Paul J. Steen | Geology Reports

USGS2011

Predicting future changes in Muskegon River Watershed game fish distributions under future land cover alteration and climate change scenarios

Future alterations in land cover and climate are likely to cause substantial changes in the ranges of fish species. Predictive distribution models are an important tool for assessing the probability that these changes will cause increases or decreases in or the extirpation of species. Classification tree models that predict the probability of game fish presence were applied to the streams of the Muskegon River watershed, Michigan. The models were used to study three potential future scenarios: (1) land cover change only, (2) land cover change and a 3°C increase in air temperature by 2100, and (3) land cover change and a 5°C increase in air temperature by 2100. The analysis indicated that the expected change in air temperature and subsequent change in water temperatures would result in the decline of coldwater fish in the Muskegon watershed by the end of the 21st century while cool- and warmwater species would significantly increase their ranges. The greatest decline detected was a 90% reduction in the probability that brook trout Salvelinus fontinalis would occur in Bigelow Creek. The greatest increase was a 276% increase in the probability that northern pike Esox lucius would occur in the Middle Branch River. Changes in land cover are expected to cause large changes in a few fish species, such as walleye Sander vitreus and Chinook salmon Oncorhynchus tshawytscha, but not to drive major changes in species composition. Managers can alter stream environmental conditions to maximize the probability that species will reside in particular stream reaches through application of the classification tree models. Such models represent a good way to predict future changes, as they give quantitative estimates of the n-dimensional niches for particular species.

Michigan↗

USGS2009

Applications of a broad-spectrum tool for conservation and fisheries analysis: Aquatic gap analysis

Natural resources support all of our social and economic activities, as well as our biological existence. Humans have little control over most of the physical, biological, and sociological conditions dictating the status and capacity of natural resources in any particular area. However, the most rapid and threatening influences on natural resources typically are anthropogenic overuse and degradation. In addition, living natural resources (i.e., organisms) do not respect political boundaries, but are aware of their optimal habitat and environmental conditions. Most organisms have wider spatial ranges than the jurisdictional boundaries of environmental agencies that deal with them; even within those jurisdictions, information is patchy and disconnected. Planning and projecting effects of ecological management are difficult, because many organisms, habitat conditions, and interactions are involved. Conservation and responsible resource use involves wise management and manipulation of the aspects of the environment and biological communities that can be effectively changed. Tools and data sets that provide new insights and analysis capabilities can enhance the ability of resource managers to make wise decisions and plan effective, long-term management strategies. Aquatic gap analysis has been developed to provide those benefits. Gap analysis is more than just the assessment of the match or mis-match (i.e., gaps) between habitats of ecological value and areas with an appropriate level of environmental protection (e.g., refuges, parks, preserves), as the name suggests. Rather, a Gap Analysis project is a process which leads to an organized database of georeferenced information and previously available tools to examine conservation and other ecological issues; it provides a geographic analysis platform that serves as a foundation for aquatic ecological studies. This analytical tool box allows one to conduct assessments of all habitat elements within an area of interest. Aquatic gap analysis naturally focuses on aquatic habitats. The analytical tools are largely based on specification of the species-habitat relations for the system and organism group of interest (Morrison et al. 2003; McKenna et al. 2006; Steen et al. 2006; Sowa et al. 2007). The Great Lakes Regional Aquatic Gap Analysis (GLGap) project focuses primarily on lotic habitat of the U.S. Great Lakes drainage basin and associated states and has been developed to address fish and fisheries issues. These tools are unique because they allow us to address problems at a range of scales from the region to the stream segment and include the ability to predict species specific occurrence or abundance for most of the fish species in the study area. The results and types of questions that can be addressed provide better global understanding of the ecological context within which specific natural resources fit (e.g., neighboring environments and resources, and large and small scale processes). The geographic analysis platform consists of broad and flexible geospatial tools (and associated data) with many potential applications. The objectives of this article are to provide a brief overview of GLGap methods and analysis tools, and demonstrate conservation and planning applications of those data and tools. Although there are many potential applications, we will highlight just three: (1) support for the Eastern Brook Trout Joint Venture (EBTJV), (2) Aquatic Life classification in Wisconsin, and (3) an educational tool that makes use of Google Earth (use of trade or product names does not imply endorsement by the U.S. Government) and Internet accessibility.

Gap Analysis Bulletin↗

USGS2006

Modeling brook trout presence and absence from landscape variables using four different analytical methods

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Book chapter↗