Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distribution and Dispersion00:54

Distribution and Dispersion

21.9K
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...
21.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

68
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...
68
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

51
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...
51
Habitat Fragmentation02:31

Habitat Fragmentation

17.7K
Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
17.7K
Manipulation and Analysis01:21

Manipulation and Analysis

48
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...
48
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Facilitative interspecific interactions in marine vertebrates across scales: from individuals to ecosystems.

Biological reviews of the Cambridge Philosophical Society·2026
Same author

Animal tracking with particle algorithms informs protected area design.

Science advances·2025
Same author

Naval sonar induces an anaerobic swimming gait in beaked whales.

Scientific reports·2025
Same author

Multi-decadal trends in biomarkers in harp seal teeth from the North Atlantic reveal the influence of prey availability on seal trophic position.

Global change biology·2023
Same author

Estimating energetic intake for marine mammal bioenergetic models.

Conservation physiology·2023
Same author

Benthic animal-borne sensors and citizen science combine to validate ocean modelling.

Scientific reports·2022

Related Experiment Video

Updated: Jul 28, 2025

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

13.4K

Understanding spatial effects in species distribution models.

Iosu Paradinas1,2, Janine Illian2, Sophie Smout2,3

  • 1Scottish Ocean's Institute, University of St Andrews, East sands, St Andrews, United Kingdom.

Plos One
|May 30, 2023
PubMed
Summary
This summary is machine-generated.

Spatial effects in Species Distribution Models can smooth over multiple unmeasured environmental drivers. This simulation study demonstrates that these spatial effects reflect the combined influence of unaccounted factors, complicating direct ecological interpretation.

More Related Videos

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.9K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.8K

Related Experiment Videos

Last Updated: Jul 28, 2025

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

13.4K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.9K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.8K

Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Modeling

Background:

  • Species Distribution Models (SDMs) often incorporate spatial effects to enhance predictions and identify environmental drivers.
  • Ecologists sometimes attempt to interpret the spatial patterns derived from these effects.
  • However, spatial autocorrelation can arise from numerous unmeasured variables, hindering ecological interpretation of spatial effects.

Purpose of the Study:

  • To demonstrate that spatial effects in statistical models can effectively smooth over the influence of multiple unaccounted drivers.
  • To illustrate the challenges in ecologically interpreting spatial effects when unmeasured variables are present.

Main Methods:

  • A simulation study was conducted to investigate the behavior of spatial effects.
  • Model-based spatial models were fitted using geostatistics and 2D smoothing splines.
  • The simulation focused on how spatial effects represent unmeasured covariates.

Main Results:

  • Fitted spatial effects were shown to approximate the combined surface of unaccounted covariates.
  • Both geostatistical and 2D smoothing spline methods produced similar results regarding the smoothing of unmeasured drivers.
  • The study provides empirical evidence for the smoothing effect of spatial components.

Conclusions:

  • Spatial effects in SDMs can obscure the individual contributions of unmeasured environmental drivers by integrating their influence.
  • Direct ecological interpretation of spatial effects should be approached with caution due to potential confounding by unmeasured variables.
  • Understanding the smoothing nature of spatial effects is crucial for accurate ecological inference in SDMs.