Ascenzi, A. | bioRxiv Science

bioRxiv2025

Deep Learning-Based Methods for Automated Estimation of Insect Length, Volume, and Biomass

We present InsectMorphoAI, an open-source, user-friendly software package that automates the measurement of insects from 2D images. The software addresses the need for high-throughput, non-invasive alternatives to laborious and often destructive manual measurement methods. InsectMorphoAI provides two analysis modes: a rapid, general-purpose method using oriented bounding boxes for linear length estimation across diverse taxa, and a high-precision, taxon-specific instance segmentation method for detailed curvilinear length, volume, and biomass estimation. We demonstrate the softwares accuracy, showing that the volume estimates from the segmentation module are strongly correlated with dry weight (R = 0.907), and the general length module achieves a mean absolute error corresponding to ~2.3% of the average specimen length. InsectMorphoAI is distributed with a graphical user interface and is freely available, with straightforward installation via a Docker container or a native Python environment. By streamlining data acquisition, InsectMorphoAI facilitates the integration of detailed trait data into large-scale ecological research, from biodiversity monitoring to functional trait analysis.

bioinformatics↗

bioRxiv2024

Neglected predatory insects trigger potential Key Biodiversity Areas in threatened coastal habitats

Key Biodiversity Areas (KBAs) are poised to become a powerful tool for identifying regions that host unique biodiversity. With their great diversity, insects hold significant potential as indicators for global KBA mapping, even in highly specialized and narrowly distributed habitats. For instance, species adapted to fragmented ecosystems like coastal sand dunes--among the most heavily impacted habitats worldwide--can serve as critical indicators to trigger KBAs in these fragile environments. Despite their relevance as indicators, the inclusion of insects in KBA assessments remains limited, especially for neglected insect species such as antlions. We tested selected KBA criteria on 26 antlion and owlfly species (Neuroptera: Myrmeleontidae) in Italy, including dunes specialists, and performed COI based genetic analysis to identify potential weaknesses of the assessment. Several endemic and dune specialist species trigger potential KBAs, showing limited (< 20% of their extent) overlap with the current protected area network, confirming the great value of these taxa in narrowly distributed habitats. Genetic data provides evidence of misidentification for widely distributed species. We advise for the integration of both spatial and genetic data to increase reliability of potential KBA assessments using neglected insect taxa.

ecology↗