Plant functional trait variability of Carapaguianensis at different … · 2018. 10. 10. · Plant...
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Plant functional trait variability of Carapa guianensis at different microclimatic and elevation gradients Manuel R. Flores 1 , Abigail Keebler, Elizabeth M. Prior, Lia Gomez, Dr. Georganne Moore 1 1 Texas A&M University, Department of Ecosystem Science and Management • Funding for this Research Experiences for Undergraduate program is provided by the National Science Foundation’s Division of Earth Sciences (EAR-1659848). • The identification of all sample trees was aided Dr. Eugenio Gonzalez. • Special thanks to Dr. Alistair Shawcross, Ethan Kidd, and Ben Eaten for assisting in field work in Pocosol. • Stomatal Density Measurements and analysis were guided by Dr. Luiza Maria Aparecido. Introduction Tropical rainforests contain some of the highest biodiversity in the world, which can vary greatly in terms of topography, elevation, and climate. To understand how biodiversity changes along environmental gradients, convergent evolution has been studied extensively to identify key plant traits that vary along those gradients. Utilizing photosynthetic rates and stomatal density measurements, this project investigated physiological trait variation within a select tropical tree species, Carapa guianensis, that thrives in a wide range of elevations and climates within Central America in order to gain insight on how elevation gradients effect the physiology of a phenotypically plastic plant. Methods Study Sites TAMU Soltis Center in San Isidro, Costa Rica – Site chosen was secondary pre-montane tropical forest bordering the Children’s Eternal Rainforest at an elevation between 450 and 600 meters. Pocosol Biological Preserve – Site chosen was in primary forest within the Children’s Eternal Rainforest at an elevation of 900 meters. • Field Methods: - Scouting days were held for each site and possible sample trees were identified. - Li-6400XT was packed into a hard case along with batteries, backup equipment. - Once at site, sample trees and leaflets were chosen, flagged, and measured by running a light curve program that measured photosynthetic output of the plant while altering light intensity. - Sample trees with the least amount of canopy cover were chose and each Leaflet measured was fully expanded and green. - Each light curve cycled incrementally light levels ranging from 100 to 3000 - Desiccant was also scrubbed and bypassed in order to maintain a constant Relative Humidity (RH). - Once all light curves were completed, each leaflet measured was collected, stored in a labeled bag, and brought back to the Soltis Center lab. Results – Light Curve • Lab Methods: - Once back in the lab, every leaf collected had films created from them using clear nail polish and were taken back to Texas A&M to be analyzed. - Once back at A&M all adequate light curve data was run through non- linear regression model derived from (Lobo, De Barros et al. 2013, Kaipiainen 2009). - Additionally, stomatal films were selected from each sample site and were scaled and analyzed using FIJI ImageJ with pictures taken with a Zeiss Axiophot microscopy instrument (Aparecido, Miller et al. 2017, Hilu and Randall 1984). Upon viewing our results, most light curves sampled at the Pocosol Biological Preserve were unusable due to negative readings, but did have some usable data. After unreliable data was omitted the rest of the data was run through the following model: Where: • P N = net photosynthesis rate • P gmax = maximum gross photosynthesis rate • I = photosynthetic photon flux density • I 50 = light saturation point • R D = dark respiration rate (Lobo, De Barros et al. 2013, Kaipiainen 2009). Results from this model yielded consistently higher rates of photosynthesis within Soltis Center Sample Trees than in Pocosol. Additionally photosynthetic peaks were also consistently higher in the Soltis Center aside from a select few curves and can be also be seen as depicted in fig. 9. Fig. 5 Sample taken from the leaflet of a Pocosol sample tree. Fig. 6 Sample taken from the leaflet of a Soltis Center sample tree. Fig. 