308

Vista previa del material en texto

Distribución Potencial de Dendroctonus rhizophagus 
(Coleoptera: Scolytinae) en la Sierra Madre Occidental y su 
Aplicación para la 
Generación de un Modelo de Riesgo para el Municipio de Casas 
Grandes, Chihuahua. 
 
 
T E S I S 
QUE COMO UNO DE LOS REQUISITOS PARA 
OBTENER EL GRADO DE 
DOCTOR EN CIENCIAS QUIMICOBIOLÓGICAS 
 
P R E S E N T A 
 
 
M. C. MARÍA GUADALUPE MENDOZA CORREA 
 
 
 MÉXICO, D. F., ENERO del 2011 
 
Instituto Politécnico Nacional 
Escuela Nacional de Ciencias Biológicas 
Sección de Estudios de Posgrado e Investigación 
 
María Guadalupe Mendoza Correa 
 
 
 
 
 
 
 
 
 
El presente trabajo se realizó en el Laboratorio de Variación Biológica y Evolución 
del Departamento de Zoología de la Escuela Nacional de Ciencias Biológicas del 
Instituto Politécnico Nacional, bajo la dirección del Dr. Gerardo Zúñiga Bermúdez 
y la codirección del Dr. Héctor Omar Mejía Guerrero. 
 
 
 
 
 
 
 
María Guadalupe Mendoza Correa 
 
 
 
 
 
 
 
La presente investigación formó parte del proyecto “Biología, ecología y 
manejo de poblaciones de Dendroctonus rhizophagus Thomas y Bright 
(Coleoptera, Curculionidae: Scolytinae) en el estado de Chihuahua”, 
CONAFOR, CLAVE 69539. 
 
 
 
 
El sustentante fue becario del Consejo Nacional de Ciencia y Tecnología 
(CONACYT) de Enero del 2007 a Diciembre del 2010 y del Programa 
Nacional de Formación de Investigadores (PIFI) del Instituto Politécnico 
Nacional, de Enero de 2007 a Junio de 2010. 
 
 
María Guadalupe Mendoza Correa 
DEDICATORIAS 
Dedico esta investigación con todo mi amor, admiración y respeto: 
 
A mi esposo: Marco Antonio 
Por seguir apoyándome en TODOS mis proyectos, 
Por ser papá y mamá durante mis ausencias, 
Por ser un padre amoroso y ejemplar, 
Por ser tan pasional, comprensivo y paciente, 
Por seguir siendo el mejor esposo, amigo y amante 
Por tus valores, creencias, defectos y virtudes 
Por ser mi complemento y mí premio mayor 
Por amarme tanto… 
¡¡¡TE ADMIRO Y TE AMO!!!! 
 
A mis hijos: Marquito y Nayeli: 
Por su comprensión, apoyo, alegría y paciencia, 
Por ser tan responsables y buenos estudiantes, 
Por darme la tranquilidad necesaria durante mis ausencias, 
Por darme tanta felicidad, 
Por su ánimo para continuar hasta el final, 
Por ser mi motivación y mi mayor logro en la vida….¡¡¡LOS AMO!!! 
María Guadalupe Mendoza Correa 
AGRADECIMIENTOS 
 
A mis Padres Ines y José Guadalupe 
Gracias por su amor, apoyo y comprensión, 
los cuales me han permitido culminar una meta más, 
Gracias por quererme tanto, no sólo a mí sino a mi esposo e hijos, 
¡¡¡Dios me escogió a los mejores padres!!! 
 
A mis hermanos Jorge, Miguel, Agustin, Ramón e Ignacio 
y mis ″hermanas” Lety, Lola, Moni, Ale y Rosalía 
Gracias también por apoyarme tanto para culminar esta nueva meta, 
Por su gran ánimo, por su cariño y 
por siempre estar a mi lado cuando los hemos necesitado, 
porque donde hay amor, la distancia no importa… 
Y si volviera a nacer y tuviera que elegir a mi familia 
¡¡¡Sin dudarlo los escogería a ustedes otra vez!!! 
 
Al Dr. Gerardo Zúñiga 
Gracias por un logro más juntos, 
Gracias por la oportunidad, el apoyo, la tolerancia y el respeto, 
Porque aunque a veces ha sido difícil el trabajo conjunto, 
Al final siempre prevalece la comprensión, la admiración 
y sobre todo el cariño mutuo… 
¡¡¡Gracias!!! 
María Guadalupe Mendoza Correa 
 
 
 
A mis sinodales: Dr. Guillermo, Dr. Fabián, Dr. Cesar, Dr. Omar y Dr. Fernando 
Gracias por su apoyo, sus revisiones y sus acertados comentarios 
para la mejora de este escrito. 
 
 
Al M. en C. Daniel Núñez López 
Gracias por todo tu apoyo, tu paciencia y sobre todo tus enseñanzas, 
las cuales fueron fundamentales para desarrollar esta investigación, 
en verdad te admiro por tu profesionalismo… 
 
 
Al Ing. José Luis Aguilar Vitela, Ing. Sergio Quiñones y Biol. Antonio Olivo 
Gracias por su colaboración en la colecta de los datos, 
Por todo su apoyo incondicional, su generosidad y su alegría, 
Gracias por esos bellos paisajes que no olvidaré, 
y los agradables momentos que pasamos juntos, 
con todo mi cariño, admiración y respeto… 
María Guadalupe Mendoza Correa 
 
 
A todos los compañeros del laboratorio de Variación Biológica y Evolución 
A Ericka, Claudia, Rosita, Fernanda, Paquito, Enrico, Javier, Gabriel, Vero Torres, 
Belinda, Faviel, Verito, Kim, Karina, Mariana, Karina Martínez, Alba y Berenice: 
 
Gracias por su amistad, alegría, apoyo y comprensión, 
Por haberme aceptado como compañera y 
hacer más agradable la estancia en el laboratorio, 
Los voy a extrañar…. 
 
 
Al CONACYT y PIFI 
Gracias por el apoyo logístico recibido y por la confianza depositada en mi persona. 
 
 
María Guadalupe Mendoza Correa 
 
ÍNDICE Páginas 
 
Índice de Figuras I 
Índice de Tablas II 
Introducción General IV 
Justificación VIII 
Objetivos IX 
Capitulo I: Factores que influyen la distribución geográfica de 
Dendroctonus rhizophagus (Coleoptera: Curculionidae: Scolytinae) 
en la Sierra Madre Occidental, México 1 
 Abstract 3 
 Resumen 4 
 Introduction 5 
 Materials and Methods 8 
 Results 12 
 Discussion 15 
 Acknowledgments 19 
 References Cited 20 
 Tables 27 
 Figure Legends 35 
 Figures 36 
María Guadalupe Mendoza Correa 
 
Capitulo II: Determinación de áreas de susceptibilidad y riesgo de 
presencia de Dendroctonus rhizophagus (Coleoptera: Curculionidae: 
Scolytinae) en los bosques de pino del municipio de Casas Grandes, 
Chihuahua. 39 
Abstract 41 
Resumen 42 
 Introduction 43 
 Materials and Methods 46 
 Results 51 
 Discussion 54 
 Acknowledgments58 
 References Cited 59 
 Tables 66 
 Figure Legends 77 
 Figures 78 
Conclusiones Generales 82 
Referencias Generales 84 
 
 
 
María Guadalupe Mendoza Correa 
 
I 
 
ÍNDICE DE FIGURAS Páginas 
 
CAPITULO I 
Fig. 1. Potential distribution of D. rhizophagus in the Sierra Madre Occidental, 
 México modeled with BIOCLIM 36 
Fig. 2. Habitat suitability (HS) for D. rhizophagus in the Sierra Madre Occidental, 
 México modeled with ENFA. 37 
Fig. 3. Potential distribution of D. rhizophagus in the Sierra Madre Occidental, 
 México modeled with MaxEnt 38 
 
CAPITULO II 
Fig. 1. Location of Casas Grandes municipality, Chihuahua, and map of the 
 potential distribution of pine forests (García-Nájera 2009) 78 
Fig. 2. Potential distribution of D.rhizophagus in Casas Grandes municipality, 
 modeled with MaxEnt 79 
Fig. 3 Susceptibility’s model of pine forests of Casas Grandes municipality, 
 Chihuahua 80 
Fig. 4. Risk Model of presence of D. rhizophagus in pine forests of Casas 
 Grandes municipality, Chihuahua 81 
 
María Guadalupe Mendoza Correa 
 
II 
 
ÍNDICE DE TABLAS Páginas 
CAPITULO I 
Table 1. Collection records and reference sources of D. rhizophagus 27 
Table 2. Bioclimatic profile of D. rhizophagus for each location (obtained 
using BIOCLIM) 28 
Table 3. Percentage of incidence of D. rhizophagus on host Pinus species 
 in the Sierra Madre Occidental, México 30 
Table 4. Principal component analysis of BIOCLIM climatic variables 31 
Table 5. Percentage of variation explained by ecogeographic variables for 
 D. rhizophagus using ENFA 33 
Table 6. Percentage of estimated contribution for environmental variables 
 using MaxEnt 34 
CAPITULO II 
Table 1. Ecogeographic (topographic, climatic and biotic) variables regarded 
as predictor variables in MaxEnt analysis 66 
Table 2. Regression coefficients used to generate models of temperature 
and precipitation variables for Casas Grandes municipality, Chihuahua 67 
Table 3. Suitability values for each alternative of the precipitation criterion, and 
their corresponding byte values 68 
Table 4. Suitability values for each alternative of the temperature criterion, and 
María Guadalupe Mendoza Correa 
 
 III 
their corresponding byte values 69 
Table 5. Suitability values for each alternative of the topographic and biotic 
criteria, and their corresponding byte values 70 
Table 6. Measurement scale for assignment of value judgments, defined 
by Saaty (1980) 71 
Table 7. Percent contribution by each ecogeographic variable as estimated 
by MaxEnt 72 
Table 8. Pairwise comparison matrix of precipitation variables and the weight 
assigned to each variable 73 
Table 9. Pairwise comparison matrix of temperature variables and the weight 
assigned to each variable 74 
Table 10. Pairwise comparison matrix of topographic variables and the weight 
assigned to each variable 75 
Table 11. Pairwise comparison matrix of the criteria taken into account in order to 
obtain the susceptibility map and the weight assigned to each criterion 76 
 
 
María Guadalupe Mendoza Correa 
 
IV 
 
INTRODUCCIÓN GENERAL 
 
Los escarabajos conocidos como descortezadores (Coleoptera: Curculionidae: Scolytinae) 
destacan como uno de los grupos de insectos que mayor impacto tienen en los bosques de 
coníferas. De manera particular, las especies del género Dendroctonus Erichson son consideradas 
como uno de los agentes destructivos más importantes en condiciones epidémicas de los bosques 
de los géneros Pinus (L), Picea (A. Dietr.), Pseudotsugae (Carr.) y Larix (Mill.). El género tiene 
una distribución Holartica y está integrado por 19 especies, 17 de las cuales se distribuyen en 
Norteamérica y 2 en Europa y Asia. En México están presentes 12 especies y entre las más 
agresivas destacan D. mexicanus, D. adjunctus, D. frontalis y D. rhizophagus. 
Las especies de este género, al igual que muchos otras, son componentes naturales del 
bosque, su función ecológica es la de coadyuvar a la regeneración y restauración natural de los 
bosques (Salinas et al. 2010) al matar árboles enfermos, dañados o debilitados por diversos 
factores (Wood 1982). Sin embargo, cuando las condiciones de equilibrio de los bosques son 
alteradas por factores naturales o por actividades antropogénicas, sus poblaciones pueden 
convertirse en plagas (Raffa et al. 2005) ocasionando pérdidas económicas importantes a la 
industria forestal (Wood 1982). 
Dado que los descortezadores del género Dendroctonus son la principal causa de 
mortalidad de las coníferas de Norteamérica (Raffa y Berryman 1987), históricamente se han 
venido aplicando métodos agresivos de control como son: el derribo y abandono de los árboles; 
el derribo, descortezado y arropado, y la inyección de químicos a los árboles plagados, entre 
otros. En Canadá y Norteamérica el conocimiento preciso del sistema de comunicación química 
de las especies más agresivas del género, ha permitido el uso exitoso de compuestos químicos 
(semioquimicos) sintéticos que alteran su conducta, al funcionar como mezclas de agregación o 
María Guadalupe Mendoza Correa 
V 
 
antiagregación. Desafortunadamente, los semioquímicos para las especies mexicanas no son 
conocidos, lo que ha dado lugar al traslado y aplicación de estos compuestos generados en otras 
latitudes (Sánchez-Martínez 2007), sin embargo, su uso no ha producido resultados satisfactorios. 
Debido a la falta de información relacionada con la biología y ecología de las especies de 
descortezadoresmexicanos, es fundamental generar información básica de manera consistente 
que contribuya en la resolución de los problemas específicos relacionados con estos insectos y 
que por consiguiente derive en un mejor manejo de los bosques. 
El estudio de la distribución geográfica de los organismos es crucial para disciplinas como 
la biogeografía, evolución, conservación y manejo de especies (Anderson et al. 2003). Desde el 
siglo pasado se ha hecho mucho hincapié en la importancia del reconocimiento y construcción de 
los patrones de distribución geográfica de las especies y de la relación de éstos con factores 
climáticos y geográficos (Brown 1984). Asimismo, se ha señalado que al igual que el ambiente, 
las interacciones bióticas limitan o favorecen la abundancia de las especies y por consiguiente 
regulan indirectamente su distribución geográfica (Lomolino et al. 2006). 
Los estudios de distribución geográfica de las especies se han realizado tradicionalmente a 
partir de la información (localidad y altitud) que acompaña a los ejemplares depositados en las 
colecciones científicas y en los museos (MacDonald 2003). A partir de esta información se han 
generado mapas en los cuales la distribución de los taxa se representa simplemente como un 
conjunto de puntos, y en donde cada punto es una localidad de colecta, de tal modo que al unir 
los puntos de la periferia, se circunscribe un área que corresponde a su distribución geográfica 
(Rapoport 1982). En la actualidad una visión más moderna en este tipo de estudios, busca 
integrar esta información básica con datos climáticos, biológicos y topográficos, con la finalidad 
de tener un conocimiento más integral de las áreas de distribución de los taxa y comprender 
mejor cuales son las variables que limitan o favorecen su expansión (Gaston 2003). 
María Guadalupe Mendoza Correa 
VI 
 
El desarrollo de modelos predictivos ayuda a estimar la distribución potencial de las 
especies mediante el uso de técnicas estadísticas y probabilísticas, las cuales correlacionan los 
datos de presencia y/o ausencia con un conjunto de variables ambientales (Guisan y Zimmermann 
2000, Pearson y Dawson 2003). Asimismo, estos modelos permiten describir las condiciones 
bioclimáticas donde están presentes las especies, así como identificar que variables están 
determinando su presencia. Dichos modelos han ganado importancia como una herramienta de 
investigación en temas de conservación, nicho ecológico de las especies, patrones espaciales de la 
biodiversidad, efecto del cambio climático sobre la distribución de las especies y amenaza 
potencial de plagas o especies invasivas (Casinello et al. 2006, Ungerer et al. 1999). 
Por otra parte, la integración de las variables ecogeográficas con la presencia real de un 
agente que potencialmente puede afectar o dañar un recurso en un área geográfica determinada, 
permite explorar el grado de susceptibilidad o riesgo que ese agente puede tener sobre el recurso. 
En el caso de los descortezadores, la inclusión de esta información ligada al manejo del bosque, 
conduce al desarrollo de estrategias preventivas en aquellas zonas boscosas que son susceptibles 
a la presencia de estos insectos (Mason et al. 1985, Bentz et al. 1993, Llanderal, 1995). 
Uno de las estrategias preventivas son los sistemas de valoración de susceptibilidad o 
modelos de riesgo, entendiendo por “riesgo” la probabilidad de que en un sitio particular ocurra 
un ataque epidémico en un tiempo determinado (Nelson et al. 2006) y por “susceptibilidad” la 
probabilidad de que un sitio sea infestado debido a sus características inherentes que lo hacen 
disponible a la presencia de las poblaciones de estos insectos (Shore y Safranyik 1992). Los 
modelos de susceptibilidad y riesgo han sido desarrollados y aplicados principalmente en Estados 
Unidos y Canadá (Lorio 1981), ya que en estos países existe un amplio conocimiento de las 
condiciones de los huéspedes, así como de la biología, ecología y dinámica poblacional de los 
descortezadores (McGregor 1986). En México, sin embargo, la aplicación de estos modelos ha 
María Guadalupe Mendoza Correa 
VII 
 
sido escasa aplicándose solo a dos especies del género: D. mexicanus y D. frontalis (Espinosa y 
Muñoz 1988, Llanderal 1995, Iñiguez 1999). 
Dendroctonus rhizophagus Thomas y Bright es una especie endémica de la Sierra Madre 
Occidental en los estados de Sonora, Chihuahua, Durango y Sinaloa, que presenta un 
comportamiento atípico dentro del género ya que no realiza ataques masivos y solo parasita 
árboles < 3 m en áreas de regeneración natural y plantaciones de once especies de pinos de 
importancia forestal, entre las que destacan Pinus engelmannii Carr., Pinus durangensis 
Martinez, Pinus leiophylla Schlecht y Cham. y Pinus arizonica Engelm. (Cibrian et al. 1995; 
Salinas-Moreno et al. 2004; Sánchez-Martínez y Wagner 2009). Los daños producidos por esta 
especie en las dos últimas décadas ascienden alrededor de 12,500 plántulas por año en toda su 
área de distribución. Una de las zonas afectadas del estado de Chihuahua, es el municipio de 
Casas Grandes, el cual posee extensas áreas de bosques de pino de importancia forestal en sus 
regiones sureñas y occidentales. 
 
 
 
 
 
 
 
 
María Guadalupe Mendoza Correa 
 
VIII 
 
JUSTIFICACIÓN 
D. rhizophagus es un descortezador endémico de la Sierra Madre Occidental, el cual 
presenta un comportamiento de ataque diferente, al parasitar únicamente a los renuevos de varias 
especies de pinos. Dado su comportamiento, este descortezador es considerado dentro de las 
especies “agresivas” de México (Sánchez-Martínez et al 2007). A pesar de la importancia de D. 
rhizophagus como una de las principales plagas forestales del país, y en particular del estado de 
Chihuahua, es muy poco el conocimiento que se tiene acerca de su distribución geográfica, 
biología y ecología (Estrada-Murrieta 1983, Salinas-Moreno et al. 2004; Sánchez-Martínez y 
Wagner 2009), el cual es básico para la aplicación en métodos de control directo o preventivo. 
Esta situación hace que dicha especie sea un ejemplo ideal para la aplicación de los modelajes 
predictivos de distribución, con la finalidad de generar este conocimiento básico a partir de datos 
de presencia del insecto, el cual pueda ser útil para obtener modelos de susceptibilidad y riesgo 
de los bosques de pino a una escala local, que pudieran contribuir a un mejor control de esta 
plaga. 
En este contexto, en el primer capítulo de este trabajo se analiza la distribución geográfica 
potencial de D. rhizophagus a lo largo de la Sierra Madre Occidental mediante varios modelos 
predictivos de distribución, con la finalidad de describir las condiciones bioclimáticas donde 
puede estar presente el insecto, así como identificar las variables que determinan su presencia en 
esta provincia morfotectónica. En el segundo capítulo se generan modelos de susceptibilidad y 
riesgo para el municipio de Casas Grandes, Chihuahua por medio de técnicas de toma de decisión 
multicriterio (TDMC) y Sistemas de información Geográfica (SIG), a partir de la información 
bioclimática y la importancia relativa de las variables eco-geográficas. 
 
