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USGS2015

Landscape-level terrestrial methane flux observed from a very tall tower

Simulating the magnitude and variability of terrestrial methane sources and sinks poses a challenge to ecosystem models because the biophysical and biogeochemical processes that lead to methane emissions from terrestrial and freshwater ecosystems are, by their nature, episodic and spatially disjunct. As a consequence, model predictions of regional methane emissions based on field campaigns from short eddy covariance towers or static chambers have large uncertainties, because measurements focused on a particular known source of methane emission will be biased compared to regional estimates with regards to magnitude, spatial scale, or frequency of these emissions. Given the relatively large importance of predicting future terrestrial methane fluxes for constraining future atmospheric methane growth rates, a clear need exists to reduce spatiotemporal uncertainties. In 2010, an Ameriflux tower (US-PFa) near Park Falls, WI, USA, was instrumented with closed-path methane flux measurements at 122 m above ground in a mixed wetland–upland landscape representative of the Great Lakes region. Two years of flux observations revealed an average annual methane (CH 4 ) efflux of 785 ± 75 mg C CH 4 m −2 yr −1 , compared to a mean CO 2 sink of −80 g C CO 2 m −2 yr −1 , a ratio of 1% in magnitude on a mole basis. Interannual variability in methane flux was 30% of the mean flux and driven by suppression of methane emissions during dry conditions in late summer 2012. Though relatively small, the magnitude of the methane source from the very tall tower measurements was mostly within the range previously measured using static chambers at nearby wetlands, but larger than a simple scaling of those fluxes to the tower footprint. Seasonal patterns in methane fluxes were similar to those simulated in the Dynamic Land Ecosystem Model (DLEM), but magnitude depends on model parameterization and input data, especially regarding wetland extent. The model was unable to simulate short-term (sub-weekly) variability. Temperature was found to be a stronger driver of regional CH 4 flux than moisture availability or net ecosystem production at the daily to monthly scale. Taken together, these results emphasize the multi-timescale dependence of drivers of regional methane flux and the importance of long, continuous time series for their characterization.

Wisconsin↗

USGS2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Agricultural and Forest Meteorology↗

USGS2022

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

Agricultural and Forest Meteorology↗

USGS2023

Operationalizing crop model data assimilation for improved on-farm situational awareness

The ability of ‘digital agriculture’ to support on-farm decision making is predicated on the real-time combination of observations and prior knowledge into an integrated digital environment. The mathematical discipline that seeks to provide this integration is known as model data assimilation (DA), with demonstrated benefits including improved predictive reliability, and the capacity to identify unexpected changes in field conditions and potential measurement errors. Despite routine adoption in other fields, the delayed adoption of DA in agriculture is due to the need to express end-of-season outcomes such as yield, update forecasts of these outcomes throughout the growing season as data become available, and enhance forecast reliability. To overcome these challenges, three guiding principles are introduced, providing a means to operationalize crop model DA for robust on-farm decision support. We apply the guiding principles using a South Australian viticulture case study. Our case study involves application of an iterative form of a widely used DA algorithm (ensemble Kalman filter) to dynamically update both static parameters and states associated with a grapevine simulation model. Daily weather data as well as fortnightly ground-based leaf area index (LAI) data are used for assimilation. It is shown how crop model DA can lead to not only significant improvements in forecasts of LAI but also to forecasts of end-of-season yield. The guiding principles also enable observations of greatest value to be identified throughout the season. This study highlights the role that formal crop model DA can play in agricultural decision support through enhancing situational awareness in real time.

Agricultural and Forest Meteorology↗

USGS2024

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R 2 ) and mean absolute error values of 0.73- and 0.95-mm d −1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

California↗

USGS2013

Deep subsurface drip irrigation using coal-bed sodic water: part II. geochemistry

