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Restoring Abandoned Pasture Land with Native Tree Species in Costa Rica: An Ecophysiological Approach to Species Selection


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RESTORING ABANDONED PASTURE LAND WITH NATIVE TREE SPECIES IN COSTA RICA: AN ECOPHYSIOLOGICAL APPROACH TO SPECIES SELECTION. By GERARDO CELIS AZOFEIFA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007 1

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Copyright 2007 by Gerardo Celis Azofeifa 2

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To my parents 3

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ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Shibu Jose, for his dedication and support throughout my research. I also wish to express my sincere appreciation to my committee members, Dr. Kaoru Kitajima and Karen Kainer for their valuable insights. I would like to thank FUNDAZOO and Gustavo Vargas for their support in the logistics of my research and the Compton Foundation for funding it. Fellowships from TCD and FLORICA were also essential to complete my masters courses and are much appreciated. Finally, I thank Gabriela Hernndez, my family, and friends for their unconditional support, without which none of this would have been possible. 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES...........................................................................................................................7 LIST OF FIGURES.........................................................................................................................8 ABSTRACT.....................................................................................................................................9 CHAPTER 1 INTRODUCTION..................................................................................................................11 2 GROWTH AND BIOMASS ALLOCATION OF TROPICAL TREE SEEDLINGS IN AN ABANDONDED PASTURE IN COSTA RICA: RESPONSE TO GRASS COMPETITION AND VARIED LIGHT REGIMES............................................................14 Introduction.............................................................................................................................14 Material and Methods.............................................................................................................16 Study Area.......................................................................................................................16 Experimental Design.......................................................................................................16 Analysis...........................................................................................................................18 Mortality...................................................................................................................18 Growth......................................................................................................................18 Results.....................................................................................................................................19 Mortality..........................................................................................................................19 Relative Growth Rate......................................................................................................19 Leaf Area.........................................................................................................................20 Leaf Area Ratio and Leaf Mass Ratio.............................................................................21 Specific Leaf Area...........................................................................................................21 Root Mass Ratio, Stem Mass Ratio, and Root: Shoot Ratio...........................................22 Discussion...............................................................................................................................23 Conclusion..............................................................................................................................25 3 PHOTOSYNTHETIC RESPONSES OF TROPICAL TREE SEEDLINGS IN AN ABANDONED PASTURE UNDER GRASS COMPETITION AND VARIED LIGHT REGIMES...............................................................................................................................36 Introduction.............................................................................................................................36 Materials and Methods...........................................................................................................37 Study Site.........................................................................................................................37 Experimental Design.......................................................................................................38 Photosynthesis..........................................................................................................39 Growth......................................................................................................................39 Analysis...........................................................................................................................40 5

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Model Fitting............................................................................................................40 Growth and photosynthesis......................................................................................41 Results.....................................................................................................................................41 Photosynthesis.................................................................................................................41 Growth and Photosynthesis.............................................................................................41 Discussion...............................................................................................................................42 Photosynthesis.................................................................................................................42 Growth and Photosynthesis.............................................................................................43 Conclusions.............................................................................................................................43 4 SUMMARY AND CONCLUSION.......................................................................................49 APPENDIX SOIL NUTRIENTS................................................................................................51 LIST OF REFERENCES...............................................................................................................52 BIOGRAPHICAL SKETCH.........................................................................................................56 6

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LIST OF TABLES Table page 2-1 Mean (standard error) initial and final height and diameter for each species....................26 2-2 Plant variables derived for growth analysis.......................................................................27 2-3 Percent mortality of species by treatment (grass and light)...............................................28 2-4 Split-plot ANOVA for Total Leaf Area, LAR, LMR and SLA with respect to each treatment and their interactions..........................................................................................29 2-5 Split-plot ANOVA for RWR, SMR and R:S with respect to each treatment and their interactions.........................................................................................................................30 3-1 Estimated parameters of photosynthesis model: light saturated point (Amax), apparent quantum yield (Aqe), and light compensation point (LCP)................................45 A-1 Soil nutrient levels.............................................................................................................51 7

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LIST OF FIGURES Figure page 2-1 Mean values of Relative growth rate and standard errors over the growth period for each species in the three light treatments (100%, 37% and 2%) and grass treatments (short and tall)....................................................................................................................31 2-2 The overall relationship between relative growth rate (RGR) and growth variables for all treatments................................................................................................................32 2-3 Relationship between mean values over growth period of 26 week for relative growth rate (RGR) and net assimilation rate (NAR) and leaf area ratio (LAR) for each light treatment (100%, 37% and 2%)........................................................................33 2-4 Growth variables: leaf area, leaf area ratio (LAR) and specific leaf area (SLA) for each species grown under low (2%), medium (37%), and high (100%) light condition in each of the grass treatments, short and tall....................................................................34 2-5 Growth variables: root mass ratio (RMR), stem mass ratio (SMR) and leaf mass ratio (LMR) for each species grown under low (2%), medium (37%), and high (100%) light condition in each of the grass treatments, short and tall............................................35 3-1 Light response curves fitted with nonlinear Mitscherlich model equations from parameter estimates obtained from nonlinear mixed models analysis using SAS.............46 3-2 Light response curves fitted with nonlinear Mitscherlich model equations from parameter estimates obtained from nonlinear mixed models analysis...............................47 8

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science RESTORING ABANDONED PASTURE LAND WITH NATIVE TREE SPECIES IN COSTA RICA: AN ECOPHYSIOLOGICAL APPROACH TO SPECIES SELECTION. By Gerardo Celis Azofeifa May 2007 Chair: Shibu Jose Major Department: Interdisciplinary Ecology The establishment of trees in the successional trajectory of tropical abandoned pastures into forest communities is confronted with several barriers: dispersal, seed predation, unfavorable conditions for germination, and intense competition once they germinate. In order to aid the restoration of abandoned pastures into forested ecosystems, we must overcome some of these barriers through manipulative efforts. The present study was designed to characterize the light requirements of six native tree species (light demanding: Pseudosamanea guachapele (Kunth) Harms (Fabaceae), Tabebuia impetiginosa (Mart. Ex DC.) Standley (Bignoniaceae), Ceiba pentandra (L.) Gaertn. (Bombacaceae); shade tolerant: Bombacopsis quinatum (Jacq.) Dugand. (Bombacaceae), and intermediate: Dalbergia retusa Hemsl. (Fabaceae), Tabebuia rosea (Bertol.) DC. (Bignoniaceae) under contrasting light environments and grass competition. Understanding their early establishment requirements could be used in selecting proper light and competition regimen for the success of restoring pastures after abandonment. Field studies were conducted in the pastures of the Santa Ana Conservation Center in Costa Rica. Two grass competition regimes were selected, one dominated by Hyparrhenia rufa (Nees) Stapf (Tall-grass) and another dominated by Cynodon mlenfluensis Vanderyst (Short9

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grass). Three light treatments were created (100%, 37% and 2% light) using either neutral shade cloth (37% and 2%) or no shade cloth (100%). Growth characteristics, biomass partitioning and light response curves of the seedlings were measured. Overall, P. guachapele had the best performance in competing with the grasses followed by D. retusa. T. impetiginosa, and T. rosea, which had similar results regardless of the grass. C. pentandra did not do well under tall grass. The 2% light treatment greatly reduced seedling performance for all species and 37% had no effect except for D. retusa. We recommend planting P. guachapele as an initial step in reforesting pastures. Once they are established and shade produced by the tree reduces grass cover, T. rosea, T. impetiginosa and C. pentandra can be planted. 10

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CHAPTER 1 INTRODUCTION Costa Rican forests have been greatly diminished in the recent past. For example, Sader and Joyce (1988) estimated a 50% decline between 1940 and 1984. The driving forces behind deforestation have generally been the expansion of agriculture and cattle operations (Snchez-Azofeifa, 2000). The latest estimates are that a 29% of the territory is under closed forest cover and that 30% of that forest is protected by national conservation policies (Snchez-Azofeifa, Harriss, and Skole, 2001). Although Costa Rica still has nearly a third of the land area under forest cover, most of it is fragmented and as a result faces further threat of being degraded into even smaller islands. This new landscape is a mosaic of forest patches surrounded by human dominated lands, primarily of agricultural activities. For various reasons, many agricultural areas are being abandoned and left as pastures. Some of these areas are able to regenerate naturally; although the speed at which they restore will depend on the existing vegetation in the pasture, the land use history and the proximity of these to forested areas (Aide, Zimmerman, Pascarella, Rivera, and Marcano-Vega, 2000; Hooper, Legendre, and Condit, 2005; Zimmerman, Pascarella, and Aide, 2000). Several studies have demonstrated that pastures may regenerate naturally into forest within a few decades, but the species composition may be drastically different compared to the original one (Aide et al., 2000; Finegan and Delgado, 2000; Parrotta and Knowles, 1999). Therefore, in order to accelerate restoration and obtain the desired species composition it is necessary to intervene with enrichment planting. One of the main factors influencing the successional trajectory of abandoned pastures into forest communities is seed dispersal. Since pastures have little or no woody vegetation, the majority of woody species that colonize abandoned pastures are wind-dispersed (Finegan and 11

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Delgado, 2000; Holl, 2002; Holl, Loik, Lin and Samuels, 2000; Toh, Gillespie, and Lamb, 1999; Zimmerman et al., 2000). Those species that eventually colonize also undergo seed predation, unfavorable conditions for germination, and intense competition once they germinate (Camargo, Ferraz, and Imakawa, 2002; Holl and Lulow, 1997; Holl et al., 2000; Wijdeven and Kuzee, 2000). Consequently, competition with other species is an important factor limiting the establishment of seedlings, following their dispersal barrier (Holl, 1998). The most evident competition is aboveground between the pasture grasses, which in many cases are exotic species and very aggressive and new colonizers. Grass cover greatly reduces light availability (Hooper, Condit, and Legendre, 2002; Vieira, Uhl and Nepstad, 1994), and hence seedling growth. Belowground, competition can have important implications as well, especially where water is scarce and soils are low in nutrients (Chapman, Chapman, Zanne, and Burgess, 2002). In order to aid the restoration of abandoned tropical pastures into forested ecosystems, we must overcome some of these barriers through manipulative efforts. In doing so, we need to learn more about the performance of native tree species in these extreme environments. For example, what are their capabilities and at what stage of the regeneration process should they be introduced? Can they all be planted in an open pasture at the same time? Should we introduce different species at different stages to mimic the natural successional trajectory? Our knowledge base is limited at this point to answer these questions. Objectives. The present study was designed to characterize the light requirements of six native tree species under contrasting light environments and grass competition. Understanding their early establishment requirements could be used in selecting proper light and competition 12

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regimen for the success of restoring a pasture after abandonment. More specifically the research was designed to determine growth characteristics and biomass partitioning of seedlings under three different light environments and two levels of grass competition; estimate light response curve parameters under three different light environments and two levels of competition; establish the sequence in which the native species should be planted in a pasture. The results of experiments carried out are presented in the two subsequent chapters. In the final chapter, a summary of these findings is presented. Preliminary recommendations for the sequence of planting in restoring an abandoned pastureland and the future research needs are also discussed in the final chapter. 13

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CHAPTER 2 GROWTH AND BIOMASS ALLOCATION OF TROPICAL TREE SEEDLINGS IN AN ABANDONDED PASTURE IN COSTA RICA: RESPONSE TO GRASS COMPETITION AND VARIED LIGHT REGIMES Introduction Traditionally, plants have been grouped by light requirements as light demanding and shade tolerant (Swaine and Whitmore, 1988). This is mainly because in a forested environment, light is one of the most varying and dynamic resources for a plant (Chazdon, Pearcy, Lee ,and Fetcher, 1996). Plant species also vary in their ability to exploit different light environments in order to optimize growth (Lambers, Chapin, and Pons, 1998). Understanding where species stand in this dichotomy will provide a guide for refining management interventions aimed at improving plant establishment and growth. Light available to plants may vary greatly when ecosystems are compared. For example, light levels in large open areas, such as pastures, can be much greater than in undisturbed forests (Holl, 1999). Moreover, within each particular ecosystem, measurements of light fluctuate vertically: higher at the canopy and lower at the base of the plants. Consequently, even in pastures competition aboveground with grasses, which in many cases are exotic species and very aggressive, may reduce seedling establishment and seedling growth (Hooper et al., 2002; Vieira et al., 1994). In any event, what is important from a restoration point of view is that species selection needs to be aimed at finding the right match between light availability and plant light requirements for successful establishment and seedling growth. Furthermore, the growth of tropical tree seedlings in these particular light environments, and their ability to adapt to changes, depends on the complex interaction of morphological and physiological attributes of each species (Garwood, 1996). Light influences growth directly through differences in carbon gain, and indirectly through differences in carbon partitioning 14

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(Veneklass and Poorter, 1998). The focus of this chapter is the growth and biomass allocation response of tree seedlings under the competition conditions posed by existing pastures and under varied light conditions. Growth analyses are commonly used to understand seedling performance. In particular the relative growth rate (RGR), which represents the increase of plant mass per unit of plant mass, is analyzed as the product of a physiological component, the net assimilation rate (NAR), which stands for the increase in plant mass per unit leaf area, and a morphological component, the leaf area ratio (LAR), which corresponds to the amount of leaf area per unit plant mass. LAR in turn can be broken down into the biomass allocated to leaves, the leaf mass ratio (LMR) or the amount of leaf mass per unit plant mass, and the specific leaf area (SLA) or the amount of leaf area per unit leaf mass. However it is important to consider that growth is the net acquisition of resources from the environment by different parts of the plant and their relative influences will have consequences on growth (Farrar and Gunn, 1998). This is examined by studying plant biomass allocation to various parts of the plant. The objectives of this study were to (1) determine growth characteristics and biomass partitioning of seedlings in three different light environments and two levels of competition and (2) establish the sequence in which the native species should be planted in a pasture based on the growth and biomass partitioning data. We hypothesized that light demanding species should exhibit higher relative growth in high light environments, whereas the shade tolerant species should perform better under low light, and the intermediate species should perform best in medium light. All seedlings under Short-grass competition should have higher relative growth rates than under Tall-grass competition. 15

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Material and Methods Study Area This study was conducted in the pastures of the Santa Ana Conservation Center in Costa Rica, a protected spot in the outskirts of San Jose (9o 56.00 N 84o 11W), in the middle of the fastest urban expansion front in the country. According to the Holdridge Classification, the Santa Ana Conservation Center is located on a Premontane Wet Forest Life Zone. The climate is seasonal, with a dry season that extends from early December to the end of April. The average annual rainfall is 2,467 mm and the average annual temperature is 23.4oC. There are two rainfall peaks: one at the beginning of the rainy season, in May, and the second at the end of the rainy season between October and November. The average maximum temperatures occur during the dry season. Soils were vertisols with a soil texture of 12.8% sand, 24% silt, and 63.2% clay. Soil pH was 5.5. Nutrient levels based on Bertsch (1986) soil standards showed deficiencies of Potassium (K) and Phosphorous (P). All other nutrient levels were normal (Table A-1). Experimental Design In the pastures of the Santa Ana Conservation Center, two grass competition regimes were selected by creating 18 (3.5 x 3.5 meter) plots in each of two areas: one dominated by Hyparrhenia rufa (Nees) Stapf (Tall-grass) and another dominated by Cynodon mlenfluensis Vanderyst (Short-grass), both of which had been hand machete once before planting to a height of about 15cm. These grasses are native to tropical Africa. C. mlenfluensis is a stoloniferous perennial without underground rhizomes, which can reach heights of 100 cm and H. rufa, also perennial, can reach heights that range from 60cm (Skerman and Riveros, 1990). In each area three light environments were created: low (2% light), medium (37% light) and high (100% light). Plots were separated by 3 meters to prevent neighbor shading. For the shaded treatments 16

