There are also several other amazing presentations this session so I recommend sticking around for the whole thing!

If you want to learn more about this work, the article is available open access here: doi.org/10.1111/1365-2745.14317. (12/12)

Science is a team sport and we as scientists do better work by bringing more people to the table and using our shared expertise to try to tackle a shared question. (11/)

This type of collaboration is important for meta-analysis of resolving opposing findings, but it also incredibly refreshing to build collaborations and do really good science as a team instead of being egotistical about who is right and who is wrong. (10/)

In addition to our results, I want to highlight just how cool it was to come together as a group and try to resolve opposing results in a rigorous, quantitative way! (9/)

At least not until #herbaria#CommunityScience databases or until community science databases grow to match the temporal extent of herbaria! (8/)

That is, it was only when we calculated the difference in sensitivity between tree and wildflower species that we found substantial differences between the two datasets. This suggests that the two sources of data should not be used interchangeably in every instance... (7/)

Our main conclusion is that #CommunityScience#Herbarium collections yield different insights into how climate change affects biotic interactions. Surprisingly, this was despite species-level sensitivities being generally consistent between the studies (6/)

The short version is that we were largely unable to resolve the differences in results. Controlling for each of the 4 differences moved results from the two datasets closer together, but none of them satisfactorily resolved all of the differences. (5/)

That is, controlling for differences in the statistical analysis, could we resolve the opposite findings? Or were the differences instead due to inherent differences in the datasets and types of data themselves? (4/)