Kim Cuddington

Alternative stable states in ecology

The idea that alternative stable states may exist in communities has been a recurring theme in ecology since the late 1960s, and is now experiencing a resurgence of interest. Since the first papers on the subject appeared, two perspectives have developed to describe how communities shift from one stable state to another. One assumes a constant environment with shifts in variables such as population density, and the other anticipates changes to underlying parameters or environmental “drivers”. We review the theory behind alternative stable states and examine to what extent these perspectives are the same, and in what ways they differ. We discuss the concepts of resilience and hysteresis, and the role of stochasticity within the two formulations. In spite of differences in the two perspectives, the same type of experimental evidence is required to demonstrate the existence of alternative stable states.

Complexity in Ecology and Conservation: Mathematical, Statistical, and Computational Challenges

Abstract Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application.

Black noise and population persistence

Biological populations are susceptible to random variation in environmental influences such as temperature and moisture. This variability (or noise) can determine population size and, ultimately, cause extinctions. Extinction risk depends on noise colour or the amount of short– and long–term variation. Most environmental noise is reddened: the variation is dominated by long–term fluctuations. Recent modelling has shown that moderately reddened noise affects populations differently from the white noise used in earlier studies. However, some geophysical phenomena, such as temperature and river height, can have deeply reddened ‘brown’ or even ‘black’ spectra. We find that, compared to environments characterized by red noise, very long population persistence times are more likely for black noise. Unlike previous work incorporating a simple autoregressive model of reddened noise, our model suggests that the large variation associated with persistence in a red–noise environment limits our ability to predict the fate of particular populations subject to this noise colour. Thus, we identify the colour of noise experienced by a population (red or black) as a crucial factor in any attempt to manage or conserve that population.

Predator‐Prey Dynamics and Movement in Fractal Environments

Previous research suggests that local interactions and limited animal mobility can affect population dynamics. However, the spatial structure of the environment can further limit the mobility of animals. For example, an animal confined to a river valley or to a particular plant cannot move with equal ease in all directions. We show that spatial architecture could influence the population dynamics of predator‐prey systems using individual‐based computer simulations parameterized with allometric relationships from the literature. Spatial forms (representing geographical features or plant architecture) of differing fractal dimension were generated, and simulated predators and prey were introduced into these computer environments. We claim that the alteration in interaction rates and population dynamics found in these simulations can be explained as a consequence of the anomalously slow rates of movement associated with fractal spaces and the diffusion‐limited nature of predator‐prey interactions. As a result, functional responses and numerical responses are substantially reduced in fractal environments, and the overall stability of the system is determined by the interaction between individual mobility and spatial architecture.

Process‐based models are required to manage ecological systems in a changing world

Several modeling approaches can be used to guide management decisions. However, some approaches are better fitted than others to address the problem of prediction under global change. Process-based models, which are based on a theoretical understanding of relevant ecological processes, provide a useful framework to incorporate specific responses to altered environmental conditions. As a result, these models can offer significant advantages in predicting the effects of global change as compared to purely statistical or rule-based models based on previously collected data. Process-based models also offer more explicitly stated assumptions and easier interpretation than detailed simulation models. We provide guidelines for identifying the appropriate type of model and level of complexity for management decisions. Finally we outline some of those factors that make modeling for local and regional management under global change a particular challenge: changes to relevant scales and processes, additional sources of uncertainty, legacy effects, threshold dynamics, and socio-economic impacts.

The "Balance of Nature" Metaphor and Equilibrium in Population Ecology

I claim that the “balance of nature” metaphoris shorthand for a paradigmatic view of natureas a beneficent force. I trace the historicalorigins of this concept and demonstrate that itoperates today in the discipline of populationecology. Although it might be suspected thatthis metaphor is a pre-theoretic description ofthe more precisely defined notion ofequilibrium, I demonstrate that “balance ofnature” has constricted the meaning ofmathematical equilibrium in population ecology.As well as influencing the meaning ofequilibrium, the metaphor has also loaded themathematical term with values.Environmentalists and critics use thisconflation of meaning and value to theiradvantage. This interplay between the “balanceof nature” and equilibrium fits aninteractionist interpretation of the role ofmetaphor in science. However, it seems theinteraction is asymmetric, and the “balance ofnature” metaphor has had a larger influence onmathematical equilibrium than vice versa. Thisdisproportionate influence suggests that themetaphor was and continues to be a constitutivepart of ecological theories.

