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Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP). Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients. [ABSTRACT FROM AUTHOR]

Changing climate may pose a threat to forest tree species, forcing three potential population-level responses: toleration/adaptation, movement to suitable environmental conditions, or local extirpation. Assessments that prioritize and classify tree species for management and conservation activities in the face of climate change will need to incorporate estimates of the risk posed by climate change to each species. To assist in such assessments, we developed a set of four quantitative metrics of potential climate change pressure on forest tree species: (1) percent change in suitable area, (2) range stability over time, (3) range shift pressure, and (4) current realized niche occupancy. All four metrics are derived from climate change environmental suitability maps generated using the Multivariate Spatio-Temporal Clustering (MSTC) technique, which combines aspects of traditional geographical information systems and statistical clustering techniques. As part of the Forecasts of Climate-Associated Shifts in Tree Species (ForeCASTS) project, we calculated the predicted climate change pressure statistics for North American tree species using occurrence data from the USDA Forest Service Forest Inventory and Analysis (FIA) program. Of 172 modeled tree species, all but two were projected to decline in suitable area in the future under the Hadley B1 Global Circulation Model/scenario combination. Eastern species under Hadley B1 were predicted to experience a greater decline in suitable area and less range stability than western species, although predicted range shift did not differ between the regions. Eastern species were more likely than western species, on average, to be habitat generalists. Along with the consideration of important species life-history traits and of threats other than climate change, the metrics described here should be valuable for efforts to determine which species to target for monitoring efforts and conservation actions. [ABSTRACT FROM AUTHOR]

A multi-stressor risk assessment was conducted at Yuma Proving Ground, Arizona, as a demonstration of the Military Ecological Risk Assessment Framework. The focus of the assessment was a testing program at Cibola Range, which involved an Apache Longbow helicopter firing Hellfire missiles at moving targets, that is, M60-A1 tanks. This article focuses on the wildlife risk assessment for the helicopter overflight. The primary stressors were sound and the view of the aircraft. Exposure to desert mule deer (Odocoileus hemionus crooki) was quantified using Air Force sound contour programs NOISEMAP and MR_NMAP, which gave very different results. Slant distance from helicopters to deer was also used as a measure of exposure that integrated risk from sound and view of the aircraft. Exposure-response models for the characterization of effects consisted of behavioral thresholds in sound exposure level or maximum sound level units or slant distance. Available sound thresholds were limited for desert mule deer, but a distribution of slant-distance thresholds was available for ungulates. The risk characterization used a weight-of-evidence approach and concluded that risk to mule deer behavior from the Apache overflight is uncertain, but that no risk to mule deer abundance and reproduction is expected. [ABSTRACT FROM AUTHOR]

A multiple stressor risk assessment was conducted at Yuma Proving Ground, Arizona, as a demonstration of the Military Ecological Risk Assessment Framework (MERAF). The focus was a testing program at Cibola Range that involved an Apache Longbow helicopter firing Hellfire missiles at moving targets, that is, M60-A1 tanks. This article describes the ecological risk assessment, using the MERAF, for the tracked vehicle movement component of the testing program. The principal stressor associated with tracked vehicle movement was soil disturbance, and a resulting, secondary stressor was hydrological change. Water loss to desert wash vegetation was hypothesized to result from increased infiltration and/or evaporation associated with vehicle disturbances to surrounding desert pavement, potentially affecting mule deer as well as vegetation. The simulated exposure of wash vegetation to water loss was quantified using estimates of disturbed land area from a digital orthogonal quarter quadrangle aerial photo and field observations, a 30-m digital elevation model, the flow accumulation feature of ESRI ArcInfo GIS, and a two-step runoff process dependent on soil characteristics and the extent of disturbance. In all simulated scenarios, the absolute amount of water lost increased with distance from the disturbance downslope in the washes; however, the percentage of water lost was greatest in land areas immediately downslope of a disturbance. Potential effects on growth and survival of desert wash trees were quantified by comparing water availability from the hydrologic model to water volume thresholds required for wash trees to survive and persist, derived from a local study. For both the incremental risk of the test program and for the combination of test and pretest disturbances, this demonstration of MERAF found no significant risk to either wash vegetation growth and survival or mule deer abundance and reproduction. [ABSTRACT FROM AUTHOR]

