skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: NetworkExtinction : An R package to simulate extinction propagation and rewiring potential in ecological networks
Abstract Earth's biosphere is undergoing drastic reorganization due to the sixth mass extinction brought on by the Anthropocene. Impacts of local and regional extirpation of species have been demonstrated to propagate through the complex interaction networks they are part of, leading to secondary extinctions and exacerbating biodiversity loss. Contemporary ecological theory has developed several measures to analyse the structure and robustness of ecological networks under biodiversity loss. However, a toolbox for directly simulating and quantifying extinction cascades and creating novel interactions (i.e. rewiring) remains absent.Here, we presentNetworkExtinction—a novel R package which we have developed to explore the propagation of species extinction sequences through ecological networks and quantify the effects of rewiring potential in response to primary species extinctions. WithNetworkExtinction, we integrate ecological theory and computational simulations to develop functionality with which users may analyse and visualize the structure and robustness of ecological networks. The core functions introduced withNetworkExtinctionfocus on simulations of sequential primary extinctions and associated secondary extinctions, allowing user‐specified secondary extinction thresholds and realization of rewiring potential.With the packageNetworkExtinction, users can estimate the robustness of ecological networks after performing species extinction routines based on several algorithms. Moreover, users can compare the number of simulated secondary extinctions against a null model of random extinctions. In‐built visualizations enable graphing topological indices calculated by the deletion sequence functions after each simulation step. Finally, the user can estimate the network's degree distribution by fitting different common distributions. Here, we illustrate the use of the package and its outputs by analysing a Chilean coastal marine food web.NetworkExtinctionis a compact and easy‐to‐use R package with which users can quantify changes in ecological network structure in response to different patterns of species loss, thresholds and rewiring potential. Therefore, this package is particularly useful for evaluating ecosystem responses to anthropogenic and environmental perturbations that produce nonrandom and sometimes targeted, species extinctions.  more » « less
Award ID(s):
2224915 2129757
PAR ID:
10472557
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Wiley
Date Published:
Journal Name:
Methods in Ecology and Evolution
Volume:
14
Issue:
8
ISSN:
2041-210X
Page Range / eLocation ID:
1952 to 1966
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Quantifying the structure and dynamics of species interactions in ecological communities is fundamental to studying ecology and evolution. While there are numerous approaches to analysing ecological networks, there is not yet an approach that can (1) quantify dissimilarity in the global structure of ecological networks that range from identical species and interaction composition to zero shared species or interactions and (2) map species between such networks while incorporating additional ecological information, such as species traits or abundances.To address these challenges, we introduce the use of optimal transport distances to quantify ecological network dissimilarity and functionally equivalent species between networks. Specifically, we describe the Gromov–Wasserstein (GW) and Fused Gromov–Wasserstein (FGW) distances. We apply these optimal transport methods to synthetic and empirical data, using mammal food webs throughout sub‐Saharan Africa for illustration. We showcase the application of GW and FGW distances to identify the most functionally similar species between food webs, incorporate additional trait information into network comparisons and quantify food web dissimilarity among geographic regions.Our results demonstrate that GW and FGW distances can effectively differentiate ecological networks based on their topological structure while identifying functionally equivalent species, even when networks have different species. The FGW distance further improves node mapping for basal species by incorporating node‐level traits. We show that these methods allow for a more nuanced understanding of the topological similarities in food web networks among geographic regions compared to an alternative measure of network dissimilarity based on species identities.Optimal transport distances offer a new approach for quantifying functional equivalence between networks and a measure of network dissimilarity suitable for a broader range of uses than existing approaches. OT methods can be harnessed to analyse ecological networks at large spatial scales and compare networks among ecosystems, realms or taxa. Optimal transport‐based distances, therefore, provide a powerful tool for analysing ecological networks with great potential to advance our understanding of ecological community structure and dynamics in a changing world. 
