The Structure of Ecological Networks Across Levels of Organization

Annual Review of Ecology Evolution and Systematics, Journal Year: 2020, Volume and Issue: 51(1), P. 433 - 460

Published: Sept. 1, 2020

Interactions connect the units of ecological systems, forming networks. Individual-based networks characterize variation in niches among individuals within populations. These individual-based merge with each other, species-based and food webs that describe architecture communities. Networks at broader spatiotemporal scales portray structure interactions across landscapes over macroevolutionary time. Here, I review patterns observed multiple levels biological organization. A fundamental challenge is to understand amount interdependence as we move from beyond. Despite uneven distribution studies, regularities network emerge due architectural shared by complex interplay between traits numerical effects. illustrate integration these organizational exploring consequences emergence highly connected species for structures scales.

Language: Английский

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Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks

Methods in Ecology and Evolution, Journal Year: 2019, Volume and Issue: 11(2), P. 281 - 293

Published: Nov. 2, 2019

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, pollination interaction may be the proportions of bee's tongue fit plant's flower shape. Empirical estimates importance trait-matching for determining interactions, however, vary significantly among different types ecological networks. Here, we show ambiguity empirical studies arisen at least parts from using overly simple statistical models. Using simulated and real data, contrast conventional generalized linear models (GLM) with flexible Machine Learning (ML) (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Support Vector Machines, naive Bayes, k-Nearest-Neighbor), testing ability predict interactions based on traits, infer trait combinations causally responsible interactions. We find best ML can successfully plant-pollinator networks, outperforming GLMs by substantial margin. Our results also demonstrate better identify than GLMs. In two case studies, predicted global database inferred ecologically plausible rules plant-hummingbird network, without any prior assumptions. conclude offer many advantages over traditional regression understanding anticipate these extrapolate other network types. More generally, our highlight potential machine learning artificial intelligence inference ecology, beyond standard tasks such as image or pattern recognition.

Language: Английский

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Seeing through the static: the temporal dimension of plant–animal mutualistic interactions

Ecology Letters, Journal Year: 2020, Volume and Issue: 24(1), P. 149 - 161

Published: Oct. 19, 2020

Abstract Most studies of plant–animal mutualistic networks have come from a temporally static perspective. This approach has revealed general patterns in network structure, but limits our ability to understand the ecological and evolutionary processes that shape these predict consequences natural human‐driven disturbance on species interactions. We review growing literature temporal dynamics including pollination, seed dispersal ant defence mutualisms. then discuss potential mechanisms underlying such variation interactions, ranging behavioural physiological at finest scales broadest. find (days, weeks, months) interactions are highly dynamic, with considerable structure. At intermediate (years, decades), still exhibit high levels variation, appears influence properties only weakly. broadest (many decades, centuries beyond), continued shifts appear reshape leading dramatic community changes, loss function. Our highlights importance considering dimension for understanding ecology evolution complex webs

Language: Английский

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Pathways for Novel Epidemiology: Plant–Pollinator–Pathogen Networks and Global Change

Trends in Ecology & Evolution, Journal Year: 2021, Volume and Issue: 36(7), P. 623 - 636

