Data-centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog-bit.

Premise: Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications. Methods: We built SDMs for the invasive aquatic plant European frog-bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large-scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence-background or presence-absence) of occurrence data. Results: Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat. Discussion: Our findings relative to European frog-bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data-centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

Introducing Daniel Gorelick - a new Editor-in-Chief for Biology Open.

In July 2023, Daniel Gorelick was appointed Editor-in-Chief of Biology Open (BiO). Dan is currently Associate Professor in the Center for Precision Environmental Health, and Department of Molecular and Cellular Biology at the Baylor College of Medicine in Houston, Texas, USA. He is also Director of the Zebrafish Advanced Technology Core Facility. Dan's lab studies how endocrine-disrupting chemicals and related toxicants influence embryonic development.

DMM Outstanding Paper Prize 2022 winners: Tamihiro Kamata, Jennifer K. Sargent and Mark A. Warner.

Disease Models & Mechanisms (DMM) is delighted to announce that the winners of the DMM Outstanding Paper Prize 2022 are Tamihiro Kamata for their Research Article (titled ' Statins mediate anti- and pro-tumourigenic functions by remodelling the tumour microenvironment'), and Jennifer K. Sargent and Mark A. Warner for their Resource Article (titled ' Genetically diverse mouse platform to xenograft cancer cells'). The two prizes of £1000 are awarded to the first author(s) of the papers that are judged by the journal's Editors to be the most outstanding contribution to the journal that year.

DMM Outstanding Paper Prize 2021 winner: Daniel Bronder.

Disease Models & Mechanisms (DMM) is delighted to announce that the winner of the DMM Outstanding Paper Prize 2021 is Daniel Bronder, for his paper entitled 'TP53 loss initiates chromosomal instability in fallopian tube epithelial cells' ( Bronder et al., 2021). The prize of £1000 is awarded to the first author of the paper that is judged by the journal's editors to be the most outstanding contribution to the journal that year. To be considered for the prize, the first author must be a student or a postdoc of no more than 5 years standing.

Aggregated occurrence records of invasive European frog-bit (Hydrocharismorsus-ranae L.) across North America.

BACKGROUND: European frog-bit (Hydrocharismorsus-ranae L.; EFB) is a free-floating aquatic plant invasive in Canada, the United States and India. It is native to Europe and northern and western Asia and is believed to have first been introduced to North America in Ottawa, Ontario in 1932. It has since spread by way of the St. Lawrence River and connected waterways to southern Ontario and Quebec and parts of the northern United States. Invasive European frog-bit occurs in freshwater coastal wetlands and inland waters, where it can form dense mats that have the potential to limit recreational and commercial use of waterways, alter water chemistry and impact native species and ecosystems. Data on the past and present distribution of this invasive species provide geospatial information that can be used to infer the pattern of invasion and inform management and monitoring targeted at preventing secondary spread. Our EFB dataset contains 12,037 preserved specimen and observation-based occurrence records, including 9,994 presence records spanning two Canadian provinces and ten U.S. states and 2,043 absence records spanning five U.S. states. The aggregated EFB dataset provides a curated resource that has been used to guide a Michigan management strategy and provide information for ongoing efforts to develop invasion risk assessments, species distribution models and decision-support tools for conservation and management. NEW INFORMATION: Specimen-based and observation-based occurrence data were accessed through nine digital data repositories or aggregators and three primary sources. Twenty-six percent of the data are new records not previously published to a data repository or aggregator prior to this study. We removed duplicate data and excluded records with incorrect species identifications. Occurrence records without coordinates were georeferenced from recorded locality descriptions. Data were standardised according to Darwin Core. This aggregated dataset is the most complete account of EFB occurrence records in its North American invasive range.

Assessing the sustainability capacity of evidence-based programs in community and health settings.

Background: Within many public health settings, there remain large challenges to sustaining evidence-based practices. The Program Sustainability Assessment Tool has been developed and validated to measure sustainability capacity of public health, social service, and educational programs. This paper describes how this tool was utilized between January 2014 and January 2019. We describe characteristics of programs that are associated with increased capacity for sustainability and ultimately describe the utility of the PSAT in sustainability research and practice. Methods: The PSAT is comprised of 8 subscales, measuring sustainability capacity in eight distinct conceptual domains. Each subscale is made up of five items, all assessed on a 7-point Likert scale. Data were obtained from persons who used the PSAT on the online website (https://sustaintool.org/), from 2014 to 2019. In addition to the PSAT scale, participants were asked about four program-level characteristics. The resulting dataset includes 5,706 individual assessments reporting on 2,892 programs. Results: The mean overall PSAT score was 4.73, with the lowest and highest scoring subscales being funding stability and program adaptation, respectively. Internal consistency for each subscale was excellent (average Cronbach's alpha = 0.90, ranging from 0.85 to 0.94). Confirmatory factor analysis highlighted good to excellent fit of the PSAT measurement model (eight distinct conceptual domains) to the observed data, with a comparative fit index of 0.902, root mean square error of approximation equal to 0.054, and standardized root mean square residual of 0.054. Overall sustainability capacity was significantly related to program size (F = 25.6; p < 0.001). Specifically, smaller programs (with staff sizes of ten or below) consistently reported lower program sustainability capacity. Capacity was not associated with program age and did not vary significantly by program level. Discussion: The PSAT maintained its excellent reliability when tested with a large and diverse sample over time. Initial criterion validity was explored through the assessment of program characteristics, including program type and program size. The data collected reinforces the ability of the PSAT to assess sustainability capacity for a wide variety of public health and social programs.

