Dataset Identification:
Resource Abstract:
- description: The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using
species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily
obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information
Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and
bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution
with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning
of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting
the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models
with 2,500 maximum iterations. Maxent was implemented in R using the dismopackage (Hijmans et al. 2011). Model evaluation
was carried out using the PresenceAbsencepackage in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model
performance. The package also computes threshold values using several accuracy metrics to translate predicted probability
maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability
of species occurrences. The output from Maxent are grid datasets in a multiband tifformat with one band for each replication.
We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low
(threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded.
Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report
at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].; abstract: The Maxent modeling algorithm was used to build
the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors
(Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database)
and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos
museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as
predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method
of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to
n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models
converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented
in R using the dismopackage (Hijmans et al. 2011). Model evaluation was carried out using the PresenceAbsencepackage in R
(Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values
using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected
the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid
datasets in a multiband tifformat with one band for each replication. We averaged the five replicated maps and created a mean
grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat
suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please
review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].
Citation
- Title Coast Horned Lizard Habitat Model for NSNF Connectivity - CDFW [ds1035].
-
- creation Date
2018-01-02T20:59:02.178268
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- Linkage for online resource
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- name Dublin Core references URL
- URL: http://bios.dfg.ca.gov/
- protocol WWW:LINK-1.0-http--link
- link function information
- Description URL provided in Dublin Core references element.
Metadata data stamp:
2018-08-06T21:05:07Z
Resource Maintenance Information
- maintenance or update frequency:
- notes: This metadata record was generated by an xslt transformation from a dc metadata record; Transform by Stephen M. Richard, based
on a transform by Damian Ulbricht. Run on 2018-08-06T21:05:07Z
Metadata contact
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pointOfContact
- organisation Name
CINERGI Metadata catalog
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- Contact information
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- Address
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- electronic Mail Address cinergi@sdsc.edu
Metadata language
eng
Metadata character set encoding:
utf8
Metadata standard for this record:
ISO 19139 Geographic Information - Metadata - Implementation Specification
standard version:
2007
Metadata record identifier:
urn:dciso:metadataabout:2b0078f4-6687-4426-87cb-f52cc32f77ca
Metadata record format is ISO19139 XML (MD_Metadata)