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The application of remote sensing techniques to solving ecological problems is discussed. The three phases of environmental ecological management are examined. The differences between discovery and exploitation of natural resources and their ecological management are described. The specific application of remote sensing to water management is developed.
The use of higher plants in a closed ecological life support system for long duration space missions involving large numbers of people is considered. The approach to planning and developing both the habitat for a long term space mission and closed ecological life support systems are discussed with emphasis on environmental compatibility and integrated systems design. The requirements of photosynthetic processes are summarized and evaluated in terms of their availability within a closed ecological life support environment. Specific references are recommended as a data base for future research on this topic.
The main problems of human adaptation at the level of the body and the population in connection with the features of current morbidity of the population and certain demographic processes are analyzed. The concepts of health and adaptation of the individual and human populations are determined. The importance of the anthropo-ecological approach to the investigation of the adaptation process of human populations is demonstrated. Certain features of the etiopathogenesis of diseases are considered in connection with the population-ecological regularities of human adaptation. The importance of research on general pathology aspects of adaptation and the ecology of man for planning, and organization of public health protection is discussed.
Self maintaining laboratory scale ecological systems completely isolated from exchanges of matter with external systems were demonstrated. These research tools are discussed in terms of their anticipated value in understanding (1) global ecological material and energy balances, (2) the dynamics of stability and instability in ecosystems, (3) the effects of man-made substances and structures on ecosystems, and (4) the precise requirements for dynamic control of controlled ecology life support systems (CELSS).
Cognitive engineering is largely concerned with creating environmental designs to support skillful and effective human activity. A set of necessary conditions are proposed for psychological models capable of supporting this enterprise. An analysis of the psychological nature of the design product is used to identify a set of constraints that models must meet if they can usefully guide design. It is concluded that cognitive engineering requires models with resources for describing the integrated human-environment system, and that these models must be capable of describing the activities underlying fluent and effective interaction. These features are required in order to be able to predict the cognitive activity that will be required given various design concepts, and to design systems that promote the acquisition of fluent, skilled behavior. These necessary conditions suggest that an ecological approach can provide valuable resources for psychological modeling to support design. Relying heavily on concepts from Brunswik's and Gibson's ecological theories, ecological task analysis is proposed as a framework in which to predict the types of cognitive activity required to achieve productive behavior, and to suggest how interfaces can be manipulated to alleviate certain types of cognitive demands. The framework is described in terms, and illustrated with an example from the previous research on modeling skilled human-environment interaction.
The application of remote sensing to ecological investigations is briefly discussed. Emphasis is given to the recruitment problem in marine population dynamics, the regional analysis of terrestrial ecosystems, and the monitoring of ecological changes. Impediments to the use of remote sensing data in ecology are addressed.
Several promising approaches to assessing biochemical and architectural properties of landscapes are outlined. Strategies for using new EOS sensors in ecological models are examined. Ways in which ecological and remote sensing models can utilize information provided by the new sensors to characterize ecological properties at coarse scales and to estimate within-ecosystem properties are addressed.
A major factor for long-term success of a lunar station is the ability to keep an agroecosystem functioning at a desirable, stable steady-state with ecological stability and reliability. Design for a long-lived extraterrestrial manned station must take into account interactions among its subsystems to insure that overall functionality is enhanced (or at least not compromised). Physical isolation of feed production, human living areas, recycling, and other systems may be straightforward, however, microbiological isolation will be very difficult. While it is possible to eliminate plant-associated microbiological communities by growing the plants asepticallly, it is not practical to keep plants germ-free on a large scale if humans are working with them. Ecological theory strongly suggests that some kinds of communities or organisms effectively increase the stability of ecosystems and will protect the plants from potential pathogens. A carefully designed and maintained (lunar-derived) soil can provide a variety of habitats for effective microbial buffers while adding structure to the agroecosystem. A soil can also increase ecosystem reliability through buffering otherwise large element and compound fluctuations (of nutrients, wastes, etc.) as well as buffering temperature level and atmosphere composition. We are doing experiments in ecological dynamics and attempting to extend the relevant theories.
Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.
