Login processing...

Trial ends in Request Full Access Tell Your Colleague About Jove


How to Extract Climate Variability from Tree-Rings

Published: March 9, 2022 doi: 10.3791/63414


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.

Subscription Required. Please recommend JoVE to your librarian.


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

  1. Determining the study area
    1. Select the most appropriate sampling area based on climatic information and forest composition (forests can be highly heterogeneous; Figure 1A,B).
    2. Check that the sampling site shows evident annual climatic seasonality and inter-annual climatic variation including dry/wet or cold/hot season during the year. Inspect the climatic records from nearby meteorological stations to determine the annual climatic seasonality and inter-annual climatic variation.
    3. Ensure that moderate to high climatic inter-annual variability is present so that the trees in the study site show enough year-to-year ring width variation to cross-date samples among trees.
    4. Carry out field trips in the area of interest to identify potential sites with the species of interest (Figure 1B)
    5. Use some of the recommended tools such as cartography, drones, and satellite images to explore a larger forested area and detect more potential sampling areas. Verify the information from these sources in the field.
    6. Gather information from complementary sources such as the regional stakeholders which include forest service providers, forest producers, rural communities, and small landowners. Choose the best study sites and the most suitable individuals to fulfill the objective based on the information obtained from both sources.
    7. Select areas where the longest-lived individuals of the species of interest are observed (Figure 2A,B). Observe standing dead trees, fallen trees, and stumps. Old dead samples are very important since they allow the chronology to be extended back in time (Figure 2A,C,D,E)
    8. Register the location of individuals with the characteristics mentioned above using a GPS.
  2. Considerations for selecting the best tree
    1. Once the best site has been located, select the trees to be appropriately sampled. Trees located in shallow and rocky soils and steep slopes are more sensitive to climatic variability. Use these ecophysiographic characteristics to determine the limiting factors that trees most likely will record (Figure 2A).
      NOTE: Avoid taking tree samples in places of high competition; in these high-density locations, trees will have a strong forest stand dynamics signal and a reduced climatic one.
    2. Record the site information in a field format. Collect geographic and ecological information on the area, such as coordinates, elevation, the slope of the terrain, location's name, vegetation type, dominant species, and current land use.
    3. Record information of the sampled trees, such as diameter, height, presence of damage, if it is located near or on a stream, on a steep slope, or ravine.
      NOTE: The above information will be handy when analyzing the samples to corroborate and better interpret the study results. Since trees or samples might be exposed to damage or the site's conditions where the trees grow can alter the annual variations in growth. This metadata will help explain variations in growth independent of climatic factors, giving the elements to consider or eliminate noisy samples, always considering highlighting the climatic signal.
    4. Give a code for each sample based on the site name and the sample number, which comprises the first three letters of the site name, tree number, and sample number. For example, the first sample taken in this site will have the following code: RMI01A, which corresponds to the site Río Miravalles (RMI), tree number one (01), and first sample (A).
      NOTE: The term sample in this case refers to an increment core or a piece of a cross section taken from one tree.
    5. Perform selective sampling as done in most of the dendroclimatic studies by selecting individuals with specific phenotypic characteristics and growing in specific environmental conditions to address the objectives of the research. Perform a non-selective sampling if the goal is to project climate effects on tree growth and to integrate tree size and stand dynamics.
    6. Select trees with a long-lived appearance, on some occasions with dry top, dieback, twisted stem (i.e., spiral shape), and dropping branches (Figure 3A-C). Long-lived individuals will extend environmental records further back in time.
    7. Identify long-lived trees by observing highly compact, narrower rings during their most recent years which are by consequence difficult to observe and cross-date. Identify younger trees growing during these same periods as they register wider and more conspicuous rings, facilitating the dating of older ones.
    8. Consider sampling between 10%-20% of young individuals among the sampled trees within the sampling strategy.
    9. Ensure that the trees have a solid trunk to obtain the longest possible increment core. In addition, avoid rotten areas because they can cause sectioned samples and loss of internal rings and may get the increment borer stuck.
    10. Ensure that the selected trees are not hollow. Gently tap the tree with a plastic hammer and listen to the resonance of the wood. If the resonance is strong or deep, it means that the tree might be hollow. If the sound is dry, there is a low probability that the tree is hollow.
      NOTE: This step is important because the increment borer may get stuck in hollow trees, making it difficult to extract the increment borer, and potentially the sample might not be of good quality.
    11. Even when the above is considered and no rot is detected, pay close attention to the following. When introducing the increment borer, apply certain degree of force to penetrate the tree. When this required force changes, the borer becomes softer, at this point stop and draw the sample.
