Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-13T00:41:34.444Z Has data issue: false hasContentIssue false
  • English
  • Français

Relationships among phenology, climate and biomass across subtropical forests in Argentina

Published online by Cambridge University Press:  17 April 2018

Cecilia Blundo ,
Nestor I. Gasparri ,
Agustina Malizia ,
Matthew Clark ,
Genoveva Gatti ,
Paula I. Campanello ,
H. Ricardo Grau ,
Leonardo Paolini ,
Lucio R. Malizia  and
Sandra E. Chediack
...Show all authors
Cecilia Blundo*
Affiliation:
Instituto de Ecología Regional, Universidad Nacional de Tucumán. CONICET. Yerba Buena, Argentina
Nestor I. Gasparri
Affiliation:
Instituto de Ecología Regional, Universidad Nacional de Tucumán. CONICET. Yerba Buena, Argentina
Agustina Malizia
Affiliation:
Instituto de Ecología Regional, Universidad Nacional de Tucumán. CONICET. Yerba Buena, Argentina
Matthew Clark
Affiliation:
Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment and Planning, Sonoma State University, CA, USA
Genoveva Gatti
Affiliation:
Instituto de Biología Subtropical, Facultad de Ciencias Forestales, Universidad Nacional de Misiones. CONICET. Puerto Iguazú, Argentina
Paula I. Campanello
Affiliation:
Instituto de Biología Subtropical, Facultad de Ciencias Forestales, Universidad Nacional de Misiones. CONICET. Puerto Iguazú, Argentina
H. Ricardo Grau
Affiliation:
Instituto de Ecología Regional, Universidad Nacional de Tucumán. CONICET. Yerba Buena, Argentina
Leonardo Paolini
Affiliation:
Instituto de Ecología Regional, Universidad Nacional de Tucumán. CONICET. Yerba Buena, Argentina
Lucio R. Malizia
Affiliation:
Centro de Estudios Territoriales Ambientales y Sociales, Facultad de Ciencias Agrarias, Universidad Nacional de Jujuy. San Salvador de Jujuy, Argentina
Sandra E. Chediack
Affiliation:
Investigadora independiente. San Cristóbal de las Casas, Chiapas, México
Patricio MacDonagh
Affiliation:
Facultad de Ciencias Forestales, Universidad Nacional de Misiones. Eldorado, Argentina
Guillermo Goldstein
Affiliation:
Laboratorio de Ecología Funcional, Universidad de Buenos Aires. CONICET. Buenos Aires, Argentina
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract:

Phenology is a key ecosystem process that reflects climate–vegetation functioning, and is an indicator of global environmental changes. Recently, it has been suggested that land-use change and timber extraction promote differences in forest phenology. We use remote-sensing data to describe regional leaf phenological patterns in combination with field data from 131 plots in old-growth and disturbed forests distributed over subtropical forests of Argentina (54–65°W). We assessed how climate is related to phenological patterns, and analysed how changes in forest structural characteristics such as stock of above-ground biomass relate to the observed phenological signals across the gradient. We found that the first three axes of a principal component analysis explained 85% of the variation in phenological metrics across subtropical forests, ordering plots mainly along indicators of seasonality and productivity. At the regional scale, the relative importance of forest biomass in explaining variation in phenological patterns was about 15%. Climate showed the highest relative importance, with temperature and rainfall explaining Enhanced Vegetation Index metrics related to seasonality and productivity patterns (27% and 47%, respectively). Within forest types, climate explains the major fraction of variation in phenological patterns, suggesting that forest function may be particularly sensitive to climate change. We found that forest biomass contributed to explaining a proportion of leaf phenological variation within three of the five forest types studied, and this may be related to changes in species composition, probably as a result of forest use.

