Jorge F. Perez-Quezada

Seasonal fluctuations in Vitis vinifera root respiration in the field

We studied the seasonal fluctuation of soil respiration (R(S)), and its root-dependent (R(R)) and basal (R(B)) components, in a Vitis vinifera (Chardonnay) vineyard. The R(S) components were estimated through independent field methods (y-intercept and trenching) and modeled on the basis of a Q(10) response to soil temperature, and fine and coarse root respiration coefficients. The effect of assimilate availability on R(R) was assessed through a trunk girdling treatment. The apparent Q(10) for R(R) was twice that of R(B) (3.5 vs 1.6) and increased linearly with increasing vine root biomass. The fastest R(R) of fine roots was during rapid fruit growth and the fastest R(R) of coarse roots was immediately following fruit development. R(S) was estimated at 32.6 kg ha(-1) d(-1) (69% as a result of R(R) ) for the hottest month and at 7.6 kg ha(-1) d(-1) (18% as a result of R(R)) during winter dormancy. Annual R(S) was low compared with other natural and cultivated ecosystems: 5.4 Mg ha(-1) (46% as a result of R(R)). Our estimates of annual vineyard R(S) are the first for any horticultural crop and suggest that the assumption that they are similar to those of annual crops or forest trees might lead to an overestimation.

Land Use Influences Carbon Fluxes in Northern Kazakhstan

Abstract A mobile, closed-chamber system (CC) was used to measure carbon and water fluxes on four land-use types common in the Kazakh steppe ecoregion. Land uses represented crop (wheat or barley, WB), abandoned land (AL), crested wheatgrass (CW), and virgin land (VL). Measurements were conducted during the growing season of 2002 in northern Kazakhstan at three locations (blocks) 15–20 km apart. The CC allowed the measurement of the carbon flux components of net ecosystem exchange (NEE), ecosystem respiration (RE) and soil respiration (RS), together with evapotranspiration (ET). Nonlinear regression analyses were used to model gross primary production (GPP) and ET as a function of photosynthetically active radiation (Q); RE and RS were modeled based on air (Tair) and soil (Ts) temperature, respectively. GPP, RE, RS, and ET were estimated for the entire year with the use of continuous 20-min means of Q, Tair, and Ts. Annual NEE indicated that AL gained 536 g CO2 · m−2, WB lost − 191 g CO2 · m−2, CW was near equilibrium (− 14 g CO2 · m−2), and VL exhibited considerable carbon accumulation (153 g CO2 · m−2). The lower GPP values of the land-use types dominated by native species (CW and VL) compared to WB and AL were compensated by positive NEE values that were maintained during a longer growing season. As expected, VL and CW allocated a larger proportion of their carbon assimilates belowground. Non–growing-season RE accounted for about 19% of annual RE in all land-use types. The results of this landscape-level study suggest that carbon lost by cultivation of VLs is partially being restored when fields are left uncultivated, and that VLs are net sinks of carbon. Estimations of carbon balances have important management implications, such as estimation of ecosystem productivity and carbon credit certification.

Predicting Vascular Plant Richness in a Heterogeneous Wetland Using Spectral and Textural Features and a Random Forest Algorithm

A method to predict vascular plant richness using spectral and textural variables in a heterogeneous wetland is presented. Plant richness was measured at 44 sampling plots in a 16-ha anthropogenic peatland. Several spectral indices, first-order statistics (median and standard deviation), and second-order statistics [metrics of a gray-level co-occurrence matrix (GLCM)] were extracted from a Landsat 8 Operational Land Imager image and a Pleiades 1B image. We selected the most important variables for predicting richness using recursive feature elimination and then built a model using random forest regression. The final model was based on only two textural variables obtained from the GLCM and derived from the Landsat 8 image. An accurate predictive capability was reported (R2 = 0.6; RMSE = 1.99 species), highlighting the possibility of obtaining parsimonious models using textural variables. In addition, the results showed that the mid-resolution Landsat 8 image provided better predictors of richness than the high-resolution Pleiades image. This is the first study to generate a model for plant richness in a wetland ecosystem.