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shade houses 2.5 meters tall were constructed to cover the entire plot using neutral shade-cloth (63% and 98% shade). Each light environment x grass competition treatment was replicated six times for a total of 36 plots. Therefore each plot was a sampling unit for this experiment. Six species pertaining to different natural life histories were used; light demanding: Pseudosamanea guachapele (Kunth) Harms (Fabaceae), Tabebuia impetiginosa (Mart. Ex DC.) Standley (Bignoniaceae), Ceiba pentandra (L.) Gaertn. (Bombacaceae); shade tolerant: Bombacopsis quinatum (Jacq.) Dugand. (Bombacaceae), and intermediate: Dalbergia retusa Hemsl. (Fabaceae), Tabebuia rosea (Bertol.) DC. (Bignoniaceae) (See Table 2-1). Six individuals of each species were directly planted into the ground in a row under each treatment (light environment and grass competition) for a total of 36 individuals per plot. Species were assigned a row by systematically rotating their position within the plot. Tree seedlings were provided by the Santa Ana Conservation Center nursery, located in the same area. All seedlings were grown under 40% shade in plastic bags (9cm x 18cm) for 11 months. They were planted during the first two weeks of the experiment starting the last week of May 2005 directly into the ground in holes of about 20cm in diameter. One week after planting, damaged seedlings were replaced. Growth. An initial census of all plants in the field was made to measure plant height (to the nearest 0.1cm), root collar diameter, (RCD to the nearest 0.01cm) and leaf number in the second week of July 2005. Ten nursery seedlings were selected randomly for each species and destructively harvested. For each individual, height, RCD, leaf number, biomass allocation (root, stem and leaf) (to the nearest 0.01g) and leaf area (cm2) were measured. Leaf area was determined from scanned images of each leaf using Scion Image (Scion Corporation, Frederick, Maryland, USA). Plant biomass was determined after drying at 70 oC for 72 hours. 17

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A second and final census was also done during the first week of January 2006 where seedling height, RCD and leaf number of all remaining seedlings were measured the same way as in the first census. Mortality was also recorded. Out of each treatment, at least nine seedlings that had leaves were randomly selected and destructively harvested and measured for biomass allocation (root, stem and leaf) and leaf area using the same method as for the first harvest. At least 1 individual from each plot of 36 was harvested. Analysis Mortality A logistic regression model was used to determine differences in mortality. Live individuals received a score of 1, whereas dead ones received a score of 0. Grass, light and species were used as main effects and, since each plot corresponded to a specific light and grass treatment, our model used the interaction of grass and light with plot nested within it as the random effect. Growth Young seedling variability in plant size is difficult to homogenize in experiments; therefore, we conducted an initial census to test for differences among treatments in seedling height and RCD after planting. A two-way ANOVA with grass and light treatments as main effects was used to determine differences between seedling height and RCD. The test indicated that between grasses there were no differences for both seedling height and RCD (F1,32=2.32 p=0.14 and F1,32=0.25 p=0.62 respectively); for the light treatment there were no differences in height (F2,32=0.25 p=0.78), however there were differences in RCD (F2,32=11.94 p<0.0001); Therefore, to compensate for these differences all other analyses were preformed with height and RCD as covariates if they were significant in the model. 18

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The growth analyses were done following the classical method (Hunt et al., 2002) to calculate mean growth variables over the growth period (using initial and final harvest data): relative growth rate (RGR), net assimilation rate (NAR or ULR), leaf area ratio (LAR), specific leaf area (SLA), leaf mass ratio (LMR), root mass ratio (RMR), and stem mass ratio (SMR) and Root: Shoot allometric coefficient (Table 2-2). Each variables mean value over the growth period was compared using a correlation analysis to determine relationships between each treatment. The nonparametric Spearmans rank correlation coefficients (rs) were used because it is robust to sample distribution problems. Final harvest values for LAR, SLA, LMR, RMR, SMR, and Root:Shoot were compared using a split-plot ANOVA with grass, light and species as main effects and their interactions to determine differences. When the main effects were significant, post hoc Tukey's test was carried out for mean separation. Since each plot corresponded to a specific light and grass treatment, our model used the interaction of grass and light with plot nested within it as the random effect. B. quinatum was eliminated from this analysis due to the fact that in some treatments they had lost completely their leaves at the time of the second harvest. All statistical analyses were conducted using JMP version 5.1, SAS Institute 2004. Results Mortality Mortality was low, ranging from 0 to 14%, during the study period (Table2-3). There was no significant difference among any of the treatments. Relative Growth Rate The species with the highest relative growth rate (RGR) was P. guachapele This species grew best in 100% and 37% light levels and there was no difference between grass treatments. RGR was positive when grown in 2% light. D. retusa and C. pentandra shared the second 19

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highest growth rates in 100% and 37% light levels, respectively. D. retusa was not affected by the grass treatment, whereas C. pentandra performed better in short grass. T. rosea and T. impetiginosa performed similarly, but the latter was affected more by the tall grass treatment (Figure 2-1). Relative growth rate overall was correlated to net assimilation ratio (NAR) (rs =0.96, p < 0.001), leaf area ratio (LAR) (rs = -0.60, p < 0.001), specific leaf area (SLA) (rs = -0.55, p = 0.002) and leaf mass ratio (LMR) (rs = -0.47, p = 0.009) (Figure 2-2). When looked at individually under each light treatment, at 100% light there was a strong correlation with NAR (rs = 0.78, p = 0.008), but LAR was not significant. At 37% light NAR (rs = 0.95, p < 0.001) and LAR (rs = -0.70, p < 0.05) and SLA (rs = -0.78, p = 0.007) were significant. At 2% light NAR (rs = 0.91, p < 0.001) and LAR (rs = -0.62, p = 0.06) were significant (Figure 2-3). Leaf Area At final harvest there was a significant difference in total leaf area in all treatments; light (F2,8=15.56; p = 0.002), grass (F1,8=10.62; p < 0.01), and species (F4,8=9.09; p = 0.005) (Table 2-4). Seedlings grown under light levels 37% and 2% had the same total leaf area whereas leaf area was significantly lower in the 100% light treatment. Seedlings grown in short grass had more leaf area than those in tall grass. P. guachapele and T. rosea shared the highest leaf area, followed by C. pentandra and T. impetiginosa and D. retusa with the lowest. As for the interactions none were significant. T. impetiginosa and D. retusa had the same leaf area in all light levels. P. guachapele had the highest leaf area in the 37% light treatment and lower, but similar leaf area in the 2% and 100% treatments. C. pentandra and T. rosea had 37% and 2% with the highest values which were greater than 100%. C. pentandra and P. guachapele had 20

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higher values in the short grass than tall. T. rosea, T. impetiginosa and D. retusa had same values in short and tall grass. Leaf Area Ratio and Leaf Mass Ratio Leaf area ratio and Leaf mass ratio were significantly different in all treatments (Table 2-4). Seedlings grown under 2% light had the highest LAR and LMR followed by 37% and then 100%. LAR and LMR were higher in seedlings grown in short grass compared to tall grass. T. rosea and C. pentandra had the highest LAR, followed by T. impetiginosa and P. guachapele and finally by D. retusa (Figure 2-4). For LAR, the only interaction with a significant difference was light *species (F8,8=9.30; p = 0.002), where P. guachapele and D. retusa had similar values at 37 and 100%, all other species showed the same trend as with the light treatment. For LMR, T. rosea had the highest values followed by T. impetiginosa and C. pentandra, then P. guachapele and finally by D. retusa (Figure 2-4). For the interaction of light *species, T. impetiginosa showed the same trend, but T. rosea, C. pentandra, and P. guachapele had similar values for their 2% and 37% light treatments and their 100% was significantly lower. D. retusa showed similar values across light levels. As for the grass *species interaction T. impetiginosa, P. guachapele, and D. retusa showed similar values in tall and short grasses, T. rosea had higher values in short than tall and C. pentandra had the inverse. Specific Leaf Area Specific leaf area showed significant differences in light (F2,8=115.91; p < 0.001) and species (F4,8=10.43; p = 0.003) treatments, but not in grass. There were significant interactions for light *species (F8,8=5.88; p = 0.011) only (Table 2-4). SLA exhibited the same trend under light treatment as LAR. However, for species, D. retusa and C. pentandra shared the highest values followed by P. guachapele, T. rosea and T. impetiginosa, which also had similar values. 21

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For the interaction of light and species all species except T. impetiginosa had higher values at 2% and 37% and 100% were less, but similar. T. impetiginosa had similar values in all light treatments (Figure 2-4). Root Mass Ratio, Stem Mass Ratio, and Root: Shoot Ratio Root mass ratio and root: shoot ratio showed significant difference in light treatments and species. There was a significant interaction between light *species (Table 2-5). The RMR of seedlings grown under 100% light treatment had the highest value followed by 37% and then 2%. For the species treatment T. impetiginosa had the highest followed by D. retusa, P. guachapele, C. pentandra and T. rosea. As for the light interaction, T. impetiginosa followed the same trend as the light treatment. D. retusa had light at 37% the same as 2%. P. guachapele C. pentandra and T. rosea all had all light levels equal except for 100% and 2% (Figure 2-5). For R: S ratios the light treatments at 37% and 2% similar, but were lower than 100%. T. impetiginosa had the highest value followed by D. retusa and P. guachapele and then by C. pentandra and T. rosea. P. guachapele, C. pentandra and T. rosea did not differ in the light treatments. D. retusa and T. impetiginosa had similar ratios at 37 and 2%, which were lower than 100% (Figure 2-5). As for the interaction between grass *species T. impetiginosa was the only one to have Short grass larger than Tall grass, the rest had same values. For SMR, C. pentandra had the highest followed by T. rosea P. guachapele D. retusa and T. impetiginosa (Figure 2-5). The interaction between light and species(F8,8=4.05; p = 0.03) (Table 2-5), all species had similar values in all light treatments except D. retusa, for which 37% and 2% were similar and greater than 100%. Tall grass had a higher value than short. For grass *species interaction all species had the same values in both types of grasses (Figure 2-5). 22

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Discussion In this study we observed a low mortality in all treatments, which may be due to the short duration of the study (only 26 weeks). Interspecific relative growth rates (RGR) can be ranked into specific groups by patterns in both net assimilation ratio (NAR) and leaf area ratio (LAR) (Veneklass and Poorter, 1998). Light demanding species have higher relative growth rates in both low and high light environments (Kitajima, 1994). However there are discrepancies as to which component, NAR or LAR, determines RGR at low-light and at high-light. For example, in low light conditions Bloor and Grubb (2003) found NAR to be significantly related to relative growth rate and Veneklass and Poorter (1998) suggest LAR. In the light levels that we examined, we did not see a shift from NAR to LAR (Figure 2-3). In all light treatments NAR had a high correlation with RGR. Nonetheless at 37% light LAR becomes significant, but not as significant as NAR. This coincides with the findings of Bloor and Grubb (2003) that under very low light condition RGR is determined by NAR. Light under tall grass canopies can be greatly reduced (Holl, 2002; Hooper et al., 2002) and this, combined with seedling height, could explain why RGRs were correlated mainly to NAR under all light conditions. However, interspecific variation in RGR with respect to light treatments varied. Overall, P. guachapele had the best performance (Figure 2-3). In the high light treatment (100%) P. guachapele and D. retusa performed better than other species. Gerhardt (1993) found that after six months after planting seedlings, the highest growth rates for the two light demanding species they examined were the individuals grown in a pasture. This means that they were able to compete with grasses above or belowground. P. guachapele and D. retusa were species with the largest stem height (Table 2-1). If plants were taller in the grass environment, they would have had better access to light. This may be true for P. guachapele; however C. pentandra, whose height was considerably large, did not perform well in the high light condition. 23

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It is important to note that P. guachapele and D. retusa are both legumes, and have nitrogen fixing nodules. This could have enhanced their capability to compete with grasses. Jones, Wishnie, Deago, Sautu, and Cerezo (2004) found that the legume species Inga spp. had the best comparative performance with 13 other species in pastures in Panama. It is also important to note that D. retusa and P. guachapele ranked second and third in root mass ratios (RMR) (Figure 2-5), which means that overall they invested more in roots than the other species. This, combined with their nitrogen fixing capabilities, could have given them an advantage when compared to the other species. Lowering the light level to 37% did not affect the performance of P. guachapele or T. rosea, but greatly reduced D. retusa, and improved C. pentandra under short grass (Figure 2-1). Shading can effectively eliminate or hinder grasses, for example Hooper et al. (2002) found that shade eliminated the Saccharum exotic grass and enhanced tree regeneration in Panama. In our study, grasses were not eliminated, but shading made them less aggressive. However, the reduction in light could also affect seedling performance, that being the case for D. retusa. C. pentandra performed better under short grass due to the reduction in competition, but this reduction was not enough under tall grass. Lewis and Tanner (2000) found that a reduction of below ground competition resulted in increased allocation to leaves and in decreased allocation to roots, a trend that we found in C. pentandra, T. impetiginosa, and T. rosea (Figure 2-5). At the lowest light level, shade effectively eliminated grass, but also reduced all species RGRs, except for P. guachapele. Contrary to our expectations of categorizing species solely by light requirements, there was clearly a grass and light effect which varied with species. Therefore, we can not make 24

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generalization as to light requirements. For example, not all light demanding species performed better under high light conditions. Conclusion Species differed in their RGR, but overall P. guachapele was the best performer. We recommend planting this species as an initial step in reforesting pastures. Once it is established and shade produced by the tree reduces grass cover T. rosea (Roble), T. impetiginosa and C. pentandra can be planted. However, C. pentandra may be affected by competition with grasses and/or P. guachapeles presence. Further research is needed to understand how different species will react under P. guachapeles shade and competition. Will its canopy be effective in reducing light quantity to have an effect on grasses? Seedling size may also become a factor in these pasture conditions, which should be examined with the other species of smaller heights that were used. 25

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Table 2-1. Mean (standard error) initial and final height and diameter for each species. 26 Species Family Species code Initial Height (cm) Initial Diameter (cm) Final Height (cm) Final Diameter (cm) Ceiba pentandra Bombacaceae Ceiba 42.0(0.6) 0.76(0.01) 48.8(1.1) 0.88(0.02) Dalbergia retusa Fabaceae Coco 41.7(1.2) 0.55(0.009) 40.4(1.3) 0.64(0.01) Tabebuia impetiginosa Bignoniaceae Cortez 24.1(0.4) 0.51(0.009) 25.9(0.4) 0.60(0.02) Pseudosamanea guachapele Fabaceae Guaya 46.1(0.9) 0.75(0.01) 52.5(1.3) 0.93(0.02) Bombacopsis quinatum Bombacaceae Pochote 21.9(0.5) 0.61(0.01) 24.5(0.7) 0.78(0.02) Tabebuia rosea Bignoniaceae Roble 28.3(0.4) 0.82(0.01) 33.2(0.5) 0.89(0.02)

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Table 2-2. Plant variables derived for growth analysis. M is total dry mass of seedling (g), A is seedling leaf area (cm-2), L is seedling leaf dry mass (g), S is stem dry mass (g), R is root dry mass (g) and T is time (weeks). Subscripts refer to initial (i) final (f). Variable Formula Units Relative growth rate (RGR) ( ) )(lnlnTMMif g g-1 week-1 Net assimilation rate (NAR) ( ) ( ) [ ] ()[] ifififAAAAMM lnln g cm-2 week-1 Leaf are ratio (LAR) MA cm2 g-1 Specific leaf area (SLA) LA cm2 g-1 Leaf mass ratio (LMR) ML g g-1 Stem mass ratio (SMR) MS g g-1 Root mass ratio (RMR) MR g g-1 Root to shoot coefficient (R:S) LSR + 27

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Table 2-3. Percent mortality of species by treatment (grass and light). See Table 2-1 for species coding. Grass Light Species Mortality Tall 100% Ceiba 3% Tall 100% Coco 6% Tall 100% Cortez 0% Tall 100% Guaya 0% Tall 100% Roble 0% Tall 37% Ceiba 0% Tall 37% Coco 3% Tall 37% Cortez 3% Tall 37% Guaya 3% Tall 37% Roble 3% Tall 2% Ceiba 14% Tall 2% Coco 8% Tall 2% Cortez 6% Tall 2% Guaya 0% Tall 2% Roble 3% Short 100% Ceiba 3% Short 100% Coco 3% Short 100% Cortez 8% Short 100% Guaya 3% Short 100% Roble 0% Short 37% Ceiba 0% Short 37% Coco 11% Short 37% Cortez 0% Short 37% Guaya 0% Short 37% Roble 0% Short 2% Ceiba 11% Short 2% Coco 14% Short 2% Cortez 0% Short 2% Guaya 3% Short 2% Roble 14% 28

