Low- and High-Temperature Tolerance and Acclimation for Chlorenchyma versus Meristem of the Cultivated Cacti Nopalea cochenillifera, Opuntia robusta, and Selenicereus megalanthus

Dividing meristematic cells are thought to be more sensitive to extreme temperatures compared to other tissues, such as chlorenchyma. This was examined for low and high temperatures for three widely cultivated cacti: Nopalea cochenillifera, Opuntia robusta, and Selenicereus megalanthus. Temperature tolerances of chlorenchyma and meristem were based on the cellular uptake of the vital stain neutral red for plants at mean day/night air temperatures of 25/20°C and plants maintained at 10/5°C or 45/40°C to examine temperature acclimation. Meristematic cells tolerated 1.8°C lower low temperatures and 4.0°C higher high temperatures than chlorenchyma cells for the three species at 25/20°C. Both tissue types showed acclimation, with a decrease or increase in temperature tolerated at 10/5°C or 45/40°C, respectively. Meristematic cells were more tolerant of extreme temperatures compared to chlorenchyma, contrary to the prevailing belief, and may reflect an additional strategy for cacti to survive extreme temperatures

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

There is a data gap in our current knowledge of the geospatial distribution, type and extent of C rich peatlands across the globe. The Pastaza Marañón Foreland Basin (PMFB), within the Peruvian Amazon, is known to store large amounts of peat, but the remoteness of the region makes field data collection and mapping the distribution of peatland ecotypes challenging. Here we review methods for developing high accuracy peatland maps for the PMFB using a combination of multi-temporal synthetic aperture radar (SAR) and optical remote sensing in a machine learning classifier. The new map produced has 95% overall accuracy with low errors of commission (1–6%) and errors of omission (0–15%) for individual peatland classes. We attribute this improvement in map accuracy over previous maps of the region to the inclusion of high and low water season SAR images which provides information about seasonal hydrological dynamics. The new multi-date map showed an increase in area of more than 200% for pole forest peatland (6% error) compared to previous maps, which had high errors for that ecotype (20–36%). Likewise, estimates of C stocks were 35% greater than previously reported (3.238 Pg in Draper et al. (2014) to 4.360 Pg in our study). Most of the increase is attributed to pole forest peatland which contributed 58% (2.551 Pg) of total C, followed by palm swamp (34%, 1.476 Pg). In an assessment of deforestation from 2010 to 2018 in the PMFB, we found 89% of the deforestation was in seasonally flooded forest and 43% of deforestation was occurring within 1 km of a river or road. Peatlands were found the least affected by deforestation and there was not a noticeable trend over time. With development of improved transportation routes and population pressures, future land use change is likely to put South American tropical peatlands at risk, making continued monitoring a necessity. Accurate mapping of peatland ecotypes with high resolution (\u3c30 m) sensors linked with field data are needed to reduce uncertainties in estimates of the distribution of C stocks, and to aid in deforestation monitoring

Prediciendo la distribución de Polylepis: bosques Andinos vulnerables y cada vez más importantes

Polylepis woodlands are a vital resource for preserving biodiversity and hydrological functions, which will be altered by climate change and challenge the sustainability of local human communities. However, these highaltitude Andean ecosystems are becoming increasingly vulnerable due to anthropogenic pressure including fragmentation, deforestation and the increase in livestock. Predicting the distribution of native woodlands has become increasingly important to counteract the negative effects of climate change through reforestation and conservation. The objective of this study was to develop and analyze the distribution models of two species that form extensive woodlands along the Andes, namely Polylepis sericea and P. weberbaueri. This study utilized the program Maxent, climate and remotely sensed environmental layers at 1 km resolution. The predicted distribution model for P. sericea indicated that the species could be located in a variety of habitats along the Andean Cordillera, while P. weberbaueri was restricted to the high elevations of southern Peru and Bolivia. For both species, elevation and temperature metrics were the most significant factors for predicted distribution. Further model refinement of Polylepis and other Andean species using increasingly available satellite data demonstrate the potential to help define areas of diversity and improve conservation strategies for the Andes.Los bosques de Polylepis son recursos vitales para la conservación de la biodiversidad y funciones hidrológicas, la cual se verá alterada por el cambio climático a nivel mundial desafiando la sostenibilidad de las comunidades locales. Sin embargo, estos ecosistemas andinos de gran altitud son cada vez más vulnerables debido a la presión antropogénica como la fragmentación, deforestación y el incremento en el ganado. La importancia para predecir la distribución de bosques nativos ha aumentado para contrarrestar los efectos negativos del cambio climático a través de la conservación y la reforestación. El objetivo de este estudio fue desarrollar y analizar los modelos de distribución de dos especies, Polylepis sericea y P. besseri, que forman bosques extensos a lo largo de los Andes. Este estudio utilizó el programa Maxent, el clima y capas ambientales de una resolución de 1 km. El modelo de distribución previsto para P. sericea indica que la especie podría estar situada en una variedad de hábitats a lo largo de la Cordillera de los Andes, mientras que P. besseri se limitaba a las grandes alturas del sur de Perú y Bolivia. Para ambas especies, los metros de elevación y la temperatura son los factores más importantes para la distribución prevista. El perfeccionamiento del modelo de Polylepis y otras especies andinas utilizando datos de satélites cada vez más disponibles al público demuestran el potencial para ayudar a definir las áreas de diversidad y mejorar las estrategias de conservación en los Andes

Benchmark map of forest carbon stocks in tropical regions across three continents

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete

Better estimates of soil carbon from geographical data:A revised global approach

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