Agro-ecoregionalization is categorization of landscapes for use in crop suitability analysis, strategic agroeconomic development, risk analysis, and other purposes. Past agro-ecoregionalizations have been subjective, expert opinion driven, crop specific, and unsuitable for statistical extrapolation. Use of quantitative analytical methods provides an opportunity for delineation of agro-ecoregions in a more objective and reproducible manner, and with use of generalized crop-related environmental inputs offers an opportunity for delineation of regions with broader application. For this study, raster (cell-based) environmental data at 1km scale were used in a multivariate geographic clustering process to delineate agroecozones. Environmental parameters included climatic, edaphic and topographic characteristics hypothesized to be generally relevant to many crops. Clustering was performed using five a priori grouping schemes of 5–25 agroecozones. Non-contiguous geographic zones were defined representing areas of similar crop-relevant environmental conditions. A red-green-blue color triplet was used for visualization of agroecozones as unique combinations of environmental factors. Concordance of the agroecozones with other widely used datasets was investigated using MapCurves, a quantitative goodness-of-fit method. The 5- and 25-agroecozone schemes had highest concordance with a map of major land resource areas and a map of major landform regions, with degree of fit judged to be good. The resulting agroecozones provide a framework for future rigorous hypothesis testing. Other applications include: quantitative evaluation of crop suitability at the landscape scale, environmental impact modeling and agricultural scenario building. [Copyright &y& Elsevier]

Changes in Earth’s climate in response to atmospheric greenhouse gas buildup impact the health of terrestrial ecosystems and the hydrologic cycle. The environmental conditions influential to plant and animal life are often mapped as ecoregions, which are land areas having similar combinations of environmental characteristics. This idea is extended to establish regions of similarity with respect to climatic characteristics that evolve through time using a quantitative statistical clustering technique called Multivariate Spatio-Temporal Clustering (MSTC). MSTC was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM). Results from an ensemble of five 99-yr Business-As-Usual (BAU) transient simulations from 2000 to 2098 were analyzed. MSTC establishes an exhaustive set of recurring climate regimes that form a “skeleton” through the “observations” (model output) throughout the occupied portion of the climate phase space formed by the characteristics being considered. MSTC facilitates direct comparison of ensemble members and ensemble and temporal averages since the derived climate regimes provide a basis for comparison. Moreover, by mapping all land cells to discrete climate states, the dynamic behavior of any part of the system can be studied by its time-varying sequence of climate state occupancy. MSTC is a powerful tool for model developers and environmental decision makers who wish to understand long, complex time series predictions of models. Strong predicted interannual trends were revealed in this analysis, including an increase in global desertification; a decrease in the cold, dry high-latitude conditions typical of North American and Asian winters; and significant warming in Antarctica and western Greenland. [ABSTRACT FROM AUTHOR]

Eddy covariance data from regional flux networks are direct in situ measurement of carbon, water, and energy fluxes and are of vital importance for understanding the spatio-temporal dynamics of the the global carbon cycle. FLUXNET links regional networks of eddy covariance sites across the globe to quantify the spatial and temporal variability of fluxes at regional to global scales and to detect emergent ecosystem properties. This study presents an assessment of the representativeness of FLUXNET based on the recently released FLUXNET2015 data set. We present a detailed high resolution analysis of the evolving representativeness of FLUXNET through time. Results provide quantitative insights into the extent that various biomes are sampled by the network of networks, the role of the spatial distribution of the sites on the network scale representativeness at any given time, and how that representativeness has changed through time due to changing operational status and data availability at sites in the network. To realize the full potential of FLUXNET observations for understanding emergent ecosystem properties at regional and global scales, we present an approach for upscaling eddy covariance measurements. Informed by the representativeness of observations at the flux sites in the network, the upscaled data reflects the spatio-temporal dynamics of the carbon cycle captured by the in situ measurements. This study presents a method for optimal use of the rich point measurements from FLUXNET to derive an understanding of upscaled carbon fluxes, which can be routinely updated as new data become available, and direct network expansion by identifying regions poorly sampled by the current network. [ABSTRACT FROM AUTHOR]

