Suboptimal foraging theory: How inaccurate predictions and approximations can make better models of adaptive behavior.

Optimal foraging theory (OFT) is based on the ecological concept that organisms select behaviors that convey future fitness, and on the mathematical concept of optimization: finding the alternative that provides the best value of a fitness measure. As implemented in, for example, state-based dynamic modeling, OFT is powerful for one key problem of modern ecology: modeling behavior as a tradeoff among competing fitness elements such as growth, risk avoidance, and reproductive output. However, OFT is not useful for other modern problems such as representing feedbacks within systems of interacting, unique individuals: When we need to model foraging by each of many individuals that interact competitively or synergistically, optimization is impractical or impossible-there are no optimal behaviors. For such problems we can, however, still use the concept of future fitness to model behavior by replacing optimization with less precise (but perhaps more realistic) techniques for ranking alternatives. Instead of simplifying the systems we model until we can find optimal behavior, we can use theory based on inaccurate predictions, coarse approximations, and updating to produce good behavior in more complex and realistic contexts. This so-called state- and prediction-based theory (SPT) can, for example, produce realistic foraging decisions by each of many unique, interacting individuals when growth rates and predation risks vary over space and time. Because SPT lets us address more natural complexity and more realistic problems, it is more easily tested against more kinds of observation and more useful in management ecology. A simple foraging model illustrates how SPT readily accommodates complexities that make optimization intractable. Other models use SPT to represent contingent decisions (whether to feed or hide, in what patch) that are tradeoffs between growth and predation risk, when both growth and risk vary among hundreds of patches, vary unpredictably over time, depend on characteristics of the individuals, are subject to feedbacks from competition, and change over the daily light cycle. Modern ecology demands theory for tradeoff behaviors in complex contexts that produce feedbacks; when optimization is infeasible, we should not be afraid to use approximate fitness-seeking methods instead.

Mechanistic simulations predict that thermal and hydrological effects of climate change on Mediterranean trout cannot be offset by adaptive behaviour, evolution, and increased food production.

Streamflow is a main driver of fish population dynamics and is projected to decrease in much of the northern hemisphere, especially in the Mediterranean region, due to climate change. However, predictions of future climate effects on cold-water freshwater fish populations have typically focused only on the ecological consequences of increasing temperatures, overlooking the concurrent and interacting effects of climate-driven changes in streamflow regimes. Here, we present simulations that contrasted the consequences of changes in thermal regime alone versus the combined effects of changes in thermal regime and streamflow for resident trout populations in distinct river types with different sensitivities to climatic change (low-altitude main river vs. high-altitude headwaters). We additionally assessed the buffering effect of increased food production that may be linked to warming. We used an eco-genetic individual-based model that integrates the behavioural and physiological effects of extrinsic environmental drivers -temperature and flow- with intrinsic dynamics -density-dependence, phenotypic plasticity and evolutionary responses - across the entire trout life cycle, with Mediterranean brown trout Salmo trutta as the model species. Our simulations indicated that: (1) Hydrological change is a critical dimension of climate change for the persistence of trout populations, in that neither river type supported viable populations under strong rates of flow change, even under scenarios of increased food production. (2) Climate-change-related environmental change most affects the largest, oldest trout via increased metabolic costs and decreased energy inputs. In both river types, populations persisted under extreme warming alone but became dominated by younger, smaller fish. (3) Density-dependent, plastic and evolutionary changes in phenology and life-history traits provide trout populations with important resilience to warming, but strong concurrent shifts in streamflow could exceed the buffering conferred by such intrinsic dynamics.

Eco-evolutionary responses to recreational fishing under different harvest regulations.

