Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

Ecological Processes - Tập 13 Số 1
Pablo Luís Peri1,2, Juan Gaitán3, Matías Enrique Mastrangelo4, Marcelo D. Nosetto5, Pablo E. Villagra6, Ezequiel Balducci1, Martín Alcides Pinazo1, Roxana Paola Eclesia1, Alejandra Von Wallis1, Sebastián Horacio Villarino4, Francisco Alaggia1, Marina González Polo7, Silvina Magdalena Manrique8, Pablo A. Meglioli6, Julián Rodríguez-Souilla9, Martín H. Mónaco10, Jimena E. Chaves9, Ariel Medina10, Ignácio Gasparri11, Eugenio Alvarez Arnesi12, María Paula Barral4, Axel Von Müller1, Norberto Manuel Pahr1, Josefina Uribe Echevarria1, Pedro Porta Fernández1,11, Marina Morsucci6, Diana María López1, Juan Manuel Cellini13, Leandro Álvarez6, Ignacio M. Barberis12, Hernán Pablo Colomb14, Ludmila La Manna15, Sebastián Barbaro1, Cecilia Blundo11, Ximena Sirimarco4, Laura Cavallero1, Gualberto Zalazar6, Guillermo Martínez Pastur9
1Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
2Universidad Nacional de la Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Río Gallegos, Argentina
3Universidad Nacional de Luján, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Luján, Argentina
4Grupo de Estudio de Agroecosistemas y Paisajes Rurales (GEAP), Universidad Nacional de Mar del Plata, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mar del Plata, Argentina
5CCT San Luis, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Luis, Argentina
6Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo, Mendoza, Argentina
7Instituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bariloche, Argentina
8Instituto de Investigaciones en Energía No Convencional, CCT Salta-Jujuy, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Salta, Argentina
9Laboratorio de Recursos Agroforestales, Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ushuaia, Argentina
10Dirección Nacional de Bosques, Ministerio de Ambiente y Desarrollo Sostenible de la Nación, Buenos Aires, Argentina
11Instituto de Ecología Regional, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Tucumán, Tucumán, Argentina
12Universidad Nacional de Rosario, Instituto de Investigaciones en Ciencias Agrarias de Rosario, Centro Científico Tecnológico CONICET, Santa Fe, Argentina
13Laboratorio de Investigaciones en Maderas (LIMAD), Universidad Nacional de La Plata (UNLP), La Plata, Argentina
14Administración de Parques Nacionales (APN), Parque Nacional Los Alerces, Esquel, Argentina
15Centro de Estudios Ambientales Integrados (CEAI), Universidad Nacional de la Patagonia San Juan Bosco (UNPASJB), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Esquel, Argentina

Tóm tắt

Abstract Background The nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter-annual climate variability. Methods We used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud-based computing platform for subsequent modelling. To determine the biodiversity threats from high inter-annual climate variability, we employed the spatial–temporal satellite-derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery. Results SOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions. Conclusions In the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.

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