    more » « less
  2. Abstract Human-driven threats are changing biodiversity, impacting ecosystem services. The loss of one species can trigger secondary extinctions of additional species, because species interact–yet the consequences of these secondary extinctions for services remain underexplored. Herein, we compare robustness of food webs and the ecosystem services (hereafter ‘services’) they provide; and investigate factors determining service responses to secondary extinctions. Simulating twelve extinction scenarios for estuarine food webs with seven services, we find that food web and service robustness are highly correlated, but that robustness varies across services depending on their trophic level and redundancy. Further, we find that species providing services do not play a critical role in stabilizing food webs – whereas species playing supporting roles in services through interactions are critical to the robustness of both food webs and services. Together, our results reveal indirect risks to services through secondary species losses and predictable differences in vulnerability across services. 
    more » « less
  3. Abstract Microbial networks offer critical insights into community structure, ecological interactions and host–microbe dynamics. However, constructing reliable microbiome networks remains challenging due to variability among existing inference methods, limited overlap between inferred networks and the absence of a gold standard (a universally accepted reference for benchmarking) for validation.We developedCMiNet, an R package and interactive Shiny App(https://cminet.wid.wisc.edu) that enables consensus microbiome network construction by integrating up to 10 widely used inference algorithms.CMiNetsupports both correlation‐based and conditional dependence‐based methods and provides users with flexible options to construct individual or consensus networks across different approaches.CMiNetintegrates results from multiple inference methods through a voting strategy that retains edges supported by a user‐defined number of methods. To assess robustness, we complement this with a bootstrap analysis that quantifies edge stability under resampling. By jointly reporting method support and bootstrap confidence,CMiNetprovides a reproducible framework that explicitly communicates both agreement across methods and stability under perturbation.We appliedCMiNetto gut and soil microbiome datasets, constructing consensus networks that retained edges supported by multiple methods and confirmed by bootstrap reproducibility values. To identify disease‐associated taxa, we developed an integrative strategy that compared results across machine learning, differential abundance and network‐based approaches, ensuring that selected taxa were consistently recovered across methods. In the soil dataset, this analysis highlighted key taxa such asKtedonobacteria, Acidobacteriae, Vicinamibacteria, MB‐A2‐108, IgnavibacteriaandAnaerolineae, all of which were confirmed by multiple independent strategies. 
    more » « less
  4. Habitat loss disrupts species interactions through local extinctions, potentially orphaning species that depend on interacting partners, via mutualisms or com- mensalisms, and increasing secondary extinction risk. Orphaned species may become functionally or secondarily extinct, increasing the severity of the cur- rent biodiversity crisis. While habitat destruction is a major cause of biodiver- sity loss, the number of secondary extinctions is largely unknown. We investigate the relationship between habitat loss, orphaned species, and bipar- tite network properties. Using a real seed dispersal network, we simulate habi- tat loss to estimate the rate at which species are orphaned. To be able to draw general conclusions, we also simulate habitat loss in synthetic networks to quantify how changes in network properties affect orphan rates across broader parameter space. Both real and synthetic network simulations show that even small amounts of habitat loss can cause up to 10% of species to be orphaned. More area loss, less connected networks, and a greater disparity in the species richness of the network’s trophic levels generally result in more orphaned spe- cies. As habitat is lost to land-use conversion and climate change, more orphaned species increase the loss of community-level and ecosystem func- tions. However, the potential severity of repercussions ranges from minimal (no species orphaned) to catastrophic (up to 60% of species within a network orphaned). Severity of repercussions also depends on how much the interac- tion richness and intactness of the community affects the degree of redun- dancy within networks. Orphaned species could add substantially to the loss of ecosystem function and secondary extinction worldwide. 
    more » « less
  5. Abstract Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype‐environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD‐seq, ddRAD‐seq, whole genome sequencing data) representing environmental variation across the species geographic range.To demonstrate multi‐species applicability, we apply our toolbox to three georeferenced genomic datasets for co‐occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatusandA. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escaleraiandM. crypticus).Our framework sets the stage for large scale, multi‐species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity and landscape barriers. 
    more » « less