Departmental quality initiative to establish turnaround times from simulation to treatment.

INTRODUCTION: The process of treatment delivery involves a series of steps from patient evaluation, therapeutic simulation (simulation), followed by dosimetric treatment planning, pre-treatment quality assurance and plan verification, and ultimately treatment delivery. Each step has a strict precedence relationship, requiring the preceding task to be completed prior to the initiation of the next task. The minimum time for a patient to undergo treatment is based on the summation of times of the individual tasks. Nevertheless, patients are often scheduled based on factors that do not directly consider the overall time required to complete these steps. MATERIALS AND METHODS: To better help in scheduling patients and to ensure quality and safety of treatment planning and delivery, we undertook a quality initiative based on team members tabulating time required to complete tasks required for treatment delivery. We established "fastest possible" turnaround times based how quickly a task could be accomplished if there were minimal or no competing obligations, as well as processing times under routine operating conditions. RESULTS: For urgent situations, we found that our center can accommodate treatment within 24 h. For routine plans using 3D conformal radiation, an approximately 1-week turnaround time is needed. For patients being treated with IMRT/VMAT an approximately 2-week turnaround time is needed. CONCLUSIONS: The growing complexity of radiotherapy delivery also requires additional steps which has increased turnaround times from simulation to treatment compared to historical standards. We report our estimates for turnaround time based on plan type and acuity level. While our turnaround times may not be applicable to all centers, we believe that this exercise was helpful to facilitate inter- and intra- departmental communication regarding reasonable start times for patients.

The Clinical Sustainability Assessment Tool: measuring organizational capacity to promote sustainability in healthcare.

BACKGROUND: Few validated assessment tools are available to increase understanding and measure factors associated with sustainment of clinical practices, an increasingly recognized need among clinicians. We describe the development of the Clinical Sustainability Assessment Tool (CSAT), designed to assess factors that contribute to sustainable practices in clinical settings. METHODS: Sixty-four participants from clinical and research fields participated in concept mapping and were recruited to brainstorm factors that lead to sustained clinical practices. Once repeated factors were removed, participants sorted items based on similarity and rated them by importance and feasibility. Using concept mapping analyses, items were grouped into meaningful domains to develop an initial tool. We then recruited pilot sites and early adopters, for a total of 286 practicing clinicians, to pilot and evaluate the tool. Individuals were recruited from clinical settings across pediatric and adult medical and surgical subspecialties. The data were analyzed using confirmatory factor analysis (CFA) to test hypothesized subscale structure in the instrument. We used root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR) to assess fit and thus the ability of CSAT to measure the identified domains. RESULTS: The concept mapping produced sorted statements that were edited into items that could be responded to, resulting in the creation of a tool with seven determinant domains and 47 items. The pilot and CFA testing resulted in a final CSAT instrument made up 35 items, five per domain. CFA results demonstrated very good fit of the seven domain structure of the CSAT (RMSEA = 0.049; SRMR = 0.049). Usability testing indicated the CSAT is brief, easy to use, easy to learn, and does not require extensive training. Additionally, the measure scored highly (18/20) on the Psychometric and Pragmatic Evidence Rating Scale (PAPERS). The seven final CSAT domains were engaged staff and leadership, engaged stakeholders, organizational readiness, workflow integration, implementation and training, monitoring and evaluation, and outcomes and effectiveness. CONCLUSIONS: The CSAT is a new reliable assessment tool which allows for greater practical and scientific understanding of contextual factors that enable sustainable clinical practices over time.

DMM Outstanding Paper Prize 2020 winner: Sarah Colijn.

Disease Models & Mechanisms (DMM) is delighted to announce that the winner of the DMM Prize 2020 is Sarah Colijn, for her paper entitled 'Cell-specific and athero-protective roles for RIPK3 in a murine model of atherosclerosis' (Colijn et al., 2020b). The prize of $1000 is awarded to the first author of the paper that is judged by the journal's editors to be the most outstanding contribution to the journal that year. To be considered for the prize, the first author must be a student or a postdoc of no more than 5 years standing.

DMM Outstanding Paper Prize 2019 winner: Alessandro Bailetti.

Disease Models & Mechanisms (DMM) is delighted to announce (with apologies for the delay) that the winner of the DMM Prize 2019 is Alessandro Bailetti, for his paper entitled 'Enhancer of Polycomb and the Tip60 complex repress hematological tumor initiation by negatively regulating JAK/STAT pathway activity' ( Bailetti et al., 2019). The prize of $1000 is awarded to the first author of the paper that is judged by the journal's editors to be the most outstanding contribution to the journal that year. To be considered for the prize, the first author must be a student or a postdoc of no more than 5 years standing.

A data management workflow of biodiversity data from the field to data users.

PREMISE: Heterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources. METHODS: In 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek). RESULTS: Over 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals. DISCUSSION: We highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data.