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Table 2-4. Split-plot ANOVA for Total Leaf Area, LAR, LMR and SLA with respect to each treatment and their interactions. Variable Treatment Total LeafArea LAR LMR SLA Grass F1,8=10.62; p < 0.01 F1,8=5.48; p = 0.047 F1,8=5.93; p = 0.041 F1,8=1.66; p < 0.23 Light F2,8=15.56; p = 0.002 F2,8=281.58; p < 0.001 F2,8=55.31; p < 0.001 F2,8=115.91; p < 0.001 Grass*light F2,8=2.96; p = 0.11 F2,8=1.75; p = 0.23 F2,8=0.094; p = 0.91 F2,8=1.05; p = 0.40 Species F4,8=9.09; p = 0.005 F4,8=24.41; p < 0.001 F4,8=9.09; p = 0.005 F4,8=10.43; p = 0.003 Species*Light F8,8=2.61; p = 0.098 F8,8=9.30; p = 0.002 F8,8=2.90; p = 0.077 F8,8=5.88; p = 0.011 Species*Grass F4,8=1.64; p = 0.26 F4,8=1.30; p = 0.35 F4,8=2.68; p = 0.11 F4,8=0.77; p = 0.58 29

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Table 2-5. Split-plot ANOVA for RWR, SMR and R:S with respect to each treatment and their interactions. Variable Treatment RWR SMR R:S Grass F1,8=0.0007; p = 0.98 F1,8=5.50; p = 0.047 F1,8=1.77; p = 0.22 Light F2,8=61.9; p < 0.001 F2,8=1.10; p = 0.37 F2,8=29.80; p < 0.001 Grass*light F2,8=0.28; p = 0.76 F2,8=0.089; p = 0.91 F2,8=0.13; p = 0.88 Species F4,8=55.63; p < 0.001 F4,8=52.44; p < 0.001 F4,8=73.11; p < 0.001 Species*Light F8,8=4.14; p = 0.03 F8,8=4.05; p = 0.03 F8,8=3.61; p = 0.044 Species*Grass F4,8=2.69; p = 0.11 F4,8=1.97; p = 0.19 F4,8=2.12; p = 0.17 30

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CeibaCocoCortezGuayaRoble RGR (g g-1 week-1) -0.020.000.020.040.060.080.10 Species CeibaCocoCortezGuayaRoble 100% 37% 2% ShortTall Figure 2-1. Mean values of relative growth rate and standard errors over the growth period for each species in the three light treatments (100%, 37% and 2%) and grass treatments (short and tall). See Table 2-1 for species coding. 31

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NAR (g cm-2 week-1) -0.0020.0000.0020.0040.0060.008 LAR (cm2 g-1) 20304050607080 Ceiba Coco Cortez Guaya Roble SLA (cm2 g-1) 140160180200220240260280300320340 LMR (g g-1) 0.120.140.160.180.200.220.240.260.280.300.320.34 ABCD RGR (g g-1 week-1) -0.04-0.020.000.020.040.060.080.10 RMR (g g-1) 0.20.30.40.50.60.7 RGR (g g-1 week-1) -0.04-0.020.000.020.040.060.080.10 SMR (g g-1) 0.150.200.250.300.350.400.450.500.55 EFrs =0.96, p < 0.001 rs = -0.60, p < 0.001 rs = -0.55, p = 0.002 rs = -0.47, p = 0.009 rs = 0.33, ns rs = -0.17, ns Figure 2-2. The overall relationship between relative growth rate (RGR) and growth variables for all treatments. A) Net assimilation rate. B) Leaf area ratio. C) Specific leaf area. D) Leaf mass ratio. E) Root mass ratio. F) Stem mass ratio. Mean values over growth period of 26 weeks. Spearmans rank correlations estimated and significance based on (p<0.05). See Table 2-1 for species coding. 32

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NAR (g cm-2 week-1) -0.0020.0000.0020.0040.0060.008 LAR (cm2 g-1) 20304050607080 NAR (g cm-2 week-1) -0.0020.0000.0020.0040.0060.008 LAR (cm2 g-1) 20304050607080 Ceiba Coco Cortez Guaya Roble Ceiba Coco Cortez Guaya Roble RGR (g g-1 week-1) -0.04-0.020.000.020.040.060.080.10 NAR (g cm-2 week-1) -0.0020.0000.0020.0040.0060.008 LAR (cm2 g-1) 20304050607080 100%37%2%NAR; rs = 0.78, p = 0.008LAR; rs = 0.38, nsNAR; rs = 0.95, p < 0.001LAR; rs = -0.70,p < 0.05 NAR; rs = 0.91, p < 0.001LAR; rs = -0.62, p = 0.06 Figure 2-3. Relationship between mean values over growth period of 26 week for relative growth rate (RGR) and net assimilation rate (NAR) -dark symbols, and leaf area ratio (LAR) -white symbols, for each light treatment (100%, 37% and 2%). Spearmans rank correlations estimates and significance based on (p<0.05). 33

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Leaf Area (cm2) 020040060080010001200140016001800 100% 37% 2% AB LAR (cm2 g-1) 020406080100120 CeibaCocoCortezGuayaRoble SLA (cm2 g-1) 0100200300400500 Species CeibaCocoCortezGuayaRoble CDEFShortTall Figure 2-4. Growth variables: Leaf area, leaf area ratio (LAR) and specific leaf area (SLA) for each species grown under low (2%), medium (37%), and high (100%) light condition in each of the grass treatments, short and tall. A) Leaf area short grass. B) Leaf area tall grass. C) Leaf area ratio short grass. D) Leaf area ratio tall grass. E) Specific leaf area short grass. F) Specific leaf area tall grass. Mean values and standard errors obtained from final harvest. See Table 2-1 for species coding. 34

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SMR (g g-1) 0.00.20.40.60.8 100% 37% 2% RMR (g g-1) 0.00.20.40.60.8 CeibaCocoCortezGuayaRoble LMR (g g-1) 0.00.20.40.60.8 Species CeibaCocoCortezGuayaRoble ShortTallEFCDAB Figure 2-5. Growth variables: Root mass ratio (RMR), stem mass ratio (SMR) and leaf mass ratio (LMR) for each species grown under low (2%), medium (37%), and high (100%) light condition in each of the grass treatments, short and tall. A) Root mass ratio short grass. B) Root mass ratio tall grass. C) Stem mass ratio short grass. D) Stem mass ratio tall grass. E) Leaf mass ratio short grass. F) Leaf mass ratio tall grass. Mean values and standard error obtained from final harvest. See Table 2-1 for species coding. 35

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CHAPTER 3 PHOTOSYNTHETIC RESPONSES OF TROPICAL TREE SEEDLINGS IN AN ABANDONED PASTURE UNDER GRASS COMPETITION AND VARIED LIGHT REGIMES Introduction The growth of tropical tree seedlings in particular light environments (Lambers et al., 1998), and their ability to adapt to changes, depends on the complex interaction of morphological and physiological attributes of each species (Garwood, 1996). Therefore, the ability of a species to acquire and utilize light is an important factor in their competitive ability (Chazdon et al., 1996). Learning more about their physiological demands will give us some insights as to when and how to introduce species into a pasture. For example, Loik and Holl (1999) experimented with several species of tree seedlings under remnant pasture trees and an open pasture in southern Costa Rica. They found higher levels of photosynthetic rates in open pastures than under remnants; however growth was higher under remnants. In order to describe, in a quantitative fashion, how plants utilize light for carbon fixation, a light-response curve is often used. This curve is a non-linear function where net photosynthesis (A) is the response variable and the estimated parameters allow representing key stages of photosynthesis, as follows: In dark; that is, in zero photosynthetic active radiation (PAR), there is net-CO2 release due to respiration. As light intensity increases, net-CO2 release is gradually reduced until the light compensation point (LCP) is attained, where net-CO2 exchange is zero because photosynthetic CO2-uptake increases until light saturation is reached. At this point, the rate of CO2 assimilation levels off. Any further increase in the amount of light striking the leaf does not cause an increase in the rate of photosynthesis-the amount of light is said to be 'saturating' for the photosynthetic process. This light-saturation point often is hard to determine precisely, because light saturation is approached gradually. Alternatively, the asymptote of net 36

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assimilation at high light (Amax) is estimated. Finally, the slope of the nearly linear part of the curve below saturation gives the apparent quantum yield (Aqe: mol CO2 per mol photons) of photosynthesis. Understanding species photosynthesis and growth performances under different light scenarios will help us determine their optimal light requirements. Based on these findings, we can then suggest the ideal setting or sequence in which these species should be introduced in a restoration project. The objectives of this study were (1) to estimate light response curve parameters: maximum net assimilation (Amax), apparent quantum yield (Aqe), and light compensation point (LCP) under three different light environments and two levels of competition; (2) to determine relative growth rates (RGR) of seedlings under three different light environments and two levels of competition; and (3) to establish the sequence in which the native species should be planted in a pasture based on the previous characteristics. We hypothesized that light response curve parameters will display the following relationship with seedlings light demand: the higher the seedlings light demand, the higher the maximum net assimilation (Amax), the higher the light compensation point (LCP), and the lower the apparent quantum yield (Aqe) Also, seedling grown under Short-grass competition should all have maximum net assimilation (Amax) higher than under Tall-grass competition. Materials and Methods Study Site This study was conducted in the pastures of the Santa Ana Conservation Center in Costa Rica, a protected spot in the outskirts of San Jose (9o 56.00 N 84o 11W), in the middle of the fastest urban expansion front in the country. According to the Holdridge Classification, the Santa Ana Conservation Center is located on a Premontane Wet Forest Life 37

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Zone. The climate is seasonal, with a dry season that extends from early December to the end of April. The average annual rainfall is 2,467 mm and the average annual temperature is 23.4 oC. There are two rainfall peaks: one at the beginning of the rainy season, in May, and the second at the end of the rainy season between October and November. The average maximum temperatures occur during the dry season. Experimental Design In the pastures of the Santa Ana Conservation Center, two grass competition regimes were selected by creating 18 (3.5 x 3.5 meter) plots in each of two areas: one dominated by Hyparrhenia rufa (Nees) Stapf (Tall-grass) and another dominated by Cynodon mlenfluensis Vanderyst (Short-grass), both of which had been hand machete once before planting to a height of about 15cm. These grasses are native to tropical Africa. C. mlenfluensis is a stoloniferous perennial without underground rhizomes, which can reach heights of 100 cm and H. rufa, also perennial, can reach heights that range from 60cm (Skerman and Riveros, 1990). In each area three light environments were created: low (2% light), medium (37% light) and high (100% light). Plots were separated by 3 meters to prevent neighbor shading. For the shaded treatments shade houses 2.5 meters tall were constructed to cover the entire plot using neutral shade-cloth (63% and 98% shade). Each light environment x grass competition treatment was replicated six times for a total of 36 plots. Therefore each plot was a sampling unit for this experiment. Six species pertaining to different natural life histories were used; light demanding: Pseudosamanea guachapele (Kunth) Harms (Fabaceae), Tabebuia impetiginosa (Mart. Ex DC.) Standley (Bignoniaceae), Ceiba pentandra (L.) Gaertn. (Bombacaceae); shade tolerant: Bombacopsis quinatum (Jacq.) Dugand. (Bombacaceae), and intermediate: Dalbergia retusa Hemsl. (Fabaceae), Tabebuia rosea (Bertol.) DC. (Bignoniaceae) (See Table 2-1). Six 38

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individuals of each species were directly planted into the ground in a row under each treatment (light environment and grass competition) for a total of 36 individuals per plot. Species were assigned a row by systematically rotating their position within the plot. Tree seedlings were provided by the Santa Ana Conservation Center nursery, located in the same area. All seedlings were grown under 40% shade in plastic bags (9cm x 18cm) for 11 months. They were planted during the first two weeks of the experiment starting the last week of May 2005 directly into the ground in holes of about 20cm in diameter. One week after planting, damaged seedlings were replaced. Photosynthesis At least six individuals from each treatment (grass, light and species) were randomly selected and a photosynthetic light-response curve was determined for the third most fully developed leaf from the apical meristem of each individual in the sample. If the leaf was missing or damaged, the second most fully developed leaf was used. B. quinatum was not taken into consideration, due to the fact that in some of the treatments individuals had lost all their leaves at the time of measurements. Measurements were done during the period of December 2005 and January 2006 using an open-mode portable photosynthesis system (Li-6400, Li-Cor, Inc. Lincoln, Nebraska, USA). Using a light-emitting diode (LED) light source, ten light (Photosynthetically Active Radiation, PAR) intensities were set: 0, 25, 50, 100, 200, 500, 800, 1,000, 1,500, and 2,000 mol m2 s1, starting from the lowest and then increasing every 2 minutes. The leaf chamber was controlled to a CO2 concentration of 380 mol mol, the temperature of the block chamber was set at 26 oC, and air flow was maintained at 500 mol s-1. Growth The relative growth rate (RGR) was calculated following the classical method 39

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( ) )(lnlnTMMif over the growth period (Hunt et al., 2002). Where M is total dry mass of seedling (g) and T is time (weeks). Subscripts refer to initial (i) final (f) harvest. (Table 2-2). Analysis Model Fitting A nonlinear mixed model was used in our analysis. Nonlinear mixed models allow both fixed (treatments) and random (experimental unit) effects to have a nonlinear relationship to the response variable (Wolfinger, 2000). In addition, fitting an appropriate nonlinear model lends biological meaning to estimated parameters (Peek, Russek-Cohen, Wait, and Forseth, 2002). For our experiment, our fixed effect treatment design was a 2x3 factorial with two grass competitions (short and tall) and three light levels (100%, 37% and 2%), for each species separately. We took repeated measurements of net photosynthesis on the same leaf at different light levels, which requires the incorporation of a random effect for each individual. For each treatment a light-response curve was fitted to a nonlinear Mitscherlich model equation (Peek et al., 2002). () =LCPPARAqeeAA1max Where Amax is the asymptote of photosynthesis at high light, Aqe is the apparent quantum yield, LCP is light compensation point, PAR is the photosynthetic active radiation and A is net photosynthesis, the response variable. We used a nonlinear mixed models procedure (NLMIXED) (SAS Institute Inc., Cary, North Carolina USA) to fit curves to photosynthetic data from each plant. Initial values of parameters to start the model iterations were obtained from the NLIN procedure of SAS, ignoring random effects. The mean for the three parameters (Amax, Aqe 40

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and LCP) for each treatment by species was compared using a multiple comparison procedure of contrasts. Growth and photosynthesis Relative growth rate (RGR) mean values over the growth period and net photosynthetic rate (Amax) for each treatment (grass, light and species) were compared using a correlation analysis to determine relationships between the two variables. A nonparametric Spearmans rank correlation coefficients (rs) were used because it is robust to sample distribution problems. Results Photosynthesis Based on the estimated parameters obtained for the model, C. pentandra had the highest Amax in the Short 37%, Tall 100% and Tall 37% treatments, but was significantly lower for the Short 100%, Short 2% and Tall 2% (Table 3-1 and Figure 3-2). D. retusa had the highest Amax in the Short 100%, Short 37%, Tall 37%, and the lowest values in the Tall 100%, Tall 2% and Short 2% (Table 3-1 and Figure 3-1). P. guachapele had the highest Amax in Short 37% and 100% and the lowest at the Tall 2% and Tall 100% (Table 3-1 and Figure 3-1). T. impetiginosa and T. rosea did not differ at any of the treatments. Regarding the parameters Aqe and LCP, for all species they did not differ significantly between treatments. Growth and Photosynthesis Overall there was a positive relationship (rs=0.49; p=0.007) between relative growth rate and Amax when all species were combined (Figure 3-3). However when each species was analyzed individually, there was a positive significant relationship for T. impetiginosa (rs=0.83; 41

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p=0.04), P. guachapele (rs=0.83; p=0.04), and T. rosea (rs=0.89; p=0.02) and no trend was observed for C. pentandra and D. retusa. Discussion Photosynthesis There are tradeoffs between persistence in low light and adaptations for exploiting high light environments. Species in general do well in one environment and not in both. However there are some species that can do well in both such as C. pentandra (Bazzaz, 1996). Studies of photosynthetic responses of tropical species have shown that pioneer species show a larger acclimation to light conditions than shade tolerant (Davidson, Mauffette, and Gagnon, 2002; Holscher, Leuschner, Bohman, Juhrbant, and Tjitrosemito, 2004; Tinoco-Ojanguren and Pearcy, 1995) Amax of pioneers in low light conditions is about 4-6 mol CO2 m2 s1 and at high light 11-22 mol CO2 m2 s1 and for their shade tolerant species at low light ranges 2-4 mol CO2 m2 s1 and at high light 2-6 mol CO2 m2 s1. Photosynthetic performances of our species were not as expected. Light demanding species T. impetiginosa (Cortez) and intermediate species T. rosea (Roble) did not vary with respect to all photosynthetic parameters for each treatment (Table 3-1). This could be due to the fact that initial seedling height (Table 2-1) was not sufficient to overcome the aboveground competition. Therefore, seedlings experienced low light levels regardless of light treatment. Holl (2002) measured light levels under grass canopies and found that light can be as low as 5 mol m2 s1 and was most likely the factor which contributed to low growth of their seedlings. Seedlings with taller initial heights did show a response to light treatment. Light demanding species C. pentandra, P. guachapele and intermediate species D. retusa had highest Amax in high light (100%) and intermediate light (37%). The potential maximum photosynthetic 42