Keywords

biomassclimateenhanced vegetation indexMODISpermanent plotsphenologysubtropical forests
Type
Research Article
Information
Journal of Tropical Ecology , Volume 34 , Issue 2 , March 2018 , pp. 93 - 107
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

4

Centro de Estudios Ambientales Integrados, Universidad Nacional de la Patagonia San Juan Bosco, CONICET. Esquel, Argentina (Present address)

References

LITERATURE CITED

ARIAS, M. & BIANCHI, A. R. 1996. Estadísticas climatológicas de la provincia de Salta. Ministerio de la Producción y el Empleo, Dirección de Medio Ambiente y Recursos Naturales e INTA, Salta. 189 pp.Google Scholar
BIANCHI, A. R. & YÁÑEZ, C. 1992. Las Precipitaciones del Noroeste Argentino. INTA, Salta. 388 pp.Google Scholar
BLUNDO, C. & MALIZIA, L. R. 2009. Impacto del aprovechamiento forestal en la estructura y diversidad de la Selva Pedemontana. Pp. 387–405 in Brown, A. D., Blendinger, P., Lomáscolo, T. & García Bes, P. (eds). Selva Pedemontana de las Yungas: historia natural, ecología y manejo de un ecosistema en peligro. Ediciones del Subtrópico, Tucumán.Google Scholar
BLUNDO, C., MALIZIA, L. R., BROWN, A. D. & BLAKE, J. G. 2012. Tree species distribution in Andean forests: influence of regional and local factors. Journal of Tropical Ecology 28:83–95.Google Scholar
BLUNDO, C., MALIZIA, L. R. & GONZÁLEZ-ESPINOSA, M. 2015. Distribution of functional traits in subtropical trees across environmental and forest use gradients. Acta Oecologica 69:96–104.CrossRefGoogle Scholar
BRADLEY, B. A. & MUSTARD, J. F. 2008. Comparison of phenology trends by land cover class: a case study in the Great Basin, USA. Global Change Biology 14:334–346.Google Scholar
BRANDO, P. M., GOETZ, S. J., BACCINI, A., NEPSTAD, D. C., BECK, P. S. A. & CHRISTMAN, M. C. 2010. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proceedings of the National Academy of Sciences USA 107:14685–14690.Google Scholar
BROWN, A. D. 1995. Fitogeografía y conservación de las selvas de montaña del noroeste de Argentina. Pp. 663–672 in Churchill, S. P., Balslev, H., Forero, E. & Luteyn, J. L. (eds). Biodiversity and conservation of Neotropical montane forests. The New York Botanical Garden, New York.Google Scholar
BROWN, A. D., GRAU, H. R., MALIZIA, L. R. & GRAU, A. 2001. Argentina. Pp. 623–659 in Kappelle, M. & Brown, A. D. (eds). Bosques nublados del Neotrópico. Instituto Nacional de Biodiversidad, San José.Google Scholar
CABRERA, A. 1976. Regiones fitogeográficas argentinas. Enciclopedia Argentina de Agricultura y Jardinería, Editorial Acme, Buenos Aires. 85 pp.Google Scholar
CABRERA, A. & WILLINK, A. 1980. Biogeografía de América Latina. (Second edition). OEA, Washington, DC. 117 pp.Google Scholar
CAMPANELLO, P. I., GATTI, M. G., ARES, A., MONTTI, L. & GOLDSTEIN, G. 2007. Tree regeneration and microclimate in a liana and bamboo-dominated semideciduous Atlantic Forest. Forest Ecology and Management 252:108–117.CrossRefGoogle Scholar
CHAVE, J., ANDALO, C., BROWN, S., CAIRNS, M. A., CHAMBERS, J. Q., EAMUS, D., FÖLSTER, H., FROMARD, N., HIGUCHI, N., KIRA, T., LESCURE, J. P., NELSON, W., OGAWA, H., PUIG, H., RIERA, B. & YAMAKURA, T. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99.CrossRefGoogle ScholarPubMed