Published: April 15, 2021

Flower sharing amongst pollinator species represents a conduit for interspecific insect pathogen transmission.Plant–pollinator network structure and traits shape dynamics in the community.Global change (climate change, invasive species, agricultural intensification, urbanisation) can modulate interactions, host susceptibility, virulence, thereby creating novel epidemiological risks.Multiple global effects interact synergistic or antagonistic ways, additional risks complicating predictions of evolution.Flower-mediated transmission provides model framework to understand interplay under change. Multiple pressures, their interplay, cause plant–pollinator extinctions modify assemblages interactions. This may alter shifts, intra- spread, emergence population community epidemics. Flowers are hubs transmission. Consequently, interaction networks be pivotal shifts modulating disease dynamics. Traits plants, pollinators, pathogens also govern spread pathogens. Pathogen spillover–spillback between managed wild pollinators driving evolution virulence Understanding this host–pathogen will crucial predicting impacts on pollination underpinning ecosystems human wellbeing. Wild provide ecosystem services diverse economic noneconomic values that support health, production, wellbeing [1.Potts S.G. et al.Safeguarding well-being.Nature. 2016; 540: 220-229Crossref PubMed Scopus (558) Google Scholar]. Multiple, potentially interacting, anthropogenic pressures threaten drive declines Scholar,2.Vanbergen A.J. Insect Pollinators Initiative Threats an service: pollinators.Front. Ecol. Environ. 2013; 11: 251-259Crossref (644) Intra- (viruses, microsporidian fungi, bacteria, protozoa, parasitic invertebrates) communities assembled changes likely emerging threat Scholar,3.Brown M.J.F. al.A horizon scan future threats opportunities pollination.PeerJ. 4e2249Crossref Horizontal occurs when individual insects share flowers providing nectar pollen resources [4.Durrer S. Schmid-Hempel P. Shared use leads horizontal transmission.Proc. R. Soc. London B. 1994; 258: 299-302Crossref (206) Scholar,5.McArt S.H. al.Arranging bouquet disease: floral plant animal pathogens.Ecol. Lett. 2014; 17: 624-636Crossref (112) Scholar] as well during social interactions [6.Chen Y.P. al.Prevalence honeybee viruses.Appl. Microbiol. 2006; 72: 606-611Crossref (184) mating, latter leading vertical generations [7.Beaurepaire A. al.Diversity distribution viruses western honey bee, Apis mellifera.Insects. 2020; 239Crossref (17) Emerging zoonotic diseases due current growing [8.Woolhouse M.E.J. al.Emerging pathogens: epidemiology jumps.Trends Evol. 2005; 20: 238-244Abstract Full Text PDF (438) Although is more limited than intraspecific Hymenoptera [9.Yañez O. al.Bee viruses: routes infection Hymenoptera.Front. 943Crossref (6) Scholar], phylogenetic studies indicate frequent Why particular jump some but not others, remains only partly understood [10.Parrish C.R. al.Cross-species virus new epidemic diseases.Microbiol. Mol. Biol. Rev. 2008; 457-470Crossref (446) Scholar]; lack coevolution means these events elevate mortality, reduce fitness, [11.Vanbergen al.Risks from alien species.Nat. 2018; 2: 16-25Crossref (61) Multi-host systems infectivity [12.Letko M. al.Bat-borne diversity, spillover, emergence.Nat. 18: 461-471Crossref (38) so it critical how mechanisms impact including spillover bee species. Given importance vital such land [13.Figueroa L.L. al.Landscape simplification shapes prevalence networks.Ecol. 23: 1212-1222Crossref chemical stressors [14.Fine J.D. al.An inert pesticide adjuvant synergizes viral pathogenicity mortality larvae.Sci. Rep. 2017; 7: 40499Crossref (35) altered habitat [15.Graystock al.Dominant abundance parasite temporal communities.Nat. 4: 1358-1367Crossref (8) climate [16.Cornelissen al.Global warming promotes biological invasion pest.Glob. Chang. 