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Forests 2018, 9(10), 628; doi:10.3390/f9100628

Article
The Potential Distribution of Tree Species in Three Periods of Time under a Climate Change Scenario
División de Estudios de Postgrado-Instituto de Estudios Ambientales, Universidad de la Sierra Juárez, Avenida Universidad S/N, Ixtlán de Juárez, 68725 Oaxaca, México
*
Author to whom correspondence should be addressed.
Received: 1 September 2018 / Accepted: 9 October 2018 / Published: 11 October 2018

Abstract

:
Species distribution models have become some of the most important tools for the assessment of the impact of climatic change, and human activity, and for the detection of failure in silvicultural or conservation management plans. In this study, we modeled the potential distribution of 13 tree species of temperate forests distributed in the Mexican state Durango in the Sierra Madre Occidental, for three periods of time. Models were constructed for each period of time using 19 climate variables from the MaxEnt (Maximum Entropy algorithm) modelling algorithm. Those constructed for the future used a severe climate change scenario. When comparing the potential areas of the periods, some species such as Pinus durangensis (Martínez), Pinus teocote (Schiede ex Schltdl. & Cham.) and Quercus crassifolia (Bonpl.) showed no drastic changes. Rather, the models projected a slight reduction, displacement or fragmentation in the potential area of Pinus arizonica (Engelm.), P. cembroides (Zucc), P. engelmanni (Carr), P. leiophylla (Schl), Quercus arizonica (Sarg), Q. magnolifolia (Née) and Q. sideroxila (Humb. & Bonpl.) in the future period. Thus, establishing conservation and reforestation strategies in the medium and long term could guarantee a wide distribution of these species in the future.
Keywords:
Bioclimatic niche; Durango; Mexican tree species; MaxEnt; non-parametric correlation

1. Introduction

In conservation biology, estimating areas of potential distribution of species through the modeling of an ecological niche, has had a variety of applications for learning the current and future state of species [1]. Yet, human activities have produced significant changes in the distribution of species in the different ecosystems of the world [2], with the temperate forests of Mexico being one of the most affected, due to the excessive extraction of some species of commercial interest, mainly of the Pinus genus like Pinus durangensis Martínez, P. arizonica Engelm., P. cooperi C. E. Blanco and P. engelmannii Carr in the Mexican northwest [3,4]. On the other hand, the changes in land-use and activities related to the extraction of timber species, are among the main causes of the disturbance of habitat and the loss of diversity in the temperate forests of Mexico [5,6,7,8]. Also, climate change is another phenomenon to be considered when studying the distribution of plants since many habitats have undergone severe changes. Even so, the survival of some plant species is at risk because of this [9]. Projecting the potential distribution of species using climatic variables becomes very important to evaluate and foresee any alteration of either natural or anthropogenic origin [1,10,11].
One way of estimating the potential distribution of a species for a future period is by modeling their ecological niche considering that the geographical distribution of a given species is not random, but obeys environmental factors such as altitude, topographic position, temperature, humidity and precipitation, among others. Likewise, the distribution of plants is associated with physiographic (aspect and slope) and edaphic factors [12,13,14].
One of the most used tools to model the distribution of species in geographic space and their environmental tolerances, is the Maximum Entropy algorithm (MaxEnt), whose predictions start with the principle of maximum probability of occurrence of the species from the presence data and the bioclimatic variables associated with each sampling site [15].
The MaxEnt method has been used successfully in studies related to the analysis of biodiversity, the conservation of the species niche, the identification of priority conservation areas and the implementation of actions focused on preventing the establishment of invasive species in a locality [16,17,18]. The maps of potential distribution projected by MaxEnt and the spatial analysis by means of a GIS have proved their effectiveness by providing a reasonable approximation of the niche of the species, even with a small sample size [19,20,21,22].
An effective way to measure the effect of environmental variations on the geographic distribution of high-interest plants is to evaluate any change manifested when going from one period of time, with specific climatic characteristics, to another, such as the period of the most recent glaciation and the contemporary period. In this way, it is possible to predict a possible displacement, fragmentation or reduction of the potential area in the face of future climate change scenarios.
This study had two objectives: (i) to identify if there is a significant change in the potential distribution of 13 tree species (highly valued both economically and ecologically) native to the temperate forests of Durango, northwestern Mexico, as a function of 19 climatic variables, considering three periods of time; and (ii) to identify species with risks of decreasing their potential distribution area in the future (2080 to 2100), under a scenario of severe climate change. In this study the spatial resolution of the data was 1 km2. The hypothesis was that the potential distribution of each species does not change significantly between one period of time and another.

