ISSN e: 2007-4018 / ISSN print: 2007-4018

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Vol. XXIII, issue 1 January - April 2017

ISSN: ppub: 2007-3828 epub: 2007-4018

Article scientific

Spatio-temporal analysis of forest modeling in Mexico

http://dx.doi.org/10.5154/r.rchscfa.2016.01.003

Martínez-Santiago, Saira Y. 1 ; Alvarado-Segura, Arturo A. 1 2 ; Zamudio-Sánchez, Francisco J. 1 * ; Cristóbal-Acevedo, David 3

  • 1 Universidad Autónoma Chapingo, División de Ciencias Forestales. km 38.5 Carretera México-Texcoco. C. P. 56230. Chapingo, Texcoco, Estado de México.
  • 2 Instituto Tecnológico Superior del Sur del Estado de Yucatán. Carretera Muna-Felipe Carrillo Puerto, tramo Oxkutzcab-Akil km 41+400. C. P. 97880. Oxkutzcab, Yucatán, México.
  • 3 Universidad Autónoma Chapingo, Departamento de Suelos. km 38.5 Carretera México-Texcoco. C. P. 56230. Chapingo, Texcoco, Estado de México.

Corresponding author. Email: fzamudios@taurus.chapingo.mx

Received: January 26, 2016; Accepted: September 13, 2016

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Abstract

There is a consensus that anthropogenic actions are degrading ecosystems at an alarming rate. Modeling and new technologies, such as information and communications technology (ICT), are increasingly being used to make decisions about the management and conservation of natural resources. In this study, the temporal evolution and spatial distribution of Mexican scientific production in forest modeling are analyzed. From 1980 to 2015, 454 authors participated in the publication of 259 papers in 37 journals (84 % of them Mexican), of which 28 are indexed in the Journal Citation Reports (JCR). Studies on forest management have been the most important but are losing relative weight, while those on environmental services and potential distribution of species are gaining importance. The authors belong to 89 institutions, of which 65 % are Mexican. During the period analyzed, the number of authors (and partnerships) increased 12 times, while the number of publications increased nine times. These increases coincide with the evolution of regulatory policies and the establishment and support of the National System of Researchers. Collaborations in the current forest-modeling network still have great growth potential.

Keywords:Scientific production; collaborative networks; forest management; environmental services; bibliometric approach.

Introduction

A forest model is a simplified representation of a phenomenon, process or system, which can explain functional relationships in a forest (Barnsley, 2007). Statistical models are mainly used in forest modeling, with regression and experimental designs being the most common (Barnsley, 2007; Sheridan, Popescu, Gatziolis, & Morgan, 2014). In the case of forests, modeling is a key tool to establish relationships and understand multifactorial phenomena or processes, such as growth, timber yield, site productivity, current and potential distribution of species and carbon sequestration (Hynynen, 2011; Li et al., 2015; Peng, 2000).

To have access to complete and organized information on forest modeling, in order to establish mediumand long-term development prospects, the historical evolution of scientific production must be known. In this sense, productivity can be measured by the number and quality of publications, mainly of papers that undergo peer review (Galeano, Amarilla, & Parra, 2007; Ríos & Herrero, 2005). With a bibliometric approach, the scientific production of individuals, institutions and countries, the magnitude and frequency of use (i.e. the number of citations) of such production and the form of collaboration of the researchers or institutions in the networks can be known (Huamaní & MaytaTristán, 2010; Prat, 2001). This information enables placing researchers and institutions into a particular field, identifying those who are subject to funding, and detecting the priorities of the collaborative networks (Huamaní & Mayta-Tristán, 2010; Prat, 2001).

Collaborative networks reflect the relationships among their members, works and academic links, which allow evaluating the process of knowledge generation (Huamaní & Mayta-Tristán, 2010). It has been reported, for example, that consolidated research networks permit increased production and citations of papers (Hill, 2008), as well as obtaining academic and logistical benefits (Gaughan & Ponomariov, 2008; Huamaní & Mayta-Tristán, 2010). Analyzing the structure of the networks allows identifying the fields of interest in each and measuring some variables related to the productivity of the groups, such as centrality and network size (Bullock & Lawler, 2015; García, 2012; Lužar, Levnajić, Povh, & Perc, 2014).

Globally, networks have been used to analyze the structure and evolution of interdisciplinarity, some particular disciplines (Bullock & Lawler, 2015; Lužar et al., 2014) and environmental management (Martínez, Brenner, & Espejel, 2015). In Mexico, network analysis has been used to investigate social sciences, economics, journal databases and natural resource management (Calderón & Flores, 2012; Martínez et al., 2015; Nuñez-Espinoza, Figueroa, & Jiménez-Sánchez, 2014). However, specific studies have not yet been made on the coauthorship of papers on forest modeling, despite the considerable number of institutions that form human capital and generate technical-scientific documents on the subject.

Based on the above, the aim of this study was to analyze the spatio-temporal evolution of forest modeling in Mexico, to identify the potential and prospects for scientific production. The results may help in forming public policies for allocating resources to projects and institutions. They can also facilitate the consultation and identification of the main forest sector´s research projects and actors, in order to invest or manage resources or to establish exchanges between research groups.

