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At least 883 records · Page 49Linked to original sources
USGS2009

Land changes and their driving forces in the Southeastern United States

The ecoregions of the Middle Atlantic Coastal Plain, Southeastern Plains, Piedmont, and Blue Ridge provide a continuum of land cover from the Atlantic Ocean to the highest mountains in the East. From 1973 to 2000, each ecoregion had a unique mosaic of land covers and land cover changes. The forests of the Blue Ridge Mountains provided amenity lands. The Piedmont forested area declined, while the developed area increased. The Southeastern Plains became a commercial forest region, and most agricultural lands that changed became forested. Forests in the Middle Atlantic Coastal Plain declined, and development related to recreation and retirement increased. The most important drivers of land conversion were associated with commercial forestry, competition between forest and agriculture, and economic and population growth. These and other drivers were modified by each ecoregion’s unique suitability and land use legacies with the result that the same drivers often produced different land changes in different ecoregions.

Regional Environmental Change↗
USGS2010

Estimation of late twentieth century land-cover change in California

We present the first comprehensive multi-temporal analysis of land-cover change for California across its major ecological regions and primary land-cover types. Recently completed satellite-based estimates of land-cover and land-use change information for large portions of the United States allow for consistent measurement and comparison across heterogeneous landscapes. Landsat data were employed within a pure-panel stratified one-stage cluster sample to estimate and characterize land-cover change for 1973–2000. Results indicate anthropogenic and natural disturbances, such as forest cutting and fire, were the dominant changes, followed by large fluctuations between agriculture and rangelands. Contrary to common perception, agriculture remained relatively stable over the 27-year period with an estimated loss of 1.0% of agricultural land. The largest net declines occurred in the grasslands/shrubs class at 5,131 km 2 and forest class at 4,722 km 2 . Developed lands increased by 37.6%, composing an estimated 4.2% of the state’s land cover by 2000.

California↗
USGS2020

Bridging the research-management gap: Landscape ecology in practice on public lands in the western United States

The field of landscape ecology has grown and matured in recent decades, but incorporating landscape science into land management decisions remains challenging. Many lands in the western United States are federally owned and managed for multiple uses, including recreation, conservation, and energy development. We argue for stronger integration of landscape science into the management of these public lands. We open by outlining the relevance of landscape science for public land planning, management, and environmental effects analysis, including pertinent laws and policies. We identify challenges to integrating landscape science into public land management, including the multijurisdictional nature and complicated spatial pattern of public lands, the capacity of agencies to identify and fill landscape science needs, and public perceptions about the meaning of landscape approaches to management. We provide several recent examples related to landscape monitoring, restoration, reclamation, and conservation in which landscape science products were developed specifically to support decision-making. We close by highlighting three actions - elevating the importance of science-management partnerships dedicated to coproducing actionable landscape science products, identifying where landscape science could foster efficiencies in the land-use planning process, and developing scenario-based landscape models for shrublands - that could improve landscape science support for public land planners and managers.

Arizona, California, Colorado, Idaho, Montana, New↗
USGS2014

Evaluating the effects of land use on headwater wetland amphibian assemblages in coastal Alabama

Anthropogenic land use is known to impact aquatic ecosystems in several ways, including increased frequency and intensity of floods, stream channel incision, sedimentation, and loss of microtopography. Amphibians are susceptible to changes in wetland and surrounding habitats. This study evaluated amphibian assemblages of fifteen headwater slope wetlands in coastal Alabama across a gradient of land uses. Amphibians were surveyed on a seasonal basis and land use was delineated within wetland watersheds and within a 200-m buffer surrounding each wetland. Amphibian presence/absence and land use data were used to develop species occupancy models. Both urban and agricultural land use were shown to influence amphibian occurrence. Species richness ranged from five to ten species across sites; however, five species only occurred in wetlands surrounded by forested lands. Many species were detected more frequently on these wetlands compared to wetlands surrounded by urban or mixed land uses. Occupancy models showed Acris gryllus was negatively associated with the amount of agriculture within a buffer around the wetland. Hyla squirella , Lithobates clamitans , and L. sphenocephalus were positively associated with agricultural land within a watershed. Anaxyrus terrestris and the non-native Eleutherodactylus planirostris were positively associated with the amount of impervious surface area within the wetland buffer.

Alabama↗
USGS2010

Response of aquatic macrophytes to human land use perturbations in the watersheds of Wisconsin lakes, U.S.A.

