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At least 451 records · Page 25Linked to original sources
USGS2015

The 3D Elevation Program: summary for West Virginia

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of West Virginia, elevation data are critical for natural resources conservation, flood risk management, forest resources management, infrastructure and construction management, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

West Virginia↗
USGS2001

A field and statistical modeling study to estimate irrigation water use at Benchmark Farms study sites in southwestern Georgia, 1995-96

A benchmark irrigation monitoring network of farms located in a 32-county area in southwestern Georgia was established in 1995 to improve estimates of irrigation water use. A stratified random sample of 500 permitted irrigators was selected from a data base--maintained by the Georgia Department of Natural Resources, Georgia Environmental Protection Division, Water Resources Management Branch--to obtain 180 voluntary participants in the study area. Site-specific irrigation data were collected at each farm using running-time totalizers and noninvasive flowmeters. Data were collected and compiled for 50 farms for 1995 and 130 additional farms for the 1996 growing season--a total of 180 farms. Irrigation data collected during the 1996 growing season were compiled for 180 benchmark farms and used to develop a statistical model to estimate irrigation water use in 32 counties in southwestern Georgia. The estimates derived were developed from using a statistical approach know as "bootstrap analysis" that allows for the estimation of precision. Five model components--whether-to-irrigate, acres irrigated, crop selected, seasonal-irrigation scheduling, and the amount of irrigation applied--compose the irrigation model and were developed to reflect patterns in the data collected at Benchmark Farms Study area sites. The model estimated that peak irrigation for all counties in the study area occurred during July with significant irrigation also occurring during May, June, and August. Irwin and Tift were the most irrigated and Schley and Houston were the least irrigated counties in the study area. High irrigation intensity primarily was located along the eastern border of the study area; whereas, low irrigation intensity was located in the southwestern quadrant where ground water was the dominant irrigation source. Crop-level estimates showed sizable variations across crops and considerable uncertainty for all crops other than peanuts and pecans. Counties having the most irrigated acres showed higher variations in annual irrigation than counties having the least irrigated acres. The Benchmark Farms Study model estimates were higher than previous irrigation estimates, with 20 percent of the bias a result of underestimating irrigation acreage in earlier studies. Model estimates showed evidence of an upward bias of about 15 percent with the likely cause being a misrepresented inches-applied model. A better understanding of the causes of bias in the model could be determined with a larger irrigation sample size and increased substantially by automating the reporting of monthly totalizer amounts.

Georgia↗
USGS2015

Modeling elk and bison carrying capacity for Great Sand Dunes National Park, Baca National Wildlife Refuge, and The Nature Conservancy's Medano Ranch, Colorado

Great Sand Dunes National Park and Preserve and the neighboring Baca National Wildlife Refuge constitute an extraordinary setting that offers a variety of opportunities for outdoor recreation and natural resource preservation in the San Luis Valley of Colorado. Adjacent to these federal lands, the Nature Conservancy (TNC) manages the historic Medano Ranch. The total land area of these three conservation properties is roughly 121,500 hectares (ha). It is a remote and rugged area in which resource managers must balance the protection of natural resources with recreation and neighboring land uses. The management of wild ungulates in this setting presents challenges, as wild ungulates move freely across public and private landscapes. The San Luis Valley was historically used for irrigated agriculture and ranching. Historically, livestock, including sheep ( Ovis aries ) and cattle ( Bos taurus ), were grazed throughout the valley. The former Luis Marie “Baca” Ranch, which makes up the northern part of Great Sand Dunes National Park (hereafter “Park”) and all of the Baca National Wildlife Refuge (hereafter “Refuge”), was actively grazed by cattle until 2004. Bison ( Bison bison ), elk ( Cervus elaphus ), mule deer ( Odocoileus hemionus ), and pronghorn ( Antilocapra americana ) were native to the area until about the 1840s, when bison, elk, and pronghorn were extirpated. Elk and pronghorn likely moved back into the area from surrounding populations to the north and south, and mule deer populations have varied through time. A population of 4,400 elk currently inhabits the area. The current bison population was established in 1986 for meat production. In 1999 TNC purchased the ranch and established a bison conservation herd, and eventually subcontracted management to a private rancher in 2005. A population of bison ranging in size from 1,200–2,000 ranges freely within the 16,100 ha Medano Ranch. Ungulate populations in the valley are regulated by hunting, with the exception of bison, which are rounded up and culled annually to maintain population levels. In an effort to create and form the basis of a multi-agency ungulate management plan for the region, the Park sought the development of an elk and bison ecological carrying capacity model to provide guidance to resource managers.