Materials and methods

Scientific production in Mexico on forest modeling, research groups and collaborative networks were identified through peer-reviewed scientific papers published from 1980 to 2015. The search was conducted in national and international journals related to forestry and natural resource management. For this purpose, the technological resources available in the major publishing houses (Elsevier, Springer and Scopus) and web sites (Latindex, Scielo, Redalyc, Thomson-Reuters and Conricyt) were used. In addition, the major journals that address the topic of interest at the national level were consulted: Agrociencia, Madera y Bosque, Revista Chapingo Serie Ciencias Forestales y del Ambiente, Botanical Science and Revista Mexicana de Ciencias Forestales. All searches were made online and only papers on modeling of forest plant resources were considered. The keywords used for the search were “model,” “growth,” “tree,” “forest,” “forestry,” “modeling,” “site index,” “volume,” “Mexico” and “biomass”, identifying them in the titles, keywords or abstracts of publications. Subsequently, the “snowball” technique, which consisted of identifying and obtaining missing papers, from the list of references of the papers initially found (Leipold, 2014), was used. The information was captured in a spreadsheet; it was systematized and classified by journal name, year of publication, article title, state of the country under study, authors, institution, keywords, language of publication and subject category of the paper. Considering the subject areas where modeling is used as an analytical tool, the papers were classified into five categories according to their purpose: (i) wood supply and technology, (ii) potential distribution, (iii) forest fires, (iv) forest management and (v) environmental services. Papers on estimating biomass and carbon sequestration were included in the category of environmental services.

The names of the authors, institutions and countries of origin were standardized in the database, since the information available in the papers is sometimes incomplete or presented with some variants (Aguado-López et al., 2009). The journals Boletín de la Sociedad Botánica de México, Revista Ciencia Forestal en México, and the Instituto Nacional de Investigaciones Forestales (INIF) were renamed as Botanical Science, Revista Mexicana de Ciencias Forestales and Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), respectively. Only the current name was used in all three renaming cases. In the case of institutions with multiple locations in Mexico, only the name of the headquarters was used. On the other hand, the authors were identified with their first surname followed by an underscore and the initials of their second surname and their name or names (e.g. Juan Pérez López was abbreviated as Pérez_LJ).

The UCINET computer program’s Nodelists1 script (Borgatti, Everett, & Freeman, 2002) was used for developing the networks and obtaining the following three indicators: network size, degree centrality (in-degree or out-degree) and density (Velázquez & Aguilar, 2005). Network size is the property that measures the number of individuals participating in it (Tichy, Tushman, & Fombrun, 1979); in-degree centrality measures the number of links that reach the node, and out-degree centrality the number of those who leave it, indicating the importance of the node in terms of its connection within the system (Newman, 2010; Wasserman & Faust, 1994); density measures the proportion of relationships in the network over the maximum number of possible relationships, being minimized when there are no relationships between actors (0) and maximized when all players are interlinked (1) (Newman, 2010; Reagans & Zuckerman, 2001; Wasserman & Faust, 1994). The most productive authors (leaders) were selected based on average degree centrality, selecting the top 2 % of all authors per category (rounding up); in categories with fewer than 100 authors, a minimum of two leaders (criteria selected by the authors) was chosen.

The graphical representation of the networks was made using NetDraw software (Borgatti, 2002). On the other hand, the geographical distribution of national scientific production was carried out using the ArcGIS package (Environmental Systems Research Institute [ESRI], 2015), with which six layers of information were generated. The first layer consisted of the classification of Mexico’s 32 states based on roundwood timber production (m3 ) (Secretaría del Medio Ambiente y Recursos Naturales [SEMARNAT], 2013) and the other five corresponded to each of the five categories of papers. The distribution of papers in the states was represented with colored bubbles (article category) of variable size (number of papers).

Results and discussion

From 1980-2015, 259 scientific papers on forest modeling were published in 37 journals, of which 28 are indexed in the Journal Citation Reports (JCR). Of the total number of papers, 84 % were published in Mexican journals. A total of 454 authors participated (37 % of them as corresponding authors), belonging to 89 institutions, of which 65 % are Mexican. These results show the evolution of scientific production, the importance at the national level and the way in which public policies conform to the regulatory framework so that science and technology develop under the required standards (Bullock & Lawler, 2015), directed by geopolitical events (Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura [UNESCO], 2015).

Temporal distribution of scientific production

The increased rate of Mexican scientific production in forest modeling is strongly related to pertinent changes in forest regulations since the 1980s (Congreso de la Unión de los Estados Unidos Mexicanos, 1986, 1988), with the establishment of the SNI or the National System of Researchers in 1984 (Consejo Nacional de Ciencia y Tecnología [CONACYT], 2006), and the creation of institutions for research and related undergraduate programs promoted by the Forestry Act of 1986 (Congreso de la Unión de los Estados Unidos Mexicanos, 1986). On the one hand, CONACYT is an institution that provides economic incentives, through the SNI, to the most productive researchers (CONACYT, 2006); also, in conjunction with other institutions, it helps researchers to address some of Mexico’s most pressing problems through sectoral programs. On the other hand, a number of the regulatory changes responded to international demands, such as the North American Free Trade Agreement -NAFTA- (Secretaría de Comercio y Fomento Industrial, 1993) and various international environmental scenarios, such as the Brundtland report, the Intergovernmental Panel on Climate Change and the Earth Summit (United Nations Framework Convention on Climate Change, 2014).

Figure 1 shows increasing annual production during the study period, with a higher rate of increase from 2005 and a concentration of 70 % of the publications in the last third (2006-2015). This trend coincides with an increase in the number of researchers, in both Mexico and globally, growth that is reflected in the explosion in the number of scientific publications (Foro Consultivo Científico y Tecnológico AC [FCCTAC], 2014a; 2014b; UNESCO, 2015). From 2006 to 2012, scientific production in Mexico increased 40 %; however, it is still very low compared with most member countries of the Organisation for Economic Co-operation and Development (OECD), and worldwide it contributes less than 1 % of the total (CONACYT, 2013).

Figure 1. Growth pattern of scientific production in forest modeling in Mexico. Bars outlined in green indicate years with outstanding production.

