The biodiversity survey of the Cape (BioSCape), integrating remote sensing with biodiversity science
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Anabelle W. Cardoso↗
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Results for “biodiversity”
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The Marine Biodiversity Observation Network Plankton Workshops: Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications
Plankton functional diversity is at the core of various ecological processes, including productivity, carbon cycling and sequestration, nutrient cycling, interspecies interactions, food web dynamics and structure. Through these functions, plankton play a critical role in the health of the coastal and open ocean and provide essential ecosystem services. Yet, at present, our understanding of plankton dynamics is insufficient to project how climate change and other human-driven impacts affect the functional diversity of plankton. That limits our ability to predict how critical ecosystem services will change in the future and develop strategies to adapt to these changes.
Plankton↗
A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models
Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections may therefore be overly optimistic because they do not always account for the role of biodiversity in maintaining ecological functions. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two integration pathways. The first uses empirical data on biodiversity–ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented globally for systems and taxa with sufficient data. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models. Pursuing both approaches will provide greater insight into biodiversity and ecosystem services projections. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance policy development to meet global sustainability goals.
biodiversity models↗
CyanoSCape: Freshwater Phytoplankton and Floating Aquatic Vegetation Biodiversity
In Southern Africa, the impacts of anthropogenic activities on biodiversity and ecosystem services are exacerbated by the climate crisis. Rapid land use change and the lack of emphasis on environmentally sustainable agricultural practices has hindered hydrological processes and compromised riverine and aquatic ecosystems. This poses obvious risks to natural/indigenous aquatic biodiversity and long-term ecosystem sustainability. Phytoplankton serve as the foundation of the freshwater food web with zooplankton as consumers, which feed fish, invertebrates, and so on up the food chain that comprises the biodiversity of the freshwater system that serves as habitat for biodiversity as well. The diversity of phytoplankton (microscopic organisms) includes photosynthesizing bacteria (cyanobacteria), plant-like diatoms, dinoflagellates, and green algae. Nutrient run-off from agricultural fertilizers and urban overflows, warm temperatures, abundant light availability and compromised hydrological systems provide an ideal environment for cyanobacteria to flourish. Increased prevalence of cyanobacteria, due to both natural and anthropogenic causes, can incur significant effects on the biodiversity of the overall phytoplankton assemblage. These bloom forming algae can significantly outcompete other phytoplankton classes in warmer and eutrophic waters where they are quick to dominate the freshwater system. Eutrophication and toxic cyanobacteria blooms (cyanoHABs) in the inland waters of the Greater Cape Floristic Region (GCFR) incur significant effects on the biodiversity of the overall phytoplankton assemblage and provide a favorable environment forthe overgrowth of floating aquatic vegetation (FAV), which is often invasive and associated with reduced aquatic biodiversity. The algal biodiversity of the GCFR’s freshwater systems is not well characterized. Hyperspectral optical observations are expected to facilitate the improvement of current phytoplankton functional type retrievals significantly, as the sensitivity is sufficient that the distinctive, fine spectral features of different phytoplankton groups can be detected. This will enable testing emerging algorithms and inform the development of new algorithms for use with upcoming hyperspectral satellite missions in this decade. Innovations in optical sensor sensitivity and next generation machine learning capabilities considerably enhance the potential for accurate and rapid detection of phytoplankton, namely the presence, extent, and diversity of cyanobacteria present in cyanoHABs and additionally, invasive FAV. Upcoming hyperspectral satellite missions such as NASA’s Surface Biology and Geology (SBG), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and the European Space Agency’s Copernicus Hyperspectral Imaging Mission (CHIME) will provide imagery with unprecedented spectral and spatial resolution which will further enable the discovery of linkages between the seasonality and dynamics of HABs and FAV. The overarching goal of this project is to utilize hyperspectral data, with recently developed and next-generation algorithms, to determine the biodiversity of freshwater systems phytoplankton assemblage with emphasis on genus level distinction, as well as monitor the prevalence and diversity of FAV.
CyanoSCape↗
Biodiversity loss reduces global terrestrial carbon storage
Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow-decaying structures such as tree bark or root systems. An ecosystem’s carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. We find that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and 10.87-145.95 PgC (fossil-fueled development scenario). This indicates a self-reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals.
