bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.02.04.636488

Consumer resilience suppresses the recovery of overgrazed ecosystems

Abstract

O_LIMany heterotroph species perish when faced with severe food limitation, others can persist, adapt, and thrive. Sea urchins are emblematic of this paradox: they can overgraze kelp forests to form barren habitats, but can then survive for decades in these nutritionally depauperate seascapes. Understanding the mechanisms enabling persistence under starvation, and rapid recovery when food returns, provides insight into how consumer resilience shapes ecosystem dynamics. C_LIO_LIWe quantified how food abundance, quality, deprivation, and reintroduction influence bioenergetic performance in the red sea urchin (Mesocentrotus franciscanus), integrating field observations of kelp forest and barren populations with a controlled feeding experiment. We measured respiration, feeding rates, gonadal growth, and fatty acid biomarkers to test how habitat history and diet jointly govern metabolic plasticity and nutrient assimilation. C_LIO_LIResting metabolic rates (RMR) were nearly twofold higher in kelp forest urchins than barrens conspecifics, yet feeding rates were equivalent across habitats, indicating that metabolic depression does not constrain food intake. Reciprocal shifts emerged in the experiment: starvation reduced RMR and lipid reserves in kelp forest urchins, while feeding elevated both traits in barrens urchins to levels comparable with kelp forest conspecifics. These results demonstrate rapid physiological compensation in response to both food deprivation and reintroduction. C_LIO_LIDiet quality strongly modulated performance. Urchins fed nutritionally poor monospecific diets consumed more biomass and calories than those on diverse, polyunsaturated fatty acid (PUFA)-rich diets, but did so with markedly lower efficiency of conversion to gonadal tissue. Fatty acid assimilation revealed that starvation elevated bacterial and biofilm biomarkers in tissues, whereas algal diets enriched essential PUFA profiles, particularly when diets were diverse. These results highlight that both quantity and quality of food influence consumer recovery trajectories, with nutritional geometry shaping efficiency of energy and nutrient use. C_LIO_LITogether, our findings show that M. franciscanus exhibits pronounced metabolic resilience, allowing persistence in barren habitats and rapid reactivation of grazing and reproduction when food becomes available. This work links nutritional ecology to ecosystem feedbacks by showing how compensatory feeding and metabolic flexibility enable consumers to maintain pressure on primary producers, thereby influencing the stability, hysteresis, and recovery of degraded ecosystems. C_LI

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Spindel, N. B., Galloway, A. W. E., Schram, J. B., Mcneill, G. D., Bellis, S. K. V., Guujaaw, N., Yakgujanaas, J., Pontier, O., Thompson, M., Lee, L. C., Okamoto, D.. 2025-02-08. Consumer resilience suppresses the recovery of overgrazed ecosystems. https://doi.org/10.1101/2025.02.04.636488

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