Colorado↗
USGS2014

The 3D Elevation Program: summary for New Mexico

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of New Mexico, elevation data are critical for infrastructure and construction management, natural resources conservation, flood risk management, agriculture and precision farming, geologic resource assessment and hazard mitigation, forest resources management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 (table 1) for the conterminous United States and quality level 5 ifsar data (table 1) for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

New Mexico↗
USGS2012

Will hunters steward wolves? A comment on Treves and Martin

As wolf conservation transitions away from federally sponsored protection and recovery toward sustainable management under state fish and game agencies, researchers and policymakers are interested to know what role hunters will play. Based upon hunters' responses to three recent surveys in Wisconsin and the northern Rockies, Treves and Martin question the assumption that hunters will steward wolves, noting that the majority of hunters that responded were unsupportive of wolf conservation. However, this conclusion largely depends upon what is meant by stewardship and what actions are required for wolves to be conserved. This article discusses the meaning of three concepts either explicitly or implicitly discussed by Treves and Martin—tolerance, acceptance, and stewardship—and offers a conceptual model of wildlife conservation behavior that clarifies the relationship among these concepts.

Society & Natural Resources: An International Jour↗
USGS2017

Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem

We examined data on white pine blister rust (blister rust) collected during the monitoring of whitebark pine trees in the Greater Yellowstone Ecosystem (from 2004-2015). Summaries of repeat observations performed by multiple independent observers are reviewed and discussed. These summaries show variability among observers and the potential for errors being made in blister rust status. Based on this assessment, we utilized occupancy models to analyze blister rust prevalence while explicitly accounting for imperfect detection. Available covariates were used to model both the probability of a tree being infected with blister rust and the probability of an observer detecting the infection. The fitted model provided strong evidence that the probability of blister rust infection increases as tree diameter increases and decreases as site elevation increases. Most importantly, we found evidence of heterogeneity in detection probabilities related to tree size and average slope of a transect. These results suggested that detecting the presence of blister rust was more difficult in larger trees. Also, there was evidence that blister rust was easier to detect on transects located on steeper slopes. Our model accounted for potential impacts of observer experience on blister rust detection probabilities and also showed moderate variability among the different observers in their ability to detect blister rust. Based on these model results, we suggest that multiple observer sampling continue in future field seasons in order to allow blister rust prevalence estimates to be corrected for imperfect detection. We suggest that the multiple observer effort be spread out across many transects (instead of concentrated at a few each field season) while retaining the overall proportion of trees with multiple observers around 5-20%. Estimates of prevalence are confounded with detection unless it is explicitly accounted for in an analysis and we demonstrate how an occupancy model can be used to do account for this source of observation error.

idaho, Montana, Utah, Wyoming↗
USGS2014

The 3D Elevation Program: summary for Michigan

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Michigan, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, water supply and quality, infrastructure and construction management, coastal zone management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features. The Michigan Statewide Authoritative Imagery and Lidar (MiSAIL) program provides statewide lidar coordination with local, State, and national groups in support of 3DEP for Michigan.

Michigan↗
USGS2000

Design, revision, and application of ground-water flow models for simulation of selected water-management scenarios in the coastal area of Georgia and adjacent parts of South Carolina and Florida

Ground-water flow models of the Floridan aquifer system in the coastal area of Georgia and adjacent parts of South Carolina and Florida, were revised and updated to ensure consistency among the various models used, and to facilitate evaluation of the effects of pumping on the ground-water level near areas of saltwater contamination. The revised models, developed as part of regional and areal assessments of ground-water resources in coastal Georgia, are--the Regional Aquifer-System Analysis (RASA) model, the Glynn County area (Glynn) model, and the Savannah area (Savannah) model. Changes were made to hydraulic-property arrays of the RASA and Glynn models to ensure consistency among all of the models; results of theses changes are evidenced in revised water budgets and calibration statistics. Following revision, the three models were used to simulate 32 scenarios of hypothetical changes in pumpage that ranged from about 82 million gallons per day (Mgal/d) lower to about 438 Mgal/d higher, than the May 1985 pumping rate of 308 Mgal/d. The scenarios were developed by the Georgia Department of Natural Resources, Environmental Protection Division and the Chatham County-Savannah Metropolitan Planning Commission to evaluate water-management alternatives in coastal Georgia. Maps showing simulated ground-water-level decline and diagrams presenting changes in simulated flow rates are presented for each scenario. Scenarios were grouped on the basis of pumping location--entire 24-county area, central subarea, Glynn-Wayne-Camden County subarea, and Savannah-Hilton Head Island subarea. For those scenarios that simulated decreased pumpage, the water level at both Brunswick and Hilton Head Island rose, decreasing the hydraulic gradient and reducing the potential for saltwater contamination. Conversely, in response to scenarios of increased pumpage, the water level at both locations declined, increasing the hydraulic gradient and increasing the potential for saltwater contamination. Pumpage effects on ground-water levels and related saltwater contamination at Brunswick and Hilton Head Island generally diminish with increased distance from these areas. Additional development of the Upper Floridan aquifer may be possible in parts of the coastal area without affecting saltwater contamination at Brunswick or Hilton Head Island, due to the presence of two hydrologic boundaries--the Gulf Trough, separating the northern and central subareas; and the hypothesized Satilla Line, separating the central and southern subareas. These boundaries diminish pumpage effects across them; and may enable greater ground-water withdrawal in areas north of the Gulf Trough and south of the Satilla Line without producing appreciable drawdown at Brunswick or Hilton Head Island.