Biodiversity↗
A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation
Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.
Bonnie J E Myers↗
Vegetation structural complexity and biodiversity in the Great Smoky Mountains
Vegetation structural complexity and biodiversity tend to be positively correlated, but understanding of this relationship is limited in part by structural metrics tending to quantify only horizontal or vertical variation, and that do not reflect internal structure. We developed new metrics for quantifying internal vegetation structural complexity using terrestrial LiDAR scanning and applied them to 12 NEON forest plots across an elevational gradient in Great Smoky Mountains National Park, USA. We asked (1) How do our newly developed structure metrics compare to traditional metrics? (2) How does forest structure vary with elevation in a high-biodiversity, high topographic complexity region? (3) How do forest structural metrics vary in the strength of their relationships with vascular plant biodiversity? Our new measures of canopy density (Depth) and structural complexity (σDepth), and their canopy height-normalized counterparts, were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Forest structure varied widely across plots spanning the elevational range of GRSM, with taller, more structurally complex forests at lower elevation. Vascular plant biodiversity was negatively correlated with elevation and more strongly positively correlated with vegetation structure variables. The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.
Jonathan A. Walter↗
Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes
There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.
Airborne lidar↗
Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems
The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration less than 2%, relative calibration of 0.2%, polarization sensitivity less than 1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.
aquatic↗
Predicting Patterns of Solar Energy Buildout to Identify Opportunities for Biodiversity Conservation
The construction of solar energy facilities can have positive or negative impacts on biodiversity depending on siting and associated land use transitions. We identified drivers of solar siting and quantified patterns of buildout in states surrounding the Chesapeake Bay watershed – a biodiversity hotspot with numerous ecosystem services. Using a convolutional neural network, we mapped the footprints of ground-mounted solar arrays present in satellite imagery annually from 2017 to 2021 in Delaware, Maryland, Pennsylvania, New York, Virginia, and West Virginia. As of 2021, we identified 958 solar arrays covering 52.3 km2 built primarily on previously cultivated land, while avoiding natural landcover. We fit a binomial-Weibull model to these solar timeseries data in a hierarchical, Bayesian framework to quantify the relationship between geospatial covariates and rate of solar development. Solar array construction rate increased in cultivated areas, areas of lower agricultural suitability, lower slope, lower forest cover, lower biodiversity protection, and greater distances from roads. We also estimated changes in the rate of solar construction over time and found differences among states: acceleration in Virginia and deceleration in New York. We used parameter estimates to map the relative likelihood of future solar development across the study area. This methodology can be used to anticipate where solar is likely to be built in different landscapes and how these patterns align with conservation goals. Around the Chesapeake Bay watershed, the selection of lower quality agricultural areas for solar energy minimizes removal of important habitat and provides opportunities for native plant and pollinator restoration.
Artificial Intelligence↗
Biodiversity: past, present, and future
Data from the fossil record are used to illustrate biodiversity in the past and estimate modern biodiversity and loss. This data is used to compare current rates of extinction with past extinction events. Paleontologists are encouraged to use this data to understand the course and consequences of current losses and to share this knowledge with researchers interested in conservation and ecology.
NASA Discipline Exobiology↗
A natural view of microbial biodiversity within hot spring cyanobacterial mat communities
This review summarizes a decade of research in which we have used molecular methods, in conjunction with more traditional approaches, to study hot spring cyanobacterial mats as models for understanding principles of microbial community ecology. Molecular methods reveal that the composition of these communities is grossly oversimplified by microscopic and cultivation methods. For example, none of 31 unique 16S rRNA sequences detected in the Octopus Spring mat, Yellowstone National Park, matches that of any prokaryote previously cultivated from geothermal systems; 11 are contributed by genetically diverse cyanobacteria, even though a single cyanobacterial species was suspected based on morphologic and culture analysis. By studying the basis for the incongruity between culture and molecular samplings of community composition, we are beginning to cultivate isolates whose 16S rRNA sequences are readily detected. By placing the genetic diversity detected in context with the well-defined natural environmental gradients typical of hot spring mat systems, the relationship between gene and species diversity is clarified and ecological patterns of species occurrence emerge. By combining these ecological patterns with the evolutionary patterns inherently revealed by phylogenetic analysis of gene sequence data, we find that it may be possible to understand microbial biodiversity within these systems by using principles similar to those developed by evolutionary ecologists to understand biodiversity of larger species. We hope that such an approach guides microbial ecologists to a more realistic and predictive understanding of microbial species occurrence and responsiveness in both natural and disturbed habitats.
