Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-10T20:49:27.479Z Has data issue: false hasContentIssue false
  • English
  • Français

Changes in the potential distribution of valuable tree species based on their regeneration in the Neotropical seasonal dry forest of north-western Argentina

Published online by Cambridge University Press:  08 April 2022

Fabio Alabar ,
Natalia Politi Open the ORCID record for Natalia Politi [Opens in a new window] ,
Paula Názaro ,
Mariano Amoroso  and
Luis Rivera
Fabio Alabar
Affiliation:
Instituto de Ecorregiones Andinas (INECOA), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Jujuy, Jujuy, Argentina
Natalia Politi*
Affiliation:
Instituto de Ecorregiones Andinas (INECOA), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Jujuy, Jujuy, Argentina
Paula Názaro
Affiliation:
Instituto de Ecorregiones Andinas (INECOA), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Jujuy, Jujuy, Argentina
Mariano Amoroso
Affiliation:
Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural (IRNAD), Universidad Nacional de Río Negro, Viedma, Argentina
Luis Rivera
Affiliation:
Instituto de Ecorregiones Andinas (INECOA), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Jujuy, Jujuy, Argentina
*
Author for Correspondence: Dr Natalia Politi, Email: npoliti@conicet.gov.ar
Get access Rights & Permissions [Opens in a new window]

Summary

The distribution of regeneration makes it possible to assess whether populations of tree species will maintain or change their distributions. For Neotropical dry forests there is little information on the potential changes in the distribution of tree species. Here, we evaluate the potential distributions of adults and seedlings of eight timber tree species of the Piedmont Forest of north-western Argentina by recording the presence of seedlings and adults in plots and modelling with MaxEnt software using three bioclimatic variables. The potential distribution areas of seedlings and adults and the percentage of overlap of seedlings with respect to adults were calculated. The potential distribution for adults was 694 457 ± 62 535 ha, and this figure was 656 564 ± 194 769 ha for seedlings. The potential distribution of seedlings of Calycophyllum multiflorum covered the smallest area (184 496 ha) and had the least overlap with the adults (18%). The difference in the overlap of the potential distribution areas between adults and seedlings suggests that there could be changes in the future distribution of this tree species and C. multiflorum should therefore be the focus of conservation strategies so that the species can follow its bioclimatic niche as the climate changes.

Keywords

MaxEntoverlapPiedmont Forestseedlings
Type
Research Paper
Information
Environmental Conservation , Volume 49 , Issue 2 , June 2022 , pp. 83 - 89
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Foundation for Environmental Conservation

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.)

