Dendroclimatological Reconstructions in the Southern Coffee Region of GuatemalaBy Diego Pons & Mackenzie Boli. Department of Geography and the Environment. University of Denver.

Literature cited: ANCHUKAITIS, K., M. Taylor, J. Martin-Fernandez, D. Pons, M. Dell, C. Chop and E. Castellanos. 2013. Annual chronology and climate response in Abies guatemalensis Rehder (pinaceae) in Central America.(2013). The Holocene, 23(2), 270-277.• CASTELLANOS, e., Tucker, c.Eakin, h. Morales,h., Barrera,j.y Diaz, r. 2013. Assessing the adaptation strategies of farmers facing multiple stressors: Lessons from the Coffee and Global Changes Project in Mesoamerica. Environmental Science and Policy 26:19-28. •COOK, ER.1985. A time series approach to tree-ring standardization. PhD Thesis, University of Arizona, Tucson. •HAGGAR, J., Medina, B., Aguilar, R. M., & Munoz, C. 2013. Land use change on coffee farms in southern Guatemala and its environmental consequences. Environmental Management, 51(4), 811-823. •HARMELING,S & ECKSTEIN,D.2012. Global Climate Risk Index 2013. Germanwatch e.V. •HOLMES, R.1983 Computer assisted quality control in tree-ring dating and measurement. Tree-Ring Bulletin 44: 69–75. •INTERNATIONAL TRADE CENTRE (ITC). 2011 The Coffee Exporter's Guide. [3rd ed.] Geneva: ITC, 2011. xvi, 247 p. •TUCKER, C., H. EAKIN, and E. CASTELLANOS. 2010. Perceptions of risk an adaptation: Coffee producers, market shocks, and extreme weather in Mexico and CentralAmerica.Global Environmental Change 20:23-32. •STOKES M and SMILEY, T. 1968 An Introduction to Tree-Ring Dating. Chicago IL: University of Chicago Press

INTRODUCTIONGuatemala is among the 10 largest coffee exporters of the world (ITC 2011) satisfying 24% of the global market on soft coffees The population directly dependent on coffee is estimated to be around 700,000 (Tucker et al 2010). At the same time, Guatemala has been recognized as one of the most vulnerable countries in the world to extreme weather events (Harmeling and Eckstein 2012). Populations dependent on agricultural systems have identified a higher frequency of extreme weather events and climate anomalies (Tucker et al 2010, Castellanos et al 2013). The absence of long-term instrumental records represents a challenge for coffee growers in the region in terms of understanding and adapting to climate change variability. Coffee still represents one of the main sources of income to the Guatemalan economy and it has been historically affected by variability of precipitation patterns, leading to several coffee crises in the country (Haggar et al 2013). In this exploratory study we collected core samples from Abies guatemalensis to try to reconstruct the climatic signal of precipitation for the region. This species in northern Guatemala has proven to be sensitive to the late dry season rainfall and negatively correlated with growing season temperature as well as negatively correlated with eastern tropical Pacific SST anomalies (Anchukaitis et al 2013). Yet, little is known about the potential of the species as a climate proxy elsewhere in the country. Here, we created a preliminary chronology to establish the potential of the species to reconstruct climate in important agricultural areas. Pearson correlation coefficients were employed against available instrumental data for calibration.

METHODSWe extracted 62 increment cores from 33 trees in Kanchej (Figure 1) , a forest located in the volcanic chain of Guatemala close to Quetzaltenango (14°,46’40.63”N , 91°,25’54.42”W 3252.8m). We ̴selected a subset of 25 cores to evaluate dating and climate signal at this site. Cores were mounted and sanded and then skeleton plots were made from each sample. A visual crossdating was used to obtain an initial dating for the samples (Stokes and Smiley 1968). Cores were then measured to 1 µm precision and crossdating was verified using COFECHA (Holmes 1983). After crossdating, we used the program ARSTAN (Cook 1985, V.44h3) to detrend the ring-width series and to perform autoregressive modeling. We selected a smoothing spline with a 50% amplitude response and 67%n of the individual series length. We verified that the growth trend was removed by this filter as shown in figure 2. We used the residual chronology in the climate analysis to minimize the effect of autocorrelation. We compared our preliminary chronology to local instrumental data available in Quetzaltenango going back to 1980 for precipitation and 1991 for the rest of variables (www.insivumeh.gob.gt/meteorologia.html). Precipitation displays a bimodal trend that follows the pattern of the rest of the country (Figure 3).

Figure 1. Map of Guatemala showing coffee areas in brown, weather stations (dots), study area (star) and proposed future research area in green circle.

RESULTS AND DISCUSSIONOur preliminary chronology includes 18 series and covers the period from 1886-2014 (Figure 4a). Despite the low number of samples (Figure 4b) and the limited average length of these (90 yrs.), our mean interseries correlation (0.402) indicates that crossdating is possible at this site. Also, the mean sensitivity is 0.295 suggesting a common climate signal . The EPS remains a little under 0.85 probably due to the sample depth. The mean interseries correlation is lower than that reported by Anchukaitis et al (2013) of 0.523 for the same species. Crossdating analysis revealed 0.061% of locally absent rings and several micro-rings were observed in our cores which are similar to those reported by Anchukaitis et al (2013). The correlation analysis with local climate data suggest a positive control on ring-width variability of A.guatemalensis in Kanchej from December precipitation. Some other statistically significant correlations are the negative minimum temperature from December and maximum temperature from February as well as a positive correlation with the maximum temperature in November (Figure 5). The positive influence of January precipitation described by Anchukaitis et al (2013) was not statistically significant in our study yet, a 0.296 correlation exists.

CONCLUSIONSThis preliminary study suggests that Abies guatemalensis in the volcanic chain of Guatemala forms annual rings that can be crossdated. Although some climate signal can be detected using limited instrumental data, more samples of longer length are needed to build a more robust chronology. If additional trees are collected in the volcanic chain (Figure 1, green circle) and more weather stations are included, paleoclimatic information can be reconstructed in this region, leading to a better understanding of the hydroclimatic variability of the recharge zone for coffee farms downslope of the volcanic chain.

Figure2. Raw measurement of a core showing the detrending line by using a smoothing spline with a 50% amplitude response on 67% of the total length. Observe that the growth trend is removed by using this filter.

Figure 3. Average monthly precipitation at the study site. The above graph shows the average monthly precipitation at the Labor Ovalle weather station in Quetzaltenango. This bimodal trend is observed at country level. The mid-summer drought is visible during July-August in this region.

Figure 5. Pearson correlation coefficients. The above table shows the statistically significant correlations that resulted from our analysis. Given the low number of records for the temperature-associated coefficients, additional weather stations are recommended.

Figure 4. Preliminary Kanchej Abies guatemalansis residual (prewhitened) chronology. (a) and sample size (b).

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b