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1. Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals

   https://doi.org/10.3390/vetsci5040092  

   Huyvaert, Kathryn; Russell, Robin; Patyk, Kelly; Craft, Meggan; Cross, Paul; Garner, M.; Martin, Michael; Nol, Pauline; Walsh, Daniel (December 2018, Veterinary Sciences)

   null (Ed.)

   Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface. 

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2. Charting a New Frontier Integrating Mathematical Modeling in Complex Biological Systems from Molecules to Ecosystems

   https://doi.org/10.1093/icb/icab165  

   White, Katharine A; McEntire, Kira D; Buan, Nicole R; Robinson, Lecia; Barbar, Elisar (July 2021, Integrative and Comparative Biology)

   Abstract Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative modeling process, and nurture a cultural shift towards interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems. 

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3. Preparing the Next Generation of Integrative Organismal Biologists

   https://doi.org/10.1093/icb/icae098  

   Padilla, Dianna K; Grünbaum, Daniel (July 2024, Integrative And Comparative Biology)

   Synopsis Pursuing cutting edge questions in organismal biology in the future will require novel approaches for training the next generation of organismal biologists, including knowledge and use of systems-type modeling combined with integrative organismal biology. We link agendas recommending changes in science education and practice across three levels: Broadening the concept of organismal biology to promote modeling organisms as systems interacting with higher and lower organizational levels; enhancing undergraduate science education to improve applications of quantitative reasoning and modeling in the scientific process; and K-12 curricula based on Next Generation Science Standards emphasizing development and use of models in the context of explanatory science, solution design, and evaluating and communicating information. Out of each of these initiatives emerges an emphasis on routine use of models as tools for hypothesis testing and prediction. The question remains, however, what is the best approach for training the next generation of organismal biology students to facilitate their understanding and use of models? We address this question by proposing new ways of teaching and learning, including the development of interactive web-based modeling modules that lower barriers for scientists approaching this new way of imagining and conducting integrative organismal biology. 

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4. Producer and consumer perspectives on supporting and diversifying local food systems in central Iowa

   https://doi.org/10.1007/s10460-023-10504-9  

   Dorneich, Michael C; Krejci, Caroline C; Schwab, Nicholas; Stone, Tiffanie F; Huckins, Erin; Thompson, Janette R; Passe, Ulrike (June 2024, Agriculture and Human Values)

   Abstract The majority of food in the US is distributed through global/national supply chains that exclude locally-produced goods. This situation offers opportunities to increase local food production and consumption and is influenced by constraints that limit the scale of these activities. We conducted a study to assess perspectives of producers and consumers engaged in food systems of a major Midwestern city. We examined producers’ willingness to include/increase cultivation of local foods and consumers’ interest in purchasing/increasing local foods. We used focus groups of producers (two groups of conventional farmers, four local food producers) and consumers (three conventional market participants, two locavores) to pose questions about production/consumption of local foods. We transcribed discussions verbatim and examined text to identify themes, using separate affinity diagrams for producers and consumers. We found producers and consumers are influenced by thestatus quoand real and perceived barriers to local foods. We also learned participants believed increasing production and consumption of local foods would benefit their community and creating better infrastructure could enhance efforts to scale up local food systems. Focus group participants also indicated support from external champions/programs could support expansion of local foods. We learned that diversifying local food production was viewed as a way to support local community, increase access to healthy foods and reduce environmental impacts of conventional production. Our research indicates that encouraging producers and consumers in local food systems will be more successful when support for the local community is emphasized. 

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5. Eight grand challenges in socio-environmental systems modeling

   https://doi.org/10.18174/sesmo.2020a16226  

   Elsawah, Sondoss; Filatova, Tatiana; Jakeman, Anthony J.; Kettner, Albert J.; Zellner, Moira L.; Athanasiadis, Ioannis N.; Hamilton, Serena H.; Axtell, Robert L.; Brown, Daniel G.; Gilligan, Jonathan M.; et al (September 2019, Socio-Environmental Systems Modelling)

   Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices. 

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