María Guadalupe Mendoza Correa 
 
IX 
 
 
OBJETIVOS 
OBJETIVO GENERAL: 
Conocer la distribución geográfica potencial de D. rhizophagus en la Sierra Madre 
Occidental, para generar modelos de susceptibilidad y riesgo de presencia de este 
descortezador en el Municipio de Casas Grandes, Chihuahua. 
 
OBJETIVOS PARTICULARES: 
1) Modelar la distribución geográfica potencial de D. rhizophagus en la Provincia 
Morfotectónica de la Sierra Madre Occidental usando modelos predictivos de 
distribución. 
 
2) Describir las condiciones bioclimáticas donde se presenta este descortezador. 
 
3) Identificar cuales variables determinan la presencia de D. rhizophagus en la 
Sierra Madre Occidental. 
 
4) Modelar la distribucióngeográfica potencial de D. rhizophagus en el Municipio de 
Casas Grandes, Chihuahua mediante un modelo predictivo de distribución. 
 
5) Describir las condiciones bioclimáticas donde se presenta este insecto en el 
Municipio de Casas Grandes, Chihuahua. 
 
6) Ponderar y evaluar las variables ecogeográficas que están relacionadas con la 
presencia de este descortezador mediante técnicas de Evaluación Multicriterio 
para obtener un modelo de susceptibilidad en el Municipio de Casas Grandes, 
Chihuahua. 
 
7) Obtener un modelo de riesgo para el municipio de Casas Grandes, en base a las 
áreas susceptibles y a la proximidad de poblaciones de D. rhizophagus. 
 
 
 
María Guadalupe Mendoza Correa 
 
1 
 
 
 
CAPITULO I 
 
 
 
 
 
Factores que influyen la distribución geográfica de Dendroctonus 
rhizophagus (Coleoptera: Curculionidae: Scolytinae) en la Sierra 
Madre Occidental, México 
 
 
 
 
 
 
 
 
Los datos de este estudio son parte del trabajo aceptado para su publicación en la 
revista Environmental Entomology 
María Guadalupe Mendoza Correa 
2 
 
Mendoza et al.: Factors influencing the 
geographical distribution of Dendroctonus 
rhizophagus 
Environmental Entomology ESA 
Population Ecology 
 
 
 
 
 
 
 
 
 
 
 
 
Factors influencing the Geographical Distribution of Dendroctonus rhizophagus 
(Coleoptera: Curculionidae: Scolytinae) in the Sierra Madre Occidental, México 
 
Ma. Guadalupe Mendoza
1
, Yolanda Salinas-Moreno
1
, Antonio Olivo-Martínez
2
 and Gerardo 
Zúñiga
1
 
1
 Laboratorio de Variación Biológica y Evolución. Escuela Nacional de Ciencias Biológicas-IPN. 
Departamento de Zoología. Carpio y Plan de Ayala s/n, Col. Santo Tomás, C. P. 11340 México 
City, México. 
2
 Sanidad Forestal, Comisión Nacional Forestal, Sección VI del Noroeste, Chihuahua México. 
Avenida Universidad piso 1. No. 3705. C. P. 31170, Colonia Magisterial. 
María Guadalupe Mendoza Correa 
3 
 
ABSTRACT 
The bark beetle, Dendroctonus rhizophagus Thomas & Bright, is endemic to the Sierra Madre 
Occidental (SMOC) in México. This bark beetle is a major pest of the seedlings and young 
saplings of several pine species that are of prime importance to the nation’s forest industry. 
Despite the significance of this bark beetle as a pest, its biology, ecology and distribution are 
poorly known. Three predictive modeling approaches were used as a first approximation to 
identify bioclimatic variables related to the presence of D. rhizophagus in the SMOC and to 
obtain maps of its potential distribution within the SMOC, which is a morphotectonic province. 
Our results suggest that the bark beetle could have an almost continuous distribution throughout 
the major mountain ranges of the SMOC. This beetle has a relatively narrow ecological niche 
with respect to some temperature and precipitation variables and inhabits areas with climatic 
conditions that are unique from those usually prevalent in the SMOC. However, the bark beetle 
has a broad ecological niche with respect to the number of hosts that it attacks. At the macro-
scale level, the D. rhizophagus distribution occurs within the wider distribution of its main hosts. 
The limit of the geographical distribution of this bark beetle coincides with the maximum 
temperature isotherms. Our results imply a preference for temperate habitats, which leads to the 
hypothesis that even minor changes in climate may have significant effects on its distribution and 
abundance. 
KEYWORDS: Bark beetles, Dendroctonus rhizophagus, geographical distribution, predictive 
models 
María Guadalupe Mendoza Correa 
4 
 
RESUMEN 
El descortezador, Dendroctonus rhizophagus Thomas & Bright, es una especie endémica de la 
Sierra Madre Occidental (SMOC) en México, que coloniza plántulas y árboles jóvenes (< 3 m) de 
varias especies de pinos de importancia económica para la industria forestal de México. A pesar 
de la importancia de este escarabajo como plaga, su biología, ecología y distribución son poco 
conocidas. En este estudio se realizaron tres aproximaciones de modelaje predictivo para 
identificar variables bioclimáticas relacionadas con la presencia de D. rhizophagus en la SMOC y 
para obtener mapas de su distribución potencial. Los resultados sugieren que el descortezador 
puede distribuirse de manera casi continua a lo largo de la SMOC, que su nicho ecológico es 
relativamente estrecho con respecto a algunas variables de temperatura y precipitación y que 
habita en zonas con condiciones climáticas particulares que no son las que prevalecen en la 
SMOC. Sin embargo, esta especie tiene un amplio nicho ecológico con respecto al número de 
huéspedes que parasita. A nivel de macro-escala la distribución de D. rhizophagus está contenida 
dentro de la distribución más amplia de sus huéspedes principales y el límite de su distribución 
geográfica coincide con las isotermas de temperatura máxima. Por último, los resultados indican 
una preferencia por hábitats templados, lo cual permite hipotetizar que aún cambios menores en 
el clima pueden tener un efecto significativo sobre su abundancia y distribución geográfica. 
 
María Guadalupe Mendoza Correa 
5 
 
Both environment and population ecology processes limit the abundance and geographic 
distribution of species (Brown 1984, Thomas et al. 2001, Lomolino et al. 2006). Traditionally, 
distribution studies have used only basic information (location and elevation) associated with 
specimens from museums and scientific collections (MacDonald 2003). Integrating this basic 
information with additional data on climate, biology and topography is essential to fully 
understand the distributional ranges of species, the potential for distributions to change, and the 
variables that limit or favor range expansion or contraction (Gaston 2003). 
The use of geographic information systems (GIS) and the development of a broad spectrum of 
spatial modeling approaches, including bioclimatic analysis (BIOCLIM) (Nix 1986), Ecological 
Niche Factor Analysis (ENFA) (Hirzel et al. 2002), and Maximum Entropy (MaxEnt) (Phillips et 
al. 2004), have made possible the integration of diverse data on biology, climate and topography. 
This has led to the development of more reliable distribution maps and the modeling of species 
responses to different past and future climatic conditions. BIOCLIM, in particular, has been used 
to predict the climate domain and potential species response to climate change at the meso-scale 
level (e.g., Mackey and Lindenmayer 2001, Ganeshaiah et al. 2003, Jiménez-Valverde et al. 
2007). ENFA and MaxEnt have been used to characterize ecological niches and to determine 
suitable areas for species conservation and natural resource management (e.g., Braunisch and 
Suchant 2007, Titeux et al. 2007, Sattler et al. 2007, Kumar and Stohlgren 2009). Frequent use of 
these tools has shown their usefulness and reliability in analyzing the potential distribution of 
species that have a relatively wide distribution range and whose biology and ecology are poorly 
known (Pearson and Dawson 2003, Beaumont et al. 2005). 
Bark and ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) are important natural 
components of forest ecosystems. The infestation of weakened mature trees promotes the 
presence of different successional stages and demographic structures within the forest. 
María Guadalupe Mendoza Correa 
6 
 
Nevertheless, these insects are a worldwide problem for the forest industry and in public parks 
and natural reserves because of the extensive tree mortality they cause under outbreak conditions 
and as vectors of diseases that reduce tree vigor (Wood 1982). Moreover, some bark beetles are 
important invaders that represent a significant threat to the health of forests (Brockerhoff et al. 
2006). Specifically, bark beetles of the genus Dendroctonus Erichson have a wide geographic 
distribution inNorth and Central America. They have caused significant economic losses and 
irreversible ecological changes in Mexican forests (Cibrián et al. 1995). 
The development of Dendroctonus species usually takes place on mature trees (> 15 years) of 
the genera Larix Mill., Picea A. Dietrich Pinus L. and Pseudotsuga Carr. (Wood 1982). The 
advanced age classes of these trees constitute an adequate resource for bark beetle nourishment 
and reproduction. The sole exception to this is Dendroctonus rhizophagus Thomas and Bright, a 
species that is endemic to the Sierra Madre Occidental (SMOC) mountain range in northwestern 
México. Dendroctonus rhizophagus colonizes and kills the seedlings and young saplings (< 10 
years) of various pine species, especially Apache pine (Pinus engelmannii Carr.), Durango pine 
(Pinus durangensis Martinez), Chihuahua pine (Pinus leiophylla Schlecht and Cham.) and 
Arizona pine (Pinus arizonica Engelm.) (Cibrián et al. 1995, Salinas-Moreno et al. 2004, 
Sánchez-Martínez and Wagner 2009). This unique bark beetle is a major threat to regeneration in 
pine forests throughout its distribution in México (Sánchez-Martínez and Wagner 2009). 
Although D. rhizophagus infests a significant number of seedlings and young saplings every 
year, its biology, ecology and geographic distribution are poorly known (Estrada-Murrieta 1983, 
Salinas-Moreno et al. 2004, Sánchez-Martínez and Wagner 2009). Thus, analyzing its geographic 
distribution is essential to identify the environmental variables associated with its presence in 
northwestern México and to evaluate its potential threats in other geographical areas of México. 
Furthermore, if we assume that ongoing climate change could affect the frequency and intensity 
María Guadalupe Mendoza Correa 
7 
 
of bark beetle outbreaks and the geographic distribution of these insects and their hosts (Logan 
and Powell 2001, Kurz et al. 2008, Waring et al. 2009), there is a possibility that D. rhizophagus 
could migrate north into forests of the southwestern United States and cause widespread tree 
mortality in these large contiguous forest. Thus, the purpose of this study is to model the potential 
distribution of D. rhizophagus in the Sierra Madre Occidental of northwestern México using 
BIOCLIM, ENFA and MaxEnt to describe the bioclimatic conditions where it occurs and to 
identify variables that determine its presence. 
María Guadalupe Mendoza Correa 
8 
 
Materials and Methods 
Biological Data. Collection records (museum specimens) for D. rhizophagus were obtained 
from the major entomological collections in México and from technical reports of the Comisión 
Nacional Forestal (CONAFOR) and the Secretaría de Medio Ambiente y Recursos Naturales 
(SEMARNAT) for the states of Chihuahua, Durango, Sonora and Sinaloa (Table 1). In addition, 
we included records gathered in the course of several field studies conducted from 2000 to 2008. 
While some areas of the SMOC morphotectonic province have been little explored, these records 
represent a reliable sample of the geographical distribution of Dendroctonus rhizophagus. A 
morphotectonic province is a zone having distinctive enough geomorphic and geologic/tectonic 
features to differentiate it from neighboring zones (Ferrusquía-Villafranca 1998). 
A preliminary database of 669 records was assembled to determine the altitudinal range where 
D. rhizophagus has been found most frequently and the frequency of its incidence (IP) on 
different hosts. The IP is a measure of the degree of occurrence of the bark beetle on its host 
plants (Salinas-Moreno et al. 2004). All location records were georeferenced on 1:50,000 
topographic maps obtained from the INEGI. For the potential distribution analysis, 412 records 
were eliminated from the database, because they differed only minimally in geographic 
coordinates and elevation. Thus, the spatial modeling database was composed of 257 presence-
only records. 
Similarly, a database of the principal hosts of D. rhizophagus was assembled from specimens 
stored in the national herbariums of México and online records from the webpage of the 
Comisión Nacional para el Conocimiento y uso de la de Biodiversidad (CONABIO). The final 
database included 294 presence-only records of 11 pine species. These records were 
georeferenced on 1:50,000 INEGI topographic maps. 
María Guadalupe Mendoza Correa 
9 
 
Study Area. The morphotectonic province of the Sierra Madre Occidental (SMOC) is located 
between 20º 30' and 31º 20' N and between 102º 20' and 109º 40' W. It covers 289,000 km² and 
runs northwest to southeast. Elevations in this mountain chain range from 200 m to slightly above 
3,000 m. The climate is temperate, and the vegetation type is primarily pine forest and, to a lesser 
extent, oak and pine-oak forest. The mean annual precipitation ranges from 400 to 1,600 mm 
(Rzedowsky 1978, Ferrusquía-Villafranca 1998). 
Potential geographic distribution of D. rhizophagus using BIOCLIM. A bioclimatic profile 
of D. rhizophagus was obtained from BIOCLIM in the DIVA-GIS v.5.2 software package 
(Hijmans et al. 2002) using 19 default temperature and precipitation variables included in 
BIOCLIM (Table 2). This profile describes the environment where the bark beetle has been 
recorded, and it is used to identify other sites where the species may reside. The suitability limits 
of the species are characterized by the mean, standard deviation, minimum and maximum 
tolerance and percentiles of each variable to a resolution of 2.5 arc minutes. 
A principal component analysis (PCA) of these climatic variables was performed with 
STATISTICS v.7.0 (StatSoft® Inc) to assess the relative contribution of each variable to the 
bioclimatic profile of the species. In addition, histograms were developed to determine the 
probabilistic distribution of these climatic variables. Variables that follow a normal distribution 
or a skewed distribution exert greater influences on the bioclimatic profile (Beaumont et al. 
2005). Thus, those variables without a clearly defined distribution or a normal distribution with a 
truncated histogram were eliminated. Subsequently, grid cells that included the bioclimatic 
profile of D. rhizophagus were identified using BIOCLIM. Grid cells were grouped into four 
categories: null (areas outside the 0-100 percentile limits), low (areas within the 0-2.5 percentile 
limits), moderate to high (areas within 2.5-10 percentile limits) and very high to excellent (areas 
María Guadalupe Mendoza Correa 
10 
 
within the10-100 percentile limits). The scatter of cells associated with these categories 
represents the potential distribution of the species. 
Potential geographic distribution of D. rhizophagus using ENFA. Maps showing the major 
climatic variables (determined by PCA), minimum annual temperature, maximum annual 
temperature and elevation were plotted with DIVA-GIS v.5.2. Additionally, BIOCLIM was used 
to draw a map of the potential distribution of the hosts of D. rhizophagus in order to incorporate 
this variable into the analysis. 
All maps were imported into ArcView v.3.2 (Environmental Systems Research Institute, 1999) 
and were transformed to the Idrisi format. Subsequent analyses were conducted with the 
Ecological Niche Factor Analysis (ENFA) algorithm available in BIOMAPPER v.3.2 (Hirzel et 
al. 2006a). ENFA conducts a PCA of ecogeographic variables and generates a series of internally 
uncorrelated factors that are used to plot potential habitat or habitat suitability (HS) maps. Only 
significant factors were considered for the HS map. Biologically significant factors included 
marginality and tolerance. Marginality is defined as the difference between the optimum 
environment for the species and the global mean of environmental variables for the study area. 
Tolerance indicates the degree of specialization of the species in relation to the range of availableenvironments (Hirzel et al. 2002, 2004). A habitat suitability score (0-100) was derived for each 
grid cell by comparing its marginality and specialization coefficients with the median of each 
factor estimated for the study area. Specialization coefficients are defined as the ratio of the 
ecological variance in mean habitat to that observed for the focal species (Hirzel et al. 2002). 
HS scores were normalized into four categories: null to low (0-25), moderate (26-50), high (51-
75) and very high to excellent (76-100). 
The robustness and predictive ability of the HS model were evaluated in BIOMAPPER by a 
jackknife-type cross-validation using k = 4 partitions of the records of the species based on 
María Guadalupe Mendoza Correa 
11 
 
Huberty’s rule, which is a heuristic method (“rule of thumb”) that determines the ratio of 
calibration and validation points (Fielding and Bell 1997). Three of the generated partitions were 
used to calibrate the HS map, and the fourth was used to evaluate the outcome. The process was 
replicated four times, and each group was used to validate the model for each replicate using 
Boyce’s continuous index. This index measures the relationship between the expected and 
observed values for different HS scores (Hirzel et al. 2006b). A value near 1 indicates congruence 
between the expected and observed values. 
Potential geographic distribution of D. rhizophagus using MaxEnt. Maps of the major 
climatic variables obtained from DIVA-GIS v.5.2, determined with the aid of PCA and 
histograms, plus maps of minimum annual temperature, maximum annual temperature, elevation 
and host distribution, were analyzed with MaxEnt v.3.3.1 (Phillips et al. 2004). The MaxEnt 
algorithm was performed using the default parameters (500 iterations with a convergence 
threshold of 10
-5
). During model development, 75% of the localities were used for model 
training, while 25% of the localities were held back to test the model. Suitable regularization 
values were included to reduce over-fitting and were selected automatically by the program. The 
output format was the logistic default option (Phillips and Dudik 2008). The model was evaluated 
by means of the area under the receiver operating characteristic curve (AUC). AUCs near 0.5 are 
similar to random predictions, which indicate an inaccurate model; in contrast, values above 0.9 
indicate a highly accurate model (Swets 1988, Phillips and Dudik 2008). The resultant map 
showed the probability (0-1) of species presence. Probability values were normalized into four 
categories: null to low (0-0.22), moderate (0.23-0.44), high (0.45-0.67) and very high to excellent 
(0.68-1.0). 
María Guadalupe Mendoza Correa 
12 
 
