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At least 1,729 records · Page 96Linked to original sources
USGS2019

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

GIScience and Remote Sensing↗
USGS2016

Disentangling density-dependent dynamics using full annual cycle models and Bayesian model weight updating

Density dependence regulates populations of many species across all taxonomic groups. Understanding density dependence is vital for predicting the effects of climate, habitat loss and/or management actions on wild populations. Migratory species likely experience seasonal changes in the relative influence of density dependence on population processes such as survival and recruitment throughout the annual cycle. These effects must be accounted for when characterizing migratory populations via population models. To evaluate effects of density on seasonal survival and recruitment of a migratory species, we used an existing full annual cycle model framework for American black ducks Anas rubripes , and tested different density effects (including no effects) on survival and recruitment. We then used a Bayesian model weight updating routine to determine which population model best fit observed breeding population survey data between 1990 and 2014. The models that best fit the survey data suggested that survival and recruitment were affected by density dependence and that density effects were stronger on adult survival during the breeding season than during the non-breeding season. Analysis also suggests that regulation of survival and recruitment by density varied over time. Our results showed that different characterizations of density regulations changed every 8–12 years (three times in the 25-year period) for our population. Synthesis and applications . Using a full annual cycle, modelling framework and model weighting routine will be helpful in evaluating density dependence for migratory species in both the short and long term. We used this method to disentangle the seasonal effects of density on the continental American black duck population which will allow managers to better evaluate the effects of habitat loss and potential habitat management actions throughout the annual cycle. The method here may allow researchers to hone in on the proper form and/or strength of density dependence for use in models for conservation recommendations.

Journal of Applied Ecology↗
USGS2017

Tracing biogeochemical subsidies from glacier runoff into Alaska's coastal marine food webs

Nearly half of the freshwater discharge into the Gulf of Alaska originates from landscapes draining glacier runoff, but the influence of the influx of riverine organic matter on the trophodynamics of coastal marine food webs is not well understood. We quantified the ecological impact of riverine organic matter subsidies to glacier-marine habitats by developing a multi-trophic level Bayesian three-isotope mixing model. We utilized large gradients in stable (δ 13 C, δ 15 N, δ 2 H) and radiogenic (Δ 14 C) isotopes that trace riverine and marine organic matter sources as they are passed from lower to higher trophic levels in glacial-marine habitats. We also compared isotope ratios between glacial-marine and more oceanic habitats. Based on isotopic measurements of potential baseline sources, ambient water and tissues of marine consumers, estimates of the riverine organic matter source contribution to upper trophic-level species including fish and seabirds ranged from 12% to 44%. Variability in resource use among similar taxa corresponded to variation in species distribution and life histories. For example, riverine organic matter assimilation by the glacier-nesting seabirds Kittlitz's murrelet ( Brachyramphus brevirostris ) was greater than that of the forest-nesting marbled murrelet ( B. marmoratus ). The particulate and dissolved organic carbon in glacial runoff and near surface coastal waters was aged (12100–1500 years BP 14 C-age) but dissolved inorganic carbon and biota in coastal waters were young (530 years BP 14 C-age to modern). Thus terrestrial-derived subsidies in marine food webs were primarily composed of young organic matter sources released from glacier ecosystems and their surrounding watersheds. Stable isotope compositions also revealed a divergence in food web structure between glacial-marine and oceanic sites. This work demonstrates linkages between terrestrial and marine ecosystems, and facilitates a greater understanding of how climate-driven changes in freshwater runoff have the potential to alter food web dynamics within coastal marine ecosystems in Alaska.

