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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
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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...
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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...
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Related Experiment Video

Updated: May 15, 2025

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

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Indigenous Knowledge as a sole data source in habitat selection functions.

Rowenna Gryba1,2,3, Andrew Von Duyke4, Henry P Huntington5

  • 1Department of Statistics, University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada.

Proceedings of the National Academy of Sciences of the United States of America
|April 8, 2025
PubMed
Summary
This summary is machine-generated.

Indigenous Knowledge (IK) statistically characterizes species-habitat relationships, informing conservation and management. This approach, including dynamic habitats, identifies critical areas for species like ringed seals, supporting Indigenous communities.

Keywords:
ArcticIndigenous Knowledgehabitat modelingringed sealsspecies conservation

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Area of Science:

  • Conservation Biology
  • Wildlife Management
  • Ethnoecology

Background:

  • Western species-habitat models often exclude Indigenous Knowledge (IK), potentially impacting Indigenous communities and species conservation.
  • Exclusion of IK in management can lead to limited access to culturally and subsistencely important species.
  • There is a need to integrate IK into scientific frameworks for effective species management.

Purpose of the Study:

  • To develop statistical methods for characterizing Indigenous Knowledge (IK) of species-habitat relationships.
  • To create species-habitat models that solely utilize IK to identify important habitats and species use areas.
  • To demonstrate the application of these methods for dynamic habitat types and species management.

Main Methods:

  • Developed statistical techniques to characterize Indigenous Knowledge (IK) regarding species-habitat associations.
  • Created species-habitat models based exclusively on documented IK, including dynamic habitat variables like ice concentration.
  • Applied the methodology to ringed seals in Alaskan waters, incorporating Iñupiat hunters' knowledge.

Main Results:

  • Successfully developed and applied methods to statistically model species-habitat relationships using Indigenous Knowledge (IK).
  • Identified important habitats and areas of species use for ringed seals based solely on IK.
  • Demonstrated that IK, especially with dynamic habitat considerations, enhances existing mapping and identification approaches.

Conclusions:

  • Indigenous Knowledge (IK) provides a valuable and statistically characterizable dataset for species-habitat modeling.
  • Integrating IK, including dynamic habitat types, offers a robust method for identifying critical species habitats.
  • This approach facilitates meaningful inclusion of IK in species management and co-management initiatives.