Results 
The preferential altitudinal range for D. rhizophagus varied between 2,000 and 2,600 m. The 
percentage of incidence showed a high frequency of attacks by this beetle on P. engelmannii, P. 
durangensis, P. arizonica and P. leiophylla (Table 3). 
Bioclimatic profile of D. rhizophagus. The profile based on 257 locations (Table 2) suggests 
that this bark beetle may be present in sites supporting the following temperatures: annual mean 
temperatures of 10-19˚C, maximum temperatures of the warmest month of 21-36˚C, mean 
temperatures of the driest quarter of 9-19˚C and mean temperatures of the coldest quarter of 3-
14˚C. In terms of precipitation, suitable sites are characterized by annual rainfalls of 305-1,406 
mm, wettest-month rainfalls of 79-321 mm, driest-month rainfalls of 2-18 mm, wettest-quarter 
rainfalls of 197-823 mm and warmest-quarter rainfalls of 170-746 mm. 
PCA showed that the first three principal components accounted for 86.79% of the total 
variance (Table 4). Of the 19 variables that were analyzed, 13 had high eigenvalues. However, of 
these 13 variables, only precipitation variables 12, 13, 14, 16 and 18 and temperature variables 1, 
5, 9 and 11 were normally distributed. Temperature variables 2 and 7 had normal but truncated 
distributions, whereas 3 and 4 did not fit any distribution. These last four variables were not used 
to generate potential distribution models with BIOCLIM, ENFA or MaxEnt. 
Potential geographic distribution of D. rhizophagus using BIOCLIM, ENFA and 
MaxEnt. The potential distribution map obtained from BIOCLIM showed a continuous area of 
moderate to high climate suitability that encompassed the entire length of the SMOC 
morphotectonic province, as well as a narrower, continuous area with very high to excellent 
values (Fig. 1). The ENFA map showed a potential distribution that was similar to the 
distribution derived by BIOCLIM but it had better definition (Fig. 2). The high and very high to 
excellent HS areas on the ENFA map appeared as small, discontinuous patches scattered along 
María Guadalupe Mendoza Correa 
13 
 
the entire length of the SMOC morphotectonic province. Areas of moderate suitability occurred 
in the gaps between these patches. The map generated with MaxEnt (Fig. 3) showed two 
separate, large areas of very high to excellent probability of occurrence in the northern part of the 
SMOC and some small, discontinuous areas in its central and southern regions. Moderate and 
high probability areas were found in almost all the SMOC. 
The global marginality factor was 2.76, suggesting that D. rhizophagus is present in areas 
supporting bioclimatic conditions that differ from the average conditions in the SMOC. Global 
tolerance was 0.17, indicating that this species has a relatively narrow ecological niche. The first 
three factors explained 93.4% of the total variation (Table 5). The marginality factor (number 1) 
explained only 4.7%, and the first two tolerance factors (numbers 2 and 3) explained 81.4% and 
7.3%, respectively. 
In terms of the marginality factor (Table 5), D. rhizophagus is essentially linked to the 
precipitation of the driest month (0.352) and the following temperature variables: annual 
maximum temperature (-0.316), maximum temperature of the warmest month (-0.340), mean 
temperature of the coldest quarter (-0.300), annual mean temperature (-0.300) and annual 
minimum temperature (0.269). Additionally, D. rhizophagus is linked to host distribution (0.304) 
and elevation (0.276). Tolerance factors indicate that D. rhizophagus is associated with annual 
mean temperature (-0.707), mean temperature of the coldest quarter (-0.707), annual minimum 
temperature (0.552) and annual maximum temperature (0.461). Jackknife-type cross-validation 
and evaluation using Boyce’s continuous index yielded a mean of 0.67 and a standard deviation 
of 0.32, both of which indicated the model is adequately robust. 
MaxEnt analysis showed that the presence of D. rhizophagus is associated mainly by annual 
mean temperature (30.1%), elevation (13.4%), maximum annual temperature (12.8%), 
precipitation of the warmest quarter (10.5%), precipitation of the driest month (9.8%), maximum 
María Guadalupe Mendoza Correa 
14 
 
temperature of the warmest month (8.1%) and precipitation of the wettest month (5.1%) (Table 
6). The AUC of the training data was 0.976 (± 0.006), and the AUC of test points was 0.970 (± 
0.006), indicating the model is robust. 
María Guadalupe Mendoza Correa 
15 
 
Discussion 
The spatial modeling of species distributions combining occurrence data with biotic, climatic 
and topographic variables can be used to determine the bioclimatic profile of a species and, as a 
consequence, the potential ranges in which it may reside (Anderson et al. 2003, Beaumont et al. 
2005). This information provides an initial explanation for the presence of a species in one area 
but not in another (Lindenmayer et al. 1991). For herbivorous insects, the host is an extremely 
important limiting factor in terms of geographical distribution, as insects cannot continue to occur 
in those places where their host plants are not present (Spellerberg and Sawyer1999, Lomolino et 
al. 2006). 
Although various studies suggest that the geographic distribution of bark beetles at the macro-
scale level is determined by variables such as host availability, temperature and elevation (e.g., 
Amman 1973, Lekander et al. 1977, Régnière and Logan 1996), few studies have examined the 
relative importance of these variables (Ungerer et al. 1999, Carroll et al. 2004). With respect to 
the host, D. rhizophagus has a narrow diet breadth, because it uses a small number (≤ 40%) of the 
available pine hosts across its distribution (Kelley and Farrell 1998). However, the results of our 
study indicate that D. rhizophagus attacks almost all (> 80%) species of pine present in its known 
distribution which is limited to specific areas within the SMOC, especially P. engelmannii, P. 
durangensis, P. arizonica and P. leiophylla (Wood 1982, Salinas-Moreno et al. 2010). The major 
incidence percentages (IPs) on these four pine species do not appear to be related to the 
abundance of them in the SMOC, as all of the hosts used by this bark beetle are similarly 
abundant elements in the pine forests of this morphotectonic province (Rzedowsky 1978, Perry 
1991, García Arevalo and González Elizondo 2003, Ortega-Rosas et al. 2008). However, the 
observed IP values could be related to the ecologically heterogeneous or patch distribution that 
these species have in the SMOC morphotectonic province. For example, while Pinus herrerae 
María Guadalupe Mendoza Correa 
16 
 
and P. leiophylla inhabit slopes and ravines, P. arizonica and P. durangensis grow on plateaus 
and mesas. 
On the other hand, our study also suggests that the distribution of D. rhizophagus may not be 
limited by the geographical distributions of its hosts (Table 6), as there are areas in the SMOC 
morphotectonic province where hosts are present but where there are no records of the beetles 
presence. In addition, the geographical distributions of the majority of them (≥ 90%) extend over 
much of the range of this bark beetle; for example, P. cembroides Zucc., P. herrerae Martínez, P. 
leiophylla, P. lumholtzii Rob. & Fernald, P. oocarpa Schiede and P. teocote Schiede ex Schltdl. 
& Cham. are present in other morphotectonic provinces in México, and others such as P. 
cembroides, P. engelmannii, P. leiophylla, P. ponderosa Dougl. and P. strobiformis Engelm. are 
located beyond the Mexican border and occur in the southwestern United States (Farjon and 
Styles 1997). Differences in the geographic distributions of D. rhizophagus and its hosts might be 
the result of their natural history, where the specific biotic interactions and particular ecological 
requirements of each of them have played important roles. 
Based on these results and those derived with ENFA and MaxEnt (Tables 5, 6), which suggest 
that the host distribution has little weight as a predictor variable, we hypothesize that at the 
macro-scale level the potential distribution of D. rhizophagus is limited more by environmental 
variables such as precipitation and temperature than by the availability or distribution of hosts 
(Lekander et al.1977). In this context, the tolerance and marginality coefficients derived from 
ENFA suggest that this beetle has a relatively narrow ecological niche with respect to some 
temperature and precipitation variables, inhabiting areas with climatic conditions that differ from 
those generally prevalent in the SMOC morphotectonic province. Areas of higher suitability may 
therefore be smaller and may appear in patches along the SMOC, as predicted by ENFA, 
compared with those predicted by BIOCLIM. 
http://es.wikipedia.org/wiki/Dougl.
http://en.wikipedia.org/wiki/George_Engelmann
María Guadalupe Mendoza Correa 
17 
 
The influence of elevation on the distribution of this beetle is not clear: while MaxEnt (Table 
6) suggests that this factor is important, ENFA (Table 5) suggests the contrary. The relationship 
between temperature and elevation could be masking the effect of this factor on the altitudinal 
distribution of D. rhizophagus. The broad elevation range of this species (1,000-2,800 m), 
matches with the altitudinal range in which pine and pine-oak communities are found in the 
SMOC. In addition, the preferential elevation range (2,000-2,600 m) of D. rhizophagus is 
correlated with the preferential altitudinal range of its main hosts (Farjon and Styles 1997, 
Lammertink et al. 1997) and represents areas identified by BIOCLIM, ENFA and MaxEnt as 
high to excellent habitats for this bark beetle. 
The actual distribution limits of D. rhizophagus into the north and south of the SMOC 
coincides with the isotherms for maximum temperature occurring between the 30º and 23º N of 
its distribution range. Specifically, these isotherms are annual temperatures of 20-25ºC and 
temperatures of the warmest month of 23-30ºC. Other bark beetle studies conducted at the macro-
scale level report similar associations with climatic variables. For example, the distribution of the 
mountain pine beetle, D. ponderosae Hopkins, in western Canada is limited by minimum 
temperatures, rather than by the availability of its preferred hosts (ponderosa pine, P. ponderosa), 
despite their wide geographic distribution (Logan and Powell 2001). Therefore, its distribution is 
limited in areas further north (Yukon and Northwest Territories) and east (Alberta) in Canada 
(Carroll et al. 2004). Ungerer et al. (1999) reported that the isotherm for minimum annual 
temperature (-16ºC) limits the dispersal of D. frontalis Zimm. into more northerly areas of the 
United States. 
Our study suggests that D. rhizophagus could be potentially distributed over a greater extent 
of the SMOC morphotectonic province. This will occur as a result of both the use of a wide range 
of pine hosts present in this province and the presence of numerous areas in the SMOC where 
María Guadalupe Mendoza Correa 
18 
 
optimal conditions of elevation, temperature and humidity exist. Moreover, our results suggest 
that limits of the D. rhizophagus distribution at the macro-scale level could be related to the 
maximum temperature isotherms and that optimal temperature ranges where the bark beetle 
occurs are related to temperate habitats. Finally, its relatively narrow ecological niche with 
respect to some temperature and precipitation variables and the preference for temperate habitats, 
lead us to hypothesize that even minor changes in climate may have significant effects on the 
distribution and abundance of this bark beetle. The apparent absence of this species from 
potentially suitable habitats within the SMOC morphotectonic province and the wide distribution 
of its hosts outside this province suggest that variations in temperature and humidity, such as 
those predicted to take place as a result of climate change (Logan and Powell 2001, Bale et al. 
2002), may significantly affect its geographic distribution. A shift in the maximum temperature 
isotherms may favor D. rhizophagus dispersal into temperate areas or beyond its current high 
altitudinal limit. More detailed modeling studies are required to determine the effects that climate 
change may have on the distribution of D. rhizophagus. It would also be important to explore 
how temperature and humidity variables directly affect the development of the life cycle and 
population dynamics of this bark beetle, given that our study suggests these variables could also 
have strong influences at the meso- and micro-scale levels, as has been demonstrated in other 
Dendroctonus species (Logan and Amman 1986, Bentz et al. 1991, Turchin et al. 1991). 
María Guadalupe Mendoza Correa 
19 
 
Acknowledgments 
We are grateful to José Luis Benito Rosas Ortiz (SEMARNAT-Sonora, México), Ricardo 
Adán Peralta Durán and Pedro Hernández Díaz (SEMARNAT-Durango, México), Sergio 
Quiñonez Barraza (CONAFOR Durango-Sinaloa, México), Oscar de León Lara(CONAFOR-
Sonora, México), Marcos Daniel Trujano Thomé and José Luis Aguilar Vitela (Secretaría de 
Recursos Naturales y Medio Ambiente, Durango, México) and Guillermo Sánchez-Martínez 
(INIFAP-Aguascalientes, México) for providing us with access to their collection records and for 
their logistic support, time and availability for consultations. We thank Jorge E. Macías Sámano 
(Colegio de la Frontera Sur, Chiapas, México), Jane L. Hayes (Pacific Northwest Research 
Station, USDA Forest Service, LaGrande, Oregon) and three anonymous reviewers for their 
critical review of the manuscript. The project was funded by Comisión Nacional Forestal 
(CONAFOR, 69539) and Secretaría de Investigación y Posgrado-IPN (SIP-20090576). This work 
was part of M.G.M.C’s Ph.D. dissertation. She was a Consejo Nacional de Ciencia y Tecnología 
(207124) and Programa Institucional de Formación de Investigadores del Instituto Politécnico 
Nacional (PIFI-IPN) fellow. 
 
María Guadalupe Mendoza Correa 
20 
 
References Cited 
Amman, G. D. 1973. Population changes of the mountain pine beetle in relation to elevation. 
Environ. Entomol. 2: 541-547. 
Anderson, R. P., D. Lew, and A. T Peterson. 2003. Evaluating predictive models of species’ 
distributions criteria for selecting optimal models. Ecol. Model. 162: 211-232. 
Bale, S. J., G. J. Masters, I. D. Hodkinson, C. Awmack, T. M. Bezemer, V. K. Brown, J. 
Butterfield, A. Buse, J. C. Coulson, J. Farrar, J. E. G. Good, R. Harrington, S. Hartley, 
T. H. Jones, R. L. Lindroth, M. C. Press, I. Symrnioudis, A. D. Watt, and J. B. 
Whittaker. 2002. Herbivory in global climate change research: direct effects of rising 
temperature on insect herbivores. Global Change Biol. 8: 1-16. 
Beaumont, L. J., L. Hughes, and M. Poulsen. 2005. Predicting species distributions: Use of 
climatic parameters in BIOCLIM and its impact on predictions of species current and future 
distributions. Ecol. Model. 186: 250-269. 
Bentz, B. J., J. A. Logan, and G. D. Amman. 1991. Temperature dependent development of the 
mountain pine beetle (Coleoptera: Scolytidae) and simulations of its phenology. Can. 
Entomol. 123: 1083-1094. 
Braunisch, V., and R. Suchant. 2007. A model for evaluating the “habitat potential” of a 
landscape for capercaillie Tetrao urogallus: a tool for conservation planning. Wildl. Biol. 13: 
21-33. 
Brown, J. H. 1984. On the relationship between abundance and distribution of species. Am. Nat. 
124: 255-279. 
Brockerhoff, E.G., A. M. Liebhold, and H. Jactel. 2006. The ecology of forest insects 
invasions and advances in their management. Can. J. For. Res. 36: 263-268. 
María Guadalupe Mendoza Correa 
21 
 
Carroll, A. L., S. W. Taylor, J. Régnière, and L. Safranyik. 2004. Effects of climate change 
on range expansion by the mountain pine beetle in British Columbia, pp. 223-232. In T. L. 
Shore, J. E. Brooks and J. E. Stone (eds.), Mountain Pine Beetle Symposium: Challenges and 
Solutions. 30-31 October 2003, Kelowna, British Columbia, Canada, Natural Resources 
Canada, Canadian Forest Service, Pacific Forestry Centre, Information Report. BC-X-399, 
Victoria, BC. 
Cibrián, T. D., J. T. Méndez M., R. Campos B., H. O. Yates III, and J. Flores L. 1995. 
Insectos Forestales de México/Forest Insects of Mexico. Universidad Autónoma Chapingo, 
México. 
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). 1997. 
Provincias Biogeográficas de México. Escala 1:4,000,000. México. 
Environmental Systems Research Institute. 1999. Arcview-Gis 3.2. Environmental Systems 
Research Institute, Redlands, California, USA. 
Estrada-Murrieta, O. 1983. Biología del descortezador del renuevo de pino Dendroctonus 
rhizophagus T. y B. (Col.: Scolytidae) en la región de la Mesa del Huracán, Chihuahua. 
Bachelor’s Thesis. Universidad Autónoma Chapingo, México. 
Farjon, A., and B. Styles. 1997. Flora Neotropica. Monograph 75. Pinus (Pinaceae). 
Organization for Flora Neotropica. New York Botanical Garden, NY. 
Ferrusquía-Villafranca, I. 1998. Geología de México: Una Sinopsis, pp. 3-108. In T. P. 
Ramamoorthy, R. Bye, A. Lot and J. Fa (eds.). Diversidad Biológica de México: Orígenes y 
Distribución. Instituto de Biología. Universidad Nacional Autónoma de México, México. 
Fielding, A. H. and J. F. Bell. 1997. A review of methods for the assessment of prediction errors 
in conservation presence/absence models. Environ. Conserv. 24: 38-49. 
María Guadalupe Mendoza Correa 
22 
 
García Arevalo, A y M. S. González Elizondo. 2003. Pináceas de Durango. 2a. ed. Instituto de 
Ecología A. C. México. 
Ganeshaiah, K. N., N. Barve, N. Nath, K. Chandrashekara, M. Swamy, and R. U. Shanker. 
2003. Predicting the potential geographical distribution of the sugarcane woolly aphid using 
GARP and DIVA-GIS. Scientific Correspondence. Current Science 85: 1526-1528. 
Gaston, J. K. 2003. The structure and dynamics of geographic ranges. Oxford University Press, 
Oxford, New York. 
Hijmans, R. J., L. Guarino, C. Bussink, and E. Rojas. 2002. DIVA-GIS, version 5.2. A 
geographic information system for the analysis of biodiversity data. Manual. International 
Potato Center, Lima, Peru. 
Hirzel, A., J. Hausser, D. Chessel, and N. Perrin. 2002. Ecological niche factor analysis: How 
to compute habitat suitability maps without absence data? Ecology 83: 2027–2036. 
Hirzel, A., B. Posse, P-A. Oggier, Y. Crettenand, C. Glenz, and R. Arlettaz. 2004. Ecological 
requirements of reintroduced species and the implications for release policy: the case of the 
Bearded vulture recolonizing the Alps. J. Appl. Ecol. 41: 1103–1116. 
Hirzel, A. H., J. Hausser, and N. Perrin. 2006a. Biomapper 3.2. Laboratory for Conservation 
Biology. Department of Ecology and Evolution. University of Lausanne, Lausanne. 
http://www.unil.ch/biomapper. 
Hirzel, A. H., G. Le Lay, V. Helfer, C. Randin, and A. Guisan. 2006b. Evaluating the ability 
of habitat suitability models to predict species presences. Ecol. Model. 199: 142–152 
Jiménez-Valverde, A., V. M. Ortuño, and J. M. Lobo. 2007. Exploring the distribution of 
Sterocorax Ortuño, 1990 (Coleoptera, Carabidae) species in the Iberian Peninsula. J. 
Biogeogr. 34: 1426-1438. 
Kelley, S. T., and B. D. Farrell. 1998. Is specialization a dead end? The phylogeny of host use 
María Guadalupe Mendoza Correa 
23 
 
in Dendroctonus Bark Beetles (Scolytidae). Evolution 52: 1731-1743. 
Kurz, W. A., C. C. Dymond, G. Stinson, G. J. Rampley, E. T. Neilson, A. L. Carroll, T. 
Ebata, and L. Safranyik. 2008. Mountain pine beetle and forest carbon feedback to climate 
change. Nature 452: 987-990. 
Kumar, S., and T. J. Stohlgren. 2009. Maxent modeling for predicting suitable habitat for 
threatened and endangered tree Canacomyrica monticola in New Caledonia. J. Ecol. Nat. 
Environ. 1: 94-98. 
Lammertink, J. M., J. A. Rojas-Tomé, F. M. Casillas-Orona, y R. L. Otto. 1997. Situación y 
conservación de los bosques antiguos de pino-encino de la Sierra Madre Occidental y sus 
aves endémicas. Consejo Internacional para la preservación de las aves, sección Mexicana 
(CIPAMEX). D. F. México. 
Lekander, B., B. Bejer-Petersen, B. Kangas, and A. Bakke. 1977. The distribution of bark 
beetles in the Nordic Countries. Acta Entomol. Fenn. 32: 1-37. 
Lindenmayer, D. B., H. A. Nix, J. P. McMahon, M. F. Hutchinson, and M. T. Tanton. 1991. 
The conservation of Leadbeater’s possum, Gymnobelideus leadbeateri (McCoy): a case study 
of the use of bioclimatic modelling. J. Biogeogr. 18: 371-383. 
Logan, J. A., and G. D. Amman. 1986. A distribution model for egg development in mountain 
pine beetle. Can. Entomol. 118: 361-372. 
Logan, J. A., and J. A. Powell. 2001. Ghost forests, global warming and the mountain pine 
beetle (Coleptera: Scolytidae). Am. Entomol. 47: 160-172. 
Lomolino, M. V., B. R. Riddle, and J. H. Brown. 2006. Biogeography. Sinauer Associates Inc., 
Sunderland, Massachussetts.MacDonald, G. M. 2003. Biogeography, space, time and life. John Wiley & Sons. Inc., United 
States of America. 
María Guadalupe Mendoza Correa 
24 
 
