As members of the Data Intensive Landscape Limnology laboratory, scientific communities, and local to global societies, our primary aim is to establish an environment and culture that emphasizes diversity and acceptance, and fosters an environment of inclusive excellence. We believe that anyone who wants to contribute to science, particularly peoples who have been historically and presently excluded or marginalized, should have an opportunity to do so in meaningful ways. We know that every individual has unique intersecting identities and lived experiences. By creating a more diverse science and valuing our diverse communities, we will be enriched as a society and solve complex environmental problems as scientists. We believe that all perspectives are necessary to help achieve this goal and we therefore continually strive to create an environment in which all people, regardless of identities, feel safe, supported, welcome and valued. 

MSU is a primarily white institution that has disparities resulting from historical and ongoing oppression of communities of color. For example, we recognize that MSU occupies the ceded ancestral, traditional, and contemporary Lands of the Anishinaabeg—Three Fires Confederacy of Ojibwe, Odawa, and Potawatomi peoples. As an institution of higher learning, MSU is also steeped in tradition and hierarchies that are exclusionary of individuals with less power. We intend to practice continuous respect for people of all backgrounds, and prioritize valuing the individual identities of those around us. It remains our mission to not only provide a good example to others, but to continue to make progress through our own self-education. By being cognizant of the past, we can pave the way towards a better future.


All members of the lab take ownership of the actions below, with special emphasis on lab leaders who establish and foster the culture of the lab needed to facilitate these actions. 

  • Empower people of all career stages, but particularly early-career scientists, to become leaders in their disciplines by instilling within them necessary scientific and interpersonal skills and confidence in those skills
  • Develop and foster a culture of respect for all scientific and personal perspectives, including conventional, new and non-conforming perspectives
  • Recognize and celebrate peoples’ scientific achievements for their intellectual merits, rather than their identities
  • Welcome and amplify the voices of scholars traditionally underrepresented in the sciences
  • Implement strategies to reach and elevate scholars excluded due to identity in all aspects of lab work, including: collaborations, citations in our articles, invitations to present to lab, recommendations of reviewers for MS submissions, award nominations, etc.
  • In all aspects of lab work, discuss how to implement ‘bias interrupters’ (https://biasinterrupters.org/) that help lab members to continually work on our culture and ensure equitable and inclusive practices
  • Broaden the lab’s professional networks across many dimensions, such as by participating with culturally-relevant professional societies (e.g., SACNAS, AISES)
  • Actively hold leaders at all levels accountable by voicing concerns at level of lab, department, college, university, and professional societies
  • Recognize and acknowledge our own positionality as individuals and as members of institutions (Coghlan and Brydon-Miller 2014; Maher and Tetreault 1993)
  • Be committed to continuously grow in this area, recognizing that many of us are 1st generation equity practitioners (Bensimon and Gray 2020)
  • Hold ourselves accountable for our behaviors, actions, and goals related to diversity, equity, and inclusion


All members of the lab take ownership of implementing these actions and assessing their impacts, with special emphasis on lab leaders who establish and foster the culture of the lab needed to facilitate implementation. 

  • Regularly review our individual and collective actions and outcomes (both short- and long-term)
  • Annually review this document and have a conversation about updates, ways to seek out opportunities for continued growth, and improvement of our goals
  • Share this document with new lab members and have a discussion about how each individual would like to contribute and grow in this area   




The members of the Data Intensive Landscape Limnology Lab at Michigan State University study inland lakes and their landscapes by the thousands. The lab, created in 2016, is co-directed by Drs. Patricia Soranno and Kendra Spence Cheruvelil, Professors in the Fisheries and Wildlife Department.

The Data Intensive Landscape Limnology Lab is an affiliate of the Institute for Biodiversity, Ecology, Evolution, and Macrosystems (IBEEM).

Our lab includes a highly collaborative group of researchers working together to conduct 2011 land cover STATE no legendresearch in landscape limnology and macrosystems ecology, with special focus on developing the critical underlying principles of these important subdisciplines of ecology, and applying those principles to the management and conservation of freshwater resources.

Landscape limnology is the spatially-explicit study of lakes, streams, and wetlands as they interact with freshwater, terrestrial, and human landscapes to determine the effects of pattern on ecosystem processes across temporal and spatial scales (Soranno et al. 2010).

Macrosystems ecology is the study of ecological phenomena (biological, geophysical, human) at regional to continental scales (Heffernan et al. 2014; Fei et al. 2016; Rose et al. 2017).

We conduct research at multiple spatial and temporal scales, including the macroscale, as well as a variety of lake physical, chemical, and biological characteristics. To conduct this research, we have had to incorporate approaches not only from data-intensive science, but also open science and team science. To do so, it has been essential for us to collaborate with other scholars from other sub-disciplines and disciplines including: stream and wetland scientists to study the integrated freshwater landscape; computer scientists and statisticians to develop and test novel methods to study ecosystems at these macroscales; and, philosophers and historians of science and psychologists to study the  cultures and practices of data-intensive science.