Bulletin of the Ecological Society of America, Год журнала: 2023, Номер 104(2)

Опубликована: Март 20, 2023

“Therefore, we attempt to treat the same problem with several alternative models each different simplifications but a common biological assumption.… Hence our truth is intersection of independent lies.” (Levins 1966). Mutualisms are bidirectional, beneficial interspecific interactions ubiquitous across taxa (Bronstein 2015). They contribute significantly ecosystem function and nutrient cycling. For instance, ~40% global food crops dependent on mutualist partner for pollination (Klein et al. 2007). Microbial mutualists, in particular mycorrhizal fungi nitrogen-fixing bacteria, responsible ~75% annual plant phosphorus uptake significant fraction nitrogen as well (van der Heijden 2008). To better understand these interactions, require an overarching theoretical framework. This because empirical results easily confounded by system-specific differences timescale issues, noise ecological data. Developing theory mutualisms beset challenges. First, highly diverse currency benefits they exchange. In plant–microbe mutualisms, exchanged usually nutritional resources (carbon phosphorus, or carbon nitrogen) (Hacskaylo 1972, Oldroyd 2011). plant–pollinator however, pollinators obtain form nectar plants facilitating increased movement pollen (Pellmyr 1996, Pellmyr 2003). The diversity forms makes it challenging develop mutualism population dynamics that can apply interaction types. Further, often temporally dynamic which between species mediated trait evolution. sanction how much provide rhizobia nodules based receives from bacteria (Denison 2000, West 2002). Similarly, evolve abort fruits have been parasitized larvae pollinators, preventing exploitation (Janzen 1979, James 1994). both cases, evolution plays vital role stabilizing mutualistic dynamics. Thus, integrating evolutionary trajectories second challenge address enhance understanding mutualisms. Finally, into whole community ecology framework includes other such predation, parasitism, competition also ongoing goal theory. order challenges, not sufficient build using studies. Mathematical serve perfect tool explore aspects complex interactions. Much like systems, modeling frameworks may be useful addressing kinds questions about mutualism. network track structure pairwise including study disturbances propagate functions emerge species-rich communities (May Valdovinos 2016, Hale 2020). Trait-explicit those quantitative genetics adaptive shape through time (Lande 1982, McPeek 2017, 2021, 2022). Community organisms, mutualism, influence coexistence, stability, subset approaches focusing emergence small web “modules” (Holt 1977, Chase Leibold 2003, Sun 2019, Koffel 2021). incorporating spatial agent-based simulations partial differential equations (PDEs) allows exploration vary space addresses maintenance cooperation (Doebeli Knowlton 1998, Parker 1999, 2001, Stump 2018a,b). Nevertheless, all draw similar principles, economic principles consumer-resource cost–benefit analyses drivers behavior create one unifying mathematical model insights multifaceted facilitate their integration general conceptual unique helps generate predictions future experiments aid its broader context These goals main focus symposium speakers, who early career researchers employing ecology. Each three speakers addressed challenges highlighted above – eco-evolutionary dynamics, unification aim introducing latest developments wide audience ecologists biologists. was moderated Naven Narayanan University Minnesota, talks given Kayla (University Michigan), Thomas (Michigan State University), Sarah Virginia). article provides brief summary presentations discussion period followed talks. portion, highlight some research directions theory, context, emerged symposium. began reviewing history beginning famous instability Lotka–Volterra use linear terms benefit exchanges subsequent delay nearly 40 years before new were developed. Empirical remarkable natural histories stimulated interest 1980s authors focused finding mechanisms could reconcile ubiquity importance observed world simple models. However, has long being forgotten rediscovered, perhaps many criticized too abstract case-specific insight nature more broadly then advocated approach developing balances detail generality: building permit parameterization interpretation empirically enough scale up networks multiple interacting without exploding computational complexity. this way, mapped qualitative patterns advance varied partners impact populations, communities, ecosystems. demonstrated seed dispersal showing requirement outcrossing causes Allee effects bistable coexistence show stable at high density destabilizing thresholds low when least obligate (Hale Reviewing historical showed prediction, robust inspiring systems level Additionally, types threshold suggest underlying play. Empirically