Mackey, B. G., and D. B. Lindenmayer. 2001. Towards a hierarchical framework for modelling 
the spatial distribution of animals. J. Biogeogr. 28: 1147-1166. 
Nix, H. 1986. A biogeographic analysis of Australian elapid snakes, pp. 4-15. In R. Longmore 
(ed.), Atlas of elapid snakes of Australia. Australian flora and fauna series number 7. 
Australian Government Publishing Service, Canberra. 
Ortega-Rosas, C. I., M. C. Peñalba, J. A. López-Sáez, y T. R. Van Devender. 2008. 
Retrospectiva del Bosque de Pino y Encino de la Sierra Madre Occidental, Sonora, Noroeste 
de México, Hace 1,000 años. Acta Bot. Mex. 83: 69-92. 
Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the 
distribution of species: are bioclimate envelope models useful? Global Ecol. 
Biogeogr. 12: 361-371. 
Perry, J. P. Jr. 1991. The Pines of Mexico and Central America. Timber Press Inc., Portland, 
Oregon. 
Phillips, S. J., R. E. Schapire, and M. Dudík. 2004. A maximum entropy approach to species 
distribution modeling, pp. 655-662. In R. Greinerand and D. Schuurmans (eds.), Proceedings 
of the Twenty-First International Conference on Machine Learning. 2004. Banff, Canada. 
ACM Press, NY. 
Phillips, S. J., and M. Dudík. 2008. Modeling of species distributions with MaxEnt: new 
extensions and a comprehensive evaluation. Ecography 31: 161-175. 
Régnière, J., and J. A. Logan. 1996. Landscape-wide projection of temperature-driven 
processes for seasonal pest management decision support: a generalized approach, pp. 43-56. 
In T. L. Shore and D. A. MacLean (eds.). Symposium Proceedings: Decision support 
systems in forest pest management. Entomological Society of Canada, FRDA Report No. 
260. Victoria, BC. 
María Guadalupe Mendoza Correa 
25 
 
Rzedowsky, J. 1978. Vegetación de México. Limusa. México. 
Salinas-Moreno, Y., M. G. Mendoza, M. A. Barrios, R. Cisneros, J. Macías-Sámano, and G. 
Zúñiga. 2004. Areography of the genus Dendroctonus (Coleoptera: Curculionidae: 
Scolytinae) in México. J. Biogeogr. 31: 1163-1177. 
Salinas-Moreno, Y., A. Ager, C. F. Vargas, J. L. Hayes, and G. Zúñiga. 2010. Determining 
the vulnerability of mexican pine forest to bark beetles of the genus Dendroctonus Erichson 
(Coleoptera: Curculionidae: Scolytinae). Forest Ecol. Manag. 260: 52-61. 
Sánchez-Martínez, G., and M. R. Wagner. 2009. Host preference and attack pattern of 
Dendroctonus rhizophagus (Coleoptera: Curculionidae: Scolytinae): a bark beetle specialist 
 on pine regeneration. Environ. Entomol. 38: 1197-1204. 
Sattler, T., F. Bontadina, A. H. Hirzel, and R. Arlettaz. 2007. Ecological niche modelling of 
two cryptic bat species calls for a reassessment of their conservation status. J. Appl. Ecol. 44: 
1188-1199. 
Spellerberg, I. F., and J. W. D. Sawyer. 1999. An introduction to applied biogeography. 
Cambridge University Press, Cambridge. 
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. 
Thomas, C. D., E. J. Bodsworth, R. J. Wilson, A. D. Simons, Z. G. Davies, M. Musche, and 
L. Conradt. 2001. Ecological and evolutionary processes at expanding range margins. 
Nature 411: 577-581. 
Titeaux, N., M. Dufrêne, J. Radoux, A. H. Hirzel, and P. Defourny. 2007. Fitness-related 
parameters improve niche-based distribution modelling: the case of the Red-backed Shrike. 
Biol. Conserv. 138: 207-223. 
Turchin, P., P. L. Jr. Lorio, A. D. Taylor, and R. F. Billings. 1991. Why do populations of 
southern pine beetles (Coleoptera: Scolytidae) fluctuate? Environ. Entomol. 20: 401-409. 
María Guadalupe Mendoza Correa 
26 
 
Ungerer, M., M. P. Ayres, and M. J. Lombardero. 1999. Climate and the northern distribution 
limits of Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae). J. Biogeogr. 26: 
1133-1145. 
Waring, K.M., D. M. Reboletti, L. A. Mork, M. Li, C.H. Huang, R. W. Hofstetter, A. M. 
Garcia, P. Z. Fulé, and T. S. Davis. 2009. Modeling the impacts of two bark beetle species 
under warming climate in the southwestern U.S.A.: ecological and economic consequences. 
Environ. Manage. 44: 824-835. 
Wood, S. L. 1982. The bark and ambrosia beetles of North and Central America (Coleoptera: 
Scolytidae) a taxonomic monograph. Great Basin Nat. Mem. 6: 1-1359. 
María Guadalupe Mendoza Correa 
27 
 
 Table 1. Collection records and reference sources of D. rhizophagus 
 
 
Reference Source 
 
CODE 
No. of 
Records 
Comisión Forestal Nacional, Chihuahua, México CONAFOR-CHIH 236 
Comisión Forestal Nacional, Sonora, México CONAFOR-SON 1 
Comisión Forestal Nacional, Sinaloa, México CONAFOR-SIN 2 
Secretaria de Medio Ambiente y Recursos Naturales, Durango, México SEMARNAT-DGO 119 
Secretaria de Medio Ambiente y Recursos Naturales de Sonora, México SEMARNAT-SON 24 
Sanidad Forestal, Secretaría del Medioambiente y Recursos Naturales, México SF-SEMARNAT 4 
Secretaría de Recursos Naturales y Medio Ambiente, Durango, México SRNyMA-DGO 31 
División de Ciencias Forestales, Universidad Autónoma Chapingo DCF-UACH 15 
Instituto de Silvicultura, Universidad Autónoma de Nuevo León. IS-UANL 2 
Colección Entomológica, Escuela Nacional de Ciencias Biológicas-IPN ENCB-IPN 14 
Lab. de Variación Biológica y Evolución, Escuela Nacional de Ciencias 
Biológicas-IPN 
LVBE-ENCB 21 
Unidad de Conservación de Ecosistemas Forestales, Madera, Chihuahua UCODEFO-CHIH 142 
Canadian National Collection of Insects, Arachnids and Nematodes-Ottawa CNCI-Ottawa 10 
Bibliographical data (Various authors) 48 
TOTAL 669 
María Guadalupe Mendoza Correa 
28 
 
 
 Table 2. Bioclimatic profile of D. rhizophagus for each location (obtained using BIOCLIM) 
 
 
No. and Climatic Variable Min. Mean Max. S.D. 5% 10% 50% 90% 95% 
1) Annual mean temperature ( ºC) 10.2 12.8 19.2 1.4 11.1 11.3 12.6 14.6 15.5 
2) Mean diurnal range (ºC) 12.4 16.7 19.1 1.9 13.1 13.6 17.4 18.4 18.7 
3) Isothermality 48.4 57.0 63.2 3.6 50.8 51.7 57.7 61.6 62.9 
4) Temperature seasonality 306.9 472.6 671.9 101.3 319.9 337.0 486.1 601.1 605.9 
5) Maximum temperature of warmest month (ºC) 21.4 27.5 35.6 2.4 23.2 24.1 27.7 30.3 30.8 
6) Minimum temperature of coldest month (ºC) -5.8 -2.1 7.0 2.6 -5.2 -5.0 -2.6 1.5 2.3 
7) Temperature annual range 21.0 29.5 35.5 4.2 21.9 22.9 31.1 33.8 34.2 
8) Mean temperature of wettest quarter (ºC) 13.9 17.8 25.0 1.6 15.5 15.9 17.6 19.9 20.9 
9) Mean temperature of driest quarter (ºC) 9.4 12.1 18.9 1.6 10.2 10.4 11.8 14.0 14.9 
10) Mean temperature of warmest quarter (ºC) 14.4 18.4 25.8 1.7 16.0 16.5 18.3 20.6 21.7 
11) Mean temperature of coldest quarter (ºC) 3.2 7.0 13.9 2.0 4.0 4.5 6.9 9.2 9.9 
12) Annual precipitation (mm) 305.0 814.2 1406.0 195.1 518.0 589.0 802.0 1101.0 1229.0 
María Guadalupe Mendoza Correa 
29 
 
13) Precipitation of wettest month (mm) 79.0 198.1 321.0 44.6 131.0 152.0 189.0 267.0 278.0 
14) Precipitation of driest month (mm) 2.0 10.9 18.0 3.2 6.0 7.0 11.0 15.0 15.0 
15) Precipitation seasonality (mm) 78.4 93.8 126.2 9.5 80.0 82.2 93.1 108.7 112.2 
16) Precipitation of wettest quarter (mm) 197.0 490.0 823.0 118.5 332.0 363.0 479.0 675.0 710.0 
17) Precipitation of driest quarter (mm) 10.0 47.8 71.0 11.5 27.0 33.0 48.0 64.0 67.0 
18) Precipitation of warmest quarter (mm) 170.0 440.4 746.0 110.6 276.0 325.0 437.0 614.0 628.0 
19) Precipitation of coldest quarter (mm) 22.0 124.0 250.0 41.6 57.0 70.0 120.0 180.0 197.0 
María Guadalupe Mendoza Correa 
 
30 
 
 Table 3. Incidence (%) of D rhizophagus on Pinus species in the Sierra 
Madre Occidental, México 
 
 
 
Pinus species 
 
Incidence (%) 
 
 
P. engelmannii 
P. durangensis 
P. arizonica 
P. leiophylla 
P. teocote 
P. herrerae 
P. lumholtzii 
P. strobiformis 
P. oocarpa 
P. cembroidesP. ponderosa 
Pinus sp 
 
Total number of records 
 
35.42 
16.60 
16.14 
8.67 
3.59 
2.84 
1.04 
0.75 
0.45 
0.15 
0.15 
14.20 
 
669 
 
María Guadalupe Mendoza Correa 
 
31 
 
Table 4. Principal component analysis of BIOCLIM climatic variables in relation to 
the occurrence of D. rhizophagus 
 
Climatic Variables 
 
Comp. 1 
Eigenvalues 
Comp. 2 
 
Comp. 3 
*1) Annual mean temperature -0.02 0.97 -0.18 
 2) Mean diurnal range [Mean of monthly (max 
temp- min temp)] 
0.71 -0.15 -0.44 
 3) Isothermality -0.74 0.28 0.07 
 4) Temperature seasonality 0.91 -0.23 -0.28 
 *5) Maximum temperature of warmest month 0.83 0.35 -0.42 
 6) Minimum temperature of coldest month -0.67 0.66 0.20 
 7) Temperature annual range 0.89 -0.21 -0.37 
 8) Mean temperature of wettest quarter 0.66 0.62 -0.41 
*9) Mean temperature of driest quarter -0.00 0.83 -0.41 
 10) Mean temperature of warmest quarter 0.70 0.60 -0.36 
*11) Mean temperature of coldest quarter -0.54 0.81 0.02 
*12) Annual precipitation -0.94 -0.01 -0.29 
*13) Precipitation of wettest month -0.81 0.11 -0.43 
*14) Precipitation of driest month -0.27 -0.56 -0.62 
 15) Precipitation seasonality 0.15 0.62 0.17 
*16) Precipitation of wettest quarter -0.92 0.14 -0.24 
 17) Precipitation of driest quarter -0.62 -0.52 -0.53 
*18) Precipitation of warmest quarter -0.88 0.14 -0.28 
María Guadalupe Mendoza Correa 
 
32 
 
 19) Precipitation of coldest quarter -0.70 -0.30 -0.49 
 % Explained variance 48.18 25.58 13.03 
 
*Most relevant variables according to histograms analysis 
María Guadalupe Mendoza Correa 
 
33 
 
Table 5. Percentage of variation explained by ecogeographic variables for D. 
rhizophagus using ENFA 
 
 
 
Ecogeographic Variable 
 
Factor 1 
Marginality 
(4.7%) 
 
Factor 2 
Specialization 
(81.4%) 
 
Factor 3 
Specialization 
(7.3%) 
 
Annual mean temperature 
 
 -0.300 
 
 -0.707 
 
 -0.471 
Maximum temperature of warmest month -0.340 0.000 0.000 
Mean temperature of driest quarter -0.165 0.000 0.000 
Mean temperature of coldest quarter -0.300 -0.707 - 0.511 
Annual precipitation 0.249 0.000 0.000 
Precipitation of wettest month 0.216 0.000 0.000 
Precipitation of driest month 0.352 0.000 0.000 
Precipitation of wettest quarter 0.202 0.000 0.000 
Precipitation of warmest quarter 0.249 0.000 0.000 
Minimum annual temperature -0.269 0.000 0.552 
Maximum annual temperature -0.316 0.000 0.461 
Elevation 0.276 0.000 0.000 
Pines distribution 0.304 0.000 0.000 
 
 
María Guadalupe Mendoza Correa 
 
34 
 
 
 Table 6. Percentage of estimated contribution for ecogeographic variables 
using MaxEnt 
 
 
Variable 
 
% Contribution 
 
Annual mean temperature 
 
30.1 
Elevation 13.4 
Maximum annual temperature 12.8 
Precipitation of warmest quarter 10.5 
Precipitation of driest month 9.8 
Maximum temperature of warmest month 8.1 
Precipitation of wettest month 5.1 
Minimum annual temperature 3.2 
Pines distribution 2.5 
Mean temperature of driest quarter 1.5 
Precipitation of wettest quarter 1.2 
Mean temperature of coldest quarter 1.1 
Annual precipitation 0.6 
María Guadalupe Mendoza Correa 
 
35 
 
Figure legends 
Fig. 1. Potential distribution of D. rhizophagus in the Sierra Madre Occidental, México 
modeled with BIOCLIM 
Fig. 2. Habitat suitability (HS) for D. rhizophagus in the Sierra Madre Occidental, 
México modeled with ENFA. 
Fig. 3. Potential distribution of D. rhizophagus in the Sierra Madre Occidental, México 
modeled with MaxEnt. 
María Guadalupe Mendoza Correa 
 
36 
 
 
María Guadalupe Mendoza Correa 
 
37 
 
 
María Guadalupe Mendoza Correa 
 
38 
 
 
 
María Guadalupe Mendoza Correa 
 
39 
 
 
 
 
CAPITULO II 
 
 
 
 
Determinación de Áreas de Susceptibilidad y Riesgo por la 
Presencia de Dendroctonus rhizophagus (Coleoptera: 
Curculionidae, Scolyitinae) en los Bosques de Pino del Municipio 
de Casas Grandes, Chihuahua 
 
 
 
 
 
 
 
 
María Guadalupe Mendoza Correa 
 
40 
 
 
 
 
 
 
Determination of Susceptibility and Risk Areas of Presence of Dendroctonus 
rhizophagus (Coleoptera: Curculionidae: Scolytinae) in Pine Forest of Casas Grandes, 
Municipality, Chihuahua 
 
 
 
 
 
 
 
 
Ma. Guadalupe Mendoza
1
, Daniel Núñez-López
2
, Yolanda-Morena
1
 and Gerardo Zúñiga
1
 
1
 Laboratorio de Variación Biológica y Evolución. Escuela Nacional de Ciencias 
Biológicas-IPN. Departamento de Zoología. Carpio y Plan de Ayala s/n, Col. Santo Tomás, 
C. P. 11340 México City, México. 
2
 Instituto de Ecología A.C., Centro Regional de Chihuahua. Carretera Chihuahua-Ojinaga, 
Aldama 32900, Chihuahua. México. 
María Guadalupe Mendoza Correa 
 
41 
 
ABSTRACT 
 
Dendroctonus rhizophagus harms and kills seedlings and young saplings of 11 species 
of pines in states of NW Mexico, particularly Chihuahua. Susceptibility and risk models for 
Casas Grandes municipality (Chihuahua) were generated by multi-criteria decision making 
(MCDM) methods based on the potential distribution of this bark beetle in Casas Grandes 
and the relative importance of the variables determining its presence in this region. Results 
suggest that D. rhizophagus is present mainly in the mountainous massifs of W and NW 
Casas Grandes, influenced mainly by precipitation and temperature variables. The preferred 
climate ranges are narrower than those reported previously for the Sierra Madre Occidental. 
The susceptibility model shows a continuous area, stretching from NW Casas Grandes to 
its S and center, where bioclimatic and topographical conditions are favorable for the 
presence of D. rhizophagus. The risk model shows a possible dispersal corridor running 
NW and S in adjacent Madera municipality. The latter model also indicates that the highest 
risk areas are in NW and W Casas Grandes. Periodical monitoring and evaluation of this 
area of the state are therefore suggested. 
KEYWORDS: Dendroctonus rhizophagus, Potential distribution, Prediction models, 
Susceptibility and risk models. 
María Guadalupe Mendoza Correa 
 
42 
 
RESUMEN 
 
Dendroctonus rhizophagus daña y mata plántulas y brinzales de once especies de pino 
en los estados del noroeste de México, particularmente en el estado de Chihuahua. Modelos 
de susceptibilidad y riesgo para el municipio de Casas Grandes, Chihuahua, fueron 
generados por técnicas de toma de decisión multicriterio (TDMC) a partir de la distribución 
potencial del insecto en este municipio y de la importancia relativa de las variables que 
determinan su presencia en esa región. Los resultados sugieren que D. rhizophagus se 
distribuye principalmente en los macizos montañosos de la región NW y W del municipio, 
influenciado principalmente por variables de precipitación y temperatura. Los intervalos 
climáticos preferenciales son más estrechos que los reportados para la Sierra Madre 
Occidental. El modelo de susceptibilidad señala una zona continua desde el noroeste hacia 
el sur y centro del municipio, con condiciones bioclimáticas y topográficas favorables para 
la presencia de D. rhizophagus. El modelo de riesgo muestra un corredor posible de 
dispersión en dirección NW y S en el municipio colindante de Madera. Asimismo, este 
modelo presenta a las regiones del NW y W del Municipio de Casas Grandes como las de 
mayor riesgo, por lo cual se sugiere una evaluación y monitoreo periódico de esta región 
del Estado. 
María Guadalupe Mendoza Correa 
 
43 
 
Introduction 
Bark beetles of the genus Dendroctonus (Curculionidae:Scolytinae) have been 
intensively studied due to the economic loss they entail in epidemic conditions for the 
lumber industry and the ecological problems caused by methods used fortheir control 
(Raffa et al. 2005, Sánchez-Martínez et al. 2007). 
To reduce the effect and intensity of beetle outbreaks, it has been resorted to preventive 
forestry practices, which are based on detailed knowledge of forest structure and 
composition as well as the characterization and modeling of environmental conditions 
favoring bark beetle population explosion (Lorio 1981). Risk assessment models or hazard 
evaluation systems are tools that have been developed and used to predict the susceptibility 
of forests to attack by these insects (Bentz et al. 1993, Shore and Safranyik 1992, Shore et 
al. 2000). 
Risk prediction models primarily integrate information on the climate, composition and 
dasonomic characteristics of forests as well as the biological and ecological attributes of 
beetles (Safranyik et al. 1978, Amman et al. 1977). Risk models have been developed at the 
micro- (local), meso- (regional), and macroscale (national) levels by the application of 
diverse criteria such as: forest manager experience (e.g. Lorio 1978) of forest conditions, 
statistical analysis of the forest characteristics favoring beetle presence (e.g. Chojnacky et 
al. 2000, Robertson et al. 2008), and evaluation of these characteristics by multi-criteria 
decision making (MCDM) methods integrated with geographic information systems (GIS) 
(e.g. Krist 2006). 
MCDM methods, such as multi-criteria evaluation (MCE) and analytic hierarchy process 
(AHP), permit identification, ordering and weighting of the relative importance of diverse 
climatic, biological and physical factors favoring presence of the risk agent. These 
María Guadalupe Mendoza Correa 
 