Mexican scientific production in forest modeling is in the multiplication stage, according to the approach described by Molina, Muñoz, and Domenech (2002). Scientific research follows a logistic curve (S-shaped) in which it is possible to first identify an appearance stage of the scientific paradigm (e. g. forest modeling in Mexico), followed by a multiplication stage, characterized by the appearance of circles of researchers that are influenced by a few highlyproductive members. Then there is a third stage, known as maturity, and a final one of stabilization (Huamaní & Mayta, 2010; Molina et al., 2002). The second stage of the logistic curve can be viewed as a phenomenon of contagion, in which the number of relationships of the first wave of adopters of an innovation is critical to the subsequent result (Crane, 1972). In line with the trend shown in Figure 2, scientific production in Mexico related to forest modeling can be expected to continue to grow in the medium term, albeit with a reshuffling of the relative weights representing each of the thematic categories.

Figure 2. Temporal evolution of scientific production in forest modeling, timber production and Protected Natural Areas. The magnitude of the circles indicates the increase in the number of papers. FM: Forest management, ES: Environmental Services, PD: Potential distribution, FF: Forest fires, WST: Wood supply and technology.

The increase in scientific production in forest modeling (more than eight times from 1980 to 2015) has been steady and significant, with the forest management category being the most important (Figure 2). However, when the analysis is done by categories, a 37 % decline in the relative importance of forest management studies and a 33 % increase in papers on environmental services and potential distribution are observed (Figure 2), while the forest fire and wood supply and technology categories have remained stable. This behavior is consistent with the following events: (i) the creation of the Forestry Act of 1960 and its amendments (Instituto Nacional de Ecología [INE] & Secretaría de Medio Ambiente y Recursos Naturales [SEMARNAT], 2003; Congreso de la Unión de los Estados Unidos Mexicanos, 1986) and (ii) the decree in 1988 of the General Law of Ecological Balance and Environmental Protection (LGEEPA) (Congreso de la Unión de los Estados Unidos Mexicanos, 1988), where the conservation trend and the increase in Protected Natural Areas are taken up again with greater force.

The diversity of forest ecosystems in Mexico and the increase in Protected Natural Areas (SEMARNAT, 2014) allow using forests as producers of ecosystem services, a situation that is related to the relative increase in research production in the environmental services and potential distribution categories (Figure 2). This situation, in turn, is related to the global demand for soil protection functions, regulation of the hydrological cycle, environmental services, conservation of biodiversity and mitigation of greenhouse gas emissions (Organización de las Naciones Unidas para la Agricultura y la Alimentación [FAO], 2005a, 2015; Ruis, 2001), established in some international agreements and conventions (FAO 2005a; Ruis, 2001; United Nations Framework Convention on Climate Change, 2014).

Throughout the period, the forest management category has been the most important and has grown considerably in magnitude, whereas the opposite has occurred in relation to timber production (Figure 2). The total consumption of roundwood from natural forests in Latin America and the Caribbean indicates a reduction of 25 % in the period 1980-2003 (FAO, 2006). Likewise, in Mexico a 40 % reduction in production from 2000 to 2011 is reported (SEMARNAT, 2015). Among the factors that have contributed to the decline and stagnation of timber production, both in Mexico and globally, are: the growing trend towards conservation, restrictions and regulations on the management of natural forests, high certification and labeling costs to enter the international market and the increasing use of timber from forest plantations (FAO, 2006).

Geographical distribution of research

Figure 3 shows the spatial distribution of scientific production in forest modeling in Mexico. Of the papers published from 1980 to 2015, 82 % are distributed in states with a varying level of timber production. The rest of the papers (18 %) were based on larger spatial scales: 10 % at the national level (e. g. Gómez-Díaz et al., 2011; Rojas-García, De Jong, Martínez-Zurimendí, & PazPellat, 2015) and 8 % in two or more states of the northeastern, southeastern and central regions (e. g. Návar, Nájera, & Jurado, 2001; Reich, Aguirre-Bravo, & Bravo, 2008). The two states with “very high” and “high” timber production (Durango and Chihuahua, respectively) account for 25 % of the production of scientific papers; another 25 % corresponds to the states of central Mexico (Hidalgo, Puebla, State of Mexico and Mexico City), where the largest number of institutions and researchers are also concentrated. The seven states with the highest number of publications (Durango, Hidalgo, State of Mexico, Oaxaca, Puebla, Veracruz and Chihuahua) account for 58 % of the total. Considering the category of papers, 55 % belong to forest management, 25 % to environmental services, 9 % to potential distribution, 7 % to forest fires and 4 % to wood supply and technology.

Figure 3. Spatial distribution of scientific production nationwide. The classification of states based on their roundwood timber production (m3) is indicated by the color scale and the number written on the abbreviation of the corresponding state. The magnitude of the bubbles indicates the number of scientific papers produced, and the colors the category to which they belong. FM: Forest management, ES: Environmental services, PD: Potential distribution, FF: Forest fires, WST: Wood supply and technology.

