Tree-ring climate reconstructions can be helpful to better understand past climate variability beyond instrumental records. This protocol shows how to reconstruct past climate using tree rings and meteorological instrumental records.
Tree rings have been used to reconstruct climatological variables in many locations around the world. Moreover, tree-rings can provide valuable insights into climatic variability of the last few centuries and, in some areas, several millennia. Despite the important development, that dendrochronology has had in recent decades to study the dendroclimatic potential of a large number of species present in different ecosystems, much remains to be done and explored. In addition to this, in the last few years more people (students, teachers and researchers) around the world are interested in implementing this science to extend the timeline of climate information backwards and understand how climate has changed on scales of decades, centuries or millennia. Therefore, the objective of this work is to describe the general aspects and basic steps needed to conduct a tree-ring climate reconstruction, from site selection and field sampling to laboratory methods and data analysis. In this method’s video and manuscript, the general basis in tree-ring climatic reconstructions is explained so newcomers and students can use it as an available guide into this field of research.
Tree rings are fundamental to our understanding of how trees respond to their environment. In addition, because climate affects tree growth, trees serve as environmental gauges recording the temporal variations during their lifespan. Thus, tree rings have been valuable to reconstruct past climates far beyond any instrumental climate record.
Growth processes in roots, stems, branches, leaves, and reproductive strategies in trees are regulated by environmental factors such as water, light, temperature, and soil nutrients1. For example, stems grow radially and the vascular cambium controls radial growth2. The vascular cambium is a meristematic tissue that will actively produce new functional cells such as xylem and bark located at the outer boundary of the stem. Additionally, the vascular cambium is primarily active during seasonal cycles. However, this growth activity can be interrupted during dormancy periods and during particular seasons of the year. This dormancy period usually happens when environmental variables are not optimal (e.g., shorter diurnal cycles, extended drought periods, cold winters, or floods). Furthermore, the growth and dormancy cycles translate in changes in the cambium activity resulting in anatomically distinct concentric boundaries in the stem called tree rings3.
Trees generally produce one tree ring every year since climatic seasonality occurs annually. Thus, tree rings are the visual manifestation of the ecophysiological response of the vascular cambium to the intra-annual climatic conditions during tree growth3. The early cluster of xylem cells formed on a tree ring during the wet season will be characterized by larger cells called earlywood4. In contrast, during the dry season and in response to water scarcity, vascular cambium produces smaller xylem cells (tracheids or vessels) with thicker cell walls called latewood. This variation in anatomical structures is more noticeable in conifers, where the earlywood shows a lighter color than latewood, showing a darker color5. The space between the beginning of the earlywood and the end of the latewood is defined as one tree ring (Figure 8F).
Trees growing on locations with a well-defined rainy and dry season could expect years with a higher or lower amount of precipitation. This variability will lead trees to produce wider rings during wet years and narrower rings during dry years. These temporal patterns of wide and narrow rings can be seen as a barcode. This tree-ring width temporal variation is the basis for applying the process of cross-dating, one of the most critical principles in tree-ring research6. The process of cross-dating is satisfactory when the patterns of wide and narrow rings in all samples are successfully synchronized in time to assign the corresponding year of formation.
In many regions of the world where seasonal climate occurs, the most dominant signal recorded in tree rings is likely related to climate variability7. However, tree rings also contain additional information related to age (young trees grow faster than older ones), competition for resources with surrounding trees, and internal and external disturbances (e.g., mortality events, pest outbreaks, or fire)8. Thus, before attempting to reconstruct past climates using tree ring widths, non-climatic signals need to be removed through several statistical procedures explained in this manuscript.
The main goal of this protocol is to show how to develop a climatic reconstruction based on tree-ring data to understand past climatic variability. Thus, this manuscript will showcase the essential field and laboratory methods such as sampling, sample preparation, cross-dating, and measuring tree-ring widths required to develop a climatic reconstruction. In addition, this protocol will also explain the fundamental statistical analyses used to extract the common variability from tree-ring widths and construct a tree-ring chronology that will be correlated with climatic data. Finally, using a simple linear regression model the protocol will show how to reconstruct past climate using the tree-ring chronology as the predictor variable and the climate data as the predictand.
