Climate is a Stronger Driver of Tree and Forest Growth Rates than Soil and Disturbance

Water, nutrients, and light availability are vital to a plant’s growth. Since these resources vary spatially and temporally within tropical rainforests, so does tree growth. Previous studies have found that tree growth increases with rainfall and decreases with drought, and tree growth increases with nutrient-rich soils. Additionally, anthropogenic and natural disturbances can initiate forest growth through the creation of canopy gaps that open up space, increase light, nutrient, and water availability, and therefore accelerate the growth of previously repressed individual trees. Although many scientific investigations have researched the effects of either climate or soil on growth rate, there are few that have simultaneously considered the effects of both environmental factors, along with disturbance, on tree growth rate. To assess the variability of tree growth rates across lowland Bolivia, and to determine the effects of climate, soil, and disturbance on growth variables, Toledo et al. 2011 collected data from 165 1-ha permanent sample plots within the Bolivian forest network. Multiple linear regression analyses demonstrated that climate variables, such as water availability, were the strongest drivers of tree growth. More rainfall, a shorter and less intense dry period, and higher temperatures led to higher tree growth. Tree growth slightly increased with soil fertility. Basal area growth was largest at intermediate soil fertility levels. Interestingly, growth did not show any relationship with total soil nitrogen or plant available soil phosphorus.  Growth rates also increased in logged plots just after logging, but this effect disappeared after 6 years. These results suggest that climate change may have a large impact on forest productivity and carbon sequestration. However, the negative impact of decreased rainfall may be offset by the positive influence of increased temperature.—Megan Smith
Toledo, M., Poorter, L., Pena-Claros, M., Alarcon, A., Balcazar, J., Leano, C., Licona, J.C., Llanque, O., Vroomans, V., Zuidema, P., Bongers, F. 2011. Climate is a Stronger Driver of Tree and Forest Growth Rates than Soil Disturbance. Journal of Ecology 99: 254–264. DOI: 10.1111/j.1365-2745.2010.01741.x

Lowland Bolivia is characterized by large differences in geomorphology and geological history. Young and nutrient-rich soils predominate in the west part of country while ancient rocks and nutrient-poor soils predominate in the east part of Bolivia. There are two rainfall gradients within the country, a south–north rainfall gradient where rainfall increases towards the equator with mean annual precipitation ranging from 1100 to 1900 mm, and an east–west gradient where mean rainfall increases from 1600 to 2200 mm. Precipitation in individual years can vary from 600 to 3000 mm per year from the driest to the wettest areas. The lowlands in Bolivia experience a 4–7 month dry season from April to September. Mean annual temperature is between 24.2 and 26.4°C.
In many tropical countries, forest management for timber harvesting is an important economic activity. In Bolivia, the current Forestry Law provides a strong incentive for sustainable forest management. The law requires the establishment and monitoring of a network of permanent sample plots in the lowland forestry areas. Plots cover different forest types, from humid evergreen Amazon forests to dry deciduous Chiquitano forests.
For this study, 165 1-ha plots were selected from the Network of Permanent Plots in lowland Bolivia. These plots were established in old-growth forests on flat terrain and in an altitude range from 100 to 500 m asl. The plots were distributed over the main environmental gradients of climate and soil, and 52% of them were affected by logging. Measurement periods varied between 2 and 11 years, with the last measurements taking place in 2007. Plots were typically square, with only 11 of them being rectangular. In each plot, every tree > 10 cm diameter at breast height (DBH) was measured with diameter tape, painted at the measurement point, and tagged and identified. Re-censuses were carried out in the same season or month as the plots were established to minimize the effect of intra-annual variation in DBH change. In most of the 85 plots that were affected by logging, logging started immediately after their establishment. Eighty plots were not logged.
The annual diameter growth per individual was calculated as: (Df – Di)/t where Df is the final diameter and Di is the initial diameter at the start of the interval. Based on this diameter growth rate (DGR), the authors calculated five growth variables per plot, representing the growth rate at the individual level: average (DGRavg), median (DGR50), 90th (DGR90), 95th(DGR95), and 99th percentile (DGR99) of annual growth diameter. Values for the 90th and 95th diameter growth rate percentile are not included in the results because they were highly correlated with DGRavg, DGR99 and between themselves. The DGR50 and DGR99 were calculated to provide information on median and upper levels of growth rate. The authors also calculated the basal area growth rate at the stand level (BAGRstand) as the net yearly basal area change per plot. The BAGRstand was calculated as: (BAf – BAi)/t, where BAf is the final total plot basal area and BAi is the plot basal area at the start of the measurement interval. t is time, in years, between both measurement dates. BAGRstand includes the effects of growth, recruitment and mortality, while DGR is based upon individuals that survived the whole monitoring period.