Researchers in the Astrobiology Technology Branch at Ames Research Center have begun investigating alternate concepts for the detection of extraterrestrial life. We suggest searching for extraterrestrial ecology, exoecology, as well as for extraterrestrial biology, exobiology. Ecology describes the interactions of living things with their environment. All ecosystems are highly constrained by their environment and are constrained by well-known system design principles. Ecology could exist wherever there is an energy source and living I things have discovered some means to capture, store, and use the available energy. Terrestrial ecosystems use as energy sources, light, organic molecules, and in thermal vents and elsewhere, simple inorganic molecules. Ecosystem behavior is controlled by matter and energy conservation laws and can be described by linear and nonlinear dynamic systems theory. Typically in an ecosystem different molecules are not in chemical equilibrium and scarce material is conserved, stored, or recycled. Temporal cycles and spatial variations are often observed. These and other -eneral principles of exoecology can help guide the search for extraterrestrial life. The chemical structure observed in terrestrial biology may be highly contingent on evolutionary accidents. Oxygen was not always abundant on Earth. Primitive sulfur bacteria use hydrogen sulfide and sulfur to perform photosynthesis instead of water and oxygen. Astrobiologists have assumed, for the sake of narrowing and focusing our life detection strategies, that extraterrestrial life will have detailed chemical similarities with terrestrial life. Such assumptions appear very reasonable and they allow us to design specific and highly sensitive life detection experiments. But the fewer assumptions we make, the less chance we have of being entirely wrong The best strategy for the detection of extraterrestrial life could be a mixed strategy. We should use detailed assumptions based on terrestrial biology to guide some but not all future searches for alien life. The systems principles of exoecology seem much more fundamental and inescapable than the terrestrial biology analogies of exobiology. We should search for exoecology as well as exobiology.
Counts of taxonomic diversity are the prevailing standards for documenting large-scale patterns of evolution in the fossil record. However, the secular pattern of relative ecological importance between the bryozoan clades Cyclostomata and Cheilostomata is not reflected fully in compilations of generic diversity or within-fauna species richness, and the delayed ecological recovery of the Cheilostomata after the mass extinction at the Cretaceous-Tertiary boundary is missed entirely. These observations demonstrate that evolutionary success and ecological dominance can be decoupled and profoundly different, even over tens of millions of years.
Phenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability, or day length are of particular interest. This article provides an overview of the observer-based plant phenology sampling conducted by the U.S. National Ecological Observatory Network (NEON), the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30-yr life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower-based cameras, satellite remote sensing, and annual high-resolution airborne remote sensing. Ground-based measurements can be used to calibrate and improve satellite-derived phenometrics. NEON's phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical, and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continental-scale inference about the status, trends, causes, and ecological consequences of phenological change.
Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.
A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.
Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.
Hyperdiverse tropical rainforests, such as the aseasonal forests in Southeast Asia, are supported by high annual rainfall. Its canopy is dominated by the species-rich tree family of Dipterocarpaceae (Asian dipterocarps), which has both ecological (e.g., supports flora and fauna) and economical (e.g., timber production) importance. Recent ecological studies suggested that rare irregular drought events may be an environmental stress and signal for the tropical trees. We assembled the genome of a widespread but near threatened dipterocarp, Shorea leprosula, and analyzed the transcriptome sequences of ten dipterocarp species representing seven genera. Comparative genomic and molecular dating analyses suggested a whole-genome duplication close to the Cretaceous-Paleogene extinction event followed by the diversification of major dipterocarp lineages (i.e. Dipterocarpoideae). Interestingly, the retained duplicated genes were enriched for genes upregulated by no-irrigation treatment. These findings provide molecular support for the relevance of drought for tropical trees despite the lack of an annual dry season.
Abstract Soil microbiomes are sensitive to current and previous soil conditions, and bacterial ‘bioindicators’ of biological, physical, and chemical soil properties have considerable potential for soil health assessment. However, the lack of ecological or physiological information for most soil microorganisms limits our ability to interpret the associations of bioindicators and, thus, their utility for guiding management. We identified bioindicators of tillage intensity and twelve soil properties used to rate soil health using a 16S rRNA gene-based survey of farmland across North America. We then inferred the genomic traits of bioindicators and evaluated their environment-wide associations (EWAS) with respect to agricultural management practice, disturbance, and plant associations with 89 studies from agroecosystems. Most bioindicators were either positively correlated with biological properties (e.g., organic matter) or negatively correlated with physical and chemical properties. Higher soil health ratings corresponded with smaller genome size and higher coding density, while lower ratings corresponded with larger genomes and higher rrn copy number. Community-weighted genome size explained most variation in health ratings. EWAS linked prominent bioindicators with the impacts of environmental disturbances. Our findings provide ecological insights into bioindicators of soil properties relevant to soil health management, illustrating the tight coupling of microbiome and soil function.
Despite decades of research on the interactions between ecology and evolution, opportunities still remain to further integrate the two disciplines, especially when considering multispecies systems. Here, we discuss two such opportunities. First, the traditional emphasis on the distinction between evolutionary and ecological processes should be further relaxed as it is particularly unhelpful in the study of microbial communities, where the very notion of species is hard to define. Second, key processes of evolutionary theory such as adaptation should be exported to hierarchical levels higher than populations to make sense of biodiversity dynamics. Together, we argue that broadening our perspective of eco-evolutionary dynamics to be more inclusive of all biodiversity, both phylogenetically and hierarchically, will open up fertile new research directions and help us to address one of the major scientific challenges of our time, that is, to understand and predict changes in biodiversity in the face of rapid environmental change.