measuring whether partner's increasing decreasing used distinguish potential dynamical drivers, presence nonlinear costs. set characterize guide herbivory, pointed out need niche concepts extending beyond current emphasis competition. presentation Contemporary Niche Theory (Chase 2003) looks exclusion along varying resource availability, connections Modern Coexistence (Chesson 2000). frameworks, difference quantifies strength competition, defined angular metric measures range conditions enables (Saavedra 2017). How quantify mutualism? answered question metabolic cross-feeding two bacterial strains, example resource-explicit (Sun 2019). metrics still positive unlike (Spaak De Laender fact, values characteristic while correspond Interestingly, extent “niche expansion” familiar concept facilitation (Bruno addition, very levels allowed states persistence extinction pair, pairwise-level effect associated ‘Allee niche’ 2009). further generality quantifying ‘apparent-mutualism’ carnivores short chain. McPeek's talk asked consider valuable generator testable predictions. Ecological help difficult essential field run impractical, if impossible, experiments. puzzle center work feedbacks nectar-producing nectar-consuming (McPeek Resource traits nectar-production rate volume via selection consumers pressures. Over generations, resource-provider resource-consumer will respond changing supply turn affect traits. Detangling consequences predict contemporary her coauthors' costs concrete growth plant's population. uses multivariate breeder's equation change generations corresponding sizes Plant pollinator populations achieve equilibrium production balance, effecting Within framework, ran numerical adjusting parameters represent facets environment: foraging activity death environment, intensity herbivory leaf tissue. Together, supply, increases species' sizes, evolves limit frequency either reductions effort (pollinator behavior) (e.g., habitat quality herbivory). highlights additional provider species: individual produce entire nectar's sizes. conclude talk, test assumptions should compare found settings environments qualities pollinators. Second, manipulate facet environment number herbivores controlled experimental treatments measure affects One standing variation experiment factors evolved provisioning, manipulates variables drives Combining designs variety resource-mediated nature. panel following speakers' talks, brought open directions. Broadly, points fell categories: cheating communities. Below outline perspectives key areas research: equilibrate steady state; otherwise, rates only increase limits. Therefore, cost interaction. example, al.'s genetic assumes pay reproductive producing studies demonstrating lower higher Pyke 1991). currently piecemeal evidence substantial occur Morris 2010, Aizen 2014). Are defining feature nature, constraints certain than biology? We assess magnitudes substantially overall services exchange systems. available estimates shown swamped demographic (Pyke 1991, Kang 2011, Brandenburg 2012, Ford despite balancing literature, benefits, most justifiable Another component saturating developed al., define investment A consume so nectar, seeds. Once seeds pollinated, there no continuing attract Many incorporated (Holland 2002, Holland DeAngelis 2009, 2010). suggests sometimes times links (Chaianunporn Hovestadt Mack 2012). Here, target investigation sets relationships generates assumptions. examine validity match deviate gain clearer governing interested break down cheating, scenario where gains greater providing good service (Soberon Mainero Martinez del Rio 1985, Bronstein 2001). accessibility strategies depends specified and, case models, acting fertilize ovules subject selection, prevent sustained ‘cheating’ phenotype. general, McPeek's's rare, particularly hamper localized. Koffel, hand, public his susceptible tragedy commons, ‘cheaters' exploit any return. Since do reciprocating, tend fitness thus displace true mutualists. sense, mirrors expected rare even nonexistent others (Jones susceptibility cheaters Koffel's potentially arise localization resources. Pollinators must visit flowers access microbes readily nutrients environment. Future geared identify degrees ease invade so, necessary. amenable analysis, limiting (Pande big remaining lies presented here account keeping tractable, opening door mentioned although studying setting critical. rewards explicitly modeled (Revilla explicit important mutualists (Valdovinos Marsland 2021) webs applied pairs species. While Saavedra (2017) provided avenues generalization competitive describing determine structures remains. NN initiated led write-up, after contributed equally manuscript. author wrote first four sections jointly writing final section. All reviewed edited Authors listed alphabetically author. No data collected study.

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