Merging dynamical and structural indicators to measure resilience in multispecies systems

Medeiros, Lucas P.; Song, Chuliang; Saavedra, Serguei

doi:10.1111/1365-2656.13421

Cited by 17 publications

(41 citation statements)

References 56 publications

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“…Additionally, the scale at which this analysis occurs, whether the population or community level, is important. Our results show that a community's distributions of abundance and information flow are potentially more informative about the habitat than its diversity, similar to [57], who demonstrated how structural and dynamical indicators were sufficient for measuring system health. Although some studies have equated diversity with ecosystem health, some have found that diseased habitats can display higher microbial diversity than healthy habitats, particularly because of an increase in viruses [58][59][60].…”

Section: Phylogenetic and Probabilistic Ecohealth Characterizationsupporting

confidence: 77%

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

Galbraith

Convertino

2021

Entropy

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The microbiome emits informative signals of biological organization and environmental pressure that aid ecosystem monitoring and prediction. Are the many signals reducible to a habitat-specific portfolio that characterizes ecosystem health? Does an optimally structured microbiome imply a resilient microbiome? To answer these questions, we applied our novel Eco-Evo Mandala to bacterioplankton data from four habitats within the Great Barrier Reef, to explore how patterns in community structure, function and genetics signal habitat-specific organization and departures from theoretical optimality. The Mandala revealed communities departing from optimality in habitat-specific ways, mostly along structural and functional traits related to bacterioplankton abundance and interaction distributions (reflected by ϵ and λ as power law and exponential distribution parameters), which are not linearly associated with each other. River and reef communities were similar in their relatively low abundance and interaction disorganization (low ϵ and λ) due to their protective structured habitats. On the contrary, lagoon and estuarine inshore reefs appeared the most disorganized due to the ocean temperature and biogeochemical stress. Phylogenetic distances (D) were minimally informative in characterizing bacterioplankton organization. However, dominant populations, such as Proteobacteria, Bacteroidetes, and Cyanobacteria, were largely responsible for community patterns, being generalists with a large functional gene repertoire (high D) that increases resilience. The relative balance of these populations was found to be habitat-specific and likely related to systemic environmental stress. The position on the Mandala along the three fundamental traits, as well as fluctuations in this ecological state, conveys information about the microbiome’s health (and likely ecosystem health considering bacteria-based multitrophic dependencies) as divergence from the expected relative optimality. The Eco-Evo Mandala emphasizes how habitat and the microbiome’s interaction network topology are first- and second-order factors for ecosystem health evaluation over taxonomic species richness. Unhealthy microbiome communities and unbalanced microbes are identified not by macroecological indicators but by mapping their impact on the collective proportion and distribution of interactions, which regulates the microbiome’s ecosystem function.

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Section: Phylogenetic and Probabilistic Ecohealth Characterizationsupporting

confidence: 77%

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

Galbraith

Convertino

2021

Entropy

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“…local asymptotic stability (May, 1972). Hence, we can hypothesize a similar negative relationship between complexity and the amplitude of the feasibility domain (Arnoldi & Haegeman, 2016;Gibbs et al, 2018;Medeiros et al, 2021). In multi-trophic communities, this complexity is encoded in the number of species across different trophic guilds and their interactions with species of the same and other guilds.…”

Section: Introductionmentioning

confidence: 81%

Non-random interactions within and across guilds shape the potential to coexist in multi-trophic ecological communities

García‐Callejas

Godoy

Buche

et al. 2021

Preprint

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Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic levels. However, we lack empirical evaluations of how the structure, strength and sign of these interactions drive the potential to coexist in diverse multi-trophic communities. Here we model community feasibility domains, a theoretically-informed measure of coexistence probability, from empirical data on communities comprising more than 50 species for three trophic guilds (plants, pollinators, and herbivores). Although feasibility domains vary depending on the number of trophic guilds considered, we show that higher network connectance leads to lower coexistence opportunities. Moreover, empirical estimations of the feasibility domains were higher with respect to random network structures but lower than a mean-field approach, suggesting that observed interaction structures tend to maximize coexistence within its imposed limits. Our results stress the importance of incorporating empirically-informed interaction structures within and across guilds to better understand how species coexist in diverse multi-trophic communities.

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“…In this Special Feature, Medeiros et al. (2021) reveal that recovery and resistance are negatively correlated with one another using both experimental microbial systems and theoretical models, suggesting that resistance could be inferred from recovery and vice versa. Likewise, Jones et al.…”

Section: Opportunities and Challenges In The Special Featurementioning

confidence: 99%

“…Combining theoretical studies with experiments and/or observational studies can also help to test the validity of resilience concepts (Li et al, 2021;Medeiros et al, 2021).…”

Section: Contributi On S To This S Pecial Fe Atu R Ementioning

confidence: 99%

“…For example, experiments might help to unravel processes promoting resilience, which might be more difficult to observe in the field (Hoover et al., 2021; Jones et al., 2020; Lipoma et al., 2021). Combining theoretical studies with experiments and/or observational studies can also help to test the validity of resilience concepts (Li et al., 2021; Medeiros et al., 2021).…”

Section: Contributions To This Special Featurementioning

confidence: 99%

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Reconciling resilience across ecological systems, species and subdisciplines

et al. 2021

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Resilience has emerged as a key concept in ecology and conservation biology to understand and predict ecosystem responses to global change. In its broadest sense, resilience describes the ability of an ecosystem to resist, and recover from, a disturbance. However, the application of such a concept in different subdisciplines of ecology and in different study systems has resulted in a wide disparity of definitions and ways of quantifying resilience. This Special Feature, which spans the Journal of Ecology, Journal of Animal Ecology and Functional Ecology, provides an overview of how ecologists define, quantify, compare and predict resilience across different study systems. The 29 contributions to this Special Feature show the broad range of approaches used by ecologists to study resilience. Almost half of the contributions (48%) study resilience at the community level, with a 30% of them studying resilience at multiple levels of biological organisation. A large proportion of these articles are observational (42%), experimental (14%) or a combination of both (17%) while a 17% utilise theoretical or computational approaches. Although 38%, 21% and 14% of the studies were based solely on plants, animals or micro‐organisms, respectively, 17% of them incorporated these multiple trophic levels. Synthesis. A unified ecological understanding of resilience across systems and taxa requires a trans‐disciplinary consensus on what resilience actually is and how to best measure it. Here, we provide an overview of how ecologists define, quantify, compare and predict resilience across different ecological systems and subdisciplines, with reference to the diverse approaches used by contributions to this Special Feature. We identify four key recommendations to harmonise future efforts in resilience research: (a) define resilience using existing theoretical frameworks; (b) use common and comparable metrics to measure resilience; (c) clearly contextualise and define the pre‐ and post‐disturbance state of the ecological system and (d) consider explicitly the disturbance type and regime impacting the system.

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Merging dynamical and structural indicators to measure resilience in multispecies systems

Cited by 17 publications

References 56 publications

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

Non-random interactions within and across guilds shape the potential to coexist in multi-trophic ecological communities

Reconciling resilience across ecological systems, species and subdisciplines

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