Peer-reviewed Articles - Computer sci

86 downloads
404 views

Background Accurate time-energy budgets summarise an animal’s energy expenditure in a given environment, and are potentially a sensitive indicator of how an animal responds to changing resources. Deriving accurate time-energy budgets requires an estimate of time...

["Animal Behavior","Ecology","Marine Biology","Data Mining and Machine Learning"]
doi:10.7717/peerj.5814
89 downloads
409 views

Background Functional groups serve two important functions in ecology: they allow for simplification of ecosystem models and can aid in understanding diversity. Despite their important applications, there has not been a universally accepted method of how to define...

["Ecology","Marine Biology","Statistics","Data Mining and Machine Learning","Data Science"]
doi:10.7717/peerj.5795
57 downloads
514 views

Background Biogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome...

["Biogeography","Ecology","Data Mining and Machine Learning","Spatial and Geographic Information Science"]
doi:10.7717/peerj.5666
1 citation
591 downloads
2,188 views

Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts...

["Ecology","Data Mining and Machine Learning","Climate Change Biology"]
doi:10.7717/peerj.4278
1 citation
231 downloads
1,047 views

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, referred to as species abundance models (SAMs), are still less commonly used to date, but increasingly receiving attention....

["Biodiversity","Biogeography","Conservation Biology","Ecology","Data Mining and Machine Learning"]
doi:10.7717/peerj.4160

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