2. Materials and Methods

2.1. Study Region

The modeled region corresponds to a portion of the mountain system of the Sierra Madre Occidental (SMO) in the State of Durango (northwest of Mexico), between geographical coordinates (WGS 84) 26°50′ and 22°17′ N and 107°09′ and 102°30′ W, covering an area of about 6.33 million ha (Figure 1). In this region, forests of oak-pine, pine-oak, oak or pine, together with other species typical of the temperate climate, are mainly distributed. Portions of temperate mesophytic as well as tropical deciduous and semi-deciduous forests are also found on the western slope of the region [23]. The annual rainfall fluctuates from 250 to 1444 mm and the mean annual temperature from 8.3 to 26.2 °C [24].

2.2. Obtaining Data

A total of 13 of the most representative species of the study area were analyzed, with eight of the genus Pinus (P. arizonica Engelm. var. stormiae Martínez, P. strobiformis Engelm., P. cembroides Zucc, P. cooperi C. E. Blanco, P. durangensis Martínez, P. engelmannii Carr, P. leiophylla Schiede ex Schltdl. et Cham., and P. teocote Schiede ex Schltdl. et Cham) and five of the genus Quercus (Q. arizonica Sarg, Q. crassifolia Humb. & Bonpl., Q. grisea Liebm, Q. magnoliifolia Née and Q. sideroxyla Humb. & Bonpl.) selected.
The presence records of the 13 species were collected using two data sources. The first data-set was obtained from on-line herbarium specimens provided by The National Herbarium (MEXU http://www.ib.unam.mx/botanica/herbario/). The second data-set was obtained directly in the field, from 1804 sampling plots, distributed systematically every five kilometers in the study area. These plots were established by the National Forestry Commission (CONAFOR) for the National Forest and Soil Inventory 2004–2009 [25]. These presence records were converted to geographical coordinates and finally a single database was generated by combining both subsets of data.
It should be mentioned that the temperate forests of the SMO are under forest management and it is possible that anthropogenic activities have altered the natural distribution of the species studied. For example, some conifer species such as Pinus durangensis Martínez, P.cooperi C. E. Blanco, P. arizonica Engelm. var. stormiae, P. teocote Schiede ex Schltdl. et Cham and P. engelmannii Carr, are extracted more than Pinus strobiformis Engelm. or Pinus leiophylla Schiede ex Schltdl. et Cham. due to having desirable phenotypic characteristics for the forest industry [26]. But since there is no precise quantification of the impact of anthropogenic activities, this factor was not incorporated into the models.

2.3. Distribution Modeling

The potential distribution of each species was modeled as a function of 19 environmental variables (Table 2) which were obtained from the WorldClim database (http://www.worldclim.org/ version 2) [27,28], with a spatial resolution of 1 km. To accomplish this, we used the Maximum Entropy algorithm (MaxEnt) [15], following the methodology used by several authors [17,29,30]. Potential distribution models for three periods of times were generated: (i) period of the most recent glaciation (21,000 years ago), (ii) present period and (iii) the future, corresponding to the period from 2080 to 2100. For this last period, the general circulation model used was the NIES 99 (http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=35) under an A2A scenario, a very severe scenario [31]. The MaxEnt method has proven its effectiveness in making predictions based on information from presence data [32,33,34,35] and whose results express the value of habitat suitability for each species as a function of probability according to environmental variables. A high value of the distribution function in a given cell suggests very favorable conditions for the presence of an analyzed species [15].
The results obtained from the MaxEnt method, were changed to Boolean layers (presence-absence data) using ArcMap software package 10.1 (http://desktop.arcgis.com/en/arcmap/), considering a cutoff threshold equal to 10% omission errors [30,36]. In each model, the variables that explained 70% or more of the data variability in the principal component analysis (PCA) were included [30,37]. The PCA analyses were carried out with R software [38].
For each species, presence records were divided into 75% (randomly selected) for model training and 25% for model validation, using 100 replicates in 500 iterations with different random partitions (cross-validation method).
In order to measure the degree of association or similarity between the relative abundance of one species (presence-absence data per cell of 1 km2) between one period of time and another, the Phi coefficient was used [39]. Finally, to identify if there are significant differences between the potential areas of one period of time and another, the average area of the 13 species was compared, using the Kruskal-Wallis test, testing the hypothesis that the average area of a period of time is similar to the other periods. The level of significance used in all the analyses was 0.05.