Before the field trips have the permission of the owners, in case of a conservation area, or the corresponding authorities. It is very important that some personnel representing the authority participate in the field work to avoid any problem.
1. Sampling strategy
Figure 1: Temperate mixed-conifer forest. (A) Mixed-conifer forest of Pinus montezumae, Pinus arizonica, and Pinus ayacahuite. (B) Mixed-conifer forest of Pseudotsuga menziesii, Pinus arizonica, and Pinus ayacahuite. Please click here to view a larger version of this figure.
Figure 2: Site selection. (A) Forested areas with limiting conditions (shallow, dry soil and a steep slope) with a high probability of finding long-lived individuals. (B) Long-lived individuals are essential for dendroclimatic studies. (C, D, E) Locating and selecting deadwood (stumps, fallen trees, and wood with a certain degree of deterioration) that allows the chronology to be extended in time. Please click here to view a larger version of this figure.
Figure 3: Selection of the best tree specimens. (A) Tree with a dead canopy top and thick branches, characteristic of long-lived individuals, and (B, C) images of trees with twisted stems and branches, that is, in a spiral shape, indicative of long-lived individuals. Please click here to view a larger version of this figure.
Figure 4: Tools used for sample collection. (A) Increment borer (Pressler), the tool to extract dendrochronological samples. (B) A 12 mm diameter borer, recommended for cases where more material is needed to define the tree rings, allowing the extraction of a larger sample volume, which improves the visualization of intricate rings, and facilitates the identification of growth problems. (C) A 5 mm diameter borer used in most cases. This type of borer is used for core sampling. Please click here to view a larger version of this figure.
Figure 5: Sample collection process. (A) Orient the drill pointing to the center of the trunk, positioned at a 90° angle, perpendicular to the axis of the trunk, simultaneously push the borer towards the tree and turn clockwise. (B) When the borer has been inserted 1 inch deep, keep turning clockwise to reach the center of the trunk, the extractor is inserted into the inner cylinder of the borer. (C) When the extractor is inserted to its full length, rotate the borer one turn counterclockwise to break the connection between the sample and the tree. (D, E) Wood sample extraction. (F) The borer is removed from the trunk by turning counterclockwise. Please click here to view a larger version of this figure.
Figure 6: Techniques to protect wood samples. Because the samples can be fragile, each sample must be stored properly after being collected. (A) The samples taken with the 5 mm diameter borer are placed in plastic straws with perforations or paper straws. The perforations allow better ventilation and prevents fungal growth. (B) The 12 mm diameter specimens are firmer. These samples are wrapped in newspaper or other paper type or manila envelopes. (C) When collecting cross-sections with a chainsaw (D, E), they should be wrapped in plastic to provide further support and avoid fragments being lost during transport. Please click here to view a larger version of this figure.
2. Sample preparation in the laboratory
Figure 7: Preparation of sample. (A) Drying samples in the shade ensures that the loss of moisture is gradual to minimize the deformation of the wood (twisted cores). (B) Example of how to mount samples on a wooden rack, fixed with glue, and (C, D) show how they are attached to the trim with tape or thin rope. (E) Indicates the correct position of the wood fibers, which must be oriented perpendicular to the growth rings. This orientation will allow clear visualization of the anatomy of the growth rings. (F, G) It is an example of the quality of sanding and polishing using sandpaper grits from 120 to 1200. This procedure allows to visualize and differentiate the growth rings. Please click here to view a larger version of this figure.
3. Tree-ring dating
4. Measuring the tree-ring
Figure 8: Cross-dating and tree-ring measurement. (A) Shows ring count and growth pattern comparison between two samples. (B) An example of how the growth variability of both samples is reflected in paper graphs (skeleton plot). This type of graph allows comparisons between the growths of many samples simultaneously (cross-dating) and is an essential techniques for achieving the correct dating. The marks at the top of the graph 0, 50, 60, etc., indicate the number of rings counted in sample shown in A. (C) Skeleton plot of a dead wood sample dated to the exact year using the master chronology. (D) Example of a master chronology, average of correctly dated living trees. (E) A measurement system with a precision of 0.001 mm was used to measure each of the annual growths. (F) Schematic showing the annual growth in Pinus lumholtzii and the three different band portions of an annual ring (total ring, earlywood, and latewood). Please click here to view a larger version of this figure.
