Dataset Information

Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data.

ABSTRACT: Geolocators are a well-established technology to reconstruct migration routes of animals that are too small to carry satellite tags (e.g. passerine birds). These devices record environmental light-level data that enable the reconstruction of daily positions from the time of twilight. However, all current methods for analysing geolocator data require manual pre-processing of raw records to eliminate twilight events showing unnatural variation in light levels, a step that is time-consuming and must be accomplished by a trained expert. Here, we propose and implement advanced machine learning techniques to automate this procedure and we apply them to 108 migration tracks of barn swallows ( Hirundo rustica). We show that routes reconstructed from the automated pre-processing are comparable to those obtained from manual selection accomplished by a human expert. This raises the possibility of fully automating light-level geolocator data analysis and possibly analysing the large amount of data already collected on several species.

SUBMITTER: Pancerasa M

PROVIDER: S-EPMC6597775 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data.

Pancerasa Mattia M Sangiorgio Matteo M Ambrosini Roberto R Saino Nicola N Winkler David W DW Casagrandi Renato R

Journal of the Royal Society, Interface 20190619 155

Geolocators are a well-established technology to reconstruct migration routes of animals that are too small to carry satellite tags (e.g. passerine birds). These devices record environmental light-level data that enable the reconstruction of daily positions from the time of twilight. However, all current methods for analysing geolocator data require manual pre-processing of raw records to eliminate twilight events showing unnatural variation in light levels, a step that is time-consuming and mus ...[more]

PMID: 31213173

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Project description:Longitudinal tracking studies have revealed consistent differences in the migration patterns of individuals from the same populations. The sources or processes causing this individual variation are largely unresolved. As a result, it is mostly unknown how much, how fast and when animals can adjust their migrations to changing environments. We studied the ontogeny of migration in a long-distance migratory shorebird, the black-tailed godwit Limosa limosa limosa, a species known to exhibit marked individuality in the migratory routines of adults. By observing how and when these individual differences arise, we aimed to elucidate whether individual differences in migratory behaviour are inherited or emerge as a result of developmental plasticity. We simultaneously tracked juvenile and adult godwits from the same breeding area on their south- and northward migrations. To determine how and when individual differences begin to arise, we related juvenile migration routes, timing and mortality rates to hatch date and hatch year. Then, we compared adult and juvenile migration patterns to identify potential age-dependent differences. In juveniles, the timing of their first southward departure was related to hatch date. However, their subsequent migration routes, orientation, destination, migratory duration and likelihood of mortality were unrelated to the year or timing of migration, or their sex. Juveniles left the Netherlands after all tracked adults. They then flew non-stop to West Africa more often and incurred higher mortality rates than adults. Some juveniles also took routes and visited stopover sites far outside the well-documented adult migratory corridor. Such juveniles, however, were not more likely to die. We found that juveniles exhibited different migratory patterns than adults, but no evidence that these behaviours are under natural selection. We thus eliminate the possibility that the individual differences observed among adult godwits are present at hatch or during their first migration. This adds to the mounting evidence that animals possess the developmental plasticity to change their migration later in life in response to environmental conditions as those conditions are experienced.

Project description:BACKGROUND:Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. METHODS:Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity - the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. RESULTS:The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. CONCLUSIONS:The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.

Dataset Information

Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data.

Publications

Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data.

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