      CAUTION: If force is continued to drive the increment borer in, the decaying wood mixed with resin will collect in the increment borer barrel and form a plug that is difficult to remove with the extractor. When this happens, do not use a knife or some steel material to free the wooden plug from the increment borer (this could damage the cutting part, rendering it useless).
    12. The edge of the increment borer is very sensitive to metal rust, use a lubricant and a piece of wood to press the plug and release the increment borer cylinder. The wood does not cause any damage to the edge of the increment borer.
    13. When working with resinous trees or with large quantities of sap, clean the borer often with oil. Use lubricants or ethanol, for cleaning the resin residues that adhere to the metal.
  3. Sample collection (collecting increment cores)
    1. Collect the samples with the Pressler increment borer (Figure 4A), a precision tool designed to extract a small core from a living tree without significant damage6. Use any of the available increment borers which come in different lengths (100-1000 mm), diameters (4, 5, 10, 12 mm), and threads (2 and 3; Figure 4B,C). As with any wood-cutting tool, keep the borer sharp and clean; unsharpened borers might lead to twisted and broken cores.
    2. Select the right borer depending on the tree species to be sampled. For most woods, use a three-threaded borer of any length or diameter for sampling. For hardwood species, use a two-threaded borer of small diameter and a short length for a slower penetration, less friction and stress on the wood, and a lower probability of being broken during the sampling process.
    3. In species that show a high frequency of false rings or interannual density fluctuation (IADF) and or micro-rings, use the 12 mm diameter borer instead of the 5 mm. This allows to extract a wider sample surface for better visualization of the difficult rings and facilitates the identification of these problems (Figure 4B). Do not attempt to use longer borers in hardwoods since there is the risk of breaking it in during the sampling process.
    4. In order to take a wood sample, orient the increment borer by aiming it toward the center of the tree, 90° (perpendicular) to the axis of the trunk.
    5. Push the increment borer into the tree and turn the handle clockwise, simultaneously. This part is essential since the lack of pressure during the initial penetration of the borer bit might cause irregular or broken cores. Once the bit has penetrated completely, relax the pressure and turn the handle until the desired depth is reached (Figure 5A).
    6. Obtain at least two samples per individual to ensure good sample quality. If trees are growing on a slope, take samples parallel to the slope's contour (Figure 4A) to avoid the reaction wood produced by the trees7.
      NOTE: In conifers, trees produce reaction wood in the form of wide rings down the slope to keep the tree upright and it is called compression wood. In angiosperms (broadleaf trees) wide rings are produced up the slope and call it tension wood. Reaction wood must be considered to find the center of the tree and avoid non-climatic influences on tree-ring widths.
    7. When the borer has been rotated deep enough to the tree's center (Figure 5B), insert the extractor into the borer and push it towards the center of the tree (Figure 5C).
    8. When the extractor is inserted to its full length, turn the borer slightly counterclockwise to break the connection between the sample and the tree (Figure 5C). Then, remove the extractor carrying the core (Figure 5D,E) and finish the extraction by removing the borer from the tree turning it counterclockwise (Figure 5F).
    9. After taking the sample, pay close attention that the tree forms a seal of resins or sap exudation followed by secondary growth. Under special conditions, the injury might be the pathway for the entry of pathogens that could damage the tree6.
    10. When working in restricted areas, for instance, protected natural areas and national parks, consider taking extra measures to protect the sampled trees. Cover the minor injury made by the increment borer with Campeche wax or beeswax.
    11. In case problems occur during fieldwork, such as having wood stuck inside the borer, a broken tip, or having the increment borer stuck on a tree refer to steps 1.2.9.-1.2.12. In addition, take more than one increment borer to the field.
      NOTE: Remember that there is no golden rule to extract the core. Avoid irregularities and attempt to get the best information needed (Figure 4). For further information about taking care of the increment core and sampling, consult Maeglin9 and Phipps10 papers freely available on-line.
    12. Handle the cores with care as they are brittle. Store each sample immediately after extraction. For samples with 5 mm or thinner diameter, place them in plastic straws with perforations or paper straws for better ventilation and to avoid fungal growth (Figure 6A). For samples with a 12 mm diameter, wrap them in newspaper or any other type of paper (Figure 6B).
    13. During fieldwork and transportation to the laboratory, protect the samples and store the samples on a solid plastic tube with plastic caps.
    14. In places where dead trees or stumps are found, extract cross-sections using a chainsaw. This allows samples from both small trees and large trees (Figure 6C).
      NOTE: The objective of this type of sample is to extend the period of the chronology and to help detect missing rings not found on the cores. The missing rings are or will be evident if the whole circumference of the tree is exposed6.
    15. For samples taken with the chainsaw and with a certain degree of wood decomposition, it is possible to lose sample fragments. Wrap the samples in plastic to avoid this (Figure 6D,E).

