutant loads is well known, but in semi‐arid regions where urbanization is significant the pollutant load in dry weather flow (DWF) may also be important. This research compares the relative contributions of potential contaminants discharged in DWF and WWF from the Ballona Creek Watershed in Los Angeles, California. Models to predict DWF and WWF loads of total suspended solids, biochemical oxygen demand, nitrate‐nitrogen, nitrite‐nitrogen, ammonia‐nitrogen, total Kjeldahl nitrogen, and total phosphorus from the Ballona Creek Watershed for six water years dating from 1991 to 1996 were developed. The contaminants studied were selected based on data availability and their potential importance in the degradation of Ballona Creek and Santa Monica Bay beneficial uses. Wet weather flow was found to contribute approximately 75 percent to 90 percent of the total annual flow volume discharged by the Ballona Creek Watershed. Pollutant loads are also predominantly due to WWF, but during the dry season, DWF is a more significant contributor. Wet weather flow accounts for 67 to 98 percent of the annual load of the constituents studied. During the dry season, however, the portion attributable to DWF increases to greater than 40 percent for all constituents except biochemical oxygen demand and total suspended solids. When individual catchments within the watershed are considered, the DWF pollutant load from the largest catchment is similar to the WWF pollutant load in two other major catchments. This research indicates WWF is the most significant source of nonpoint source pollution load on an annual basis, but management of the effects of the nonpoint source pollutant load should consider the seasonal importance of DWF.     

MODELING LIGHT ATTENUATION,SECCHI DISK,AND EFFECTS OF TRIPTON IN SENACA RIVER,NEW YORK,USA1

The development, testing, and application of a probabilistic model framework for the light attenuation coefficient for downwelling irradiance (K_d) and Secchi disc transparency (SD) that resolves the effects of several light attenuating constituents, including phytoplankton and nonliving particles (tripton), is documented. The model is consistent with optical theory, partitioning the magnitudes of the light attenuating processes of absorption and scattering according to the contributions of attenuating constituents as simple summations. The probabilistic framework accommodates variations in the character and concentrations of these constituents and ambient conditions during measurements, and recognizes a linear relationship between the magnitudes of absorption and scattering by tripton. The model is tested and applied for a 21 km reach of the Seneca River, New York, that features optical gradients caused by an intervening hypereutrophic lake and dam, and a severe infestation of the exotic zebra mussel. The model is applied to resolve the roles of phytoplankton and tripton in regulating measured longitudinal patterns of SD along the study reach of the river and increases in SD since the zebra mussel invasion, and to predict decreases in K_d since the invasion.     

MODELING THE DISTRIBUTION OF DIFFUSE NITROGEN SOURCES AND SINKS IN THE NEUSE RIVER BASIN OF NORTH CAROLINA,USA1

This study quantified nonpoint source nitrogen (NPS‐N) sources and sinks across the 14,582 km² Neuse River Basin (NRB) located in North Carolina, to provide tabular data summaries and graphic overlay products to support the development of management approaches to best achieve established N reduction goals. First, a remote sensor derived, land cover classification was performed to support modeling needs. Modeling efforts included the development of a mass balance model to quantify potential N sources and sinks, followed by a precipitation event driven hydrologic model to effectively transport excess N across the landscape to individual stream reaches to support subsequent labeling of transported N values corresponding to source origin. Results indicated that agricultural land contributed 55 percent of the total annual NPS‐N loadings, followed by forested land at 23 percent (background), and urban areas at 21 percent. Average annual N source contributions were quantified for agricultural (1.4 kg/ha), urban (1.2 kg/ha), and forested cover types (0.5 kg/ha). Nonpoint source‐N contributions were greatest during the winter (40 percent), followed by spring (32 percent), summer (28 percent), and fall (0.3 percent). Seasonal total N loadings shifted from urban dominated and forest dominated sources during the winter, to agricultural sources in the spring and summer. A quantitative assessment of the significant NRB land use activities indicated that high (greater than 70 percent impervious) and medium (greater than 35 percent impervious) density urban development were the greatest contributors of NPS‐N on a unit area basis (1.9 and 1.6 kg/ha/yr, respectively), followed by row crops and pasture/hay cover types (1.4 kg/ha/yr).     

A comparative study of US EPA 1996 and 1999 emission inventories in the west Gulf of Mexico coast region, USA

Emission inventory is one of the required inputs to air quality models. To assist in the urban and regional modeling efforts, United States Environmental Protection Agency (EPA) has compiled a National Emission Inventory (NEI) for criterion pollutants, and the precursors of ozone and particulate matter (PM). In December 2002, EPA released the 1999 NEI estimates (NEI99), which represent the most recent national emission data. However, the data sets are not in model-ready format for air quality simulations. This present work converts the NEI99 Final Version 2 data sets into Inventory Data Analyzer (IDA) format and processes the data using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system to generate a gridded emission inventory in a domain covering the west Gulf Coast Region, USA. The spatial and diurnal emission characteristics of the gridded emission inventories are then assessed and compared with those of the National Emission Trend 1996 (NET96). The NEI99 database contains more complete emission records in both area and point sources. It is also found that NEI99 data exhibit greater emissions with respect to point and mobile sources but smaller emissions with respect to area sources when compared to the corresponding gridded NET96 data in the same study domain. The most distinct differences between the NEI99 and NET96 databases are CO emission of mobile sources, SO2 emissions of point sources, and VOC/PM/NH3/NOx emissions of area and non-road sources. The gridded NEI99 data show low VOC/NOx ratios (<2-5) in the urban areas of the study domain.     

