Methods for imputing trait and community data in ecological and evolutionary research

Abstract

Species trait data are widely used in ecology and evolution to approximate ecological niche. Despite this, the data housed in species trait databases are often patchy or biased toward charismatic species and easy-to-study traits. Filling data gaps can be achieved by determining the most likely values via data imputation. We conducted a systematic literature review to identify data imputation methods for species trait data in ecological and evolutionary studies, as well as in data science studies, and compared the two domains to identify whether alternative methods might be suitable for adoption in ecology and evolution. We focus on novel and state-of-the-art deep learning (DL) imputation methods. We use evidence maps to identify missing data contexts where DL alternatives have been shown to be effective in data science. Differences in preferred methods were identified between the two domains. DL methods were rarely used in ecological and evolutionary studies, whereas they were frequently employed in data science. Several DL methods that have not been adopted in ecology and evolution demonstrated state-of-the-art performance when imputing data in contexts that are similar to those faced by ecologists wishing to impute species trait data. We identified a clear gap in the ecological and evolutionary literature in relation to the uptake and use of imputation methods based on DL. We then identify autoencoders, transformer-based architectures, Bayesian neural networks, and feed-forward neural networks as primary architectures worth further investigation for their capacity to impute missing species trait data and potential for multiple imputation.