Alaska↗
USGS2004

Predicting patterns of non-native plant invasions in Yosemite National Park, California, USA

One of the major issues confronting management of parks and reserves is the invasion of non-native plant species. Yosemite National Park is one of the largest and best-known parks in the United States, harbouring significant cultural and ecological resources. Effective management of non-natives would be greatly assisted by information on their potential distribution that can be generated by predictive modelling techniques. Our goal was to identify key environmental factors that were correlated with the percent cover of non-native species and then develop a predictive model using the Genetic Algorithm for Rule-set Production technique. We performed a series of analyses using community-level data on species composition in 236 plots located throughout the park. A total of 41 non-native species were recorded which occurred in 23.7% of the plots. Plots with non-natives occurred most frequently at low- to mid-elevations, in flat areas with other herbaceous species. Based on the community-level results, we selected elevation, slope, and vegetation structure as inputs into the GARP model to predict the environmental niche of non-native species. Verification of results was performed using plot data reserved from the model, which calculated the correct prediction of non-native species occurrence as 76%. The majority of the western, lower-elevation portion of the park was predicted to have relatively low levels of non-native species occurrence, with highest concentrations predicted at the west and south entrances and in the Yosemite Valley. Distribution maps of predicted occurrences will be used by management to: efficiently target monitoring of non-native species, prioritize control efforts according to the likelihood of non-native occurrences, and inform decisions relating to the management of non-native species in postfire environments. Our approach provides a valuable tool for assisting decision makers to better manage non-native species, which can be readily adapted to target non-native species in other locations.

Diversity and Distributions↗
USGS2019

Applying circuit theory and landscape linkage maps to reintroduction planning for California condors

Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor ( Gymnogyps californianus ) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor’s historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.

Central California↗
USGS2009

Perturbation analysis for patch occupancy dynamics

Perturbation analysis is a powerful tool to study population and community dynamics. This article describes expressions for sensitivity metrics reflecting changes in equilibrium occupancy resulting from small changes in the vital rates of patch occupancy dynamics (i.e., probabilities of local patch colonization and extinction). We illustrate our approach with a case study of occupancy dynamics of Golden Eagle ( Aquila chrysaetos ) nesting territories. Examination of the hypothesis of system equilibrium suggests that the system satisfies equilibrium conditions. Estimates of vital rates obtained using patch occupancy models are used to estimate equilibrium patch occupancy of eagles. We then compute estimates of sensitivity metrics and discuss their implications for eagle population ecology and management. Finally, we discuss the intuition underlying our sensitivity metrics and then provide examples of ecological questions that can be addressed using perturbation analyses. For instance, the sensitivity metrics lead to predictions about the relative importance of local colonization and local extinction probabilities in influencing equilibrium occupancy for rare and common species.

Ecology↗
USGS2003

Uncertainty in spatially explicit animal dispersal models

Uncertainty in estimates of survival of dispersing animals is a vexing difficulty in conservation biology. The current notion is that this uncertainty decreases the usefulness of spatially explicit population models in particular. We examined this problem by comparing dispersal models of three levels of complexity: (1) an event-based binomial model that considers only the occurrence of mortality or arrival, (2) a temporally explicit exponential model that employs mortality and arrival rates, and (3) a spatially explicit grid-walk model that simulates the movement of animals through an artificial landscape. Each model was fitted to the same set of field data. A first objective of the paper is to illustrate how the maximum-likelihood method can be used in all three cases to estimate the means and confidence limits for the relevant model parameters, given a particular set of data on dispersal survival. Using this framework we show that the structure of the uncertainty for all three models is strikingly similar. In fact, the results of our unified approach imply that spatially explicit dispersal models, which take advantage of information on landscape details, suffer less from uncertainly than do simpler models. Moreover, we show that the proposed strategy of model development safeguards one from error propagation in these more complex models. Finally, our approach shows that all models related to animal dispersal, ranging from simple to complex, can be related in a hierarchical fashion, so that the various approaches to modeling such dispersal can be viewed from a unified perspective.