Aquatic macrophyte communities were assessed in 53 lakes in Wisconsin, U.S.A. along environmental and land use development gradients to determine effects human land use perturbations have on aquatic macrophytes at the watershed and riparian development scales. Species richness and relative frequency were surveyed in lakes from two ecoregions: the Northern Lakes and Forests Ecoregion and the Southeastern Wisconsin Till Plain Ecoregion. Lakes were selected along a gradient of watershed development ranging from undeveloped (i.e., forested), to agricultural to urban development. Land uses occurring in the watershed and in perimeters of different width (0–100, 0–200, 0–500, and 0–1000 m from shore, in the watershed) were used to assess effects on macrophyte communities. Snorkel and SCUBA were used to survey aquatic macrophyte species in 18 quadrats of 0.25 m 2 along 14 transects placed perpendicular to shore in each lake. Effects of watershed development (e.g., agriculture and/or urban) were tested at whole-lake (entire littoral zone) and near-shore (within 7 m of shore) scales using canonical correspondence analysis (CCA) and linear regression. Overall, species richness was negatively related to watershed development, while frequencies of individual species and groups differed in level of response to different land use perturbations. Effects of land use in the perimeters on macrophytes, with a few exceptions, did not provide higher correlations compared to land use at the watershed scale. In lakes with higher total watershed development levels, introduced species, particularly Myriophyllum spicatum , increased in abundance and native species, especially potamids, isoetids, and floating-leaved plants, declined in abundance. Correlations within the northern and southeastern ecoregions separately were not significant. Multivariate analyses suggested species composition is driven by environmental responses as well as human development pressures. Both water chemistry and land use variables loaded positively with the first CCA axis indicating that these factors are correlated. Land use pressures in Wisconsin are greater in the southeastern portion of the state where lakes have higher concentrations of water chemistry variables including alkalinity, conductivity, pH, calcium, magnesium, and nitrogen. This creates a complex gradient that influences species composition of macrophyte communities from lake to lake.

Wisconsin↗
USGS2008

Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.

Advances in Space Research↗
USGS2022

Wildlife migrations highlight importance of both private lands and protected areas in the Greater Yellowstone Ecosystem

Formally protected areas are an important component of wildlife conservation, but face limitations in their effectiveness for migratory species. Improved stewardship of working lands around protected areas is one solution for conservation planning, but private working lands are vulnerable to development. In the Greater Yellowstone Ecosystem (GYE), ungulates such as elk ( Cervus canadensis ) use both protected areas and private lands throughout their annual migrations. We studied patterns of landownership, protection, and conservation challenges within the ranges of migratory elk in the GYE. We used GPS data from 1088 elk in 26 herds to define herd-level seasonal ranges, and extracted covariates related to landownership and protection, land use, and human infrastructure and development. All elk herds used land encompassing >1 ownership type. Most elk herds (92.3 % of herds, n = 24) used the highest proportion of private land in the winter (mean = 36.2 % private land). Most elk herds' winter ranges contained the highest building densities (mean = 1.24 buildings/km 2 ), fence densities (mean = 1.02 km fence/km 2 ), and cattle grazing (mean = 1.9 cows/km 2 ), compared to migratory and summer ranges. Out of all ranges, 36.5 % of ranges did not have any zoning regulations, indicating the potential for future development. Our results show that elk in the GYE rely on habitat outside of protected areas, and face landscape-scale conservation challenges such as habitat fragmentation from human development, particularly in winter ranges. Future conservation strategies for wildlife in this system need to encompass coordination across both public and private land to ensure migratory connectivity.

Idaho, Montana, Wyoming↗
USGS2022

Linkages between land-use change and groundwater management foster long-term resilience of water supply in California

Study Region We created a 270-m coupled model of land-use and groundwater conditions, LUCAS-W[ater], for California’s Central Coast. This groundwater-dependent region is undergoing a dramatic reorganization of groundwater management under California’s 2014 Sustainable Groundwater Management Act (SGMA). Study Focus Understanding land-use and land-cover change supports long-term sustainable water management. Anthropogenic water demand has depleted groundwater aquifers worldwide, while future water shortages will likely affect land-use change, creating system feedbacks. Our novel participatory approach fused changes in land-use and associated water use from county-scale data to local water agencies’ estimates of total sustainable supply, scaling up local hydro-geologic knowledge from heterogeneous aquifers and diverse management approaches to a regional level. We assessed five stakeholder-driven scenarios with the same historic rates of urban and agricultural land-use change, but different water and land-use management, analyzing how management strategies altered both the spatial pattern of development and subsequent water sustainability from 2001 to 2061. New Hydrological Insights for the Region Transformative strategies using demand-side interventions that coupled water availability to land-use more effectively achieved long-term sustainability than adaptive strategies using supply-side interventions to increase water supplies. Limiting water withdrawals within SGMA regulated basins resulted in leakage of development into unregulated basins, increasing groundwater pumping there. Protecting ecosystems, farmlands, and recharge areas from development reduced leakage into undeveloped basins without negatively affecting water sustainability.