5. Verification of cross-dating
6. Chronology development
Figure 9: Examples of detrending and standardization procedures of tree-ring width measurements (RW), from measurements to indices. The standardization to a ring width index (RWI) is calculated, so the mean is around one and has a homogeneous variance. (A) Ring width series RW indicates the exponential decrease in growth due to the age effect, the detrending curve of best fit is applied, and in this example, we use a negative exponential curve (red color). (C) This is a second example of a straight line (red color). (B, D) Normalized indices (RWI) are generated after dividing the value of the curve by the RW series. This division eliminates the trends fitted with the curve, maximizing the climatic signal (time series in gray color) and a 20 year smoothing spline (red color) to observe low-frequency events such as droughts and wet periods. Please click here to view a larger version of this figure.
7. Monthly correlation analysis
8. Simple linear regression model and reconstruction of the climatic variable
Following steps 1.1 and 1.2 of the protocol, Pinus lumholtzii B.L. Rob. & Fernald was selected for this study. Among the most important aspects that were considered, a few are as follows: It is a conifer of the genus Pinus with a wide geographical distribution and very few studies from the dendrochronological point of view; it develops in poor sites with rocky outcrops, with low water storage capacity, and its growth is limited by low water and nutritional availability, which causes slow growth rates and is of little commercial value; due to its phenotypic conformation and its little commercial interest, it is possible to find sites with low disturbance and with long-lived individuals; some previous studies indicate that it is a species with high climatic sensitivity and dendrochronological potential.
During a week of field work and following step 1.3 of the protocol, 50 samples were collected. Sample preparation following step 2 took one week. Out of the 50 samples, 41 samples were cross dated, showing a high and significant inter-series correlation value (r = 0.60, p <0.01). The cross-dating process of all the tree ring series was conducted using the COFECHA software. Warning flags were encountered during the cross-dating process that were later revised and corroborated, making sure there were no potential dating errors (following steps 3.4 to 3.8). We identified five missing rings and zero false rings. The overall dataset of 41 series and 6960 rings showed a mean sensitivity of 0.34.
After corroborating that all samples were correctly dated and correctly measured with a precision of 0.001 mm, a 294 year ring-width chronology was developed, from 1722 to 2015 CE (following the steps 6.1 to 6.6 of the protocol; Figure 10A). The ring-width chronology showed a mean EPS of 0.92, above the conventional threshold of 0.85. Additionally, the EPS analysis indicated that this dataset requires eight trees to obtain a significant EPS, making this chronology most reliable and robust from 1769 to 2015 (Figure 10B).
Figure 10: Tree-ring chronology and expressed population signal. (A) Tree-ring chronology of Pinus lumholtzii extending from 1722 to 2015 (gray line). The thick blue line represents the smoothing spline of 10 years and the black line is the number of core samples used to develop the tree-ring width chronology. (B) Expressed population signal (EPS, red line) and Rbar (green line). Rbar is the average pairwise correlation between all series where for each series this is the correlation between a series and a master chronology, estimated on a moving window of 25 years overlapped by 13 years. The horizontal dashed red lines denote the EPS threshold value of 0.85, while the gray box represents the series period with EPS < 0.85. Please click here to view a larger version of this figure.
Correlation analysis
After developing the chronology, it was compared with the 24 year monthly mean precipitation records from the two meteorological stations closest to the study area and with the most complete records (following the steps 7.1 to 7.6 of the protocol). In this protocol, the data's from January-December, 2021 (current growth year) and July-December, 2020 (previous growth year; monthly and cumulative) precipitation were correlated with the ring-width index chronology without autocorrelation (residual; Figure 11A,B). The correlation analysis revealed a positive relationship between tree growth chronology and the rainfall in June, September, and December of the previous growth year, and January, February, March, April, May, June, July, and September of the current growth year (Figure 11A). Moreover, the months of January, February, and March showed significant correlation values (p < 0.05), where March was the month with the highest correlation (r = 0.57; p < 0.01). However, the accumulated precipitation showed positive and significant correlations (p <0.01) with the chronology in multiple periods throughout the year (Figure 11B), where the total rainfall from January to July showed the highest seasonal correlation (r = 0.73; p < 0.01) (Figure 11). These correlations between the chronology and the seasonal precipitation showed a high potential to reconstruct the seasonal rainfall variability from January-July, explaining 52% of the instrumental climatic variability.
