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

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.5K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.5K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

28
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...
28
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

74
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
74
Manipulation and Analysis01:21

Manipulation and Analysis

28
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...
28
Levels of Use of a GIS01:29

Levels of Use of a GIS

56
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...
56
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

Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests.

Nature communications·2024
Same author

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany.

Ecology and evolution·2024
Same author

Multispectral analysis-ready satellite data for three East African mountain ecosystems.

Scientific data·2024
Same author

Nature 4.0: A networked sensor system for integrated biodiversity monitoring.

Global change biology·2024
Same author

Seasonal variation in dragonfly assemblage colouration suggests a link between thermal melanism and phenology.

Nature communications·2023
Same author

Consistent signals of a warming climate in occupancy changes of three insect taxa over 40 years in central Europe.

Global change biology·2022

Related Experiment Video

Updated: Jul 12, 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

spatialMaxent: Adapting species distribution modeling to spatial data.

Lisa Bald1, Jannis Gottwald1, Dirk Zeuss1

  • 1Department of Geography, Environmental Informatics Philipps-University Marburg Marburg Germany.

Ecology and Evolution
|October 26, 2023
PubMed
Summary

New spatialMaxent software improves species distribution models by accounting for spatial data structure, leading to more reliable predictions for biodiversity conservation. It outperforms conventional methods in most cases.

Keywords:
MaxentNCEAS datasetmodel tuningopen‐source softwarespatial validationspecies distribution modeling

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.3K

Related Experiment Videos

Last Updated: Jul 12, 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
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.3K

Area of Science:

  • Ecological modeling
  • Biodiversity informatics
  • Computational biology

Background:

  • Conventional species distribution models often lack predictive accuracy due to unaddressed spatial data structures.
  • Overfitting during model training compromises predictive performance on independent, spatially separated data.
  • Reliable species distribution models are crucial for effective biodiversity conservation strategies.

Purpose of the Study:

  • To introduce spatialMaxent, a novel software integrating advanced spatial modeling with Maxent.
  • To enhance species distribution modeling by addressing spatial dependency and overfitting.
  • To provide a user-friendly tool for ecological research and conservation practice.

Main Methods:

  • spatialMaxent incorporates spatial cross-validation for variable selection, feature selection, and regularization multiplier tuning.
  • The software explicitly considers the impact of spatial dependency within training data to mitigate overfitting.
  • Performance was evaluated using a large, diverse dataset (NCEAS) comprising over 200 species globally.

Main Results:

  • spatialMaxent demonstrated superior performance compared to conventional Maxent and non-spatially tuned models in 80% of evaluated cases.
  • The implemented spatial tuning strategies significantly improved model reliability and predictive power.
  • The software proved effective across diverse species and geographic regions.

Conclusions:

  • spatialMaxent offers a significant advancement in species distribution modeling by effectively handling spatial data structures.
  • Its user-friendly nature makes it accessible to a broad range of users, including researchers and conservation practitioners.
  • The tool has strong potential to aid in addressing critical biodiversity conservation challenges through improved ecological predictions.