44 
 
methods, integrated with GIS, allow susceptibility or risk assessment maps to be drawn in 
order to be able to visualize the degree of potential affectation that the risk agent may have 
in a particular area or region. The first step in developing such maps is identification of the 
major variables related to the risk agent. This is done by using species distribution models 
(SDMs) such as BIOCLIM, GARP, and MaxEnt. SDMs enable estimation of the potential 
distribution of species, define the bioclimatic conditions in which they occur, and identify 
the variables that determine their presence (Guisan and Zimmermann 2000). 
The control methods used to restrain the more aggressive species of the genus 
Dendroctonus in Mexico are characterized by a wide use of treatments such as felling trees 
and leaving them on site, debarking, or the application of chemical compounds (Sánchez-
Martínez et al. 2007) that directly suppress beetle activity during or after outbreaks. These 
methods are costly, slow in operation, and aggressive due to the loss not only of infested 
trees, but also of those infested during sanitary actions. In spite of the scarcity of reliable 
information at the national level on the condition of Mexican forests, existing information 
at the local and regional levels has allowed susceptibility and risk models to be developed 
for some of the more aggressive bark beetle species, such as D. mexicanus Hopkins and D. 
frontalis Zimmermann (e.g. Iñiguez 1999, Llanderal 1995, Espinosa and Muñoz 1988). 
The bark beetle D. rhizophagus Thomas and Bright harms and kills seedlings and young 
saplings of 11 species of pine that are commercially valuable in the states of Sonora, 
Sinaloa, Chihuahua and Durango in northwestern Mexico (Estrada-Murrieta 1983, Cibrián 
et al. 1995). Chihuahua forests have been significantly affected by this beetle. For example, 
between 1977 and 1982 control actions were undertaken in over two million seedlings and 
saplings in ~ 12,000 ha of the Mesa del Huracán (29º 38' N, 108º 14' W) (Estrada-Murrieta 
1983). More recent reports indicate that D. rhizophagus affected some 1,200 ha in its 
María Guadalupe Mendoza Correa 
 
45 
 
distribution range between 2003 and 2008. Furthermore, conservative estimates suggest 
that the surface area susceptible to attack by this bark beetle covers ~ 1000 km
2
 
(SEMARNAT 2005). 
Since D. rhizophagus is an aggressive agent in pine forests of the state of Chihuahua in 
northwest Mexico, the aim of this study was to generate susceptibility and risk models in 
Casas Grandes municipality (Chihuahua) through the use of MCDM methods. In this study, 
the term “risk” is understood to mean the potential damage to a forest caused by exposure 
to an agent (Kaplan 1977), in this case presence of D. rhizophagus, while “susceptibility” is 
the probability that the forest will be attacked (Mott 1963), given the set of characteristics 
favoring presence of this bark beetle (Shore and Safranyik 1992). 
 
María Guadalupe Mendoza Correa 
 
46 
 
Materials and Methods 
Biological data. A total of 102 presence-only records for D. rhizophagus were obtained 
based on technical reports from the Babícora-Casas Grandes Forest Management Unit in 
the state of Chihuahua, as well as major entomological collections in México. 
Study area. Casas Grandes municipality is located between 30º 23' N latitude and 107º 
57' W longitude in the northwest part of the state of Chihuahua (Fig. 1). It covers a surface 
area of 3,719 km
2
; the climate is semiarid and harsh; and its altitude ranges from 1,430 to 
2,600 m.a.s.l. Surface relief in Casas Grandes is divided into the North Central Plateau on 
the E and the Sierra Madre of Chihuahua on the W. The North Central Plateau includes 
extensive arid areas with a desert climate, where the vegetation is mainly grassland, brush, 
and oak forest. The Sierra Madre of Chihuahua has a temperate climate and its vegetation 
cover comprises mainly pine forest and to a lesser extent oak forest (INEGI 2004). 
Potential geographic distribution of D. rhizophagus using MaxEnt, and 
determination of percent contribution by ecogeographic predictor variables. Digital 
maps of Casas Grandes showing 16 ecogeographic (topographic, climatic and biotic) 
variables that have been cited as significant for occurrence of D. rhizophagus (Mendoza et 
al. in review) and other bark beetles (Shore and Safranyik 1992, Wulder et al. 2006) were 
drawn at a spatial resolution of 25 x 25 m. These variables (Table 1) were regarded as 
predictor variables in order to obtain a model of the potential distribution of D. rhizophagus 
using MaxEnt v.3.3.1 (Phillips et al. 2004). 
Topographic variables. The digital elevation model (DEM) of the study area generated 
by García-Nájera (2009) was used to derive digital maps of the surface slope, aspect, and 
solar radiation with the spatial analysis tools provided in ArcGis v.9.2 software (ESRI, 
María Guadalupe Mendoza Correa 
 
47 
 
Redlands CA). The DEM was obtained from the contour lines in the eight vector 
topographic charts from INEGI (scale 1:50,000) that comprise the municipality. 
Climatic variables. Digital maps of the different temperature and precipitation variables 
were obtained (Table 1) through spatial interpolation using regression functions (Ninyerola 
et al. 2000). These methods are based on existing correlations between the altitude, latitude 
and longitude of weather stations, and temperature and precipitation values (Muñoz et al. 
2005). Regression coefficients were derived for each of the temperature and precipitation 
variables under consideration (Table 2) based on mean values obtained from the databases 
of 96 weather stations in the state of Chihuahua (Medina et al. 2006) as well as nine 
stations in Sonora (4), Arizona (4) and New Mexico (1) that are closest to Casas Grandes. 
These databases comprised the years 1961-1990. Digital maps of the different temperature 
and precipitation variables were derived through map algebra with ArcView v.3.2 software, 
using the following general regression equation: 
Temperature or precipitation variable = B0 + B1(map-x) + B2(map-y) + B3(map-z) 
where map-x, map-y and map-z are independent variablesof digital coverages of longitude, 
latitude and altitude; and B0, B1, B2 and B3 are regression coefficients obtained for each of 
the temperature and precipitation variables. 
Biotic variable. A digital map of the potential distribution of pine forests in Casas 
Grandes generated by García-Nájera (2009) by means of MCE (Fig. 1) was used. The latter 
method included topographic variables (elevation, slope, aspect), climatic variables (annual 
mean precipitation), the normalized difference vegetation index (NDVI) obtained from 
LandSat TM and SPOT5 satellite images, and soil attributes (texture and physical phase). 
The map shows values at byte scale, where the highest values (>146) represent pine 
communities. 
María Guadalupe Mendoza Correa 
 
48 
 
The model of the potential distribution of D. rhizophagus was obtained by entering in 
MaxEnt data on presence of the species as well as from the 16 maps of ecogeographic 
predictor variables. The MaxEnt algorithm was derived using default parameters of 500 
iterations with a convergence threshold of 0.00001 and a logistic output format (Phillips 
and Dudik 2008). During model development, 80% of the localities were used for model 
training and 20% were held back to test the model. The latter was evaluated by means of 
the area under the receiver operating characteristic curve (AUC). AUCs close to 0.5 are 
similar to random predictions and indicate poor model performance, while values > 0.9 
suggest high model reliability (Swets 1988, Phillips and Dudik 2008). The resulting map 
shows the probability (0-1) of presence of the species. 
Based on analysis of the data entered in MaxEnt, a table was constructed to show the 
percent contribution of each predictor variable to the final potential distribution model. This 
percentage was determined by the algorithm through the increase in gain in the model 
provided by each variable incorporated (Phillips et al. 2004). 
Susceptibility model. Digital maps of the ecogeographic variables used with MaxEnt 
were standardized, weighted and analyzed by MCE in order to obtain a model for assessing 
the susceptibility of pine forests to presence of D. rhizophagus. 
Standardization of factor maps. The MCE method used was weighted linear 
combination (WLC) with the use of overlay techniques based on GIS, which require that all 
factor maps (maps of ecogeographic variables) be standardized or converted into units 
capable of being subsequently compared (Ceballos-Silva and López-Blanco 2003). The 
nine different ranges obtained by default with ArcView v.3.2 (e.g. slope 0.001-16.48, 
16.49-32.95, etc.) for each alternative (e.g. aspect, slope, elevation, etc.) in the factor maps 
María Guadalupe Mendoza Correa 
 
49 
 
were therefore assigned to one of five possible suitability values (low, medium-low, 
medium, medium-high, and high). Classification of the ranges was based on the number of 
location records for D. rhizophagus in each of them. Each range was then standardized to 
byte scale (Tables 3, 4 and 5), consisting in 256 byte values, of which 0 is the lowest value 
and 255 the highest. The 256 byte values were divided into five intervals that represent the 
different suitability values. 
Weighting of standardized factor maps. Standardized factor maps were weighted at 
both the alternative (elevation, slope, etc.) and criterion (topographic, temperature, etc.) 
levels by analytic hierarchy process (AHP). Weighting of maps was based on the percent 
contribution of each variable to the model of the potential distribution of the bark beetle. 
AHP requires using a pairwise comparison matrix (PCM), which simplifies the preferred 
values among the different alternatives or criteria under analysis (Saaty 1980, Tienwong et 
al. 2009). The method for constructing the PCM uses a basic scale of values from 1/9 to 9 
in order to evaluate preferences between the two criteria or alternatives compared (Table 6). 
Consistency ratio (CR). There is a potential risk that each weight assigned to each of 
the alternatives or criteria will be inadequate as a result of incorrect or inconsistent pairwise 
comparisons. Therefore, to ensure credibility of the relative importance assigned, the CR 
was estimated. This ratio indicates the probability that values have been randomly assigned. 
Saaty (1980) suggested that if the CR is < 0.1, consistency is largely acceptable, but if it is 
> 0.1 then inconsistencies are present and the AHP is unable to produce significant results 
(Tienwong et al. 2009). 
Multi-criteria Evaluation (MCE). To obtain the susceptibility model for the pine 
forest, MCE was carried out through weighted linear combination (WLC) using map 
algebra and overlay techniques available in ArcView v.3.2. To this end, standardized factor 
María Guadalupe Mendoza Correa 
 
50 
 
maps of the same criterion were multiplied by their corresponding weight and added 
according to WLC, using the following equations: 
Topographic_Map = (Std_Slp*0.5842) + (Std_Elev*0.2725) + (Std_Asp*0.0943) + 
(Std_Sol-Rad* 0.0490) 
Precipitation_Map = (Std_An-Pp*0.5028) + (Std_Pp-D-M*0.2602) + (Std_Pp-Wm-
Q*0.1344) + (Std_Pp-Wt-M*0.0678) + (Std_Pp-Wt-Q*0.0348) 
Temperature_Map = (Std_Mn-Tp-D-Q*0.4615) + (Std_An-Max-Tp*0.2515) + (Std_Mx-
Tp-Wm-M*0.1330) + (Std_An-Mn-Tp*0.0619) + (Std_Mn-Tp-C-Q*0.0619) + (Std_An-
Min-Tp*0.0302). 
The susceptibility map was obtained by multiplying each criterion by its respective 
weight, and their sum through the equation: 
Susceptibility_Map = (Std_Pot-Dist-Pines*0.5579) + (Precipitation_Map*0.2633) + 
(Temperature_Map*0.1219) + (Topographic_Map*0.0569) 
Risk assessment model. After the susceptibility map was obtained, a map of 
georeferenced localities of D. rhizophagus in Casas Grandes and Madera municipalities 
was overlaid on it in order to establish the potential risk areas of presence of this insect, by 
generating zones of influence based on probable dispersal distances previously reported for 
these bark beetles (Atkins 1961, Byers 2000, Sánchez-Martínez verbal comm.), for 
example, is reported that D. valens can flight distances near to 2 km (Kinn 1986). These 
zones of influence were defined by 1.5 and 3 km dispersal radius using ArcView v.3.2. 
 
María Guadalupe Mendoza Correa 
 
51 
 
Results 
Potential geographic distribution of D. rhizophagus. The map of the potential 
geographic distribution of D. rhizophagus (Fig. 2) shows two separate areas of high 
probability of occurrence (> 0.502). The smaller of the two is in NW Casas Grandes and 
the other in its W part. Both are surrounded by areas of moderate probability (0.196 - 
0.501). The north, east, south, and southeast parts of Casas Grandes are the least suitable 
areas (< 0.196) for presence of this bark beetle. The AUC of training data was 0.956 and 
that of test points 0.946, indicating robustness of the model. 
Determination of percent contribution of ecogeographic predictor variables to the 
potential distribution model. According to MaxEnt results, the ecogeographic variables 
contributing the most to the potential distribution model of D. rhizophagus were annual 
precipitation (42.1%), mean temperature of driest quarter (14.1%), slope (10.0%), 
precipitation of driest month (9.6%), potential distribution of pines (8.5%), elevation 
(4.6%) and precipitation of warmest quarter (3.5%) (Table 7). Variables contributing the 
least were: precipitation of wettest quarter (0.1), solar radiation (0.1) and minimum annual 
temperature (0.01). 
Standardization of factor maps. Tables 3, 4 and 5 show the suitability values assigned 
to each of the alternatives of the different criteria under analysis, and their corresponding 
byte values. This bark beetle occurs preferentially (>70% of the records) in areas with the 
following precipitation ranges: annual 541-592 mm, driest month 7.7-9.0 mm, warmest 
quarter 258-286 mm, wettest month 125-137mm, and wettest quarter 300-330 mm. The 
preferred temperature ranges are: mean of driest quarter 11.3-13.6 ºC, mean of coldest 
quarter 4.7-6.2 ºC, maximum of warmest month 28.4-31.4 ºC, maximum annual 20.6-22.2 
ºC, mean annual 11.5-13.2 ºC, and minimum annual 2.2-4.1 ºC. In addition, this beetle 
María Guadalupe Mendoza Correa 
 
52 
 
occurs most frequently at elevations between 1,976 and 2,267 m, on gentle slopes (< 33%) 
facing NE, E or SE with solar radiation from 1,779,502 to 2,065,497 Wh/m
2
. 
Determination of weight for each criterion and alternative, and consistency ratio 
(CR). In analyzing the alternatives of the precipitation criterion with PCM (Table 8), the 
variable with highest weight was annual precipitation (W=0.5028), followed in importance 
by precipitation of driest month (W=0.2602), precipitation of warmest quarter (W=0.1344), 
precipitation of wettest month (W=0.0678) and precipitation of wettest quarter 
(W=0.0348). The CR was 0.093 (< 0.1) and consistency is therefore acceptable. The 
following weights were assigned for the temperature criterion (Table 9) in descending 
order: temperature of driest quarter (W=0.4615), maximum annual temperature 
(W=0.2515), maximum temperature of warmest month (W=0.1330), mean annual 
temperature (W=0.0619), temperature of coldest quarter (W=0.0619) and minimum annual 
temperature (W=0.0302). The CR was 0.067 (< 0.1), indicating that this ratio is within the 
interval established as acceptable. 
As regards the PCM of the topographic criterion (Table 10), the variable that was 
assigned the highest weight was surface slope (W=0.5842), followed in order by elevation 
(W=0.2725), aspect (W=0.0943), and last of all solar radiation (W=0.0490). The CR was 
0.044 (< 0.10), meaning the value judgments assigned were also consistent. 
Finally, in the AHP of the susceptibility model (Table 11) the criterion assigned the 
highest weight (W=0.5579) was the potential distribution of pines, followed in descending 
order by precipitation (W=0.2633), temperature (W=0.1219), and last of all the topographic 
criterion (W=0.0569). The CR was also acceptable, showing a value of 0.065 (<0.1). 
Susceptibility model. The susceptibility map of the pine forests in Casas Grandes 
obtained by MCE (Fig. 3) shows a large continuous area of high susceptibility in the NW, 
María Guadalupe Mendoza Correa 
 
53 
 
stretching towards the S and going around the W side of a low elevation zone in the central 
part of the municipality. This high susceptibility area encloses the latter zone on the E up to 
its middle portion, where it then extends as scattered patches. Additional tiny discontinuous 
high susceptibility areas are evident in SE Casas Grandes. All these high susceptibility 
areas extend over mountain terrain that forms part of the Sierra Madre of Chihuahua at 
elevations over 1,600 m. 
Risk model. For purposes of this study, all zones of influence of D. rhizophagus as 
determined by its dispersal radius and located in high susceptibility areas were considered 
potential risk areas. Thus, the model suggests as potential risk areas, the entire high 
susceptibility area in NW Casas Grandes that runs southward bordering on the west the 
region of low elevations in the central part of the municipality (Fig. 4). Also, two additional 
potential risk areas are evident: one in the SW and a second one that is more isolated from 
the rest, towards the S on small discontinuous areas of medium-high and high 
susceptibility. Analysis of the zones of influence of D. rhizophagus located in the adjacent 
Madera municipality evidences their proximity to the high susceptibility areas in S and SE 
Casas Grandes. 
 