Figure 1
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
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
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
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
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
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

  1. Follow the standard procedures indicated by Stokes and Smiley6 for preparation and dating of samples in the laboratory.
  2. Let the samples dry in shade so that the loss of moisture from the wood gradually minimizes wood deformations (Figure 7A). After the cores have lost enough moisture, mount them on wooden mounts or rails, fix with glue (Figure 7B) and fasten them with tape or thread (Figure 7C,D).
  3. Pay attention to the orientation of the wooden cores when placing them on the mounts. Fix the cores such as the xylem cells of the wood, which are oriented perpendicular to the plane, are observed and surfaced (Figure 7E). This orientation allows clear visualization of the wood anatomy of the tree rings.
  4. Sand and polish the samples using sandpaper of different grits, ranging between 120 to 1200 grit. In cross-sections that can show significant surface irregularities, follow one of two possible options.
    1. Option 1: Work with an electric brush and later sand the sample. Option 2: Begin the sanding process with a coarser sandpaper grit, in the range of 30 and gradually increase the grit to 1200. This will allow the growth rings to be seen and differentiated more easily (Figure 7F,G).
  5. Polish the entire upper part of the sample (Figure 7E). Polish a minimum of 30% and a maximum of 50% of the sample part opposite to the section glued to the wooden rack. This will allow to have enough portion of wood for later polishing processes with the objective of greater clarity of the rings, erase points and marks that are placed during the dating process.

Figure 7
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

  1. After the samples have been polished, analyze each core under a stereoscope at 10x to 15x magnification. Consider a stereoscope that allows observing and comparing several growth rings at the same time.
  2. Once the researcher has a good idea of what a growth ring is, depending on the species used, count the growth rings of each sample. This step will provide a tree age approximation. Additionally, recognize the type of variations that might be encountered during the cross-dating process (Figure 8A). For tree ring counting, start from the inner ring (center of the tree) to the outer ring (bark).
  3. Make small marks on the sample to go back and revisit the sample knowing the place in time. Place a tiny dot for each decade, two dots for every fifty-year segment, and three dots for every hundred years (Figure 8A).
  4. Use other types of marks to highlight rings that have particular characteristics. For example, when a micro ring with only a tiny part of the growth band is evident, use two parallel points to mark them. When there is a suspicion or certainty of the absence of a ring, use two alternate points or a circle to mark it, and when a false ring is identified, use a diagonal line to indicate that it is a single ring.
    NOTE: For more details on the counting technique, follow the standard procedures indicated by Stokes and Smiley6.
  5. Once the tree rings are counted, use growth graphs or skeleton plots to compare the temporal patterns and variability between wide and narrow rings. This graphical part allows comparing several samples simultaneously and determining common and synchronized growth patterns (Figure 8B). This technique allows detecting growth discrepancies that could have been mistakenly marked when counting the rings.
    NOTE: Please see reference6 and the link below for more details on making a skeleton plot: https://www.ltrr.arizona.edu/skeletonplot/plotting.htm.
  6. In samples of young living trees, where the date of the last outer ring (besides the bark) is known, perform a preliminary tree ring dating directly on the sample. For example, if the sample was collected from a forest in northern hemisphere in December 2021, which is the end of the growing season and tree ring formation is completed, the date of the last fully formed ring will be 2021. Using this, count the rings from the outer part (bark) to the center of the sample.
  7. The samples from older trees show periods of narrower rings, generally at the outermost part of the core. Generate a skeleton plot for these cores (Figure 8C) to compare their growth pattern with a known well-dated sample or with a previous regional ring-width master chronology (Figure 8D).
  8. To compare the sample look for the synchrony between thin and wide rings between different trees (Figure 8A,B). The sample is considered dated when successful match is found based on the cross-dating technique.
  9. In samples where growth synchrony patterns are not clear, due to differences in growth variability, absent rings, or false rings, detect the problem by reviewing ring by ring between the samples and compare it with perfectly dated samples. Use climate records from nearby stations to verify suspicious missing rings, since this kind of ring anomaly occurs in years with extreme dry or cold conditions.
  10. After potential problems are identified, correct the count and test if the synchrony is achieved.
  11. After all living trees are cross-dated, develop an average growth chart commonly called Master Chronology (Figure 8D), which is the average of all dated growth plots and indicates the growth pattern of the site on a time-domain6. It is useful as a dating tool for more samples that need to be cross dated, like dead trees with unknown date of death (Figure 8C).

