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Climate change is generating novel communities composed of new combinations of species. These result from different degrees of species adaptations to changing biotic and abiotic conditions, and from differential range shifts of species. To determine whether the responses of organisms are determined by particular species traits and how species interactions and community dynamics are likely to be disrupted is a challenge. Here, we focus on two key traits: body size and ecological specialization. We present theoretical expectations and empirical evidence on how climate change affects these traits within communities. We then explore how these traits predispose species to shift or expand their distribution ranges, and associated changes on community size structure, food web organization and dynamics. We identify three major broad changes: (i) Shift in the distribution of body sizes towards smaller sizes, (ii) dominance of generalized interactions and the loss of specialized interactions, and (iii) changes in the balance of strong and weak interaction strengths in the short term. We finally identify two major uncertainties: (i) whether large-bodied species tend to preferentially shift their ranges more than small-bodied ones, and (ii) how interaction strengths will change in the long term and in the case of newly interacting species.

A common approach to analyse stability of biological communities is to calculate the interaction strength matrix. Problematic in this approach is defining intraspecific interaction strengths, represented by diagonal elements in the matrix, due to a lack of empirical data for these strengths. Theoretical studies have shown that an overall increase in these strengths enhances stability. However, the way in which the pattern in intraspecific interaction strengths, i.e. the variation in these strengths between species, influences stability has received little attention. We constructed interaction strength matrices for 11 real soil food webs in which four patterns for intraspecific interaction strengths were chosen, based on the ecological literature. These patterns included strengths that were (1) similar for all species, (2) trophic level dependent, (3) biomass dependent, or (4) death rate dependent. These four patterns were analysed for their influence on (1) ranking food webs by their stability and (2) the response in stability to variation of single interspecific interaction strengths. The first analysis showed that ranking the 11 food webs by their stability was not strongly influenced by the choice of diagonal pattern. In contrast, the second analysis showed that the response of food web stability to variation in single interspecific interaction strengths was sensitive to the choice of diagonal pattern. Notably, stability could increase using one pattern and decrease using another. This result asks for deliberate approaches to choose diagonal element values in order to make predictions on how particular species, interactions, or other food web parameters affect food web stability.

Quantifying species interaction strengths enhances prediction of community dynamics, but variability in the strength of species interactions in space and time complicates accurate prediction. Interaction strengths can vary in response to density, indirect effects, priority effects or a changing environment, but the mechanism(s) causing direction and magnitudes of change are often unclear. We designed an experiment to characterize how environmental factors influence the direction and the strength of priority effects between sessile species. We estimated per capita non-trophic effects of barnacles (Semibalanus balanoides) on newly settled germlings of the fucoid, Ascophyllum nodosum, in the presence and absence of consumers in experiments on rocky shores throughout the Gulf of Maine, USA. Per capita effects on germlings varied among environments and barnacle life stages, and these interaction strengths were largely unaltered by changing consumer abundance. Whereas previous evidence shows adult barnacles facilitate fucoids, here, we show that recent settlers and established juveniles initially compete with germlings. As barnacles mature, they switch to become facilitators of fucoids. Consumers caused variable mortality of germlings through time comparable to that from competition. Temporally variable effects of interactors (e.g. S. balanoides), or spatial variation in their population structure, in different regions differentially affect target populations (e.g. A. nodosum). This may affect abundance of critical stages and the resilience of target species to environmental change in different geographical regions.

Land use is known to reduce the diversity of species and complexity of biotic interactions. In theory, interaction networks can be used to predict the sensitivity of species against co-extinction, but this has rarely been applied to real ecosystems facing variable land-use impacts. We investigated plant-pollinator networks on 119 grasslands that varied quantitatively in management regime, yielding 25 401 visits by 741 pollinator species on 166 plant species. Species-specific plant and pollinator responses to land use were significantly predicted by the weighted average land-use response of each species' partners. Moreover, more specialized pollinators were more vulnerable than generalists. Both predictions are based on the relative interaction strengths provided by the observed interaction network. Losses in flower and pollinator diversity were linked, and mutual dependence between plants and pollinators accelerates the observed parallel declines in response to land-use intensification. Our findings confirm that ecological networks help to predict natural community responses to disturbance and possible secondary extinctions.

Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time—a requirement for managing resources in a nonlinear, non-equilibrium world.

Knowledge of feeding rates is the basis to understand interaction strength and subsequently the stability of ecosystems and biodiversity. Feeding rates, as all biological rates, depend on consumer and resource body masses and environmental temperature. Despite five decades of research on functional responses as quantitative models of feeding rates, a unifying framework of how they scale with body masses and temperature is still lacking. This is perplexing, considering that the strength of functional responses (i.e. interaction strengths) is crucially important for the stability of simple consumer–resource systems and the persistence, sustainability and biodiversity of complex communities. Here, we present the largest currently available database on functional response parameters and their scaling with body mass and temperature. Moreover, these data are integrated across ecosystems and metabolic types of species. Surprisingly, we found general temperature dependencies that differed from the Arrhenius terms predicted by metabolic models. Additionally, the body-mass-scaling relationships were more complex than expected and differed across ecosystems and metabolic types. At local scales (taxonomically narrow groups of consumer–resource pairs), we found hump-shaped deviations from the temperature and body-mass-scaling relationships. Despite the complexity of our results, these body-mass- and temperature-scaling models remain useful as a mechanistic basis for predicting the consequences of warming for interaction strengths, population dynamics and network stability across communities differing in their size structure.