2.4. Model Evaluation

Models were evaluated using the area under the curve (AUC) described by Phillips et al. [15], whose values are calculated from the Receiver Operating Characteristic (ROC). When AUC showed values equal to or greater than 0.9, we considered the models to be robust; values of AUC 0.7–0.9 were considered moderately robust; and values close to 0.5 were considered not robust [33]. In all cases, the logistic output format was used [34].

3. Results

Observing the AUC values, the models for the scenarios of the three periods were consistent and robust showing values higher than 0.90 for all species, except for Quercus arizonica and Quercus sideroxyla (Table 1). The climatic variables used to model the potential niche of the species are listed in Table 2, which mainly includes average, maximum and minimum temperatures and rainfall in specific periods. The areas projected for the three periods of time, are shown in Figure 2 and Figure 3.
The Phi coefficients revealed a high and positive correlation between presence/absence records of the contemporary and future period with an average of 0.81 for 92% of the species studied. The highest Phi coefficients were observed between the contemporary period and the future for P. durangensis and P. cooperi with a value of 0.87 for each species, followed by P. strobiformis with a coefficient of 0.85. In contrast, the smallest absolute coefficients (three of them negative) were observed between the period of the most recent glaciation and the contemporary period, and between the period of the most recent glaciation and the future period for Pinus cembroides and strobiformis, whose Phi coefficients were less than 0.05 (Table 3).
When comparing the average values of the projected surfaces of the 13 species studied, no significant differences were found among the three periods of time, according to the Kruskal-Wallis test, with a significance level of 0.05. However, observing the areas of each species separately, several of them showed changes, as was the case of Pinus arizonica, P. leiophylla, P. cooperi and Quercus grisea, whose projected areas decreased as they passed from one period to another. This was more evident for the first two species between the period of the most recent glaciation and the contemporary period (Figure 4). The potential area of some species showed a change from one period to another. For example, P. cembroides projected an increase of 19,701 km2 from the present to the future period and P. strobiformis projected an increase of 9633 km2 (Figure 2 and Figure 3).
In general, the projected potential areas were variable for each species, and although in most cases there were minor changes in the projected surface, slight displacements, a possible fragmentation of the habitats or discontinuity in the distribution of species were observed from one period of time to another (Figure 4). Habitat fragmentation was clearer for P. arizonica, P. leiophylla, Q. arizonica, Q. grisea and Q. magnolifolia (Figure 2 and Figure 3).

4. Discussion

MaxEnt is a modeling instrument that has gained relevance in recent years for analyzing ecological characteristics of species using presence records, which are usually collected from specimen records in plant collection centers (e.g., MEXU) or field survey records. In this study, the areas projected (km2) by models, apparently did not change from one period to another for most of the species (Figure 2 and Figure 3). Yet, the fragmentation or displacement of areas with a suitable climate was evident in the maps for several species, as in the case of Pinus arizonica, P. cembroides, P. engelmanni, P. leiophylla, Quercus arizonica, Q. magnolifolia and Q. sideroxila (Figure 3 and Figure 4). Considering that the real interval of climate tolerance (optimal interval) is variable between one species and another, although they cohabit in the same region [26], it is possible that there were overestimates of the models. For this reason, the small difference between the areas projected does not necessarily suggest that the species have broad climatic tolerances or that the plants have a high capacity to adapt to contrasting climatic conditions.
The observations of which species show a fragmentation or discontinuity of the areas projected are valuable findings since they can be indicators of a high sensitivity to changes in climate values. In the end, a fragmentation can trigger a reduction in the real area of a species in the medium- or long-term, a problem that has been observed in the last decades in the temperate forests of the SMO [40] and in some Mexican terrestrial ecosystems [41,42]. Moreover, in the SMO, there are no studies on the real anthropogenic effects, since they could accelerate the alteration of the natural distribution of some species. For example, during the harvesting of roundwood, regeneration and other herbaceous species are damaged in many zones of the study region due to the poor design of the road network used to transport the roundwood. If the quantitative effects of anthropogenic activity were known, a specific weight could be included and assigned when making the potential distribution models.
The Phi coefficients revealed different degrees of similarity between one period of time and another. For example, when comparing the corresponding values of the contemporary period and the future period of both Quercus magnolifolia and Q. grisea, a high correlation was observed (Phi = 0.87), suggesting a high similarity in the pattern of distribution of these species in these two periods of time. Conversely, a weak correlation was observed between the values of the past and present period of both Quercus crassifolia and Quercus arizonica (Table 3).
Potential distribution models have been useful for making important decisions in the area of ecology [14,43,44], although they should be used with caution as several authors have warned [45,46,47,48], because this type of modeling has limitations, which include the iterative procedure of the MaxEnt algorithm [49] as well as the possible errors of commission and omission when using climatic factors to characterize the habitat of a species [48]. A commission error related to climatic variables could occur when the affinity of a species in a site is attributable to an event or situation that cannot be conditioned by climatic variables [48] whereas an error of omission is any error related to the underestimation of the climatic range [50] which can be translated as a false prediction of absence [48].
In some cases, the areas projected by the models are larger than the real distributions of the species studied, since the models do not consider biotic interactions [32] or the effects of human activities whose impact on the distribution pattern has increased significantly in recent years [2,51]. Given that there is no precise quantification of the magnitude of the impact of human activities on the presence/absence of the species, this factor was not included in the model. Likewise, the models also exclude the dynamic nature of the individuals studied, the alteration by pests or diseases, or other situations that, for a short period, could drastically alter the distribution and abundance of forest species [52].
On the other hand, changes in the fundamental niche of a species can occur as a result of the plasticity of traits of morphological, physiological or behavioral type or by the classification of genotypes. For example, they could occur by the spatial segregation of individuals with certain functional features [53,54,55]. Finally, the niche of a species can expand or move, after a long period of time, as a genetic response to the new environmental conditions [56,57].
In general, the statistical results suggest that the potential areas of most species are not experiencing drastic changes in the three modeled time periods. Nevertheless, establishing strategies for restoration, conservation, and reforestation in the medium- and long-term can guarantee not only the survival, but also the wide distribution, of the species of greatest economic and ecological interest, particularly those whose potential areas were reduced, displaced or fragmented, such as Pinus engelmanni, P. arizonica, P. leiophylla, P. cooperi and Quercus grisea, whose loss of potential area before the change from one period to another was evident (Figure 2 and Figure 3). In these models, historical factors, biotic interactions or other limitations that affect the dispersion of the plants are not incorporated, since the magnitude of impact (quantitative values) of each of them on the distribution and abundance of organisms is unknown, and thus it is impossible to assign them an appropriate weight in the models. In future studies, we foresee incorporating physiographic variables, topographic variables and soil properties, as well as testing other analysis methods.