4. Measuring the tree-ring

  1. Once the samples have been dated, measure tree ring widths. Measure total ring width as well as interannual band - earlywood and latewood- widths if possible. Use a measuring system with a precision of 0.001 mm11 to perform these measurements (Figure 8E).
  2. Measure the growth rings and partial rings one by one by sliding the measuring system stage and observing the sample through a stereoscope with a cross-linked eyepiece. Depending on the measuring system, begin the measurement with the innermost ring to the outer ring (Figure 8F).
  3. If a mechanical measurement system is not available, in that case, use a scanner to take high-resolution images and perform tree-ring measurements using a specialized software such as CooRecorder or R measuring from CRAN.

Figure 8
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

  1. Once the ring widths have been measured, test them for their dating accuracy and quality. Use the software COFECHA12 (https://ltrr.arizona.edu/research/software) and dplR13 for the statistical verification of cross-dating. Identify not significant correlations (< 0.3281; p > 0.01) between segments of the ring series amongst a master chronology built using the same samples in COFECHA analysis software.
  2. Look for flags in the software which identify segment correlation values that are not significant, making it easy to identify the potential discrepancies between the segments of any sample with the overall master chronology (the average of all standardized values of each sample analyzed).
  3. The discrepancies may be related to errors due to measuring or ring identification attributed to specific microsite conditions for selected trees, which is not synchronized with the overall variability among the rest of the samples. Verify these with observations and notes taken in the field and decide whether to conserve or eliminate this sample.
  4. For more details on the interpretation of the COFECHA statistics, see Speer7.

6. Chronology development

  1. Detrend or standardize the tree-rings measurements to remove all the non-climatic information (noise), such as the age, tree geometry, stand dynamics and disturbance effects as described.
    1. Fit a mathematical equation to the sample data-negative exponential (Figure 9A), straight line (Figure 9C), or cubic splines- depending on the criteria and temporal trends found on the samples (Figure 9A). Then divide each measured ring width by its fitted or expected value.
    2. Average the standardized values of individual trees together into a mean-value function and adjust for differential growth rates due to differing tree ages and differences in the overall growth rate. This will generate a standardized time series with a relatively constant variance and a mean equal to one8 (Figure 9B,D)
  2. There is not a perfect recipe to standardize tree ring width series; perform a graphical inspection of all the ring-width measurements to identify the embedded trends in the samples before applying any detrending method.
  3. Use any statistical platform to achieve standardization. Software like ARSTAN or dplR are especially for this type of analysis13,18 and are freely available at https://ltrr.arizona.edu/research/software and as an R package from CRAN.
  4. Perform autocorrelation removal using the statistical procedure called autoregressive moving average modelling (ARMA modelling) which is automatically applied in the two programs already mentioned. This is required to study the effect of interannual climatic variability on the tree rings.
  5. Once the tree-ring measurements have been standardized and autocorrelation has been removed, develop the site chronology (Figure 10A). The tree ring site chronology is the average of all standardized series using a robust bi-weighted mean that, unlike the arithmetic mean, attenuates the influence of atypical years (outliers).
  6. Assess the quality and the climate reconstruction potential using the three key statistical indicators from the site chronology generated by ARSTAN or dplR, namely the expressed population signal (EPS), the mean sensitivity (MS), and the intercorrelation between series (ISC).
  7. Estimate the degree of similarity between the different samples used in the chronology and a hypothetical chronology with an infinite number of samples by using the EPS (Figure 10B). A value greater than 0.85 is considered acceptable and suggests that the chronology has a sufficient number of samples to express the common signal of a given site19.
  8. Measure the relative variability between ring widths by using the MS. The value of the mean sensitivity ranges from zero to two, with zero values meaning there is no difference between two adjacent rings and two meaning that a ring has a zero value next to a ring where the value is greater than zero3. A mean sensitivity value greater than 0.3 indicates sufficient interannual variation and potential for climatic reconstruction.
    NOTE: The mean sensitivity can be interpreted as a metric of the potential relationship between tree growth and climate.
  9. Calculate the mean Pearson correlation coefficient of each sample against its master chronology produced from all the other time series at the site using the ISC. This statistic indicates the common signal of tree growth among trees.

