Spatio-temporal dynamics of woody plant-cover in Argentine savannas: encroachment, agriculture conversion and changes in carbon stocks at varying scales

<p>Land use and land cover changes significantly affect C storage in terrestrial ecosystems. Programs intended to compensate land owners for the maintenance or enhancement to C stocks are promising, but require detailed and spatially explicit C distribution estimates to monitor the effectiveness of management interventions. Savanna ecosystems are significant components of the global C cycle, however, they have not received much attention for the development of C monitoring approaches. In this dissertation I have investigated three of the aspects related to woody plant cover dynamics in semiarid savannas of central Argentina: spatio-temporal dynamics, precise field surveying and scaling from field to region with the use of freely available remotely sense data. </p><p>To examine the long term changes in woody plant cover, I first carefully extracted information from historical maps of the Caldenal savannas of central Argentina (190,000 km2) in the 1880s to generate a woody cover map that was compared to a 2000s dataset. Over the last ~120 years, woody cover increased across ~12,200 km2 (14.2 % of the area). During the same period, ~5,000 km2 of the original woody area was converted to croplands and ~7,000 km2 to pastures, about the same total land area as was affected by woody plant encroachment. A smaller area, fine scale analysis between the 1960s and the 2000s revealed that tree cover increased overall by 27%, shifting from open savannas to a mosaic of dense woodlands along with additional agricultural clearings. Statistical models indicate that woody cover dynamics in this region were affected by a combination of environmental and human factors.</p><p>To assess the consequences of woody cover dynamics on C, we also measured ecosystem C stocks along a gradient of woody plant density. I characterized changes in C stocks in live biomass (woody and herbaceous, above- and belowground), litter, and soil organic carbon (to 1.5 m depth) pools along a woody plant cover gradient (0 to 94 %). I found a significant increase in ecosystem C stocks with increasing woody cover, with mean values of 4.5, 8.4, 12.4, and 16.5 kg C m-2 for grasslands, shrublands, open and closed forests, respectively. Woody plant cover and soil silt content were the two primary factors accounting for the variability of ecosystem C. I developed simple regression models that reliably predict soil, tree and ecosystem C stocks from basic field measurements of woody plant cover and soil silt content. These models are valuable tools for broad scale estimation if linked to regional soil maps and remotely sensed data, allowing for precise and spatially explicit estimation of C stocks and change at regional scales.</p><p>Finally, I used the field survey data and high resolution panchromatic images (2.5 m resolution) to identify tree canopies and train a regional tree percent cover model using the Random Forests (RF) algorithm. I found that a model with summer and winter tasseled cap spectral indices, climate and topography performed best. Sample spatial distribution highly affected the performance of the RF models. The regression model built to predict tree C stocks from percent tree cover explained 83 % of the variability, and the spatially explicit tree C model prediction presented an root mean squared error (RMSE) of 8.2 tC/ha which represented ~30% of the mean C stock for areas with tree cover. Our analysis indicates that regionally over the last ~120 years, increases in woody plant cover have stored significant amounts of C (95.9 TgC), but not enough to compensate for in C generated by the conversions of woodlands and natural grasslands to croplands and pastures (166.7 TgC), generating a regional net loss of 70.9 TgC. C losses could be even larger in the future if, as predicted, energy crops would trigger a new land cover change phase in this region.</p>Dissertatio

Shifting carbon pools along a plant cover gradient in woody encroached savannas of central Argentina

Woody plant encroachment is a widespread process of land cover change driven by a combination of local land use practices and regional to global environmental changes. Increases in woody plant cover alter the distribution of carbon in the ecosystem and can affect water and nutrient cycling. Although semiarid rangelands comprise almost half of the global land surface, our understanding on the effects of woody plant encroachment on carbon stocks in these ecosystems is uncertain ? studies have found both net C gains and losses. We measured, for the first time in South American semiarid savannas, ecosystem C stocks along a gradient of woody plant density across 30,000 km2 of the Caldenal in central Argentina. We characterized changes in C stocks in live biomass (woody and herbaceous, above- and belowground), litter, and soil organic carbon (to 1.5 m depth) pools along a woody plant cover gradient (0?94%). We found a significant increase in ecosystem C stocks with increasing woody cover, with mean values of 4.5, 8.4, 12.4, and 16.5 kg C m-2 for grasslands, shrublands, open and closed forests, respectively. Using dendrochronological data we estimated the average C accrual rate to be 104 g C m-2 yr-1 at the ecosystem (plant + soil) level. Woody plant cover and soil silt content were the two primary factors accounting for the variability of ecosystem C. We developed simple regression models that reliably predict soil, tree and ecosystem C stocks from basic field measurements of woody plant cover and soil silt content. These models could prove to be valuable tools for broad scale estimation if linked to regional soil maps and remotely sensed data, allowing for precise and spatially explicit estimation of C stocks and change at regional scales.Fil: Gonzalez Roglich, Mariano. University Of Duke; Estados UnidosFil: Swanson, Jennifer. University Of Duke; Estados UnidosFil: Jobbagy Gampel, Esteban Gabriel. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; ArgentinaFil: Jackson, Robert B.. University Of Stanford; Estados Unido