References

Arambarri, AM, Novoa, MC, Bayón, ND, Hernández, MP, Colares, MN, Monti, C (2011) Anatomía foliar de arbustos y árboles medicinales de la región chaqueña semiárida de la Argentina. Dominguezia 27: 5–24.Google Scholar
Arias, M, Bianchi, A (1996) Estadısticas climatologicas de la Provincia de Salta. Salta, Argentina: INTA.Google Scholar
Balducci, ED, Eliano, P, Iza, HR, Sosa, I (2012) Bases para el manejo sostenible de los bosques nativos de Jujuy. Jujuy, ArgentinaL INCOTEDES.Google Scholar
Banda, K, Delgado-Salinas, A, Dexter, KG, Linares-Palomino, R, Oliveira-Filho, A, Prado, D et al. (2016) Plant diversity patterns in Neotropical dry forests and their conservation implications. Science 353: 1383–1387.Google Scholar
Barros, V, Carlinom, H, Magnasco, E, Magrin, G (2014) Tercera Comunicación Nacional de la República Argentina a la Convención Marco de las Naciones Unidas sobre el cambio climático – Buenos Aires, Argentina [www document]. URL https://www.argentina.gob.ar/ambiente/cambio-climatico/tercera-comunicacion Google Scholar
Bianchi, A, Yañez, C (1992) Las precipitaciones en el noroeste de Argentina. Salta, Argentina: INTA.Google Scholar
Blundo, C, Gasparri, NI, Malizia, A, Clark, M, Gatti, G, Campanello, PI et al. (2018) Relationships among phenology, climate and biomass across subtropical forests in Argentina. Journal of Tropical Ecology 34: 93–107.CrossRefGoogle Scholar
Blundo, C, Malizia, LR, Blake, JG, Brown, AD (2012) Tree species distribution in Andean forests: influence of regional and local factors. Journal of Tropical Ecology 28: 83–95.CrossRefGoogle Scholar
Bohren, A, Gartland, M, Keller, H, Grance, L (2007) Ficha técnica árboles de Misiones Cordia trichotoma (Vell.) Arráb. ex Steud. Yvyrareta 14: 51–55.Google Scholar
Brown, A (2009) Manejo sustentable y conservación de la biodiversidad de un ecosistema prioritario del noroeste argentino. In: Las selvas pedemontanas de las Yungas, Historia natural, ecología y manejo de un ecosistema en peligro, eds A Brown, PG Blendinger, T Lomáscolo, P García Bes (pp. 36–13). Tucumán, Argentina: Ediciones del Subtrópico.Google Scholar
Brown, JL (2014) SDMtoolbox: a Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution 5: 694–700.CrossRefGoogle Scholar
Cabrera, AL (1976) Regiones fitogeográficas de la República Argentina, Enciclopedia de Agricultura, Jardinería y Fruticultura. Buenos Aires, Argentina: ACME.Google Scholar
Chaudhary, A, Burivalova, Z, Koh, LP, Hellweg, S (2016) Impact of forest management on species richness: global meta-analysis and economic trade-offs. Scientific Reports 6: 23954.CrossRefGoogle ScholarPubMed
Correa-Gómez, DF, Vargas-Ríos, O (2009) Regeneración de palmas en bosques nativos y plantaciones del santuario de fauna y flora otún – Quimbaya (Risaralda, Colombia). Caldasia 31: 195–212.Google Scholar
Easdale, TA, Healey, JR, Grau, HR, 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.CrossRefGoogle Scholar
Gillson, L, Willis, KJ (2004) ‘As Earth’s testimonies tell’: wilderness conservation in a changing world. Ecology Letters 7: 990–998.CrossRefGoogle Scholar
Giménez, AM, Moglia, JG (2003) Árboles del Chaco Argentino, Guía para el reconocimiento dendrológico. Santiago del Estero, Argentina: Universidad Nacional de Santiago del Estero.Google Scholar
Guisan, A, Zimmermann, NE (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135: 147–186.CrossRefGoogle Scholar
Gustafson, EJ, Shvidenko, AZ, Sturtevant, BR, Scheller, RM (2010) Predicting global change effects on forest biomass and composition in south-central Siberia. Ecological Applications 20: 700–715.CrossRefGoogle ScholarPubMed
Higgins, SI, Lavorel, S, Revilla, E (2003) Estimating plant migration rates under habitat loss and fragmentation. Oikos 101: 354–366.CrossRefGoogle Scholar
Hijmans, RJ, Phillips, S, Leathwick, J, Elith, J (2020) dismo: species distribution modeling. R package version 1.3-3 [www document]. URL https://CRAN.R-project.org/package=dismo Google Scholar
Hulme, PE (2005) Adapting to climate change: is there scope for ecological management in the face of a global threat? Journal of Applied ecology 42: 784–794.CrossRefGoogle Scholar
Humano, C (2020) Modelado del crecimiento de especies nativas forestales de la Selva Pedemontana de Yungas, Argentina. Quebracho 28: 5–19.Google Scholar
Jadán, O, Cedillo, H, Pillacela, P, Guallpa, D, Gordillo, A, Zea, P et al. (2019) Regeneración de Pinus patula (Pinaceae) en ecosistemas naturales y plantaciones, en un gradiente altitudinal andino, Azuay, Ecuador. Revista de Biología Tropical 67: 182–195.CrossRefGoogle Scholar