CHAVE, J., MULLER-LANDAU, H. C., BAKER, T. R., EASDALE, T. A., TER STEEGE, H. & WEBB, C.O. 2006. Regional and phylogenetic variation in woody density across neotropical tree species. Ecological Applications 16:2356–2367.CrossRefGoogle Scholar
CLARK, J. S., CARPENTER, S. R., BARBER, M., COLLINS, S., DOBSON, A., FOLEY, J. A., LODGE, D. M., PASCUAL, M., PIELKE, R. JR., PIZER, W., PRINGLE, A., REID, W. V., ROSE, K. A., SALA, O., SCHLESINGER, W. H., WALL, D. H. & WEAR, D. 2001. Ecological forecasts: an emerging imperative. Science 293:657–660.Google Scholar
CRESPO, J. A. 1982. Ecología de la comunidad de mamíferos del Parque Nacional Iguazú, Misiones. Revista del Museo Argentino de Ciencias Naturales “Bernardino Rivadavia” 3:1–162.Google Scholar
CRISTIANO, P. M., MADANES, N., CAMPANELLO, P. I., DI FRANCESCANTONIO, D., RODRIGUEZ, S. A., ZHANG, Y. J., OLIVA CARRASCO, L. & GOLDSTEIN, G. 2014. High NDVI and potential canopy photosynthesis of South American subtropical forests despite seasonal changes in leaf area index and air temperature. Forests 5:287–308.Google Scholar
CUSTÓDIO TALORA, D. & MORELLATO, P. C. 2000. Fenologia de espécies arbóreas em floresta de planície litorânea do sudeste do Brasil. Revista Brasileira de Botânica 23 (1): 13–26.Google Scholar
DAVISON, J. E., BRESHEARS, D. D., VAN LEEUWEN, W. J. & CASADY, G. M. 2010. Remotely sensed vegetation phenology and productivity along a climatic gradient: on the value of incorporating the dimension of woody plant cover. Global Ecology and Biogeography 20:101–113.Google Scholar
DE BEURS, K. M. & HENEBRY, G. M. 2004. Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 89:497–509.CrossRefGoogle Scholar
DI FRANCESCANTONIO, D. 2017. Características ecológicas, fisiológicas y anatómicas de especies arbóreas del Bosque Atlántico y su relación con los diferentes patrones fenológicos. Tesis doctoral, Universidad de Buenos Aires, Argentina. 128 pp.Google Scholar
EASDALE, T., HEALEY, J. R., GRAU, H. R. & MALIZIA, A. 2007. Tree life histories in a montane subtropical forest: species differ independently by shade-tolerance, turnover rate and substrate preference. Journal of Ecology 95:1234–1249.Google Scholar
FERRERO, M. E. & VILLALBA, R. 2009. Potential of Schinopsis lorentzii for dendrochronological studies in subtropical dry Chaco forests of South America. Trees 23:1275–1284.CrossRefGoogle Scholar
FRANGI, J. L. & LUGO, A. E. 1985. Ecosystem dynamics of a subtropical floodplain forest. Ecological Monographs 55:351–369.CrossRefGoogle Scholar
GALINDO-LEAL, C. & GUSMÃO CÂMARA, I. 2003. Atlantic forest hotspot status: an overview. Pp. 3–11 in Galindo-Leal, C. & Gusmão Câmara, I. (eds). The Atlantic Forest of South America: biodiversity status, threats and outlook (First edition). Island Press, Washington.Google Scholar
GASPARRI, N. I. & BALDI, G. 2013. Regional patterns and controls of biomass in semiarid woodlands: lessons from the Northern Argentina Dry Chaco. Regional Environmental Change 13:1131–1144.Google Scholar
GASPARRI, N. I., PARMUCHI, M. G., BONO, J., KARSZENBAUM, H. & MONTENEGRO, C. L. 2010. Assessing multi-temporal Landsat 7 ETMþ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments 74:1262–1270.Google Scholar
GIMENEZ, A. M. & MOGLIA, J. G. 2003. Árboles del Chaco argentino: guía de reconocimiento dendrológico. SAyDS y Facultad de Ciencias forestales UNSE, Santiago del Estero. 307 pp.Google Scholar