5. Conclusions

The projections reported here could be useful for decision makers. For example, for a preliminary assessment of risks of the decrease, displacement, or fragmentation of the spaces with ideal conditions for the species studied. The results could also be used to define the degree of change in the climatic domain for each of these species. Incorporating other variables like soil properties (pH, electrical conductivity, texture, etc.), physiographic variables, the annual deforestation index, or any variable that could modify the natural distribution of plants, could increase the certainty of the models. The potential areas for most of the species did not vary between one period and another. Still, establishing integral management plans including conservation and reforestation strategies, at a regional level can guarantee the continued wide distribution of the species studied. Likewise, it is highly recommended that strategies designed to minimize the impacts generated by the excessive extraction of some species, such as Pinus durangensis (cataloged as Near Threatened by the IUCN), are established.

Author Contributions

P.A. conceived and coordinated the research project, statistical analysis, and the primary writing of the text. M.E.S.-M. designed the models, analyzed the data, and helped in writing the text. C.V.-E. conducted formatting, and was the advisor and text editor. F.R.-A.: was the advisor and text editor.

Acknowledgments

We are grateful to the Secretaría de Educación Publica (SEP; IDCA: 24332 and Proyect PRODEP-UNSIJ-PTC-028). We acknowledge and wish to show our appreciation for the technical assistance provided by Irene Bautista Juárez, Teresa Martínez Martínez, Diana Nava Juárez, and Marleny B. Ramírez Aguirre. We are grateful to Colin M. Gee and two anonymous reviewers for constructive comments on earlier versions of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Soberón, J.; Nakamura, M. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl. Acad. Sci. USA 2009, 106, 19644–19650. [Google Scholar] [CrossRef] [PubMed]
  2. Crowther, T.W.; Glick, H.B.; Covey, K.R.; Bettigole, C.; Maynard, D.S.; Thomas, S.M.; Tuanmu, M.N. Mapping tree density at a global scale. Nature 2015, 525, 201–205. [Google Scholar] [CrossRef] [PubMed]
  3. Perosa, M.; Rojas, J.F.; Villagra, P.E.; Tognelli, M.F.; Carrara, R.; Alvarez, J.A. Distribución potencial de los bosques de Prosopis flexuosa en la Provincia Biogeográfica del Monte (Argentina). Ecol. Austral. 2014, 24, 238–248. [Google Scholar]
  4. Martínez-Antúnez, P.; Hernández-Díaz, J.C.; Wehenkel, C.; López-Sánchez, C.A. Estimación de la densidad de especies de coníferas a partir de variables ambientales. Madera Bosques 2015, 21, 23–33. [Google Scholar] [CrossRef]
  5. Sánchez-Cordero, V.; Cirelli, V.; Murguíal, M.; Sarkar, S. Place prioritization for biodiversity representation using species ecological niche modelling. Biodivers. Inform. 2005, 2, 11–23. [Google Scholar] [CrossRef]
  6. Rodríguez, F.J.; Pereda, M.E. La dinámica espacial de los ecosistemas del estado de Durango. Ra Ximhai 2012, 8, 91–96. [Google Scholar]
  7. Calderón-Aguilera, L.E.; Rivera-Monroy, V.H.; Porter-Bolland, L.; Martínez-Yrízar, A.; Ladah, L.B.; Martínez-Ramos, M.; Alcocer, J.; Santiago-Pérez, A.L.; Hernandez-Arana, H.A.; Reyes-Gómez, V.M.; et al. An assessment of natural and human disturbance effects on Mexican ecosystems: Current trends and research gaps. Biodivers. Conserv. 2012, 21, 589–617. [Google Scholar] [CrossRef]
  8. Galicia, L.; Potvin, C.; Messier, C. Maintaining the high diversity of pine and oak species in Mexican temperate forests: A new management approach combining functional zoning and ecosystem adaptability. Can. J. For. Res. 2015, 45, 1358–1368. [Google Scholar] [CrossRef]