Synergizing global tools to monitor progress towards land degradation neutrality: Trends.Earth and the World Overview of Conservation Approaches and Technologies sustainable land management database

As part of the Sustainable Development Goals, countries are striving to achieve by 2030 a land degradation neutral world. Land degradation neutrality (LDN) is the state whereby the amount and quality of land resources remains stable or increases within specified temporal and spatial scales. Achieving this will require the uptake of sustainable land management (SLM) practices to increase the sustainable provision of ecosystem goods and services the human population will require. It will also require the development of systematic, robust, and validated methods for tracking progress at project, subnational and national scales. However, to date, no systematic comparison between the SLM practices and the indicators proposed for monitoring LDN has been performed. In this article, we used the United Nations Convention to Combat Desertification primary recommended global sustainable land management database of World Overview of Conservation Approaches and Technologies (WOCAT), and an innovative tool designed to assess and monitor land condition via changes in land productivity, Trends.Earth, to evaluate the agreement between self-reported sustainable land management technologies and indicators derived from satellite-based earth observations. We found that a combination of two primary productivity indicators derived from annual integrals of normalized difference vegetation indices (NDVI), trajectory and state, were able to identify increases in primary productivity in the locations where the SLM practices are implemented in comparison to control sites where SLM practices are not known to have occurred. Moreover, different SLM practices showed unique responses in terms of proportional area which experienced increase, decrease, or remained stable terms of primary productivity. We also found that the time since establishment of the SLM technology was critical for identifying improvements in the SLM sites, as only technologies with more than 10 years since implementation show statistically significant improvements. Our results show that satellite-derived land productivity indicators are successful at detecting the impacts of SLM practices on primary productivity, positioning them as essential elements of the monitoring and assessment tools needed to track land condition to assure the achievement of a land degradation neutral world

Woody plant-cover dynamics in Argentine Savannas from the 1880s to 2000s: The interplay of encroachment and agricultural conversion at varying scales

Woody plant-cover dynamics can alter the provisioning of ecosystem services that humans rely on. However, our understanding of such dynamics today is often limited by the availability of reliable and detailed land-cover information in the past, before the onset of remote sensing technologies. In this study, we carefully extracted information from historical maps of the Caldenal savannas of central Argentina in the 1880s to generate a woody cover map that we compared to a 2000s dataset. Over about the last 120 years, woody cover increased across approximately 12,200 km2 (14.2% of the area). During the same period, about 5,000 km2 of the original woody area was converted to croplands and around 7,000 km2 to pastures, about the same total land area as was affected by encroachment. A smaller area, fine-scale analysis between the 1960s and the 2000s revealed that tree cover increased overall by 27%, shifting from open savannas to a mosaic of dense woodlands along with additional agricultural clearings. Statistical models indicate that woody cover dynamics in this region were affected by a combination of environmental and human factors. Over about the last 120 years, increases in woody plant cover have stored significant amounts of C (95.9 TgC), but not enough to compensate for losses from conversions to croplands and pastures (166.7 TgC), generating a regional net loss of 70.9 TgC. C losses could be even larger in the future if, as predicted, energy crops such as switchgrass, would trigger a new land-cover change phase in this region.Fil: Gonzalez Roglich, Mariano. University Of Duke; Estados UnidosFil: Swanson, Jennifer. University Of Duke; Estados UnidosFil: Villareal, Diego. Universidad Nacional de La Pampa; ArgentinaFil: Jobbagy Gampel, Esteban Gabriel. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; ArgentinaFil: Jackson, Robert B.. University Of Duke; Estados Unidos. University Of Stanford; Estados Unido

Land degradation assessment in the Argentinean Puna: Comparing expert knowledge with satellite-derived information