A multiple stressor risk assessment was conducted at Yuma Proving Ground, Arizona, as a demonstration of the Military Ecological Risk Assessment Framework. The focus was a testing program at Cibola Range, which involved an Apache Longbow helicopter firing Hellfire missiles at moving targets, that is, M60-A1 tanks. This article describes the ecological risk assessment for the missile launch and detonation. The primary stressor associated with this activity was sound. Other minor stressors included the detonation impact, shrapnel, and fire. Exposure to desert mule deer (Odocoileus hemionus crooki) was quantified using the Army sound contour program BNOISE2, as well as distances from the explosion to deer. Few effects data were available from related studies. Exposure-response models for the characterization of effects consisted of human “disturbance” and hearing damage thresholds in units of C-weighted decibels (sound exposure level) and a distance-based No-Observed-Adverse-Effects Level for moose and cannonfire. The risk characterization used a weight-of-evidence approach and concluded that risk to mule deer behavior from the missile firing was likely for a negligible number of deer, but that no risk to mule deer abundance and reproduction is expected. [ABSTRACT FROM AUTHOR]

Theoretical work suggests that species’ ecological niches should remain relatively constant over long-term ecological time periods, but empirical tests are few. We present longitudinal studies of 23 extant mammal species, modelling ecological niches and predicting geographical distributions reciprocally between the Last Glacial Maximum and present to test this evolutionary conservatism. This study covered distributional shifts in mammal species across the lower 48 states of the United States. We used a machine-learning tool for modelling species’ ecological niches, based on known occurrences and electronic maps summarizing ecological dimensions, to assess the ability of ecological niches as modelled in one time period to predict the geographical distribution of the species in another period, and vice versa. High intertemporal predictivity between niche models and species’ occurrences indicate that niche conservatism is widespread among the taxa studied, particularly when statistical power is considered as a reason for failure of reciprocal predictions. Niche projections to the present for 8 mammal taxa that became extinct at the end of the Pleistocene generally increased in area, and thus do not support the hypothesis of niche collapse as a major driving force in their extinction. Ecological niches represent long-term stable constraints on the distributional potential of species; indeed, this study suggests that mammal species have tracked consistent climate profiles throughout the drastic climate change events that marked the end of the Pleistocene glaciations. Many current modelling efforts focusing on anticipating climate change effects on species’ potential geographical distributions will be bolstered by this result — in essence, the first longitudinal demonstration of niche conservatism. [ABSTRACT FROM AUTHOR]

A dendrone is a hierarchical thresholding structure that can be automatically generated from a complex image. The dendrone structure captures the connectedness of objects and subobjects during successive brightness thresholding. Based on connectedness and changes in intensity contours, dendronic representations of objects in images capture the coarse-to-fine unfolding of finer and finer detail, creating a unique signature for target objects that is invariant to lighting, scale, and placement of the object within the image. Subdendrones within the hierarchy are recognizable as objects within the picture. Complex composite images can be autonomously analyzed to determine if they contain the unique dendronic signatures of particular target objects of interest. In this paper, we describe the initial design of the dendronic image characterization environment (DICE) for the generation of dendronic signatures from complex multiband remote imagery. By comparing subdendrones within an image to dendronic signatures of target objects of interest, DICE can be used to match/retrieve target features from a library of composite images. The DICE framework can organize and support a number of alternative object recognition and comparison techniques, depending on the application domain. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 243–253, 2000 [ABSTRACT FROM AUTHOR]