Figure 11: Monthly correlation analysis between the total ring-width chronology with monthly precipitation. (A) Previous and current year monthly correlation analysis, the x-axis shows the months and the y-axis the correlation values between the chronology and the corresponding precipitation record. The best correlation value between the chronology and the precipitation record was determined with the January-July period of the current year (blue-shaded region). (B) Correlation analysis using accumulated precipitation records, the x-axis, indicates the accumulated precipitation from January-December. The y-axis values are the correlation coefficients between the chronology and the corresponding precipitation records. * = P < 0.05 and ** = P < 0.01. The month names for a given period are abbreviated using the first alphabet for the month. The months are in chronological order. Please click here to view a larger version of this figure.
Rainfall reconstruction using a simple linear regression model
Given the association between the ring width index and the seasonal January-July precipitation (r = 0.73; p <0.01) (Figure 12A). The linear regression model generated for the reconstruction (Figure 12B) was as follows:
Yt = 75.475 + 391.02 * Xt
where Yt = January-July total precipitation in mm, reconstructed for a given year t; Xt = ring width index for a given year t.
Model calibration and verification
Once the model was developed, it was statistically validated following steps 8.1 to 8.6 of the protocol. The calibration period was selected from 2005-2014 and showed a significant correlation between the chronology and the seasonal precipitation (r = 0.85, p < 0.01), which accounted for 72% of the rainfall variability (Figure 12C). The verification (subperiod 1991-2004) indicated a highly significant correlation r = 0.64 (p < 0.001), which explained 41% of the rainfall variability (Figure 12C). Both the calibration and the verification subperiods of the model showed a significant relationship (Table 1 and Table 2). However, the model that includes the total period of available climate data (1991-2014) is considered statistically acceptable r = 0.73 (r2 = 0.53; p < 0.01) (Table 1, Table 2, and Figure 12B) to reconstruct the precipitation variability in the total length of the chronology.
Figure 12: Association between January-July seasonal precipitation and the regional ring width index for the period of 1991-2014. (A) Association between January-July seasonal precipitation during the 1991-2014 period and the ring width index (r = 0.73; p < 0.001, n = 24). (B) Linear regression model between the two variables using a commercial statistical program with 95% confidence interval (0.95 Conf. Interv.) and (C) comparison of the reconstructed January-July precipitation (solid line) and the observed precipitation (dotted line) for the verification period (r = 0.64; p < 0.001) and calibration (r = 0.85; p < 0.001) of the regression model. This figure has been adapted from Chávez-Gándara et al.22. Please click here to view a larger version of this figure.
Precipitation reconstruction
Once the model was validated, January-July precipitation was reconstructed for the period 1722-2015 (294 years). The reconstruction shows high variability, which has historically characterized the seasonal January-July precipitation regime in the study site (Figure 13). This climate reconstruction made it possible to reconstruct important drought events (those consecutive years with values below the reconstructed mean) of the last three centuries (Figure 13). Due to their extension and intensity, the droughts of the periods 1766-1780 (15 years), 1890-1900 (11 years), 1950-1957 (8 years), and 2011-2015 (5 years) are the most extended and driest periods recorded in this area. Likewise, the droughts that are observed approximately every 100 years (around every mid-century 1740-1750, 1840-1850, and 1940-1950), are events with a large-scale coverage, reported in studies in different regions of the country, which shows the effects of climatic phenomena on an extensive geographical scale in specific periods.
Figure 13: Dendroclimatic reconstruction of three centuries of January-July total precipitation. The gray line in the background indicates the inter-annual variability. The thick black smooth line represents a 10 year spline allowing the low frequency to be visible (long-term droughts and wet periods). The horizontal line represents the 300 year average rainfall. Red areas highlight the most substantial documented droughts. Dates highlighted in blue indicate droughts with a recurrence period near 100 years, documented in different studies with broad geographic coverage in Mexico23. The gray box represents the period of the series with EPS < 0.85. The 1769-2015 period registers a statistically robust sample size (EPS> 0.85). Please click here to view a larger version of this figure.