9 All Usable data from both sights overlaid on the same graph depicting 15 points of light and respective photosynthetic output for that point. Results- Stomatal Density Images taken from the nail polish films yielded significantly higher stomatal density counts in all sample trees analyzed from the Soltis Center than from the Pocosol site. Additionally, ocular observations seem to suggest significant differences in size as depicted by figures 3 and 4. These results have been consistent with other studies and are believed to be an adaptive responses to light intensity (Camargo and Marenco 2012). Results – Temperature Measurements Leaf temperatures recorded from the Li-6400XT depict minimal variance in leaf temperature between either sites. This suggests that leaf temperature itself does not have significant effects on the photosynthetic output of the sample trees we studied. Fig. 7 Averages Stomatal Density of 3 separate sample trees taken from both the Soltis Center and Pocosol Site. Fig. 10 Leaf Temperature plotted against photosynthetic output. = × + 50 − Fig 10. Output table of all calculated variables from the non-linear regression model. Discussion Due to this study’s focus on light curves and photosynthetic rates, the complications that were encountered with obtaining adequate measurements at the Pocosol site, can still potentially provide us with insight. If weather data is collected at both sites and it is found that the Pocosol site receives higher precipitation and lower solar radiation, the unusable data could potentially be attributed to unsuitable environmental conditions for photosynthesis. This would also further support the stomatal density findings of both this study and (Camargo and Marenco 2012). As for the light curves that were used, results do seem consistent but it should be noted that at least one curve taken from each site selected did depict a noticeably large sum of square error, which suggest that using an alternative model to analyze the data may become necessary for future use. Fig. 1 Li-6400XT running through a light curve on a sample tree at the Pocosol Site. Fig. 2 IRGA Extension of the Li-Unit being adjusted just before a measurement. Fig. 3 Leaves are carefully painted on their adaxial surface with nail polish. Fig. 4 The films taken from Costa Rica being analyzed through a Zeiss Axiophot light microscope. Acknowledgements: Conclusions • Photosynthetic rates were higher at lower elevations than at higher elevations • Stomatal Density was higher at lower elevations, and size appears to vary significantly between sites. • Leaf Temperature had no significant effect on photosynthetic rate within the ranges tested for either sites. Parameter Unity Soltis 111 Soltis112 Soltis121 Soltis122 Soltis211 Soltis212 Soltis221 Soltis222 Soltis311 Soltis312 Soltis321 Soltis322 Pocosol111 Pocosol112 Pocosol221 Pocosol222 P gmax = µmol (CO 2 ) m -2 s -1 9.10 11.83 10.80 10.92 13.74 19.46 4.04 3.79 7.77 8.63 6.64 6.41 6.78 2.00 5.20 4.01 I (50 ) = µmol (photons) m -2 s -1 129 102 173 105 800 216 96 104 50 91 82 86 50 119 69 50 R D = µmol (CO 2 ) m -2 s -1 0.00 1.60 0.00 1.39 1.33 1.83 0.00 0.00 2 0 0 0 0.75 2.69 0 0 I comp = µmol (photons) m -2 s -1 0 16 0 15 86 23 0 0 13.91 0 0 0 6.18 -463.09 0 0 I sat(50 ) = µmol (photons) m -2 s -1 129 134 173 135 972 261 96 104 77.81 90.63 82.29 85.73 62.36 -807.62 69.37 50.00 I sat(85) = µmol (photons) m -2 s -1 731 687 982 695 5106 1376 545 589 376 514 466 486 325 -2415 393 283 I sat(90 ) = µmol (photons) m -2 s -1 1160 1081 1560 1095 8059 2173 866 935 589 816 741 772 512 -3564 624 450 I sat(95) = µmol (photons) m -2 s -1 2449 2265 3293 2294 16918 4562 1829 1973 1228 1722 1564 1629 1074 -7009 1318 950 I max = µmol (photons) m -2 s -1 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 P N(I max) = µmol (CO 2 ) m -2 s -1 8.53 9.64 9.92 8.98 8.41 15.69 3.85 3.60 5.89 8.25 6.37 6.14 5.87 -0.80 5.02 3.91 φ (Io) = µmol (CO 2 ) µmol -1 (photons) 0.07 0.12 0.06 0.10 0.02 0.09 0.04 0.04 0.16 0.10 0.08 0.07 0.14 0.02 0.07 0.08 φ (I comp) = µmol (CO 2 ) µmol -1 (photons) 0.07 0.09 0.06 0.08 0.01 0.07 0.04 0.04 0.10 0.10 0.08 0.07 0.11 0.00 0.07 0.08 φ (I o_I comp) = µmol (CO 2 ) µmol -1 (photons) NAN 0.10 NAN 0.09 0.02 0.08 NAN NAN 0.12 NAN NAN NAN 0.12 NAN NAN NAN φ (I comp_I 200) = µmol (CO 2 ) µmol -1 (photons) 0.03 0.03 0.03 0.03 0.01 0.04 0.01 0.01 0.02 0.03 0.02 0.02 0.02 0.01 0.02 0.01 0.91 0.59 1.48 1.16 6.52 0.39 1.87 1.17 0.57 1.21 1.04 0.89 2.34 0.66 0.55 22.03 Quantum yield at specific I SSE Parameters and variables estimates Soltis Center Site Trees Pocosol Site Trees Light compensation point Light saturation point Light-saturated net CO 2 uptake References Aparecido, L. M., G. R. Miller, A. T. Cahill, and G. W. Moore. 