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The temporal trend of aboveground net primary production (ANPP) is frequently used to estimate the effect of humans on ecosystems. In water-limited ecosystems, like most grazing areas in the world, the effect of humans act upon ANPP in combination with environmental variations. Our main objective was to quantify long-term (1981-2012) changes of ANPP and discriminate the causes of these changes between environmental and human at a subcontinental scale, across vast areas of Patagonia. We estimated ANPP through a radiative model based on remote sensing data. Then, we evaluated the relation between ANPP and environmental interannual variations of two hierarchically related factors: El Niño Southern Oscillation (ENSO) through the Southern Oscillation Index (SOI), and precipitation. We described the effect of humans through the shape of the temporal trends of the residuals (RESTREND) of the environmental model and quantified human relative impact through the RESTREND: ANPP trend ratio. ANPP interannual variation was significantly explained by ENSO (through SOI) and precipitation in 65% of the study area. The SOI had a positive association with annual precipitation. The association between ANPP and annual precipitation was positive. RESTREND analysis was statistically significant in 92% of the area where the tested environmental model worked, representing 60% of the study area, and it was mostly negative. However, its magnitude, revealed through the RESTREND: ANPP trend ratio, was relatively mild. Our analysis revealed that most of ANPP trends were associated with climate and that even when human density is low, its incidence seems to be mainly negative.

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Degradation processes often lead to species loss. Such losses would impact on ecosystem functioning depending on the extinction order and the functional and structural aspects of species. For the Patagonian arid steppe, we used a simulation model to study the effects of species loss on the rate and variability (i.e. stability) of transpiration as a key attribute of ecosystem functioning. We addressed (1) the differences between the overgrazing extinction order and other potential orders, and (2) the role of biomass abundance, biomass distribution, and functional diversity on the effect of species loss due to overgrazing. We considered a community composed of ten species which were assigned an order of extinction due to overgrazing based on their preference by livestock. We performed four model simulations to test for overgrazing effects through different combinations of species loss, and reductions of biomass and functional diversity. In general, transpiration rate and variability were positively associated to species richness and remained fairly constant until half the species were lost by overgrazing. The extinction order by overgrazing was the most conservative of all possible orders. The amount of biomass was more important than functional diversity in accounting for the impacts of species richness on transpiration. Our results suggest that, to prevent Patagonian steppes from shifting to stable, low-production systems (by overgrazing), maintaining community biomass is more important than preserving species richness or species functional diversity.

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In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative "Land ecosystem change utility for South America", which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.

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El Índice de Vegetación Normalizado (IVN) es un índice espectral derivado de sensores a bordo de satélites que muestra una relación positiva y lineal con la fracción de la radiación fotosintéticamente activa absorbida por el dosel. El IVN es un buen estimador de la productividad primaria, un importante atributo ecosistémico. En este trabajo fueron caracterizados los ecosistemas del Uruguay en base a tres atributos derivados de la curva estacional del IVN a partir de 20 años de imágenes de los satélites NOAA/AVHRR (1981-2000). Se calculó la integral anual del IVN (IVN-I), usada como estimador de la productividad primaria neta aérea; el mes de máximo de IVN (IVN-MDM) y el rango relativo de IVN (IVN-RREL), atributos que dan cuenta de la estacionalidad de la productividad primaria y reflejan aspectos centrales del funcionamiento de los ecosistemas. De acuerdo al comportamiento de estos tres atributos, cada porción del territorio uruguayo se clasificó como un tipo funcional de ecosistema (TFE), combinando métodos de clasificación no supervisados, supervisados y análisis jerárquico. Se identificaron 6 TFE que difieren significativamente entre sí. La variación espacial de los tres atributos utilizados se asocia principalmente a las regiones geomorfológicas y a los patrones de uso del suelo, y no a las variables climáticas

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Numerosos actores socioeconómicos y políticos utilizan las estimaciones de la superficie cultivada para planificar, reducir la incertidumbere o mejorar la asiganción de recursos. Para resultar confiables y útiles, las estimaciones deben basarse en una metodología debidamente documentada, reproducible para el espacio y en el tiempo, idependiente del observador, evaluable de manera cuantitativa. ¿En qué medida se satisfacen los criterio anteriores en Argentina?. Más allá de su utilidad, la información disponible incorpora fuentes de incertidumbre que afectan seriamente las estimaciones. Estas icluyen las dificulatdes para referir las estimaciones a un área determinada, las posibilidades de los informantes de integrar la información local, la ausencia de protocolos claros y diferencias asociadas a la heterogeneidad de formación, motivación y compromiso de los informantes. La comparación de las estimaciones en dos agencias independientes para una año particular arroja, para las mismas áreas, diferencias de hasta el 24 por ciento en el área sembrada con trigo. Esta diferencia es muy superior a las variaciones interanuales que pretenden detectarse. El análisis multiespectral y multitemporal de imágenes satelitales permite discriminar tipos de cobertura del suelo sobre la base de su comportamiento fenológico. La combinación de información satelital provenientes de sensores con distinta resolución espacial ofrece enormes posibilidades para descripción de los tipos de cobertura del suelo y la estimación de superficies agrícolas. En tal sentido se presenta una propuesta operativa, basada en el uso de imágenes Landsat TM, SAC-C y AVHRR/NOAA, para la evolución regional de la superficie cultivada en Mercosur

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