Simulation of within-field yield variability in a four-crop rotation field using SSURGO Soil-Unit definitions and the Epic Model

Soil data were collected from a 30 ha commercial field using a 60 m sampling grid. Monitored yield data were also collected in this field between 1996 and 1999, when it had a wheat–processing tomato–bean–sunflower crop rotation. A comparison between SSURGO–NRCS soil–unit definition and field–measured soil data showed that in this field the former are a good approximation and starting point for precision agriculture studies and management. In a second test, the EPIC model, using the SSURGO database soil type definitions, was found to reproduce the yield variability within this field with reasonable accuracy. The model’s performance was not as good when tested with data from soil samples, apparently due to the way EPIC simulates water holding capacity from texture information and the lack of some key variables (not sampled), such as water content at field–capacity (FC), wilting–point (WP), and soil saturated conductivity. A set of runs was performed to simulate the yield at 13 point–locations in the field using FC, WP, and bulk density. Although the accuracy of the simulation did not improve greatly, the model reproduced the yield trend of two of the crops (wheat and sunflower).

Evaluation of impacts of management in an anthropogenic peatland using field and remote sensing data

Peatlands are a type of wetland characterized by the accumulation of organic matter, called peat, and are important carbon reservoirs. In areas with poor drainage, human-induced forest fires and logging can produce flooded conditions and organic matter accumulation, which generates an ecosystem called anthropogenic peatland. Productive management activities such as Sphagnum moss harvesting and livestock grazing take place there. Our hypothesis was that productive management has a strong impact on the aboveground C reservoir and increases the presence of exotic species. We established 44 sampling points in a 16-ha anthropogenic peatland on Chiloe Island, Chile, comparing productive and conservation types of managements. Carbon stocks, vegetation structure and composition variables were quantified. These variables were used to classify the ecosystem into microsites to analyze the different locations in the peatland. In addition, predictive models of aboveground carbon were created using Landsat 8 OLI and Pleiades images. The results revealed a carbon stock of 11.99 ± 0.77 kg C m−2, which is smaller than in natural peatlands, and showed a wide variability of conditions within the peatland itself. This variability, mainly expressed in aboveground carbon, produces microsites dominated by either shrubs, species of the genus Juncus or grasses. Productive management reduced accumulated carbon in the aboveground stock and in the woody debris. However, the strongest impact was found on the vegetation variables, with a decrease in total cover, cover of shrubs and herbaceous plants, and in vegetation height. There was also an increase in the richness and presence of exotic species. The spatial prediction of aboveground carbon yielded significant results using only spectral indices, showing also that the impact of productive management is not homogenous, being less intense in waterlogged areas. This study is the first to quantify carbon reservoirs in this type of ecosystem and to propose variables that can be used as indicators of the impact of human activities.

Tree carbon stock in evergreen forests of Chiloé, Chile

The carbon stock associated with tree biomass was estimated in evergreen forests near the town of Inio in Chiloe Island, Chile (43o21’ S, 74o07’ W), analyzin...

Temporal Gap Between Knowledge and Conservation Needs in High Andean Anurans: The Case of the Ascotán Salt Flat Frog in Chile (Anura: Telmatobiidae: Telmatobius)

Abstract. The natural history of most species of Telmatobius that live in the Altiplano of the Andes is unknown due to the difficulty of performing long-term studies and the logistics of working in these remote areas with extreme environmental conditions. One of these anurans inhabits the springs of the Ascotán salt flat of Chile. Here, we provide for the first time information on its distribution, habitat, microhabitat, density, diet and reproductive activity. Suitable habitat for the specie is restricted to a few springs that drain into the salt flat. Amphibian density was variable among springs and seasons and diet was composed mainly of bottom-dwelling invertebrates. We also found evidence of selection for some benthonic prey. Reproductive activity occurred mainly at night. Larvae were found during the whole year, which suggests a long larval developmental period. The salt flat is under strong anthropic pressure, although so far this anuran has managed to persist in spite of habitat perturbations. Our biological and ecological data might increase our ability to act and protect this high Andean anuran species.

Actual evapotranspiration and its relation with floristic composition and topographical features in an arid watershed

Actual evapotranspiration (ET) represents an ecosystem key functional variable, directly related with vegetation properties, particularly in water limited environments. The present study aims to map ET using a surface energy fluxes modelling approach, and explore its relation with topographical and floristic composition features, in an arid watershed of Central Chile. Several topographical and vegetation variables were significantly related to ET, however the most important were altitude, northing, easting, the height of herbaceous plants and tree species richness. The patterns of ET rate of the selected variables were explained by the singular eco-hydrological condition of the watershed. These variables were used in a multivariate linear model which allowed an accurate prediction of daily ET for a spring day corresponding to a critical growing vegetation condition.