2017. Leaf surface traits and water storage retention affect photosynthetic responses to leaf surface wetness among wet tropical forest and semiarid savanna plants. Tree physiology 37:1285-1300. Camargo, M. A. B., and R. A. Marenco. 2012. Growth, leaf and stomatal traits of crabwood (Carapa guianensis Aubl.) in central Amazonia. Revista Árvore 36:07-16. Hilu, K. W., and J. L. Randall. 1984. Convenient method for studying grass leaf epidermis. Taxon:413-415. Iluz, D., I. Alexandrovich, and Z. Dubinsky. 2012. The enhancement of photosynthesis by fluctuating light. Artificial Photosynthesis. InTech. Kaipiainen, E. 2009. Parameters of photosynthesis light curve in Salix dasyclados and their changes during the growth season. Russian journal of plant physiology 56:445-453. Lobo, F. d. A., M. De Barros, H. Dalmagro, Â. Dalmolin, W. Pereira, É. de Souza, G. Vourlitis, and C. R. Ortíz. 2013. Fitting net photosynthetic light-response curves with Microsoft Excel—a critical look at the models. Photosynthetica 51:445-456. Fig. 8 Light curve concept with labeled parameters (Iluz et al. 2012)
Transcript of Plant functional trait variability of Carapaguianensis at different … · 2018. 10. 10. · Plant...
Plant functional trait variability of Carapa guianensis at different microclimatic and elevation gradients
Manuel R. Flores1, Abigail Keebler, Elizabeth M. Prior, Lia Gomez, Dr. Georganne Moore1
1Texas A&M University, Department of Ecosystem Science and Management
• Funding for this Research Experiences for Undergraduate program is provided by the National Science Foundation’s Division of Earth Sciences (EAR-1659848).
• The identification of all sample trees was aided Dr. Eugenio Gonzalez.• Special thanks to Dr. Alistair Shawcross, Ethan Kidd, and Ben Eaten for
assisting in field work in Pocosol.• Stomatal Density Measurements and analysis were guided by Dr. Luiza Maria
Aparecido.
Introduction Tropical rainforests contain some of the highest biodiversity in the world, which can vary greatly in terms of topography, elevation, and climate. To understand how biodiversity changes along environmental gradients, convergent evolution has been studied extensively to identify key plant traits that vary along those gradients.Utilizing photosynthetic rates and stomatal density measurements, this project investigated physiological trait variation within a select tropical tree species, Carapa guianensis, that thrives in a wide range of elevations and climates within Central America in order to gain insight on how elevation gradients effect the physiology of a phenotypically plastic plant.
Methods
Study SitesTAMU Soltis Center in San Isidro, Costa Rica – Site chosen was
secondary pre-montane tropical forest bordering the Children’s Eternal Rainforest at an elevation between 450 and 600 meters.
Pocosol Biological Preserve – Site chosen was in primary forest within the Children’s Eternal Rainforest at an elevation of 900 meters.
• Field Methods:- Scouting days were held for each site and possible sample trees were identified. - Li-6400XT was packed into a hard case along with batteries, backup
equipment.- Once at site, sample trees and leaflets were chosen, flagged, and
measured by running a light curve program that measured photosynthetic output of the plant while altering light intensity.
- Sample trees with the least amount of canopy cover were chose and each Leaflet measured was fully expanded and green.
- Each light curve cycled incrementally light levels ranging from 100 to 3000
- Desiccant was also scrubbed and bypassed in order to maintain a constant Relative Humidity (RH).