Ecological Applications↗
USGS2020

Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the northeastern United States

SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed to quantify and improve the understanding of the sources, fate, and transport of nitrogen, phosphorus, and suspended sediment in the northeastern United States. Excessive nutrients and suspended sediment from upland watersheds and tributary streams have contributed to ecological and economic degradation of northeastern surface waters. Recent efforts to reduce the flux of nutrients and suspended sediment in northeastern streams and to downstream estuaries have met with mixed results, and expected ecological improvements have been observed in some areas but not in others. Effective watershed management and restoration to improve surface-water quality are complicated by the multitude of nutrient sources in the Northeast and the multitude of natural and human landscape processes affecting the delivery of nutrients and suspended sediment from upland areas to and within surface waters. Individual models were constructed representing streamflow and the loads of total nitrogen, total phosphorus, and suspended sediment from watersheds draining to the Atlantic Ocean from southern Virginia through Maine. Northeastern streams contribute 303,000 metric tons (t) of nitrogen, 25,300 t of phosphorus, and 14,700,000 t of suspended sediment, annually (on average), to waters along the Atlantic Coast of North America. Although atmospheric deposition and natural mineral erosion contribute to nitrogen and phosphorus loads, respectively, in northeastern streams, most of the contributions are attributable to urban or agricultural sources. Within the Northeast, average yields of nutrients are therefore generally greater from densely populated or intensively cultivated areas of the mid-Atlantic region, the Hudson, Mohawk, and Connecticut River valleys, and the coastal areas of southern New England than in predominantly forested areas such as northern New England. Average upland sediment yields are similarly greater from agricultural areas than from urban or forested areas and are therefore generally greatest in areas yielding the greatest nutrients. Landscape conditions that are significant to nitrogen delivery from uplands to streams likely reflect the importance of groundwater transport in carbonate settings and of denitrification for removing nitrogen from uplands. Nitrogen losses to streams in agricultural areas are apparently mitigated by the use of cover crops but are exacerbated by the use of conservation tillage or no-till practices. The transport of phosphorus and suspended sediment from uplands to streams is greater in areas of more erodible soils but mitigated in agricultural areas with greater use of conservation tillage or no-till practices. Loads of nutrients and suspended sediment are significantly reduced within the stream network in impounded reaches, and nitrogen load is also significantly reduced in small flowing reaches.

Scientific Investigations Report↗
USGS2022

Using paleoecological data to inform decision making: A deep-time perspective

Latest climate models project conditions for the end of this century that are generally outside of the human experience. These future conditions affect the resilience and sustainability of ecosystems, alter biogeographic zones, and impact biodiversity. Deep-time records of paleoclimate provide insight into the climate system over millions of years and provide examples of conditions very different from the present day, and in some cases similar to model projections for the future. In addition, the deep-time paleoecologic and sedimentologic archives provide insight into how species and habitats responded to past climate conditions. Thus, paleoclimatology provides essential context for the scientific understanding of climate change needed to inform resource management policy decisions. The Pliocene Epoch (5.3–2.6 Ma) is the most recent deep-time interval with relevance to future global warming. Analysis of marine sediments using a combination of paleoecology, biomarkers, and geochemistry indicates a global mean annual temperature for the Late Pliocene (3.6–2.6 Ma) ∼3°C warmer than the preindustrial. However, the inability of state-of-the-art climate models to capture some key regional features of Pliocene warming implies future projections using these same models may not span the full range of plausible future climate conditions. We use the Late Pliocene as one example of a deep-time interval relevant to management of biodiversity and ecosystems in a changing world. Pliocene reconstructed sea surface temperatures are used to drive a marine ecosystem model for the North Atlantic Ocean. Given that boundary conditions for the Late Pliocene are roughly analogous to present day, driving the marine ecosystem model with Late Pliocene paleoenvironmental conditions allows policymakers to consider a future ocean state and associated fisheries impacts independent of climate models, informed directly by paleoclimate information.