There are only two states where studies have been conducted in all categories: Durango and Hidalgo. In the former, the forest management category accounts for 75 % of its scientific production, which is consistent with the fact it is the state with the largest timber production. In Hidalgo, the percentages among categories are more evenly distributed, with environmental services having a greater relative importance with respect to Durango, due to there is a greater incentive to this alternative because it is a state with low timber production. Nayarit, Querétaro, Aguascalientes and Baja California Sur have very low timber production and do not have any type of work (Figure 3); however, they represent opportunity options for research in environmental services and potential distribution. Although there are a wide variety of environmental services, those that have the greatest potential are: carbon sequestration, water capture, biodiversity and bioprospecting (FAO, 2005b). In Mexico, most studies related to environmental services are focused on the issue of carbon sequestration and, to a lesser extent, hydrological services and soil protection (e. g. Acosta, Carrillo, & Gómez, 2011; Gómez-Díaz et al., 2011; Návar, 2009). On the other hand, the scientific production reported in each state can be associated with the domestic demand for the professionalization of technical forest services, as well as the training of skilled personnel, which has been brought about by the creation of educational institutions. Specifically, more than 15 undergraduate, nine masters and nine doctoral programs have been created in a total of 23 institutions. Although some programs have been implemented in areas with forest production, most are located in the center of the country (State of Mexico and Mexico City), which coincides with the fact that 32 % of the SNI researchers in area VI of Biotechnology and Agricultural Sciences are concentrated in that region alone (Atlas de la Ciencia Mexicana, 2012; FCCTAC, 2014b). On the other hand, 70 % of the scientific production was generated in educational institutions and 30 % in research institutions. More than 60 % of the papers were generated in five national institutions (Figure 4), of the 89 that have scientific production. Three institutions accounted for 46 % of the production, all three of which also have their own journal: INIFAP (Revista Mexicana de Ciencias Forestales), the Colegio de Postgraduados (ColPos, Agrociencia) and the Universidad Autónoma Chapingo (UACh, Revista Chapingo Serie Ciencias Forestales y del Ambiente). These three institutions publish a percentage of papers authored by their members in their own journals: INIFAP 70 %, ColPos 46 % and UACh 32 %. The journals that published the highest number of papers are presented in Table 1; 78 % of the papers are published in Spanish and 22 % in English. It is important to mention that Mexican journals began publishing in the two languages in 2000, in order to attract a greater international audience.

Table 1. Journals analyzed for spatio-temporal analysis of forest modeling in Mexico.

Journal Country Institution / Organization JCR impact factor (2015) No. papers published
Revista Mexicana de Ciencias Forestales Mexico Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) Does not have 81
Agrociencia Mexico Colegio de Postgraduados 0.305 46
Madera y Bosques Mexico Instituto de Ecología (INECOL) 0.434 35
Revista Chapingo Serie Ciencias Forestales y del Ambiente Mexico Universidad Autónoma Chapingo (UACh) 0.243 31
Forest Ecology and Management Netherlands Elsevier Science Publisher B. V. 2.826 9
Revista Fitotecnia Mexicana Mexico Sociedad Mexicana de Fitogenética 0.318 8
Annals of Forest Science France Institut National de la Recherche Agronomique 2.086 5
Botanical Sciences Mexico Sociedad Botánica de México 0.624 5
Journal of Forestry Research China Northeast Forestry University and Ecological Society of China 0.658 4
Canadian Journal of Forest Research Canada NRC Research Press 1.682 3
Forestry United Kingdom Oxford University Press 1.921 2
Investigaciones Geográficas Mexico Universidad Nacional Autónoma de México (UNAM) Does not have / No tiene 2
Journal of Arid Environments United States Academic Press Inc 1.623 2
Revista Mexicana de Biodiversidad Mexico Universidad Nacional Autónoma de México 0.493 2
Revista Mexicana de Ciencias Agrícolas Mexico INIFAP Does not have 2
Other journals Various - 0.17 to 3.709 22
JCR: Journal Citation Reports. Other journals: Agriculture, Ecosystems & Environment, Agronomía Mesoamericana, Atmósfera, Biomass and Bioenergy, Chinese Geographical Science, Dendrochronologia, Ecological Modelling, Forest Ecosystems, Forest Systems, Interciencia, International Journal of Biometeorology, Journal of Latin American Geography, Journal of Tropical Ecology, Journal of Vegetation Science, Política y Cultura, Plant Ecology, Revista Latinoamericana de Recursos Naturales, Tropical and Subtropical Agroecosystems, Water, Air, & Soil Pollution, Forestra Veracruzana y Agrofaz.

Figure 4. Institutions of corresponding authors and types of research that they conduct. Categories: FM = Forest management, ES = Environmental services, WST = Wood supply and technology, FF = Forest fires, PD = Potential distribution. Institutions: Universidad Juárez del Estado de Durango (UJED), Universidad Autónoma de Nuevo León (UANL), Universidad Autónoma Chapingo (UACh), Colegio de Postgraduados (ColPos), Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP).

Authorship by networks

A total of 454 authors were counted in the period analyzed (1980-2015), distributed in almost 90 institutions, of which 169 are corresponding authors and 173 main authors, with a co-authorship index of 1.8 (collaboration among authors). Based on information from the Science Citation Index, international collaborations rose from 19 to 34 % from 1980 to 1990 (Russell, Ainsworth, & Narváez-Berthelemot, 2006). This contrasts with the behavior observed in Mexico, as there is a higher percentage of individual participation (Aguado-López et al., 2009). In the present study, the tendency to have papers with one and two authors has been maintained, while collaborations with more than three authors increased from 2005. Forest modeling studies from 1980 to 1995 showed a similar situation of low collaboration with 36 authors involved; individual authorship accounted for 29 % and small group collaboration schemes (six subnets), 71 %. In the next study period (1996-2005), a medium-sized network was established and developed; collaborations (two or more members) increased to 85 % and the number of subnets doubled. In the following 10 years (2006-2015), almost 30 small groups (2-20 authors), representing 28 % of the network, and a main group of more than 300 authors, representing 70 %, were identified; in this period, individual authorships fell to 2 %. Considering the publications of the 454 authors, 22 published papers are of individual authorship, 51 double, 33 triple and 153 with more than four authors. The network of forest modelers, belonging to area VI of Biotechnology and Agricultural Sciences (formerly Agrosciences), accumulated more than 200 researchers from 1942 to 1979, while the medicine network in just three years, from 1976 to 1979, grew by more than five times (Atlas de la Ciencia Mexicana, 2012). In other words, the area VI network has had very slow growth compared to that in other areas such as medicine area III.