Waters with low salinity and high sodium adsorption ratios (SARs) present a challenge to irrigation because they degrade soil structure and infiltration capacity. In the Powder River Basin of Wyoming, such low salinity (electrical conductivity, EC 2.1 mS cm -1 ) and high-SAR (54) waters are co-produced with coal-bed methane and some are used for subsurface drip irrigation(SDI). The SDI system studied mixes sulfuric acid with irrigation water and applies water year-round via drip tubing buried 92 cm deep. After six years of irrigation, SAR values between 0 and 30 cm depth (0.5-1.2) are only slightly increased over non-irrigated soils (0.1-0.5). Only 8-15% of added Na has accumulated above the drip tubing. Sodicity has increased in soil surrounding the drip tubing, and geochemical simulations show that two pathways can generate sodic conditions. In soil between 45-cm depth and the drip tubing, Na from the irrigation water accumulates as evapotranspiration concentrates solutes. SAR values >12, measured by 1:1 water-soil extracts, are caused by concentration of solutes by factors up to 13. Low-EC (<0.7 mS cm -1 ) is caused by rain and snowmelt flushing the soil and displacing ions in soil solution. Soil below the drip tubing experiences lower solute concentration factors (1-1.65) due to excess irrigation water and also contains relatively abundant native gypsum (2.4 ± 1.7 wt.%). Geochemical simulations show gypsum dissolution decreases soil-water SAR to <7 and increases the EC to around 4.1 mS cm-1, thus limiting negative impacts from sodicity. With sustained irrigation, however, downward flow of excess irrigation water depletes gypsum, increasing soil-water SAR to >14 and decreasing EC in soil water to 3.2 mS cm-1. Increased sodicity in the subsurface, rather than the surface, indicates that deep SDI can be a viable means of irrigating with sodic waters.

Wyoming↗

USGS2016

Cultivation of a native alga for biomass and biofuel accumulation in coal bed methane production water

Coal bed methane (CBM) production has resulted in thousands of ponds in the Powder River Basin of low-quality water in a water-challenged region. A green alga isolate, PW95, was isolated from a CBM production pond, and analysis of a partial ribosomal gene sequence indicated the isolate belongs to the Chlorococcaceae family. Different combinations of macro- and micronutrients were evaluated for PW95 growth in CBM water compared to a defined medium. A small level of growth was observed in unamended CBM water (0.15 g/l), and biomass increased (2-fold) in amended CBM water or defined growth medium. The highest growth rate was observed in CBM water amended with both N and P, and the unamended CBM water displayed the lowest growth rate. The highest lipid content (27%) was observed in CBM water with nitrate, and a significant level of lipid accumulation was not observed in the defined growth medium. Growth analysis indicated that nitrate deprivation coincided with lipid accumulation in CBM production water, and lipid accumulation did not increase with additional phosphorus limitation. The presented results show that CBM production wastewater can be minimally amended and used for the cultivation of a native, lipid-accumulating alga.

Montana, Wyoming↗

USGS2022

Connecting regional-scale tree distribution models with seed dispersal kernels

Regional scale forest distribution models are important tools for biogeography and understanding the structure of forest communities in space. These models take climate and geographic variables as input and are therefore helpful for long-term decision support and climate adaptation planning. Generally, local processes of tree germination and seedling survival are resolved probabilistically with explanatory variables such as elevation, latitude, exposure, soil type, moisture availability, climate and weather inputs and `trained’ using landscape and regional presence-absence data and machine learning techniques. How seeds are distributed in these models, that is, determining the dispersal kernel, is far more problematic. The challenge is that variables conditioning vertebrate seed dispersal (motility and probability of utilization or caching in response to cover type) are not represented in large scale distribution models, and in fact vary on scales (10-100 meters) that are much smaller than the smallest pixel size for the distribution model (1-10 kilometers). We present a homogenized seed digestion kernel (HSDK) which incorporates this scale separation. Homogenization naturally links highly variable small-scale processes (like seed foraging and caching by birds and rodents) with large scale effects (like dispersal of seeds over tens of kilometers). We develop a homogenization strategy to predict seed dispersal on landscape scales, analytically linking small-scale variables (landscape fraction cover by tree type, gut residence times and cover type utilization by frugivorous birds) with large scale behaviors. Closed form approximations are developed in two dimensions for two limiting cases of seed handling behavior, and the approach is illustrated using landscape data and piñon-pine dispersal in a 630,000 square kilometer region in the southwestern US.

Applied Mathematics and Computation↗

USGS2023

Linking vocal behaviours to habitat structure to create behavioural landscapes

The recent development of animal-borne sensors coupled with location data can provide insights into how individuals modify their behaviour with respect to specific habitat features. Animals can express a diverse array of behaviours as they navigate heterogenous landscapes, yet few studies have specifically evaluated the interaction of behaviours with habitat characteristics. We used a novel broadcast acoustic transmitter to investigate the interaction between vocal behaviours of an endemic Hawaiian thrush, the ʻōmaʻo, Myadestes obscurus , and habitat features across a naturally fragmented forest landscape. Through the development of behavioural landscape models that link specific vocalizations with space use, we found that the use of different vocalization types (calls, songs, whisper songs) were highly variable across the landscape but were associated with distinct habitat features. The likelihood of calls increased in an open lava matrix between forest patches, while whisper songs were more strongly associated with the dense interior areas of forest fragments. In contrast, the rate of ʻōmaʻo vocalizations overall decreased in the open lava matrix, suggesting that ʻōmaʻo may shift behaviours from territory defence to foraging as they transition through different habitats. Our study revealed context-specific changes in behaviour across ʻōmaʻo home ranges, including courtship, aggression and social interactions between individuals. Combining the use of a novel acoustic tool with automated radiotelemetry allowed us to overcome challenges associated with detection and analysis of variation in behaviour and resource selection across a highly heterogeneous landscape that would have been otherwise difficult to impossible.

Hawaii↗

USGS2022

Field testing a high-frequency acoustic attenuation system for measuring fine suspended sediments and algal movements

Acoustic measurements of suspended sediment have the potential to allow remote, autonomous monitoring of sediment movements at much higher temporal resolution than traditional manual sampling methods. Although suspended sands present a challenging measurement problem due to their logarithmic distribution with depth, fine clay sediments are distributed evenly throughout a stream cross section, making them amenable to point measurements. In order to improve measurement capabilities for fine sediments in stream channels, The National Center for Physical Acoustics at The University of Mississippi has developed a remote, autonomous acoustic system to monitor fine sediments transported in streams. The system was tested on the Middle Rio Grande near San Acacia, New Mexico, and in Goodwin Creek in Panola County, Mississippi. The acoustic instruments were compared to sediment concentrations from physical samples in both deployments. Diurnal patterns were found in the acoustic signals from the Middle Rio Grande, and a follow up experiment at The University of Mississippi Biological Field Station was used to investigate the potential effects of algal biomass on acoustic attenuation measurements. The results showed diurnal patterns in attenuation were associated with patterns in light, temperature, and dissolved oxygen. These results combined with information from the literature indicate diel movement of algal colonies in the water column of some water bodies may interfere with high-frequency acoustic measurements in natural environments and that acoustic methods have the potential to allow ecological researchers to evaluate mass algal movements in the field. Results from Goodwin Creek demonstrate that the acoustic system is able to provide measurements of sediment concentration with high temporal resolution that track well with expected sediment transport patterns in response to discharge hydrographs.

Applied Acoustics↗

USGS2005

Statistical characterization of a large geochemical database and effect of sample size

The authors investigated statistical distributions for concentrations of chemical elements from the National Geochemical Survey (NGS) database of the U.S. Geological Survey. At the time of this study, the NGS data set encompasses 48,544 stream sediment and soil samples from the conterminous United States analyzed by ICP-AES following a 4-acid near-total digestion. This report includes 27 elements: Al, Ca, Fe, K, Mg, Na, P, Ti, Ba, Ce, Co, Cr, Cu, Ga, La, Li, Mn, Nb, Nd, Ni, Pb, Sc, Sr, Th, V, Y and Zn. The goal and challenge for the statistical overview was to delineate chemical distributions in a complex, heterogeneous data set spanning a large geographic range (the conterminous United States), and many different geological provinces and rock types. After declustering to create a uniform spatial sample distribution with 16,511 samples, histograms and quantile-quantile (Q-Q) plots were employed to delineate subpopulations that have coherent chemical and mineral affinities. Probability groupings are discerned by changes in slope (kinks) on the plots. Major rock-forming elements, e.g., Al, Ca, K and Na, tend to display linear segments on normal Q-Q plots. These segments can commonly be linked to petrologic or mineralogical associations. For example, linear segments on K and Na plots reflect dilution of clay minerals by quartz sand (low in K and Na). Minor and trace element relationships are best displayed on lognormal Q-Q plots. These sensitively reflect discrete relationships in subpopulations within the wide range of the data. For example, small but distinctly log-linear subpopulations for Pb, Cu, Zn and Ag are interpreted to represent ore-grade enrichment of naturally occurring minerals such as sulfides. None of the 27 chemical elements could pass the test for either normal or lognormal distribution on the declustered data set. Part of the reasons relate to the presence of mixtures of subpopulations and outliers. Random samples of the data set with successively smaller numbers of data points showed that few elements passed standard statistical tests for normality or log-normality until sample size decreased to a few hundred data points. Large sample size enhances the power of statistical tests, and leads to rejection of most statistical hypotheses for real data sets. For large sample sizes (e.g., n > 1000), graphical methods such as histogram, stem-and-leaf, and probability plots are recommended for rough judgement of probability distribution if needed. ?? 2005 Elsevier Ltd. All rights reserved.

Applied Geochemistry↗

USGS2009

Soil chemistry in lithologically diverse datasets: the quartz dilution effect

National- and continental-scale soil geochemical datasets are likely to move our understanding of broad soil geochemistry patterns forward significantly. Patterns of chemistry and mineralogy delineated from these datasets are strongly influenced by the composition of the soil parent material, which itself is largely a function of lithology and particle size sorting. Such controls present a challenge by obscuring subtler patterns arising from subsequent pedogenic processes. Here the effect of quartz concentration is examined in moist-climate soils from a pilot dataset of the North American Soil Geochemical Landscapes Project. Due to variable and high quartz contents (6.2–81.7 wt.%), and its residual and inert nature in soil, quartz is demonstrated to influence broad patterns in soil chemistry. A dilution effect is observed whereby concentrations of various elements are significantly and strongly negatively correlated with quartz. Quartz content drives artificial positive correlations between concentrations of some elements and obscures negative correlations between others. Unadjusted soil data show the highly mobile base cations Ca, Mg, and Na to be often strongly positively correlated with intermediately mobile Al or Fe, and generally uncorrelated with the relatively immobile high-field-strength elements (HFS) Ti and Nb. Both patterns are contrary to broad expectations for soils being weathered and leached. After transforming bulk soil chemistry to a quartz-free basis, the base cations are generally uncorrelated with Al and Fe, and negative correlations generally emerge with the HFS elements. Quartz-free element data may be a useful tool for elucidating patterns of weathering or parent-material chemistry in large soil datasets.

Applied Geochemistry↗

USGS2011

Effects of slow recovery rates on water column geochemistry in aquitard wells

Monitoring wells are often installed in aquitards to verify effectiveness for preventing migration of surface contaminants to underlying aquifers. However, water sampling of aquitard wells presents a challenge due to the slow recovery times for water recharging the wells, which can take as long as weeks, months or years to recharge depending on the sample volume needed. In this study, downhole profiling and sampling of aquitard wells was used to assess geochemical changes that occur in aquitard wells during water level recovery. Wells were sampled on three occasions spanning 11 years, 1 year and 1 week after they were purged and casing water showed substantial water chemistry variations. Temperature decreased with depth, whereas pH and specific conductance increased with depth in the water column after 11 years of water level recovery. Less stable parameters such as dissolved O 2 (DO) and Eh showed strong zonation in the well column, with DO stratification occurring as the groundwater slowly entered the well. Oxidation of reduced till groundwater along with degassing of CO 2 from till pore water affects mineral solubility and dissolved solid concentrations. Recommendations for sampling slowly recovering aquitard wells include identifying the zone of DO and Eh stratification in the well column and collecting water samples from below the boundary to better measure unstable geochemical parameters.