  9. Thuiller, W.; Albert, C.; Araújo, M.B.; Berry, P.M.; Cabeza, M.; Guisan, A.; Sykes, M.T. Predicting global change impacts on plant species’ distributions: Future challenges. Perspect. Plant. Ecol. 2008, 9, 137–152. [Google Scholar] [CrossRef]
  10. Sykes, M.T.; Prentice, I.C.; Cramer, W. A bioclimatic model for the potential distributions of north European tree species under present and future climates. J. Biogeogr. 1996, 23, 203–233. [Google Scholar]
  11. Kearney, M.; Porter, W.P. Mechanistic niche modelling: Combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 2009, 12, 334–350. [Google Scholar] [CrossRef] [PubMed]
  12. Hanson, H.C.; Churchill, E.D. The Plant Community; Reinhold Publishing Corp.: New York, NY, USA, 1961; pp. 1–218. [Google Scholar]
  13. Chapman, S.B. Methods in Plant Ecology; Blackwell Scientific: Oxford, UK, 1976; pp. 1–580. [Google Scholar]
  14. Guisan, A.; Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef]
  15. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  16. Anderson, R.P.; Peterson, A.T.; Gómez-Laverde, M. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 2002, 98, 3–16. [Google Scholar] [CrossRef]
  17. Suárez-Mota, M.E.; Villaseñor, J.L.; López-Mata, L. La región del Bajío, México y la conservación de su diversidad florística. Rev. Mex. Biod. 2015, 86, 799–808. [Google Scholar] [CrossRef]
  18. Suárez-Mota, M.E.; Ortiz, E.; Villaseñor, J.L.; Espinosa-Garcia, F.J. Ecological niche modeling of invasive plant species according to invasion status and management needs: The case of Chromolaena odorata (Asteraceae) in South Africa. Pol. J. Ecol. 2016, 64, 369–383. [Google Scholar] [CrossRef]
  19. Townsend, P.; Klusa, D.A. New distributional modelling approaches for gap analysis. Animal Conservation. Zool. Soc. Lond. 2003, 6, 47–54. [Google Scholar] [CrossRef]
  20. Leal-Nares, O.; Mendoza, M.E.; Pérez-Salicrup, D.; Geneletti, D.; López-Granados, E.; Carranza, E. Distribución potencial del Pinus martinezii: Un modelo espacial basado en conocimiento ecológico y análisis multicriterio. Rev. Mex. Biodivers. 2012, 83, 1152–1170. [Google Scholar] [CrossRef]
  21. Fourcade, Y.; Engler, J.O.; Rödder, D.; Secondi, J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: A performance assessment of methods for correcting sampling bias. PLoS ONE 2014, 9, e97122. [Google Scholar] [CrossRef] [PubMed]
  22. Chefaoui, R.M.; Hortal, J.; Lobo, J.M. Potential distribution modelling, niche characterization and conservation status assessment using GIS tools: A case study of Iberian Copris species. Biol. Conserv. 2005, 122, 327–338. [Google Scholar] [CrossRef]
  23. Rzedowski, J. Vegetación de México; Limusa: México, D.F., Mexico, 1978; pp. 1–432. [Google Scholar]
  24. Silva-Flores, R.; Pérez-Verdín, G.; Wehenkel, C. Patterns of tree species diversity in relation to climatic factors on the Sierra Madre Occidental, Mexico. PLoS ONE 2014, 9, 105034. [Google Scholar] [CrossRef] [PubMed]
  25. CONAFOR (Comisión Nacional Forestal). Manual and Procedures for Field Sampling—National Forest and Soil Inventory. 2009. Available online: http://www.snieg.mx/contenidos/espanol/iin/Acuerdo_3_X/Manual_y_Procedimientos_para_el_Muestreo_de_Campo_INFyS_2004–2009.pdf (accessed on 17 November 2017).
  26. Antúnez, P.; Wehenkel, C.; López-Sánchez, C.A.; Hernández-Díaz, J.C. The role of climatic variables for estimating probability of abundance of tree species. Pol. J. Ecol. 2017, 65, 324–338. [Google Scholar] [CrossRef]
  27. Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Clim. 2005, 25, 1965–1978. [Google Scholar] [CrossRef]
