Jc science coursework b 2013

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Subscribe to our Newsletter Helpful articles and useful tips for adults considering a college degree. Thank you for signing up for our science You will soon receive [URL] email confirming your request. Business Forensics Degree Program Program Overview Program Details Coursework Choose Franklin Expose occupational fraud and questionable business practices White-collar crime doesn't always leave clues, which makes it easy to miss—and hard to prosecute.

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Ecological Applications — Tracing impacts of partner abundance in facultative pollination mutualisms: from individuals to populations. Ecology Galen, C. Kaczorowski, S. Todd, J.


Geib , and R. Dosage-dependent impacts of a floral volatile compound on pollinators, larcenists, and the potential for floral evolution in the Alpine Skypilot Polemonium viscosum. The American Naturalist Gei b.

Conditions for evolution in a pollination mutualism: density dependent effects of nectar-thieving ants on selection for bumblebee pollination in the alpine wildflower, Polemonium viscosu m. Chandrasekhar, M.. Litherland, and J. August Undergraduates mentored Kimberly Bowman B. The design allows statistical inferences to be made about an entire population, including the unselected units. Its main limitations are the restriction of inference to a population of sampling units with nonzero sampling probabilities and a limited ability to address analytical or causal hypotheses Sterba Models typically include parameters for hypothesized factors e.

The assumed applicability of models to all potential observations allows inference to be extended beyond the sample to the population.


Yuanyuan Liang, PhD

Purposive sampling is linked directly to a model, and data are collected deliberately to strengthen inferences about population parameters. With opportunistic sampling the linkage is missing, and the data may or may not prove useful for reliably estimating parameters. In either case the models include covariates that are hypothesized to influence observations, and analysis involves estimating covariate parameters with the sample data.

This approach may produce reliable inferences if the relevant environmental and observer factors are accounted for in the model, and the necessary covariate information about them is collected Sterba These approaches contrast with opportunistic data collection without any sampling design, which is unlikely to result in reliable inferences about a target population without information beyond the survey.

John Tukey , , a pioneer in statistics, distinguished between exploratory and confirmatory research paradigms and how each affects hypothesis formation, study design, data collection, and analytical methods. In the confirmatory paradigm, hypotheses are generated a priori, before data collection, whereas in the exploratory paradigm hypotheses are generated a posteriori, after collection.

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Ecology involves both exploratory and confirmatory analysis, depending on the circumstances. Nonetheless, it is important not to confuse one with the other. The distinction between them is particularly relevant to citizen science, which often involves opportunistic collection of large amounts of data and exploratory analysis with new Web 2. Several factors influence the quality and usefulness of data for estimation and inference, particularly in population research and monitoring.

In our typology, overall design of projects ranges from rigorous protocols designed for clearly articulated questions to opportunistic data collection without protocols.

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Our grouping builds on the pyramid framework of the U. In intensive scientific research projects, volunteers work in small teams under close supervision by lead scientists.

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Volunteers may perform complex tasks and collect data according to explicit protocols that often allow confirmatory hypothesis testing and statistical inference. Longitudinal monitoring studies consist of standardized repeat monitoring, often with many participants and a high degree of institutional coordination. Some level of biological expertise is frequently a prerequisite. Data may consist of lists of species occurrence or checklists, sometimes designed to yield relative abundance within grid cells.

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An atlas can be a single survey of species spatial distribution, or can include repeat sampling. Objectives that guide project design and data collection influence data quality. Objectives of applied ecological research can be pursued in projects with strong designs, expertise and training of volunteers, and professional oversight. For these purposes it is unnecessary to meet the temporal replication and other design requirements needed to distinguish real ecological patterns from error or natural variation.

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For survey and monitoring design in general, factors including the number of taxa, number of sites, sampling variability, sampling frequency, and duration of monitoring are major determinants of the analysis techniques that can be used for reliable inference. Databases from such projects are often usable to address a wide range of questions. Other common sources of variation result in nonrepresentative sampling of geographic areas. Statistical adjustments can sometimes reduce variability and bias and compensate for data features that violate statistical assumptions.

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  • The potential for citizen science to produce reliable and useful information in ecology.

Although exploratory analysis of patterns in data collected opportunistically is always possible, it is still necessary at a minimum to deal with survey effort and detection issues. Otherwise the patterns that are discovered may confound sampling error with ecological processes. A further factor influencing data quality is volunteer training and performance tracking. Some opportunistic monitoring projects may provide no training of any kind e. Observer bias in citizen science projects has been investigated by experimentally comparing data collection by volunteers and professionals.

Of course, professionals frequently fail to detect organisms, too. Project design, rather than citizen involvement per se, is a fundamental constraint limiting the use of citizen science data in ecological research. The issue is how data are collected, not by whom professionals or volunteers. Project planners with these goals must take into account the importance of data quality for decision makers, especially in government. Much ecological research carried out by agencies e.

Department of the Interior is applied research to meet ecological management or policy objectives, including conservation. Endangered Species Act and invasive species control, which can be very costly. For biodiversity conservation, 1 way citizen science can potentially make valuable contributions is by increasing the number of observers and geographic scope of species monitoring efforts. This can be an important source of information about changes in species relative abundance or distribution.

In sum, measures for strengthening the science aspect of citizen science in ecology will increase its acceptance by scientists and decision makers.