D. Richard Cutler

Bio: D. Richard Cutler is an academic researcher from Utah State University. The author has contributed to research in topics: Rare species & Population. The author has an hindex of 14, co-authored 25 publications receiving 3681 citations.

Random Forests for Classification in Ecology

Abstract: Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

Dietary protein intake and risk of osteoporotic hip fracture in elderly residents of Utah.

Abstract: The role of protein intake in osteoporosis is unclear. In a case-control study in Utah (n = 2501), increasing level of protein intake was associated with a decreased risk of hip fracture in men and women 50–69 years of age but not in those 70–89 years of age. Protein intake may be important for optimal bone health. Introduction: Protein is an important component of bone, but the role of dietary protein intake in osteoporosis and fracture risk remains controversial. Material and Methods: The role of dietary protein intake in osteoporotic hip fracture was evaluated in a statewide case-control study in Utah. Patients, 50–89 years of age, with hip fracture (cases) were ascertained through surveillance of 18 Utah hospitals during 1997–2001. Age- and gender-matched controls were randomly selected. Participants were interviewed in their place of residence, and diet was assessed using a picture-sort food frequency questionnaire previously reported to give a useful measure of usual dietary intake in the elderly Utah population. The association between protein intake and risk of hip fracture was examined across quartiles of protein intake and stratified by age group for 1167 cases (831 women, 336 men) and 1334 controls (885 women, 449 men). Results: In logistic regression analyses that controlled for gender, body mass index, smoking status, alcohol use, calcium, vitamin D, potassium, physical activity, and estrogen use in women, the odds ratios (OR) of hip fracture decreased across increasing quartiles of total protein intake for participants 50–69 years of age (OR: 1.0 [reference]; 0.51 [95% CI: 0.30–0.87]; 0.53 [0.31–0.89]; 0.35 [0.21–0.59]; p < 0.001). No similar associations were observed among participants 70–89 years of age. Results from analyses stratified by low and high calcium and potassium intake did not differ appreciably from the results presented above. Conclusion: Higher total protein intake was associated with a reduced risk of hip fracture in men and women 50–69 years of age but not in men and women 70–89 years of age. The association between dietary protein intake and risk of hip fracture may be modified by age. Our study supports the hypothesis that adequate dietary protein is important for optimal bone health in the elderly 50–69 years of age.

Antioxidant Intake and Risk of Osteoporotic Hip Fracture in Utah: An Effect Modified by Smoking Status

Abstract: The role of antioxidant intake in osteoporotic hip fracture risk is uncertain and may be modified by smoking. In the Utah Study of Nutrition and Bone Health, a statewide, population-based case-control study, the authors investigated whether antioxidant intake was associated with risk of osteoporotic hip fracture and whether this association was modified by smoking status. The analyses included data on 1,215 male and female cases aged > or = 50 years who incurred a hip fracture during 1997-2001 and 1,349 age- and sex-matched controls. Diet was assessed by food frequency questionnaire. Among ever smokers, participants in the highest quintile of vitamin E intake (vs. the lowest) had a lower risk of hip fracture after adjustment for confounders (odds ratio = 0.29, 95% confidence interval (CI): 0.16, 0.52; p-trend < 0.0001). The corresponding odds ratio for beta-carotene intake was 0.39 (95% CI: 0.23, 0.68; p-trend = 0.0004), and for selenium intake it was 0.27 (95% CI: 0.12, 0.58; p-trend = 0.0003). Vitamin C intake did not have a significant graded association with hip fracture risk among ever smokers. Similar findings were obtained when an overall antioxidant intake score was used (odds ratio = 0.19, 95% CI: 0.10, 0.37; p-trend < 0.0001). No similar associations were found in never smokers. Antioxidant intake was associated with reduced risk of osteoporotic hip fracture in these elderly subjects, and the effect was strongly modified by smoking status.

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

Random Forests for Classification in Ecology

Abstract: Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

An assessment of the effectiveness of a random forest classifier for land-cover classification

Abstract: Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

Literacy and health outcomes

Abstract: OBJECTIVE: To review the relationship between literacy and health outcomes DATA SOURCES: We searched MEDLINE, Cumulative Index to Nursing and Allied Health (CINAHL), Educational Resources Information Center (ERIC), Public Affairs Information Service (PAIS), Industrial and Labor Relations Review (ILLR), PsychInfo, and Ageline from 1980 to 2003 STUDY SELECTION: We included observational studies that reported original data, measured literacy with any valid instrument, and measured one or more health outcomes Two abstractors reviewed each study for inclusion and resolved disagreements by discussion DATA EXTRACTION: One reviewer abstracted data from each article into an evidence table; the second reviewer checked each entry The whole study team reconciled disagreements about information in evidence tables Both data extractors independently completed an 11-item quality scale for each article; scores were averaged to give a final measure of article quality DATA SYNTHESIS: We reviewed 3,015 titles and abstracts and pulled 684 articles for full review; 73 articles met inclusion criteria and, of those, 44 addressed the questions of this report Patients with low literacy had poorer health outcomes, including knowledge, intermediate disease markers, measures of morbidity, general health status, and use of health resources Patients with low literacy were generally 15 to 3 times more likely to experience a given poor outcome The average quality of the articles was fair to good Most studies were cross-sectional in design; many failed to address adequately confounding and the use of multiple comparisons CONCLUSIONS: Low literacy is associated with several adverse health outcomes Future research, using more rigorous methods, will better define these relationships and guide developers of new interventions