María Guadalupe Mendoza Correa 
 
54 
 
Discussion 
Predictive modeling of the potential distribution of D. rhizophagus using MaxEnt 
enabled identification of the major bioclimatic variables and conditions that determine the 
presence of this species. Results suggest that D. rhizophagus presence is correlated mainly 
with precipitation and temperature, rather than topographic, variables (Table 7). Our results 
are consistent with those of other authors, who recognize a link between precipitation and 
temperature variables and the life cycle development and population dynamics (mortality, 
reproduction, emergence and dispersal) of bark beetles (Safranyik 1986, Bentz et al. 1991, 
Régnière and Logan 1996, Logan and Bentz 1999). For example, temperature has been said 
to determine the type of life cycle in some species, as in D. rufipennis Kirby, which usually 
displays semivoltine cycles, but may have univoltine cycles when environmental conditions 
become warmer (Hansen et al. 2001). Also, particular temperature and humidity thresholds 
have been seen to regulate population dynamics and the different life stages of these insects 
(Powell et al. 2000). Thus, the optimal temperatures for development of all life stages of D. 
ponderosae Hopkins have been said to fall between 23 and 25 ºC (Bentz et al. 1991) while 
its emergence and flight are said to be usually initiated after a threshold temperature of 19º 
C is reached (Rasmussen 1974). In regard to humidity, Coster et al. (1978), for example, 
found a significant correlation between this variable and the number of beetles present on 
trees infested by D. frontalis. Furthermore, humidity has been linked to the emergence of 
outbreaks, since when humidity is low tree resistance has been found to decrease, making 
trees more vulnerable to successful attack by bark beetles (Hicks 1980). 
The lesser relevance of topographic variables for determination of presence of D. 
rhizophagus may be due to their indirect effect, since variables such as elevation, aspect 
and slope modify both temperature and precipitation (Furniss and Carolin 1977, Nelson et 
María Guadalupe Mendoza Correa 
 
55 
 
al. 2007). Thus, temperatures in the microhabitat in which D. ponderosae occurs have been 
said to vary due to these topographic variables (Bolstad et al. 1997). Likewise, topographic 
variables are known to be more important in determination of the distribution, composition 
and structure of plant communities (Guisan and Hofer 2003), indirectly affecting therefore 
the distribution of insects that parasitize them (Wulder et al. 2006). The higher occurrence 
of D. rhizophagus on gentle slopes facing NE, E and SE appears in turn to be the result of 
preference for these conditions by the pine communities in Casas Grandes (García-Najera 
2009). 
Analysis of the ranges of the precipitation and temperature variables at which D. 
rhizophagus occurs is consistent with reports by Mendoza et al. (in review) for the Sierra 
Madre Occidental (SMOC). However, the former are much narrower, suggesting that this 
bark beetle is present in highly specific local conditions relative to those found throughout 
its geographic distribution range. Not so in the case of elevation, since the presence of this 
beetle in Casas Grandes was associated with preference for elevations over 1,900 m, which 
is consistent with the preferred range reported for its entire distribution range (Mendoza et 
al. in review). 
Determination of susceptible areas and risk areas in Casas Grandes municipality was 
based on ranking of the criteria for potential presence of the host as well as of climatic and 
topographic criteria. The highest weight was assigned to host presence, since hosts are the 
source of food for the beetle without which it could not become established. However, both 
the potential distribution model and the susceptibility model evidence the fact that not all 
areas with pines have suitable conditions for presence of this insect. This suggests that its 
distribution in Casas Grandes may be limited by environmental variables (precipitation and 
María Guadalupe Mendoza Correa 
 
56 
 
temperature) more than host presence. This same situation wasalso observed to occur 
throughout its distribution range (Mendoza et al. in review). 
The susceptibility model obtained by MCE concurs with the potential distribution model 
in pointing to mountain areas in W and NW Casas Grandes as those with a higher 
probability of having the most suitable conditions for D. rhizophagus presence. However, 
the susceptibility map also indicates as important other areas in S and SE Casas Grandes. 
These results suggest that the potential distribution model tends to underestimate certain 
areas with a higher probability of presence of the beetle, perhaps due to a spatial bias in 
sampling efforts since most location records fall within the W and NW regions of Casas 
Grandes. Phillips et al. (2009) state that a spatial bias in the sampling of data may impact 
the quality of the prediction model obtained. 
As regards high susceptibility areas, it is worth stressing that these run continuously 
along the mountainous massifs in W and NW Casas Grandes descending southwards to the 
central part of the municipality and going around a central region with low elevations. Such 
continuity of the areas of high suitability for presence of this bark beetle is important since 
they may constitute a corridor favoring dispersal of this beetle along the pine communities 
of Casas Grandes. 
Comparison of these areas of high susceptibility to the zones of influence of D. 
rhizophagus determined on the basis of its dispersal radius permitted the establishment of 
risk areas. The latter coincide in areas of high susceptibility with land surfaces given over 
to pine forest exploitation where the dominant species are P. durangensis, P. engelmannii, 
P. leiophylla and P. arizonica, which are the major hosts of this beetle (Cibrian et al. 1995, 
Mendoza et al. in review). This allow to suggest that priority be given to the monitoring 
and management of such risk areas, in view of the potential for populations of this beetle to 
María Guadalupe Mendoza Correa 
 
57 
 
become established or surge out of control, since felling is carried out continuously in some 
of these areas and the presence of new seedlings is therefore feasible. It may also be 
advisable in managing these areas to take into account the type of felling as well as the 
continuity with which it is practiced, since on this will depend the density and availability 
of new seedlings and saplings for the insect. Ideally, selective felling practices should be 
favored. Proximity of the zones of influence of D. rhizophagus in Madera to high 
susceptibility areas in south Casas Grandes is worth noting, as this may also imply an 
increased probability of risk in the former region, given that bark beetles do not recognize 
the boundaries between municipalities. Similarly, these observations suggest the existence 
of a corridor between the two municipalities. 
Finally, it is worth stressing that occurrence of this bark beetle in high susceptibility 
areas does not necessarily imply that it will develop in epidemic conditions, since this will 
depend on the presence of other factors in these susceptible areas including, among others, 
high populations of this insect, presence of seedlings of its hosts, presence of competitors 
or predators, as well as microclimatic conditions. This does not diminish the importance of 
risk models, which are intended as a helping guide for forest managers and are not meant to 
reproduce exactly the complex character of nature, but rather to identify and interrelate 
major or key biological and ecological characteristics in the insect-host system that may be 
of predictive value (Bentz et al. 1993). 
 
 
María Guadalupe Mendoza Correa 
 
58 
 
Acknowledgments 
We are grateful to Roberto Nevarez (Unidad de Manejo Forestal de Casas Grandes, 
Chihuahua) and Antonio Olivo Martínez (SEMARNAT-Chihuahua, México) for providing 
us with only-presence data of D. rhizophagus and for their time and availability. We also 
are grateful to Ing. Arturo Cedillo Quiñones (Unidad de Aprovechamiento y Restauración 
de Recursos Naturales de la SEMARNAT), for his availability to providing us with 
silvicultural information of the forest management plans from Municipality of Casas 
Grandes. The Project was funded by Comisión Nacional Forestal (CONAFOR, 69539) and 
Secretaría de Investigación y Posgrado-IPN (SIP-20090576). This work was part of M. G. 
M. C’s Ph. D. dissertation. She was a Consejo Nacional de Ciencia y Tecnología (207124) 
and Programa Institucional de Formación de Investigadores del Instituto Politécnico 
Nacional (PIFI-IPN) fellow. 
 
María Guadalupe Mendoza Correa 
 
59 
 
References Cited 
Atkins, M. D. 1961. A study of the flight behaviour of the Douglas-fir beetle Dendroctonus 
pseudotsugae Hopk. (Coleoptera: Scolytidae) III. Flight capacity. Can. Entomol. 93: 
467-474. 
Amman, G. D., M. D. McGregor, D. B. Cahill, and W. H. Klein. 1977. Guidelines for 
reducing losses of lodgepole pine to the mountain pine beetle in unmanaged stands in 
the Rocky Mountains. U.S.D.A. For. Serv., Intermountain Forest and Range Experiment 
Station. Gen. Tech. Rept. INT-36. 19 p. 
Bentz, B. J., J. A. Logan, and G. D. Amman. 1991. Temperature dependent development of 
the mountain pine beetle (Coleoptera: Scolytidae) and simulations of its phenology. Can. 
Entomol. 123: 1083-1094. 
Bentz, B. J., G. D. Amman, and J. A. Logan. 1993. A critical assessment of risk 
classification systems for the Mountain Pine Beetle. Forest Ecol. Manag. 61: 349-366. 
Bolstad, P. V., B. J. Bentz y J. A. Logan. 1997. Modelling micro-habitat temperature for 
Dendroctonus ponderosae (Coleoptera: Scolytidae). Ecol. Model. 94: 287-297. 
Byers, J. A. 2000. Wind-aided dispersal of simulated bark beetles flying through forests. Ecol. 
Model. 125: 231-243. 
Ceballos-Silva, A. y J. López-Blanco. 2003. Delineation of suitable areas for crops using a 
Multi-criteria Evaluation approach and land use/cover mapping: a case study in Central 
México. Agr. Syst. 77: 117-136. 
María Guadalupe Mendoza Correa 
 
60 
 
Cibrián, T. D., J. T. Méndez M., R. Campos B., H. O. Yates III, and J. Flores L. 1995. 
Insectos Forestales de México / Forest Insects of Mexico. Universidad Autónoma 
Chapingo, México. 
Coster, J. E., T. L. Payne, L. J. Edson, and E. R. Hart. 1978. Influence of weather on mass 
aggregation of southern pine beetles at attractive host trees. The Southwestern 
Entomologist. 3: 14-20. 
Chojnacky, D. C., B. J. Bentz, and J. A. Logan. 2000. Mountain pine beetle attack in 
ponderosa pine: Comparing methods for rating susceptibility. U.S.D.A. For. Serv., 
Ogden, UT: Rocky Mountain Research Station. Res. Pap. RMRS-RP-26. 10p. 
Environmental Systems Research Institute. 1999. Arcview-Gis 3.2. Environmental Systems 
Research Institute, Redlands, California, USA. 
Espinosa, C. G. M. y M. A. Muñoz. 1988. Sistema de clasificación de riesgo para 
Dendroctonus mexicanus en los bosques de la Unidad Forestal de San Rafael, México. 
Tesis Profesional. Universidad Autónoma Chapingo, México. 
Estrada-Murrieta, O. 1983. Biología del descortezador del renuevo de pino Dendroctonus 
rhizophagus T. y B. (Col.: Scolytidae) en la región de la Mesa del Huracán, Chihuahua. 
Bachelor’s Thesis. Universidad Autónoma Chapingo, México. 
Furniss, R. L., and V. M. Carolin. 1977. Western Forest Insects. Washington, D. C., USDA 
Forest Service. Miscellaneous Publication No. 1339. 654 p. 
García-Nájera, E. A. 2009. Distribución Potencial de las Comunidades de Pinos a Través de 
un proceso de Evaluación Multicriterio basado en SIG en el Municipio de Casas 
María Guadalupe Mendoza Correa 
 
61 
 
Grandes, Chihuahua. Tesis de Maestría. Escuela Nacional de Ciencias Biológicas, 
Instituto Politécnico Nacional. México, D.F. 
Guisan, A. and U. Hofer. 2003. Predicting reptile distributions at the mesoscale: relation to 
climate and topography. J. Biogeogr. 30: 1233-1243.Guisan, A. and N. Zimmermann. 2000. Predictive habitat distribution models in ecology. 
Ecol. Model. 135: 147-186. 
Hansen, E. M., B. J. Bentz, and D. L. Turner. 2001. Temperature-based model for 
predicting univoltine brood proportions in spruce beetle (Coleoptera: Scolytidae). Can. 
Entomol. 133: 827-841. 
Hicks, R. R. Jr., J. E. Howard, K. G. Watterson, and J. E. Coster. 1980. Rating forest 
stand susceptibility to southern pine beetle in east Texas. Forest Ecol. Manag. 2: 269-
283. 
INEGI. 2004. Síntesis de Información Geográfica del Estado de Chihuahua. Instituto 
Nacional de Estadística Geografía e Informática, México. 
Iñiguez, H. G. 1999. Sistemas de Clasificación de riesgo para Dendroctonus frontalis y D. 
mexicanus en el Manzano, en Villa de Santiago, Nuevo León, México. Tesis de 
Maestria. Universidad Autónoma de Nuevo León. 84p. 
Kaplan, S. 1997. The Words of Risk Analysis. Risk Analysis. 17: 407-417. 
Kinn, D. N. 1986. Studies on the Flight Capabilities of Dendroctonus frontalis and Ips 
calligraphus: Preliminary Findings Using Tethered Beetles. U.S.D.A. For. Serv., Res. 
Pap. SO-324, 3 p. South. For. Exp. Stn., New Orleans, La. 
Krist, F. J. 2006. A Multi-Criteria framework for producing local, regional and national insect 
and disease risk maps. Advances in threat assessment and their application to forest and 
rangeland management. July 18-20. Boulder, Colorado. 
María Guadalupe Mendoza Correa 
 
62 
 
Logan, J.A. and B. J. Bentz. 1999. Model analysis of mountain pine beetle (Coleoptera: 
Scolytidae) seasonality. Environ. Entomol. 28: 924–934. 
Lorio, P. L. Jr. 1978. Developing stand risk classes for the southern pine beetle. U.S.D.A. 
For. Serv., South. For. Exp. Stn. New Orleans. LA. Res. Pap. SO-144. 9 p. 
Lorio, L. P. Jr. 1981. Rating stands for susceptibility to Southern Pine Beetle. Chapter 8. In: 
The Southern Pine Beetle, by Thatcher, C. R., Searcy, L. J., Coster, E. J. Y Hertel, D. G. 
U.S.D.A. Expanded Southern pine beetle. Research and applications program. Forest 
Service Science and Education Administration. Tech. Bull. No. 1631. 153-163. 
Llanderal, O. T. 1995. Sistema de Clasificación de Susceptibilidad para Dendroctonus 
frontalis Zimm. en el Estado de México. Tesis Profesional. Universidad Autónoma 
Chapingo, México. 
Medina, G. G., P. G. Díaz, M. M. Berzoza, S. M. M. Silva y G. A. D. Baez. 2006. 
Estadísticas Climatológicas Básicas del Estado de Chihuahua (Periodo 1961-2003). 
Centro de Investigación Regional Norte Centro. Dirección de Coordinación y 
Vinculación Estatal en Chihuahua. INIFAP. Libro Técnico No. 1. 235p. 
Mendoza, G., Y. Salinas-Moreno, A. Olivo-Martínez y G. Zúñiga. Factors influencing the 
Geographical Distribution of Dendroctonus rhizophagus (Coleoptera: Curculionidae: 
Scolytinae) in the Sierra Madre Occidental, México. Environ. Entomol. In Review. 
Mott, D.G. 1963. The Forest and the Spruce Budworm in the Dynamics of Epidemic spruce 
Budworm Populations. R.F. Morris ed. Mem. Entomol. Soc. Can. 31:189-202. 
Muñoz, R. C. A., E. J. Treviño, J. Verástegui, J. Jiménez y O. A. Aguirre. 2005. 
Desarrollo de un Modelo Espacial para la evaluación del Peligro de incendios Forestales 
en la Sierra Madre Oriental de México. Invest. Geogr. 56: 101-117. 
María Guadalupe Mendoza Correa 
 
63 
 
Nelson, T. A., B. Boots, M. A. Wulder and A. L. Carroll. 2007. Environmental 
characteristics of mountain pine beetle infestation hot spots. BC J. Ecosys. Manage. 8: 
91-108. 
Ninyerola, M., X. Pons, and J. Roure. 2000. Climatological modeling. A methodological 
approach of climatological modeling of temperature and precipitation through GIS 
techniques. http://www.creaf.uab.es/MIRAMON/publicat/papers/lisboa98/climodel.htm 
(Fecha de consulta: 18 de Mayo del 2007). 
Phillips, S. J., R. E. Schapire, and M. Dudík. 2004. A maximum entropy approach to 
species distribution modeling, pp 655-662. In R. Greinerand and D. Schurmans (eds.), 
Proceedings of the Twenty-First International Conference on Machine Learning. 2004. 
Banff, Canada. ACM Press, NY. 
Phillips, S. J., and M. Dudík. 2008. Modeling of species distributions with MaxEnt: new 
extensions and a comprehensive evaluation. Ecography 31: 161-175. 
Phillips, S. J., M. Dudík, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. 
Ferrier. 2009. Sample Selection bias and presence-only distribution models: 
implications for background and pseudo-absence data. Ecol. Appl. 19: 181-197. 
Powell, J., B. P. Kennedy, B. White, J. Bentz, J. A. Logan and D. Roberts. 2000. 
Mathematical elements of attack risk analysis for Mountain Pine Beetles. J. Theor. Biol. 
204: 601-620. 
Raffa, K.F., B.H. Aukema, N. Erbilgin, D.K. Klepzing, K.F.Wallin. 2005. Interactions 
among conifer terpenoids and bark beetles across multiple levels of scale: an 
http://www.creaf.uab.es/MIRAMON/publicat/papers/lisboa98/climodel.htm
María Guadalupe Mendoza Correa 
 
64 
 
attempt to understand links between population patterns and physiological 
processes. Rec. Adv. Phytochem. 39: 79–118. 
Rasmussen, L. A. 1974. Flight and attack behavior of mountain pine beetles in lodgepole pine 
of Northern Utah and Southern Idaho. USDA Forest Service. Intermountain Forest and 
Range Experiment Station. Research Note, INT-180. 
Régnière, J. and J. A. Logan. 1996. Landscape-wide projection of temperature-driven 
processes for seasonal pest management decision support: a generalized approach, pp 
43-56. In T. L. Shore and D. A. MacLean (eds.). Symposium Proceedings: Decision 
support systems in forest pest management. Entomological Society of Canada, FRDA 
Report No. 260. Victoria, BC. 
Robertson, C., M. A. Wulder, T. A. Nelson, and J. C. White. 2008. Risk rating for 
mountain pine beetle infestation of lodgepole pine forests over large areas with ordinal 
regression modeling. Forest Ecol. Manag. 256: 900-912. 
Saaty, T. L. 1980. The Analytic Hierarchy Process, MacGraw-Hill, New York. 
Safranyik, L. 1978. Effects of climate and weather on mountain pine beetle populations. pp. 
77-86. .In: A. A. Berryman, G. D. Amman and R. W. Stark (eds.). Theory and Practice 
of Mountain Pine Beetle Management in Lodgepole Pine Forest. University of Idaho, 
Moscow. 
Safranyik, L. 1986. Effect of climatic factors on development, survival and life cycle of the 
MPB. pp. 14-24. In: Proceedings of the mountain pine beetle symposium: April 16, 17 
18, 1985. P. Hall and T. F. Maher (eds.). Smithers, B. C. British Columbia Ministry of 
Forests, Prince Rupert Region, Smithers, B. C. Pest Management Report Number 7. 
María Guadalupe Mendoza Correa 
 
65 
 
Sánchez-Martínez, G., L. M. Torres-Espinosa, I. Vázquez-Collazo, E. González-Gaona, y 
R. Narváez-Flores. 2007. Monitoreo y Manejo de Insectos Descortezadores. INIFAP. 
Centro de Investigación Regional Norte Centro. Campo Experimental Pabellón. Libro 
Técnico No. 4. 107 p. 
SEMARNAT. 2005. Aprovechamiento de los recursos forestales pesqueros y de la vida 
Silvestre. México. 
Shore, T. L. y L. Safranyik. 1992. Susceptibility and risk rating systems for the mountain 
pine beetle in lodgepole pine stands. Inf. Rep. BC-X336, Pacific and Yukon Region. 
Forestry Canada, Pacific Forestry Centre, Victoria, B. C., 12 pp. 
Shore, T. L., L. Safranyik and J. P. Lemieux. 2000. Susceptibility of lodgepole pine stands 
to the mountain pine beetle: testing of a rating system. Can. J. For. Res. 30: 44-49. 
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. 
Tienwong, K., S. Dasananda, and Ch. Navanugraha. 2009. Integration of land evaluation 
and the analytical hierarchical process methods for energy crops in Kanchanaburi, 
Tahiland. Sci. Asia 35: 170-177. 
Wulder, M. A., J. C. White, C. C. Dyamond, T. Nelson, B. Boots, and T. L. Shore. 2006. 
Calculating risk of mountain pine beetle attack: comparison of distance-and density-
based estimates of beetle pressure. J. Environ. Inf. 8:1-18. 
María Guadalupe Mendoza Correa 
 
66 
 
Table 1. Ecogeographic (topographic, climatic and biotic) variables regarded as 
predictor variables in MaxEnt analysis 
 
Ecogeogreaphic Variables 
 
Key 
Topographic 
1) Elevation (m.a.s.l) Elev 
2) Slope (%) Slp 
3) Aspect Asp 
4) Solar Radiation (WH/m²) Sol-Rad 
Climatic 
5) Annual Precipitation (mm) An-Pp 
6) Precipitation of wettest month (mm) Pp-Wt-M 
7) Precipitation of driest month (mm) Pp-D-M 
8) Precipitation of wettest quarter (mm) Pp-Wt-Q 
9) Precipitation of warmest quarter (mm) Pp-Wm-Q 
10) Annual maximum temperature ( ºC) An-Max-Tp 
11) Annual mean temperature (ºC) An-Mn-Tp 
12) Annual minimum temperature (ºC) An-Min-Tp 
13) Mean temperature of driest quarter (ºC) Mn-Tp-D-Q 
14) Mean temperature of coldest quarter (ºC) Mn-Tp-C-Q 
15) Maximum temperature of warmest month (ºC) Mx-Tp-Wm-M 
Biotic 
16) Potential distribution of Pines (Bytes) Pot-Dist-Pines 
María Guadalupe Mendoza Correa 
 