Florida, Georgia, South Carolina↗
USGS2009

Effects of Water-Management Strategies on Water Resources in the Pawcatuck River Basin, Southwestern Rhode Island and Southeastern Connecticut

The Pawcatuck River Basin in southwestern Rhode Island and southeastern Connecticut is an important high-quality water resource for domestic and public supplies, irrigation, recreation, and the aquatic ecosystem. Concerns about the effects of water withdrawals on aquatic habitat in the basin have prompted local, State, and Federal agencies to explore water-management strategies that minimize the effects of withdrawals on the aquatic habitat. As part of this process, the U.S. Geological Survey in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service and the Rhode Island Water Resources Board completed a study to assess the effects of current (2000-04) and potential water withdrawals on streamflows and groundwater levels using hydrologic simulation models developed for the basin. The major findings of the model simulations are: *Moving highly variable seasonal irrigation withdrawals from streams to groundwater wells away from streams reduces short-term fluctuations in streamflow and increases streamflow in the summer when flows are lowest. This occurs because of the inherent time lag between when water is withdrawn from the aquifer and when it affects streamflow. *A pumped well in the vicinity of small streams indicates that if withdrawals exceed available streamflow, groundwater levels drop substantially as a consequence of water lost from aquifer storage, which may reduce the time wetlands and vernal pools are saturated, affecting the animal and plant life that depend on these habitats. *The effects of pumping on water resources such as ponds, streams, and wetlands can be minimized by relocating pumping wells, implementing seasonal pumping schemes that utilize different wells and pumping rates, or both. *The effects of projected land-use change, mostly from forest to low- and medium density housing, indicate only minor changes in streamflow at the subbasin scale examined; however, at a local scale, high flows could increase, and low flows could decrease as a result of increased impervious area. In some instances, low flows could increase slightly as a result of decreased evapotranspiration from the loss of deeprooted vegetation (forest) associated with development. *In some subbasins where large areas of agricultural lands were converted to low- and medium-density housing, low flows increase because the consumptive domestic water use was projected to be less than consumptive agricultural water use. All agricultural water use was for irrigation purposes and was assumed to be lost from the basin through evapotranspiration.

Circular↗
USGS2023

The 3D Elevation Program—Supporting Oregon's economy

Introduction Oregon’s physical environments and vegetation are diverse. The varied geologic and climatic conditions combined with increasing population have created the need for high-quality elevation data that can be used for infrastructure management, forestry and wildfire management, agriculture, natural resources conservation, and other business uses. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Oregon↗
USGS2023

The value of scientific information — An overview

Introduction The U.S. Geological Survey (USGS) provides reliable science, data, information, and models (hereafter collectively referred to as “information”) to describe and understand the Earth. This information is used to minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect quality of life. USGS science informs public and private decisions, operations, and risk management in all major United States economic sectors, as defined by the Bureau of Economic Analysis, and provides critical information for natural resource and natural hazard management and stewardship decisions. Understanding the value of scientific information supports applications of USGS science in land- and water-management decisions, and better informs the public about the return on investment of USGS programs. USGS economists, social scientists, and physical scientists are engaged in collaborative efforts to advance methods to estimate the value of information (VOI) produced by the USGS. These efforts involve collaborating with an international community to develop and refine estimation methods, establish best practices to determine VOI, develop a study repository, and conduct projects to assess the VOI of specific information products and their application. This report focuses on economic valuation conducted by USGS specifically, although the methodology has much broader applicability within the U.S. government, academia, and beyond. Noneconomic valuation techniques for assessing the VOI also exist but are not addressed in this report.