Justiniano, MJ, Fredericksen, TS (1998) Ecología y silvicultura de especies menos conocidas: Curupaú Anadenanthera colubrina (Vell. Conc.) Benth. Mimosoideae. Santa Cruz, Bolivia: Proyecto de Manejo Forestal Sostenible.Google Scholar
Kennard, D (2004) Commercial tree regeneration 6 years after high-intensity burns in a seasonally dry forest in Bolivia. Canadian Journal of Forest Research 34: 2199–2207.CrossRefGoogle Scholar
Killeen, TJ, García, E, Beck, SG (1993). Guía de árboles de Bolivia (No. C/581.984 G8). La Paz, Bolivia: Herbario Nacional de Bolivia.Google Scholar
Laundré, JW, Hernández, L, López Medina, P, Campanella, A, López-Portillo, J, González-Romero, A et al. (2014) The landscape of fear: the missing link to understand top-down and bottom-up controls of prey abundance?. Ecology 95: 1141–1152.CrossRefGoogle ScholarPubMed
Leite, EJ (2002) State-of-knowledge on Myracrodruon urundeuva Fr. Allemão (Anacardiaceae) for genetic conservation in Brazil. Perspectives in Plant Ecology, Evolution and Systematics 5: 193–206.CrossRefGoogle Scholar
Lenoir, J, Gégout, JC, Pierrat, JC, Bontemps, JD, Dhôte, JF (2009) Differences between tree species seedling and adult altitudinal distribution in mountain forests during the recent warm period (1986–2006). Ecography 32: 765–777.CrossRefGoogle Scholar
Liang, Y, Duveneck, MJ, Gustafson, EJ, Serra-Diaz, JM, Thompson, JR (2018) How disturbance, competition, and dispersal interact to prevent tree range boundaries from keeping pace with climate change. Global Change Biology 24: 335–351.CrossRefGoogle ScholarPubMed
Lloyd, CT, Sorichetta, A, Tatem, AJ (2017) High resolution global gridded data for use in population studies. Scientific Data 4: 170001.CrossRefGoogle ScholarPubMed
Lo, YH, Blanco, JA, Kimmins, JP (2010) A word of caution when planning forest management using projections of tree species range shifts. Forestry Chronicle 86: 312–316.CrossRefGoogle Scholar
Mangueira, JRS, Holl, D, Rodrigues, RR (2019) Enrichment planting to restore degraded tropical forest fragments in Brazil. Ecosystems and People 15: 3–10.CrossRefGoogle Scholar
Manish, K, Telwala, Y, Nautiyal, DC, Pandit, MK (2016) Modelling the impacts of future climate change on plant communities in the Himalaya: a case study from Eastern Himalaya, India. Modeling Earth Systems and Environment 2: 2–12.CrossRefGoogle Scholar
Martínez, OG, Barrandeguy, ME, García, MV, Cacharani, DA, Prado, DE (2013). Presencia de Anadenanthera colubrina var. colubrina (Fabaceae, Mimosoideae) en Argentina. Darwiniana 1: 279–288.CrossRefGoogle Scholar
Martinuzzi, S, Rivera, L, Politi, N, Bateman, BL, de Los Llanos, ER, Lizarraga, L et al. (2018) Enhancing biodiversity conservation in existing land-use plans with widely available datasets and spatial analysis techniques. Environmental Conservation 45: 252–260.CrossRefGoogle Scholar
Milad, M, Schaich, H, Bürgi, M, Konold, W (2011) Climate change and nature conservation in Central European forests: a review of consequences, concepts and challenges. Forest Ecology and Management 261: 829–843.CrossRefGoogle Scholar
Minetti, J, Bessonart, S, Balducci, E (2009) La actividad forestal en la Selva Pedemontana del norte de Salta. In Las selvas pedemontanas de las Yungas, Historia natural, ecología y manejo de un ecosistema en peligro, eds A Brown, PG Blendinger, T Lomáscolo, P García Bes (pp. 367–386). Tucumán, Argentina: Ediciones del Subtrópico.Google Scholar
Mok, HF, Arndt, SK, Nitschke, CR (2012) Modelling the potential impact of climate variability and change on species regeneration potential in the temperate forests of south-eastern Australia. Global Change Biology 18: 1053–1072.CrossRefGoogle Scholar
Mostacedo, CB, Fredericksen, TS (1999) Regeneration status of important tropical forest tree species in Bolivia: assessment and recommendations. Forest Ecology and Management 124: 263–273.CrossRefGoogle Scholar
Naoki, K, Gómez, MI, López, RP, Meneses, RI, Vargas, J (2006) Comparación de modelos de distribución de especies para predecir la distribución potencial de vida silvestre en Bolivia. Ecología en Bolivia 41: 65–78.Google Scholar
National Geographic Institute (2019) Digital elevation model MDE-Ar [www document]. URL https://www.ign.gob.ar/NuestrasActividades/Geodesia/ModeloDigitalElevaciones/Introduccion Google Scholar
Názaro, P, Rivera, L, Martínez Pastur, G, Alabar, F, Politi, N (2021) Preliminary assessment of the conservation status of timber species in the threatened piedmont dry forest of northwestern Argentina. Journal for Nature Conservation 59: 125947.CrossRefGoogle Scholar
