Join us in a study of SDM methodology!

Map of prediction from a species distribution model of suitable climate for Geum radiatum

We invite you to be part of a worldwide study about structural uncertainty in species distribution models (SDMs)!

Specifically, we are interested in how choices along the modeling workflow affect final outcomes of models. While differences in algorithms, spatial extents, etc., have been widely explored, we are interested in the cumulative effect of choices and how the culture of modeling varies by lab group and individual and thus affects model outcomes. These kinds of exercises are common in other fields in which different analysts are provided the same dataset for analysis in a blind manner (example from ecology and evolution). We seek to conduct a similar study, using SDMs. 

Our target journal is Methods in Ecology and Evolution, for which we were invited to submit a paper broadly on methods in SDMing. After some consideration and discussion with the MEE editors, we have decided to use this opportunity to conduct an experiment. If you participate, we would welcome you as a co-author.

Our intent is to compare model outcomes across modeling teams. Our intent is not to find the “best” workflow or model. Rather, we want to assess the diversity of model outputs arising from the diversity of choices in modeling. To stay true to the intent, we have some ground rules that we request all participants follow to ensure that model outcomes are as similar as possible to the kind of modeling that you would normally do. 

Ground rules:

  • Please follow the scenario provided in the attached PDF for instructions about what to do. We have left them intentionally vague to provide as much freedom in interpretation and implementation as possible. Picture yourself as an expert with whom a non-profit staff member without modeling expertise is consulting. You will have to acquire the relevant inputs and produce at least three rasters (one present and two future), plus an ODMAP (Overview, Data, Model, Assessment and Prediction) protocol.
  • If you have questions about the exercise/experiment, please contact us. We will try to answer as best we can, and we may share the question (anonymously) and answer with other participants.
  • Please do not communicate about this project with others outside of your lab. As a rule-of-thumb, speaking about it with people who are currently in your lab (i.e., students, postdocs, PIs, etc.) is OK, but talking about it to students, postdocs, or others who have left the lab is verboten. We ask you to do this because different labs have different approaches, and knowing that another lab was invited may change how you may do your work. If you would like to work on this with someone else in your lab, or ask that they head the project, that is certainly acceptable (and anyone who assists would thus be on the author team). However, we would like just one set of model outcomes from any one lab (if you have questions about this, please contact us).
  • We would like all model outputs (rasters and ODMAP form) completed by July 15, 2024. For objectivity, we will keep the people conducting the analysis of workflows and model products separate from the teams doing the modeling. The analysis team will conduct a comparison of outputs and workflows and then present the results to the modeling teams in an all-participant meeting. We will then draft an article for which we seek your input and participation to the degree you desire to provide it. We will seek everyone’s feedback as we develop the draft and before final submission.

If you choose to participate, please complete this Google form.

Please download the project prompt and protocol, plus spreadsheets for each of the two species (for you to complete and submit later) are attached. 

Additionally, if you know of other individuals/teams that might be interested, please prompt them to contact us at (vs. simply forwarding them the invitation, to help us keep teams as “independent” as possible).

Please contact us at with any questions. 

Thank you,

Anna Thonis, Uzma Ashraf, C. Nikki Cavalieri, Toni Lyn Morelli, and Adam B. Smith