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capacity (Amax) often is not obtained due to limiting resources such as water, nutrients, herbivore damage and internal feedback mechanisms (Bazzaz, 1996). The parameters apparent quantum yield (Aqe) and light compensation point (LCP) for all species did not differ significantly between treatments. However this only represents leaf level traits and does not take into account other plant tissues physiological activities that influence whole plant carbon economy (Givnish, 1988). Growth and Photosynthesis Photosynthesis parameters have been correlated with growth parameters (Zipperlen and Press, 1996). In this study the only varying photosynthetic parameter was Amax for C. pentandra, P. guachapele and D. retusa. When relating Amax to relative growth rate we found that there was no relationship for D. retusa and C. pentandra. However, P. guachapele, T. impetiginosa and T. rosea exhibited significant relationships between Amax and RGR. It must be recalled though, that correlation simply shows how the parameters move when observed together; it does no represent a cause-effect relationship. The way they move together may be determined by other factors. In fact, photosynthetic measurements are not the only determinant of dry matter gain. Biomass partitioning, and factors which control assimilate distribution may therefore be more diagnostic of the ecological status of a species than its rate of carbon dioxide fixation per unit area of leaf (Press, Brown, Baker, and Zipperlen, 1996; Zipperlen and Press, 1996). Conclusions P. guachapele was the best performer with the highest Amax and relative growth rates. We recommend planting this species as an initial step in reforesting pastures. As for the other species, it is hard to determine their performance solely based on their Amax and relative growth rates. Further research is needed to understand why species performances varied and what are 43

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the factors controlling this variation other than the photosynthetic capacities. Also it will be important to see how P. guachapele shade and competition will affect grasses and other seedlings once it establishes a canopy. Seedling height may also be a factor one should consider when planting in a pasture and should be examined for each species. 44

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Table 3-1. Estimated parameters of photosynthesis model: light saturated point (Amax), apparent quantum yield (Aqe), and light compensation point (LCP). Means and standard errors. See Table 2-1 for species coding. Treatment Amax Aqe LCP -------------------------------------Ceiba------------------------------------Tall 100% 7.912.35ab 0.00210.0011a -12.4250.03a Tall 37% 9.502.71a 0.00260.0015a 2.6955.77a Tall 2% 5.212.74b 0.00700.0051a -7.2659.18a Short 100% 4.873.24b 0.00170.0023a 3.5693.63a Short 37% 10.452.84a 0.00390.0016a 19.7751.79a Short 2% 5.462.69b 0.00640.0045a -5.1559.01a -------------------------------------Coco------------------------------------Tall 100% 4.491.70ab 0.00180.0021a -50.67121.30a Tall 37% 6.152.07ab 0.00710.0037a 17.30121.99a Tall 2% 4.292.18b 0.00800.0061a 5.25123.50a Short 100% 9.242.08a 0.00510.0026a 20.52121.88a Short 37% 7.332.19ab 0.00750.0034a 11.00121.98a Short 2% 3.682.19b 0.00790.0076a 0.36125.17a -------------------------------------Cortez------------------------------------Tall 100% 5.251.53a 0.00210.0018a -13.5859.69a Tall 37% 4.832.02a 0.00490.0041a 10.8466.89a Tall 2% 3.812.20a 0.00320.0054a -31.01124.86a Short 100% 5.451.91a 0.00490.0034a 19.6263.31a Short 37% 6.972.00a 0.00600.0031a 14.8461.69a Short 2% 3.882.14a 0.00420.0066a -12.62100.23a -------------------------------------Guaya------------------------------------Tall 100% 5.411.29cd 0.00270.0014a 30.8224.93a Tall 37% 9.181.51bc 0.00390.0022a 14.0331.56a Tall 2% 5.071.91d 0.00300.0036a -33.3087.38a Short 100% 12.611.86ab 0.00280.0015a 24.6729.31a Short 37% 14.181.85a 0.00420.0017a 23.9926.95a Short 2% 7.441.91cd 0.00390.0029a -10.1444.80a -------------------------------------Roble------------------------------------Tall 100% 7.361.52a 0.00230.0011a 14.3734.73a Tall 37% 7.742.08a 0.00500.0027a 9.2339.71a Tall 2% 4.991.82a 0.00620.0041a -6.9645.48a Short 100% 8.832.23a 0.00350.0020a 7.8342.50a Short 37% 8.392.31a 0.00590.0022a 13.1937.18a Short 2% 4.602.30a 0.00320.0065a -42.59163.74a Note: Different superscript letters assigned to means in the same column designate statistically significant differences at the 0.05 level; the same letters indicate that no statistically significant differences exist. 45

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100% 37% 2% -202468101214 -202468101214 PAR vs Col 20 PAR vs Col 21 PAR vs Col 22 Plot 1 Upper specification Net CO2 uptake (mol m-2 s-1) -202468101214 PAR vs Col 24 PAR vs Col 25 PAR vs Col 26 Plot 1 Upper specification 05001000150020002500 -202468101214 Incindent PPF (mol m-2 s-1) 05001000150020002500 CocoCocoGuayaGuayaCortezCortezRobleRobleShortTallShortTallShortTallShortTall Figure 3-1. Light response curves fitted with nonlinear Mitscherlich model equations from parameter estimates obtained from nonlinear mixed models analysis using SAS. See Table 2-1 for species coding. 46

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05001000150020002500 Net CO2 uptake (mol m-2 s-1) -202468101214 Incindent PPF (mol m-2 s-1) 05001000150020002500 100% 37% 2% CeibaCeibaShortTall Figure 3-2. Light response curves fitted with nonlinear Mitscherlich model equations from parameter estimates obtained from nonlinear mixed models analysis. See Table 2-1 for species coding. 47

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Amax(mol m-2 s-1) 24681012141 6 RGR (gg-1) -0.04-0.020.000.020.040.060.080.10 Ceiba Coco Cortez Guaya Roble Rs = 0.49; p < 0.007 Figure 3-3. Relationship between net photosynthesis and relative growth rate for all species. 48

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CHAPTER 4 SUMMARY AND CONCLUSION One of the main factors influencing the successional trajectory of abandoned pastures into forest communities is seed dispersal. Since pastures have little or no woody vegetation, the majority of woody species that colonizes abandoned pastures are wind-dispersed (Finegan and Delgado, 2000; Holl, 2002; Holl et al., 2000; Toh et al., 1999; Zimmerman et al., 2000). Those species that eventually colonize have to undergo seed predation, unfavorable conditions for germination, and intense competition once they germinate (Camargo et al., 2002; Holl and Lulow, 1997; Holl et al., 2000; Wijdeven and Kuzee, 2000). In order to aid the restoration of abandoned tropical pastures into forested ecosystems, we must overcome some of these barriers through manipulative efforts. In doing so, we need to learn more about the performance of native tree species in these extreme environments. The present study was designed to characterize the light requirements of six native tree species under contrasting light environments and grass competition. Understanding their early establishment requirements could be used in selecting proper light and competition regimen for the success of restoring a pasture after abandonment. Based on our morphological and physiological findings, overall, Pseudosamanea guachapele was the best performer under open pasture conditions. We recommend planting this species as an initial step in reforesting pastures. Once this species is established the canopy may produce enough shade to reduce grass cover. Possibly under this scenario, Tabebuia rosea, Tabebuia impetiginosa and Ceiba pentandra can be planted as a second step in the restoration process. However, C. pentandra may be affected by competition with grasses and/or P. guachapele presence. 49

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Further research is needed to understand why species performances varied and what are the factors controlling this variation other than light. It is also important to examine how P. guachapele canopy shade and competition will affect grasses and other seedlings once it established. It seems that seedling height may also become an important factor needed to overcome pasture competition and should be more closely examined by each species. 50

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APPENDIX SOIL NUTRIENTS Table A-1. Soil nutrient levels. pH/ water K Ca Mg P Fe Cu Zn Mn ------------cmol+/L------------------------ppm------------5.5 0.17 15.7 8.65 4.7 67 15 2.6 21 51

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LIST OF REFERENCES Aide, T.M., J.K. Zimmerman, J.B. Pascarella, L. Rivera, and H. Marcano-Vega. 2000. Forest regeneration in a chronosequence of tropical abandoned pastures: Implications for restoration ecology. Restoration Ecology 8:328-338. Bazzaz, F.A. 1996. Plants in changing environments: Linking physiological, population, and community ecology. Cambridge University Press, Cambridge, UK. Bertsch, F. 1986. Manual para interpretar la fertilidad de los suelos de Costa Rica. Oficina de Publicaciones, UCR, San Jos, Costa Rica. Bloor, J.M.G., and P.J. Grubb. 2003. Growth and mortality in high and low light: Trends among 15 shade-tolerant tropical rain forest tree species. Journal of Ecology 91:77-85. Camargo, J.L.C., I.D.K. Ferraz, and A.M. Imakawa. 2002. Rehabilitation of degraded areas of Central Amazonia using direct sowing of forest tree seeds. Restoration Ecology 10:636-644. Chapman, C.A., L.J. Chapman, A. Zanne, and M.A. Burgess. 2002. Does weeding promote regeneration of an indigenous tree community in felled pine plantations in Uganda? Restoration Ecology 10:408-415. Chazdon, R., R. Pearcy, D. Lee, and N. Fetcher, (eds.) 1996. Photosynthetic responses of tropical forest plants to contrasting light environments. Chapman & Hall, London, UK. Davidson, R., Y. Mauffette, and D. Gagnon. 2002. Light requirements of seedlings: A method for selecting tropical trees for plantation forestry. Basic and Applied Ecology 3:209-220. Farrar, J., and S. Gunn. 1998. Allocation: Allometry, acclimation and alchemy? Pages 183-198 in H. Lambers, H. Poorter, and M. M. I. Van Vuuren editors. Inherent variation in plant growth: Physiological mechanisms and ecological consequences. Backhuys Publishers, Leiden, Netherlands. Finegan, B., and D. Delgado. 2000. Structural and floristic heterogeneity in a 30-year-old Costa Rican rain forest restored on pasture through natural secondary succession. Restoration Ecology 8:380-393. Garwood, N.C. 1996. Functional morphology of tropical tree seedlings. Pages 59-129 in M. D. Swaine, editor. The ecology of tropical forest tree seedlings. UNESCO, Paris, France. Gerhardt, K. 1993. Tree seedling development in tropical dry abandoned pasture and secondary forest in Costa Rica. Journal of Vegetation Science 4:95-102. Givnish, T.J. 1988. Adaptation to sun and shade a whole-plant perspective. Australian Journal of Plant Physiology 15:63-92. 52

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Holl, K.D. 1998. Do bird perching structures elevate seed rain and seedling establishment in abandoned tropical pasture? Restoration Ecology 6:253-261. Holl, K.D. 1999. Factors limiting tropical rain forest regeneration in abandoned pasture: Seed rain, seed germination, microclimate, and soil. Biotropica 31:229-242. Holl, K.D. 2002. Effect of shrubs on tree seedling establishment in an abandoned tropical pasture. Journal of Ecology 90:179-187. Holl, K.D., and M.E. Lulow. 1997. Effects of species, habitat, and distance from edge on post-dispersal seed predation in a tropical rainforest. Biotropica 29:459-468. Holl, K.D., M.E. Loik, E.H.V. Lin, and I.A. Samuels. 2000. Tropical montane forest restoration in Costa Rica: Overcoming barriers to dispersal and establishment. Restoration Ecology 8:339-349. Holscher, D., C. Leuschner, K. Bohman, J. Juhrbandt, and S. Tjitrosemito. 2004. Photosynthetic characteristics in relation to leaf traits in eight co-existing pioneer tree species in Central Sulawesi, Indonesia. Journal of Tropical Ecology 20:157-164. Hooper, E., R. Condit, and P. Legendre. 2002. Responses of 20 native tree species to reforestation strategies for abandoned farmland in Panama. Ecological Applications 12:1626-1641. Hooper, E., P. Legendre, and R. Condit. 2005. Barriers to forest regeneration of deforested and abandoned land in Panama. Journal of Applied Ecology 42:1165-1174. Hunt, R., D.R. Causton, B. Shipley, and A.P. Askew. 2002. A Modern Tool for Classical Plant Growth Analysis. Annals of Botany 90:485-488. Jones, E.R., M.H. Wishnie, J. Deago, A. Sautu, and A. Cerezo. 2004. Facilitating natural regeneration in Saccharum spontaneum (L.) grasslands within the Panama Canal Watershed: Effects of tree species and tree structure on vegetation recruitment patterns. Forest Ecology and Management 191:171-183. Kitajima, K. 1994. Relative importance of photosynthetic traits and allocation patterns as correlates of seedling shade tolerance of 13 tropical trees. Oecologia 98:419-428. Lambers, H., F.S. Chapin, and T.L. Pons. 1998. Plant physiological ecology. Springer, New York, USA. Lewis, S.L., and E.V.J. Tanner. 2000. Effects of above and belowground competition on growth and survival of rain forest tree seedlings. Ecology 81:2525-2538. Loik, M.E., and K.D. Holl. 1999. Photosynthetic responses to light for rainforest seedlings planted in abandoned pasture, Costa Rica. Restoration Ecology 7:382-391. 53

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Parrotta, J.A., and O.H. Knowles. 1999. Restoration of tropical moist forests on bauxite-mined lands in the Brazilian Amazon. Restoration Ecology 7:103-116. Peek, M., E. Russek-Cohen, A. Wait, and I. Forseth. 2002. Physiological response curve analysis using nonlinear mixed models. Oecologia 132:175-180. Press, M.C., N.D. Brown, M.G. Baker, and S.W. Zipperlen. 1996. Photosynthetic responses to light in tropical rain forest tree seedlings. Pages 41-58 M. D. Swaine, editor. The ecology of tropical forest tree seedlings, UNESCO, Paris, France. Sader, S.A., and A.T. Joyce. 1988. Deforestation rates and trends in Costa Rica 1940 to 1983. Biotropica 20:11-19. Snchez-Azofeifa, G.A. 2000. Land use and cover changes in Costa Rica. Pages 473-501 in C. A. S. Hall editor. Quantifying sustainable development: The future of tropical economies. Academic Press, San Diego, CA, USA. Snchez-Azofeifa, G.A., R.C. Harriss, and D.L. Skole. 2001. Deforestation in Costa Rica: A quantitative analysis using remote sensing imagery. Biotropica 33:378-384. Skerman, P.J., and F. Riveros. 1990. Tropical grasses. Food and Agriculture Organization of the United Nations, Rome, Italy. Swaine, M.D., and T.C. Whitmore. 1988. On the definition of ecological species groups in tropical rain forests. Vegetatio 75:81-86. Tinoco-Ojanguren, C., and R.W. Pearcy. 1995. A comparison of light quality and quantity effects on the growth and steady-state and dynamic photosynthetic characteristics of three tropical tree species. Functional Ecology 9:222-230. Toh, I., M. Gillespie, and D. Lamb. 1999. The role of isolated trees in facilitating tree seedling recruitment at a degraded sub-tropical rainforest site. Restoration Ecology 7:288-297. Veneklass, E.J., and L. Poorter. 1998. Growth and carbon partitioning of tropical tree seedlings in contrasting light environments. Pages 337-361 in H. Lambers, H Poorter, and M. M. I. Van Vuuren editors. Inherent variation in plant growth: Physiological mechanisms and ecological consequences. Backhuys Publishers, Leiden, Netherlands. Vieira, I.C.G., C. Uhl, and D. Nepstad. 1994. The role of the shrub (Cordia multispicata) as a succession facilitator in an abandoned pasture, Paragominas, Amazonia. Vegetatio 115:91-99. Wijdeven, S.M.J., and M.E. Kuzee. 2000. Seed availability as a limiting factor in forest recovery processes in Costa Rica. Restoration Ecology 8:414-424. Wolfinger, R.D. 2000. Fitting nonlinear mixed models with the new NLMIXED procedure. SAS Institute, Cary, North Carolina, USA. 54

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Zimmerman, J.K., J.B. Pascarella, and T.M. Aide. 2000. Barriers to forest regeneration in an abandoned pasture in Puerto Rico. Restoration Ecology 8:350-360. Zipperlen, S.W., and M. Press. 1996. Photosynthesis in relation to growth and seedling ecology of two dipterocarp rain forest tree species. Journal of Ecology 84:863-876. 55