On the other hand, the six national institutions with the most collaborations are: INIFAP, ColPos, UACh, Universidad Juárez del Estado de Durango (UJED), Universidad Autónoma de Nuevo León (UANL) and the Instituto Tecnológico de El Salto (ITES). These institutions have also collaborated with some foreign ones, with the Universidad de Compostela and the United States Forest Service being the two most important.

Regarding the international collaborations of the authors, 12 % are with groups in the United States, Germany and France, because they are market, science and technology leaders (Altbach, Reisberg, & Rumbley, 2009 technology; UNESCO, 2015), and with Spain, possibly because of language compatibility. This percentage of contributions is equal to a third of those recorded on average globally (Aguado-López et al., 2009; Russell et al., 2006). The increase in collaborations can be attributed to the ease of establishing them beginning with the rise of information and communications technologies (ICTs) in the 1990s, when a significant increase in researchers, scientific production and mobility was also generated (UNESCO, 2015). This has resulted in the strengthening of multidisciplinarity and collaborations among researchers.

Interrelationships among the members of the collaborative network of Mexican forest modelers in 2015 was still very low, which is reflected in the very low density value (0.003), leaving a universe of possibilities to establish collaborations among them and increase their scientific production (Reagans & Zuckerman, 2001). Some Latin American countries (Argentina, Brazil, Chile and Uruguay) have increased investment in strategic sectors such as agriculture, energy, ICTs, biotechnologies and nanotechnologies to encourage scientific production and collaborations (Altbach et al., 2009; UNESCO, 2015). These investments in strategic sectors seek an improvement in higher education and greater scientific production and international collaboration. In the current network of Mexican forest modelers, the 16 most productive and interrelated authors were identified (Figure 5), according to the degree centrality of the nodes in each of the article categories (García, 2012; Hou, Kretschmer, & Liu, 2008; Leij & Goyal, 2011). These authors, due to having an advantageous position, are more likely to secure resources for projects, because they have more alternatives for collaboration with other researchers who also have significant networks (Hanneman & Riddle, 2005; Newman, 2004). Comparatively, in the physical science networks, there are more than 20 subnets with visibility, of which only three have a few leading authors (Atlas de la Ciencia Mexicana, 2012), which means that, in this case, productivity does not depend on a small group of researchers.

Figure 5. Collaborative network in forest modeling. In this medium-sized network with over 450 members, the 16 authors with the greatest degree centrality in the different categories are highlighted: forest management (blue), environmental services (green), potential distribution (yellow), forest fires (orange) and wood supply and technology (purple).

Of the forest modeling categories analyzed, the forest management’s network, accounts for 50 % of the authors, whereas, at the other extreme, the supply and technology grouping accounts for only 7 %. In forest management, of 227 authors, 3 % individual authorships, as well as the largest group with 181 collaborators, are reported. The categories of environmental services and potential distribution have the highest number of collaborative groups of more than four members, without reaching one hundred and have no individual authorships. Finally, the supply and technology category has 6 % individual authorships and five groups of two to 10 members. Previous studies have found that the productivity of a group is influenced by the number of its collaborators and that the most productive researchers, who have many interactions in their categories, are identified as experts in their area; this allows establishing new collaborations and an increase in their productivity (García, 2012; Hill, 2008; Hou et al., 2008; Leij & Goyal, 2011; Oh, Labianca, & Chung, 2006). This coincides with the results, since the forest management network, which is the largest, had more leaders and scientific production.

Conclusions

In Mexico, scientific production in forest modeling has grown steadily since 1980, but with greater force from 2005. The creation of the National System of Researchers and the evolution of the regulatory framework governing natural resource management and conservation are probably the main factors behind this growth. The three most productive institutions are INIFAP, ColPos and UACh, each of which also has its own journal. Scientific production in environmental services and potential distribution is beginning to surge, possibly due to increased global demand for ecosystem functions and to habitat diversity in Mexico. Thus, all states, regardless of their timber production, are candidates for work in environmental services. With regard to the networks, there is a potential for growth of more than 98 % in collaborations between researchers and institutions and, therefore, for greater scientific production. The 16 leading authors identified in the five forest modeling categories are the most interrelated and most influential. The results of this study can serve as a basis for planning forest modeling and to promote effective collaborations among research groups.

References

Acosta, M. M., Carrillo, A. F., & Gómez, V. R. G. (2011). Estimación de biomasa y carbono en dos especies de bosque mesófilo de montaña. Revista Mexicana de Ciencias Agrícolas, 2(4), 529-543. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S2007-09342011000400005&lng=es&tln g=es

Aguado-López, E., Rogel-Salazar, R., Garduño-Oropeza, G., Becerril-García, A., Zúñiga-Roca, M., & Velázquez-Álvarez, A. (2009). Patrones de colaboración científica a partir de redes de coautoría. Convergencia, Revista de Ciencias Sociales, 16(1), 225-258. Retrieved from http://www.redalyc.org/pdf/105/10512244010.pdf

Altbach, P. G., Reisberg, L., & Rumbley, L. E. (2009). Tras la pista de una revolución académica: Informe sobre las tendencias actuales. Paris, Francia: Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura (UNESCO). Retrieved from: de http://unesdoc.unesco.org/images/0018/001831/183168s.pdf

Atlas de la Ciencia Mexicana. (2012). Atlas de la Ciencia mexicana 2012 (Vol. 1). México: Autor. Retrieved from http://atlasdelacienciamexicana.org/atlas_version_impresa_2012-volI/pdf/atlas2012-vol1.pdf

Barnsley, M. J. (2007). Environmental modeling: A practical introduction. Florida, USA: CRC Press.