2019; 25: 3642-3655Crossref (9) [17.Schweiger al.Multiple biotic interactions: affect pollination.Biol. 2010; 85: 777-795PubMed pollinator–pathogen Such raise risk by altering dominance, distribution, primary secondary asymptomatic vectors. populations, communities, functions (Figure 1). The landscape key concentrating diluting potential transfer. Pollinator obligate flower visitors, foraging feeding and/or at least part life cycle, therefore both compete time space makes hotspots co-occurrence, direct contact, indirect (mediated nectar) Scholar,15.Graystock Scholar,18.Graystock al.Parasites bloom: aid dispersal parasites within species.Proc. 2015; 28220151371Crossref (126) Accordingly, serve 'travel' through crossing boundaries infecting hosts [18.Graystock Studying tripartite plant–pollinator–pathogen help identify flower-mediated elucidate disruptions create determine susceptibility different environmental transmit [19.Williams N.M. al.Ecological life-history predict responses disturbances.Biol. Conserv. 143: 2280-2291Crossref (417) Species' diet breadth preference probability encountering nectar/pollen, infected hosts, deposited flowers. Relatively specialised forage few taxa have lower exposure with broader diets. Less function generalist networks, being highly connected multiple thus indirectly other generalists greater (novel otherwise) accordingly vector across network. Generalist however, differ preferences foragers select specific subsets which mitigate [20.Ellner S.P. al.Individual specialization multihost epidemics: networks.Am. Nat. 195: 118-131Crossref (2) Moreover, dilute per emergent spreading visits over Sociality trait affecting Eusocial (Apis, Bombus, Meliponini) play disproportionately large role history, cooperative brood care, overlapping densely populated colonies exacerbating intra-colony Along generalised diets long flying season bridging phenology eusocial bees contact spillover. However, increased burden immunity (e.g., behaviours combat removing diseased larvae dead workers colony [21.Meunier J. Social group living insects.Philos. Trans. B Sci. 37020140102Crossref (94) Scholar]). Elucidating gradients uptake deposition rates, transportation mode), sizes, plasticity facultative sociality Halictidae) among transmission, largely unexplored important direction eco-epidemiology. Plant risk, facilitation inhibition transfer [5.McArt Floral architecture, constraining access requisite morphological adaptations, filters visitor contacts Plants structurally simple, displays Apiaceae, Rosaceae) attract numerous facilitating [22.Truitt al.Trait-based modeling multi-host transmission: 193: 149-167Crossref (11) Additionally, plants central position often contain high load [23.Piot N. al.Network centrality indicator via flowers.Insects. 872Crossref (0) More complex structures requiring intimate sustained limit functional Alternatively, characteristics, organic volatiles produced mutualists, toxic certain [24.Hammerbacher al.Roles defence against microbial exploitation volatiles.Plant Cell 42: 2827-2843Crossref (56) Nectar rewards phytochemicals inhibit consumed reducing Crithidia sp. bumble [25.Giacomini J.J. al.Medicinal value sunflower pathogens.Sci. 8: 14394Crossref (32) Scholar]), loads spread. Combined resistance degradation temperature, UV-radiation), survival subsequent infections [26.Adler L.S. al.Disease where you dine: associated bees.Ecology. 99: 2535-2545Crossref (23) Scholar,27.Figueroa : , persistence, acquisition flowers.Proc. Lond. 28620190603PubMed physiology alternative has implications levels. Certain individuals immune vectors, whereas others suffer disease, physiological stress, behaviour, reduced fitness death [28.Manley al.Contrasting specialist bees.Mol. 29: 380-393Crossref (3) according nutritional status, influence stressors, context [29.Brown al.Strong context-dependent host-parasite system: reconciling genetic evidence theory.J. Anim. 2003; 994-1002Crossref (208) While research heavily biased towards bees, recent point ranges, bombi families [30.Ngor L. al.Cross-infectivity bee-associated three families.Parasitology. 