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BIOGRAPHICAL SKETCH Gerardo Celis was born in Costa Rica. At the age of four, his entire family moved to the US where his father was to pursue his graduate studies in Resource Economics. When they returned to Costa Rica, eight years later, he was fascinated by the rainforests and began to develop an interest for the environment and for understanding the impact of humans on it. This motivated him to pursue a career in this area. As a first step, upon conclusion of High School, he initiated a program in Environmental Studies at the University of British Columbia in Vancouver. After one year there, he then returned to Costa Rica, where he completed his undergraduate studies in Biology at Universidad Latina. His undergraduate research, entitled: Seed germination of two sympatric palm species: Chamaedorea tepejilote Liebm. and Chamaedorea Costaricana Oerst (Arecaceae) in natural conditions and in a nursery, was the result of a pro bono collaboration with the National Museum. After concluding his undergraduate studies he taught Biostatistics at the same university and was awarded a scholarship by the Organization for Tropical Studies (OTS) to participate in the program Research Experiences for Undergraduates (REU) at La Selva Biological Station. The research conducted was entitled: Do patterns of seed germination and seedling biomass allocation reflect a shade tolerance syndrome in Gnetum leybodii Tul. (GNETACEAE)?. Later on, he became TA, under Professor Luis Diego Gmez, for OTS course Plantains, Iguanas and Shamans: An Introduction to Field Ethnobiology. At this point in his career, he felt that he needed to develop a broader understanding of environmental processes by incorporating the interdisciplinary dimension; in particular, how humans could help restore the environment. Thus, he decided to pursue a masters in interdisciplinary ecology at the University of Florida (UF). In 2004, he obtained a 9-credits out56

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of-state tuition exemption from the Florida-Costa Rica Linkage Institute (FLORICA). For the second year he was awarded a fellowship by UFs Tropical Conservation and Development Program (TCD) within the Center for Latin American Studies. His masters thesis was entitled: Restoring abandoned pasture land with native tree species in Costa Rica: An ecophysiological approach to species selection funded by the Comptons fellowship. Upon completion of his masters degree he enrolled in a PhD in Urban restoration ecology at the University of Florida. He plans to continue research in the area of urban restoration ecology, teach courses at universities, become part of teams performing environmental impact assessments and designing policy reforms, and develop community level activities. With the information generated from his research, he wants to create programs that will help to establish a better interpretation of the environmental impacts of urban expansions and to give a solid basis for urban planning and policy design. 57


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Permanent Link: http://ufdc.ufl.edu/UFE0018200/00001

Material Information

Title: Restoring Abandoned Pasture Land with Native Tree Species in Costa Rica: An Ecophysiological Approach to Species Selection
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0018200:00001

Permanent Link: http://ufdc.ufl.edu/UFE0018200/00001

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Title: Restoring Abandoned Pasture Land with Native Tree Species in Costa Rica: An Ecophysiological Approach to Species Selection
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Copyright Date: 2008

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RESTORING ABANDONED PASTURE LAND WITH NATIVE TREE SPECIES IN COSTA
RICA: AN ECOPHYSIOLOGICAL APPROACH TO SPECIES SELECTION.




















By

GERARDO CELIS AZOFEIFA


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2007



























Copyright 2007

by

Gerardo Celis Azofeifa



































To my parents









ACKNOWLEDGMENTS

I would like to thank my advisor, Dr. Shibu Jose, for his dedication and support throughout

my research. I also wish to express my sincere appreciation to my committee members, Dr.

Kaoru Kitajima and Karen Kainer for their valuable insights. I would like to thank FUNDAZOO

and Gustavo Vargas for their support in the logistics of my research and the Compton

Foundation for funding it. Fellowships from TCD and FLORICA were also essential to

complete my master's courses and are much appreciated. Finally, I thank Gabriela Hernandez,

my family, and friends for their unconditional support, without which none of this would have

been possible.









TABLE OF CONTENTS

page

A CK N O W LED G M EN T S ................................................................. ........... ............. .....

L IST O F T A B L E S ...................... ............... ....................................................... . 7

LIST OF FIGURES .................................. .. ..... ..... ................. .8

A B S T R A C T ......... ....................... .................. .......................... ................ .. 9

CHAPTER

1 IN T R O D U C T IO N ....................................................................................... .......... .. .. .. 11

2 GROWTH AND BIOMASS ALLOCATION OF TROPICAL TREE SEEDLINGS IN
AN ABANDONED PASTURE IN COSTA RICA: RESPONSE TO GRASS
COMPETITION AND VARIED LIGHT REGIMES ..........................................................14

Intro du action ................... .......................................................... ................ 14
M material and M methods ............... ................. ........... ......................... 16
S tu d y A re a ...................................... ......................................................16
Experim mental D design ................................. ... .. .......... .. .............16
A naly sis ................................. .................. ..... ......... ............................ 18
M o rta lity ............................................................................................................. 1 8
G ro w th .........................................................................................1 8
R e su lts ................... ...................1...................9..........
M mortality .................................19...............................................
R elative G row th R ate ...............................................................19
L eaf A rea .................................................................................................................. 2 0
Leaf Area Ratio and Leaf M ass Ratio ........................................................ ......... 21
Specific L eaf A rea ............................................................................................2 1
Root Mass Ratio, Stem Mass Ratio, and Root: Shoot Ratio ........................................22
D iscu ssio n ................... ...................2...................3..........
C o n clu sio n ................... ...................2...................5..........

3 PHOTOSYNTHETIC RESPONSES OF TROPICAL TREE SEEDLINGS IN AN
ABANDONED PASTURE UNDER GRASS COMPETITION AND VARIED LIGHT
R E G IM E S ................... ...................3...................6..........

In tro d u ctio n ................... ...................3...................6..........
M materials an d M eth o d s ...........................................................................................................3 7
S tu d y S ite .......................................................3 7
E x p erim en tal D esig n ....................................................................................................... 3 8
P photosynthesis .................................................................................................39
G ro w th .........................................................................................3 9
A naly sis ....................................................... .................................40









M o d el F ittin g ................................................................................. 4 0
G row th and photosynthesis ........................................................... .....................4 1
R e su lts .................................................................................................................4 1
Photosynthesis ................................. .......................... ..... .... ......... 41
Grow th and Photosynthesis ................................. ......................................... 41
D iscu ssio n ................. ....... ... .. ...............................................................................................4 2
Photosynthesis .................................. ................................. ......... 42
G row th and Photosynthesis ........................................................... .......43
C onclusions.................. ......................................................................43

4 SUMMARY AND CONCLUSION ........................................................ ..............49

A PPEN D IX SO IL N U TR IEN T S ............................................................................... ........ 51

L IST O F R E F E R E N C E S ..................................................................................... ....................52

B IO G R A PH IC A L SK E T C H .............................................................................. .....................56





































6









LIST OF TABLES


Table page

2-1 Mean (standard error) initial and final height and diameter for each species....................26

2-2 Plant variables derived for growth analysis................................................ 27

2-3 Percent mortality of species by treatment (grass and light).................. ............... 28

2-4 Split-plot ANOVA for Total Leaf Area, LAR, LMR and SLA with respect to each
treatm ent and their interactions............................................................... .....................29

2-5 Split-plot ANOVA for RWR, SMR and R: S with respect to each treatment and their
interactions ................ ...................................... ............................30

3-1 Estimated parameters of photosynthesis model: light saturated point (Amax),
apparent quantum yield (Aqe), and light compensation point (LCP) .............................45

A-1 Soil nutrient levels .......................... ..... .. ................. ............ 51









LIST OF FIGURES


Figure page

2-1 Mean values of Relative growth rate and standard errors over the growth period for
each species in the three light treatments (100%, 37% and 2%) and grass treatments
(sh ort an d tall) ................................................. ........................................ 3 1

2-2 The overall relationship between relative growth rate (RGR) and growth variables
for all treatm ents. ........................................................ ................. 32

2-3 Relationship between mean values over growth period of 26 week for relative
growth rate (RGR) and net assimilation rate (NAR) and leaf area ratio (LAR) for
each light treatment (100%, 37% and 2%) ........... ........ ..................................33

2-4 Growth variables: leaf area, leaf area ratio (LAR) and specific leaf area (SLA) for
each species grown under low (2%), medium (37%), and high (100%) light condition
in each of the grass treatments, short and tall ........................... ..... ............................ 34

2-5 Growth variables: root mass ratio (RMR), stem mass ratio (SMR) and leaf mass ratio
(LMR) for each species grown under low (2%), medium (37%), and high (100%)
light condition in each of the grass treatments, short and tall............................... 35

3-1 Light response curves fitted with nonlinear Mitscherlich model equations from
parameter estimates obtained from nonlinear mixed models analysis using SAS.............46

3-2 Light response curves fitted with nonlinear Mitscherlich model equations from
parameter estimates obtained from nonlinear mixed models analysis.............................. 47









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

RESTORING ABANDONED PASTURE LAND WITH NATIVE TREE SPECIES IN COSTA
RICA: AN ECOPHYSIOLOGICAL APPROACH TO SPECIES SELECTION.

By

Gerardo Celis Azofeifa

May 2007

Chair: Shibu Jose
Major Department: Interdisciplinary Ecology

The establishment of trees in the successional trajectory of tropical abandoned pastures

into forest communities is confronted with several barriers: dispersal, seed predation,

unfavorable conditions for germination, and intense competition once they germinate. In order to

aid the restoration of abandoned pastures into forested ecosystems, we must overcome some of

these barriers through manipulative efforts.

The present study was designed to characterize the light requirements of six native tree

species (light demanding: Pseudosamanea guachapele (Kunth) Harms (Fabaceae), Tabebuia

impetiginosa (Mart. Ex DC.) Standley (Bignoniaceae), Ceibapentandra (L.) Gaertn.

(Bombacaceae); shade tolerant: Bombacopsis quinatum (Jacq.) Dugand. (Bombacaceae), and

intermediate: Dalbergia retusa Hemsl. (Fabaceae), Tabebuia rosea (Bertol.) DC. (Bignoniaceae)

under contrasting light environments and grass competition. Understanding their early

establishment requirements could be used in selecting proper light and competition regimen for

the success of restoring pastures after abandonment.

Field studies were conducted in the pastures of the Santa Ana Conservation Center in

Costa Rica. Two grass competition regimes were selected, one dominated by Hyparrhenia rufa

(Nees) Stapf (Tall-grass) and another dominated by Cynodon mlenfluensis Vanderyst (Short-









grass). Three light treatments were created (100%, 37% and 2% light) using either neutral shade

cloth (37% and 2%) or no shade cloth (100%). Growth characteristics, biomass partitioning and

light response curves of the seedlings were measured. Overall, P. guachapele had the best

performance in competing with the grasses followed by D. retusa. T impetiginosa, and T. rosea,

which had similar results regardless of the grass. C. pentandra did not do well under tall grass.

The 2% light treatment greatly reduced seedling performance for all species and 37% had no

effect except for D. retusa.

We recommend planting P. guachapele as an initial step in reforesting pastures. Once they

are established and shade produced by the tree reduces grass cover, T. rosea, T. impetiginosa and

C. pentandra can be planted.









CHAPTER 1
INTRODUCTION

Costa Rican forests have been greatly diminished in the recent past. For example, Sader

and Joyce (1988) estimated a 50% decline between 1940 and 1984. The driving forces behind

deforestation have generally been the expansion of agriculture and cattle operations (Sanchez-

Azofeifa, 2000). The latest estimates are that a 29% of the territory is under closed forest cover

and that 30% of that forest is protected by national conservation policies (Sanchez-Azofeifa,

Harriss, and Skole, 2001).

Although Costa Rica still has nearly a third of the land area under forest cover, most of it

is fragmented and as a result faces further threat of being degraded into even smaller islands.

This new landscape is a mosaic of forest patches surrounded by human dominated lands,

primarily of agricultural activities. For various reasons, many agricultural areas are being

abandoned and left as pastures. Some of these areas are able to regenerate naturally; although the

speed at which they restore will depend on the existing vegetation in the pasture, the land use

history and the proximity of these to forested areas (Aide, Zimmerman, Pascarella, Rivera, and

Marcano-Vega, 2000; Hooper, Legendre, and Condit, 2005; Zimmerman, Pascarella, and Aide,

2000). Several studies have demonstrated that pastures may regenerate naturally into forest

within a few decades, but the species composition may be drastically different compared to the

original one (Aide et al., 2000; Finegan and Delgado, 2000; Parrotta and Knowles, 1999).

Therefore, in order to accelerate restoration and obtain the desired species composition it is

necessary to intervene with enrichment planting.

One of the main factors influencing the successional trajectory of abandoned pastures

into forest communities is seed dispersal. Since pastures have little or no woody vegetation, the

majority of woody species that colonize abandoned pastures are wind-dispersed (Finegan and









Delgado, 2000; Holl, 2002; Holl, Loik, Lin and Samuels, 2000; Toh, Gillespie, and Lamb, 1999;

Zimmerman et al., 2000). Those species that eventually colonize also undergo seed predation,

unfavorable conditions for germination, and intense competition once they germinate (Camargo,

Ferraz, and Imakawa, 2002; Holl and Lulow, 1997; Holl et al., 2000; Wijdeven and Kuzee,

2000).

Consequently, competition with other species is an important factor limiting the

establishment of seedlings, following their dispersal barrier (Holl, 1998). The most evident

competition is aboveground between the pasture grasses, which in many cases are exotic species

and very aggressive and new colonizers. Grass cover greatly reduces light availability (Hooper,

Condit, and Legendre, 2002; Vieira, Uhl and Nepstad, 1994), and hence seedling growth.

Belowground, competition can have important implications as well, especially where water is

scarce and soils are low in nutrients (Chapman, Chapman, Zanne, and Burgess, 2002).

In order to aid the restoration of abandoned tropical pastures into forested ecosystems, we

must overcome some of these barriers through manipulative efforts. In doing so, we need to learn

more about the performance of native tree species in these extreme environments. For example,

what are their capabilities and at what stage of the regeneration process should they be

introduced? Can they all be planted in an open pasture at the same time? Should we introduce

different species at different stages to mimic the natural successional trajectory? Our knowledge

base is limited at this point to answer these questions.

Objectives. The present study was designed to characterize the light requirements of six

native tree species under contrasting light environments and grass competition. Understanding

their early establishment requirements could be used in selecting proper light and competition









regimen for the success of restoring a pasture after abandonment. More specifically the research

was designed to

* determine growth characteristics and biomass partitioning of seedlings under three
different light environments and two levels of grass competition;

* estimate light response curve parameters under three different light environments and two
levels of competition;

* establish the sequence in which the native species should be planted in a pasture.

The results of experiments carried out are presented in the two subsequent chapters. In the

final chapter, a summary of these findings is presented. Preliminary recommendations for the

sequence of planting in restoring an abandoned pastureland and the future research needs are also

discussed in the final chapter.









CHAPTER 2
GROWTH AND BIOMASS ALLOCATION OF TROPICAL TREE SEEDLINGS IN AN
ABANDONED PASTURE IN COSTA RICA: RESPONSE TO GRASS COMPETITION
AND VARIED LIGHT REGIMES

Introduction

Traditionally, plants have been grouped by light requirements as light demanding and

shade tolerant (Swaine and Whitmore, 1988). This is mainly because in a forested environment,

light is one of the most varying and dynamic resources for a plant (Chazdon, Pearcy, Lee ,and

Fetcher, 1996). Plant species also vary in their ability to exploit different light environments in

order to optimize growth (Lambers, Chapin, and Pons, 1998). Understanding where species stand

in this dichotomy will provide a guide for refining management interventions aimed at

improving plant establishment and growth.

Light available to plants may vary greatly when ecosystems are compared. For example,

light levels in large open areas, such as pastures, can be much greater than in undisturbed forests

(Holl, 1999). Moreover, within each particular ecosystem, measurements of light fluctuate

vertically: higher at the canopy and lower at the base of the plants. Consequently, even in

pastures competition aboveground with grasses, which in many cases are exotic species and very

aggressive, may reduce seedling establishment and seedling growth (Hooper et al., 2002; Vieira

et al., 1994). In any event, what is important from a restoration point of view is that species

selection needs to be aimed at finding the right match between light availability and plant light

requirements for successful establishment and seedling growth.