Frontiers in Ecology and Evolution↗
USGS2009

Hierarchical models for estimating density from DNA mark-recapture studies

Genetic sampling is increasingly used as a tool by wildlife biologists and managers to estimate abundance and density of species. Typically, DNA is used to identify individuals captured in an array of traps ( e. g., baited hair snares) from which individual encounter histories are derived. Standard methods for estimating the size of a closed population can be applied to such data. However, due to the movement of individuals on and off the trapping array during sampling, the area over which individuals are exposed to trapping is unknown, and so obtaining unbiased estimates of density has proved difficult. We propose a hierarchical spatial capture-recapture model which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to (via movement) and detection by traps. Detection probability is modeled as a function of each individual's distance to the trap. We applied this model to a black bear (Ursus americanus) study conducted in 2006 using a hair-snare trap array in the Adirondack region of New York, USA. We estimated the density of bears to be 0.159 bears/km2, which is lower than the estimated density (0.410 bears/km2) based on standard closed population techniques. A Bayesian analysis of the model is fully implemented in the software program WinBUGS.

Ecology↗
USGS2017

Identifying ecologically relevant scales of habitat selection: diel habitat selection in elk

Although organisms make resource selection decisions at multiple spatiotemporal scales, not all scales are ecologically relevant to any given organism. Ecological patterns and rhythms such as behavioral and climatic patterns may provide a consistent method for identifying ecologically relevant scales of habitat selection. Using elk ( Cervus canadensis ) as an example species, we sought to test the ability of behavioral patterns to empirically partition diel scales for modeling habitat selection. We used model selection to partition diel scales by shifts in dominant behavior and then used resource selection probability functions to model elk habitat selection hierarchically at diel scales within seasons. Model selection distinguished four diel temporal partitions following elk crepuscular behavioral patterns: dawn, midday, dusk, and night. Across seasons, model‐averaged coefficients indicated that elk shifted from selecting grassland cover at dawn/dusk, to selecting for greater canopy and forest cover at midday, and then to areas with greater herbaceous biomass at night. Top models changed between diel intervals in spring and fall but stayed the same across diel intervals in winter and summer. In winter, elk selected for southern aspects during midday, for unburned areas at dawn/dusk, and for areas burned within 1–3 yr at dawn/dusk and night. In spring, elk selected for northern aspects and for areas burned within 1–3 yr at midday, for areas farther from roads at dawn/dusk and midday, and for areas farther from water at midday. In summer, elk changed diel preferences for fewer covariates: At dawn/dusk and midday, elk selected for areas farther from water and avoided forest cover, and at night, elk selected for areas burned within 1–3 yr. In fall, elk selected for areas burned the previous year at dawn/dusk and night, for higher elevations at midday, and for areas closer water at night. Using behavioral patterns to identify ecologically relevant scales can help identify overlooked habitat requirements such as diel changes in preference for fire history, forage availability, and cover. We show that the ecological relevancy of a given scale (e.g., a diel temporal scale) can change throughout a given extent (e.g., across seasons).

New Mexico↗
USGS2020

Using remote sensing products to predict recovery of vegetation across space and time following energy development

Using localized studies to understand how ecosystems recover can create uncertainty in recovery predictions across landscapes. Large archives of remote sensing data offer opportunities for quantifying the spatial and temporal factors influencing recovery at broad scales and predicting recovery. For example, energy production is a widespread and expanding land use among many semi-arid ecosystems of the Western United States dominated by sagebrush ( Artemisia spp.), a keystone species providing a variety of ecological services. With remotely-sensed (Landsat) estimates of vegetation cover collected every 2–5 years from southwestern Wyoming, USA, over nearly three decades (1985–2015), we modeled changes in sagebrush cover on 375 former oil and gas well pads in response to weather and site-level conditions. We then used modeled relationships to predict recovery time across the landscape as an indicator of resilience for vegetation after well pad disturbances, where faster recovery indicates a greater capacity to recover when similarly disturbed. We found the rate of change in sagebrush cover generally increased with moisture and temperature, particularly at higher elevations. Rate of change in sagebrush cover also increased and decreased with greater percent sand and larger well pads, respectively. We predicted 21% of the landscape would recover to pre-disturbance conditions within 60 years, whereas other areas may require >100 years for recovery. These predictions and maps could inform future restoration efforts as they reflect resilience. This approach also is applicable to other disturbance types (e.g., fires and vegetation removal treatments) across landscapes, which can further improve conservation efforts by characterizing past conditions and monitoring trends in subsequent years.

