<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-9028</journal-id>
<journal-title><![CDATA[Ecosistemas y recursos agropecuarios]]></journal-title>
<abbrev-journal-title><![CDATA[Ecosistemas y recur. agropecuarios]]></abbrev-journal-title>
<issn>2007-9028</issn>
<publisher>
<publisher-name><![CDATA[Universidad Juárez Autónoma de Tabasco, Dirección de Investigación y Posgrado]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-90282025000200007</article-id>
<article-id pub-id-type="doi">10.19136/era.a12n2.4127</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación de la configuración de tres algoritmos para realizar modelos de distribución potencial de especies forestales]]></article-title>
<article-title xml:lang="en"><![CDATA[Evaluation of the configuration of three algorithms to model the potential distribution of forest species]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montoya-Jimenez]]></surname>
<given-names><![CDATA[Juan Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valdez-Lazalde]]></surname>
<given-names><![CDATA[José René]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ángeles-Pérez]]></surname>
<given-names><![CDATA[Gregorio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Santos-Posadas]]></surname>
<given-names><![CDATA[Héctor Manuel De los]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz-Cárdenas]]></surname>
<given-names><![CDATA[Gustavo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,TES Valle de Bravo División de Ingeniería Forestal ]]></institution>
<addr-line><![CDATA[Valle de Bravo Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Politécnico Nacional  ]]></institution>
<addr-line><![CDATA[Jiquilpan Michoacán]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>12</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-90282025000200007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-90282025000200007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-90282025000200007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los modelos de distribución potencial son una herramienta útil para identificar condiciones ambientales óptimas para que un organismo prevalezca. El objetivo del estudio fue evaluar la configuración de parámetros de los algoritmos Maxent, Random Forest y Modelos Aditivos Generalizados (GAM) a través de la generación de modelos de distribución de seis especies forestales de México. Con registros de presencia de seis especies forestales se ajustaron modelos de distribución potencial con tres algoritmos, se usaron dos configuraciones en sus parámetros (afinados y predeterminados), los modelos se evaluaron a través del área bajo la curva, para comparar la configuración afinada y predeterminada se realizaron gráficos de violín, gráficos de valores de idoneidad predicha con ambas configuraciones y un análisis de coincidencia global difusa. Los conjuntos de cuatro, cinco y seis variables mejoraron las predicciones. Los mejores valores en el multiplicador de regularización oscilaron entre 0.1 y 0.4, las clases de características que describen mejor la distribución potencial de las seis especies fueron lineal, cuadrática y producto. Random Forest mostró que con 750 y 1 000 árboles y dos variables en cada división no mejora el ajuste de los modelos para las seis especies. Los mejores valores de suavización del algoritmo GAM oscilaron entre 0.0001 para P. pseudostrobus hasta 1.5 para P. durangensis, pero no se encontraron diferencias entre el ajuste de modelos con configuración afinada y predeterminada. Los algoritmos obtuvieron buen rendimiento, sin embargo, el efecto del ajuste de los parámetros en la capacidad predictiva fue marginal y varió según el algoritmo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Potential distribution models are a useful tool to identify optimal environmental conditions for an organism to prevail. The objective of the study was to evaluate the parameter configuration of the Maxent, Random Forest and Generalized Additive Models (GAM) algorithms through the generation of distribution models of six forest species from Mexico. With presence records of six forest species, potential distribution models were adjusted with three algorithms, two configurations were used in their parameters (tuned and default), the models were evaluated through the area under the curve, to compare the tuned and default configuration. violin plots, plots of predicted fitness values with both configurations, and a fuzzy global matching analysis were performed. Sets of four, five, and six variables improved predictions. The best values in the regularization multiplier ranged between 0.1 and 0.4, the feature classes that best describe the potential distribution of the six species were linear, quadratic and product. Random Forest showed that with 750 and 1 000 trees and two variables in each division the fit of the models for the six species does not improve. The best smoothing values for the GAM algorithm ranged from 0.0001 for P. pseudostrobus to 1.5 for P. durangensis, however, no differences were found between fitting models with fine-tuned and default settings. The algorithms obtained good performance, however, the effect of parameter tuned on the predictive capacity was marginal and varied depending on the algorithm.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Random Forest]]></kwd>
<kwd lng="es"><![CDATA[Maxent]]></kwd>
<kwd lng="es"><![CDATA[Modelos aditivos generalizados]]></kwd>
<kwd lng="es"><![CDATA[afinación]]></kwd>
<kwd lng="es"><![CDATA[Pinus]]></kwd>
<kwd lng="en"><![CDATA[Random Forest]]></kwd>
<kwd lng="en"><![CDATA[Maxent]]></kwd>
<kwd lng="en"><![CDATA[generalized additive model]]></kwd>
<kwd lng="en"><![CDATA[tuning]]></kwd>
<kwd lng="en"><![CDATA[Pinus]]></kwd>
</kwd-group>
</article-meta>
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