Curlis, J. D. | Search bioRxiv

bioRxiv2023

A highly resolved multiplex network reveals the structural role of insects and plants in terrestrial food webs

Comparative studies suggest remarkable similarities among food webs across habitats, including systematic changes in their structure with diversity and complexity (scale-dependence). However, historic aboveground terrestrial food webs (ATFWs) have coarsely grouped plants and insects such that these webs are generally small, and herbivory is disproportionately underrepresented compared to vertebrate predator-prey interactions. Furthermore, terrestrial herbivory is thought to be structured by unique processes compared to size-structured feeding in other systems. Here, we present the richest ATFW to date, including [~]580,000 feeding links among [~]3,800 taxonomic species, sourced from [~]27,000 expert-vetted interaction records annotated as feeding upon one of six different resource types: leaves, flowers, seeds, wood, prey, and carrion. By comparison to historical ATFWs and null ecological hypotheses, we show that our temperate forest web displays a potentially unique structure characterized by two properties: a) a large fraction of carnivory interactions dominated by a small number of hyper-generalist, opportunistic bird and bat predators, and b) a smaller fraction of herbivory interactions dominated by a hyper-rich community of insects with variably-sized but highly-specific diets. We attribute our findings to the large-scale, even resolution of vertebrate, insect, and plant guilds in our food web.

ecology↗

bioRxiv2021

Batch-Mask: An automated Mask R-CNN workflow to isolate non-standard biological specimens for color pattern analysis

O_LIEfficient comparisons of biological color patterns are critical for understanding the mechanisms by which organisms evolve in ecosystems, including sexual selection, predator-prey interactions, and thermoregulation. However, elongate or spiral-shaped organisms do not conform to the standard orientation and photographic techniques required for automated analysis. Currently, large-scale color analysis of elongate animals requires time-consuming manual landmarking, which reduces their representation in coloration research despite their ecological importance. C_LIO_LIWe present Batch-Mask: an automated and customizable workflow to facilitate the analysis of large photographic data sets of non-standard biological subjects. First, we present a user guide to run an open-source region-based convolutional neural network with fine-tuned weights for identifying and isolating a biological subject from a background (masking). Then, we demonstrate how to combine masking with existing manual visual analysis tools into a single streamlined, automated workflow for comparing color patterns across images. C_LIO_LIBatch-Mask was 60x faster than manual landmarking, produced masks that correctly identified 96% of all snake pixels, and produced pattern energy results that were not significantly different from the manually landmarked data set. C_LIO_LIThe fine-tuned weights for the masking neural network, user guide, and automated workflow substantially decrease the amount of time and attention required to quantitatively analyze non-standard biological subjects. By using these tools, biologists will be able to compare color, pattern, and shape differences in large data sets that include significant morphological variation in elongate body forms. This advance will be especially valuable for comparative analyses of natural history collections, and through automation can greatly expand the scale of space, time, or taxonomic breadth across which color variation can be quantitatively examined. C_LI

zoology↗