GRÖMPING, U. 2006. Relative importance for linear regression in R: The Package relaimpo. Journal of Statistical Software 17:1–27.Google Scholar
HIJMANS, R. J., CAMERON, S. E., PARRA, J. L., JONES, P. G. & JARVIS, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965–1978.CrossRefGoogle Scholar
HUNZINGER, H. 1997. Hydrology of montane forests in the Sierra de San Javier, Tucumán, Argentina. Mountain Research and Development 17:299–308.Google Scholar
JACKSON, D. A. 1993. Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 74:2204–2214.Google Scholar
JOHNSON, J. W. & LEBRETON, J. M. 2004. History and use of relative importance indices in organizational research. Organizational Research Methods 7:238–257.Google Scholar
JONES, M. O., KIMBALL, J. S. & NEMANI, R. R. 2014. Asynchronous Amazon forest canopy phenology indicates adaptation to both water and light availability. Environmental Research Letters 9:124021.Google Scholar
JÖNSSON, P. & EKLUNDH, L. 2004. TIMESAT − a program for analyzing time-series of satellite sensor data. Computers and Geosciences 30:833–845.Google Scholar
KESSLER, M. & BECK, S. 2001. Bolivia. Pp. 581–622 in Kappelle, M. & Brown, A. D. (eds). Bosques nublados del Neotrópico. Instituto Nacional de Biodiversidad, San José.Google Scholar
KINDT, R. & COE, R. 2005. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre, Nairobi. 196 pp.Google Scholar
KNAPP, A. K., BRIGGS, J. M., COLLINS, S. L., ARCHER, S. R., BRET-HARTE, M. S., EWERKS, B. E., PETERS, D. P., YOUNG, D. R., SHAVER, G. R., PENDALL, E. & CLEARLY, M. 2008. Shrub encroachment in North American grasslands: shifts in growth form dominance rapidly alters control of ecosystem carbon inputs. Global Change Biology 14:615–623.CrossRefGoogle Scholar
KOLTUNOV, A., USTIN, S. L., ASNER, G. P. & FUNG, I. 2009. Selective logging changes forest phenology in the Brazilian Amazon: evidence from MODIS image time series analysis. Remote Sensing of Environment 113:2431–2440.CrossRefGoogle Scholar
LEGNAME, P. 1982. Árboles indígenas del noroeste argentino. Opera Lilloana 34:1–226.Google Scholar
LEITE, P. F. & KLEIN, R. M. 1990. Geografía do Brazil: Regiaõ Sul. Pp. 113–150 in IBGE (ed.). Vegetaçaõ, Vol. 2. Instituto Brasileiro de Geografia e Estadística, Rio de Janeiro.Google Scholar
LIMA PILON, N. A., GIASSI UDULUTSCH, R. & DURIGAN, G. 2015. Padrões fenológicos de 111 espécies de Cerrado em condições de cultivo. Hoehnea 42:425–443.Google Scholar
LINDEMAN, R. H., MERENDA, P. F. & GOLD, R. Z. 1980. Introduction to bivariate and multivariate analysis. Scott, Foresman, Glenview.Google Scholar
LINDERMAN, M., ROWHANI, P., BENZ, D., SERNEELS, S. & LAMBIN, E. F. 2005. Land-cover change and vegetation dynamics across Africa. Journal of Geophysical Research 11010.1029/2004JD005521.CrossRefGoogle Scholar
MALIZIA, L., PACHECO, S., BLUNDO, C. & BROWN, A. D. 2012. Caracterización altitudinal, uso y conservación de las yungas subtropicales de Argentina. Ecosistemas 21:53–73.Google Scholar
MARQUES, M. C. M., ROPER, J. J. & BAGGIO SALVALAGGIO, A. P. 2004. Phenological patterns among plant life-forms in a subtropical forest in southern Brazil. Plant Ecology 173:203– 213.CrossRefGoogle Scholar
MARTÍN, G. O., NICOSIA, M. G. & LAGOMARSINO, E. D. 1997. Fenología foliar en leñosas nativas del Chaco semiárido de Tucumán y algunas consideraciones para su aprovechamiento forrajero. Revista Agronómica del Noroeste Argentino 29:65–85.Google Scholar