Borgatti, S. P. (2002). NetDraw software for network visualization. Lexington, KY, USA: Analytic Technologies Retrieved from http://www.analytictech.com/netdraw/netdraw.htm

Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for Windows: Software for Social Network Analysis. Harvard, MA, USA: Analytic Technologies. Retrieved from http://www.analytictech.com/ucinet/

Bullock, R., & Lawler, J. (2015). Community forestry research in Canada: A bibliometric perspective. Forest Policy and Economics, 59, 47-55. doi: 10.1016/j.forpol.2015.05.009

Calderón, M. M. G., & Flores, P. J. (2012). Redes de conocimiento en empresas de la industria electrónica en México: Una propuesta metodológica. Economía: teoría y práctica, 37, 121−143. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-33802012000200006&lng=es&tlng=es

Congreso de la Unión de los Estados Unidos Mexicanos. (1986). Ley Forestal. México: Diario Oficial de la Federación. Retrieved from http://dof.gob.mx/nota_detalle.php?codigo=4795460&fecha=30/05/1986

Congreso de la Unión de los Estados Unidos Mexicanos. (1988). Ley General de Equilibrio Ecológico y Protección al Ambiente. México: Diario Oficial de la Federación . Retrieved from http://www.diputados.gob.mx/LeyesBiblio/pdf/148_130516.pdf

Consejo Nacional de Ciencia y Tecnología (CONACYT). (2006). Reglamento del Sistema Nacional de Investigadores. México: Diario Oficial de la Federación (DOF). Retrieved 08/09/16 from http://dof.gob.mx/nota_detalle.php?codigo=4932567&fecha=26/09/2006

Consejo Nacional de Ciencia y Tecnología (CONACYT). (2013). Programa especial de ciencia, tecnología e innovación 2014-2018. Retrieved from http://www.conacyt.mx/images/conacyt/PECiTI_2014-2018.pdf

Crane, D. (1972). Invisible colleges. Diffusion of knowledge in scientific communities. USA: The University of Chicago Press.

Environmental Systems Research Institute (ESRI). (2015). ArcGIS (Versión 10.3) Software de procesamiento digital de imágenes satelitales. Redlands, CA, USA. Retrieved from http://www.esri.com/software/arcgis/arcgis-for-desktop

Foro Consultivo Científico y Tecnológico AC (FCCTAC). (2014a). Sistema Nacional de Investigadores. Retrieved from http://www.foroconsultivo.org.mx/asuntos/academicos/sni2014/resultados_comisiones_ dictaminadoras_2014.pdf

Foro Consultivo Científico y Tecnológico AC . (2014b). Sistema Nacional de Investigadores. Retrieved from http://www.foroconsultivo.org.mx/documentos/acertadistico/conacyt/sistema_nacional_ de_investigadores.pdf

Galeano, M. E., Amarilla, A., & Parra, G. (2007). Productividad científica del Paraguay en el área de biomedicina: Un análisis bibliométrico. Memorias del Instituto de Investigaciones en Ciencias de la Salud, 5(1), 26−30. Retrieved from http://scielo.iics.una.py/pdf/iics/v5n1/v5n1a06.pdf

García, H. A. (2012). Las redes de colaboración científica y su efecto en la productividad. Un análisis bibliométrico. Investigación Bibliotecológica, 27(59), 159−175. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0187-358X2013000100008

Gaughan, M., & Pnomariov, B. (2008). Faculty publication productivity, collaboration, and grants velocity: Using curricula vitae to compare center-affiliated and unaffiliated scientists. Research Evaluation, 17(2), 103−110. doi: 10.3152/095820208X287180

Gómez-Díaz, J. D., Monterroso-Rivas, A. I., Tinoco-Rueda, J. A., Toledo-Medrano, M. L., Conde-Álvarez, C., & Gay- García, C. (2011). Assessing current and potential patterns of 16 forest species driven by climate change scenarios in México. Atmósfera, 24(1), 31−52. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0187-62362011000100004&lng=es&tlng=en

Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA, USA: University of California. Retrieved from http://faculty.ucr.edu/~hanneman/nettext/

Hill, V. A. (2008). Collaboration in an academic setting: Does the network structure matter? Retrieved from http://www.casos.cs.cmu.edu/publications/papers/CMU-ISR-08-128.pdf

Hou, H., Kretschmer, H., & Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2), 189−202. doi: 10.1007/s11192-007-1771-3

Huamaní, C., & Mayta-Tristán, P. (2010). Producción científica peruana en medicina y redes de colaboración, análisis del Science Citation Index 2000-2009. Revista Peruana de Medicina Experimental y Salud Pública, 27(3), 315−325. Retrieved from http://www.scielo.org.pe/pdf/rins/v27n3/a03v27n3.pdf

Hynynen, J. (2011). Conceptos básicos para la modelación del crecimiento forestal. Recursos Naturales y Ambiente, 64, 22−27. Retrieved from http://bco.catie.ac.cr/portal-revistas/index.php/rrna/article/view/3/12

Instituto Nacional de Ecología (INE) & Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT). (2003). Memorias del primer encuentro internacional de derecho ambiental. México: Autor . Retrieved from http://www2.inecc.gob.mx/publicaciones/consultaPublicacion.html?id_pub=398

Leij, M., & Goyal, S. (2011). Strong ties in a small world. Review of Network Economics, 10(2). doi: 10.2202/1446-9022.1278

Leipold, S. (2014). Creating forests with words ̶ A review of forest-related discourse studies. Forest Policy Economics, 40, 12-20. doi: 10.1016/j.forpol.2013.12.005

Li, Y. Q., Deng, X. W., Huang, Z. H., Xiang, W. H., Yan, W., Lei, P. F., Peng, C. H. (2015). Development and evaluation of models for the relationship between tree height and diameter at breast height for Chinese- Fir plantations in subtropical China. PLoS ONE, 10(4), e0125118. doi: 10.1371/journal.pone.0125118

