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.

Melt ponds are puddles of water on Arctic summer sea ice that play an important role in the Arctic climate system, mainly through their influence on the surface energy balance. The albedo of polar oceans is primarily determined by the area fractions of sea ice and darker open ocean. However, the presence of melt ponds also significantly reduces the reflective properties of sea ice. This allows more solar energy to be absorbed, accelerating regional ice melt in a phenomenon known as ice-albedo feedback. Despite their importance, there is a lack of observational datasets that explicitly parameterize melt ponds next to sea ice and open water, and the existing data are mainly based on optical imagery. In this study, we present a new helicopter-borne dataset based on (TIR) and employ a deep learning-based semantic segmentation approach to classify surface types (melt ponds, sea ice, and open water). We evaluate the performance of several model architectures: U-Net, UNet++, PSPNet, DeepLabV3+, SwinTransformer, SAM2-UNet, and AutoSAM. Among these models, UNet++ pre-trained on the AID dataset demonstrates the highest performance, achieving a melt pond (IoU) of 0.486, a sea ice IoU of 0.764, and an open water IoU of 0.660. Furthermore, we apply the most effective model to the datasets of five helicopter flights, resulting in (MPF) estimates with a bias of approximately 11%, which is higher than for melt pond classifications based on visible observations.

We present an end-to-end deep learning framework for short-term forecasting of global sea surface dynamics based on sparse satellite altimetry data. Building on two state-of-the-art architectures: U-Net and 4DVarNet, originally developed for image segmentation and spatiotemporal interpolation, respectively, we adapt the models to forecast the sea level anomaly (SLA) and sea surface currents over a seven-day horizon using sequences of sparse nadir altimeters observations. The model is trained on data from the GLORYS12 operational ocean reanalysis, with synthetic nadir sampling patterns applied to simulate realistic observational coverage. The forecasting task is formulated as a sequence-to-sequence mapping, with the input comprising partial SLA snapshots and the target being the corresponding future full-field SLA maps. We evaluate model performance using (i) normalized root mean squared error (nRMSE), (ii) averaged effective resolution, (iii) percentage of correctly predicted velocities, magnitudes and angles, and benchmark results against the operational Mercator Ocean forecast product. Results show that end-to-end neural forecasts outperform the baseline across all lead times, with particularly notable improvements in high variability regions. Our framework is developed within the OceanBench benchmarking initiative, promoting reproducibility and standardized evaluation in ocean machine learning. These results demonstrate the feasibility and potential of end-to-end neural forecasting models for operational oceanography, even in data-sparse conditions.