Furthermore, the growth of tropical tree seedlings in these particular light environments,

and their ability to adapt to changes, depends on the complex interaction of morphological and

physiological attributes of each species (Garwood, 1996). Light influences growth directly

through differences in carbon gain, and indirectly through differences in carbon partitioning









(Veneklass and Poorter, 1998). The focus of this chapter is the growth and biomass allocation

response of tree seedlings under the competition conditions posed by existing pastures and under

varied light conditions.

Growth analyses are commonly used to understand seedling performance. In particular the

relative growth rate (RGR), which represents the increase of plant mass per unit of plant mass, is

analyzed as the product of a physiological component, the net assimilation rate (NAR), which

stands for the increase in plant mass per unit leaf area, and a morphological component, the leaf

area ratio (LAR), which corresponds to the amount of leaf area per unit plant mass. LAR in turn

can be broken down into the biomass allocated to leaves, the leaf mass ratio (LMR) or the

amount of leaf mass per unit plant mass, and the specific leaf area (SLA) or the amount of leaf

area per unit leaf mass. However it is important to consider that growth is the net acquisition of

resources from the environment by different parts of the plant and their relative influences will

have consequences on growth (Farrar and Gunn, 1998). This is examined by studying plant

biomass allocation to various parts of the plant.

The objectives of this study were to (1) determine growth characteristics and biomass

partitioning of seedlings in three different light environments and two levels of competition and

(2) establish the sequence in which the native species should be planted in a pasture based on the

growth and biomass partitioning data.

We hypothesized that light demanding species should exhibit higher relative growth in

high light environments, whereas the shade tolerant species should perform better under low

light, and the intermediate species should perform best in medium light. All seedlings under

Short-grass competition should have higher relative growth rates than under Tall-grass

competition.









Material and Methods


Study Area

This study was conducted in the pastures of the Santa Ana Conservation Center in Costa

Rica, a protected spot in the outskirts of San Jose (90 56'26.00" N 840 11'44"10W), in the

middle of the fastest urban expansion front in the country. According to the Holdridge

Classification, the Santa Ana Conservation Center is located on a Premontane Wet Forest Life

Zone. The climate is seasonal, with a dry season that extends from early December to the end of

April. The average annual rainfall is 2,467 mm and the average annual temperature is 23.4C.

There are two rainfall peaks: one at the beginning of the rainy season, in May, and the second at

the end of the rainy season between October and November. The average maximum

temperatures occur during the dry season.

Soils were vertisols with a soil texture of 12.8% sand, 24% silt, and 63.2% clay. Soil pH

was 5.5. Nutrient levels based on Bertsch (1986) soil standards showed deficiencies of

Potassium (K) and Phosphorous (P). All other nutrient levels were normal (Table A-i).

Experimental Design

In the pastures of the Santa Ana Conservation Center, two grass competition regimes were

selected by creating 18 (3.5 x 3.5 meter) plots in each of two areas: one dominated by

Hyparrhenia rufa (Nees) Stapf (Tall-grass) and another dominated by Cynodon mlenfluensis

Vanderyst (Short-grass), both of which had been hand machete once before planting to a height

of about 15cm. These grasses are native to tropical Africa. C. mlenfluensis is a stoloniferous

perennial without underground rhizomes, which can reach heights of 100 cm and H. rufa, also

perennial, can reach heights that range from 60-240cm (Skerman and Riveros, 1990). In each

area three light environments were created: low (2% light), medium (37% light) and high (100%

light). Plots were separated by 3 meters to prevent neighbor shading. For the shaded treatments









shade houses 2.5 meters tall were constructed to cover the entire plot using neutral shade-cloth

(63% and 98% shade). Each light environment x grass competition treatment was replicated six

times for a total of 36 plots. Therefore each plot was a sampling unit for this experiment.

Six species pertaining to different natural life histories were used; light demanding:

Pseudosamanea guachapele (Kunth) Harms (Fabaceae), Tabebuia impetiginosa (Mart. Ex DC.)

Standley (Bignoniaceae), Ceibapentandra (L.) Gaertn. (Bombacaceae); shade tolerant:

Bombacopsis quinatum (Jacq.) Dugand. (Bombacaceae), and intermediate: Dalbergia retusa

Hemsl. (Fabaceae), Tabebuia rosea (Bertol.) DC. (Bignoniaceae) (See Table 2-1). Six

individuals of each species were directly planted into the ground in a row under each treatment

(light environment and grass competition) for a total of 36 individuals per plot. Species were

assigned a row by systematically rotating their position within the plot. Tree seedlings were

provided by the Santa Ana Conservation Center nursery, located in the same area. All seedlings

were grown under 40% shade in plastic bags (9cm x 18cm) for 11 months. They were planted

during the first two weeks of the experiment starting the last week of May 2005 directly into the

ground in holes of about 20cm in diameter. One week after planting, damaged seedlings were

replaced.

Growth. An initial census of all plants in the field was made to measure plant height (to

the nearest 0.1cm), root collar diameter, (RCD to the nearest 0.01cm) and leaf number in the

second week of July 2005. Ten nursery seedlings were selected randomly for each species and

destructively harvested. For each individual, height, RCD, leaf number, biomass allocation (root,

stem and leaf) (to the nearest 0.01g) and leaf area (cm2) were measured. Leaf area was

determined from scanned images of each leaf using Scion Image (Scion Corporation, Frederick,

Maryland, USA). Plant biomass was determined after drying at 70 C for 72 hours.









A second and final census was also done during the first week of January 2006 where

seedling height, RCD and leaf number of all remaining seedlings were measured the same way

as in the first census. Mortality was also recorded. Out of each treatment, at least nine seedlings

that had leaves were randomly selected and destructively harvested and measured for biomass

allocation (root, stem and leaf) and leaf area using the same method as for the first harvest. At

least 1 individual from each plot of 36 was harvested.

Analysis

Mortality

A logistic regression model was used to determine differences in mortality. Live

individuals received a score of 1, whereas dead ones received a score of 0. Grass, light and

species were used as main effects and, since each plot corresponded to a specific light and grass

treatment, our model used the interaction of grass and light with plot nested within it as the

random effect.

Growth

Young seedling variability in plant size is difficult to homogenize in experiments;

therefore, we conducted an initial census to test for differences among treatments in seedling

height and RCD after planting. A two-way ANOVA with grass and light treatments as main

effects was used to determine differences between seedling height and RCD. The test indicated

that between grasses there were no differences for both seedling height and RCD (F1,32=2.32

p=0.14 and F1,32=0.25 p=0.62 respectively); for the light treatment there were no differences in

height (F2,32=0.25 p=0.78), however there were differences in RCD (F2,32=11.94p<0.0001);

Therefore, to compensate for these differences all other analyses were preformed with height and

RCD as covariates if they were significant in the model.









The growth analyses were done following the "classical" method (Hunt et al., 2002) to

calculate mean growth variables over the growth period (using initial and final harvest data):

relative growth rate (RGR), net assimilation rate (NAR or ULR), leaf area ratio (LAR), specific

leaf area (SLA), leaf mass ratio (LMR), root mass ratio (RMR), and stem mass ratio (SMR) and

Root: Shoot allometric coefficient (Table 2-2). Each variable's mean value over the growth

period was compared using a correlation analysis to determine relationships between each

treatment. The nonparametric Spearman's rank correlation coefficients (rs) were used because it

is robust to sample distribution problems. Final harvest values for LAR, SLA, LMR, RMR,

SMR, and Root: Shoot were compared using a split-plot ANOVA with grass, light and species as

main effects and their interactions to determine differences. When the main effects were

significant, post hoc Tukey's test was carried out for mean separation. Since each plot

corresponded to a specific light and grass treatment, our model used the interaction of grass and

light with plot nested within it as the random effect. B. quinatum was eliminated from this

analysis due to the fact that in some treatments they had lost completely their leaves at the time

of the second harvest.

All statistical analyses were conducted using JMP version 5.1, SAS Institute 2004.

Results

Mortality

Mortality was low, ranging from 0 to 14%, during the study period (Table2-3). There was

no significant difference among any of the treatments.

Relative Growth Rate

The species with the highest relative growth rate (RGR) was P. guachapele This species

grew best in 100% and 37% light levels and there was no difference between grass treatments.

RGR was positive when grown in 2% light. D. retusa and C. pentandra shared the second









highest growth rates in 100% and 37% light levels, respectively. D. retusa was not affected by

the grass treatment, whereas C. pentandra performed better in short grass. T. rosea and T.

impetiginosa performed similarly, but the latter was affected more by the tall grass treatment

(Figure 2-1).

Relative growth rate overall was correlated to net assimilation ratio (NAR) (r, =0.96, p <

0.001), leaf area ratio (LAR) (r, = -0.60, p < 0.001), specific leaf area (SLA) (r, = -0.55, p =

0.002) and leaf mass ratio (LMR) (r, = -0.47, p = 0.009) (Figure 2-2). When looked at

individually under each light treatment, at 100% light there was a strong correlation with NAR

(r, = 0.78, p = 0.008), but LAR was not significant. At 37% light NAR (rs = 0.95, p < 0.001) and

LAR (r, = -0.70, p < 0.05) and SLA (r, = -0.78, p = 0.007) were significant. At 2% light NAR (r,

= 0.91, p < 0.001) and LAR (r, = -0.62, p = 0.06) were significant (Figure 2-3).

Leaf Area

At final harvest there was a significant difference in total leaf area in all treatments; light

(F2,=15.56;p = 0.002), grass (F,8=10.62;p < 0.01), and species (F4,=9.09;p = 0.005) (Table

2-4). Seedlings grown under light levels 37% and 2% had the same total leaf area whereas leaf

area was significantly lower in the 100% light treatment. Seedlings grown in short grass had

more leaf area than those in tall grass. P. guachapele and T. rosea shared the highest leaf area,

followed by C. pentandra and T. impetiginosa and D. retusa with the lowest. As for the

interactions none were significant. T impetiginosa and D. retusa had the same leaf area in all

light levels. P. guachapele had the highest leaf area in the 37% light treatment and lower, but

similar leaf area in the 2% and 100% treatments. C. pentandra and T. rosea had 37% and 2%

with the highest values which were greater than 100%. C. pentandra and P. guachapele had









higher values in the short grass than tall. T rosea, T impetiginosa and D. retusa had same values

in short and tall grass.

Leaf Area Ratio and Leaf Mass Ratio

Leaf area ratio and Leaf mass ratio were significantly different in all treatments (Table 2-

4). Seedlings grown under 2% light had the highest LAR and LMR followed by 37% and then

100%. LAR and LMR were higher in seedlings grown in short grass compared to tall grass. T

rosea and C. pentandra had the highest LAR, followed by T impetiginosa and P. guachapele

and finally by D. retusa (Figure 2-4). For LAR, the only interaction with a significant difference

was light *species (F,,g=9.30; p = 0.002), where P. guachapele and D. retusa had similar values

at 37 and 100%, all other species showed the same trend as with the light treatment.

For LMR, T. rosea had the highest values followed by T. impetiginosa and C. pentandra,

then P. guachapele and finally by D. retusa (Figure 2-4). For the interaction of light *species, T

impetiginosa showed the same trend, but T. rosea, C. pentandra, and P. guachapele had similar

values for their 2% and 37% light treatments and their 100% was significantly lower. D. retusa

showed similar values across light levels. As for the grass *species interaction T impetiginosa,

P. guachapele, and D. retusa showed similar values in tall and short grasses, T rosea had higher

values in short than tall and C. pentandra had the inverse.

Specific Leaf Area

Specific leaf area showed significant differences in light (F2,8=115.91; p < 0.001) and

species (F4,=10.43;p = 0.003) treatments, but not in grass. There were significant interactions

for light *species (F,,g=5.88; p = 0.011) only (Table 2-4). SLA exhibited the same trend under

light treatment as LAR. However, for species, D. retusa and C. pentandra shared the highest

values followed by P. guachapele, T. rosea and T. impetiginosa, which also had similar values.









For the interaction of light and species all species except T. impetiginosa had higher values at 2%

and 37% and 100% were less, but similar. T impetiginosa had similar values in all light

treatments (Figure 2-4).

Root Mass Ratio, Stem Mass Ratio, and Root: Shoot Ratio

Root mass ratio and root: shoot ratio showed significant difference in light treatments and

species. There was a significant interaction between light *species (Table 2-5). The RMR of

seedlings grown under 100% light treatment had the highest value followed by 37% and then

2%. For the species treatment T. impetiginosa had the highest followed by D. retusa, P.

guachapele, C. pentandra and T. rosea. As for the light interaction, T impetiginosa followed the

same trend as the light treatment. D. retusa had light at 37% the same as 2%. P. guachapele C.

pentandra and T. rosea all had all light levels equal except for 100% and 2% (Figure 2-5). For R:

S ratios the light treatments at 37% and 2% similar, but were lower than 100%. T impetiginosa

had the highest value followed by D. retusa and P. guachapele and then by C. pentandra and T

rosea. P. guachapele, C. pentandra and T. rosea did not differ in the light treatments. D. retusa

and T. impetiginosa had similar ratios at 37 and 2%, which were lower than 100% (Figure 2-5).

As for the interaction between grass *species T. impetiginosa was the only one to have Short

grass larger than Tall grass, the rest had same values.

For SMR, C. pentandra had the highest followed by T. rosea P. guachapele D. retusa and

T. impetiginosa (Figure 2-5). The interaction between light and species(F,,g=4.05; p = 0.03)

(Table 2-5), all species had similar values in all light treatments except D. retusa, for which 37%

and 2% were similar and greater than 100%. Tall grass had a higher value than short. For grass

*species interaction all species had the same values in both types of grasses (Figure 2-5).









Discussion

In this study we observed a low mortality in all treatments, which may be due to the short

duration of the study (only 26 weeks). Interspecific relative growth rates (RGR) can be ranked

into specific groups by patterns in both net assimilation ratio (NAR) and leaf area ratio (LAR)

(Veneklass and Poorter, 1998). Light demanding species have higher relative growth rates in

both low and high light environments (Kitajima, 1994). However there are discrepancies as to

which component, NAR or LAR, determines RGR at low-light and at high-light. For example, in

low light conditions Bloor and Grubb (2003) found NAR to be significantly related to relative

growth rate and Veneklass and Poorter (1998) suggest LAR. In the light levels that we examined,

we did not see a shift from NAR to LAR (Figure 2-3). In all light treatments NAR had a high

correlation with RGR. Nonetheless at 37% light LAR becomes significant, but not as significant

as NAR. This coincides with the findings of Bloor and Grubb (2003) that under very low light

condition RGR is determined by NAR. Light under tall grass canopies can be greatly reduced

(Holl, 2002; Hooper et al., 2002) and this, combined with seedling height, could explain why

RGRs were correlated mainly to NAR under all light conditions.

However, interspecific variation in RGR with respect to light treatments varied. Overall,

P. guachapele had the best performance (Figure 2-3). In the high light treatment (100%) P.

guachapele and D. retusa performed better than other species. Gerhardt (1993) found that after

six months after planting seedlings, the highest growth rates for the two light demanding species

they examined were the individuals grown in a pasture. This means that they were able to

compete with grasses above or belowground. P. guachapele and D. retusa were species with the

largest stem height (Table 2-1). If plants were taller in the grass environment, they would have

had better access to light. This may be true for P. guachapele; however C. pentandra, whose

height was considerably large, did not perform well in the high light condition.









It is important to note that P. guachapele and D. retusa are both legumes, and have

nitrogen fixing nodules. This could have enhanced their capability to compete with grasses.

Jones, Wishnie, Deago, Sautu, and Cerezo (2004) found that the legume species Inga spp. had

the best comparative performance with 13 other species in pastures in Panama. It is also

important to note that D. retusa and P. guachapele ranked second and third in root mass ratios

(RMR) (Figure 2-5), which means that overall they invested more in roots than the other species.

This, combined with their nitrogen fixing capabilities, could have given them an advantage when

compared to the other species.

Lowering the light level to 37% did not affect the performance of P. guachapele or T

rosea, but greatly reduced D. retusa, and improved C. pentandra under short grass (Figure 2-1).

Shading can effectively eliminate or hinder grasses, for example Hooper et al. (2002) found that

shade eliminated the Saccharum exotic grass and enhanced tree regeneration in Panama. In our

study, grasses were not eliminated, but shading made them less aggressive. However, the

reduction in light could also affect seedling performance, that being the case for D. retusa. C.

pentandra performed better under short grass due to the reduction in competition, but this

reduction was not enough under tall grass. Lewis and Tanner (2000) found that a reduction of

below ground competition resulted in increased allocation to leaves and in decreased allocation

to roots, a trend that we found in C. pentandra, T. impetiginosa, and T. rosea (Figure 2-5). At

the lowest light level, shade effectively eliminated grass, but also reduced all species RGRs,

except for P. guachapele.