- Once all light curves were completed, each leaflet measured was collected, stored in a labeled bag, and brought back to the Soltis Center lab.
Results – Light Curve
• Lab Methods: - Once back in the lab, every leaf collected had films created from them
using clear nail polish and were taken back to Texas A&M to be analyzed.
- Once back at A&M all adequate light curve data was run through non-linear regression model derived from (Lobo, De Barros et al. 2013, Kaipiainen 2009).
- Additionally, stomatal films were selected from each sample site and were scaled and analyzed using FIJI ImageJ with pictures taken with a Zeiss Axiophot microscopy instrument (Aparecido, Miller et al. 2017, Hilu and Randall 1984).
Upon viewing our results, most light curves sampled at the Pocosol Biological Preserve were unusable due to negative readings, but did have some usable data. After unreliable data was omitted the rest of the data was run through the following model:
Where:
• PN = net photosynthesis rate• Pgmax = maximum gross photosynthesis rate• I = photosynthetic photon flux density• I50 = light saturation point• RD = dark respiration rate(Lobo, De Barros et al. 2013, Kaipiainen 2009).
Results from this model yielded consistently higher rates of photosynthesis within Soltis Center Sample Trees than in Pocosol. Additionally photosynthetic peaks were also consistently higher in the Soltis Center aside from a select few curves and can be also be seen as depicted in fig. 9.
Fig. 5 Sample taken from the leaflet of a Pocosol sample tree.
Fig. 6 Sample taken from the leaflet of a Soltis Center sample tree.
Fig. 9 All Usable data from both sights overlaid on the same graph depicting 15 points of light and respective photosynthetic output for that point.
Results- Stomatal DensityImages taken from the nail polish films yielded significantly higher stomatal density counts in all sample trees analyzed from the Soltis Center than from the Pocosol site. Additionally, ocular observations seem to suggest significant differences in size as depicted by figures 3 and 4. These results have been consistent with other studies and are believed to be an adaptive responses to light intensity (Camargo and Marenco 2012).
Results – Temperature MeasurementsLeaf temperatures recorded from the Li-6400XT depict minimal variance in leaf temperature between either sites. This suggests that leaf temperature itself does not have significant effects on the photosynthetic output of the sample trees we studied.
Fig. 7 Averages Stomatal Density of 3 separate sample trees taken from both the Soltis Center and Pocosol Site.
Fig. 10 Leaf Temperature plotted against photosynthetic output.
𝑃𝑃𝑁𝑁 = 𝐼𝐼 × 𝑃𝑃𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔
𝐼𝐼+𝐼𝐼50− 𝑅𝑅𝐷𝐷
Fig 10. Output table of all calculated variables from the non-linear regression model.
Discussion
Due to this study’s focus on light curves and photosynthetic rates, the complications that were encountered with obtaining adequate measurements at the Pocosol site, can still potentially provide us with insight. If weather data is collected at both sites and it is found that the Pocosol site receives higher precipitation and lower solar radiation, the unusable data could potentially be attributed to unsuitable environmental conditions for photosynthesis. This would also further support the stomatal density findings of both this study and (Camargo and Marenco 2012).
As for the light curves that were used, results do seem consistent but it should be noted that at least one curve taken from each site selected did depict a noticeably large sum of square error, which suggest that using an alternative model to analyze the data may become necessary for future use.
Fig. 1 Li-6400XT running through a light curve on a sample tree at the
Pocosol Site.
Fig. 2 IRGA Extension of the Li-Unit being adjusted just before a
measurement.
Fig. 3 Leaves are carefully painted on their adaxial surface
with nail polish.
Fig. 4 The films taken from Costa Rica being analyzed through a Zeiss
Axiophot light microscope.
Acknowledgements:
Conclusions • Photosynthetic rates were higher at lower elevations than at
higher elevations • Stomatal Density was higher at lower elevations, and size appears
to vary significantly between sites.• Leaf Temperature had no significant effect on photosynthetic rate
within the ranges tested for either sites.