Timely and accurate knowledge about the geospatial distribution of crops at regional to continental scales is crucial for forecasting crop production and estimating crop water use. The United States (US) is one of the leading food-producing countries, but lacks a nationwide high resolution crop-specific land cover map available publicly during the current growing season. The goal of this study was to map crops across the Continental US (CONUS) before the harvest, and to estimate the earliest date of classification by which crops can be mapped with sufficient accuracy (90% of full-season accuracy). The study employed a scalable cluster-then-label model that was trained on multiple years of MODIS NDVI using ground truth data in the form of US Department of Agriculture (USDA) Cropland Data Layer (CDL) products. The first step in the crop classification was to perform Multivariate Spatio-Temporal Clustering (MSTC) of annual MODIS-derived NDVI trajectories to create phenologically similar regions, or phenoregions. The second step was to assign crop labels to phenoregions based on spatial concordance between phenoregions and crop classes from CDL using Mapcurves. Assigning crop labels to phenoregions was performed within ecoregions to reduce classification errors due to spatial variability in phenology caused by variations in climate, agricultural practices, and growing conditions. The crop classifier was trained and validated on the years 2008–2014, then tested independently on 2015–2018. Ecoregion -level crop classification performed better than state-level and CONUS-level classification. Pixel-wise accuracy of classification for eight major crops by area was around 70% across the major corn-, soybeans- and winter wheat-producing areas, whereas regions characterized by high crop diversity had slightly lower accuracy. Classification accuracy for dominant crops like corn, soybeans, winter wheat, fallow/idle cropland and other hay/non alfalfa improved with time as they grew, reaching 90% of year-end accuracy by the end of August over each of the four unseen years in the test period. For corn and soybeans, the earliest dates of classification were found to be much earlier in the central regions of the Corn Belt (parts of Iowa, Illinois and Indiana) than in peripheral areas. The ability to map growing crops may permit near real-time monitoring of the health status and vigor of agricultural crops nationally. • Crop maps for the Continental US (CONUS) are not available during the growing season. • We developed a MODIS NDVI-based generalized "cluster-then-label" model to map crops. • User's accuracy of >70% was achieved in major crop growing regions across CONUS. • We developed a method for within season crop mapping. • Major crops can be mapped with sufficient accuracy across CONUS by late-August. [ABSTRACT FROM AUTHOR]

Global climate change is having marked influences on species distributions, phenology and ecosystem composition and raises questions as to the effectiveness of current conservation strategies. Conservation planning has only recently begun to adequately account for dynamic threats such as climate change. We propose a method to incorporate climate-dynamic environmental domains, identified using specific environmental correlates of floristic composition, into conservation strategies, using the province of KwaZulu-Natal, South Africa as a case study. The environmental domains offer an approach to conservation that conserves diversity under current and future climates, recognising that the species constituting diversity may change through time. We mapped current locations of domains by identifying their positions in a multi-dimensional environmental space using a non-hierarchical iterative k -means clustering algorithm. Their future locations were explored using an ensemble of future climate scenarios. The HadCM2 and GFDL2.1 models represented the extreme ranges of the models. The magnitude of change in each environmental domain was calculated using Euclidean distances to determine areas of greatest and least stability for each future climate projection. Domains occurring in the savanna biome increase at the expense of domains occurring in the grassland biome, which has significant negative consequences for the species rich grasslands. The magnitude of change maps represents areas of changed climatic conditions or edaphic disjunctions. The HadCM2 model predicted the greatest overall magnitude of change across the province. Species with specific soil requirements may not be able to track changing climatic conditions. A vulnerability framework was developed that incorporated climatic stability and habitat intactness indices. The mean magnitude of change informed the potential speed of transition of domains between the vulnerability quadrants. The framework informs appropriate conservation actions to mitigate climate change impacts on biodiversity. The study explicitly links floristic pattern and climate variability and provides useful insights to facilitate conservation planning for climate change. [ABSTRACT FROM AUTHOR]