147: 1290-1304Crossref strains behave specialists terms range, infectivity, ability cross Single-stranded RNA tend pose highest co-infecting mutation rate, short generation time, [31.McMahon D.P. sting spit: widespread cross-infection bees.J. 84: 615-624Crossref (129) Scholar, 32.Dobelmann al.Genetic strain diversity infect wide range associates shaped geographic origins.Viruses. 12: 358Crossref (1) 33.Dalmon al.Possible same resource.Insects. 2021; 122Crossref co-infections (RNA viruses, microsporidia, Acari) further For example, mellifera infested Varroa destructor mites show higher Deformed wing (DWV) [34.Wilfert al.Deformed honeybees driven mites.Science. 351: 594-597Crossref asymmetric intra-host competition inhibiting manifestation [35.Doublet V. al.Within-host Nosema ceranae disadvantage virus.J. Invertebr. Pathol. 124: 31-34Crossref Pathogens behaviour increase potential. trypanosomatid defecate frequently [27.Figueroa fungal scent lure [36.Cellini al.Pathogen-induced honeybee-mediated Erwinia amylovora.ISME 13: 847-859Crossref (18) spp.) forming cysts spores promote persistence susceptible [37.Evison S.E.F. Jensen A.B. biology bees.Curr. Opin. 26: 105-113Crossref Beyond pathogens, biota gut microbiota) interacting mutualistic, commensal, Lactobacilli inadvertently collected improve [38.McFrederick Q.S. al.Environment kin: whence do obtain acidophilic bacteria?.Mol. 2012; 21: 1754-1768Crossref (95) Arbuscular mycorrhizal fungi roots soil fertility chemistry alkaloids) ways [39.Davis J.K. al.From parasites: mycorrhizae nutrients pathogens.Ecology. 100e02801Crossref Interactions virus-vectoring non-vector herbivores mediated chemicals reduction flavonoids) [40.Su Q. herbivore increases vector-borne defences.Funct. 34: 1091-1101Crossref It unknown whether similar trophic might pollinators. Plant–pollinator describe [41.Nielsen Bascompte Ecological nestedness, sampling effort.J. 2007; 95: 1134-1141Crossref (148) Scholar,42.Thébault E. Fontaine C. Stability ecological architecture mutualistic networks.Science. 329: 853-856Crossref (826) pool landscape, metabolic dietary needs, phenological synchrony mutualisms dictate [43.Vázquez al.Uniting pattern process plant-animal networks: review.Ann. Bot. 2009; 103: 1445-1457Crossref (369) 1A). typically asymmetrical form (Box Specialist number single family) represent subset visited nested consist weakly interlinked modules comprise sets strongly [44.Olesen J.M. al.The modularity networks.Proc. Natl. Acad. U. 104: 19891-19896Crossref (957) arise convergent mutualist coevolution, caused example inter- flexible level visitation available [45.Spiesman B.J. Gratton Flexible topology networks.Ecology. 97: 1431-1441Crossref (19) Scholar].Box 1Key Network Metrics TransmissionPlant–pollinator displayed bipartite nodes connecting lines. Connectance, I) arguably most metrics throughout used unweighted (binary) indices exist characterise weighted (i.e., frequency), common accurate way display networks.Connectance proportion realised Hence, containing five ten connectance 0.20, since 50 theoretically possible.Modularity refers division separate modules, within-module connectance, modules. As express modularity. Newman's (Q), calculated fraction each module minus expected random [115.Newman Modularity 8577-8582Crossref (6380) Scholar].Nestedness described tendency resulting species' [116.Bascompte assembly plant–animal 100: 9383-9387Crossref (1355) There several nestedness indices, metric based overlap decreasing fill (NODF) considered robust [117.Almeida-Neto consistent analysis systems: concept measurement.Oikos. 117: 1227-1239Crossref (960) networks. Connectance possible. Nestedness Stochastic environmentally variation fundamental assembl

