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Related Concept Videos

Distribution and Dispersion00:54

Distribution and Dispersion

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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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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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Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
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Related Experiment Video

Updated: Jul 27, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Building use-inspired species distribution models: Using multiple data types to examine and improve model

Camrin D Braun1, Martin C Arostegui1, Nima Farchadi2

  • 1Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA.

Ecological Applications : a Publication of the Ecological Society of America
|June 7, 2023
PubMed
Summary

Different data types can build robust species distribution models (SDMs) for marine conservation. Combining data through ensembles or pooled models improves ecological realism and predictions for species like the blue shark.

Keywords:
climate changeecological forecastinghighly migratory speciespredictionspatial ecologyspecies distribution models

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Area of Science:

  • Marine ecology
  • Conservation biology
  • Fisheries science

Background:

  • Species distribution models (SDMs) are crucial for marine conservation and management.
  • Increasing marine biodiversity data necessitates guidance on leveraging diverse data types for robust SDMs.

Purpose of the Study:

  • To explore the effect of different data types on SDM fit, performance, and predictive ability.
  • To compare models trained with fishery-dependent and fishery-independent data for the blue shark (Prionace glauca).

Main Methods:

  • Compared SDMs trained with conventional mark-recapture tags, fisheries observer records, satellite-linked electronic tags, and pop-up archival tags.
  • Evaluated model fit, performance, and spatial predictions for the blue shark in the Northwest Atlantic.
  • Assessed model ensembles and pooled data models for integrating inferences.

Main Results:

  • All four data types produced robust SDMs, but spatial predictions varied due to sampling biases.
  • Differences in data sampling and absence representation influenced model outcomes.
  • Model ensembles and pooled data models yielded more ecologically realistic predictions than individual models.

Conclusions:

  • Guidance is provided for practitioners developing SDMs with diverse data sources.
  • Future work should focus on integrative modeling approaches that leverage individual data type strengths and account for limitations.
  • Ecological realism in model selection and interpretation is vital, regardless of data type used.