Lužar, B., Levnajić, Z., Povh, J., & Perc, M. (2014). Community structure and the evolution of interdisciplinarity in Slovenia’s Scientific Collaboration Network. PLoS ONE, 9(4), e94429. doi: 10.1371/journal.pone.0094429

Martínez, N., Brenner, L., & Espejel, I. (2015). Red de participación institucional en las áreas naturales protegidas de la península de Baja California. Región y sociedad, 27(62), 27−62. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-39252015000100002&lng=es&tlng=es

Molina, J. L., Muñoz, J. M., & Domènech, M. (2002). Redes de publicaciones científicas: un análisis de la estructura de coautorías. Redes, 1(3), 1−16. Retrieved from http://www.redalyc.org/articulo.oa?id=93100103

Návar, J. (2009). Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. Forest Ecology and Management, 257(2), 427-434. doi: 10.1016/j.foreco.2008.09.028

Návar, J., Nájera, J., & Jurado, E. (2001). Preliminary estimates of biomass growth in the Tamaulipan thornscrub in north-eastern Mexico. Journal of Arid Environments, 47(3), 281−290. doi: 10.1006/jare.2000.0708

Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. The National Academy of Sciences, 101, 5200-5205. doi: 10.1073/pnas.0307545100

Newman, M. (2010). Networks: An introduction. New York, USA: Oxford University Press.

Nuñez-Espinoza, J. F., Figueroa, R. O. L., & Jiménez-Sánchez, L. (2014). Elementos para analizar redes sociales para el desarrollo rural en México: El caso RENDRUS. Agricultura, sociedad y desarrollo, 11(1), 1−24. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-54722014000100001&lng=es&tlng=es

Oh, H., Labianca, G., & Chung, M. H. (2006). A multilevel model of group social capital. Academy of Management Review, 31(3), 569−582. doi: 10.5465/AMR.2006.21318918

Organización de las Naciones Unidas para la Agricultura y la Alimentación (FAO). (2005a). Estudio de tendencias y perspectivas del sector forestal en América Latina. Informe subregional Centroamérica y México. Retrieved from ftp://ftp.fao.org/docrep/fao/009/j7354s/j7354s00.pdf

Organización de las Naciones Unidas para la Agricultura y la Alimentación (FAO). (2005b). Estudio de tendencias y perspectivas del sector forestal en América Latina al año 2020. Retrieved October 13, 2015 from http://www.fao.org/docrep/006/j2215s/j2215s00.htm

Organización de las Naciones Unidas para la Agricultura y la Alimentación (FAO). (2006). Los desafíos del desarrollo y el sector forestal en América Latina y el Caribe. Retrieved from ftp://ftp.fao.org/docrep/fao/009/a0470s/a0470s00.pdf

Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura (UNESCO). (2015). Informe de la UNESCO sobre la ciencia. Hacia 2030. Retrieved from http://unesdoc.unesco.org/images/0023/002354/235407s.pdf

Organización de las Naciones Unidas para la Agricultura y la Alimentación (FAO). (2015). Global Forest Resources Assessment 2015. Retrieved September, 12, 2015 from http://www.fao.org/forest-resources-assessment/current-assessment/en/

Peng, C. (2000). Understanding the role of forest simulation models in sustainable forest management. Environmental Impact Assessment Review, 20(4), 481−501. doi: 10.1016/S0195-9255(99)00044-X

Prat, A. M. (2001). Evaluación de la producción científica como instrumento para el desarrollo de la ciencia y la tecnología. ACIMED, 9(4), 111−114. Retrieved from http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1024-94352001000400016&lng=es

Reagans, R., & Zuckerman, E. W. (2001). Networks, diversity, and productivity: The social capital of corporate R&D teams. Organization Science, 12(4), 502−517. doi: 10.1287/orsc.12.4.502.1063712(4):502

Reich, R. M., Aguirre-Bravo, C., & Bravo, V. A. (2008). New approach for modeling climatic data with applications in modeling tree species distributions in the states of Jalisco and Colima, Mexico. Journal of Arid Environments, 72(7), 1343−1357. doi: 10.1016/j.jaridenv.2008.02.004

Ríos, G. C., & Herrero, S. V. (2005). La producción científica latinoamericana y la ciencia mundial: una revisión bibliográfica (1989-2003). Revista Interamericana de Bibliotecología, 28(1), 43−61. Retrieved from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-09762005000100003&lng=en&tlng=es

Rojas-García, F., De Jong, H. J., Martínez-Zurimendí, P., & Paz- Pellat, F. (2015). Database of 478 allometric equations to estimate biomass for Mexican trees and forests. Annals of Forest Science, 72(6), 835−864. doi: 10.1007/s13595-015-0456-y

Ruis, M. G. S. B. (2001). Convenios mundiales sobre los bosques. A falta de un convenio forestal, diez tratados sobre árboles. Unasylva: Revista internacional de silvicultura e industrias forestales, 206. Retrieved 30- 10−2015 from http://www.fao.org/docrep/003/y1237s/y1237s02.htm#P0_0

Russell, J. M., Ainsworth, S., & Narváez-Berthelemot, N. (2006). Colaboración científica de la Universidad Nacional Autónoma de México y su política institucional. Revista Española de Documentación Científica, 29(1). doi: 10.3989/redc.2006.v29.i1.287

Secretaría de Comercio y Fomento Industrial. (1993). Tratado de Libre Comercio de América del Norte. México: Diario Oficial de la Federación . Retrieved from http://www.imcine.gob.mx/sites/536bfc0fa137610966000002/content_entry537f86d693e05abc55000284/53d2770d9d72796e24000089/files/1.pdf