Figure 9
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

  1. Perform correlation analysis to identify the relationship between annual tree growth (site chronology) and monthly climatic variables (precipitation, temperature, evaporation, relative humidity, among others). Use this analysis to assesses the period to reconstruct the tree ring based on the highest correlations values between seasonal climatic variables and the site chronology.
  2. Carry out the correlation analysis with the previous and current year monthly climatic records (Figure 11A). Use one of the several programs available to run this type of analysis, see references 3,20,13.
    NOTE: The analysis at a monthly to seasonal resolution indicates the degree of association, using Pearson correlation coefficients, between climatic variables and the tree ring site chronology.
  3. Use the climate records from the nearest meteorological stations or the nearest grid point from a gridded dataset for performing the correlation analysis. Additionally, assess the records' quality and use the best available option.
  4. When performing correlation analysis on multiple meteorological stations, explore each regional record one by one before compiling one regional climate record. Use only those stations that display the highest correlation values with the tree-ring chronology.
  5. Compare the regional average with the best available gridded dataset to assess and avoid losing climate variability when combining multiple meteorological stations.

8. Simple linear regression model and reconstruction of the climatic variable

  1. Once the seasonal period that shows the strongest climate-growth is identified (Figure 11B and Figure 12A), perform a simple or multiple linear regression analysis to build up the reconstruction model (Figure 12B).
  2. Perform this procedure on an extensive range of monthly combinations to obtain the best reconstruction model (the one with the highest explanatory power, adjusted R2 value). In this analysis, consider the tree-ring chronology index as the independent variable, and the precipitation for a seasonal accumulated monthly period as dependent variable.
  3. After the regression model has been generated, apply it to the chronology in the common period of the observed data.
  4. Subsequently, divide the common period of observed and reconstructed data into two periods each one containing half the data used in the entire common regression model to statistically validate the model and perform a calibration and verification test.
  5. Determine the following statistical variables to verify the statistical predictive power and uncertainty of the regression model (see Discussion for a detailed description): Correlation coefficient (r), adjusted R2, reduction of error (RE), signs test, paired sample t-test, standard error of estimate (SE), root-mean-square error of validation (RMSEv), and Durbin-Watson test.
  6. Once the regression model has been statistically validated, use it to reconstruct the climatic variable of response using the tree ring chronology.
  7. Finally, to provide additional reliability and certainty to any climate reconstruction, verify the reconstruction with historical documented records or other dendroclimatic reconstructions from nearby locations.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

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
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
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
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
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.

Subscription Required. Please recommend JoVE to your librarian.


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.

Subscription Required. Please recommend JoVE to your librarian.


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.


Name Company Catalog Number Comments
ARSTAN Software https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software
Belt Sander Dewalt Dwp352vs-b3 3x21 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