Language: Английский

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Impacts of plant invasions in native plant–pollinator networks

New Phytologist, Journal Year: 2021, Volume and Issue: 230(6), P. 2117 - 2128

Published: March 12, 2021

Summary The disruption of mutualisms by invasive species has consequences for biodiversity loss and ecosystem function. Although plant effects on the pollination individual native been subject much study, their impacts entire plant–pollinator communities are less understood. Community‐level studies invasion have mainly focused two fronts: understanding mechanisms that mediate integration; network structure. Here we briefly review current knowledge propose a more unified framework evaluating integration communities. We further outline gaps in our ways to advance this field. Specifically, modeling approaches so far yielded important predictions regarding outcome drivers However, experimental test these field lacking. emphasize need understand link between structure population dynamics (population growth). Integrating demographic with those networks is thus key order achieve predictive pollinator‐mediated persistence biodiversity.

Language: Английский

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Native and exotic plants play different roles in urban pollination networks across seasons

Oecologia, Journal Year: 2023, Volume and Issue: 201(2), P. 525 - 536

Published: Jan. 24, 2023

Urban areas often host exotic plant species, whether managed or spontaneous. These plants are suspected of affecting pollinator diversity and the structure pollination networks. However, in dense cityscapes, also provide additional flower resources during periods scarcity, consequences for seasonal dynamics networks still need to be investigated. For two consecutive years, we monitored monthly plant-pollinator 12 green spaces Paris, France. We focused on variations availability attractiveness resources, comparing native at both species community levels. considered their respective contributions network properties over time (specialization nestedness). Exotic provided more abundant diverse than plants, especially from late summer on. received visits attracted level; certain times year level as well. were involved generalist interactions, increasingly so seasons. In addition, they contributed nestedness plants. results show that major components interactions a urban landscape, even though less attractive natives. They constitute core increase can participate overall stability network. most seldom visited by insects. Pollinator communities may benefit including when managing spaces.

Language: Английский

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Mutualism increases diversity, stability, and function of multiplex networks that integrate pollinators into food webs

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: May 1, 2020

Abstract Ecosystems are composed of complex networks many species interacting in different ways. While ecologists have long studied food webs feeding interactions, recent studies increasingly focus on mutualistic including plants that exchange for reproductive services provided by animals such as pollinators. Here, we synthesize both types consumer-resource interactions to better understand the controversial effects mutualism ecosystems at species, guild, and whole-community levels. We find mechanisms underlying plant-pollinator mutualisms can increase persistence, productivity, abundance, temporal stability mutualists non-mutualists webs. These strongly with floral reward productivity qualitatively robust variation prevalence pollinators upon resources addition rewards. This work advances ability mechanistic network theory illustrates how enhance diversity, stability, function ecosystems.

Language: Английский

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Temporal flexibility in the structure of plant–pollinator interaction networks

Oikos, Journal Year: 2020, Volume and Issue: 129(9), P. 1369 - 1380

Published: May 25, 2020

Ecological communities consist of species that are joined in complex networks interspecific interaction. These interactions often form and dissolve rapidly, but this temporal variation is not well integrated into our understanding the causes consequences network structure. If exhibit flexibility across time periods over which organisms co-occur, then emergent structure corresponding may also be flexible, something a temporally-static perspective will miss. Here, we use an empirical plant–pollinator system to examine short-term (week-to-week) (connectance, nestedness specialization) individual contribute three summer growing seasons subalpine ecosystem. We compared properties weekly cumulative aggregate field observations each full season. As test potential robustness perturbation, simulated random loss from networks. A week-to-week view reveals considerable their contributions For example, would considered relatively generalized entire activity period much more specialized at certain times, no point as suggest. Furthermore, throughout conclude leads properties, cumulative, season-long miss important aspects way interact, with implications for ecology, evolution conservation.

Language: Английский

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Dynamic network modeling of gut microbiota during Alzheimer’s disease progression in mice

Gut Microbes, Journal Year: 2023, Volume and Issue: 15(1)

Published: Feb. 1, 2023

The intimate association between the gut microbiota (GM) and central nervous system points to potential intervention strategies for neurological diseases. Nevertheless, there is currently no theoretical framework selecting window period target bacteria GM interventions owing complexity of microecosystem. In this study, we constructed a complex network-based modeling approach evaluate topological features infer bacterial candidates interventions. We used Alzheimer's disease (AD) as an example traced dynamic changes in AD wild-type mice at one, two, three, six, nine months age. results revealed alterations from scale-free network into random during progression, indicating severe disequilibrium late stage AD. Through stability vulnerability assessments networks, identified third month after birth optimal mice. Further computational simulations robustness evaluations determined that hub were Moreover, our functional analysis suggested Lachnospiraceae UCG-001 – enriched bacterium was keystone its contributions quinolinic acid synthesis. conclusion, study established practical strategy perspective

Language: Английский

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Predicting plant–pollinator interactions: concepts, methods, and challenges

Trends in Ecology & Evolution, Journal Year: 2024, Volume and Issue: 39(5), P. 494 - 505

Published: Jan. 22, 2024

Language: Английский

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