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At least 73 records · Page 4

Space-Based Ornithology: On the Wings of Migration and Biophysics

Understanding bird migration and avian biodiversity is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Public interest is high with concerns about avian flu, Katrina and its impacts, and the recent sightings of the ivory-billed woodpecker. Space based technology gives us new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. Together with the USGS and other agencies, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. In our work, we use individual organism biophysical models of energy and water balance and drive these models with satellite measurements of spatio-temporal gradients in climate and habitat. Dynamic state variable modeling provides an additional tool for studying bird migration across multiple scales and can be linked to our mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. We also build upon the canopy done by the Environmental Laboratory over the past few years.

Smith, James A.↗

Tradeoffs between uniform land protection and biodiversity-specific land protection with <2 °C global warming

Nearly 200 countries have pledged to conserve 30% of terrestrial ecosystems to stop the global biodiversity crisis. However, biodiversity is not uniformly distributed across countries. Adequately addressing this crisis requires a scientific basis for selecting protected land that considers both ecological benefits and impacts to humans. We use the global change analysis model to evaluate land use tradeoffs of four land protection cases under two climate cases. We find that biodiversity-specific land protection up to 39% globally can reduce land use constraints and food prices compared to protecting 30% of land uniformly in each country (’30 × 30’ initiative). Valuing terrestrial carbon for climate change mitigation reduces land conversion pressure and can complement protection strategies. Global impacts to agriculture of additional land protection are small, but regional impacts vary and may be considerable. Overall, biodiversity-specific land protection has greater potential than a uniform target to meet both ecological and human needs.

biodiversity↗

Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

59 BASIC BIOLOGICAL SCIENCES↗

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

36 MATERIALS SCIENCE↗

Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.

54 ENVIRONMENTAL SCIENCES↗

Landscape-scale characterization of Arctic tundra vegetation composition, structure, and function with a multi-sensor unoccupied aerial system

The Arctic is experiencing some of the most rapid climate change on Earth, with strong impacts on tundra ecosystems that are characterized by high land-surface and vegetation heterogeneity. Previous studies have explored this complexity using satellite remote sensing, however these typically coarse spatial resolution data have generally missed sub-pixel heterogeneity, leaving critical gaps in our understanding of tundra vegetation dynamics from the community to landscape scales. To address these gaps, we collected very high-resolution (1-5 cm) optical, structural, and thermal data at three low-Arctic tundra sites on the Seward Peninsula, Alaska, using a multi-sensor unoccupied aerial system (UAS). We examined the application of these data to studying tundra vegetation dynamics, by quantifying (1) canopy height and thermoregulation (leaf-air temperature) of representative plant functional types (PFTs), (2) fine-scale patterns of vegetation composition across landscapes, and (3) impacts of fine-scale vegetation composition on landscape-scale variation of canopy height and thermoregulation. Our results show that deciduous tall shrubs (those that can potentially grow >2 m) had a strong cooling effect, with canopy temperatures significantly lower than local air temperatures and other PFTs. Increased cover of tall shrubs also had the potential to reduce the cover of low-stature PFTs across the landscape, potentially associated with their closed canopy (i.e., increased light competition) and strong thermoregulation. To understand the connections between fine-scale vegetation composition and large-scale ecosystem processes, we produced a random forest model which showed that fine-scale PFT composition accounted for 86.8% and 74.2% of the landscape-scale variation in canopy height and thermoregulation, respectively. These findings highlight the importance of spatially detailed characterization of tundra PFTs to improve our ecological understanding and model representation of tundra vegetation, also transcend our study to show the need for continued collection of similar datasets to better understand the impacts of surface heterogeneity on the mapping and modeling of tundra ecosystem dynamics, as well as assist with conservation management and biodiversity monitoring strategies.

54 ENVIRONMENTAL SCIENCES↗

SERVIR Presentation for the Hyperwall at the ESRI International Users Conference

From space, we can view our planet in new ways. As a joint initiative of NASA and USAID working in partnership with leading regional organizations, SERVIR connects space to village by helping people in developing countries use that view to gain knowledge and insights about their environments. We teach regional decision-makers to use Earth observation satellite data, Geographic Information Systems, and predictive models for addressing issues such as water and land use, natural disasters, agricultural problems, biodiversity conservation, and more to improve the lives, livelihoods, safety, and future of the people in their countries.