California↗
USGS2013

An integrated multi-criteria scenario evaluation web tool for participatory land-use planning in urbanized areas: The Ecosystem Portfolio Model

Land-use land-cover change is one of the most important and direct drivers of changes in ecosystem functions and services. Given the complexity of the decision-making, there is a need for Internet-based decision support systems with scenario evaluation capabilities to help planners, resource managers and communities visualize, compare and consider trade-offs among the many values at stake in land use planning. This article presents details on an Ecosystem Portfolio Model (EPM) prototype that integrates ecological, socio-economic information and associated values of relevance to decision-makers and stakeholders. The EPM uses a multi-criteria scenario evaluation framework, Geographic Information Systems (GIS) analysis and spatially-explicit land-use/land-cover change-sensitive models to characterize changes in important land-cover related ecosystem values related to ecosystem services and functions, land parcel prices, and community quality-of-life (QoL) metrics. Parameters in the underlying models can be modified through the interface, allowing users in a facilitated group setting to explore simultaneously issues of scientific uncertainty and divergence in the preferences of stakeholders. One application of the South Florida EPM prototype reported in this article shows the modeled changes (which are significant) in aggregate ecological value, landscape patterns and fragmentation, biodiversity potential and ecological restoration potential for current land uses compared to the 2050 land-use scenario. Ongoing refinements to EPM, and future work especially in regard to modifiable sea level rise scenarios are also discussed.

Environmental Modelling and Software↗
USGS2017

Importance of scale, land cover, and weather on the abundance of bird species in a managed forest

Climate change and habitat loss are projected to be the two greatest drivers of biodiversity loss over the coming century. While public lands have the potential to increase regional resilience of bird populations to these threats, long-term data are necessary to document species responses to changes in climate and habitat to better understand population vulnerabilities. We used generalized linear mixed models to determine the importance of stand-level characteristics, multi-scale land cover, and annual weather factors to the abundance of 61 bird species over a 20-year time frame in Chippewa National Forest, Minnesota, USA. Of the 61 species modeled, we were able to build final models with R-squared values that ranged from 26% to 69% for 37 species; the remaining 24 species models had issues with convergence or low explanatory power (R-squared < 20%). Models for the 37 species show that stand-level characteristics, land cover factors, and annual weather effects on species abundance were species-specific and varied within guilds. Forty-one percent of the final species models included stand-level characteristics, 92% included land cover variables at the 200 m scale, 51% included land cover variables at the 500 m scale, 46% included land cover variables at the 1000 m scale, and 38% included weather variables in best models. Three species models (8%) included significant weather and land cover interaction terms. Overall, models indicated that aboveground tree biomass and land cover variables drove changes in the majority of species. Of those species models including weather variables, more included annual variation in precipitation or drought than temperature. Annual weather variability was significantly more likely to impact abundance of species associated with deciduous forests and bird species that are considered climate sensitive. The long-term data and models we developed are particularly suited to informing science-based adaptive forest management plans that incorporate climate sensitivity, aim to conserve large areas of forest habitat, and maintain an historical mosaic of cover types for conserving a diverse and abundant avian assemblage.

Minnesota↗
USGS2016

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4–8. Training data were selected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2022

Development of the LCMAP annual land cover product across Hawai'i

Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23 .

Hawaii↗
USGS2023

The concept of land bridge marshes in the Mississippi River Delta and implications for coastal restoration

Louisiana has high coastal wetland loss rates due to natural processes such as subsidence and anthropogenic activities such as construction of river levees and dams, pervasive alteration of surface hydrology by local industries such as oil and gas, and navigation. With the exception of the Atchafalaya River discharge area, most of Louisiana's marsh coastline is retreating and coastal marshes are degrading. In the inactive degrading delta regions, there exists a previously uncharacterized landform referred to colloquially as coastal ‘land bridge’ marshes. Land bridge marshes are saline or brackish marshes fronting large estuarine bays or lakes with sufficient fetch and wave energy to supply high levels of resuspended sediments to the marsh surface. They are generally linear features that are oriented parallel to the coast and the shoreline front retreats landward due to erosion from wave energy. These marshes persist over time vertically due to input of resuspended sediments but are experiencing rapid edge erosion due to wave attack. Comparison of data from Louisiana's Coastal Reference Monitoring System (CRMS) sites show that land bridge marshes have a greater frequency of higher soil surface elevation and higher soil bulk density than non-land bridge marshes. Because land bridges are vertically stable relative to other coastal wetlands, identification of measures to sustain these landscape features is important. Simulations using MarshMorpho2D, a process-based reduced-complexity morphology model, suggest that protection barriers installed on the seaward side of land bridge marshes will attenuate wave energy and, thus, edge erosion. Shoreline protection that can reduce wave energy but still allow sediment input to marshes include living shorelines, rock barriers, and/or breakwaters. Periodic thin layer nourishment of the marsh surface may be necessary to help sustain vertical growth. Further, marsh creation projects directly landward of land bridge marshes may benefit from their protection from waves and as a source of sediment. Consideration of land bridge marshes as distinct marsh types in the State Master Plan and integrated modeling could help to identify measures to sustain these landscape features.

