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Toward reliable estimates of abundance: comparing index methods to assess the abundance of a Mammalian predator.

ABSTRACT: Due to time and financial constraints indices are often used to obtain landscape-scale estimates of relative species abundance. Using two different field methods and comparing the results can help to detect possible bias or a non monotonic relationship between the index and the true abundance, providing more reliable results. We used data obtained from camera traps and feces counts to independently estimate relative abundance of red foxes in the Black Forest, a forested landscape in southern Germany. Applying negative binomial regression models, we identified landscape parameters that influence red fox abundance, which we then used to predict relative red fox abundance. We compared the estimated regression coefficients of the landscape parameters and the predicted abundance of the two methods. Further, we compared the costs and the precision of the two field methods. The predicted relative abundances were similar between the two methods, suggesting that the two indices were closely related to the true abundance of red foxes. For both methods, landscape diversity and edge density best described differences in the indices and had positive estimated effects on the relative fox abundance. In our study the costs of each method were of similar magnitude, but the sample size obtained from the feces counts (262 transects) was larger than the camera trap sample size (88 camera locations). The precision of the camera traps was lower than the precision of the feces counts. The approach we applied can be used as a framework to compare and combine the results of two or more different field methods to estimate abundance and by this enhance the reliability of the result.

SUBMITTER: Guthlin D

PROVIDER: S-EPMC3990582 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Toward reliable estimates of abundance: comparing index methods to assess the abundance of a Mammalian predator.

Güthlin Denise D Storch Ilse I Küchenhoff Helmut H

PloS one 20140417 4

Due to time and financial constraints indices are often used to obtain landscape-scale estimates of relative species abundance. Using two different field methods and comparing the results can help to detect possible bias or a non monotonic relationship between the index and the true abundance, providing more reliable results. We used data obtained from camera traps and feces counts to independently estimate relative abundance of red foxes in the Black Forest, a forested landscape in southern Ger ...[more]

PMID: 24743565

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Project description:BACKGROUND:Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE:The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS:Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e-Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS:Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS:The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning-based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Project description:ObjectivesIn patients with nonischemic dilated cardiomyopathy (NIDCM), native T1, partition coefficient (λGd), and extracellular volume fraction (ECV) mapping may offer prognostic values beyond late gadolinium enhancement (LGE), by scaling the range of myocardial changes.BackgroundIn patients with NIDCM, LGE is seen in 30% of patients and it indicates adverse prognosis.MethodsThe study mapped 6 anatomical locations using all 4 cardiac magnetic resonance (CMR) tissue-characterizing methods and associated with outcome. The authors performed T1 mapping of the myocardium and the blood pool, before and serially after contrast injection, using a Look-Locker cine gradient-echo technique to obtain T1 and the corresponding reciprocal R1 values. λGd values were derived from the slopes of the least-squares regression lines for myocardial versus blood R1, then adjusted to serum hematocrit to yield ECV.ResultsConsecutive 240 NIDCM patients (49 ± 16 years of age; 38% women) underwent CMR for cardiac function, LGE, native T1, λGd, and ECV. After a median of 3.8 years, 36 (15%) experienced major adverse cardiac events (MACE), including 22 heart failure hospitalizations and 14 deaths. Nonischemic LGE was detected in 34%, whereas ECV was elevated (≥1 location) in 58%. Comparing the 4 methods, mean ECV and λGd both demonstrated strong association with MACE (both p < 0.001). In contrast to native T1 and LGE, ECV values from all 6 locations were associated with MACE and death, with the anteroseptum being the most significant (p < 0.0001). The number of abnormal ECV locations correlated linearly with annual MACE rates (p = 0.0003). Mean ECV was the only predictor to enter a prognostic model that contained age, sex, New York Heart Association functional class, and left ventricular ejection fraction. For every 10% increase, mean ECV portended to a 2.8-fold adjusted increase risk to MACE (p < 0.001).ConclusionsIn this study of patients with NIDCM, mapping the myocardial extent of abnormality using ECV offers prognostication toward heart failure outcomes incremental to LGE or native T1 mapping.

Dataset Information

Toward reliable estimates of abundance: comparing index methods to assess the abundance of a Mammalian predator.

Publications

Toward reliable estimates of abundance: comparing index methods to assess the abundance of a Mammalian predator.

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