Condicion futura del paisaje Andes Amazonas (2020),NatureServe

Index

Metadata file identifier
  • 7db96744-2cdb-11e4-917e-bc5ff4764ffa

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    Language
  • UNKNOWN

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    Contact
    Individual name
  • Jon Hak

  • Organisation name
  • NatureServe

  • Position name
  • Spatial Analyst

  • Contact information
    Address
    Delivery point
  • 4600 N. Fairfax Dr., 7th Floor

  • Administrative area
  • VA

  • Postal code
  • 22203

  • Role
  • publisher

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    Metadata date stamp
  • 2014-05-18

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    Metadata standard name
  • ISO 19115 Geographic Information - Metadata

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    Metadata standard version
  • http://metadata.dgiwg.org/ISO19115/ISO19115_v0_7.htm

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    Spatial representation information
    Number of dimensions
  • 0

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    Identification Information
    Citation
    Title
  • Condicion futura del paisaje Andes Amazonas (2020),NatureServe

  • Date
    Date
  • 2014-05-18

  • Date Type
  • publication

  • Cited responsible party
    Organisation name
  • Jon Hak, NatureServe, Spatial Analyst

  • Role
  • originator

  • Abstract
  • Anthropogenic stressors come in many forms, from regional patterns of acid deposition or climate-induced ecosystem change, to local-scale patterns in agricultural drainage ditches and tiles, point-source pollution, land-conversion, and transportation corridors, among others. To be effective, a landscape condition model needs to incorporate multiple stressors, their varying individual intensities, the combined and cumulative effect of those stressors, and if possible, some measure of distance away from each stressor where negative effects remain likely. Since our knowledge of natural ecosystems is varied and often limited, a primary challenge is to identify those stressors that likely have the most degrading effects on ecosystems or species of interest. A second challenge is to acquire mapped information that realistically portrays those stressors. In addition, there are tradeoffs in costs, complexity, the often varying spatial resolutions in available maps, and the variable ways stressors operate across diverse land and waterscapes. Typically, expert knowledge forms the basis of stressor selection, and relative weighting. Once models are developed, they may be calibrated with field measurements. Developing empirical relationships between stress variables and ecological response variables is a key to providing insights into how human activities impact ecological condition (Danz et al. 2007).The Landscape Condition Model tool (Comer & Hak 2009) was used to link the various land use features to their expected effect on the landscape condition. The model output is an index of the relative effect of overlapping land uses and infrastructure that describes the resulting landscape condition in values from 0 (poor) to 1 (very good) as a spatial grid. The model allows the user to individually set weighting values for both direct impacts (“site intensity”) and indirect impacts (“reach distance”) across all land use types and infrastructure inputs.Current (2012) and a future (2020) landscape condition models were run for the project area at 250 meter resolution, with the current model incorporating data on the presence of existing roads, agricultural lands, cattle grazing areas, current population densities, and active mining concessions. The future model is based on the same, but with the addition of planned roads and planned mines. We computed the projected change in landscape condition by subtracting the projected future condition value from the value for current condition, with higher values thus representing places most directly threatened by planned roads and mines. We then calculated the mean change in condition for each watershed to characterize level of threat.

  • Purpose
  • The Andes Amazon current (2012) and future (2020) Landscape Condition Models were created to characterize condition and level of threat for biodiversity in the Andes Amazon region as a component of NatureServe's assessment of watersheds in the Andes and Amazon for effective conservation investment There are two primary uses for NatureServes landscape condition model: 1) to map the predicted ecological conditions one would encounter in the field, based on apparent stressors present across the landscape of interest, and 2) facilitate repeated predictions of ecological condition within the same landscape over time, or given alternative land use proposals. Maps predicting relative ecological condition can provide a screening tool for gauging anthropogenic stress in locations including any mapped point or polygon. Repeated predictions of ecological condition assist with evaluating likely effects of changes in overlapping land uses on the condition of the landscape for an element or group of elements. This can provide a powerful tool understanding cumulative effects of land use change over time and/or for modeling environmental restoration options. The landscape condition model is integrated into NatureServes Vista software (NatureServe 2009).

  • Credit
  • NatureServe 2013

  • Point of contact
    Role
  • Point of contact

  • Resource maintenance
    Maintenance and update frequency
  • unknown

  • Descriptive keywords
    Keyword
  • Threat

  • Type
  • theme

  • Thesaurus name
    Title
  • None

  • Date
    Date
  • 0001-01-01

  • Date Type
  • publication

  • Descriptive keywords
    Keyword
  • Landscape Condition

  • Type
  • theme

  • Thesaurus name
    Title
  • None

  • Date
    Date
  • 0001-01-01

  • Date Type
  • publication

  • Spatial representation type
  • grid

  • Language
  • eng

  • Environment description
  • Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; Esri ArcGIS 10.1.1.3143

  • Extent
    Geographic element
    West bound longitude
  • -79.883731

  • East bound longitude
  • -59.622230

  • South bound latitude
  • -20.713528

  • North bound latitude
  • 3.203049

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    Application schema information
    Schema ASCII
  • <?xml version = '1.0' encoding = 'ISO-8859-1'?> <eainfo> </eainfo>

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