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Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
Manipulation and Analysis01:21

Manipulation and Analysis

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...
Levels of Use of a GIS01:29

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
Distribution and Dispersion00:54

Distribution and Dispersion

Ecology is the study of how organisms interact with their environment and with one another. An important aspect of ecology is understanding where species are found and how individuals are distributed within those areas. The geographic range of a species refers to the total area where its members are located, while dispersion describes the pattern of spacing of individuals within that range.Geographic Range and Dispersion PatternsWithin a species’ geographic range, individuals may be distributed...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Related Experiment Video

Updated: Jun 10, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Spatial modelling and landscape-level approaches for visualizing intra-specific variation.

Henri A Thomassen1, Zachary A Cheviron, Adam H Freedman

  • 1Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, Los Angeles, CA 90095, USA. hathomassen@ucla.edu

Molecular Ecology
|August 21, 2010
PubMed
Summary
This summary is machine-generated.

Spatial analytical methods are accelerating in biology, integrating environmental data for predictive mapping. This review covers methods for spatial prediction of biological variation and future opportunities.

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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Last Updated: Jun 10, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

  • Ecology
  • Biogeography
  • Geographic Information Systems (GIS)

Background:

  • Spatial analytical methods have long been used in biology.
  • Recent advances in modeling and data availability, particularly satellite remote sensing, are accelerating their application.
  • Current approaches move beyond purely spatial analysis to explore environmental drivers of biological variation.

Purpose of the Study:

  • To review methods for making continuous spatial predictions of biological variation.
  • To provide examples of these methods in use.
  • To critically evaluate the advantages and limitations of spatial prediction techniques.

Main Methods:

  • Utilizing spatial and environmental predictor variables for biological variation analysis.
  • Employing advanced modeling approaches for spatial prediction.
  • Integrating satellite remote sensing data for environmental variable assessment.

Main Results:

  • Spatial methods can now infer relationships and causes of biological heterogeneity.
  • Predictive maps of biological variation under changing environmental conditions can be generated.
  • The review critically evaluates the strengths and weaknesses of various spatial prediction techniques.

Conclusions:

  • Spatial analytical methods offer powerful tools for understanding and predicting biological patterns.
  • Integrating diverse environmental data, especially from remote sensing, enhances predictive capabilities.
  • Future work should address key challenges and capitalize on emerging opportunities in spatial ecology.