Watershed Condition Classification Technical Guide

Watershed Condition Classification Technical Guide
Author: U.s. Department of Agriculture Forest Service
Publisher: Createspace Independent Pub
Total Pages: 48
Release: 2012-08-15
Genre: Nature
ISBN: 9781479315130

The U.S. Department of Agriculture (USDA) Strategic Plan for fiscal year (FY) 2010–2015 targets the restoration of watershed and forest health as a core management objective of the national forests and grasslands. To achieve this goal, the Forest Service, an agency of USDA, is directed to restore degraded watersheds by strategically focusing investments in watershed improvement projects and conservation practices at landscape and watershed scales. The Forest Service formed the National Watershed Condition Team and tasked it with developing a nationally consistent, science-based approach to classify the condition of all National Forest System (NFS) watersheds and to develop outcome-based performance measures for watershed restoration. The team evaluated alternative approaches for classifying watersheds (USDA Forest Service 2007) and developed the watershed condition classification (WCC) system described in this technical guide. The team designed the WCC system to—Classify the condition of all NFS watersheds; Be quantitative to the extent feasible; Rely on Geographic Information System (GIS) technology; Be cost effective; Be implementable within existing budgets; Include resource areas and activities that have a significant influence on watershed condition. National forests are required to revise the classification on an annual basis. The WCC system is a national forest-based, reconnaissance level evaluation of watershed condition achievable within existing budgets and staffing levels that can be aggregated for a national assessment of watershed condition. The WCC system offers a systematic, flexible means of classifying watersheds based on a core set of national watershed condition indicators. The system relies on professional judgment exercised by forest interdisciplinary (ID) teams, GIS data, and national databases to the extent they are available, and on written rule sets and criteria for indicators that describe the three watershed condition classes (functioning properly, functioning at risk, and impaired function). The WCC system relies on Washington Office and regional office oversight for flexible and consistent application among national forests. The WCC system is a first approximation of watershed condition, and we will revise and refine it over time. The expectation is that we will improve and refine individual resource indicators and that we will develop databases and map products to assist with future classifications. The WCC information will be incorporated into the watershed condition framework, which will ultimately be employed to establish priorities, evaluate program performance, and communicate watershed restoration successes to interested stakeholders and Congress. The watershed condition goal of the Forest Service is “to protect National Forest System watersheds by implementing practices designed to maintain or improve watershed condition, which is the foundation for sustaining ecosystems and the production of renewable natural resources, values, and benefits” (FSM 2520). U.S. Secretary of Agriculture Tom Vilsack reemphasized this policy in his “Vision for the Forest Service” when he stated that achieving restoration of watershed and forest health would be the primary management objective of the Forest Service (USDA 2010). This Watershed Condition Classification Technical Guide helps to implement this policy objective by—1. Establishing a systematic process for determining watershed condition class that all national forests can apply consistently; 2. Improving Forest Service reporting and tracking of watershed condition; 3. Strengthening the effectiveness of the Forest Service to maintain and restore the productivity and resilience of watersheds and their associated aquatic systems on NFS lands.

Alaska Subsistence

Alaska Subsistence
Author: Frank Blaine Norris
Publisher:
Total Pages: 326
Release: 2002
Genre: Alaska
ISBN:

"This study is a chronicle of how subsistence management in Alaska has grown and evolved"--P. viii.

Machine Learning for Ecology and Sustainable Natural Resource Management

Machine Learning for Ecology and Sustainable Natural Resource Management
Author: Grant Humphries
Publisher: Springer
Total Pages: 442
Release: 2018-11-05
Genre: Science
ISBN: 3319969781

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Fishes of the Salish Sea

Fishes of the Salish Sea
Author: Theodore W. Pietsch
Publisher:
Total Pages: 0
Release: 2019
Genre: Nature
ISBN: 9780295743745

Fishes of the Salish Sea is the definitive guide to the identification and history of the marine and anadromous fishes of Puget Sound and the Straits of Georgia and Juan de Fuca. This comprehensive three-volume set, featuring striking illustrations of the Salish Sea's 260 fish species by noted illustrator Joseph Tomelleri, details the ecology and life history of each species and recounts the region's rich heritage of marine research and exploration. Beginning with jawless hagfishes and lampreys and ending with the distinctive Ocean Sunfish, leading scientists Theodore Wells Pietsch and James Orr present the taxa in phylogenetic order, based on classifications that reflect the most current scientific knowledge. Illustrated taxonomic keys facilitate fast and accurate species identification. These in-depth, thoroughly documented, and yet accessible volumes will prove invaluable to marine biologists and ecologists, natural resource managers, anglers, divers, students, and all who want to learn about, marvel over, and preserve the vibrant diversity of Salish Sea marine life. Comprehensive accounts of 260 fish species Brilliant color plates of all treated species Illustrated taxonomic keys for easy species identification In-depth history of Salish Sea research and exploration