Another development for biodiversity ecology was Hubbell’s unified neutral theory of biodiversity (NTB). The simplicity of this theory ( MacArthur and Wilson, 1967) allowed many developments using metapopulation-type communities ( Tilman, 1994 Matter et al., 2002). It ignores interactions entirely, so extinction and colonization processes drive the system’s biodiversity. One of the most widely applied community theories is that of island biogeography ( MacArthur and Wilson, 1967). Assigning interaction strengths in an ecological community of hundreds of species remains a formidable problem and drives the development of various other community models, though these could arguably be seen as special cases of the generalized Lotka–Volterra system ( Kessler and Shnerb, 2015). A more persistent challenge is how to populate the S 2 interaction terms with plausible parameter values. The growth in computing power has circumvented many of the obvious technical difficulties of solving a system of this size. This generalized Lotka–Volterra system has had enormous impact on community ecology, including classical treatments such as that of May ( May, 1973) and continues to be the subject of novel approaches ( Forte and Vrscay, 1996 Bertuzzo et al., 2011 Fisher and Mehta, 2014 Kessler and Shnerb, 2015). A natural development for dynamic community modeling is to generalize this system to S species described by a set of first-order non-linear coupled differential equations. While the Lotka–Volterra system is phenomenological, it can also be related to more mechanistic resource models ( Schoener, 1973). This pair of equations with four interaction terms can describe relationships of competition, predation, herbivory and mutualism. The basis of most modeling in community ecology is the Lotka–Volterra model. That said, the greater attention to time calls for model development to help insights specifically into dynamic effects in communities. The simplifications of assuming an equilibrium will remain useful and compelling, such as for flux calculations and for the species-area relationship. Thus, we expect ecological equilibria to be more dynamic, more provisional and more diffuse ( Halley and Inchausti, 2004). Variability exists on all timescales both within the ecological community itself ( Pimm and Redfearn, 1988 Halley, 1996) and in the environment to which it responds ( Wunsch, 2003 Franzke et al., 2020). The “balance of nature” paradigm has receded somewhat ( Cuddington, 2001 Ergazaki and Ampatzidis, 2012 Simberloff, 2014) and we now have a more dynamic conception of persistence in natural ecosystems ( Pimm, 1991). Additionally, in the last 50 years, there has been a reassessment of the dominant paradigm of community organization. This call is urgent as we live in an age of major changes in biodiversity across the Earth via landscape transformation or migration ( Dornelas et al., 2014). It also provides a powerful framework to explore significant ideas in ecology, such as the drift of ecological communities into evolutionary time.Įmpirical studies of ecological communities often call for a greater emphasis on time and on longer time intervals ( Ripa and Lundberg, 2000 Hastings, 2004 Magurran, 2007). This dynamic hypercube model reproduces several key patterns in communities: lognormal species abundance distributions, 1/ f-noise population abundance, multiscale patterns of extinction debt and logarithmic species-time curves. While the community’s size remains constant, the relative volumes of the niches within it change continuously, thus allowing the populations of different species to rise and fall in a zero-sum fashion. We describe the community’s size through the volume of the hypercube and the dynamics of the populations in it through the fluctuations of the axes of the niche hypercube on different timescales. Here, we develop the niche-hypervolume concept of the community into a powerful model of community dynamics.
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