Ecosystem Engineers: Feedback and Population Dynamics

All organisms alter their abiotic environment, but ecosystem engineers are species with abiotic effects that may have to be explicitly accounted for when making predictions about population and community dynamics. The goal of this analysis is to identify those conditions in which engineering leads to population dynamics that are qualitatively different than one would predict using models that incorporate only biotic interactions. We present a simple model coupling an ecosystem engineer and the abiotic environment. We assume that the engineer alters environmental conditions at a rate dependent on engineer density and that the environment decays back to original conditions at an exponential rate. We determine when the feedback to population dynamics through environmental state can lead to altered equilibrium densities, bistability, or runaway growth of the engineer population. The conditions leading to changes in dynamics, such as susceptibility of a system to engineering or alteration of density‐dependent and density‐independent controls, define cases in which the engineering concept is essential for ecological understanding.

Influences of pea morphology and interacting factors on pea aphid (Homoptera: Aphididae) reproduction.

ABSTRACT It has been claimed that plant architecture can alter aphid reproductive rates, but the mechanism driving this effect has not been identified. We studied interactions between plant architecture, aphid density, environmental conditions, and nutrient availability on the reproduction of pea aphids [Acyrthosiphon pisum (Harris)] using four near-isogenic peas (Pisum sativum L.) that differ in morphology. Manipulations of aphid density (1, 5, and 10 adults per plant) allowed us to examine any effects of plant morphology on crowding and consequently reproduction. Pea morphology per se did not alter pea aphid crowding, as measured by mean nearest neighbor distance, and there was no effect on reproduction. In addition, reproduction increased with increasing adult density, indicating positive density dependence. In a separate experiment, peas were fertilized to determine whether differences between nutrient availability of the four different morphologies might drive any observed differences in aphid reproduction. Although plant nitrogen content was altered by fertilization treatments, this did not have an impact on aphid reproduction. Greenhouse experiments, however, suggested that pea morphology can interact with environmental conditions to reduce aphid reproduction under some conditions. We conclude that plant morphology only influences aphid reproduction when environmental conditions are less than optimal.

13 - Balancing the Engineer–Environment Equation: the Current Legacy

This chapter defines the ecosystem engineering models as those that include a relationship between the engineer species and the environmental state, and that subsequently link between the environmental state and some biotic characteristic of the system. The chapter provides a survey of the current state of ecosystem engineer modeling with the goals of deducing general principles and paving the way for future efforts. The majority of the models that is included in this review are those at the population level (reflecting the current state of the art), where the action of engineering affects the environment, which in turn affects the engineer. This chapter also explores some of the general conclusions to be drawn from models of ecosystem engineering, and point out some new directions. Often the relationships between the engineer and the environment have not been investigated in the field, and the modelers must simply select those functions they judge to be reasonable. However, many theoreticians are more familiar with either physical or biological relationships

Population Dynamics and Food Web Structure-Predicting Measurable Food Web Properties with Minimal Detail and Resolution

Publisher Summary This chapter argues that two major deficiencies of food web theory to date are the almost mutually exclusive treatment of detail and resolution in food web models and the lack of tangible timescales in mapping theory to empirical research on food webs. It highlights some recent advances in food web theory that represent the first steps towards integrating detail at the individual and population level with resolution at the whole system level and suggests that future progress depends on continued integration of these historically separate lines of investigation. Finally, a working conceptual model for integrating detail and resolution to make predictions about the links between population and whole system persistence has been suggested. Despite recent advances in the integration of structural and dynamic approaches to food webs, few theoretical approaches satisfactorily combine detail and resolution to identify links between population dynamics and the stability of system structure. Population models analyze population persistence as a function of individual properties, while models of larger food webs evaluate the resilience of whole systems of interacting species as a function of observed structural properties of entire food webs. The analysis of persistence is achieved in both cases via the same methods—by evaluating the sign and magnitude of the real components of eigenvalues from the determinant of an n x n community matrix, where n is the number of species included in the model.