The Puna region, located in NW Argentina, is a dry highland with many endemic species and significant traditional cultural heritage. The Puna was a pilot region for the Land Degradation Assessment in Drylands project (LADA-FAO) which aimed at assessing desertification status in different land use systems (LUS). The results of these assessments are used for reporting to the United Nations Convention to Combat Desertification (UNCCD) and for Land Degradation Neutrality (LDN) monitoring. The assessment was performed using an expert knowledge questionnaire following the World Overview of Conservation Approaches and Technologies initiative methodology (LADA-WOCAT), on LUS map units obtained by a participatory mapping methodology. In this article we compare the inferences on land degradation status and its temporal trends derived from LADA-WOCAT method with those obtained from remotely sensed data. Our aim is to understand similarities and differences in the assessments in order to provide recommendations and suggestions on improved LDN assessment methods, reporting and monitoring. Our results suggest that the LADA-WOCAT participatory mapping successfully delineated LUSs with different phenological characteristics over an extended area. However, the trend and the degradation processes described by experts at the LUS scale did not agree with the ones derived from satellite data. Results obtained at pixel scale help in identifying areas undergoing potential human induced degradation processes and specific areas of agreement between methods. The spatial scale of land degradation assessments obtained by either satellite or expert knowledge may impact the accuracy of the final results. A methodology integrating satellite and expert opinion data at an appropriate scale needs to coalesce to make the most of both data sources and gain accuracy in land degradation and LDN assessments.Instituto de Fisiología y Recursos Genéticos VegetalesFil: García, César Luis. Universidad Católica de Córdoba. Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Teich, Ingrid. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fisiología y Recursos Genéticos Vegetales; ArgentinaFil: Gonzalez Roglich, Mariano. Conservation International. Betty & Gordon Moore Center for Science; Estados UnidosFil: Kindgard, Adolfo Federico. Food and Agriculture Organization. FAO Forestry Department; ItaliaFil: Ravelo, Andres Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Liniger, Hanspeter. University of Bern. Centre for Development and Environment (CDE); Suiz

Global hotspots for coastal ecosystem-based adaptation.

Helping the world's coastal communities adapt to climate change impacts requires evaluating the vulnerability of coastal communities and assessing adaptation options. This includes understanding the potential for 'natural' infrastructure (ecosystems and the biodiversity that underpins them) to reduce communities' vulnerability, alongside more traditional 'hard' infrastructure approaches. Here we present a spatially explicit global evaluation of the vulnerability of coastal-dwelling human populations to key climate change exposures and explore the potential for coastal ecosystems to help people adapt to climate change (ecosystem-based adaptation (EbA)). We find that mangroves and coral reefs are particularly well situated to help people cope with current weather extremes, a function that will only increase in importance as people adapt to climate change now and in coming decades. We find that around 30.9 million people living within 2km of the coast are highly vulnerable to tropical storms and sea-level rise (SLR). Mangroves and coral reefs overlap these threats to at least 5.3 and 3.4 million people, respectively, with substantial potential to dissipate storm surges and improve resilience against SLR effects. Significant co-benefits from mangroves also accrue, with 896 million metric tons of carbon stored in their soils and above- and below-ground biomass. Our framework offers a tool for prioritizing 'hotspots' of coastal EbA potential for further, national and local analyses to quantify risk reduction and, thereby, guide investment in coastal ecosystems to help people adapt to climate change. In doing so, it underscores the global role that conserving and restoring ecosystems can play in protecting human lives and livelihoods, as well as biodiversity, in the face of climate change

Global hotspots for coastal ecosystem-based adaptation.

Helping the world's coastal communities adapt to climate change impacts requires evaluating the vulnerability of coastal communities and assessing adaptation options. This includes understanding the potential for 'natural' infrastructure (ecosystems and the biodiversity that underpins them) to reduce communities' vulnerability, alongside more traditional 'hard' infrastructure approaches. Here we present a spatially explicit global evaluation of the vulnerability of coastal-dwelling human populations to key climate change exposures and explore the potential for coastal ecosystems to help people adapt to climate change (ecosystem-based adaptation (EbA)). We find that mangroves and coral reefs are particularly well situated to help people cope with current weather extremes, a function that will only increase in importance as people adapt to climate change now and in coming decades. We find that around 30.9 million people living within 2km of the coast are highly vulnerable to tropical storms and sea-level rise (SLR). Mangroves and coral reefs overlap these threats to at least 5.3 and 3.4 million people, respectively, with substantial potential to dissipate storm surges and improve resilience against SLR effects. Significant co-benefits from mangroves also accrue, with 896 million metric tons of carbon stored in their soils and above- and below-ground biomass. Our framework offers a tool for prioritizing 'hotspots' of coastal EbA potential for further, national and local analyses to quantify risk reduction and, thereby, guide investment in coastal ecosystems to help people adapt to climate change. In doing so, it underscores the global role that conserving and restoring ecosystems can play in protecting human lives and livelihoods, as well as biodiversity, in the face of climate change

Mapping and characterizing social-ecological land systems of South America

Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world