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Climate Change Vulnerability Assessment for the Chugach National Forest and the Kenai Peninsula

Author : Gregory Dale Hayward
Publisher :
Page : 344 pages
File Size : 15,76 MB
Release : 2017
Category : Climatic changes
ISBN :

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This assessment evaluates the effects of future climate change on a select set of ecological systems and ecosystem services in Alaska’s Kenai Peninsula and Chugach National Forest regions. The focus of the assessment was established during a multi-agency/organization workshop that established the goal to conduct a rigorous evaluation of a limited range of topics rather than produce a broad overview. The report explores the potential consequences of climate change for: (a) snowpack, glaciers, and winter recreation; (b) coastal landscapes and associated environments, (c) vegetation, (d) salmon, and (e) a select set of wildlife species. During the next half century, directional change associated with warming temperatures and increased precipitation will result in dramatic reductions in snow cover at low elevations, continued retreat of glaciers, substantial changes in the hydrologic regime for an estimated 8.5 percent of watersheds, and potentially an increase in the abundance of pink salmon. In contrast to some portions of the Earth, apparent sealevel rise is likely to be low for much of the assessment region owing to interactions between tectonic processes and sea conditions. Shrubs and forests are projected to continue moving to higher elevations, reducing the extent of alpine tundra and potentially further affecting snow levels. Opportunities for alternative forms of outdoor recreation and subsistence activities that include sled-dog mushing, hiking, hunting, and travel using across-snow vehicles will change as snowpack levels, frozen soils, and vegetation change over time. There was a projected 66-percent increase in the estimated value of human structures (e.g. homes, businesses) that are at risk to fire in the next half century on the Kenai Peninsula, and a potential expansion of invasive plants, particularly along roads, trails, and waterways.

Proposed Chugach National Forest Additions, Alaska

Author : United States. Department of the Interior. Alaska Planning Group
Publisher :
Page : 448 pages
File Size : 18,76 MB
Release : 1974
Category : Chugach National Forest (Alaska)
ISBN :

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Climate Change

Author : J. B. Haufler
Publisher : DIANE Publishing
Page : 57 pages
File Size : 48,35 MB
Release : 2010-10
Category : Science
ISBN : 1437933742

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Summarizes potential impacts that are likely from predicted climate change (CC) in Southern Alaska (SA), identifies on-going collaborative efforts directed at climate change, and suggests some possible responses that the Alaska Region (AR) could take to address this challenge. Contents: (1) Intro.; (2) Overview of the AR; (3) Ecosystem Services of the SC and SE Landscapes; (4) CC Threats to Ecosystem Services in Southern Coastal Alaska: Observed Changes in Alaska¿s Climate; Predicted CC in Alaska Climate; (5) Impacts of CC on Ecosystem Services: Changing Sea Levels; Increased Ocean Temp. and Changing Circulation Patterns; Increased Ocean Acidification; Increased Storm Intensities; Changes to Stream Temp. and Flows; Loss of Glaciers; Changes to Wetlands; Forest Temp. and Precipitation Changes; Increases in Invasive Species; (6) Initiatives for CC in Southern Alaska Coastal Landscapes; (7) Strategic Plan for CC. Figures.

A Climate Change Vulnerability Assessment for Aquatic Resources in the Tongass National Forest

Author :
Publisher :
Page : 123 pages
File Size : 30,70 MB
Release : 2014
Category : Aquatic resources
ISBN :

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"This vulnerability assessment is an initial science-based effort to identify how and why important resources (snow, ice, and water features; riparian vegetation; fish species) across the Tongass National Forest are likely to be affected by both non-climate stressors and future climate conditions."--Page 9.

Forest Dynamics and Conservation

Author : Manoj Kumar
Publisher : Springer Nature
Page : 490 pages
File Size : 22,51 MB
Release : 2022-05-16
Category : Technology & Engineering
ISBN : 981190071X

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This book unveils forestry science and its policy and management that connect past and present understanding of forests. The aggregated knowledge is presented to cover the approaches adopted in studying forest structure, its growth, functioning, and degradation, especially in the context of the surrounding environment. The application of advance computation, instrumentation, and modelling has been elaborated in various chapters. Forest ecosystems are rapidly changing due to forest fires, deforestation, urbanization, climate change, and other natural and anthropogenic drivers. Understanding the dynamics of forest ecosystems requires contemporary methods and measures, utilizing modern tools and big data for developing effective conservation plans. The book also covers discussion on policies for sustainable forestry, agroforestry, environmental governance, socio-ecology, nature-based solutions, and management implication. It is suitable for a wide range of readers working in the field of scientific forestry, policy making, and forest management. In addition, it is a useful material for postgraduate and research students of forestry sciences.

Machine Learning for Ecology and Sustainable Natural Resource Management

Author : Grant Humphries
Publisher : Springer
Page : 442 pages
File Size : 47,30 MB
Release : 2018-11-05
Category : Science
ISBN : 3319969781

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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.