  28. Fick, S.E.; Hijmans, R.J. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  29. Cuervo-Robayo, A.; Téllez-Valdés, O.; Gómez, M.A.; Venegas-Barrera, C.S.; Manjarrez, J.F.; Mártinez-Meyer, E. An update of high-resolution monthly climate surface for Mexico. Int. J. Climatol. 2013, 34, 2427–3437. [Google Scholar] [CrossRef]
  30. Cruz-Cárdenas, G.; López-Mata, L.; Villaseñor, J.L.; Ortiz, E. Potential species distribution modeling and the use of principal component analysis as predictor variables. Rev. Mex. Biodiver. 2014, 85, 189–199. [Google Scholar] [CrossRef]
  31. Nakicenovic, N.; Swart, R. IPCC: Special Report on Emissions Scenarios; Cambridge University Press: Cambridge, UK, 2000. [Google Scholar]
  32. Elith, J.; Graham, J.C.H.; Anderson, P.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
  33. Peterson, A.T.; Soberón, J.; Pearson, R.G.; Anderson, R.; Martínez-Meyer, E.; Nakamura, M.; Araujo, M. Ecological Niches and Geographic Distributions; Princeton University Press: Princeton, NJ, USA, 2011; pp. 1–314. [Google Scholar]
  34. Phillips, S.J.; Dudik, M. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  35. Phillips, S.J. Transferability, sample selection bias and background data in presenceonly modelling: A response to Peterson et al. (2007). Ecography 2008, 31, 272–278. [Google Scholar] [CrossRef]
  36. Pearson, R.G.; Raxworthy, C.J.; Nakamura, M.; Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 2007, 34, 102–117. [Google Scholar] [CrossRef]
  37. Jolliffe, I.T. Principal component analysis and factor analysis. In Principal Component Analysis; Springer: New York, NY, USA, 1986; pp. 115–128. [Google Scholar]
  38. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2018. Available online: https://www.r-project.org/ (accessed on 1 January 2017).
  39. Kuhn, G.M. The phi coefficient as an index of ear differences in dichotic listening. Cortex 1973, 9, 450–457. [Google Scholar] [CrossRef]
  40. González-Elizondo, M.S.; González-Elizondo, M.; González, L.R.; Enríquez, I.L.; Rentería, F.R.; Flores, J.T. Ecosystems and diversity of the Sierra Madre Occidental. In Merging Science and Management in a Rapidly Changing World: Biodiversity and Management of the Madrean Archipelago III and 7th Conference on Research and Resource Management in the Southwestern Deserts, Tucson, AZ, USA, 1–5 May 2012; Gottfried, G.J., Ffolliott, P.F., Gebow, B.S., Eskew, L.G., Collins, L.C., Eds.; Proceedings RMRS-P-67; US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2013; pp. 204–211. [Google Scholar]
  41. Toledo, V.M.; Ordóñez, M. The biodiversity scenario of Mexico: A review of terrestrial habitats. In Biological Diversity of Mexico. Origins and Distribution; Ramamoorthy, T.P., Bye, R., Lot, A., Eds.; Oxford University Press: New York, NY, USA, 1993; pp. 757–777. [Google Scholar]
  42. Challenger, A. Utilización y Conservación de los Ecosistemas Terrestres de México: Pasado, Presente y futuro; Conabio-Instituto de Biología, UNAM-Agrupación Sierra Madre SC. Distrito Federal: Tlalpan, Mexico, 1998. [Google Scholar]
  43. Guisan, A.; Zimmermann, N.E. Predictive habitat distribution models in ecology. Ecol. Model. 2000, 135, 147–186. [Google Scholar] [CrossRef]
  44. Barve, N.; Barve, V.; Jiménez-Valverde, A.; Lira-Noriega, A.; Maher, S.P.; Peterson, A.T.; Villalobos, F. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 2011, 222, 1810–1819. [Google Scholar] [CrossRef]
  45. Loiselle, B.A.; Howell, C.A.; Graham, C.H.; Goerck, J.M.; Brooks, T.; Smith, K.G.; Williams, P.H. Avoiding pitfalls of using species distribution models in conservation planning. Conserv. Biol. 2003, 17, 1591–1600. [Google Scholar] [CrossRef]
  46. Araújo, M.B.; Pearson, R.G.; Thuiller, W.; Erhard, M. Validation of species–climate impact models under climate change. Glob. Chang. Biol. 2005, 11, 1504–1513. [Google Scholar] [CrossRef]
