Modeling a cold-air drainage event with a wireless sensor network

A wireless network of sensors was used to characterize a cold-air drainage event in the canyon surrounding the James Reserve. The flow of cold air at night and the first hours of sunrise have major ecological consequences by limiting the vegetation types to those tolerant of freeze and thaw cycles. A network of wireless sensors provides the opportunity to track this event in real time and fully characterize the cold air flow down the canyon, which may last 1.5 hours, and the pooling of cold air in low lying areas. By adjusting the spatial extent of the wireless sensors and the time interval between data capture, we can optimize the spatial and temporal extent of a sensor network and its ability to describe multiple cold-air drainage events

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

Deforestation risk in the Peruvian Amazon basin

The prevention of tropical forest deforestation is essential for mitigating climate change. We tested the machine learning algorithm Maxent to predict deforestation across the Peruvian Amazon. We used official annual 2001-2019 deforestation data to develop a predictive model and to test the model's accuracy using near-real-time forest loss data for 2020. Distance from agricultural land and distance from roads were the predictor variables that contributed most to the final model, indicating that a narrower set of variables contribute nearly 80% of the information necessary for prediction at scale. The permutation importance indicating variable information not present in the other variables was also highest for distance from agricultural land and distance from roads, at 40.5% and 14.3%, respectively. The predictive model registered 73.2% of the 2020 early alerts in a high or very high risk category; less than 1% of forest cover in national protected areas were registered as very high risk, but buffer zones were far more vulnerable, with 15% of forest cover being in this category. To our knowledge, this is the first study to use 19 years of annual data for deforestation risk. The open-source machine learning method could be applied to other forest regions, at scale, to improve strategies for reducing future deforestation

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

Satellite-derived forest canopy greenness shows differential drought vulnerability of secondary forests compared to primary forests in Peru

Understanding tropical secondary forest canopy greenness and responses to climatic conditions is important for climate change mitigation, particularly in the tropics where secondary forest growth is a substantial carbon sink and a promoted natural climate solution. We here test three hypotheses: (a) forest canopy greenness is higher in younger, secondary forests than in older, primary or mature forests, (b) secondary forests are more vulnerable to climatic pressures and (c) there are significant differences between forest types regarding primary–secondary canopy greenness and their differential responses to drought anomalies. To explore these relationships, we monitored wet and dry seasonal greenness from 2001 to 2020, estimated through the enhanced vegetation index (EVI), of Peruvian tropical dry, montane and lowland secondary forests and compared it to nearby primary forests. We developed predictive models of seasonal EVI using remotely sensed variables, including land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), ratio of ET and PET (ETn), and the standard precipitation index (SPI). Overall, there was a higher change in annual and seasonal EVI for secondary forests compared to primary forests. However, primary forests maintained relatively stable EVI levels during the wet season despite drought anomalies. When decoupling forest type canopy greenness and drought response, primary forest greenness in dry and lowland ecosystems were temporally more stable. Secondary montane had a lower increase in greenness when drought anomalies held during different seasons. Stepwise multiple linear regression models indicated that LST and ETn, a plant water use index, were the most significant factors to predict greening fluctuations in dry and montane forest types. ET and SPI mostly drove wet season mean EVI across all forest types. Predictors of dry season mean EVI varied, but mostly including water availability. Our results suggest that tropical secondary forests are more productive overall yet more vulnerable to prolonged drought

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

Soils hold the largest pool of organic carbon (C) on Earth" yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes

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

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