Irwin, Dan↗

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗

Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar

Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world's biodiversity hotspots, Madagascar's lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model's ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with r(squared) values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m(squared)/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and -1 t Cha(exp -1)year(exp -1), consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests.

Forest Productivity↗

Algorithm Development for Predicting Biodiversity Based on Phytoplankton Absorption

Ocean color remote sensing has provided the scientific community with unprecedented global coverage of chlorophyll a, an indicator of phytoplankton biomass. Together, satellite-derived chlorophyll a and knowledge of Phytoplankton Functional Types (PFTs) will improve our limited understanding of marine ecosystem responses to physiochemical climate drivers involved in carbon cycle dynamics and linkages. Using cruise data from the Gulf of Maine and the Middle Atlantic Bight (N 269 pairs of HPLC and phytoplankton absorption samples), two modeling approaches were utilized to predict phytoplankton absorption and pigments. Algorithm I predicts the chlorophyll-specific absorption coefficient (a*(sub ph) (m(exp 2) mg chl a(exp - 1)) using inputs of temperature, light, and chlorophyll a. Modeled r(exp 2) values (400700 nm) ranged from 0.79 to 0.99 when compared to in situ observations with approximately 25 percent lower r(exp 2) values in the UV region. Algorithm II-a utilizes matrix inversion analysis to predict a(sub ph)(m(exp -1), 400700 nm) and r(exp 2) values ranged from 0.89 to 0.99. The prediction of phytoplankton pigments with Algorithm II-b produced r(exp 2) values that ranged from 0.40 to 0.93. When used in combination, Algorithm I, and Algorithm II-a are able to use satellite products of SST, PAR, and chlorophyll a (Algorithm I) to predict pigment concentrations and ratios to describe the phytoplankton community. The results of this study demonstrate that the spatial variation in modeled pigment ratios differ significantly from the 10-year SeaWiFS average chlorophyll a data set. Contiguous observations of chlorophyll a and phytoplankton biodiversity will elucidate ecosystem responses with unprecedented complexity.

phytoplankton↗

Toward a coordinated understanding of hydro‐biogeochemical root functions in tropical forests for application in vegetation models

Summary Tropical forest root characteristics and resource acquisition strategies are underrepresented in vegetation and global models, hampering the prediction of forest–climate feedbacks for these carbon‐rich ecosystems. Lowland tropical forests often have globally unique combinations of high taxonomic and functional biodiversity, rainfall seasonality, and strongly weathered infertile soils, giving rise to distinct patterns in root traits and functions compared with higher latitude ecosystems. We provide a roadmap for integrating recent advances in our understanding of tropical forest belowground function into vegetation models, focusing on water and nutrient acquisition. We offer comparisons of recent advances in empirical and model understanding of root characteristics that represent important functional processes in tropical forests. We focus on: (1) fine‐root strategies for soil resource exploration, (2) coupling and trade‐offs in fine‐root water vs nutrient acquisition, and (3) aboveground–belowground linkages in plant resource acquisition and use. We suggest avenues for representing these extremely diverse plant communities in computationally manageable and ecologically meaningful groups in models for linked aboveground–belowground hydro‐nutrient functions. Tropical forests are undergoing warming, shifting rainfall regimes, and exacerbation of soil nutrient scarcity caused by elevated atmospheric CO 2 . The accurate model representation of tropical forest functions is crucial for understanding the interactions of this biome with the climate.

Plant Sciences↗

Fire, insect and disease‐caused tree mortalities increased in forests of greater structural diversity during drought