  47. Carneiro, L.R.D.A.; Lima, A.P.; Machado, R.B.; Magnusson, W.E. Limitations to the use of species-distribution models for environmental-impact assessments in the Amazon. PLoS ONE 2016, 11, e0146543. [Google Scholar] [CrossRef] [PubMed]
  48. Kadmon, R.; Farber, O.; Danin, A. A systematic analysis of factors affecting the performance of climatic envelope models. Ecol. Appl. 2003, 13, 853–867. [Google Scholar] [CrossRef]
  49. Ward, G.; Hastie, T.; Barry, S.; Elith, J.; Leathwick, J.R. Presence-only data and the EM algorithm. Biometrics 2009, 65, 554–563. [Google Scholar] [CrossRef] [PubMed]
  50. Walker, P.A.; Cocks, K.D. HABITAT: A procedure for modelling a disjoint environmental envelope for a plant or animal species. Glob. Ecol. Biogeogr. Lett. 1991, 1, 108–118. [Google Scholar] [CrossRef]
  51. Hunter, P. The human impact on biological diversity. How species adapt to urban challenges sheds light on evolution and provides clues about conservation. EMBO Rep. 2007, 8, 316–318. [Google Scholar] [CrossRef] [PubMed]
  52. Antúnez, P.; Hernández-Díaz, J.C.; Wehenkel, C.; Clark-Tapia, R. Generalized models: An application to identify environmental variables that significantly affect the abundance of three tree species. Forests 2017, 8, 59. [Google Scholar] [CrossRef]
  53. Ackerly, D. Canopy gaps to climate change -extreme events, ecology and evolution. New Phytol. 2003, 160, 2–4. [Google Scholar] [CrossRef]
  54. Ackerly, D.D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant. Sci. 2003, 164, 165–184. [Google Scholar] [CrossRef]
  55. Eiserhardt, W.L.; Svenning, J.C.; Borchsenius, F.; Kristiansen, T.; Balslev, H. Separating environmental and geographical determinants of phylogenetic community structure in Amazonian palms (Arecaceae). Bot. J. Linn. Soc. 2013, 171, 244–259. [Google Scholar] [CrossRef]
  56. Davis, M.B.; Shaw, R.G.; Etterson, J.R. Evolutionary responses to changing climate. Ecology 2005, 86, 1704–1714. [Google Scholar] [CrossRef]
  57. Davis, M.B.; Shaw, R.G. Range shifts and adaptive responses to quaternary climate change. Science 2001, 292, 673–679. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Study area, located in northwest of Mexico. The axes are geographic coordinates.
Figure 1. Study area, located in northwest of Mexico. The axes are geographic coordinates.
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Figure 2. Potential distribution areas of Pinus species for the period of the most recent glaciation (21,000 years ago), present period and into the future (2080 to 2100).
Figure 2. Potential distribution areas of Pinus species for the period of the most recent glaciation (21,000 years ago), present period and into the future (2080 to 2100).
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Figure 3. Potential distribution areas of Quercus species for the period of the most recent glaciation (21,000 years ago), present period and into the future (2080 to 2100).
Figure 3. Potential distribution areas of Quercus species for the period of the most recent glaciation (21,000 years ago), present period and into the future (2080 to 2100).
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Figure 4. (A) Potential areas projected for each species for different periods of time. (B) Comparison of means by the Kruskal-Wallis test, using the potential areas of the 13 species of each period; means sharing a letter are not significantly different (p < 0.05). sp1: P. arizonica, sp2: P. ayacahuite, sp3: P. cembroides, sp4: P. cooperi, sp5: P. duranguensis, sp6: P. engelmanni, sp7: P. leiophylla, sp8: P. teocote, sp9: Q. arizonica, sp10: Q. crassifolia, sp11: Q. grisea, sp12: Q. magnolifolia, sp13: Q. sideroxyla.