  1. Oliver, C., Larson, B. Brief Notice: Forest Stand Dynamics (Update Edition). Forest Science. 42 (3), 397 (1996).
  2. Dickison, W. C. Integrative Plant Anatomy. Integrative Plant Anatomy. , Academic press. (2000).
  3. Fritts, H. C. Tree-Ring and Climate. UCAR Center for Science Education. , Available from: https://scied.ucar.edu/learning-zone/how-climate-works/tree-rings-and-climate (2022).
  4. Creber, G. T., Chaloner, W. G. Environmental influences on cambial activity. The Vascular Cambium. , Research Studies Press. Tauton, UK. 159-199 (1990).
  5. Schweingruber, F. H. Tree Rings: Basics and Applications of Dendrochronology. , Springer. Dordrecht. (1988).
  6. Stokes, M. A., Smiley, T. L. An introduction to tree-ring dating. , The University of Arizona Press. (1996).
  7. Speer, J. H. Fundamentals of tree-ring research. , University of Arizona Press. (2010).
  8. Cook, E. R., Kairiukstis, L. A. Methods of dendrochronology: applications in the environmental sciences. , Springer. Dordrecht. (1990).
  9. Maeglin, R. R. Increment Core Specific Gravity of Wisconsin Hardwoods and Minor Softwoods. Department of Agriculture, Forest Service, Forest Products Laboratory. , (1973).
  10. Phipps, R. L. Collecting, preparing, crossdating, and measuring tree increment cores. Water-Resources Investigations Report. U.S. Department of the Interior, Geological Survey. , (1985).
  11. Robinson, W. J., Evans, R. A Microcomputer-Based Tree-Ring Measuring System. Tree-Ring Bulletin. , (1980).
  12. Holmes, R. L. Computer-Assisted Quality Control in Tree-Ring Dating and Measurement. Tree-Ring Bulletin. 43, 51-67 (1983).
  13. Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia. 26, 115-124 (2008).
  14. Cook, E. R. The Decomposition of Tree-Ring Series for Environmental Studies. Tree-Ring Bulletin. 47, 37-59 (1987).
  15. Lorimer, C. G. Methodological considerations in the analysis of forest disturbance history. Canadian Journal of Forest Research. 15 (1), 200-213 (1985).
  16. Nowacki, G. J., Abrams, M. D. Radial-growth averaging criteria for reconstructing disturbance histories from presettlement-origin oaks. Ecological Monographs. 67 (2), 225-249 (1997).
  17. Cook, E. R., Peters, K. The Smoothing Spline: A New Approach to Standardizing Forest Interior Tree-Ring Width Series for Dendroclimatic Studies. Tree-Ring Bulletin. 41, 45-53 (1981).
  18. Cook, E. R. A Time Series Analysis Approach to Tree Ring Standardization. School of Renewable Natural Resources. , (1985).
  19. Briffa, K. R. Interpreting High-Resolution Proxy Climate Data - The Example of Dendroclimatology. Analysis of Climate Variability. , Springer. Berlin, Heidelberg. 77-94 (1995).
  20. Biondi, F., Waikul, K. DENDROCLIM2002: A C++ program for statistical calibration of climate signals in tree-ring chronologies. Computers and Geosciences. 30, 303-311 (2004).
  21. Ostrom, C. W. Time Series Analysis (Regression Techniques). Journal of the Royal Statistical Society Series D (The Statistician). , (1991).
  22. Chávez-Gándara, M. P., et al. Reconstrucción de la precipitación invierno-primavera con base en anillos de crecimiento de árboles para la región de San Dimas, Durango, México. Bosque. 38 (2), 387-399 (2017).
  23. Cerano-Paredes, J., Villanueva-Díaz, J., Valdez-Cepeda, R. D., Méndez-González, J., Constante-García, V. Reconstructed droughts in the last 600 years for northeastern Mexico. Revista mexicana de ciencias agrícolas. 2, 235-249 (2011).
  24. Bradley, R. S. Paleoclimatology: Reconstructing Climates of the Quaternary: Third Edition. , Academic Press. (2013).
  25. Bull, W. B. Tectonic Geomorphology of Mountains: A New Approach to Paleoseismology. , Wiley Online Library. (2007).
  26. Stahle, D. W., et al. Major Mesoamerican droughts of the past millennium. Geophysical Research Letters. 38 (5), (2011).
  27. Black, B. A., Copenheaver, C. A., Frank, D. C., Stuckey, M. J., Kormanyos, R. E. Multi-proxy reconstructions of northeastern Pacific sea surface temperature data from trees and Pacific geoduck. Palaeogeography, Palaeoclimatology, Palaeoecology. 278 (1-4), 40-47 (2009).
