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Understanding the forces shaping ecological communities is of crucial importance for basic science and conservation. After 50 years in which ecological theory has focused on either stable communities driven by niche-based forces or nonstable "neutral" communities driven by demographic stochasticity, contemporary theories suggest that ecological communities are driven by the simultaneous effects of both types of mechanisms. Here we examine this paradigm using the longest available records for the dynamics of tropical trees and breeding birds. Applying a macroecological approach and fluctuation analysis techniques borrowed from statistical physics, we show that both stabilizing mechanisms and demographic stochasticity fail to play a dominant role in shaping assemblages over time. Rather, community dynamics in these two very different systems is predominantly driven by environmental stochasticity. Clearly, the current melding of niche and neutral theories cannot account for such dynamics. Our results highlight the need for a new theory of community dynamics integrating environmental stochasticity with weak stabilizing forces and suggest that such theory may better describe the dynamics of ecological communities than current neutral theories, deterministic niche-based theories, or recent hybrids. [ABSTRACT FROM AUTHOR]

Ecological spatial patterns are structured by a multiplicity of processes acting over a wide range of scales. We propose a new method, based on the scalewise variance_that is, the variance as a function of spatial scale, calculated here with wavelet kernel functions_ to disentangle the signature of processes that act at different and similar scales on observed spatial patterns. We derive exact and approximate analytical solutions for the expected scalewise variance under different individual-based, spatially explicit models for sessile organisms (e.g., plants), using moment equations. We further determine the probability distribution of independently observed scalewise variances for a given expectation, including complete spatial randomness. Thus, we provide a new analytical test of the null model of spatial randomness to understand at which scales, if any, the variance departs significantly from randomness. We also derive the likelihood function that is needed to estimate parameters of spatial models and their uncertainties from observed patterns. The methods are demonstrated through numerical examples and case studies of four tropical tree species on Barro Colorado Island, Panama. The methods developed here constitute powerful new tools for investigating effects of ecological processes on spatial point patterns and for statistical inference of process models from spatial patterns. [ABSTRACT FROM AUTHOR]