The success of a biological invasion is context dependent, and yet two key concepts—the invasiveness of species and the invasibility of recipient ecosystems—are often defined and considered separately. We propose a framework that can elucidate the complex relationship between invasibility and invasiveness. It is based on trait-mediated interactions between species and depicts the response of an ecological network to the intrusion of an alien species, drawing on the concept of community saturation. Here, invasiveness of an introduced species with a particular trait is measured by its per capita population growth rate when the initial propagule pressure of the introduced species is very low. The invasibility of the recipient habitat or ecosystem is dependent on the structure of the resident ecological network and is defined as the total width of an opportunity niche in the trait space susceptible to invasion. Invasibility is thus a measure of network instability. We also correlate invasibility with the asymptotic stability of resident ecological network, measured by the leading eigenvalue of the interaction matrix that depicts trait-based interaction intensity multiplied by encounter rate (a pairwise product of propagule pressure of all members in a community). We further examine the relationship between invasibility and network architecture, including network connectance, nestedness and modularity. We exemplify this framework with a trait-based assembly model under perturbations in ways to emulate fluctuating resources and random trait composition in ecological networks. The maximum invasiveness of a potential invader (greatest intrinsic population growth rate) was found to be positively correlated with invasibility of the recipient ecological network. Additionally, ecosystems with high network modularity and high ecological stability tend to exhibit high invasibility. Where quantitative data are lacking we propose using a qualitative interaction matrix of the ecological network perceived by a potential invader so that the structural network stability and invasibility can be estimated from the literature or from expert opinion. This approach links network structure, invasiveness and invasibility in the context of trait-mediated interactions, such as the invasion of insects into mutualistic and antagonistic networks.

The structure of ecological interaction networks is often interpreted as a product of meaningful ecological and evolutionary mechanisms that shape the degree of specialization in community associations. However, here we show that both unweighted network metrics (connectance, nestedness, and degree distribution) and weighted network metrics (interaction evenness, interaction strength asymmetry) are strongly constrained and biased by the number of observations. Rarely observed species are inevitably regarded as \"specialists,\" irrespective of their actual associations, leading to biased estimates of specialization. Consequently, a skewed distribution of species observation records (such as the lognormal), combined with a relatively low sampling density typical for ecological data, already generates a \"nested\" and poorly \"connected\" network with \"asymmetric interaction strengths\" when interactions are neutral. This is confirmed by null model simulations of bipartite networks, assuming that partners associate randomly in the absence of any specialization and any variation in the correspondence of biological traits between associated species (trait matching). Variation in the skewness of the frequency distribution fundamentally changes the outcome of network metrics. Therefore, interpretation of network metrics in terms of fundamental specialization and trait matching requires an appropriate control for such severe constraints imposed by information deficits. When using an alternative approach that controls for these effects, most natural networks of mutualistic or antagonistic systems show a significantly higher degree of reciprocal specialization (exclusiveness) than expected under neutral conditions. A higher exclusiveness is coherent with a tighter coevolution and suggests a lower ecological redundancy than implied by nested networks.

The community matrix is among ecology's most important mathematical abstractions, formally encapsulating the interconnected network of effects that species have on one another's populations. Despite its importance, the term \"community matrix\" has been applied to multiple types of matrices that have differing interpretations. This has hindered the application of theory for understanding community structure and perturbation responses. Here, we clarify the correspondence and distinctions among the Interaction matrix, the Alpha matrix, and the Jacobian matrix, terms that are frequently used interchangeably as well as synonymously with the term \"community matrix.\" We illustrate how these matrices correspond to different ways of characterizing interaction strengths, how they permit insights regarding different types of press perturbations, and how these are related by a simple scaling relationship. Connections to additional interaction strength characterizations encapsulated by the Beta matrix, the Gamma matrix, and the Removal matrix are also discussed. Our synthesis highlights the empirical challenges that remain in using these tools to understand actual communities.

Trophic cascades are indirect positive effects of predators on resources via control of intermediate consumers. Larger-bodied predators appear to induce stronger trophic cascades (a greater rebound of resource density toward carrying capacity), but how this happens is unknown because we lack a clear depiction of how the strength of trophic cascades is determined. Using consumer resource models, we first show that the strength of a trophic cascade has an upper limit set by the interaction strength between the basal trophic group and its consumer and that this limit is approached as the interaction strength between the consumer and its predator increases. We then express the strength of a trophic cascade explicitly in terms of predator body size and use two independent parameter sets to calculate how the strength of a trophic cascade depends on predator size. Both parameter sets predict a positive effect of predator size on the strength of a trophic cascade, driven mostly by the body size dependence of the interaction strength between the first two trophic levels. Our results support previous empirical findings and suggest that the loss of larger predators will have greater consequences on trophic control and biomass structure in food webs than the loss of smaller predators.