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Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Distribution and Dispersion

To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
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Updated: May 28, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
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Predicting species distributions from samples collected along roadsides.

Kyle P McCarthy1, Robert J Fletcher, Christopher T Rota

  • 1Department of Wildlife Ecology and Conservation, University of Florida, P.O. Box 110430, Gainesville, FL 32611-0430, USA.

Conservation Biology : the Journal of the Society for Conservation Biology
|October 21, 2011
PubMed
Summary
This summary is machine-generated.

Roadside sampling for species distribution models is accurate, even when validated off-road. This study found roadside data provides unbiased information for predicting species distributions, benefiting ecological research.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Wildlife Management

Background:

  • Species distribution models (SDMs) often rely on roadside data, raising concerns about potential sampling bias.
  • The impact of roadside sampling on the predictive accuracy of SDMs remains largely unquantified.
  • Understanding potential biases is crucial for reliable ecological modeling and conservation planning.

Purpose of the Study:

  • To assess whether roadside sampling affects the accuracy of species distribution models.
  • To compare the predictive performance of SDMs built with roadside data against validations using both roadside and off-road data.
  • To investigate if species' ecological traits (generalist, edge, or interior) influence model accuracy based on sampling location.

Main Methods:

  • Developed SDMs for 15 bird species using point-count data collected along unpaved roads in Montana and Idaho.
  • Validated model predictions using independent data from paired roadside and off-road (200 m away) locations.
  • Employed hierarchical occupancy models to account for imperfect detection probabilities.

Main Results:

  • Model performance metrics (kappa, AUC, TSS) did not significantly differ between roadside and off-road predictions.
  • Accuracy measures were consistent across species categorized as generalists, edge-associated, or interior forest specialists.
  • Despite vegetation differences and some species preferring roadsides, roadside data proved reliable for model building.

Conclusions:

  • Roadside sampling, when capturing a representative range of environmental gradients, can yield unbiased data for SDMs.
  • Surveys along unpaved roads are a valuable and efficient data source for predicting species distributions.
  • Findings support the use of roadside surveys as a cost-effective method in ecological research and conservation.