Review↗
Building Capacity to Use Earth Observations for Ecosystem Services & Biodiversity Applications: A Summary of NASA’s DEVELOP Program and Projects in Central and South America
NASA’s DEVELOP program engages individuals and organizations in 10-week feasibility studies to apply NASA Earth observations (EO) to decision makers’ needs. Since 2018, DEVELOP conducted 21 projects in Central and South America, eight of those in collaboration with Sistema de la Integración Centroamericana (SICA) and its member state partner agencies. These projects addressed a diverse set of community needs across multiple themes, including 11 addressing issues related to biodiversity and ecosystem services that utilized a suite of EO to measure and monitor deforestation, habitat suitability, and land change impacts on watershed health in Peru, Costa Rica, Panama, Honduras, and Colombia. This poster introduces DEVELOP, its approach to capacity building, key partnerships, and project results highlighting the capabilities of EO for enhanced decision making in the area of ecosystem services and biodiversity.
Capacity Building↗
The Internet of Animals: How to Use NASA's Assets and Computing for Biodiversity and Conservation Needs
There are many internal and external drivers of animal movement. For example, where might an animal go if it’s hungry, or how will it move if it’s really windy? The Internet of Animals project aims to combine animal telemetry data with earth observations to better support applied ecological management, architect a next-generation space-based animal tracking system and develop technology that can link to biodiversity and habitats via remote sensing. In this talk, Morgan Gilmour will provide an overview of movement ecology, remote sensing, and how earth observations with NASA’s assets can help inform biodiversity and conservation. Morgan is a Research Scientist at NASA Ames Research Center. Her research focuses on marine animal movement ecology. Some recent projects include investigating animal movement in relation to marine protected areas in the central Pacific and quantifying at-sea habitat use by albatrosses.
animal telemetry↗
Effects of Amazonian Flying Rivers on Frog Biodiversity and Populations in the Atlantic Rainforest
Given the speed at which humans are changing the climate, species with high degrees of endemism may not have time to avoid extinction through adaptation. We investigated through teleconnection analysis the origin of rainfall that determines the phylogenetic diversity of rainforest frogs and the effects of microclimate differences in shaping the morphological traits of isolated populations (which contribute to greater phylogenetic diversity and speciation). We also investigated through teleconnection analysis how deforestation in Amazonia can affect ecosystem services that are fundamental to maintaining the climate of the Atlantic rainforest biodiversity hotspot. Seasonal winds known as flying rivers carry water vapor from Amazonia to the Atlantic Forest, and the breaking of this ecosystem service could lead Atlantic Forest species to population decline and extinction in the short term. Our results suggest that the selection of morphological traits that shape Atlantic Forest frog diversity and their population dynamics are influenced by the Amazonian flying rivers. Our results also suggest that the increases of temperature anomalies in the Atlantic Ocean due to global warming and in the Amazon forest due to deforestation are already breaking this cycle and threaten the biodiversity of the Atlantic Forest hotspot.
anurans↗
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)↗
Global change and biodiversity loss: Some impediments to response
Discussed here are the effects of anthropogenic global climate change on biodiversity. The focus is on human responses to the problem. Greenhouse warming-induced climate change may shift agricultural growing belts, reduce forests of the Northern Hemisphere and drive many species to extinction, among other effects. If these changes occur together with the mass extinctions already occurring, we may suffer a profound loss of biological diversity.
Borza, Karen↗
A remote sensing research agenda for mapping and monitoring biodiversity
A remote sensing research agenda designed to expand the knowledge of the spatial distribution of species richness and its ecological determinants and to predict its response to global change is proposed. Emphasis is placed on current methods of mapping species richness of both plants and animals, hypotheses concerning the biophysical factors believed to determine patterns of species richness, and anthropogenic processes causing the accelerating rate of extinctions. It is concluded that biodiversity should be incorporated more prominently into the global change and earth system science paradigms.
Stoms, D. M.↗