Secretaría del Medio Ambiente y Recursos Naturales (SEMARNAT). (2013). Anuario Estadístico de la Producción Forestal. Retrieved from http://www.cnf.gob.mx:8090/snif/portal/economica/anuarios-estadisticos-de-la-produccion-forestal

Secretaría del Medio Ambiente y Recursos Naturales (SEMARNAT). (2014). Áreas Naturales Protegidas. Retrieved November 19, 2015 from http://www.conanp.gob.mx/regionales/

Secretaría del Medio Ambiente y Recursos Naturales (SEMARNAT). (2015). Anuarios forestales. Retrieved October 7, 2015 from http://www.semarnat.gob.mx/temas/gestion-ambiental/forestal-y-suelos/anuarios-forestales

Sheridan, R., Popescu, S., Gatziolis, D., & Morgan, C. L. (2014). Modeling forest aboveground biomass and volume using airborne LiDAR metrics and forest inventory and analysis data in the Pacific Northwest. Remote Sensing, 7(1), 229−255. doi: 10.3390/rs70100229

Tichy, N. M., Tushman, M. L., & Fombrun, C. (1979). Social network analysis for organizations. The Academy of Management Review, 4(4), 507−519. Retrieved from https://www.sfu.ca/cmns/courses/marontate/2009/801/ClassFolders/jmckinnon/Alternatives/Social%20Network%20Analysis%20for%20Organizations.pdf

United Nations Framework Convention on Climate Change (2014). Historia de la CMNUCC. Retrieved November 15, 2015 from http://unfccc.int/portal_espanol/informacion_basica/la_convencion/historia/items/6197.php

Velázquez, O. A., & Aguilar, G. (2005). Manual introductorio al análisis de redes sociales. Medidas de centralidad. Ejemplos prácticos con UCINET 6.85 y NetDraw 1.48. Retrieved from http://revista-redes.rediris.es/webredes/talleres/Manual_ARS.pdf

Wasserman, S., & Faust, K. (1994). Social network analysis, methods and applications. UK: Cambridge University Press

Figures:

Figure 1. Growth pattern of scientific production in forest modeling in Mexico. Bars outlined in green indicate years with outstanding production.
Figure 2. Temporal evolution of scientific production in forest modeling, timber production and Protected Natural Areas. The magnitude of the circles indicates the increase in the number of papers. FM: Forest management, ES: Environmental Services, PD: Potential distribution, FF: Forest fires, WST: Wood supply and technology.
Figure 3. Spatial distribution of scientific production nationwide. The classification of states based on their roundwood timber production (m3) is indicated by the color scale and the number written on the abbreviation of the corresponding state. The magnitude of the bubbles indicates the number of scientific papers produced, and the colors the category to which they belong. FM: Forest management, ES: Environmental services, PD: Potential distribution, FF: Forest fires, WST: Wood supply and technology.
Figure 4. Institutions of corresponding authors and types of research that they conduct. Categories: FM = Forest management, ES = Environmental services, WST = Wood supply and technology, FF = Forest fires, PD = Potential distribution. Institutions: Universidad Juárez del Estado de Durango (UJED), Universidad Autónoma de Nuevo León (UANL), Universidad Autónoma Chapingo (UACh), Colegio de Postgraduados (ColPos), Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP).
Figure 5. Collaborative network in forest modeling. In this medium-sized network with over 450 members, the 16 authors with the greatest degree centrality in the different categories are highlighted: forest management (blue), environmental services (green), potential distribution (yellow), forest fires (orange) and wood supply and technology (purple).

Tables:

Table 1. Journals analyzed for spatio-temporal analysis of forest modeling in Mexico.
Journal Country Institution / Organization JCR impact factor (2015) No. papers published
Revista Mexicana de Ciencias Forestales Mexico Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) Does not have 81
Agrociencia Mexico Colegio de Postgraduados 0.305 46
Madera y Bosques Mexico Instituto de Ecología (INECOL) 0.434 35
Revista Chapingo Serie Ciencias Forestales y del Ambiente Mexico Universidad Autónoma Chapingo (UACh) 0.243 31
Forest Ecology and Management Netherlands Elsevier Science Publisher B. V. 2.826 9
Revista Fitotecnia Mexicana Mexico Sociedad Mexicana de Fitogenética 0.318 8
Annals of Forest Science France Institut National de la Recherche Agronomique 2.086 5
Botanical Sciences Mexico Sociedad Botánica de México 0.624 5
Journal of Forestry Research China Northeast Forestry University and Ecological Society of China 0.658 4
Canadian Journal of Forest Research Canada NRC Research Press 1.682 3
Forestry United Kingdom Oxford University Press 1.921 2
Investigaciones Geográficas Mexico Universidad Nacional Autónoma de México (UNAM) Does not have / No tiene 2
Journal of Arid Environments United States Academic Press Inc 1.623 2
Revista Mexicana de Biodiversidad Mexico Universidad Nacional Autónoma de México 0.493 2
Revista Mexicana de Ciencias Agrícolas Mexico INIFAP Does not have 2
Other journals Various - 0.17 to 3.709 22
JCR: Journal Citation Reports. Other journals: Agriculture, Ecosystems & Environment, Agronomía Mesoamericana, Atmósfera, Biomass and Bioenergy, Chinese Geographical Science, Dendrochronologia, Ecological Modelling, Forest Ecosystems, Forest Systems, Interciencia, International Journal of Biometeorology, Journal of Latin American Geography, Journal of Tropical Ecology, Journal of Vegetation Science, Política y Cultura, Plant Ecology, Revista Latinoamericana de Recursos Naturales, Tropical and Subtropical Agroecosystems, Water, Air, & Soil Pollution, Forestra Veracruzana y Agrofaz.