Pacheco, S, Malizia, LR, Cayuela, L (2010) Effects of climate change on subtropical forests of South America. Tropical Conservation Science 3: 423–437.CrossRefGoogle Scholar
Phillips, SJ, Dudík, M, Schapire, RE (2020) Software MaxEnt para modelar nichos y distribuciones de especies MaxEnt (Versión 3.4.1) [www document]. URL https://biodiversityinformatics.amnh.org/open_source/maxent/ Google Scholar
Pidgeon, AM, Rivera, L, Martinuzzi, S, Politi, N, Bateman, B (2015) Will representation targets based on area protect critical resources for the conservation of the Tucuman parrot? Condor 117: 503–517.CrossRefGoogle Scholar
Politi, N, Rivera, L (2019) Limitaciones y avances para lograr el manejo forestal sostenible en las yungas australes de Argentina. Ecologia Austral 29: 138–145.CrossRefGoogle Scholar
Quinn, GP, Keough, MJ (2002) Experimental Design and Data Analysis for Biologists. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
R Core Team (2021). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing [www document]. URL https://www.R-project.org/.Google Scholar
Santos Biloni, J (1990) Árboles autóctonos argentinos: De las selvas, bosques y montes de la Argentina. Buenos Aires, Argentina: Tipográfica Editora.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
Schulze, M, Grogan, J, Uhl, C, Lentini, M, Vidal, E (2008) Evaluating ipê (Tabebuia, Bignoniaceae) logging in Amazonia: sustainable management or catalyst for forest degradation? Biological Conservation 141: 2071–2085.CrossRefGoogle Scholar
Serra-Diaz, JM, Franklin, J, Dillon, WW, Syphard, AD, Davis, FW, Meentemeyer, RK (2016) California forests show early indications of both range shifts and local persistence under climate change. Global Ecology and Biogeography 25: 164–175.CrossRefGoogle Scholar
Silvestre, EA, Schwarcz, KS, Grando, C, Bueno de Campos, J, Sujii, PS, Tambarussi, EV et al. (2018) Mating system and effective population size of the overexploited neotropical tree (Myroxylon peruiferum L.f.) and their impact on seedling production. Journal of Heredity 109: 264–271.CrossRefGoogle ScholarPubMed
Steenberg, JWN, Duinker, PN, Bush, PG (2013) Modelling the effects of climate change and timber harvest on the forests of central Nova Scotia, Canada. Annals of Forest Science 70: 61–73.CrossRefGoogle Scholar
Stephan, J, Bercachy, C, Bechara, J, Charbel, E, López-Tirado, J (2020) Local ecological niche modelling to provide suitability maps for 27 forest tree species in edge conditions. iForest 13: 230–237.CrossRefGoogle Scholar
Svoboda, M, Fuchs, BA (2016) Handbook of Drought Indicators and Indices. Geneva, Switzerland: World Meteorological Organization.Google Scholar
Tu, W, Xiong, Q, Qiu, X, Zhang, Y (2021) Dynamics of invasive alien plant species in China under climate change scenarios. Ecological Indicators 129: 107919.CrossRefGoogle Scholar
Urbieta, IR, García, LV, Zavala, MA, Marañón, T (2011) Mediterranean pine and oak distribution in southern Spain: is there a mismatch between regeneration and adult distribution? Journal of Vegetation Science 22: 18–31.CrossRefGoogle Scholar
VanDerWal, J, Shoo, LP, Graham, C, Williams, SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecological Modelling 220: 589–594.CrossRefGoogle Scholar
Vela Diaz, DM, Blundo, C, Cayola, L, Fuentes, AF, Malizia, LR, Myers, JA (2020). Untangling the importance of niche breadth and niche position as drivers of tree species abundance and occupancy across biogeographic regions. Global Ecology and Biogeography 29: 1542–1553.CrossRefGoogle Scholar
Verrico, BM, Weiland, J, Perkins, TD, Beckage, B, Keller, SR (2020) Long-term monitoring reveals forest tree community change driven by atmospheric sulphate pollution and contemporary climate change. Diversity and Distributions 26: 270–283.CrossRefGoogle Scholar
Walther, GR (2010) Community and ecosystem responses to recent climate change. Philosophical Transactions of the Royal Society B: Biological Sciences 365: 2019–2024.CrossRefGoogle ScholarPubMed
Young, N, Carter, L, Evangelista, P (2011) A MaxEnt model v3.3.3e tutorial (ArcGIS v10). Natural Resource Ecology Laboratory, Colorado State University and the National Institute of Invasive Species Science [www document]. URL http://www.coloradoview.org/wp-content/coloradoviewData/trainingData/a-maxent-model-v8.pdf Google Scholar
Supplementary material: File

Alabar et al. supplementary material

Alabar et al. supplementary material

Download Alabar et al. supplementary material(File)
File 247.6 KB