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Range overlap between the sword-billed hummingbird and its guild of long-flowered species: An approach to the study of a coevolutionary mosaic.

ABSTRACT: The coevolutionary process among free-living mutualists with extremely long matching traits may favor the formation of mutualistic interaction networks through coevolutionary escalation, complementarity and convergence. These networks may be geographically structured; the links among the species of a local network are shaped by the biotic composition of the community, thus creating selection mosaics at broader geographical scales. Therefore, to fully understand a coevolutionary process, it is crucial to visualize the geographical structure of the interaction network across the landscape. In this study we focused on the poorly known interaction system between Ensifera ensifera and its guild of long-flowered plant species. We combined occurrence data and environmental variables to predict E. ensifera distribution, in addition to range polygons available for plant species in order to evaluate the geographical variation in bill length and plant species richness. A positive relationship between bill length and plant species richness within the E. ensifera range suggests a geographical structuring of the interaction networks. At mid-latitude locations of E. ensifera range, where hummingbirds attained the longest bills, richness of long-flowered plant species was higher than at low latitude locations. These locations likely represent coevolutionary vortices where long-lasting reciprocal selection probably drove the evolution of long traits, consequently drawing new plant species into the coevolutionary network. Conversely, areas where the sword-billed hummingbird was absent or had shorter bills probably represent coevolutionary coldspots. Our results provide a first insight into this phenotypically specialized plant-pollinator network across the landscape and show candidate areas to test the predictions of the coevolutionary hypothesis, such as reciprocal selection.

SUBMITTER: Soteras F

PROVIDER: S-EPMC6306261 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Range overlap between the sword-billed hummingbird and its guild of long-flowered species: An approach to the study of a coevolutionary mosaic.

Soteras Florencia F Moré Marcela M Ibañez Ana C AC Iglesias María Del Rosario MDR Cocucci Andrea A AA

PloS one 20181226 12

The coevolutionary process among free-living mutualists with extremely long matching traits may favor the formation of mutualistic interaction networks through coevolutionary escalation, complementarity and convergence. These networks may be geographically structured; the links among the species of a local network are shaped by the biotic composition of the community, thus creating selection mosaics at broader geographical scales. Therefore, to fully understand a coevolutionary process, it is cr ...[more]

PMID: 30586466

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Project description:Distant populations of animals may share their non-breeding grounds or migrate to distinct areas, and this may have important consequences for population differentiation and dynamics. Small burrow-nesting seabirds provide a suitable case study, as they are often restricted to safe breeding sites on islands, resulting in a patchy breeding distribution. For example, Thin-billed prions Pachyptila belcheri have two major breeding colonies more than 8,000 km apart, on the Falkland Islands in the south-western Atlantic and in the Kerguelen Archipelago in the Indian Ocean. We used geolocators and stable isotopes to compare at-sea movements and trophic levels of these two populations during their non-breeding season, and applied ecological niche models to compare environmental conditions in the habitat. Over three winters, birds breeding in the Atlantic showed a high consistency in their migration routes. Most individuals migrated more than 3000 km eastwards, while very few remained over the Patagonian Shelf. In contrast, all Indian Ocean birds migrated westwards, resulting in an overlapping nonbreeding area in the eastern Atlantic sector of the Southern Ocean. Geolocators and isotopic signature of feathers indicated that prions from the Falklands moulted at slightly higher latitudes than those from Kerguelen Islands. All birds fed on low trophic level prey, most probably crustaceans. The phenology differed notably between the two populations. Falkland birds returned to the Patagonian Shelf after 2-3 months, while Kerguelen birds remained in the nonbreeding area for seven months, before returning to nesting grounds highly synchronously and at high speed. Habitat models identified sea surface temperature and chlorophyll a concentration as important environmental parameters. In summary, we show that even though the two very distant populations migrate to roughly the same area to moult, they have distinct wintering strategies: They had significantly different realized niches and timing which may contribute to spatial niche partitioning.

Project description:BackgroundThe Centers for Medicare and Medicaid Services projects that health care costs will continue to grow over the next few years. Rising readmission costs contribute significantly to increasing health care costs. Multiple areas of health care, including readmissions, have benefited from the application of various machine learning algorithms in several ways.ObjectiveWe aimed to identify suitable models for predicting readmission charges billed by hospitals. Our literature review revealed that this application of machine learning is underexplored. We used various predictive methods, ranging from glass-box models (such as regularization techniques) to black-box models (such as deep learning-based models).MethodsWe defined readmissions as readmission with the same major diagnostic category (RSDC) and all-cause readmission category (RADC). For these readmission categories, 576,701 and 1,091,580 individuals, respectively, were identified from the Nationwide Readmission Database of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality for 2013. Linear regression, lasso regression, elastic net, ridge regression, eXtreme gradient boosting (XGBoost), and a deep learning model based on multilayer perceptron (MLP) were the 6 machine learning algorithms we tested for RSDC and RADC through 10-fold cross-validation.ResultsOur preliminary analysis using a data-driven approach revealed that within RADC, the subsequent readmission charge billed per patient was higher than the previous charge for 541,090 individuals, and this number was 319,233 for RSDC. The top 3 major diagnostic categories (MDCs) for such instances were the same for RADC and RSDC. The average readmission charge billed was higher than the previous charge for 21 of the MDCs in the case of RSDC, whereas it was only for 13 of the MDCs in RADC. We recommend XGBoost and the deep learning model based on MLP for predicting readmission charges. The following performance metrics were obtained for XGBoost: (1) RADC (mean absolute percentage error [MAPE]=3.121%; root mean squared error [RMSE]=0.414; mean absolute error [MAE]=0.317; root relative squared error [RRSE]=0.410; relative absolute error [RAE]=0.399; normalized RMSE [NRMSE]=0.040; mean absolute deviation [MAD]=0.031) and (2) RSDC (MAPE=3.171%; RMSE=0.421; MAE=0.321; RRSE=0.407; RAE=0.393; NRMSE=0.041; MAD=0.031). The performance obtained for MLP-based deep neural networks are as follows: (1) RADC (MAPE=3.103%; RMSE=0.413; MAE=0.316; RRSE=0.410; RAE=0.397; NRMSE=0.040; MAD=0.031) and (2) RSDC (MAPE=3.202%; RMSE=0.427; MAE=0.326; RRSE=0.413; RAE=0.399; NRMSE=0.041; MAD=0.032). Repeated measures ANOVA revealed that the mean RMSE differed significantly across models with P<.001. Post hoc tests using the Bonferroni correction method indicated that the mean RMSE of the deep learning/XGBoost models was statistically significantly (P<.001) lower than that of all other models, namely linear regression/elastic net/lasso/ridge regression.ConclusionsModels built using XGBoost and MLP are suitable for predicting readmission charges billed by hospitals. The MDCs allow models to accurately predict hospital readmission charges.

Dataset Information

Range overlap between the sword-billed hummingbird and its guild of long-flowered species: An approach to the study of a coevolutionary mosaic.

Publications

Range overlap between the sword-billed hummingbird and its guild of long-flowered species: An approach to the study of a coevolutionary mosaic.

Similar Datasets

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