20 soil samples were collected within each plot from the first 30 cm of soil. A pooled sample of 500 g was analyzed within a week after collection. The analyses included 12 edaphic variables: percentage of clay, silt, and sand, exchangeable Ca, Mg, Na, K, cation exchange capacity, acidity, plant available phosphorus, organic matter, and total nitrogen. For each plot the authors obtained five climatic variables that were interpolated from available data from 45 weather stations in the region.
The authors performed two independent Principal Component Analyses (PCA) to summarize the climatic and soil variables. The PCA was done for 220 1-ha plots that are a part of the Network of Permanent Plots in lowland Bolivia and included the 165 plots that were analyzed in the study. The climatic PCA considered annual temperature, annual precipitation, precipitation of the three driest months, length of the dry period (number of months < 100 mm), and length of the drought period (# months < 50 mm). The first axis (65%) correlated positively with annual precipitation and negatively with dry period length. The second axis (29%) correlated positively with mean annual temperature and negatively with the precipitation of the driest months. The edaphic PCA considered the 12 edaphic variables. The first two axes of the edaphic PCA explained 68% of the variation. The first axis (48%) correlated positively with variables related with soil fertility and negatively with acidity. The second axis (20%) represented variation in soil texture and correlated positively with clay and silt and negatively with sand.
Four logging-related variables were used to describe forest disturbance in each plot. Logging Presence (LP) was based on whether logging occurred (1) or not (0) in the plots and Logging Impact (LI) was described as whether the impact was high (1) or low (0) in logged plots (based on the number and location of logged trees and number of additional trees that died due to logging operations). Other variables representing logging disturbance were the Logged Basal Area (LBA, in m3ha-1, based upon the number and diameter of the trees logged) and the Time After Logging (TAL in years).
The four growth variables were correlated with the individual environmental variables to evaluate what components of these composite axes were most important. Each of the four growth rate variables was regressed on the four main environmental axes and the four disturbance variables were analyzed using a series of multiple backwards regressions. The authors used PCA axes for this regression analysis to avoid problems with multicollinearity.
Growth variables demonstrated high variation across plots, with the largest variation in DGR50 and BAGRstand. On average, BAGRstand was 0.49 m2 ha-1 year-1. Mean DGRavg was 0.31 cm year-1, with the lowest value in a plot with low rainfall and the highest value in a plot with intermediate rainfall. In regards to the median and upper growth rate limits (DGR50 and DGR99), highest values were found in plots of higher rainfall and lowest values were found in plots of lower rainfall. Although low DGRavg was found mostly in plots with lower rainfall, low BAGRstand was found in plots with both low and high amount of rainfall. A table displaying the standard deviation and ranges of tree and forest growth variables was constructed, as was a figure displaying the variation in average diameter (DGRavg) and stand basal area (BAGRstand).
The four growth variables had similar relationships to the environmental axes and variables, except for the texture axis, drought period, P, acidity, and LBA. Most of the significant relationships were found for DGRavg, DGR50, and BAGRstand. Growth variables were always positively and significantly correlated with climate axes and, in most of the cases, negatively and non-significantly to the soil axes. Growth variables increased significantly with annual precipitation and decreased with the dry period. Soil variables generally had negative relationships to growth variables, but OM content was the only variable that was consistently significant. All disturbance variables were positively related to the four growth variables except TAL. A table displaying the Pearson correlation coefficients of four tree growth variables with four environmental axes, four disturbance, and 18 environmental variables from the 165 1-ha plots was constructed.
