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Among the myriad complications involved in the current food crisis, the relationship between agriculture and the rest of nature is one of the most important yet remains only incompletely analyzed. Particularly in tropical areas, agriculture is frequently seen as the antithesis of the natural world, where the problem is framed as one of minimizing land devoted to agriculture so as to devote more to conservation of biodiversity and other ecosystem services. In particular, the \"forest transition model\" projects an overly optimistic vision of a future where increased agricultural intensification (to produce more per hectare) and/or increased rural-to-urban migration (to reduce the rural population that cuts forest for agriculture) suggests a near future of much tropical aforestation and higher agricultural production. Reviewing recent developments in ecological theory (showing the importance of migration between fragments and local extinction rates) coupled with empirical evidence, we argue that there is little to suggest that the forest transition model is useful for tropical areas, at least under current sociopolitical structures. A model that incorporates the agricultural matrix as an integral component of conservation programs is proposed. Furthermore, we suggest that this model will be most successful within a framework of small-scale agroecological production.

Historically, those ecological communities thought to be dominated by competitive interactions among their component species have been assumed to exhibit transitive competition, that is, a hierarchy of competitive strength from most dominant to most submissive. A surge of recent literature takes issue with this assumption and notes that some species in some communities are intransitive, where a rock/scissors/paper arrangement characterizes some components of some communities. We here propose a merging of these two ideas, wherein an intransitive subgroup of species connects with a distinct subcomponent that is organized hierarchically, such that the expected eventual takeover by the dominant competitor in the hierarchy is thwarted, and the entire community can be sustained. This means that the combination of transitive and intransitive structures can maintain many species even when competition is strong. Here, we develop this theoretical framework using a simple variant on the Lotka–Volterra competition equations to illustrate the process. We also present data for the ant community in a coffee agroecosystem in Puerto Rico, that appears to be organized in this way. A detailed study on one typical coffee farm illustrates an intransitive loop of three species that seems to maintain a distinct competitive community of at least 13 additional species.

Endogenous (or autonomous, or emergent) spatial pattern formation is a subject transcending a variety of sciences. In ecology, there is growing interest in how spatial patterns can ‘emerge’ from internal system processes and simultaneously affect those very processes. A classic situation emerges when a predator's focus on a dominant competitor releases competitive pressure on a subdominant competitor, allowing coexistence of the two. If this idea is formulated spatially, two interesting consequences immediately arise. First, a spatial predator/prey system may take the form of a Turing instability, in which an activator (the dispersing prey population) is contained by a repressor (the more rapidly dispersing predator population) generating a spatial pattern of clusters of prey and predators, and second, an indirect intransitive loop (where A beats B beats C beats A) emerges from the simple fact that the system is spatial. Two common invasive ant species, Wasmannia auropunctata and Solenopsis invicta, and the parasitic phorid flies of S. invicta commonly coexist in Puerto Rico. Emergent spatial patterns generated by the combination of the Turing mechanism and the indirect intransitive loop are likely to be common here. This theoretical framework and the realities of the natural history in the field could explain both the long-term coexistence of these two species, and the highly variable pattern of their occurrence across a large landscape.

Tipping points in complex systems may imply risks of unwanted collapse, but also opportunities for positive change. Our capacity to navigate such risks and opportunities can be boosted by combining emerging insights from two unconnected fields of research. One line of work is revealing fundamental architectural features that may cause ecological networks, financial markets, and other complex systems to have tipping points. Another field of research is uncovering generic empirical indicators of the proximity to such critical thresholds. Although sudden shifts in complex systems will inevitably continue to surprise us, work at the crossroads of these emerging fields offers new approaches for anticipating critical transitions.