67 
 
Tabla 2. . Regression coefficients used to generate models of temperature and precipitation variables for Casas Grandes 
municipality, Chihuahua 
 Regression Coefficients 
Variables B0 B1 B2 B3 
An-Pp 4811.78943676 -0.0009906486 -0.0009054882 0.09136841 
Pp-Wt-M 1105.37880770 -0.0002187836 -0.0002112725 0.02070681 
Pp-D-M 35.30690517 -0.0000115821 -0.0000048834 0.00275391 
Pp-Wt-Q 2540.64633851 -0.0004437705 -0.0005102990 0.06037525 
Pp-Wm-Q 2414.00920504 -0.0004226468 -0.0004876615 0.05000667 
An-Max-Tp 36.10514593 0.0000025423 -0.0000016608 -0.00593993 
An-Mn-Tp 29.56201031 0.0000016611 -0.0000018158 -0.00631206 
An-Min-Tp 22.71528438 0.0000013909 -0.0000020566 -0.00683407 
Mn-Tp-D-Q 31.26635547 -0.0000077283 0.0000008181 -0.00530769 
Mn-Tp-C-Q 38.09394556 -0.0000014466 -0.0000057050 -0.00533746 
Mx-Tp-Wm-Q 33.11896571 0.0000023654 0.0000021388 -0.00678242 
María Guadalupe Mendoza Correa 
 
68 
 
Table 3. Suitability values for each alternative of the precipitation criterion, and their corresponding byte values 
 
Suitability 
Values 
Ranges of Byte Ranges of 
Pp-D-M 
Byte 
Value 
Ranges of Byte Ranges of 
Pp-Wt-M 
 Byte 
Value 
Ranges of 
Pp-Wt-Q 
 Byte 
Value Pp-Wm-Q Value An-Pp Value 
LOW 191.6-205.0 25 5.7-6.1 25 388.7-414.1 15 90.1-95.9 10 224.1-239.2 10 
LOW 205.1-218.4 50 6.2-6.6 50 414.2-439.6 30 96.0-101.7 20 239.3-254.4 20 
LOW 439.7-465.0 45 101.8-107.5 30 254.5-269.6 30 
LOW 107.6-113.3 40 269.7-284.8 40 
LOW 113.4-119.2 50 345.6-360.7 50 
MEDIUM-LOW 218.5-231.8 75 6.7-7.1 75 465.1-490.5 65 136.7-142.4 75 284.9-299.9 75 
MEDIUM-LOW 231.9-245.3 100 9.6-10.0 100 592.4-617.8 80 
MEDIUM-LOW 490.6-515.9 100 
MEDIUM 245.4-258.7 115 7.2-7.6 125 516-541.4 125 119.3-125.0 125 330.4-345.5 125 
MEDIUM 299.1-312.4 130 9.1-9.5 150 
MEDIUM 285.7-299.0 145 
MEDIUM-HIGH 258.8-272.1 175 7.7-8.0 175 566.9-592.3 175 130.9-136.6 175 300.0-315.1 175 
MEDIUM-HIGH 
HIGH 272.2-285.6 250 8.6-9.0 230 541.5-566.8 250 125.1-130.8 250 315.2-330.3 250 
HIGH 8.1-8.5 250 
 
María Guadalupe Mendoza Correa 
 
69 
 
Table 4. Suitability values for each alternative of the temperature criterion, and their corresponding byte values 
 
Suitability 
Values 
Ranges of Byte Ranges of 
Mn-Tp-C-Q 
Valor 
Byte 
Ranges of Byte Ranges of 
An-Max-Tp 
Byte 
Value 
Ranges of Byte Ranges of 
An-Min-Tp 
Byte 
Mn-Tp-D-Q Value Mx-Tp-Wm-Q Value An-Mn-Tp Value Value 
LOW 15.3-16.0 25 2.3-3.1 15 33.5-34.4 15 24.9-25.7 15 16.0-16.9 15 7.1-8.1 15 
LOW 8.8-9.6 50 8.6-9.3 30 32.5-33.4 30 17.9-18.8 30 8.6-9.5 30 -0.7-0.2 30 
LOW 7.7-8.5 45 25.3-26.3 45 24.1-24.8 45 15.1-15.9 45 6.2-7.0 45 
LOW 
LOW 
MEDIUM-LOW 14.5-15.2 75 3.2-3.8 75 26.4-27.3 75 18.9-19.7 75 9.6-10.5 75 0.2-1.1 75 
MEDIUM-LOW 9.7-10.4 100 
MEDIUM-LOW 
MEDIUM 13.7-14.4 125 7.0-7.6 1155 31.5-32.4 125 22.3-23.1 115 10.6-11.4 115 1.2-2.1 115 
MEDIUM 10.5-11.2 150 3.9-4.6 130 27.4-28.3 150 23.2-24.0 130 14.2-15.0 130 5.2-6.1 130 
MEDIUM 6.3-6.9 145 19.8-20.5 145 13.3-14.1 145 4.2-5.1 145 
MEDIUM-HIGH 12.9-13.6 175 4.7-5.4 200 30.4-31.4 175 20.6-21.4 200 11.5-12.3 200 2.2-3.1 200 
MEDIUM-HIGH 11.3-12.0 200 
HIGH 12.1-12.8 250 5.5-6.2 250 28.4-29.3 225 21.5-22.2 250 12.4-13.2 250 3.2-4.1 250 
HIGH 29.4-30.3 250 
 
 
María Guadalupe Mendoza Correa 
 
70 
 
Table 5. Suitability values for each alternative of the topographic and biotic criteria, and their corresponding byte values 
 
Suitability 
Ranges of Slope 
Byte 
Value 
Ranges of 
Aspect 
 Byte 
Value 
Ranges of 
Elevation 
 Byte 
Value 
Ranges of Byte 
Value 
Pot-Dist-Pines Byte 
Value Values Solar Radiation 
LOW 131.82-148.28 10 292.5-337.5 50 1393-1538 15 778516-921514 10 26.19-48.56 10 
LOW 115.34-131.81 20 1539-1684 30 921515-1064512 20 48.57-70.93 20 
LOW 98.87-115.33 30 2559-2704 45 1064513-1207510 30 70.94-93.30 30 
LOW 82.39-98.86 50 1207511-1350508 40 93.31-115.67 40 
LOW 1350509-1493505 50 115.68-138.04 50 
MEDIUM-LOW 65.91-82.38 75 247.5-292.5 75 2413-2558 75 1493506-1636503 75 
MEDIUM-LOW 0-22.5 100 138.05-160.41 100 
MEDIUM-LOW 337.5-360 100 
MEDIUM 157.5-202.5 130 1685-1830 115 1636504-1779501 125 
MEDIUM 49.44-65.90 125 202.5-247.5 145 2268-2412 130 160.42-182.78 150 
MEDIUM 32.96-49.43 150 1831-1975 145 
MEDIUM-HIGH 16.49-32.95 175 67.5-112.5 175 2122-2267 200 1922500-2065497 175 
MEDIUM-HIGH 112.5-157.5 200 182.79-205.15 200 
HIGH 0.001-16.48 250 1976-2121 250 205.16-227.52 250 
HIGH 22.5-67.5 250 1779502-1922499 250 
 
 
María Guadalupe Mendoza Correa 
 
71 
 
 
Table 6. Measurement scale for assignment of value judgments, defined by Saaty 
(1980) 
 
1/9 1/7 1/5 1/3 1 3 5 7 9 
Extremely 
Very 
Strongly 
Strongly Moderately Equal Moderately Strongly 
Very 
Strongly 
Extremely 
 Less Important More Important 
 
 
 
María Guadalupe Mendoza Correa 
 
72 
 
 
Table 7. Percent contribution by each ecogeographic variable as estimated by 
MaxEnt 
 
Ecogeographic 
Variables 
Percentage of 
Contribution 
An-Pp 42.1 
Mn-Tp-D-Q 14.1 
Slp 10.0 
Pp-D-M 9.6 
Pot-Dist-Pines 8.5 
Elev 4.6 
Pp-Wm-Q 3.5 
An-Max-Tp 2.8 
Mx-Tp-Wm-M 1.8 
Asp 1.1 
Pp-Wt-M 0.7 
An-Mn-Tp 0.6 
Mn-Tp-C-Q 0.4 
Pp-Wt-Q 0.1 
Sol-Rad 0.1 
An-Min-Tp 0.01 
 
María Guadalupe Mendoza Correa 
 
73 
 
 
Table 8. Pairwise comparison matrix of precipitation variables and the weight 
assigned to each variable 
 
 An-Pp Pp-D-M Pp-Wm-Q Pp-Wt-M Pp-Wt-Q Weight (W) 
An-Pp 1 3 5 7 9 0.5028 
Pp-D-M 1/3 1 3 5 7 0.2602 
Pp-Wm-Q 1/5 1/3 1 3 5 0.1344 
Pp-Wt-M 1/7 1/5 1/3 1 3 0.0678 
Pp-Wt-Q 1/9 1/7 1/5 1/3 1 0.0348 
 
María Guadalupe Mendoza Correa 
 
74 
 
 
Table 9. Pairwise comparison matrix of temperature variables and the weight 
assigned to each variable 
 
 
 Mn-Tp-D-Q An-Max-Tp Mx-Tp-Wm-M An-Mn-Tp Mn-Tp-C-Q An-Min-Tp Weight (W) 
Mn-Tp-D-Q 1 3 5 7 7 9 0.4615 
An-Max-Tp 1/3 1 3 5 5 7 0.2515 
Mx-Tp-Wm-M 1/5 1/3 1 3 3 5 0.1330 
An-Mn-Tp 1/7 1/5 1/3 1 1 3 0.0619 
Mn-Tp-C-Q 1/7 1/5 1/3 1 1 3 0.0619 
An-Min-Tp 1/9 1/7 1/5 1/3 1/3 1 0.0302 
 
María Guadalupe Mendoza Correa 
 
75 
 
 
Table 10. Pairwise comparison matrix of topographic variables and the weight 
assigned to each variable 
 
 
 
 Slope Aspect Elevation Sol-Rad Weight (W) 
Slope 1 7 3 9 0.5842 
Aspect 1/7 1 1/5 3 0.0943 
Elevation 1/3 5 1 5 0.2725 
Sol-Rad 1/9 1/3 1/5 1 0.0490 
 
 
María Guadalupe Mendoza Correa 
 
76 
 
 
Table 11. Pairwise comparison matrix of the criteria taken into account in order to 
obtain the susceptibility map and the weight assigned to each criterion 
 
 
 Pot-Dist-Pines Precipitation Temperature Topography Weight (W) 
Pot-Dist-Pines 1 3 5 7 0.5579Precipitation 1/3 1 3 5 0.2633 
Temperature 1/5 1/3 1 3 0.1219 
Topography 1/7 1/5 1/3 1 0.0569 
 
 
María Guadalupe Mendoza Correa 
 
77 
 
 
Figure legends 
 
Fig. 1. Location of Casas Grandes municipality, Chihuahua, and map of the potential 
distribution of pine forests (García-Nájera 2009). 
Fig. 2. Potential distribution of D.rhizophagus in Casas Grandes municipality, modeled 
with MaxEnt. 
Fig. 3. Susceptibility’s model of pine forests in Casas Grandes municipality, Chihuahua. 
Fig 4. Risk Model of presence of D. rhizophagus in pine forests of Casas Grandes 
municipality, Chihuahua. 
 
María Guadalupe Mendoza Correa 
 
78 
 
 
 
 
 
María Guadalupe Mendoza Correa 
 
79 
 
 
 
 
María Guadalupe Mendoza Correa 
 
80 
 
 
 
María Guadalupe Mendoza Correa 
 
81 
 
 
 
María Guadalupe Mendoza Correa 
 
82 
 
CONCLUSIONES GENERALES 
 
1.- El modelaje espacial de distribución de especies combinando datos de presencia, con 
variables bióticas, climáticas y topográficas puede ser usado para determinar la distribución 
potencial de las especies a diferentes niveles, desde locales hasta regionales o nacionales. 
En este estudio fue posible determinar la distribución potencial de D. rhizophagus tanto a 
nivel de Municipio (Casas Grandes), como regional (Sierra Madre Occidental). 
2.- Estas técnicas de modelaje permiten conocer cuales variables influyen mas en 
determinar la presencia de la especie en un sitio determinado. 
3.- El modelaje predictivo fue una herramienta útil para generar el perfil bioclimático de D. 
rhizophagus a lo largo de toda su área de distribución. 
4.- La incorporación de datos digitales, SIG y técnicas de EMC son una alternativa para 
modelar la susceptibilidad y el riesgo de los bosques de pino a agentes de riesgo como D. 
rhizophagus. 
5.- D. rhizophagus es un descortezador endémico de la SMOC, el cual se distribuye de 
manera casi continua a lo largo de esta provincia morfotectónica, 
6.- D. rhizophagus presenta un nicho ecológico relativamente estrecho con respecto a 
algunas variables de temperatura y precipitación, habitando condiciones climáticas 
particulares que no son las que prevalecen en la SMOC, teniendo una preferencia por 
hábitats templados. 
María Guadalupe Mendoza Correa 
 
83 
 
7.- D. rhizophagus presenta un nicho ecológico amplio con respecto al número de 
huéspedes que parasita (once especies); sin embargo su límite de distribución no parece 
estar determinado por la presencia de sus húespedes. 
8.- A nivel de la SMOC, el límite de la distribución geográfica de D. rhizophagus coincide 
con las isotermas de temperatura máxima. 
9.- A nivel de Casas Grandes, D. rhizophagus se distribuye principalmente en los macizos 
montañosos de la región NW y W del municipio,influenciado principalmente por variables 
de precipitación y temperatura. 
10.- Los rangos climáticos preferenciales en los que se presenta D. rhizophagus en Casas 
Grandes, son más estrechos que los reportados para toda su área de distribución en la 
SMOC. 
11.- En Casas Grandes, se presenta una zona continua de alta susceptibilidad que abarca 
desde el noroeste hacia el sur y centro del municipio, la cual presenta condiciones 
bioclimáticas y topográficas favorables para la presencia de D. rhizophagus. 
12.- El modelo de riesgo señala a las regiones del NW y W de Casas Grandes como las de 
mayor riesgo a la presencia de D. rhizophagus, por lo que se sugiere una evaluación y 
monitoreo continuo de estas zonas. 
 
 
 
María Guadalupe Mendoza Correa 
 
84 
 
REFERENCIAS GENERALES 
 
Amman, G. D. 1973. Population changes of the mountain pine beetle in relation to 
elevation. Environ. Entomol. 2: 541-547. 
Amman, G. D., M. D. McGregor, D. B. Cahill, and W. H. Klein. 1977. Guidelines for 
reducing losses of lodgepole pine to the mountain pine beetle in unmanaged stands in 
the Rocky Mountains. U.S.D.A. For. Serv., Intermountain Forest and Range Experiment 
Station. Gen. Tech. Rept. INT-36. 19 p. 
Anderson, R. P., D. Lew, and A. T Peterson. 2003. Evaluating predictive models of 
species’distributions criteria for selecting optimal models. Ecol. Model. 162: 211-232. 
Atkins, M. D. 1961. A study of the flight behaviour of the Douglas-fir beetle 
Dendroctonus pseudotsugae Hopk. (Coleoptera: Scolytidae) III. Flight capacity. Can. 
Entomol. 93: 467-474. 
Bale, S. J., G. J. Masters, I. D. Hodkinson, C. Awmack, T. M. Bezemer, V. K. Brown, 
J. Butterfield, A. Buse, J. C. Coulson, J. Farrar, J. E. G. Good, R. Harrington, S. 
Hartley, T. H. Jones, R. L. Lindroth, M. C. Press, I. Symrnioudis, A. D. Watt, and 
J. B. Whittaker. 2002. Herbivory in global climate change research: direct effects of 
rising temperature on insect herbivores. Global Change Biol. 8: 1-16. 
Bentz, B. J., J. A. Logan, and G. D. Amman. 1991. Temperature dependent development 
of the mountain pine beetle (Coleoptera: Scolytidae) and simulations of its phenology. 
Can. Entomol. 123: 1083-1094. 
Bentz, B. J., G. D. Amman, and J. A. Logan. 1993. A critical assessment of risk 
classification systems for the Mountain Pine Beetle. Forest Ecol. Manag. 61: 349-366. 
María Guadalupe Mendoza Correa 
 
85 
 
Beaumont, L. J., L. Hughes, and M. Poulsen. 2005. Predicting species distributions: Use 
of climatic parameters in BIOCLIM and its impact on predictions of species current and 
future distributions. Ecol. Model. 186: 250-269. 
Braunisch, V., and R. Suchant. 2007. A model for evaluating the “habitat potential” of a 
landscape for capercaillie Tetrao urogallus: a tool for conservation planning. Wildl. 
Biol. 13: 21-33. 
Brown, J. H. 1984. On the relationship between abundance and distribution of species. 
Am. Nat.124: 255-279. 
Brockerhoff, E.G., A. M. Liebhold, and H. Jactel. 2006. The ecology of forest insects 
invasions and advances in their management. Can. J. For. Res. 36: 263-268. 
Byers, J. A. 2000. Wind-aided dispersal of simulated bark beetles flying through forests. 
Ecol. Model. 125: 231-243. 
Carroll, A. L., S. W. Taylor, J. Régnière, and L. Safranyik. 2004. Effects of climate 
change on range expansion by the mountain pine beetle in British Columbia, pp. 223-
232. In T. L. Shore, J. E. Brooks and J. E. Stone (eds.), Mountain Pine Beetle 
Symposium: Challenges and Solutions. 30-31 October 2003, Kelowna, British 
Columbia, Canada, Natural Resources Canada, Canadian Forest Service, Pacific 
Forestry Centre, Information Report. BC-X-399, Victoria, BC. 
Ceballos-Silva, A. y J. López-Blanco. 2003. Delineation of suitable areas for crops using 
a Multi-criteria Evaluation approach and land use/cover mapping: a case study in 
Central México. Agricultural Systems 77: 117-136. 
Cibrián, T. D., J. T. Méndez M., R. Campos B., H. O. Yates III, and J. Flores L. 1995. 
María Guadalupe Mendoza Correa 
 
86 
 
Insectos Forestales de México/Forest Insects of Mexico. Universidad Autónoma 
Chapingo, México. 
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). 
1997. Provincias Biogeográficas de México. Escala 1:4,000,000. México. 
Chojnacky, D. C., B. J. Bentz, and J. A. Logan. 2000. Mountain pine beetle attack in 
ponderosa pine: Comparing methods for rating susceptibility. U.S.D.A. For. Serv., 
Ogden, UT: Rocky Mountain Research Station. Res. Pap. RMRS-RP-26. 10p. 
Environmental Systems Research Institute. 1999. Arcview-Gis 3.2. Environmental 
Systems Research Institute, Redlands, California, USA. 
Estrada-Murrieta, O. 1983. Biología del descortezador del renuevo de pino Dendroctonus 
rhizophagus T. y B. (Col.: Scolytidae) en la región de la Mesa del Huracán, Chihuahua. 
Bachelor’s Thesis. Universidad Autónoma Chapingo, México. 
Espinosa, C. G. M. y M. A. Muñoz. 1988. Sistema de clasificación de riesgo para 
Dendroctonus mexicanus en los bosques de la Unidad Forestal de San Rafael, México. 
Tesis Profesional. Universidad AutónomaChapingo, México. 
Farjon, A., and B. Styles. 1997. Flora Neotropica. Monograph 75. Pinus (Pinaceae). 
Organization for Flora Neotropica. New York Botanical Garden, NY. 
Ferrusquía-Villafranca, I. 1998. Geología de México: Una Sinopsis, pp. 3-108. In T. P. 
Ramamoorthy, R. Bye, A. Lot and J. Fa (eds.). Diversidad Biológica de México: 
Orígenes y Distribución. Instituto de Biología. Universidad Nacional Autónoma de 
México, México. 
Fielding, A. H. and J. F. Bell. 1997. A review of methods for the assessment of prediction 
errors in conservation presence/absence models. Environ. Conserv. 24: 38-49. 
María Guadalupe Mendoza Correa 
 