The backward regression models demonstrated the relative importance, and explained the effects of, environmental axes and disturbance variables on growth rates. Rainfall was the most important axis, affecting all growth rates significantly and positively. Similarly, temperature and soil fertility had positive effects, although sometimes with a plateau, while BAGRstanddeclined at higher soil fertility levels. The soil texture axis had significant negative effects only on growth rates at tree level. Disturbance variables ‘logging’ and ‘logging intensity’ had a significant positive effect on DGRavg and DGR50 whereas time after logging (TAL) had a significant negative effect. A figure displaying the relationships between average tree diameter growth rate (DGRavg) and stand basal area growth rate (BAGRstand) with environmental axes: rainfall, temperature, soil fertility, and time after logging were constructed. A table displaying the backward multiple regression analysis of forest and tree growth variables on environmental and disturbance variables was also constructed.
            Overall the authors found high variation in tree growth rates at individual and at stand level in lowland Bolivia. The DGRavg of 0.31 + 0.10 cm year-1 and its range (0.12–0.70 cm year-1) is within the range (0.08 – 0.80 cm year-1) of diameter growth rates reported for other tropical forests. The average of BAGRstand (0.49 + 0.21 m2 ha-1year-1) was similar, but the range (0.17–1.22 m2 ha-1year-1) was larger than for other tropical forests.
In general, growth rates increased with water availability. The lowest annual growth rate at tree level occurred at the drier end of the rainfall gradient. Tropical dry forests are likely to have lower annual growth rates than moist forests due to their shorter growing period accompanied by lower rainfall. Other studies have shown that lower annual rainfall, or a more intense drought period, decreases growth rates. However, while basal area growth was positively correlated with rainfall, some moister forests were found to have lower growth, which can possibly be attributed to lower stem density or to a high abundance of slow-growing, drought-adapted species. The authors also found significant positive effects of temperature on all growth variables, which is surprising. Plants becoming closer to their photosynthetic optimum could cause increased growth. Moreover, due to the forests’ geographical location, the trees often experienced cold fronts that caused reduced photosynthetic activity and chilling injury. These results are significant when considering climate change predictions. The negative effects of predicted increased rainfall seasonality may be partially offset by the positive effects of temperature on tree growth for this study region.
Non-significant or even weak negative correlations were found between growth rates and individual and composite soil variables. The edaphic traits may have been so weak in comparison to the climatic variables because the soils were sampled at a small scale at one point in time, whereas climate was based on a long-term average and averaged out over large spatial areas. The authors also believed that the weak edaphic effects were more likely due to the confounding effects of water availability in their study. Some plots in high rainfall areas were characterized by higher growth rates and highly weathered and nutrient poor soils. The multiple regression analysis removed this effect and found that tree growth did actually increase with soil fertility, while stand basal area growth was optimal at intermediate levels of soil fertility. Furthermore, most lowland forest soils have large amounts of N but small amounts of available P, so scientists assume that P limits plant growth. However, the authors found weak relationships between growth, N and P. Plants in tropical forests may obtain nutrients directly from litter fall rather than after they enter the soils, from the atmosphere, or from mychorrhizal fungi that obtain nutrients from litter and soils.
Growth rates increased in logged plots, especially those that had a high logging impact, and decreased with time after logging. The opening up of the canopy could enhance light availability, photosynthetic carbon gain, and tree growth.  Logging removes larger trees, increasing canopy openness with associated changes in micro-environmental conditions that affect forest growth rates. The authors’ results showed that logged plots had significantly higher DGRavg. Basal area growth also increased with time. The amount of logged basal area did not affect BAGRstand probably due to the small variation in logging intensity among the study plots. Conventional logging intensity in Bolivia is usually low (1–3 trees per ha). Logging affected mainly DGRavg and DGR50, which emphasized the effects on the small and suppressed trees that benefit from canopy opening by logging. DGR99 was not affected by logging because these fast-growing individuals receive high light conditions and are already in the forest canopy. Time after logging had negative effects on growth rate at tree level. Diameter growth rate peaked after 2 years and returned to original levels 6 years after logging, most likely because the forest canopy closed again.
In conclusion, the results from this study revealed that both environment and disturbance explained growth rate variation in Bolivian lowland forests. This variation was most strongly influenced by climate and water availability. Soil fertility and soil texture did not show strong effects, while growth rates increased with logging-related disturbances. Climate change scenarios for the tropics predict a future decrease in rainfall and an increase in temperature. The positive effects of higher temperature in these Bolivian forests may then offset the negative effects of increased seasonality on tree growth. 

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