Open-File Report↗
USGS2020

Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.

Nevada↗
USGS2014

The 3D Elevation Program: summary for Kentucky

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the Commonwealth of Kentucky, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, forest resources management, geologic resource assessment and hazards mitigation, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. “Kentucky from Above,” the Kentucky Aerial Photography and Elevation Data Program ( http://kygeonet.ky.gov/kyfromabove/ ), provides statewide lidar coordination with local, Commonwealth, and national groups in support of 3DEP for the Commonwealth. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Kentucky↗
USGS2016

Foraging at the wildland–urban interface decouples weather as a driver of recruitment for desert bighorn sheep

A growing number of ungulate populations are living within or near the wildland–urban interface. When resources at the interface are of greater quality than that of adjacent natural habitat, wildlife can be attracted to these developed areas. Little is known about how use of the wildland–urban interface by wildlife may affect vital rates. Under natural conditions, recruitment by desert bighorn sheep ( Ovis canadensis nelsoni ) correlates with variation in the timing and amount of rainfall that initiates and enhances growth of annual plant species. However, for populations that forage in developed areas, this relationship may become decoupled. In the River Mountains of Nevada, USA, desert bighorn sheep have been feeding in a municipal park at the wildland–urban interface since its establishment in 1985. Approximately one-third of the population now uses the park during summer months when nutritional content of natural forage is low. We hypothesized that use of this municipal area, with its abundant vegetation and water resources, may have decoupled the previous relationship between precipitation and lamb recruitment. We assessed variables known to affect lamb recruitment before (1971–1986) and after (1987–2006) establishment of the park using linear regression models. Our top candidate model for the pre-park period indicated that total November precipitation was the greatest driver of lamb recruitment in this population. After park establishment, this relationship became decoupled because lamb recruitment was no longer driven by weather variables. These results raise questions about the effects of decoupling drivers of population growth and maintaining natural populations near urban areas.

Nevada↗
USGS2014

The 3D Elevation Program: summary for New York

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of New York, elevation data are critical for coastal zone management, natural resources conservation, agriculture and precision farming, flood risk management, infrastructure and construction management, water supply and quality, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

New York↗
USGS2019

Natural resource management decision-making under climate uncertainty: Building social-ecological resilience in southwestern Colorado

The goal of this project was to facilitate climate change adaptation that contributes to social-ecological resilience, ecosystem and species conservation, and sustainable human communities in southwestern Colorado. The team developed and piloted integrated adaptation planning tools and principles that merge the strengths of the iterative scenario process, the Adaptation for Conservation Targets (ACT) planning framework, institutional analysis, and climate modeling. These tools and principles generated practical strategies and scientific knowledge to advance climate change adaptation in both the Gunnison River Basin and San Juan River Basin and, potentially, other landscapes. A key objective of this project was to work with decision-makers to develop social-ecological adaptation strategies and coordinate actions to reduce the impacts of a changing climate on nature and people. To accomplish this, the project blended science (biophysical and social) and participatory approaches to integrate expert knowledge, land management decision making, and local needs. These adaptation strategies and their creation process were documented by the project team to assist communities and scientists elsewhere who are grappling with the challenges posed by climate change.

Colorado↗
USGS2013

The 3D Elevation Program: summary for Texas

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Texas, elevation data are critical for natural resources conservation; wildfire management, planning, and response; flood risk management; agriculture and precision farming; infrastructure and construction management; water supply and quality; and other business uses. Today, high-quality light detection and ranging (lidar) data are the source for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Texas↗
USGS2008

Understanding and predicting ecological dynamics: Are major surprises inevitable

Ecological surprises, substantial and unanticipated changes in the abundance of one or more species that result from previously unsuspected processes, are a common outcome of both experiments and observations in community and population ecology. Here, we give examples of such surprises along with the results of a survey of well-established field ecologists, most of whom have encountered one or more surprises over the course of their careers. Truly surprising results are common enough to require their consideration in any reasonable effort to characterize nature and manage natural resources. We classify surprises as dynamic-, pattern-, or intervention-based, and we speculate on the common processes that cause ecological systems to so often surprise us. A long-standing and still growing concern in the ecological literature is how best to make predictions of future population and community dynamics. Although most work on this subject involves statistical aspects of data analysis and modeling, the frequency and nature of ecological surprises imply that uncertainty cannot be easily tamed through improved analytical procedures, and that prudent management of both exploited and conserved communities will require precautionary and adaptive management approaches.

Ecology↗