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A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments. [ABSTRACT FROM AUTHOR]

In many complex, real‐world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high‐quality e‐courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web‐based software platform and Delphi‐style social processes. The result is an end‐to‐end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN‐building and collaboration combine beneficially and cumulatively. [ABSTRACT FROM AUTHOR]

The possible role of climate change in late Quaternary animal extinctions is hotly debated, yet few studies have investigated its direct effects on animal physiology to assess whether past climate changes might have had significant impacts on now‐extinct species. Here we test whether climate change could have imposed physiological stress on the Tasmanian devil Sarcophilus harrisii during the mid‐Holocene, when the species went extinct on mainland Australia. Physiological values for the devil were quantified using mechanistic niche models of energy and water requirements for thermoregulation, and soil‐moisture‐based indices of plant stress from drought to indirectly represent food and water availability. The spatial pervasiveness, extremity and frequency of physiological stresses were compared between a period of known climatic and presumed demographic stability (8000–6010 BP) and the extinction period (5000–3010 BP). We found no evidence of widespread negative effects of climate on physiological parameters for the devil on the mainland during its extinction window. This leaves cultural and demographic changes in the human population or competition from the dingo Canis dingo as the main contending hypotheses to explain mainland loss of the devil in the mid‐Holocene. [ABSTRACT FROM AUTHOR]

Several research studies have shown that code status documentation is misinterpreted or incorrectly defined by a significant number of medical professionals. This misinterpretation among the medical team (i.e. equating Do Not Resuscitate (DNR) with comfort care measures only) may lead to false reporting, poor symptom management, and potentially adverse clinical outcomes. Most Hospice and Palliative Care providers are aware of these distinctions, however a shortage (and continued foreseen shortage) of Hospice and Palliative Care providers may mean these conversations and distinctions will fall to non-subspecialists, or providers of other medical specialties or degrees. The literature has demonstrated that these shortfalls and misinterpretations are present and constitute potential harm to our patients. [ABSTRACT FROM AUTHOR]

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively., (© 2021 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

We report a case of posttraumatic syringomyelia (PTS) that developed 10 months after spinal cord injury (SCI), A 46-year-old man was involved in a motorcycle accident, in which he received a severe spinal cord injury due to a burst fracture at the T6 level. The patient underwent posterior fixation without decompression at another hospital, and was transferred to our hospital for rehabilitation. Ten months after the SCI, he complained of back and neck pain caused by bending his head backward. MRI showed syringomyelia shaped like a cone extending from the T6 to the C6 level, enlarged by cerebrospinal fluid (CSF) flow toward the syringomyelia at the T6 level. Pain was relieved by syringosubarachnoid shunt implantation and the syrinx disappeared after the operation. PTS emerging 10 months after spinal cord injury is relatively rare. From the radiological and operative findings, PTS was enlarged by the CSF flow, which was hindered at the T6 level by compression of the vertebral body to the spinal cord. In cases of SCI in patients who undergo posterior fixation with insufficient decompression, close attention to PTS is required in the postoperative follow-up.