Abstract-The negative effects of chemical contaminants on tropical marine ecosystems are of increasing concern as human populations expand adjacent to these communities. Watershed streams and ground water carry a variety of chemicals from agricultural, industrial, and domestic activities, while winds and currents transport pollutants from atmospheric and oceanic sources to these coastal ecosystems. The implications of the limited information available on impacts of chemical stressors on mangrove forests, seagrass meadows, and coral reefs are discussed in the context of ecosystem management and ecological risk assessment. Three classes of pollutants have received attention: heavy metals, petroleum, and synthetic organics such as herbicides and pesticides. Heavy metals have been detected in all three ecosystems, causing physiological stress, reduced reproductive success, and outright mortality in associated invertebrates and fishes. Oil spills have been responsible for the destruction of entire coastal shallow-water communities, with recovery requiring years. Herbicides are particularly detrimental to mangroves and seagrasses and adversely affect the animal-algal symbioses in corals. Pesticides interfere with chemical cues responsible for key biological processes, including reproduction and recruitment of a variety of organisms. Information is lacking with regard to long-term recovery, indicator species, and biomarkers for tropical communities. Critical areas that are beginning to be addressed include the development of appropriate benchmarks for risk assessment, baseline monitoring criteria, and effective management strategies to protect tropical marine ecosystems in the face of mounting anthropogenic disturbance.

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ABSTRACT1. The distribution and composition of in-stream habitats are reflections of landscape scale geomorphic and climatic controls. Correspondingly, Pacific salmon (Oncorhynchus spp.) are largely adapted to and constrained by the quality and complexity of those in-stream habitat conditions. The degree to which lands have been fragmented and managed can disrupt these patterns and affect overall habitat availability and quality.2. Eleven in-stream habitat features were modelled as a function of landscape composition. In total, 121 stream reaches within coastal catchments of Oregon were modelled. For each habitat feature, three linear regression models were applied in sequence; final models were composed of the immutable and management-influenced landscape predictors that best described the variability in stream habitat.3. Immutable landscape predictors considered proxies for stream power described the majority of the variability seen in stream habitat features. Management-influenced landscape predictors, describing the additional human impacts beyond that which was inherently entwined with the immutable predictors, explained a sizeable proportion of variability. The largest response was seen in wood volume and pool frequency.4. By using a sequential linear regression analysis, management-influenced factors could be segregated from natural gradients to identify those stream habitat features that may be more sensitive to land-use pressures. These results contribute to the progressing notion that the conservation of freshwater resources is best accomplished by investigating and managing stream systems from a landscape perspective.

Distribution of fishes, both occupancy and abundance, is often correlated with landscape-scale characteristics (e.g., geology, climate, and human disturbance). Understanding these relationships is essential for effective conservation of depressed populations. We used landscape characteristics to explain the distribution of coho salmon ( Oncorhynchus kisutch ) in the Oregon Plan data set, one of the first long-term, probabilistic salmon monitoring data sets covering the full range of potential habitats. First we compared data structure and model performance between the Oregon Plan data set and two published data sets on coho salmon distribution. Most of the variation in spawner abundance occurred between reaches but much also occurred between years, limiting potential model performance. Similar suites of landscape predictors are correlated with coho salmon distribution across regions and data sets. We then modeled coho salmon spawner distribution using the Oregon Plan data set and determined that landscape characteristics could not explain presence vs. absence of spawners but that the percentage of agriculture, winter temperature range, and the intrinsic potential of the stream could explain some variation in abundance (weighted average R2 = 0.30) where spawners were present. We conclude that the previous use of nonrandom monitoring data sets may have obscured understanding of species distribution, and we suggest minor modifications to large-scale monitoring programs.

Salmon occupy large areas over which comprehensive surveys are not feasible owing to the prohibitive expense of surveying thousands of kilometers of streams. Studies of these populations generally rely on sampling a small portion of the distribution of the species. However, managers often need information about areas that have not been visited. The availability of geographical information systems data on landscape features over broad extents makes it possible to develop models to comprehensively predict the distribution of spawning salmon over large areas. In this study, the density of spawning coho salmon Oncorhynchus kisutch was modeled from landscape features at multiple spatial extents to identify regions or conditions needed to conserve populations of threatened fish, identify spatial relationships that might be important in modeling, and evaluate whether seventh-field hydrologic units might serve as a surrogate for delineated catchments. We used geospatial data to quantify landscape characteristics at four spatial 440 LANDSCAPE MODELS OF ADULT COHO SALMON DENSITY 441 extents (a 100-m streamside buffer, a 500-m streamside buffer, all adjacent seventh-field hydrologic units [mean area = 18 km 2 ], and the catchment upstream from the reach [mean area = 17 km 2 ]). Predictions from models incorporating land use, land ownership, geology, and climate variables were significantly correlated (r = 0.66-0.75, P < 0.0001) with observed adult coho salmon in the study area. In general, coho salmon densities (peak count of adults per kilometer) were greatest in river reaches within landscapes of undeveloped forest land with little area in weak rock types, areas with low densities of cattle and roads, and areas with a relatively large range in winter temperatures. The ability to predict the spatial distribution of coho salmon spawners from landscape data has great utility in guiding conservation, monitoring, and restoration efforts.