Period | R2Adj | Coefficient | Standard error | t-Statistic | Probability | ||||
β0 | β1 | β0 | β1 | β0 | β1 | β0 | β1 | ||
1991 – 2004 | 0.41 | 37.84 | 419.05 | 117.02 | 111.45 | 0.32 | 3.75 | 0.751 | 0.002 |
2005 – 2014 | 0.72 | 157.25 | 316.24 | 76.69 | 81.71 | 2.05 | 3.87 | 0.074 | 0.004 |
1991 – 2014 | 0.53 | 75.47 | 391.01 | 78.26 | 78.24 | 0.96 | 4.99 | 0.000 | 0.000 |
Table 1. Calibration for the reconstruction of January-July precipitation from the P. lumholtzii ring-width chronology.
Period | Pearson corr. (r) | Reduction of error | Signs test | t-Value |
1991-2004 | 0.64* | 0.36ns | 3* | 1.76ns |
2005-2014 | 0.85* | 0.66* | 1* | 1.80ns |
1991-2014 | 0.73* | 0.12* | 6* | 2.68* |
ns = Not significant | ||||
*= Significant p < 0.05 |
Table 2. Verification statistics for the tree-ring reconstruction January-July precipitation from the P. lumholtzii ring-width chronology.
Proxy records are natural systems that depend on the weather, which were present in the past and still exist, such as lake and marine sediments, pollen, coral reefs, ice cores, packrat middens, and tree rings, so information can be derived from them24. However, from most climate-sensitive proxies, tree rings represent the proxy with the highest precision and interannual resolution, allowing the dating of climatic and ecological events to the exact year of occurrence, spanning for centuries, and sometimes up to several millennia3,5,25,26. The cross-dating technique is now used to check and verify the correct dating of other proxy records that form regular growth bands (sometimes annual), such as ice cores, corals, rings in clam shells27, and carbon 1428,29. Dendrochronology is one of the most relevant techniques for understanding past environmental processes, and it is a critical source for monitoring anthropogenic environmental changes, such as pollution7,30.
The limitations of this method are as follows: (1) a short extension (50 years or less) and quality of climatic data observed in many regions of the world is essential to calibrate the tree ring series and reconstruct climatic variables of interest. (2) Climate reconstructions studies are feasible only in woody species that produce conspicuous and reliable annual ring and are also sensitive to climatic variables. A poor quality in the marking of the annual ring increases the probability of error, and if the species is not sensitive to recording environmental changes (complacent trees), it is not possible to obtain the required climatic signal. (3) The longevity of the species is another limitation as it is difficult to find and obtain sufficient samples of long-lived tree. This reduces the representativeness or favors a non-robust statistical sample size (as detailed in steps 6.6 and 6.7 of the protocol), which limits the use of a chronology in its total length. (4) Dendrochronology is challenging to be carried out in tropical species in locations with homogeneous climatic conditions throughout the year. (5) Selective sampling might cause modern sample bias that might distorts the recovered climate signal since31. This problem can be partially resolved by using flexible curve standardization procedures such as the cubic-smoothing spline32,33. However, in most cases, the time window provided by tree ring series is usually longer than the observed climatic records.
Any reconstruction of climate variability based on tree rings requires a good quality control for the different stages involved in this type of research. Selecting correct trees and collecting and preparing samples (as indicated in steps 1.1, 1.2, 1.3, and 2 of the protocol) is crucial. Another essential step is to achieve an exact dating of each growth band, as indicated in section 3 of the protocol and standard procedures indicated by Stokes and Smiley6. For this study, statistically significant dating between series was achieved (r = 0.60; p < 0.01). The intercorrelation between series is statistically robust to consider the series correctly dated12. The average mean sensitivity was above 0.2, indicating sufficient interannual variation, which is ideal for dendrochronological studies to reconstruct past climate7,34. The chronology's statistical parameters (series intercorrelation, mean sensitivity, and EPS) indicated that P. lumholtzii is a suitable species for dendroclimatic reconstructions.