Contrary to our expectations of categorizing species solely by light requirements, there was

clearly a grass and light effect which varied with species. Therefore, we can not make









generalization as to light requirements. For example, not all light demanding species performed

better under high light conditions.

Conclusion

Species differed in their RGR, but overall P. guachapele was the best performer. We

recommend planting this species as an initial step in reforesting pastures. Once it is established

and shade produced by the tree reduces grass cover T. rosea (Roble), T. impetiginosa and C.

pentandra can be planted. However, C. pentandra may be affected by competition with grasses

and/or P. guachapele 's presence. Further research is needed to understand how different species

will react under P. guachapele's shade and competition. Will its canopy be effective in reducing

light quantity to have an effect on grasses? Seedling size may also become a factor in these

pasture conditions, which should be examined with the other species of smaller heights that were

used.











Table 2-1. Mean (standard error) initial and final height and diameter for each species.


Species

Ceiba pentandra
Dalbergia retusa
Tabebuia impetiginosa
Pseudosamanea guachapele
Bombacopsis quinatum
Tabebuia rosea


Family

Bombacaceae
Fabaceae
Bignoniaceae
Fabaceae
Bombacaceae
Bignoniaceae


Species
code
Ceiba
Coco
Cortez
Guaya
Pochote
Roble


Initial
Height
(cm)
42.0(0.6)
41.7(1.2)
24.1(0.4)
46.1(0.9)
21.9(0.5)
28.3(0.4)


Initial
Diameter
(cm)
0.76(0.01)
0.55(0.009)
0.51(0.009)
0.75(0.01)
0.61(0.01)
0.82(0.01)


Final
Height
(cm)
48.8(1.1)
40.4(1.3)
25.9(0.4)
52.5(1.3)
24.5(0.7)
33.2(0.5)


Final
Diameter
(cm)
0.88(0.02)
0.64(0.01)
0.60(0.02)
0.93(0.02)
0.78(0.02)
0.89(0.02)










Table 2-2. Plant variables derived for growth analysis. M is total dry mass of seedling (g), A is
seedling leaf area (cm-2), L is seedling leaf dry mass (g), S is stem dry mass (g), R is
root dry mass (g) and T is time (weeks). Subscripts refer to initial (i) final (f).


Variable


Formula


Units


Relative growth rate (RGR)


Net assimilation rate (NAR)


Leaf are ratio (LAR)


Specific leaf area (SLA)


Leaf mass ratio (LMR)


Stem mass ratio (SMR)


Root mass ratio (RMR)

Root to shoot coefficient
(R:S)


(lnMf -lnM,)
(T)

[f M, AlnAf- lnA)]
L(Af-


g g1 week-


g cm-2 week1
gcm week-


2 -1
cm g

2 -1
cm g


-1
gg


g-1
gg


R
SL L









Table 2-3. Percent mortality of species by treatment (grass and light). See Table 2-1 for species
coding.
Grass Light Species Mortality
Tall 100% Ceiba 3%
Tall 100% Coco 6%
Tall 100% Cortez 0%
Tall 100% Guaya 0%
Tall 100% Roble 0%
Tall 37% Ceiba 0%
Tall 37% Coco 3%
Tall 37% Cortez 3%
Tall 37% Guaya 3%
Tall 37% Roble 3%
Tall 2% Ceiba 14%
Tall 2% Coco 8%
Tall 2% Cortez 6%
Tall 2% Guaya 0%
Tall 2% Roble 3%
Short 100% Ceiba 3%
Short 100% Coco 3%
Short 100% Cortez 8%
Short 100% Guaya 3%
Short 100% Roble 0%
Short 37% Ceiba 0%
Short 37% Coco 11%
Short 37% Cortez 0%
Short 37% Guaya 0%
Short 37% Roble 0%
Short 2% Ceiba 11%
Short 2% Coco 14%
Short 2% Cortez 0%
Short 2% Guaya 3%
Short 2% Roble 14%












Table 2-4. Split-plot ANOVA for Total Leaf Area, LAR, LMR and SLA with respect to each treatment and their interactions.
Variable


Treatment
Grass
Light
Grass*light
Species
Species*Light
Species*Grass


Total Leaf Area
=10.62;p < 0.01
=15.56;p = 0.002
=2.96; p = 0.11
=9.09;p = 0.005
=2.61;p 0.098
=1.64; p = 0.26


F1,
F2,8
F2,8
F4,8
F4,8
F4,8


LAR
=5.48; p = 0.047
=281.58;p < 0.001
=1.75;p 0.23
=24.41;p < 0.001
=9.30; p = 0.002
=1.30; p 0.35


F1
F2,,
F2,8

F4,8
F4,8
F4,8


LMR
=5.93;p= 0.041
=55.31;p < 0.001
=0.094;p = 0.91
=9.09; p 0.005
=2.90; p = 0.077
=2.68;p 0.11


F1,8
F ,8
F2,8
F4, 8
F4,8
F4,8


SLA
=1.66;p < 0.23
=115.91;p < 0.001
=1.05;p 0.40
=10.43;p 0.003
=5.88;p 0.011
=0.77; p 0.58


F18
F2,8
F2,8
F4,8
F ,8=
F4,8











Table 2-5. Split-plot ANOVA for RWR, SMR and R: S with respect to each treatment and their interactions.
Variable
Treatment RWR SMR R: S
Grass F1,8=0.0007; p = 0.98 F1,8=5.50; p = 0.047 F1,8=1.77; p = 0.22
Light F2,8=61.9;p < 0.001 F2,8=1.10;p = 0.37 F28=29.80;p < 0.001
Grass*light F2,8=0.28;p = 0.76 F28=0.089;p = 0.91 F28=0.13;p = 0.88
Species F4,8=55.63;p < 0.001 F4,8=52.44;p < 0.001 F4,8=73.11;p < 0.001
Species*Light F,8=4.14;p = 0.03 F,8=4.05;p = 0.03 F,8=3.61; p = 0.044
Species*Grass F4,8=2.69; p 0.11 F4,8=1.97;p 0.19 F4,8=2.12; p 0.17















0.08

0.06

0.04

0.02

0.00

-0.02


Ceiba Coco Cortez Guaya Roble


Ceiba Coco Cortez Guaya Roble


Species


Figure 2-1. Mean values of relative growth rate and standard errors over the growth period for
each species in the three light treatments (100%, 37% and 2%) and grass treatments
(short and tall). See Table 2-1 for species coding.


Short Tall i- 10o
I 37%
2%


: r Y















80
B Ceiba
V 0 Coco
9 V Cortez 70
A Guaya
0 o Roble 60
S0160
A 0 50 o

*
9 r, = -0.60, p < 0.001 40 J
A
A 30


-- 20


r, = -0.55, p = 0.002


V V
V


r, = 0.33, ns


0 0.5
03

H 0.4 o
Sabo AO A

a0.3 .
0.3 n '


r, = -0.47, p = 0.009


|
.o
o


av


*


* *bS
U
U


0.55

0.50
r,= -0.17, ns
0.45

A 0.40
A n)
0.35

0.30 m


V
VV


0.2 --I 0.15
-0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 -0.04-0.02 0.00 0.02 0.04 0.06 0.08 0.10

RGR (g g-1 week-1) RGR (g g-1 week-l)

Figure 2-2. The overall relationship between relative growth rate (RGR) and growth variables for
all treatments. A) Net assimilation rate. B) Leaf area ratio. C) Specific leaf area. D)
Leaf mass ratio. E) Root mass ratio. F) Stem mass ratio. Mean values over growth
period of 26 weeks. Spearman's rank correlations estimated and significance based on
(p<0.05). See Table 2-1 for species coding.


0.008


0.006


0.004


0.002


0.000


-0 002


A
A







aa
r, =0.96, p < 0.001



0 ^
-A
oo




































































-0.002 -
-0.04


100%


NAR; rs = 0.78, p= 0.008
LAR; rs = 0.38, ns


0.008


0.006


0.004


0.002


0.000


-0.002

0.008


0.006


0.004


0.002


0.000


-0.002

0.008


0.006


0.004


0.002


0.000


-0.02 0.00 0.02 0.04 0.06


* Ceiba
* Coco
v Cortez
A Guaya
* Roble
* Ceiba
o Coco
v Cortez
A Guaya
o Roble


0.08 0


80


70


60


50 E

40


30


20

80


-70


-60


50 E

40


-30


20


80


70


60


50 E


40


30


20
.10


RGR (g g-1 week-l)
Figure 2-3. Relationship between mean values over growth period of 26 week for relative growth
rate (RGR) and net assimilation rate (NAR) -dark symbols, and leaf area ratio (LAR)
-white symbols, for each light treatment (100%, 37% and 2%). Spearman's rank
correlations estimates and significance based on (p<0.05).


V

* I


NAR; rs = 0.95, p < 0.001
37% LAR; rs = -0.70,p < 0.05


0

0

A
A
S*
*
A
.. A


* NAR; rs= 0.91, p< 0.001
V% LAR; rs = -0.62, p= 0.06

S '

A

A





vt











1800
1600 A B W 100%
1400 W 37%
E 1200
1000
S800
S600
400
200
0- ,il A Ai J
120 C D
100
) 80
o 60
S40
20

500 E F

400

E 300

1 200



0
Ceiba Coco Cortez Guaya Roble Ceiba Coco Cortez Guaya Roble
Short Species Tall

Figure 2-4. Growth variables: Leaf area, leaf area ratio (LAR) and specific leaf area (SLA) for
each species grown under low (2%), medium (37%), and high (100%) light condition
in each of the grass treatments, short and tall. A) Leaf area short grass. B) Leaf area
tall grass. C) Leaf area ratio short grass. D) Leaf area ratio tall grass. E) Specific leaf
area short grass. F) Specific leaf area tall grass. Mean values and standard errors
obtained from final harvest. See Table 2-1 for species coding.























0.2


0.0
0.8 -C E F


0.6
0)

r 0.4
C/)
0.2

0.0
0.8 1E FI


0.6


0.4


0.2


0.0
Ceiba Coco Cortez Guaya Roble Ceiba Coco Cortez Guaya Roble
Short Species Tall

Figure 2-5. Growth variables: Root mass ratio (RMR), stem mass ratio (SMR) and leaf mass
ratio (LMR) for each species grown under low (2%), medium (37%), and high
(100%) light condition in each of the grass treatments, short and tall. A) Root mass
ratio short grass. B) Root mass ratio tall grass. C) Stem mass ratio short grass. D)
Stem mass ratio tall grass. E) Leaf mass ratio short grass. F) Leaf mass ratio tall
grass. Mean values and standard error obtained from final harvest. See Table 2-1 for
species coding.









CHAPTER 3
PHOTOSYNTHETIC RESPONSES OF TROPICAL TREE SEEDLINGS IN AN
ABANDONED PASTURE UNDER GRASS COMPETITION AND VARIED LIGHT
REGIMES

Introduction

The growth of tropical tree seedlings in particular light environments (Lambers et al.,

1998), and their ability to adapt to changes, depends on the complex interaction of morphological

and physiological attributes of each species (Garwood, 1996). Therefore, the ability of a species

to acquire and utilize light is an important factor in their competitive ability (Chazdon et al.,

1996). Learning more about their physiological demands will give us some insights as to when

and how to introduce species into a pasture. For example, Loik and Holl (1999) experimented

with several species of tree seedlings under remnant pasture trees and an open pasture in

southern Costa Rica. They found higher levels of photosynthetic rates in open pastures than

under remnants; however growth was higher under remnants.

In order to describe, in a quantitative fashion, how plants utilize light for carbon fixation, a

light-response curve is often used. This curve is a non-linear function where net photosynthesis

(A) is the response variable and the estimated parameters allow representing key stages of

photosynthesis, as follows: In dark; that is, in zero photosynthetic active radiation (PAR), there is

net-CO2 release due to respiration. As light intensity increases, net-CO2 release is gradually

reduced until the light compensation point (LCP) is attained, where net-CO2 exchange is zero

because photosynthetic CO2-uptake increases until light saturation is reached. At this point, the

rate of CO2 assimilation levels off. Any further increase in the amount of light striking the leaf

does not cause an increase in the rate of photosynthesis-the amount of light is said to be

'saturating' for the photosynthetic process. This light-saturation point often is hard to determine

precisely, because light saturation is approached gradually. Alternatively, the asymptote of net









assimilation at high light (Amax) is estimated. Finally, the slope of the nearly linear part of the

curve below saturation gives the apparent quantum yield (Aqe: mol CO2 per mol photons) of

photosynthesis.

Understanding species photosynthesis and growth performances under different light

scenarios will help us determine their optimal light requirements. Based on these findings, we

can then suggest the ideal setting or sequence in which these species should be introduced in a

restoration project.

The objectives of this study were (1) to estimate light response curve parameters:

maximum net assimilation (Amax), apparent quantum yield (Aqe), and light compensation point

(LCP) under three different light environments and two levels of competition; (2) to determine

relative growth rates (RGR) of seedlings under three different light environments and two levels

of competition; and (3) to establish the sequence in which the native species should be planted in

a pasture based on the previous characteristics.

We hypothesized that light response curve parameters will display the following

relationship with seedlings light demand: the higher the seedlings light demand, the higher the

maximum net assimilation (Amax), the higher the light compensation point (LCP), and the lower

the apparent quantum yield (Aqe) Also, seedling grown under Short-grass competition should all

have maximum net assimilation (Amax) higher than under Tall-grass competition.

Materials and Methods

Study Site

This study was conducted in the pastures of the Santa Ana Conservation Center in Costa

Rica, a protected spot in the outskirts of San Jose (90 56'26.00" N 840 11'44"10W), in the

middle of the fastest urban expansion front in the country. According to the Holdridge

Classification, the Santa Ana Conservation Center is located on a Premontane Wet Forest Life









Zone. The climate is seasonal, with a dry season that extends from early December to the end of

April. The average annual rainfall is 2,467 mm and the average annual temperature is 23.4 C.

There are two rainfall peaks: one at the beginning of the rainy season, in May, and the second at

the end of the rainy season between October and November. The average maximum

temperatures occur during the dry season.

Experimental Design

In the pastures of the Santa Ana Conservation Center, two grass competition regimes were

selected by creating 18 (3.5 x 3.5 meter) plots in each of two areas: one dominated by

Hyparrhenia rufa (Nees) Stapf (Tall-grass) and another dominated by Cynodon mlenfluensis

Vanderyst (Short-grass), both of which had been hand machete once before planting to a height

of about 15cm. These grasses are native to tropical Africa. C. mlenfluensis is a stoloniferous

perennial without underground rhizomes, which can reach heights of 100 cm and H. rufa, also

perennial, can reach heights that range from 60-240cm (Skerman and Riveros, 1990). In each

area three light environments were created: low (2% light), medium (37% light) and high (100%

light). Plots were separated by 3 meters to prevent neighbor shading. For the shaded treatments

shade houses 2.5 meters tall were constructed to cover the entire plot using neutral shade-cloth

(63% and 98% shade). Each light environment x grass competition treatment was replicated six

times for a total of 36 plots. Therefore each plot was a sampling unit for this experiment.

Six species pertaining to different natural life histories were used; light demanding:

Pseudosamanea guachapele (Kunth) Harms (Fabaceae), Tabebuia impetiginosa (Mart. Ex DC.)

Standley (Bignoniaceae), Ceibapentandra (L.) Gaertn. (Bombacaceae); shade tolerant:

Bombacopsis quinatum (Jacq.) Dugand. (Bombacaceae), and intermediate: Dalbergia retusa

Hemsl. (Fabaceae), Tabebuia rosea (Bertol.) DC. (Bignoniaceae) (See Table 2-1). Six









individuals of each species were directly planted into the ground in a row under each treatment

(light environment and grass competition) for a total of 36 individuals per plot. Species were

assigned a row by systematically rotating their position within the plot. Tree seedlings were

provided by the Santa Ana Conservation Center nursery, located in the same area. All seedlings

were grown under 40% shade in plastic bags (9cm x 18cm) for 11 months. They were planted

during the first two weeks of the experiment starting the last week of May 2005 directly into the

ground in holes of about 20cm in diameter. One week after planting, damaged seedlings were

replaced.

Photosynthesis

At least six individuals from each treatment (grass, light and species) were randomly

selected and a photosynthetic light-response curve was determined for the third most fully

developed leaf from the apical meristem of each individual in the sample. If the leaf was missing

or damaged, the second most fully developed leaf was used. B. quinatum was not taken into

consideration, due to the fact that in some of the treatments individuals had lost all their leaves at

the time of measurements. Measurements were done during the period of December 2005 and

January 2006 using an open-mode portable photosynthesis system (Li-6400, Li-Cor, Inc.