Using long-term data for a reintroduced population to empirically estimate future consequences of inbreeding

Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F ) over time based on kinship of possible breeding pairs and to estimate empirically N_e/N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331–1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887–1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty.     

Defining and Evaluating the Umbrella Species Concept for Conserving and Restoring Landscape Connectivity

Conserving or restoring landscape connectivity between patches of breeding habitat is a common strategy to protect threatened species from habitat fragmentation. By managing connectivity for some species, usually charismatic vertebrates, it is often assumed that these species will serve as conservation umbrellas for other species. We tested this assumption by developing a quantitative method to measure overlap in dispersal habitat of 3 threatened species—a bird (the umbrella), a butterfly, and a frog—inhabiting the same fragmented landscape. Dispersal habitat was determined with Circuitscape, which was parameterized with movement data collected for each species. Despite differences in natural history and breeding habitat, we found substantial overlap in the spatial distributions of areas important for dispersal of this suite of taxa. However, the intuitive umbrella species (the bird) did not have the highest overlap with other species in terms of the areas that supported connectivity. Nevertheless, we contend that when there are no irreconcilable differences between the dispersal habitats of species that cohabitate on the landscape, managing for umbrella species can help conserve or restore connectivity simultaneously for multiple threatened species with different habitat requirements. Definición y Evaluación del Concepto de Especie Paraguas para Conservar y Restaurar la Conectividad de Paisajes     

Using environmental heterogeneity to plan for sea‐level rise

Environmental heterogeneity is increasingly being used to select conservation areas that will provide for future biodiversity under a variety of climate scenarios. This approach, termed conserving nature's stage (CNS), assumes environmental features respond to climate change more slowly than biological communities, but will CNS be effective if the stage were to change as rapidly as the climate? We tested the effectiveness of using CNS to select sites in salt marshes for conservation in coastal Georgia (U.S.A.), where environmental features will change rapidly as sea level rises. We calculated species diversity based on distributions of 7 bird species with a variety of niches in Georgia salt marshes. Environmental heterogeneity was assessed across six landscape gradients (e.g., elevation, salinity, and patch area). We used 2 approaches to select sites with high environmental heterogeneity: site complementarity (environmental diversity [ED]) and local environmental heterogeneity (environmental richness [ER]). Sites selected based on ER predicted present‐day species diversity better than randomly selected sites (up to an 8.1% improvement), were resilient to areal loss from SLR (1.0% average areal loss by 2050 compared with 0.9% loss of randomly selected sites), and provided habitat to a threatened species (0.63 average occupancy compared with 0.6 average occupancy of randomly selected sites). Sites selected based on ED predicted species diversity no better or worse than random and were not resilient to SLR (2.9% average areal loss by 2050). Despite the discrepancy between the 2 approaches, CNS is a viable strategy for conservation site selection in salt marshes because the ER approach was successful. It has potential for application in other coastal areas where SLR will affect environmental features, but its performance may depend on the magnitude of geological changes caused by SLR. Our results indicate that conservation planners that had heretofore excluded low‐lying coasts from CNS planning could include coastal ecosystems in regional conservation strategies.     

Conservation implications of anthropogenic impacts on visual communication and camouflage

Anthropogenic environmental impacts can disrupt the sensory environment of animals and affect important processes from mate choice to predator avoidance. Currently, these effects are best understood for auditory and chemosensory modalities, and recent reviews highlight their importance for conservation. We examined how anthropogenic changes to the visual environment (ambient light, transmission, and backgrounds) affect visual communication and camouflage and considered the implications of these effects for conservation. Human changes to the visual environment can increase predation risk by affecting camouflage effectiveness, lead to maladaptive patterns of mate choice, and disrupt mutualistic interactions between pollinators and plants. Implications for conservation are particularly evident for disrupted camouflage due to its tight links with survival. The conservation importance of impaired visual communication is less documented. The effects of anthropogenic changes on visual communication and camouflage may be severe when they affect critical processes such as pollination or species recognition. However, when impaired mate choice does not lead to hybridization, the conservation consequences are less clear. We suggest that the demographic effects of human impacts on visual communication and camouflage will be particularly strong when human‐induced modifications to the visual environment are evolutionarily novel (i.e., very different from natural variation); affected species and populations have low levels of intraspecific (genotypic and phenotypic) variation and behavioral, sensory, or physiological plasticity; and the processes affected are directly related to survival (camouflage), species recognition, or number of offspring produced, rather than offspring quality or attractiveness. Our findings suggest that anthropogenic effects on the visual environment may be of similar importance relative to conservation as anthropogenic effects on other sensory modalities.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado