A New Model to Estimate Daily Energy Expenditure for Wintering Waterfowl

Current models to estimate daily energy expenditure (DEE) for free-living birds are limited to either those that use fixed thermoregulatory costs or those that more accurately estimate thermoregulatory costs, but require extensive and often logistically difficult measurements. Here, we propose a model based on basal metabolic rate (BMR), activity budgets, and site-specific energetic costs of thermoregulation that requires only simple measures of ambient temperature and wind speed to provide estimates of DEE. We use the model to calculate the DEE of Buffleheads (Bucephala albeola) wintering at six habitats that afford differing degrees of protection from exposure within Narragansett Bay, Rhode Island. Bufflehead activity budget data collected during the winters of 2001–2002 and 2002–2003, along with average temperatures and wind speeds at the sites, were used to calculate DEE that ranged from 46.9 to 52.4 kJ/hr and increased with increasing wind speed. The energetic cost of thermoregulation composed as much as 28% of total DEE and increased with wind speed. Our DEE values were 13.4% higher, and thermoregulatory costs were up to 2× higher than those calculated using an existing model that incorporates fixed thermoregulatory costs. We also saw an increase in feeding activity with increasing wind speed; sensitivity analysis of the effects of wind speed and feeding activity showed that a 1 m/sec increase in wind speed at our sites increased DEE by 2.5%, whereas a corresponding increase in feeding activity increased DEE by 4.5%. This suggests that in temperate winter habitats, increased feeding activity may have a greater impact on Bufflehead DEE than wind exposure. Site-specific model estimates of DEE could also provide additional insight into the relative contribution of environmental conditions and changes in waterfowl behavior to DEE

Energy-Based Carrying Capacities of Bufflehead \u3cem\u3eBucephala albeola\u3c/em\u3e Wintering Habitats

We present a model for calculating energy-based carrying capacities for bufflehead (Bucephala albeola), a small North American sea duck wintering in coastal and estuarine habitats. Our model uses estimates of the seasonal energy expenditures that incorporate site-specific energetic costs of thermoregulation, along with available prey energy densities to calculate carrying capacities in numbers of birds per winter. The model was used to calculate carrying capacities under several foraging scenarios for bufflehead wintering at three urban and three rural sites in the coastal northeast U.S. We found that energy-based carrying capacities varied from 20 – 320 birds per site per winter (0.38 – 6.22 birds per hectare), and showed a trend towards increasing with prey energy density (r = 0.53) and with decreasing average daily energy expenditure (r2 = 0.57, p = 0.08). We found greater prey species richness at rural sites, but similar prey biomass and productivity across all sites. Bufflehead density averaged 1.89 ± 2.34 birds per hectare (range 0.38 – 6.22 birds per hectare) across the sites. Bufflehead abundance at urban sites was reduced by an average of 43.7% from that predicted using the relationship between per-hectare carrying capacity and bufflehead abundance at rural sites. This difference may arise from natural or human induced factors that act to limit sea duck populations on wintering habitats

How Machines Learn: Where Do Companies Get Data for Machine Learning and What Licenses Do They Need?

Machine learning services ingest customer data in order to provide refined, customized services. Machine learning algorithms are increasingly prominent in multiple sectors within the software-as-a-service industry including online advertising, health diagnostics, and travel. However, very little has been written on the rights a company utilizing machine learning needs to obtain in order to use customer data to improve its own products or services. Machine learning encompasses multiple types of data use and analysis, including (a) supervised machine learning algorithms, which take specific data provided in a tagged and classified format to deliver specific predictable output; and (b) unsupervised machine learning algorithms, where untagged data is processed in order to look for patterns and correlations without a specified output. This Article introduces the reader to the types of data use involved in various machine learning models, the level of data retention normally required for each model, and the risks of using personal information or re-identifiable data in connection with machine learning. The paper also discusses the type of license a commercial provider and consumer would need to enter into for various types of machine learning software. Finally, the paper proposes best practices for ensuring adequate rights are obtained through legal agreements so that machines may self-improve and innovate

Smart Contracts, Blockchain, and the Next Frontier of Transactional Law

Smart contracts are an emerging technology that could revolutionize commercial transactions by eliminating inefficiencies and uncertainty created by the current transactional ecosystem of lawyers, courts, regulators, banks, and other parties with divergent interests. However, a lack of consensus around how smart contracts are implemented, uncertainty regarding enforceability, and scarcity of on point statutes and case law means that a stable legal, commercial and technical smart contract landscape has yet to emerge. The implementation of universal legal, technical and commercial standards and best practices will reduce uncertainty and promote widespread adoption and use of smart contracts

Habitat Characteristics Associated with the Distribution and Abundance of \u3cem\u3eHistrionicus histrionicus\u3c/em\u3e (Harlequin Ducks) Wintering in Southern New England