Figure 4. (A) Potential areas projected for each species for different periods of time. (B) Comparison of means by the Kruskal-Wallis test, using the potential areas of the 13 species of each period; means sharing a letter are not significantly different (p < 0.05). sp1: P. arizonica, sp2: P. ayacahuite, sp3: P. cembroides, sp4: P. cooperi, sp5: P. duranguensis, sp6: P. engelmanni, sp7: P. leiophylla, sp8: P. teocote, sp9: Q. arizonica, sp10: Q. crassifolia, sp11: Q. grisea, sp12: Q. magnolifolia, sp13: Q. sideroxyla.
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Table 1. List of species studied and their records used in the potential distribution models.
Table 1. List of species studied and their records used in the potential distribution models.
SpeciesAUC Values
PastPresentFuture
Pinus arizonica Engelm. var. stormiae Martínez0.9600.9610.962
Pinusstrobiformis Engelm0.9270.9450.945
Pinus cembroides Zucc0.9480.9440.950
Pinus cooperi C.E.Blanco0.9610.9650.966
Pinus durangensis Martínez0.9150.9090.908
Pinus engelmanni Carr0.9260.9290.908
Pinus leiophylla Schiede ex Schltdl. et Cham.0.9540.9370.943
Pinus teocote Schiede ex Schltdl. et Cham.0.9510.9420.947
Quercus arizonica Sarg0.7910.7840.794
Quercus crassifolia Humb. & Bonpl.0.9660.9640.967
Quercus grisea Liebm0.9420.9390.939
Quercus magnolifolia Née0.9640.9670.967
Quercus sideroxyla Humb. & Bonpl.0.8790.8970.898
Table 2. List of climatic variables used in the models and their respective acronyms. The variables that showed the highest and significant correlation coefficients (highlighted in bold) were used to model the potential areas.
Table 2. List of climatic variables used in the models and their respective acronyms. The variables that showed the highest and significant correlation coefficients (highlighted in bold) were used to model the potential areas.
AcronymsDescriptionPC1PC2
bio_01Mean Annual Temperature (°C)0.340.06
bio_02Mean Diurnal Range (Mean of monthly max. temp. min. temp.) (°C)0.930.07
bio_03Isothermality (Bio_02/Bio_07) (×100)0.31−0.41
bio_04Temperature Seasonality (standard deviation × 100) (Coefficient of Variation)0.980.19
bio_05Max Temperature of Warmest Month (°C)−0.310.13
bio_06Min Temperature of Coldest Month (°C)0.72−0.03
bio_07Temperature Annual Range (Bio_05–Bio_06) (°C)0.960.13
bio_08Mean Temperature of Wettest Quarter (°C)0.190.13
bio_09Mean Temperature of Driest Quarter (°C)0.440.16
bio_10Mean Temperature of Warmest Quarter (°C)0.120.11
bio_11Mean Temperature of Coldest Quarter (°C)0.500.027
bio_12Annual Precipitation (mm)0.880.45
bio_13Precipitation of Wettest Month (mm)0.880.42
bio_14Precipitation of Driest Month (mm)0.220.49
bio_15Precipitation Seasonality (Coefficient of Variation)−0.33−0.24
bio_16Precipitation of Wettest Quarter (mm)0.880.44
bio_17Precipitation of Driest Quarter (mm)0.520.45
bio_18Precipitation of Warmest Quarter (mm)0.790.42
bio_19Precipitation of Coldest Quarter (mm)0.720.43
Table 3. Phi correlation coefficients for the presence/absence values, comparing the degree of association among the three periods of time.
Table 3. Phi correlation coefficients for the presence/absence values, comparing the degree of association among the three periods of time.
Species StudiedPeriods of TimePastPresent
P. arizonicaPresent0.75-
Future0.670.79
P. strobiformisPresent0.73-
Future0.750.84
P. cembroidesPresent0.52-
Future0.650.46
P. cooperiPresent0.80-
Future0.780.82
P. duranguensisPresent0.72-
Future0.730.82
P. engelmanniPresent0.73-
Future0.740.84
P. leiophyllaPresent0.67-
Future0.710.78
P. teocotePresent0.72-
Future0.670.81
Q. arizonicaPresent−0.04-
Future0.020.71
Q. crassifoliaPresent−0.04-
Future−0.050.85
Q. griseaPresent0.79-
Future0.740.87
Q. magnolifoliaPresent0.79-
Future0.740.87
Q. sideroxylaPresent0.75-
Future0.690.69

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