Iowa↗

USGS2015

Modeling and management of pit lake water chemistry 1: Theory

Pit lakes are permanent hydrologic/landscape features that can result from open pit mining for metals, coal, uranium, diamonds, oil sands, and aggregates. Risks associated with pit lakes include local and regional impacts to water quality and related impacts to aquatic and terrestrial ecosystems. Stakeholders rely on predictive models of water chemistry to prepare for and manage these risks. This paper is the first of a two part series on the modeling and management of pit lakes. Herein, we review approaches that have been used to quantify wall-rock runoff geochemistry, wall-rock leachate geochemistry, pit lake water balance, pit lake limnology (i.e. extent of vertical mixing), and pit lake water quality, and conclude with guidance on the application of models within the mine life cycle. The purpose of this paper is to better prepare stakeholders, including future modelers, mine managers, consultants, permitting agencies, land management agencies, regulators, research scientists, academics, and other interested parties, for the challenges of predicting and managing future pit lakes in un-mined areas.

Applied Geochemistry↗

USGS2015

Arsenic and antimony geochemistry of mine wastes, associated waters and sediments at the Giant Mine, Yellowknife, Northwest Territories, Canada

Elevated levels of arsenic (As) and antimony (Sb) in water and sediments are legacy residues found downstream from gold-mining activities at the Giant Mine in Yellowknife, Northwest Territories (NWT), Canada. To track the transport and fate of As and Sb, samples of mine-waste from the mill, and surface water, sediment, pore-water, and vegetation downstream of the mine were collected. Mine waste, pore-water, and sediment samples were analyzed for bulk chemistry, and aqueous and solid-state speciation. Sediment and vegetation chemistry were evaluated using scanning electron microscope imaging, synchrotron-based element mapping and electron microprobe analysis. The distributions of As and Sb in sediments were similar, yet their distributions in the corresponding pore-waters were mostly dissimilar, and the mobility of As was greater than that of Sb. Competition for sorption sites is the most likely cause of elevated Sb concentrations in relatively oxidized pore-water and surface water. The aqueous and solid-state speciation of As and Sb also differed. In pore-water, As(V) dominated in oxidizing environments and As(III) in reducing environments. In contrast, the Sb(V) species dominated in all but one pore-water sample, even under reducing conditions. Antimony(III) appears to preferentially precipitate or adsorb onto sulfides as evidenced by the prevalence of an Sb(III)-S secondary solid-phase and the lack of Sb(III)(aq) in the deeper zones. The As(V)–O solid phase became depleted with depth below the sediment–water interface, and the Sb(V)–O phase persisted under relatively reducing conditions. In the surficial zone at a site populated by Equisetum fluviatile (common horsetail), As and Sb were associated with organic material and appeared mobile in the root zone. In the zone below active plant growth, As and Sb were associated primarily with inorganic phases suggesting a release and reprecipitation of these elements upon plant death. The co-existence of reduced and oxidized As and Sb species, instability of some phases under changing redox conditions, and plant uptake and release pose challenges for remediation efforts at the mine.

Northwest Territories↗

USGS2014

Comparing population exposure to multiple Washington earthquake scenarios for prioritizing loss estimation studies

Scenario-based, loss-estimation studies are useful for gauging potential societal impacts from earthquakes but can be challenging to undertake in areas with multiple scenarios and jurisdictions. We present a geospatial approach using various population data for comparing earthquake scenarios and jurisdictions to help emergency managers prioritize where to focus limited resources on data development and loss-estimation studies. Using 20 earthquake scenarios developed for the State of Washington (USA), we demonstrate how a population-exposure analysis across multiple jurisdictions based on Modified Mercalli Intensity (MMI) classes helps emergency managers understand and communicate where potential loss of life may be concentrated and where impacts may be more related to quality of life. Results indicate that certain well-known scenarios may directly impact the greatest number of people, whereas other, potentially lesser-known, scenarios impact fewer people but consequences could be more severe. The use of economic data to profile each jurisdiction’s workforce in earthquake hazard zones also provides additional insight on at-risk populations. This approach can serve as a first step in understanding societal impacts of earthquakes and helping practitioners to efficiently use their limited risk-reduction resources.