Histrionicus histrionicus (Harlequin Ducks) that winter along the east coast of North America are listed as a population of special concern in Canada, and they use several coastal wintering sites in southern New England that are subject to varying degrees of urbanization. We studied patterns of habitat use by Harlequin Ducks at 12 known wintering sites in southern New England. An average of 327 ± 114 Harlequin Ducks were found at the sites during the winters of 2001–2003. More Harlequin Ducks wintered at sites south of Cape Cod, MA that had greater mollusk (709,133 ± 504,568 versus 97,154 ± 72,427 kcal ha−1) and crustacean (27,907 ± 16,312 versus 1412 ± 1675 kcal ha−1) prey energy density, and a higher index of hunting activity (2.4 ± 1.2 versus 1.4 ± 0.5) than sites to the north. We used logistic regression analysis at 12 sites inhabited by Harlequin Ducks and 12 nearby sites of similar geomorphology that did not support Harlequin Ducks to identify habitat characteristics that best explained their distribution in southern New England. Our analysis identified two habitat characteristics that affected the likelihood a site was used by Harlequin Ducks: 1) the proportion of residential, commercial, and industrial land use within a 100-m radius of the perimeter of the site; and 2) distance to the nearest Harlequin Duck wintering site. However, other factors, including those related to their extremely low population size, need to also be considered as recommendations are developed for the conservation of east coast Harlequin Ducks

Modeling water resources management at the basin level: review and future directions

Water quality / Water resources development / Agricultural production / River basin development / Mathematical models / Simulation models / Water allocation / Policy / Economic aspects / Hydrology / Reservoir operation / Groundwater management / Drainage / Conjunctive use / Surface water / GIS / Decision support systems / Optimization methods / Water supply

Allometric length-weight relationships for benthic prey of aquatic wildlife in coastal marine habitats

We developed models to estimate the soft tissue content of benthic marine invertebrates that are prey for aquatic wildlife. Allometric regression models of tissue wet weight with shell length for 10 species of benthic invertebrates had r2 values ranging from 0.29 for hermit crabs Pagurus longicarpus to 0.98 for green crabs Carcinus maenas. As a class, bivalves had the highest r2 values (0.84) and crustaceans the lowest (0.48). Energy and nutrient content of soft tissue is also presented for the 10 benthic species. The energy content was lowest in crabs, and ranged within 2.20–4.71 kcal g-1 dry weight. Fat content was highly variable (range: 3.5–16.0%), and protein content ranged within 43.1–68.1% and was highest for shrimp Palaemonetes pugio. Comparison between classes of organisms of the amount of soft tissue per unit shell length showed that crustaceans yield five times more soft tissue per unit shell length than bivalves, and four times more than gastropods. The models we present use simple measures, such as the length of shell or wet weight of the entire animal, to quantitatively estimate the amount of available soft tissue in benthic prey that are usually consumed in total (with shell and soft tissue intact) but for which only the soft tissue is used for nutritional gain. This information can be combined with energy and nutrient content data to calculate energy or nutrient based carrying capacities that can help assess available resources for shorebirds, waterfowl and marine mammals

Exposure to cold but not exercise increases carbon turnover rates in specific tissues of a passerine

Carbon turnover differs between tissues within an animal, but the extent to which ecologically relevant increases in metabolism affect carbon turnover rates is largely unknown. We tested the energy expenditure and protein turnover hypotheses that predict increased carbon turnover, either in association with increased daily energy expenditure, or in concert with tissue-specific increased protein metabolism. We used stable-isotope-labeled diets to quantify the rate of carbon turnover in 12 different tissues for three groups of zebra finches (Taeniopygia guttata): cold-exposed birds kept at ambient temperatures below their thermoneutral zone, exercised birds that were flown for 2 h per day in a flight arena, and control birds that were kept at ambient temperatures within their thermoneutral zone and that were not exercised. We found that increases in metabolism associated with cold-exposure but not exercise produced measurable increases in carbon turnover rate of, on average, 2.4±0.3 days for pectoral muscle, gizzard, pancreas and heart, even though daily energy intake was similar for exercised and cold-exposed birds. This evidence does not support the energy expenditure hypothesis, and we invoke two physiological processes related to protein metabolism that can explain these treatment effects: organ mass increase and tissue-specific increase in activity. Such changes in carbon turnover rate associated with cold temperatures translate into substantial variation in the estimated time window for which resource use is estimated and this has important ecological relevance

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

<p>Abstract</p> <p>Background</p> <p>Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.</p> <p>Results</p> <p>In this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad-sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and specific detection was 59.4% versus detection using classification error at 62% and specific detection was 52.2%.</p> <p>Conclusion</p> <p>Of the 10 measures evaluated, the likelihood ratio and normalized mutual information (NMI) are measures that consistently improve the detection and power of MDR in simulated data over using classification error. These measures also reduce the inclusion of spurious variables in a multi-locus model. Thus, MDR, which has already been demonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alternative fitness functions.</p