Parameter Unity Soltis 111 Soltis112 Soltis121 Soltis122 Soltis211 Soltis212 Soltis221 Soltis222 Soltis311 Soltis312 Soltis321 Soltis322 Pocosol111 Pocosol112 Pocosol221 Pocosol222
P gmax = µmol (CO2) m-2 s-1 9.10 11.83 10.80 10.92 13.74 19.46 4.04 3.79 7.77 8.63 6.64 6.41 6.78 2.00 5.20 4.01
I (50) = µmol (photons) m-2 s-1 129 102 173 105 800 216 96 104 50 91 82 86 50 119 69 50R D = µmol (CO2) m
-2 s-1 0.00 1.60 0.00 1.39 1.33 1.83 0.00 0.00 2 0 0 0 0.75 2.69 0 0
I comp = µmol (photons) m-2 s-1 0 16 0 15 86 23 0 0 13.91 0 0 0 6.18 -463.09 0 0
I sat(50) = µmol (photons) m-2 s-1 129 134 173 135 972 261 96 104 77.81 90.63 82.29 85.73 62.36 -807.62 69.37 50.00I sat(85) = µmol (photons) m-2 s-1 731 687 982 695 5106 1376 545 589 376 514 466 486 325 -2415 393 283I sat(90) = µmol (photons) m-2 s-1 1160 1081 1560 1095 8059 2173 866 935 589 816 741 772 512 -3564 624 450I sat(95) = µmol (photons) m-2 s-1 2449 2265 3293 2294 16918 4562 1829 1973 1228 1722 1564 1629 1074 -7009 1318 950
I max = µmol (photons) m-2 s-1 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949 1949
P N(Imax) = µmol (CO2) m-2 s-1 8.53 9.64 9.92 8.98 8.41 15.69 3.85 3.60 5.89 8.25 6.37 6.14 5.87 -0.80 5.02 3.91
φ(Io) = µmol (CO2) µmol-1 (photons) 0.07 0.12 0.06 0.10 0.02 0.09 0.04 0.04 0.16 0.10 0.08 0.07 0.14 0.02 0.07 0.08φ(Icomp) = µmol (CO2) µmol-1 (photons) 0.07 0.09 0.06 0.08 0.01 0.07 0.04 0.04 0.10 0.10 0.08 0.07 0.11 0.00 0.07 0.08
φ(Io_Icomp) = µmol (CO2) µmol-1 (photons) NAN 0.10 NAN 0.09 0.02 0.08 NAN NAN 0.12 NAN NAN NAN 0.12 NAN NAN NAN
φ(Icomp_I200) = µmol (CO2) µmol-1 (photons) 0.03 0.03 0.03 0.03 0.01 0.04 0.01 0.01 0.02 0.03 0.02 0.02 0.02 0.01 0.02 0.010.91 0.59 1.48 1.16 6.52 0.39 1.87 1.17 0.57 1.21 1.04 0.89 2.34 0.66 0.55 22.03
Quantum yield at specific I
SSE
Parameters and variables estimates Soltis Center Site Trees Pocosol Site Trees
Light compensation point
Light saturation point
Light-saturated net CO2 uptake
ReferencesAparecido, L. M., G. R. Miller, A. T. Cahill, and G. W. Moore. 2017. Leaf surface
traits and water storage retention affect photosynthetic responses to leaf surface wetness among wet tropical forest and semiarid savanna plants. Tree physiology 37:1285-1300.
Camargo, M. A. B., and R. A. Marenco. 2012. Growth, leaf and stomatal traits of crabwood (Carapa guianensis Aubl.) in central Amazonia. Revista Árvore36:07-16.
Hilu, K. W., and J. L. Randall. 1984. Convenient method for studying grass leaf epidermis. Taxon:413-415.
Iluz, D., I. Alexandrovich, and Z. Dubinsky. 2012. The enhancement of photosynthesis by fluctuating light. Artificial Photosynthesis. InTech.
Kaipiainen, E. 2009. Parameters of photosynthesis light curve in Salix dasyclados and their changes during the growth season. Russian journal of plant physiology 56:445-453.
Lobo, F. d. A., M. De Barros, H. Dalmagro, Â. Dalmolin, W. Pereira, É. de Souza, G. Vourlitis, and C. R. Ortíz. 2013. Fitting net photosynthetic light-response curves with Microsoft Excel—a critical look at the models. Photosynthetica51:445-456.
Fig. 8 Light curve concept with labeled parameters(Iluz et al. 2012)