MINETTI, J. L. 1999. Atlas climático del Noroeste Argentino. Laboratorio Climatológico sudamericano. Fundación ZonCaldenius, Tucumán.Google Scholar
MYERS, N., MITTERMEIER, R. A., MITTERMEIER, C. G., DA FONSECA, G. A. B. & KENT, J. 2000. Biodiversity hotspots for conservation priorities. Nature 403:853–858.CrossRefGoogle ScholarPubMed
PARUELO, J. M. & LAUENROTH, W. K. 1995. Regional patterns of normalized difference vegetation index in North American shrublands and grasslands. Ecology 76:1888–1898.Google Scholar
PENNINGTON, R. T., LAVIN, M. & OLIVEIRA-FILHO, A. 2009. Woody plant diversity, evolution, and ecology in the tropics: perspectives from seasonally dry tropical forests. Annual Review of Ecology, Evolution, and Systematic 40:437–457.Google Scholar
PIZARRO, M. J., MEZHER, R., MERCURI, P. & ESPÍNDOLA, A. 2013. Tendencias de extremos climáticos en Argentina. El caso de la provincia de Misiones. Informe del Proyecto PNUD ARG/10/013, SAyDS. 12 pp.Google Scholar
PRADO, D. 1993. What is the Gran Chaco vegetation in South America? Candollea 48:45–172.Google Scholar
PRINCE, S. D. & GOWARD, S. N. 1995. Global primary production: a remote sensing approach. Journal of Biogeography 22:815–835.Google Scholar
SARMIENTO, G. 1972. Ecological and floristic convergences between seasonal plant formations of tropical and subtropical South America. Journal of Ecology 60:367–410.CrossRefGoogle Scholar
SALAZAR, L. F., NOBRE, C. A. & OYAMA, M. D. 2007. Climate change consequences on the biome distribution in tropical South America. Geophysical Research Letters 34, L09708.CrossRefGoogle Scholar
SUEPA, T., QI, J., LAWAWIROJWONG, S. & MESSINA, J. 2016. Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environmental Research 147:621–629.Google Scholar
VAN LEEUWEN, W. J. D., DAVISON, J. E., CASADY, G. M. & MARCH, S. E. 2010. Phenological characterization of Desert Sky Island vegetation communities with remotely sensed and climate time series data. Remote Sensing 2:388–415.Google Scholar
WEAVER, P. L. 2000. Elfin woodland recovery 30 years after a plane wreck in Puerto Rico's Luquillo mountains. Caribbean Journal of Science 36:1–9.Google Scholar
WERNECK, F. P., COSTA, G. C., COLLI, G. R., PRADO, D. E. & SITES, J. W. 2011. Revisiting the historical distribution of seasonally dry tropical forests: new insights based on palaeodistribution modeling and palynological evidence. Global Ecology and Biogeography 20:271–288.Google Scholar
WESSELS, K., STEENKAMP, K., VON MALTITZ, G. & ARCHIBALD, S. 2011. Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa. Applied Vegetation Science 14:49–66.Google Scholar
ZHANG, X. Y., FRIEDL, M. A., SCHAAF, C. B., STRAHLER, A. H. & LIU, Z. 2005. Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research 110: D12103.CrossRefGoogle Scholar
ZHANG, X. Y., FRIEDL, M. A. & SCHAAF, C. B. 2006. Global vegetation phenology from moderate resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research 111, G04017.Google Scholar
ZHANG, Y. J., CRISTIANO, P. M., ZHANG, Y. F., CAMPANELLO, P. I., TAN, Z. H., ZHANG, Y. P., CAO, K. F. & GOLDSTEIN, G. 2016. Carbon economy of subtropical forests. Pp. 337–355 in Goldstein, G. & Santiago, L. (eds). Tropical tree physiology: adaptations and responses in a changing environment. Springer International Publishing, Basel.Google Scholar