87 
 
Furniss, R. L., and V. M. Carolin. 1977. Western Forest Insects. Washington, D. C. 
USDA Forest Service. Miscellaneous Publication No. 1339. 654 p. 
García Arevalo, A y M. S. González Elizondo. 2003. Pináceas de Durango. 2a. ed. 
Instituto de Ecología A. C. México. 
Ganeshaiah, K. N., N. Barve, N. Nath, K. Chandrashekara, M. Swamy, and R. U. 
Shanker. 2003. Predicting the potential geographical distribution of the sugarcane 
woolly aphid using GARP and DIVA-GIS. Scientific Correspondence. Current Science 
85: 1526-1528. 
García-Nájera, E. A. 2009. Distribución Potencial de las Comunidades de Pinos a Través 
de un proceso de Evaluación Multicriterio basado en SIG en el Municipio de Casas 
Grandes, Chihuahua. Tesis de Maestría. Escuela Nacional de Ciencias Biológicas, 
Instituto Politécnico Nacional. México, D.F. 
Gaston, J. K. 2003. The structure and dynamics of geographic ranges. Oxford University 
Press, Oxford, New York. 
Guisan, A. and N. Zimmermann. 2000. Predictive habitat distribution models in ecology. 
Ecol. Model. 135: 147-186. 
Hicks, R. R. Jr., J. E. Howard, K. G. Watterson, and J. E. Coster. 1980. Rating forest 
stand susceptibility to southern pine beetle in east Texas. Forest Ecol. Manag. 2: 269-
283. 
Hijmans, R. J., L. Guarino, C. Bussink, and E. Rojas. 2002. DIVA-GIS, version 5.2. A 
geographic information system for the analysis of biodiversity data. Manual. 
International Potato Center, Lima, Peru. 
Hirzel, A., J. Hausser, D. Chessel, and N. Perrin. 2002. Ecological niche factor analysis: 
How to compute habitat suitability maps without absence data? Ecology 83: 2027–2036. 
María Guadalupe Mendoza Correa 
 
88 
 
Hirzel, A., B. Posse, P-A. Oggier, Y. Crettenand, C. Glenz, and R. Arlettaz. 2004. 
Ecological requirements of reintroduced species and the implications for release policy: 
the case of the Bearded vulture recolonizing the Alps. J. Appl. Ecol. 41: 1103–1116. 
Hirzel, A. H., J. Hausser, and N. Perrin. 2006a. Biomapper 3.2. Laboratory for 
Conservation Biology. Department of Ecology and Evolution. University of Lausanne, 
Lausanne. http://www.unil.ch/biomapper. 
Hirzel, A. H., G. Le Lay, V. Helfer, C. Randin, and A. Guisan. 2006b. Evaluating the 
ability of habitat suitability models to predict species presences. Ecol. Model. 199: 142–
152 
INEGI. 2004. Síntesis de Información Geográfica del Estado de Chihuahua. Instituto 
Nacional de Estadística Geografía e Informática, México. 
Iñiguez, H. G. 1999. Sistemas de Clasificación de riesgo para Dendroctonus frontalis y D. 
mexicanus en el Manzano, en Villa de Santiago, Nuevo León, México. Tesis de 
Maestria. Universidad Autónoma de Nuevo León. 84p. 
Jiménez-Valverde, A., V. M. Ortuño, and J. M. Lobo. 2007. Exploring the distribution 
of Sterocorax Ortuño, 1990 (Coleoptera, Carabidae) species in the Iberian Peninsula. J. 
Biogeogr. 34: 1426-1438. 
Kaplan, S. 1997. The Words of Risk Analysis. Risk Analysis. 17: 407-417. 
Kelley, S. T., and B. D. Farrell. 1998. Is specialization a dead end? The phylogeny of host 
use in Dendroctonus Bark Beetles (Scolytidae). Evolution 52: 1731-1743. 
Kinn, D. N. 1986. Studies on the Flight Capabilities of Dendroctonus frontalis and Ips 
calligraphus: Preliminary Findings Using Tethered Beetles. U.S.D.A. For. Serv.South, 
For. Exp. Stn., New Orleans, LA. Res. Pap. SO-324. 3 p 
María Guadalupe Mendoza Correa 
 
89 
 
Krist, F. J. 2006. A Multi-Criteria framework for producing local, regional and national 
insecte and disease risk maps. Advances in threat assessment and their application to 
forest and rangeland management. July 18-20. Boulder, Colorado. 
Kurz, W. A., C. C. Dymond, G. Stinson, G. J. Rampley, E. T. Neilson, A. L. Carroll, 
T. Ebata, and L. Safranyik. 2008. Mountain pine beetle and forest carbon feedback to 
climate change. Nature 452: 987-990. 
Kumar, S., and T. J. Stohlgren. 2009. Maxent modeling for predicting suitable habitat for 
threatened and endangered tree Canacomyrica monticola in New Caledonia. J. Ecol. 
Nat. Environ. 1: 94-98. 
Lammertink, J. M., J. A. Rojas-Tomé, F. M. Casillas-Orona, y R. L. Otto. 1997. 
Situación y conservación de los bosques antiguos de pino-encino de la Sierra Madre 
Occidental y sus aves endémicas. Consejo Internacional para la preservación de las aves, 
sección Mexicana (CIPAMEX). D. F. México. 
Lekander, B., B. Bejer-Petersen, B. Kangas, and A. Bakke. 1977. The distribution of 
bark beetles in the Nordic Countries. Acta Entomol. Fenn. 32: 1-37. 
Lindenmayer, D. B., H. A. Nix, J. P. McMahon, M. F. Hutchinson, and M. T. Tanton. 
1991. The conservation of Leadbeater’s possum, Gymnobelideus leadbeateri (McCoy): a 
case study of the use of bioclimatic modelling. J. Biogeogr. 18: 371-383. 
Logan, J. A., and G. D. Amman. 1986. A distribution model for egg development in 
mountain pine beetle. Can. Entomol. 118: 361-372. 
Logan, J.A. and B. J. Bentz. 1999. Model analysis of mountain pine beetle (Coleoptera: 
Scolytidae) seasonality. Environ. Entomol. 28: 924–934. 
María Guadalupe Mendoza Correa 
 
90 
 
Logan, J. A., and J. A. Powell. 2001. Ghost forests, global warming and the mountain 
pine beetle (Coleptera: Scolytidae). Am. Entomol. 47: 160-172. 
Lomolino, M. V., B. R. Riddle, and J. H. Brown. 2006. Biogeography. Sinauer 
Associates Inc., Sunderland, Massachussetts. 
Lorio, P. L. Jr. 1978. Developing stand risk classes for the southern pine beetle. U.S.D.A. 
For. Serv., South. For. Exp. Stn. New Orleans. LA. Res. Pap. SO-144. 9 p. 
Lorio, L. P. Jr. 1981. Rating stands for susceptibility to Southern Pine Beetle. Chapter 8. 
In: The Southern Pine Beetle, by Thatcher, C. R., Searcy, L. J., Coster, E. J. Y Hertel, D. 
G. USDA Expanded Southern pine beetle. Research and applications program. Forest 
Service Science and Education Administration. Tech. Bull. No. 1631. 153-163. 
Llanderal, O. T. 1995. Sistema de Clasificación de Susceptibilidad para Dendroctonus 
frontalis Zimm. en el Estado de México. Tesis Profesional. Universidad Autónoma 
Chapingo, México. 
MacDonald, G. M. 2003. Biogeography, space, time and life. John Wiley & Sons. Inc., 
United States of America. 
McGregor, M. D. 1986. Stand hazard and risk rating for mountain pine beetle 
susceptibility and losses. In: Proceedings of the mountain pine beetle symposium: April 
16, 17, 18, 1985. P. M. Hall and T. F. Maher (ed.) Smithers, B. C. British Columbia 
Ministry of Forests, Prince Rupert Region, Smithers, B. C. Pest Management Report 
Number 7. Pp. 87-101. 
Mackey, B. G., and D. B. Lindenmayer. 2001. Towards a hierarchical framework for 
modeling the spatial distribution of animals. J. Biogeogr. 28: 1147-1166. 
María Guadalupe Mendoza Correa 
 
91 
 
Mason, G. N., L. P. Jr. Lorio. R. P. Belanger y W. A. Nettleton. 1985. Rating the 
susceptibility of stands to southern pine beetle attack. Integrated Pest Management 
Handbook, USDA Forest Service. Agriculture Handbook. Nm. 65. 31 p 
Medina, G. G., P. G. Díaz, M. M. Berzoza, S. M. M. Silva y G. A. D. Baez. 2006. 
Estadísticas Climatológicas Básicas del Estado de Chihuahua (Periodo 1961-2003). 
Centro de Investigación Regional Norte Centro. Dirección de Coordinación y 
Vinculación Estatal en Chihuahua. INIFAP. Libro Técnico No. 1. 235p. 
Mendoza, G., Y. Salinas-Moreno,A. Olivo-Martínez y G. Zúñiga. Factors influencing 
the Geographical Distribution of Dendroctonus rhizophagus (Coleoptera: Curculionidae: 
Scolytinae) in the Sierra Madre Occidental, México. Environ. Entomol. In Review. 
Mott, D.G. 1963. The Forest and the Spruce Budworm in the Dynamics of Epidemic 
spruce Budworm Populations. R.F. Morris ed. Mem. Entomol. Soc. Can. 31:189-202. 
Muñoz, R. C. A., E. J. Treviño, J. Verástegui, J. Jiménez y O. A. Aguirre. 2005. 
Desarrollo de un Modelo Espacial para la evaluación del Peligro de incendios Forestales 
en la Sierra Madre Oriental de México. Investigaciones Geográficas 56: 101-117. 
Nelson, W. A., A. Potapov, M. A. Lewis,, A. E. Hundsdörfer and F. He. 2006. The 
balance of complexity in mechanistic modeling: Risk analysis in the mountain pine 
beetle. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre. 
Mountain Pine Beetle Initiative. Working Paper 2006-03. 
Nelson, T. A., B. Boots, M. A. Wulder and A. L. Carroll. 2007. Environmental 
characteristics of mountain pine beetle infestation hot spots. BC Journal of Ecosystems 
and Management. 8: 91-108. 
María Guadalupe Mendoza Correa 
 
92 
 
Ninyerola, M., X. Pons, and J. Roure. 2000. Climatological modeling. A methodological 
approach of climatological modeling of temperature and precipitation through GIS 
techniques. http://www.creaf.uab.es/MIRAMON/publicat/papers/lisboa98/climodel.htm 
(Fecha de consulta: 18 de Mayo del 2007). 
Nix, H. 1986. A biogeographic analysis of Australian elapid snakes, pp. 4-15. In R. 
Longmore (ed.), Atlas of elapid snakes of Australia. Australian flora and fauna series 
number 7. Australian Government Publishing Service, Canberra. 
Ortega-Rosas, C. I., M. C. Peñalba, J. A. López-Sáez, y T. R. Van Devender. 2008. 
Retrospectiva del Bosque de Pino y Encino de la Sierra Madre Occidental, Sonora, 
Noroeste de México, Hace 1,000 años. Acta Bot. Mex. 83: 69-92. 
Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the 
distribution of species: are bioclimate envelope models useful? Global Ecol. 
Biogeogr. 12: 361-371. 
Perry, J. P. Jr. 1991. The Pines of Mexico and Central America. Timber Press Inc., 
Portland, Oregon. 
Phillips, S. J., R. E. Schapire, and M. Dudík. 2004. A maximum entropy approach to 
species distribution modeling, pp. 655-662. In R. Greinerand and D. Schuurmans (eds.), 
Proceedings of the Twenty-First International Conference on Machine Learning. 2004. 
Banff, Canada. ACM Press, NY. 
Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of 
species geographic distributions. Ecol. Model. 190: 231-259. 
Phillips, S. J., and M. Dudík. 2008. Modeling of species distributions with MaxEnt: new 
extensions and a comprehensive evaluation. Ecography 31: 161-175. 
http://www.creaf.uab.es/MIRAMON/publicat/papers/lisboa98/climodel.htm
María Guadalupe Mendoza Correa 
 
93 
 
Phillips, S. J., M. Dudík, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. 
Ferrier. 2009. Sample Selection bias and presence-only distribution models: 
implications for background and pseudo-absence data. Ecological Applications 19: 181-
197. 
Powell, J., B. P. Kennedy, B. White, J. Bentz, J. A. Logan and D. Roberts. 2000. 
Mathematical elements of attack risk analysis for Mountain Pine Beetles. J. Theor. Biol. 
204: 601-620. 
Raffa, K. F. and A. A. Berryman. 1987. Interacting Selective Pressures in Conifer-Bark 
Beetle Systems. A basis for Reciprocal Adaptations?. The American Naturalist. 129: 
234-262. 
Raffa,K. F., B. H. Aukema, N. Erbilgin, D. K. Klepzing, K. F. Wallin. 2005. 
Interactions among conifer terpenoids and bark beetles across multiple levels of scale: 
an attempt to understand links between populations patterns and physiological processes. 
Rec. Adv.Phytochem. 39: 79-118. 
Rapoport, E. H. 1982. Areografía. Estrategias geográficas de las especies. Fondo de 
Cultura Económica. México, 214 pp. 
Régnière, J., and J. A. Logan. 1996. Landscape-wide projection of temperature-driven 
processes for seasonal pest management decision support: a generalized approach, pp. 
43-56. In T. L. Shore and D. A. MacLean (eds.). Symposium Proceedings: Decision 
support systems in forest pest management. Entomological Society of Canada, FRDA 
Report No. 260. Victoria, BC. 
María Guadalupe Mendoza Correa 
 
94 
 
Robertson, C., M. A. Wulder, T. A. Nelson, and J. C. White. 2008. Risk rating for 
mountain pine beetle infestation of lodgepole pine forests over large areas with ordinal 
regression modeling. Forest Ecol. Manag. 256: 900-912. 
Rzedowsky, J. 1978. Vegetación de México. Limusa. México. 
Saaty, T. L. 1980. The Analytic Hierarchy Process, MacGraw-Hill, New York. 
Safranyik, L. 1978. Effects of climate and weather on mountain pine beetle populations. 
pp. 77-86. .In: A. A. Berryman, G. D. Amman and R. W. Stark (eds.). Theory and 
Practice of Mountain Pine Beetle Management in Lodgepole Pine Forest. University of 
Idaho, Moscow. 
Safranyik, L. 1986. Effect of climatic factors on development, survival and life cycle of 
the MPB. pp. 14-24. In: Proceedings of the mountain pine beetle symposium: April 16, 
17 18, 1985. P. Hall and T. F. Maher (eds.). Smithers, B. C. British Columbia Ministry 
of Forests, Prince Rupert Region, Smithers, B. C. Pest Management Report Number 7. 
Salinas-Moreno, Y., M. G. Mendoza, M. A. Barrios, R. Cisneros, J. Macías-Sámano, 
and G. Zúñiga. 2004. Areography of the genus Dendroctonus (Coleoptera: 
Curculionidae: Scolytinae) in México. J. Biogeogr. 31: 1163-1177. 
Salinas-Moreno, Y., A. Ager, C. F. Vargas, J. L. Hayes, and G. Zúñiga. 2010. 
Determining the vulnerability of mexican pine forest to bark beetles of the genus 
Dendroctonus Erichson (Coleoptera: Curculionidae: Scolytinae). Forest Ecol. Manag. 
260: 52-61. 
Sánchez-Martínez, G., L. M. Torres-Espinosa, I. Vázquez-Collazo, E. González-
Gaona, y R. Narváez-Flores. 2007. Monitoreo y Manejo de Insectos Descortezadores. 
María Guadalupe Mendoza Correa 
 
95 
 
INIFAP. Centro de Investigación Regional Norte Centro. Campo Experimental 
Pabellón. Libro Técnico No. 4. 107 p. 
Sánchez-Martínez, G., and M. R. Wagner. 2009. Host preference and attack pattern of 
Dendroctonus rhizophagus (Coleoptera: Curculionidae: Scolytinae): a bark beetle 
specialist on pine regeneration. Environ. Entomol. 38: 1197-1204. 
Sattler, T., F. Bontadina, A. H. Hirzel, and R. Arlettaz. 2007. Ecological niche 
modelling of two cryptic bat species calls for a reassessment of their conservation status. 
J. Appl. Ecol. 44: 1188-1199. 
SEMARNAT. 2005. Aprovechamiento de los recursos forestales pesqueros y de la vida 
Silvestre. México. 
Shore, T. L. y L. Safranyik. 1992. Susceptibility and risk rating systems for the mountain 
pine beetle in lodgepole pine stands. Inf. Rep. BC-X336, Pacific and Yukon Region. 
Forestry Canada, Pacific Forestry Centre, Victoria, B. C., 12 pp. 
Shore, T. L., L. Safranyik and J. P. Lemieux. 2000. Susceptibility of lodgepole pine 
stands to the mountain pine beetle: testing of a rating system. Can. J. For. Res. 30: 44-
49. 
Spellerberg, I. F., and J. W. D. Sawyer. 1999. An introduction to applied biogeography. 
Cambridge University Press, Cambridge. 
Swets, J. A. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. 
Thomas, C. D., E. J. Bodsworth, R. J. Wilson, A. D. Simons, Z. G. Davies, M. Musche, 
and L. Conradt. 2001. Ecological and evolutionary processes at expanding range 
margins. Nature 411: 577-581. 
María Guadalupe Mendoza Correa 
 
96 
 
Tienwong, K., S. Dasananda, and Ch. Navanugraha. 2009. Integration of land 
evaluation and the analytical hierarchical process methods for energy crops in 
Kanchanaburi, Tahiland. Science Asia 35: 170-177. 
Titeaux, N., M. Dufrêne, J. Radoux, A. H. Hirzel, and P. Defourny. 2007. Fitness-
related parameters improve niche-baseddistribution modelling: the case of the Red-
backed Shrike. Biol. Conserv. 138: 207-223. 
Turchin, P., P. L. Jr. Lorio, A. D. Taylor, and R. F. Billings. 1991. Why do populations 
of southern pine beetles (Coleoptera: Scolytidae) fluctuate? Environ. Entomol. 20: 401-
409. 
Ungerer, M., M. P. Ayres, and M. J. Lombardero. 1999. Climate and the northern 
distribution limits of Dendroctonus frontalis Zimmermann (Coleoptera: Scolytidae). J. 
Biogeogr. 26: 1133-1145. 
Waring, K.M., D. M. Reboletti, L. A. Mork, M. Li, C.H. Huang, R. W. Hofstetter, A. 
M. Garcia, P. Z. Fulé, and T. S. Davis. 2009. Modeling the impacts of two bark beetle 
species under warming climate in the southwestern U.S.A.: ecological and economic 
consequences. Environ. Manage. 44: 824-835. 
Wood, S. L. 1982. The bark and ambrosia beetles of North and Central America 
(Coleoptera: Scolytidae) a taxonomic monograph. Great Basin Nat. Mem. 6: 1-1359. 
Wulder, M. A., J. C. White, C. C. Dyamond, T. Nelson, B. Boots, and T. L. Shore. 
2006. Calculating risk of mountain pine beetle attack: comparison of distance-and 
density-based estimates of beetle pressure. Journal of Environmental Informatics 8: 1-
18.