Washington↗

USGS2014

Behavior of feral horses in response to culling and GnRH immunocontraception

Wildlife management actions can alter fundamental behaviors of individuals and groups,which may directly impact their life history parameters in unforeseen ways. This is especially true for highly social animals because changes in one individual’s behavior can cascade throughout its social network. When resources to support populations of social animals are limited and populations become locally overabundant, managers are faced with the daunting challenge of decreasing population size without disrupting core behavioral processes. Increasingly, managers are turning to fertility control technologies to supplement culling in efforts to suppress population growth, but little is quantitatively known about how either of these management tools affects behavior. Gonadotropin releasing hormone (GnRH) is a small neuropeptide that performs an obligatory role in mammalian reproduction and has been formulated into the immunocontraceptive GonaCon-BTM. We investigated the influences of this vaccine on behavior of feral horses ( Equus caballus ) at Theodore Roosevelt National Park, North Dakota, USA, for a year preceding and a year following nonlethal culling and GnRH-vaccine treatment. We observed horses during the breeding season and found only minimal differences in time budget behaviors of free-ranging female feral horses treated with GnRH and those treated with saline. The differences observed were consistent with the metabolic demands of pregnancy and lactation. We observed similar social behaviors between treatment groups, reflecting limited reproductive behavior among control females due to high rates of pregnancy and suppressed reproductive behavior among treated females due to GnRH-inhibited ovarian activity. In the treatment year, band stallion age was the only supported factor influencing herding behavior (P < 0.001), harem-tending behavior (P < 0.001), and agonistic behavior (P = 0.02). There was no difference between the mean body condition of control females (4.9 (95% CI = 4.7–5.1)) and treated females(4.8 (95% CI = 4.7–4.9)). Band fidelity among all females increased 25.7% in the year fol-lowing vaccination and culling, despite the social perturbation associated with removal of conspecifics. Herding behavior by stallions decreased 50.7% following treatment and culling (P < 0.001), while harem-tending behavior increased 195.0% (P < 0.001). The amount of available forage influenced harem-tending, reproductive, and agonistic behavior in the year following culling and treatment (P < 0.04). These changes reflected the expected nexus between a species with polygynous social structure and strong group fidelity and the large instantaneous change in population density and demography coincident with culling.Behavioral responses to such perturbation may be synergistic in reducing grazing pressure by decreasing energetically expensive competitive behaviors, but further investigation is needed to explicitly test this hypothesis.

Applied Animal Behaviour Science↗

USGS2005

Performance of juvenile steelhead trout ( Oncorhynchus mykiss ) produced from untreated and cryopreserved milt

Despite the expanding use of milt cryopreservation in aquaculture, the performance of fish produced from this technique has not been fully explored beyond initial rearing stages. We compared the performance of juvenile steelhead Oncorhynchus mykiss produced from untreated (UM) and cryopreserved milt (CM) and reared for 4–9 months. For the 1996 brood, CM alevins were heavier (&sim; 1.7%, P < 0.01) than UM alevins and length was influenced by a significant milt-by-family interaction ( P < 0.03) suggesting a greater treatment effect for some families. No significant differences were found in length or weight ( P > 0.05) for 1997 brood alevins and percent yolk was similar for both broods ( P > 0.34). In growth and survival experiment I (GSE-I, 1996), UM and CM juveniles reared in separate tanks and fed to satiation (130 days) showed no significant differences in survival, length or weight ( P > 0.05) between milt groups. In contrast, for UM and CM siblings reared in the same tank for 210 days on a low food ration (GSE-II), survival was similar ( P > 0.05), but length (UM 4% > CM, P < 0.05) and possibly weight (UM 15% > CM, P = 0.08), were influenced by cryopreservation. Fish from the 1997 brood (GSE-III) were reared for 313 days in a repeat of GSE-II and no differences were found in survival ( P = 0.47), length ( P = 0.75) or weight ( P = 0.76) suggesting considerable heterogeneity between broods. Performance of the 1996 brood was also tested for response to stress and a disease challenge. Cortisol responses of juveniles exposed to acute stress were not significantly different ( P = 0.19), but mean cortisol was consistently and significantly greater ( P < 0.01) for CM than UM fish exposed to a 48-h stress (increased density). After exposure to three dosages of the bacteria, Listonella anguillarum , we found similar mortality proportions ( P = 0.72) for UM and CM fish. Variable juvenile performance for the parameters tested indicated significant differences among broods and families and suggests a cautionary approach to the widespread use of cryopreservation for steelhead.

Aquaculture↗