Biological field stations are distributed throughout North America, capturing much of the ecological variability present at the continental scale and encompassing many unique habitats. In addition to their role in supporting research and education, field stations offer legacies of data, specimens, and accumulated knowledge. Such legacies often provide the only framework for documenting and understanding the nature and pace of ecosystem, regional, and global changes in environmental conditions; ecological processes; and biodiversity, Because of these legacies and because they serve as gathering places for a rich diversity of highly creative and motivated scientists, students, and citizens, biological field stations are frequently where serendipitous scientific discoveries take place. The inclusion of biological field stations in environmental observatories and research networks ensures that these places will continue to foster future serendipitous scientific discoveries. [ABSTRACT FROM AUTHOR]

Abstract: Habitat valuation methods are most often developed and used to prioritize candidate lands for conservation. In this study the intent of habitat valuation was to inform the decision-making process for remediation of chemical contaminants on specific lands or surface water bodies. Methods were developed to summarize dimensions of habitat value for six representative aquatic and terrestrial contaminated sites at the East Tennessee Technology Park (ETTP) on the US Department of Energy Oak Ridge Reservation in Oak Ridge, TN, USA. Several general valuation metrics were developed for three broad categories: site use by groups of organisms, site rarity, and use value added from spatial context. Examples of use value metrics are taxa richness, a direct measure of number of species that inhabit an area, complexity of habitat structure, an indirect measure of potential number of species that may use the area, and land use designation, a measure of the length of time that the area will be available for use. Measures of rarity included presence of rare species or communities. Examples of metrics for habitat use value added from spatial context included similarity or complementarity of neighboring habitat patches and presence of habitat corridors. More specific metrics were developed for groups of organisms in contaminated streams, ponds, and terrestrial ecosystems. For each of these metrics, cutoff values for high, medium, and low habitat value were suggested, based on available information on distributions of organisms and landscape features, as well as habitat use information. A companion paper describes the implementation of these habitat valuation metrics and scoring criteria in the remedial investigation for ETTP. [Copyright &y& Elsevier]

Abstract: Habitat valuation methods were implemented to support remedial decisions for aquatic and terrestrial contaminated sites at the East Tennessee Technology Park (ETTP) on the US Department of Energy (DOE) Oak Ridge Reservation in Oak Ridge, TN, USA. The habitat valuation was undertaken for six contaminated sites: Contractor''s Spoil Area, K-901-N Disposal Area, K-770 Scrapyard, K-1007-P1 pond, K-901 pond, and the Mitchell Branch stream. Four of these sites are within the industrial use area of ETTP and two are in the Black Oak Ridge Conservation Easement. These sites represent terrestrial and aquatic habitat for vertebrates, terrestrial habitat for plants, and aquatic habitat for benthic invertebrates. Current and potential future, no-action (no remediation) scenarios were evaluated primarily using existing information. Valuation metrics and scoring criteria were developed in a companion paper, this volume. The habitat valuation consists of extensive narratives, as well as scores for aspects of site use value, site rarity, and use value added from spatial context. Metrics for habitat value were expressed with respect to different spatial scales, depending on data availability. There was significant variation in habitat value among the six sites, among measures for different taxa at a single site, between measures of use and rarity at a single site, and among measures for particular taxa at a single site with respect to different spatial scales. Most sites had aspects of low, medium, and high habitat value. Few high scores for current use value were given. These include: wetland plant communities at all aquatic sites, Lepomid sunfish and waterbirds at 1007-P1 pond, and Lepomid sunfish and amphibians at K-901 pond. Aquatic sites create a high-value ecological corridor for waterbirds, and the Contractor''s Spoil Area and possibly the K-901-N Disposal Site have areas that are part of a strong terrestrial ecological corridor. The only example of recent observations of rare species at these sites is the gray bat observed at the K-1007-P1 pond. Some aspects of habitat value are expected to improve under no-action scenarios at a few of the sites. Methods are applicable to other contaminated sites where sufficient ecological data are available for the site and region. [Copyright &y& Elsevier]