Wyoming↗
USGS2006

Estimating the abundance of mouse populations of known size: promises and pitfalls of new methods

Knowledge of animal abundance is fundamental to many ecological studies. Frequently, researchers cannot determine true abundance, and so must estimate it using a method such as mark-recapture or distance sampling. Recent advances in abundance estimation allow one to model heterogeneity with individual covariates or mixture distributions and to derive multimodel abundance estimators that explicitly address uncertainty about which model parameterization best represents truth. Further, it is possible to borrow information on detection probability across several populations when data are sparse. While promising, these methods have not been evaluated using mark?recapture data from populations of known abundance, and thus far have largely been overlooked by ecologists. In this paper, we explored the utility of newly developed mark?recapture methods for estimating the abundance of 12 captive populations of wild house mice (Mus musculus). We found that mark?recapture methods employing individual covariates yielded satisfactory abundance estimates for most populations. In contrast, model sets with heterogeneity formulations consisting solely of mixture distributions did not perform well for several of the populations. We show through simulation that a higher number of trapping occasions would have been necessary to achieve good estimator performance in this case. Finally, we show that simultaneous analysis of data from low abundance populations can yield viable abundance estimates.

Ecological Applications↗
USGS2006

A framework for spatial risk assessments: Potential impacts of nonindigenous invasive species on native species

Many populations of wild animals and plants are declining and face increasing threats from habitat fragmentation and loss as well as exposure to stressors ranging from toxicants to diseases to invasive nonindigenous species. We describe and demonstrate a spatially explicit ecological risk assessment that allows for the incorporation of a broad array of information that may influence the distribution of an invasive species, toxicants, or other stressors, and the incorporation of landscape variables that may influence the spread of a species or substances. The first step in our analyses is to develop species models and quantify spatial overlap between stressor and target organisms. Risk is assessed as the product of spatial overlap and a hazard index based on target species vulnerabilities to the stressor of interest. We illustrate our methods with an example in which the stressor is the ecologically destructive nonindigenous ant, Solenopsis invicta, and the targets are two declining vertebrate species in the state of South Carolina, USA. A risk approach that focuses on landscapes and that is explicitly spatial is of particular relevance as remaining undeveloped lands become increasingly uncommon and isolated and more important in the management and recovery of species and ecological systems. Effective ecosystem management includes the control of multiple stressors, including invasive species with large impacts, understanding where those impacts may be the most severe, and implementing management strategies to reduce impacts. Copyright ?? 2006 by the author(s).

Ecology and Society↗
USGS2025

A model for regional-scale oak savanna management: The roles of fire, canopy, and soils for understory plant diversity

Predicting the outcomes of land management on biodiversity is difficult without a mechanistic understanding of how management approaches, ecosystem structure, environmental conditions, and biodiversity interact. Management effects may be direct or indirect, context- or scale-dependent, or obscured by local environmental conditions. Resolving these relationships at the regional scale may be difficult, given heterogeneity in local environmental conditions, yet understanding broad-scale patterns can elucidate context dependencies and improve restoration outcomes. We confronted these challenges within globally rare oak savannas in the midwestern United States, which have been altered by fire exclusion and resulting woody encroachment. By modeling direct and indirect pathways by which management influences diversity, we test a general framework for savanna restoration. Across 100 oak savannas spanning five US states, management by prescribed fire and mechanical thinning of woody vegetation affected groundlayer plant species richness through changes to ecosystem structure (canopy openness and litter depth), and these effects were both context- and scale-dependent. Frequent prescribed fires and canopy thinning promoted greater canopy openness, which in turn increased richness at small (1 m 2 ), but not larger (1000 m 2 ) scales. Frequent fire additionally increased richness at small and larger scales through effects independent of ecosystem structure. While management effects were large relative to the influence of local edaphic conditions, soil productivity had two largely offsetting effects on small-scale richness, increasing richness directly but decreasing richness indirectly by promoting closed canopy structure. These results suggest using a combination of fire and canopy thinning to reverse the effects of decades of fire exclusion. However, management effects were also context-dependent, emphasizing that management outcomes vary regionally. Here, 1-m 2 plant species richness increased with both fire frequency and canopy thinning under low, but not high, productivity soil conditions. By demonstrating how specific management practices influence savanna structure and biodiversity by manipulating ecological processes across broad geographic and edaphic gradients, our findings provide a framework for understanding management outcomes at short and medium intervals (e.g., within and between decades, respectively), in the form of a model that can be refined by testing additional hypotheses to better predict savanna restoration outcomes.