Tree ring widths can be defined as the accumulation of biological and environmental factors that limit secondary growth (additive model)14. The biological factor can be a dominant signal in the trees expressed as a decreasing trend in growth related to age. For example, a tree grows more in its juvenile stage than in its adult or senile stage. The second dominant signal in tree growth in several locations around the world is climate. To reconstruct climatic variables using tree rings, the most significant environmental signal can be related to regional climate variability, affecting most trees on the same stand. Another environmental factor influencing ring widths is the effect of the disturbances, which can affect some or all the individuals in a forest stand and occurs sporadically in time. Disturbances include gap formation, competition between trees, wildfires, insect outbreaks, logging, or contamination; and can lead to events of sustained growth suppression or growth releases15,16.
Forest stand dynamics can play an essential role in tree growth trends. Therefore, these community-dependent dynamics will influence what type of detrending methods is the most appropriate to use. For example, it is common to observe a substantial age effect in low-density open-canopy forest, so adjusting a negative exponential function is reasonable. However, forests with a higher tree density are most likely to compete with neighboring trees, resulting in growth variations not related to the common signal among trees in the stand. In this case, detrending can be carried out using a cubic spline17 where the age effect and the forest stand dynamic are removed, leaving the common variation amongst trees that typically is the climatic one.
A time-series characteristic that needs to be considered is autocorrelation. When looking at tree growth in any given year, one tree ring is likely influenced by past years' conditions. For example, if in the past few years, a tree has been exposed to conditions of water limitation, probably, the rooting and canopy systems will not be ready to respond independently and quickly during a sporadic wet year. Therefore, the growth during the wet year will be influenced by the past dry conditions. Thus, under the assumption that the temporal climate variability does not have significant autocorrelation does not hold. Then, this type of time-dependent information (i.e., autocorrelation) embedded in tree rings must be removed when the interest of the study is the interannual climatic variability.
To verify the statistical predictive power and uncertainty of the regression model the following statistical variables were determined (see Fritts3 for a detailed description). (1) Correlation coefficient (r): This measures the strength of the degree of the linear relationship between two data sets. For example, a coefficient of one means that both datasets have the exact variability, and when the coefficient approaches zero, both datasets are different or unrelated. (2) Adjusted R2: This statistic quantifies the explanatory power of the regression while accounting for reducing the degrees of freedom with an increasing number of predictors. Similar to the r, the adjusted R2 is a measure of the strength of the regression model. (3) Reduction of error (RE): This is a rigorous measure of association between a series of measurements and their modeled estimates; its range goes from negative infinity to one, where positive values indicate prediction capacity. (4) Signs test: This is a nonparametric statistic involving the number of times that departures from the sample means agree or disagree. Means are subtracted from each series and the residuals are multiplied3. A positive product is a hit and a negative one miss. If either observed or reconstructed data lie near the mean, the year is omitted from the test. The number of signs is significant whenever it exceeds the number expected from random numbers. (5) Paired sample t-test: This statistical procedure determines whether the mean difference between a series of measurements and their modeled estimates is zero. (6) Standard error of estimate (SE): After computing a linear regression, the standard error of estimate (SE) was used to measure the uncertainty related to the model. SE measure the variation of the measurements series made around the computed regression line. It is used to check the accuracy of the predictions made with the regression line. (7) Root-mean-square error of validation (RMSEv): During the cross-validation test, the root-mean-square error of validation (RMSEv) measures the differences between values predicted by a model in the calibration period and the values observed during the validation period and vice versa. In other words, it is a measure of the uncertainty that the model estimates over the validation using the regression model from the calibration. (8) Durbin-Watson test: Durbin-Watson test indicates the presence or absence of autocorrelation in the regression residuals21.
Finally, it was possible to reconstruct the variability of the January-July precipitation for the last three centuries (Figure 13). This tree ring series allowed to analyze the climate variability over several centuries and determine the frequency of extreme events (droughts) and their effect on different geographical regions35. Added to this, it is possible to analyze the long-term influence of general ocean-atmospheric modes on the historical climatic behavior of the region. The applications of dendrochronology in different scientific fields is enormous (see Speer7). This great potential is derived of the large variety of inferences that can be done with tree-ring records; for example, the use of tree-ring networks have been used in drawing several drought reconstruction atlases35,36,37. Similarly, it has been possible to reconstruct regional temperatures in the northern hemisphere38,39 or to analyze long-term streamflow variability over large basins in several regions of the American continent by using the tree-ring records40,41,42,43.