Louisiana↗
USGS2017

Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting

The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.

Remote Sensing Applications: Society and Environme↗
USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗
USGS2008

Sensitivity of June near‐surface temperatures and precipitation in the eastern United States to historical land cover changes since European settlement

Land cover changes alter the near surface weather and climate. Changes in land surface properties such as albedo, roughness length, stomatal resistance, and leaf area index alter the surface energy balance, leading to differences in near surface temperatures. This study utilized a newly developed land cover data set for the eastern United States to examine the influence of historical land cover change on June temperatures and precipitation. The new data set contains representations of the land cover and associated biophysical parameters for 1650, 1850, 1920, and 1992, capturing the clearing of the forest and the expansion of agriculture over the eastern United States from 1650 to the early twentieth century and the subsequent forest regrowth. The data set also includes the inferred distribution of potentially water‐saturated soils at each time slice for use in the sensitivity tests. The Regional Atmospheric Modeling System, equipped with the Land Ecosystem‐Atmosphere Feedback (LEAF‐2) land surface parameterization, was used to simulate the weather of June 1996 using the 1992, 1920, 1850, and 1650 land cover representations. The results suggest that changes in surface roughness and stomatal resistance have caused present‐day maximum and minimum temperatures in the eastern United States to warm by about 0.3°C and 0.4°C, respectively, when compared to values in 1650. In contrast, the maximum temperatures have remained about the same, while the minimums have cooled by about 0.1°C when compared to 1920. Little change in precipitation was found.

Water Resources Research↗
USGS2020

Land use effects on sediment nutrient processes in a heavily modified watershed using structural equation models

Contemporary land use can affect sediment nutrient processes in rivers draining heavily modified watersheds; however, studies linking land use to sediment nutrient processes in large river networks are limited. In this study, we developed and evaluated structural equation models (SE models) for denitrification and phosphorus retention capacity to determine direct and indirect linkages between current land use and sediment nutrient processes during baseflow in the Fox River watershed, Wisconsin USA. A large spatial-scale dataset used for this study included sediment nitrogen and phosphorus retention measurements and land use information for 106 sites. The SE models for the Fox River watershed identified direct links between current land use and in-stream sediment nutrient processes. Sub-watersheds with agricultural land consisting of more natural land cover had lower surface water nitrate concentrations and higher denitrification enzyme activity than sub-watersheds with less alternative cover. This suggests that best management practices implemented in the Fox River watershed that restore natural land cover can improve water quality through nitrogen removal on the agricultural landscape and in the river network. Best management practices are not having the same measurable affect on phosphorus in the river network, most likely due to legacy phosphorus stored in the sediment.

Wisconsin↗
USGS2013

Channel unit use by Smallmouth Bass: Do land-use constraints or quantity of habitat matter?

I examined how land use influenced the distribution of Smallmouth Bass Micropterus dolomieu in channel units (discrete morphological features—e.g., pools) of streams in the Midwestern USA. Stream segments ( n = 36), from four clusters of different soil and runoff conditions, were identified that had the highest percent of forest ( n = 12), pasture ( n = 12), and urban land use ( n = 12) within each cluster. Channel units within each stream were delineated and independently sampled once using multiple gears in summer 2006. Data were analyzed using a generalized linear mixed model procedure with a binomial distribution and odds ratio statistics. Land use and channel unit were strong predictors of age-0, age-1, and age->1 Smallmouth Bass presence. Each age-class was more likely to be present in streams within watersheds dominated by forest land use than in those with pasture or urban land uses. The interaction between land use and channel unit was not significant in any of the models, indicating channel unit use by Smallmouth Bass did not depend on watershed land use. Each of the three age-classes was more likely to use pools than other channel units. However, streams with high densities of Smallmouth Bass age >1 had lower proportions of pools suggesting a variety of channel units is important even though habitat needs exist at the channel-unit scale. Management may benefit from future research addressing the significance of channel-unit quality as a possible mechanism for how land use impacts Smallmouth Bass populations. Further, management efforts aimed at improving stream habitat would likely be more beneficial if focused at the stream segment or landscape scale, where a variety of quality habitats might be supported.

MIssouri↗