Lincoln, Nebraska, USA). Using a light-emitting diode (LED) light source, ten light

(Photosynthetically Active Radiation, PAR) intensities were set: 0, 25, 50, 100, 200, 500, 800,

1,000, 1,500, and 2,000 tmol m 2 s 1, starting from the lowest and then increasing every 2

minutes. The leaf chamber was controlled to a CO2 concentration of 380 .imol mol1, the

temperature of the block chamber was set at 26 C, and air flow was maintained at 500 [tmol s-1

Growth

The relative growth rate (RGR) was calculated following the "classical" method









(lnMf -InM)
(T)

over the growth period (Hunt et al., 2002). Where M is total dry mass of seedling (g) and T is

time (weeks). Subscripts refer to initial (i) final (f) harvest. (Table 2-2).

Analysis

Model Fitting

A nonlinear mixed model was used in our analysis. Nonlinear mixed models allow both

fixed (treatments) and random (experimental unit) effects to have a nonlinear relationship to the

response variable (Wolfinger, 2000). In addition, fitting an appropriate nonlinear model lends

biological meaning to estimated parameters (Peek, Russek-Cohen, Wait, and Forseth, 2002). For

our experiment, our fixed effect treatment design was a 2x3 factorial with two grass competitions

(short and tall) and three light levels (100%, 37% and 2%), for each species separately. We took

repeated measurements of net photosynthesis on the same leaf at different light levels, which

requires the incorporation of a random effect for each individual. For each treatment a light-

response curve was fitted to a nonlinear Mitscherlich model equation (Peek et al., 2002).


A=Amax 1-e -Aq(PAR-LCP)]


Where Amax is the asymptote of photosynthesis at high light, Aqe is the apparent quantum

yield, LCP is light compensation point, PAR is the photosynthetic active radiation and A is net

photosynthesis, the response variable. We used a nonlinear mixed models procedure

(NLMIXED) (SAS Institute Inc., Cary, North Carolina USA) to fit curves to photosynthetic data

from each plant. Initial values of parameters to start the model iterations were obtained from the

NLIN procedure of SAS, ignoring random effects. The mean for the three parameters (Amax, Aqe









and LCP) for each treatment by species was compared using a multiple comparison procedure of

contrasts.

Growth and photosynthesis

Relative growth rate (RGR) mean values over the growth period and net photosynthetic

rate (Amax) for each treatment (grass, light and species) were compared using a correlation

analysis to determine relationships between the two variables. A nonparametric Spearman's rank

correlation coefficients (rs) were used because it is robust to sample distribution problems.

Results

Photosynthesis

Based on the estimated parameters obtained for the model, C. pentandra had the highest

Amax in the Short 37%, Tall 100% and Tall 37% treatments, but was significantly lower for the

Short 100%, Short 2% and Tall 2% (Table 3-1 and Figure 3-2). D. retusa had the highest Amax in

the Short 100%, Short 37%, Tall 37%, and the lowest values in the Tall 100%, Tall 2% and Short

2% (Table 3-1 and Figure 3-1). P. guachapele had the highest Amax in Short 37% and 100% and

the lowest at the Tall 2% and Tall 100% (Table 3-1 and Figure 3-1). T. impetiginosa and T. rosea

did not differ at any of the treatments.

Regarding the parameters Aqe and LCP, for all species they did not differ significantly

between treatments.

Growth and Photosynthesis

Overall there was a positive relationship (r,=0.49; p=0.007) between relative growth rate

and Amax when all species were combined (Figure 3-3). However when each species was

analyzed individually, there was a positive significant relationship for T. impetiginosa (rs=0.83;









p=0.04), P. guachapele (r,=0.83; p=0.04), and T rosea (r,=0.89; p=0.02) and no trend was

observed for C. pentandra and D. retusa.

Discussion

Photosynthesis

There are tradeoffs between persistence in low light and adaptations for exploiting high

light environments. Species in general do well in one environment and not in both. However

there are some species that can do well in both such as C. pentandra (Bazzaz, 1996). Studies of

photosynthetic responses of tropical species have shown that pioneer species show a larger

acclimation to light conditions than shade tolerant (Davidson, Mauffette, and Gagnon, 2002;

Holscher, Leuschner, Bohman, Juhrbant, and Tjitrosemito, 2004; Tinoco-Ojanguren and Pearcy,

1995) Ama of pioneers in low light conditions is about 4-6 [tmol CO2 m 2 s-1 and at high light

11-22 [tmol CO2 m 2 s-1 and for their shade tolerant species at low light ranges 2-4 [tmol CO2

m 2 s-1 and at high light 2-6 Inmol CO2 m 2 -1.

Photosynthetic performances of our species were not as expected. Light demanding species

T. impetiginosa (Cortez) and intermediate species T. rosea (Roble) did not vary with respect to

all photosynthetic parameters for each treatment (Table 3-1). This could be due to the fact that

initial seedling height (Table 2-1) was not sufficient to overcome the aboveground competition.

Therefore, seedlings experienced low light levels regardless of light treatment. Holl (2002)

measured light levels under grass canopies and found that light can be as low as 5 [mol m 2 S-1

and was most likely the factor which contributed to low growth of their seedlings.

Seedlings with taller initial heights did show a response to light treatment. Light

demanding species C. pentandra, P. guachapele and intermediate species D. retusa had highest

Amax in high light (100%) and intermediate light (37%). The potential maximum photosynthetic









capacity (Amax) often is not obtained due to limiting resources such as water, nutrients,

herbivore damage and internal feedback mechanisms (Bazzaz, 1996).

The parameters apparent quantum yield (Aqe) and light compensation point (LCP) for all

species did not differ significantly between treatments. However this only represents leaf level

traits and does not take into account other plant tissues physiological activities that influence

whole plant carbon economy (Givnish, 1988).

Growth and Photosynthesis

Photosynthesis parameters have been correlated with growth parameters (Zipperlen and

Press, 1996). In this study the only varying photosynthetic parameter was Amax for C.

pentandra, P. guachapele and D. retusa. When relating Amax to relative growth rate we found

that there was no relationship for D. retusa and C. pentandra. However, P. guachapele, T

impetiginosa and T rosea exhibited significant relationships between Amax and RGR. It must be

recalled though, that correlation simply shows how the parameters move when observed

together; it does no represent a cause-effect relationship. The way they move together may be

determined by other factors. In fact, photosynthetic measurements are not the only determinant

of dry matter gain. Biomass partitioning, and factors which control assimilate distribution may

therefore be more diagnostic of the ecological status of a species than its rate of carbon dioxide

fixation per unit area of leaf (Press, Brown, Baker, and Zipperlen, 1996; Zipperlen and Press,

1996).

Conclusions

P. guachapele was the best performer with the highest Amax and relative growth rates. We

recommend planting this species as an initial step in reforesting pastures. As for the other

species, it is hard to determine their performance solely based on their Amax and relative growth

rates. Further research is needed to understand why species performances varied and what are









the factors controlling this variation other than the photosynthetic capacities. Also it will be

important to see how P. guachapele shade and competition will affect grasses and other

seedlings once it establishes a canopy. Seedling height may also be a factor one should consider

when planting in a pasture and should be examined for each species.










Table 3-1. Estimated parameters of photosynthesis model: light saturated point (Amax), apparent
quantum yield (Aqe), and light compensation point (LCP). Means and standard errors.


See Table 2-1
Treatment


Tall 100%
Tall 37%
Tall 2%
Short 100%
Short 37%
Short 2%

Tall 100%
Tall 37%
Tall 2%
Short 100%
Short 37%
Short 2%

Tall 100%
Tall 37%
Tall 2%
Short 100%
Short 37%
Short 2%

Tall 100%
Tall 37%
Tall 2%
Short 100%
Short 37%
Short 2%

Tall 100%
Tall 37%
Tall 2%
Short 100%
Short 37%
Short 2%


for species coding.
Amax


7.91+2.35ab
9.5 02.71a
5.212.74b
4.873.24b
10.452.84a
5.462.69b

4.491.70ab
6.15+2.07ab
4.292.18b
9.242.08a
7.33+2.19ab
3.682.19b

5.251.53a
4.832.02a
3.812.20a
5.451.91a
6.972.00a
3.882.14a

5.411.29cd
9.181.51bc
5.071.91d
12.611.86ab
14.181.85a
7.441.91cd
-----------------
7.361.52a
7.742.08a
4.991.82a
8.832.23a
8.3 92.31a
4.602.30a


-Ceiba--------------
0.00210.001 1a
0.0026ti.0015a
0.0070t0.0051a
0.0017ti.0023a
0.0039i.0016a
0.0064ti.0045a
-Coco-----------
0.0018ti.0021a
0.0071i.0037a
0.0080i.0061a
0.0051i.0026a
0.0075ti.0034a
0.0079ti.0076a
.Cortez---------------
0.0021i3.0018a
0.0049ti.0041a
0.0032ti.0054a
0.0049ti.0034a
0.00600.003 1a
0.0042:i.0066a
.Guaya---------
0.0027ti.0014a
0.0039Mi.0022a
0.0030i.0036a
0.0028ti.0015a
0.0042ti.0017a
0.0039ti.0029a
-Roble------------------
0.002300.0011a
0.0050ti.0027a
0.0062i.0041a
0.0035ti.0020a
0.0059ti.0022a
0.0032ti.0065a


LCP
-----------
-12.4250.03a
2.6955.77a
-7.2659.18a
3.56:93.63a
19.7751.79a
-5.1559.01a
----------
-50.67121.30a
17.30121.99a
5.25123.50a
20.52121.88a
11.00121.98a
0.36125.17a
-----------
-13.5859.69a
10.8466.89a
-31.01124.86a
19.6263.31a
14.8461.69a
-12.62100.23a
-----------
30.8224.93a
14.0331.56a
-33.3087.38a
24.6729.31a
23.9926.95a
-10.14--4.80a
-----------
14.3734.73a
9.2339.71a
-6.9645.48a
7.8342.50a
13.1937.18a
-42.59163.74a


Note: Different superscript letters assigned to means in the same column designate statistically
significant differences at the 0.05 level; the same letters indicate that no statistically significant
differences exist.














14 Coco Coco 100%
12 ..... 37%
10 -2%
8 1.
,...................................
6

2 -,- .-
2 Short Tall
0 --- ---f
-2 I I I I I I I I I
14 Guaya ............................ Guay a
14 Guaya ..- Guaya
12- .
10
8 .....-........................
8,.
6
S4
E 2 Short Tall
0
E 0
-2 I I I
14 Cortez Cortez
S12
10
S8
z ..............................
6 -- -
4- ..
2 Short Tall


-2
14 Roble Roble
12
10
8 ......... ....................
6 -
4
2 Short Tall


-2 I I I I I I I I I
0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500

Incindent PPF (pmol m-2 s-1)
Figure 3-1. Light response curves fitted with nonlinear Mitscherlich model equations from
parameter estimates obtained from nonlinear mixed models analysis using SAS. See
Table 2-1 for species coding.


















Ceiba Ceiba -- 1%
*..... 37%
2%
S. .. ........ ...............
.. ... ...............




F T/

Short Tall

I I I I I


0 500 1000 1500 2000 2500 0


500 1000 1500 2000 2500


Incindent PPF (pmol m-2 -1)


Figure 3-2. Light response curves fitted with nonlinear Mitscherlich model equations from
parameter estimates obtained from nonlinear mixed models analysis. See Table 2-1
for species coding.





















0.10

R, = 0.49; p< 0.007
0.08 -


0.06
V
V

r 0.04 O
ry O

( 0.02-

O v
0.00 V* Ceiba
Ot 0 0 Coco
v Cortez
-0.02 v Guaya
Roble

-0.04 -
2 4 6 8 10 12 14 16

Amax(pmoI m-2 -1)


Figure 3-3. Relationship between net photosynthesis and relative growth rate for all species.









CHAPTER 4
SUMMARY AND CONCLUSION

One of the main factors influencing the successional trajectory of abandoned pastures

into forest communities is seed dispersal. Since pastures have little or no woody vegetation, the

majority of woody species that colonizes abandoned pastures are wind-dispersed (Finegan and

Delgado, 2000; Holl, 2002; Holl et al., 2000; Toh et al., 1999; Zimmerman et al., 2000). Those

species that eventually colonize have to undergo seed predation, unfavorable conditions for

germination, and intense competition once they germinate (Camargo et al., 2002; Holl and

Lulow, 1997; Holl et al., 2000; Wijdeven and Kuzee, 2000).

In order to aid the restoration of abandoned tropical pastures into forested ecosystems, we

must overcome some of these barriers through manipulative efforts. In doing so, we need to learn

more about the performance of native tree species in these extreme environments.

The present study was designed to characterize the light requirements of six native tree

species under contrasting light environments and grass competition. Understanding their early

establishment requirements could be used in selecting proper light and competition regimen for

the success of restoring a pasture after abandonment.

Based on our morphological and physiological findings, overall, Pseudosamanea

guachapele was the best performer under open pasture conditions. We recommend planting this

species as an initial step in reforesting pastures. Once this species is established the canopy may

produce enough shade to reduce grass cover. Possibly under this scenario, Tabebuia rosea,

Tabebuia impetiginosa and Ceibapentandra can be planted as a second step in the restoration

process. However, C. pentandra may be affected by competition with grasses and/or P.

guachapele presence.









Further research is needed to understand why species performances varied and what are

the factors controlling this variation other than light. It is also important to examine how P.

guachapele canopy shade and competition will affect grasses and other seedlings once it

established. It seems that seedling height may also become an important factor needed to

overcome pasture competition and should be more closely examined by each species.









APPENDIX
SOIL NUTRIENTS


Table A-1. Soil nutrient levels.
pH/ water K Ca


Mg P Fe Cu Zn Mn


------------cmol+/L------------ -------------ppm--------
5.5 0.17 15.7 8.65 4.7 67 15 2.6 21









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BIOGRAPHICAL SKETCH

Gerardo Celis was born in Costa Rica. At the age of four, his entire family moved to the

US where his father was to pursue his graduate studies in Resource Economics. When they

returned to Costa Rica, eight years later, he was fascinated by the rainforests and began to

develop an interest for the environment and for understanding the impact of humans on it. This

motivated him to pursue a career in this area.

As a first step, upon conclusion of High School, he initiated a program in Environmental

Studies at the University of British Columbia in Vancouver. After one year there, he then

returned to Costa Rica, where he completed his undergraduate studies in Biology at Universidad

Latina. His undergraduate research, entitled: "Seed germination of two sympatric palm species:

Chamaedorea tepejilote Liebm. and Chamaedorea Costaricana Oerst (Arecaceae) in natural

conditions and in a nursery," was the result of a pro bono collaboration with the National

Museum.

After concluding his undergraduate studies he taught Biostatistics at the same university

and was awarded a scholarship by the Organization for Tropical Studies (OTS) to participate in

the program Research Experiences for Undergraduates (REU) at La Selva Biological Station.

The research conducted was entitled: "Do patterns of seed germination and seedling biomass

allocation reflect a shade tolerance syndrome in Gnetum leybodii Tul. (GNETACEAE)?." Later

on, he became TA, under Professor Luis Diego G6mez, for OTS' course "Plantains, Iguanas and

Shamans: An Introduction to Field Ethnobiology."

At this point in his career, he felt that he needed to develop a broader understanding of

environmental processes by incorporating the interdisciplinary dimension; in particular, how

humans could help restore the environment. Thus, he decided to pursue a master's in

interdisciplinary ecology at the University of Florida (UF). In 2004, he obtained a 9-credits out-









of-state tuition exemption from the Florida-Costa Rica Linkage Institute (FLORICA). For the

second year he was awarded a fellowship by UF's Tropical Conservation and Development

Program (TCD) within the Center for Latin American Studies. His master's thesis was entitled:

"Restoring abandoned pasture land with native tree species in Costa Rica: An ecophysiological

approach to species selection" funded by the Compton's fellowship.

Upon completion of his master's degree he enrolled in a PhD in Urban restoration ecology

at the University of Florida. He plans to continue research in the area of urban restoration

ecology, teach courses at universities, become part of teams performing environmental impact

assessments and designing policy reforms, and develop community level activities. With the

information generated from his research, he wants to create programs that will help to establish a

better interpretation of the environmental impacts of urban expansions and to give a solid basis

for urban planning and policy design.