Illinois, Indiana, Michigan, Ohio, Wisconsin↗
USGS2012

A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring

Aim: To develop a novel global spatial framework for the integration and analysis of ecological and environmental data. Location: The global land surface excluding Antarctica. Methods: A broad set of climate-related variables were considered for inclusion in a quantitative model, which partitions geographic space into bioclimate regions. Statistical screening produced a subset of relevant bioclimate variables, which were further compacted into fewer independent dimensions using principal components analysis (PCA). An ISODATA clustering routine was then used to classify the principal components into relatively homogeneous environmental strata. The strata were aggregated into global environmental zones based on the attribute distances between strata to provide structure and support a consistent nomenclature. Results: The global environmental stratification (GEnS) consists of 125 strata, which have been aggregated into 18 global environmental zones. The stratification has a 30 arcsec resolution (equivalent to 0.86 km2 at the equator). Aggregations of the strata were compared with nine existing global, continental and national bioclimate and ecosystem classifications using the Kappa statistic. Values range between 0.54 and 0.72, indicating good agreement in bioclimate and ecosystem patterns between existing maps and the GEnS. Main conclusions: The GEnS provides a robust spatial analytical framework for the aggregation of local observations, identification of gaps in current monitoring efforts and systematic design of complementary and new monitoring and research. The dataset is available for non-commercial use through the GEO portal (http://www.geoportal.org).

Global Ecology and Biogeography↗
USGS2017

The fishery resources of the Mississippi River: A model for conservation and management

The Mississippi River is a multijurisdictional and multiuse resource that has been variously altered and is foremost managed for navigation and flood control throughout much of its 3,734‐km passage from its origin at Lake Itasca, Minnesota, to its outlet at the Gulf of Mexico. Despite alterations summarized herein, the native fish fauna remains largely intact and only five nonnative species have colonized segments of the river. Diverse habitats still remain, but loss of habitat, declining habitat suitability, and reduced floodplain functionality warrant concern. Fisheries monitoring and assessment, ecological research, and habitat rehabilitation vary from adequate in the upper reaches of the river to minimal in the lower reaches of the river, and these efforts parallel the recreational use, local values, and visibility of the river. A conceptual model is proposed to depict the value of the social, economic, and many ecosystem services the Mississippi River ecosystem offers that can be used to achieve the social and economic support needed to conserve and restore this valuable fishery resource.

Mississippi River↗
USGS2017

Vectors, hosts, and control measures for Zika virus in the Americas

We examine Zika virus (ZIKV) from an ecological perspective and with a focus on the Americas. We assess (1) the role of wildlife in ZIKV disease ecology, (2) how mosquito behavior and biology influence disease dynamics, and (3) how nontarget species and ecosystems may be impacted by vector control programs. Our review suggests that free-ranging, non-human primates may be involved in ZIKV transmission in the Old World; however, other wildlife species likely play a limited role in maintaining or transmitting ZIKV. In the Americas, a zoonotic cycle has not yet been definitively established. Understanding behaviors and habitat tolerances of Aedes aegypti and Aedes albopictus , two ZIKV competent vectors in the Americas, will allow more accurate modeling of disease spread and facilitate targeted and effective control efforts. Vector control efforts may have direct and indirect impacts to wildlife, particularly invertebrate feeding species; however, strategies could be implemented to limit detrimental ecological effects.

EcoHealth↗