  28. Suess, H. E. The Radiocarbon Record in Tree Rings of the Last 8000 Years. Radiocarbon. 22 (2), 200-209 (1980).
  29. Reimer, P. J. IntCal04 terrestrial radiocarbon age calibration, 0-26 Cal Kyr BP. Radiocarbon. 46 (3), 1029-1058 (2004).
  30. Muñoz, A. A., et al. Multidecadal environmental pollution in a mega-industrial area in central Chile registered by tree rings. Science of the Total Environment. 696, 133915 (2019).
  31. Briffa, K. R., Melvin, T. M. A Closer Look at Regional Curve Standardization of Tree-Ring Records: Justification of the Need, a Warning of Some Pitfalls, and Suggested Improvements in Its Application. Dendroclimatology. Developments in Paleoenvironmental Research. Hughes, M., Swetnam, T., Diaz, H. 11, Springer. Dordrecht. (2011).
  32. Cook, E. R., Peters, K. Calculating unbiased tree-ring indices for the study of climatic and environmental change. The Holocene. 7 (3), 361-370 (1997).
  33. Brienen, R. J. W., Gloor, E., Zuidema, P. A. Detecting evidence for CO2 fertilization from tree ring studies: The potential role of sampling biases. Global Biogeochemical Cycles. 26 (1), 1-13 (2012).
  34. Wright, W. E., Baisan, C., Streck, M., Wright, W. W., Szejner, P. Dendrochronology and middle Miocene petrified oak: Modern counterparts and interpretation. Palaeogeography, Palaeoclimatology, Palaeoecology. 445, 38-49 (2016).
  35. Stahle, D. W., et al. The Mexican Drought Atlas: Tree-ring reconstructions of the soil moisture balance during the late pre-Hispanic, colonial, and modern eras. Quaternary Science Reviews. 149, 34-60 (2016).
  36. Cook, E. R., et al. Old World megadroughts and pluvials during the Common Era. Science Advances. 1 (10), 1500561 (2015).
  37. Morales, M. S., et al. Six hundred years of South American tree rings reveal an increase in severe hydroclimatic events since mid-20th century. Proceedings of the National Academy of Sciences of the United States of America. 117 (29), 16816-16823 (2020).
  38. Wilson, R., et al. Last millennium northern hemisphere summer temperatures from tree rings: Part I: The long term context. Quaternary Science Reviews. 134, 1-18 (2016).
  39. Anchukaitis, K. J., et al. Last millennium Northern Hemisphere summer temperatures from tree rings: Part II, spatially resolved reconstructions. Quaternary Science Reviews. 163, 1-22 (2017).
  40. Villanueva-Diaz, J., et al. Hydroclimatic variability of the upper Nazas basin: Water management implications for the irrigated area of the Comarca Lagunera, Mexico. Dendrochronologia. 22 (3), 215-223 (2005).
  41. Sauchyn, D. J., St-Jacques, J. M., Luckman, B. H. Long-term reliability of the Athabasca River (Alberta, Canada) as the water source for oil sands mining. Proceedings of the National Academy of Sciences of the United States of America. 112 (41), 12621-12626 (2015).
  42. Woodhouse, C. A., Pederson, G. T., Morino, K., McAfee, S. A., McCabe, G. J. Increasing influence of air temperature on upper Colorado River streamflow. Geophysical Research Letters. 43 (5), 2174-2181 (2016).
  43. Muñoz, A. A., et al. Streamflow variability in the Chilean Temperate-Mediterranean climate transition (35°S-42°S) during the last 400 years inferred from tree-ring records. Climate Dynamics. 47, 4051-4066 (2016).
This article has been published
Video Coming Soon

Cite this Article

Cerano-Paredes, J., Szejner, P.,More

Cerano-Paredes, J., Szejner, P., Gutiérrez-García, G., Cervantes-Martínez, R., Cambrón-Sandoval, V. H., Villanueva-Díaz, J., Estrada-Arellano, J. R., Franco-Ramos, O., Vázquez-Selem, L., Castruita-Esparza, L. U. How to Extract Climate Variability from Tree-Rings. J. Vis. Exp. (181), e63414, doi:10.3791/63414 (2022).

Copy Citation Download Citation Reprints and Permissions
View Video

Get cutting-edge science videos from JoVE sent straight to your inbox every month.

Waiting X
Simple Hit Counter