Given the lack of extensive instrumental climate records and current climate change scenarios, it is essential to continue developing networks of tree-ring chronologies, hydroclimatic reconstructions, and keep exploring the potential of new species. These actions will allow the reconstruction and extension of climate records in different regions of the world lacking long climate records. The aim should be to develop robust chronology networks that facilitate the analysis of climate variability at the local level and large geographic scales on an annual resolution basis.
The authors have nothing to disclose.
The research project was carried out thanks to the financing through the projects CONAFOR-2014, C01-234547 and UNAM-PAPIIT IA201621.
ARSTAN Software | https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software | ||
Belt Sander | Dewalt Dwp352vs-b3 3×21 PuLG | For sanding samples | |
Chain Saw Chaps | Forestry Suppliers | PGI 5-Ply Para-Aramid | https://www.forestry-suppliers.com/Search.php?stext=Chain%20Saw%20Chaps |
Chainsaw | Stihl or Husqvarna for example | MS 660 | Essential equipment for taking cross sections samples (Example: 18-24 inch bar) |
Clinometer | Forestry Suppliers | Suunto PM5/360PC with Percent and Degree Scales | https://www.forestry-suppliers.com/Search.php?stext=Clinometer |
COFECHA Software | https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software | ||
Compass | Forestry Suppliers | Suunto MC2 Navigator Mirror Sighting | https://www.forestry-suppliers.com/Search.php?stext=compass |
Dendroecological fieldwork programs | Programs where dating skills can be acquired or honed | http://dendrolab.indstate.edu/NADEF.htm | |
Diameter tape | Forestry Suppliers | Model 283D/10M Fabric or Steel. | https://www.forestry-suppliers.com/Search.php?stext=Diameter%20tape |
Digital camera | CANON | EOS 90D DSLR | To take pictures of the site and the samples collected (https://www.canon.com.mx/productos/fotografia/camaras-eos-reflex) |
Digital camera for microscope | OLYMPUS | DP27 | https://www.olympus-ims.com/es/microscope/dp27/ |
Electrical tape or Plastic wrap to protect samples | uline.com | https://www.uline.com/Product/Detail/S-6140/Mini-Stretch-Wrap-Rolls/ | |
Field format | There is no any specific characteristic | To collect information from each of the samples | |
Field notebook | To take notes on study site information | ||
Gloves | For field protection | ||
Haglöf Increment Borer Bit Starter | Forestry Suppliers | https://www.forestry-suppliers.com/Search.php?stext=Increment%20borer | |
Hearing protection | Forestry Suppliers | There is no any specific characteristic | https://www.forestry-suppliers.com/Search.php?stext=Hearing%20protection |
Helmet | Forestry Suppliers | There is no any specific characteristic | https://www.forestry-suppliers.com/Search.php?stext=Wildland%20Fire%20Helmet |
Increment borer | Forestry Suppliers | Haglof | https://www.forestry-suppliers.com/Search.php?stext=Increment%20borer |
Large backpacks | There is no any specific characteristic | Strong backpack for transporting cross-sections in the field | |
Safety Glasses | Forestry Suppliers | There is no any specific characteristic | https://www.forestry-suppliers.com/Search.php?stext=Safety%20Glasses |
Sandpaper | From 40 to 1200 grit | ||
Software Measure J2X | Version 4.2 | http://www.voortech.dreamhosters.com/projectj2x/tringSubscribeV2.html | |
STATISTICA | Kernel Release 5.5 program (Stat Soft Inc. 2000) | Statistical analysis program | |
Stereomicroscope | OLYMPUS | SZX10 | https://www.olympus-ims.com/en/microscope/szx10/ |
Topographic map, land cover map | Obtained from a public institution or generated in a first phase of research | ||
Tube for drawings | There is no any specific characteristic | Strong tube for transporting samples in the field | |
Velmex equipment | Velmex, Inc. | 0.001 mm precision | www.velmex.com |
.