Harvesting alters demography and life histories of exploited populations, and there is mounting evidence that rapid phenotypic changes at the individual level can occur when harvest is intensive. Therefore, recreational fishing is expected to induce both ecological and rapid evolutionary changes in fish populations and consequently requires rigorous management. However, little is known about the coupled demographic and evolutionary consequences of alternative harvest regulations in managed freshwater fisheries. We used a structurally realistic individual-based model and implemented an eco-genetic approach that accounts for microevolution, phenotypic plasticity, adaptive behavior, density-dependent processes, and cryptic mortality sources (illegal harvest and hooking mortality after catch and release). We explored the consequences of a range of harvest regulations, involving different combinations of exploitation intensity and minimum and maximum-length limits, on the eco-evolutionary trajectories of a freshwater fish stock. Our 100-year simulations of size-selective harvest through recreational fishing produced negative demographic and structural changes in the simulated population, but also plastic and evolutionary responses that compensated for such changes and prevented population collapse even under intense fishing pressure and liberal harvest regulations. Fishing-induced demographic and evolutionary changes were driven by the harvest regime, and the strength of responses increased with increasing exploitation intensity and decreasing restriction in length limits. Cryptic mortality strongly amplified the impacts of harvest and might be exerting a selective pressure that opposes that of size-selective harvest. "Slot" limits on harvestable length had overall positive effects but lower than expected ability to buffer harvest impacts. Harvest regulations strongly shape the eco-evolutionary dynamics of exploited fish stocks and thus should be considered in setting management policies. Our findings suggest that plastic and evolutionary responses buffer the demographic impacts of fishing, but intense fishing pressure and liberal harvest regulations may lead to an unstructured, juvenescent population that would put the sustainability of the stock at risk. Our study also indicates that high rates of cryptic mortality may make harvest regulations based on harvest slot limits ineffective.

Making Predictions in a Changing World: The Benefits of Individual-Based Ecology.

Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research.

Effects of land use on bird populations and pest control services on coffee farms.

Global increases in both agriculture and biodiversity awareness raise a key question: Should cropland and biodiversity habitat be separated, or integrated in mixed land uses? Ecosystem services by wildlife make this question more complex. For example, birds benefit agriculture by preying on pest insects, but other habitat is needed to maintain the birds. Resulting land use questions include what areas and arrangements of habitat support sufficient birds to control pests, whether this pest control offsets the reduced cropland, and the comparative benefits of "land sharing" (i.e., mixed cropland and habitat) vs. "land sparing" (i.e., separate areas of intensive agriculture and habitat). Such questions are difficult to answer using field studies alone, so we use a simulation model of Jamaican coffee farms, where songbirds suppress the coffee berry borer (CBB). Simulated birds select habitat and prey in five habitat types: intact forest, trees (including forest fragments), shade coffee, sun coffee, and unsuitable habitat. The trees habitat type appears to be especially important, providing efficient foraging and roosting sites near coffee plots. Small areas of trees (but not forest alone) could support a sufficient number of birds to suppress CBB in sun coffee; the degree to which trees are dispersed within coffee had little effect. In simulations without trees, shade coffee supported sufficient birds to offset its lower yield. High areas of both trees and shade coffee reduced pest control because CBB was less often profitable prey. Because of the pest control service provided by birds, land sharing was predicted to be more beneficial than land sparing in this system.

Trait-mediated trophic interactions: is foraging theory keeping up?

Many ecologists believe that there is a lack of foraging theory that works in community contexts, for populations of unique individuals each making trade-offs between food and risk that are subject to feedbacks from behavior of others. Such theory is necessary to reproduce the trait-mediated trophic interactions now recognized as widespread and strong. Game theory can address feedbacks but does not provide foraging theory for unique individuals in variable environments. 'State- and prediction-based theory' (SPT) is a new approach that combines existing trade-off methods with routine updating: individuals regularly predict future food availability and risk from current conditions to optimize a fitness measure. SPT can reproduce a variety of realistic foraging behaviors and trait-mediated trophic interactions with feedbacks, even when the environment is unpredictable.

Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology.

Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

Pattern-oriented modeling of agent-based complex systems: lessons from ecology.

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.