Abstract Structural diversity is an emerging dimension of biodiversity that accounts for size variations in organs among individuals in a community. Previous studies show significant effects of structural diversity on forest growth, but its effects on forest mortality are not known, particularly at a large scale. To address this knowledge gap, we quantified structural diversity using stem structural diversity (SSD) based on both tree diameter and height. We obtained U.S. Forest Service Forest Inventory and Analysis (FIA) data from over 2400 plots across southcentral U.S. forests that have suffered a recent drought. Using data from multiple sampling times, we calculated SSD and compared the relative importance of SSD, species diversity, functional diversity and other stand attributes in determining tree mortalities caused by fire, insects and diseases. We also used FIRETEC, a physics‐based fire model, to test the effect of SSD on canopy consumption by fire. Our results showed that (1) SSD was positively associated with tree mortalities caused by all three disturbances; (2) species richness was negatively associated with insect‐ and disease‐caused mortalities; (3) functional diversity was negatively associated with fire‐ and disease‐caused mortalities and (4) more phylogenetically related species had more similar mortality rates by insect and disease but not fire. Moreover, the FIRETEC model showed increasing canopy consumption by fire in stands with greater SSD. Together, the different tree mortalities during drought associated with SSD more consistently than the other biodiversity metrics were evaluated. Synthesis . Our results suggest that SSD could be considered in modelling forest dynamics and planning management to sustain forest health under disturbances.

54 ENVIRONMENTAL SCIENCES↗

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018

The sensitivity of vegetation productivity to precipitation (S ppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of S ppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of S ppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of S ppt with different satellite products: S ppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest S ppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, S ppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in S ppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, S ppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in S ppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in S ppt . Our finding of the overall decline in S ppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed S ppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

54 ENVIRONMENTAL SCIENCES↗

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗

Microbial astronauts: assembling microbial communities for advanced life support systems

Extension of human habitation into space requires that humans carry with them many of the microorganisms with which they coexist on Earth. The ubiquity of microorganisms in close association with all living things and biogeochemical processes on Earth predicates that they must also play a critical role in maintaining the viability of human life in space. Even though bacterial populations exist as locally adapted ecotypes, the abundance of individuals in microbial species is so large that dispersal is unlikely to be limited by geographical barriers on Earth (i.e., for most environments "everything is everywhere" given enough time). This will not be true for microbial communities in space where local species richness will be relatively low because of sterilization protocols prior to launch and physical barriers between Earth and spacecraft after launch. Although community diversity will be sufficient to sustain ecosystem function at the onset, richness and evenness may decline over time such that biological systems either lose functional potential (e.g., bioreactors may fail to reduce BOD or nitrogen load) or become susceptible to invasion by human-associated microorganisms (pathogens) over time. Research at the John F. Kennedy Space Center has evaluated fundamental properties of microbial diversity and community assembly in prototype bioregenerative systems for NASA Advanced Life Support. Successional trends related to increased niche specialization, including an apparent increase in the proportion of nonculturable types of organisms, have been consistently observed. In addition, the stability of the microbial communities, as defined by their resistance to invasion by human-associated microorganisms, has been correlated to their diversity. Overall, these results reflect the significant challenges ahead for the assembly of stable, functional communities using gnotobiotic approaches, and the need to better define the basic biological principles that define ecosystem processes in the space environment. Copyright 2004 Springer-Verlag.

Review↗

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

plant communities↗

Effects of habitat map generalization in biodiversity assessment

Species richness is being mapped as part of an inventory of biological diversity in California (i.e., gap analysis). Species distributions are modeled with a GIS on the basis of maps of each species' preferred habitats. Species richness is then tallied in equal-area sampling units. A GIS sensitivity analysis examined the effects of the level of generalization of the habitat map on the predicted distribution of species richness in the southern Sierra Nevada. As the habitat map was generalized, the number of habitat types mapped within grid cells tended to decrease with a corresponding decline in numbers of species predicted. Further, the ranking of grid cells in order of predicted numbers of species changed dramatically between levels of generalization. Areas predicted to be of greatest conservation value on the basis of species richness may therefore be sensitive to GIS data resolution.

Stoms, David M.↗

Pixel Based Model for High Latitude Dust Detection

High Latitude Dust (HLD, ≥ 50°𝑁 𝑎𝑛𝑑 ≥ 40°𝑆 ) load has implications on the energy budget, ocean biodiversity and economy on a regional and global scale. Current methods of dust detection rely on spectral sensitivity at visible (RGB) and infrared wavelengths. The characteristics of HLD vary according to the sediments and sedimentary processes operating on the land surface that are the source of the dust particles. Leveraging machine learning (ML) methods, we propose a new detection method based on convolutional neural network (CNN) using true color images.

Priftis, Georgios↗