bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.12.26.573354

Mutualism Destabilizes Communities, but Competition Pays the Price

Abstract

A classic result in theoretical ecology states that an increase in the proportion of cooperative interactions in unstructured ecological communities leads to a loss of stability to external perturbations. However, the fate and composition of the species that constitute an unstable ecological community following such perturbations remains relatively unexplored. In this paper, we use an individual-based model to study the population dynamics of unstructured communities following external perturbations to species abundances. We find that while increasing the number of cooperative interactions does indeed increase the probability that a community will experience an extinction following a perturbation, the entire community is rarely wiped out following a perturbation. Instead, only a subset of the ecological community is driven to extinction, and the species that go extinct are more likely to be those engaged in a greater number of competitive interactions. Thus, the resultant community formed after a perturbation has a higher proportion of cooperative interactions than the original community. We show that this result can be explained by studying the dynamics of the species engaged in the highest number of competitive interactions: After an external perturbation, those species that compete with such a top competitor are more likely to go extinct than expected by chance alone, whereas those that are engaged in cooperative interactions with such a species are less likely to go extinct than expected by chance alone. Our results provide a potential explanation for the ubiquity of cooperative interactions in nature despite the known negative effects of cooperation on community stability.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bhat, A. S., Nag, S., Dey, S.. 2023-12-27. Mutualism Destabilizes Communities, but Competition Pays the Price. https://doi.org/10.1101/2023.12.26.573354

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↗