Significance Although forest succession has been approached as a predictable process, successional trajectories vary widely, even among nearby stands with similar environmental conditions and disturbance histories. We quantified predictability and uncertainty during tropical forest succession using dynamical models describing the interactions among stem density, basal area, and species density over time. We showed that the trajectories of these forest attributes were poorly predicted by stand age and varied significantly within and among sites. Our models reproduced the general successional trends observed, but high levels of noise were needed to increase model predictability. These levels of uncertainty call into question the premise that successional processes are consistent over space and time, and challenge the way ecologists view tropical forest regeneration. Although forest succession has traditionally been approached as a deterministic process, successional trajectories of vegetation change vary widely, even among nearby stands with similar environmental conditions and disturbance histories. Here, we provide the first attempt, to our knowledge, to quantify predictability and uncertainty during succession based on the most extensive long-term datasets ever assembled for Neotropical forests. We develop a novel approach that integrates deterministic and stochastic components into different candidate models describing the dynamical interactions among three widely used and interrelated forest attributes—stem density, basal area, and species density. Within each of the seven study sites, successional trajectories were highly idiosyncratic, even when controlling for prior land use, environment, and initial conditions in these attributes. Plot factors were far more important than stand age in explaining successional trajectories. For each site, the best-fit model was able to capture the complete set of time series in certain attributes only when both the deterministic and stochastic components were set to similar magnitudes. Surprisingly, predictability of stem density, basal area, and species density did not show consistent trends across attributes, study sites, or land use history, and was independent of plot size and time series length. The model developed here represents the best approach, to date, for characterizing autogenic successional dynamics and demonstrates the low predictability of successional trajectories. These high levels of uncertainty suggest that the impacts of allogenic factors on rates of change during tropical forest succession are far more pervasive than previously thought, challenging the way ecologists view and investigate forest regeneration.

There are two interrelated issues that seem to be emerging as central to the understanding of ecological systems more generally, particularly relevant to agroecosystems. First is the key insights of Alan Turing in which spatial pattern emerges from a system in which there is a reaction between two objects, both of which are diffusing in space, a pest and its natural enemy, for example. Secondly, as small-scale farmers make complex decisions about their farm's ecosystem management, they are forced to contemplate market forces as much as the background ecology. This necessity automatically involves a time lag in that remuneration for produce is realized substantially after the decision to plant is made. Here, behavioral economics intersects with non-linear ecological dynamics to produce an expectation of chaotic patterns. It is suggested that these two core ideas, spatial dynamics (e.g., Turing's dynamic instability in space) and chaos (e.g., Simon's constrained rationality in farm decisions) form a qualitative theoretical foundation for understanding the ecology of agroecosystems.

Exclosure experiments have demonstrated the effects of bird predation on arthropods. In a Mexican coffee plantation, we excluded foliage-gleaning bird and bat predators from coffee plants. Effects of bats and birds were additive. In the dry season, birds reduced arthropods in coffee plants by 30%; birds and bats together reduced arthropods by 46%. In the wet season, bats reduced arthropods by 84%, whereas birds reduced them by only 58%. We conclude that previous \"bird\" exclosure experiments may have systematically underestimated the effects of bats.

It has long been a goal of farm policy to manage production in such a way that expensive off-farm inputs and negative environmental consequences can be simultaneously minimized. One generalized philosophy that has gained currency in recent years is autonomous pest control, in which complex ecological interactions are encouraged to maintain the ecosystem in a state of permanence with the pest below economic thresholds. Early experience with biological control was hampered significantly by the inherent instability of many of the control agents, suggesting that pursuit of the autonomous strategy could be difficult. Here we show that combining two unstable two-dimensional systems (pest–predator and pest–pathogen) produces a stable three-dimensional system (pest–predator–pathogen) that is robust to perturbations in initial conditions. Contrary to expectations, the inclusion of negative interactions, which are arguably a necessary consequence of increased complexity, can stabilize unstable conditions and rescue biological control of simpler, ineffective pest management systems. Control of pests in agriculture by introduced natural enemies may be hampered because of unstable oscillations in the dynamics of the two populations. Here, the authors show that stable conditions can be maintained by combining two unstable systems utilizing different biological control agents.

The idea of niche construction is regarded as an interesting, if not essential, element of population dynamics. Classical results from population biology emerge from such a formulation, such as chaotic behavior, the self-organized emergence of Allee points, critical transitions, and hysteresis. Relaxing the assumption of a descending limb in classic 1-D map representations of chaos, the application of the Pomeau–Manneville map is suggested as more realistic in some situations. This alternative formulation does not allow for the emergence of a “self-organized” Allee point but introduces a new concept to the potential behavior of populations in discrete time, intermittency sensitively dependent on initial conditions, but with a distorted form referred to as weak chaos.

Dynamic modeling of plant pathogens has usually been accomplished with either a mean field or spatially explicit approach, searching generally for either broad generalization or precise prediction. In search of a qualitative intermediate that is able to query spatial particulars of transmission, we take an approximate approach using network theory. Some elements of network theory are applied to a specific case of the early spread of the coffee rust disease (agent = Hemileia vastatrix) on a single large shaded coffee farm in Chiapas, Mexico. We find that infection rates within connected components are more homogeneous than infection rates among components, suggesting that the initial stages of this disease show pattern that can be detected using simple ideas from network theory.