Our studies suggest a combination of its transmission dynamics and how they are affected by management issues, such as the quantity of shade and the density of planting, plus a variety of control from above elements represent a source of control, which sometimes fails .Understanding the general structure of ecological communities has long been a central goal of ecology, from Haeckel to us. Empiricists commonly, and probably necessarily, focus on the community of X, which is to say an assemblage of species defined by some set of criteria: the fungal community of Lake Wobegon, the community of gall-forming insects of oak trees, the microbial community of the human gut, the community of four ciliate species, and so on. Theoreticians perhaps feel less constraint. In the present article, we have defined the community as the herbivores of the coffee plant and their associates, in which top-down control is the goal of management . The framing of regulation from above from theoretical ecology translates directly into biological control from agroecology. Indeed, in agroecology regulation from above is elementary, in that the top-down agents are frequently obvious . However, stopping at that level of understanding may obscure more than clarify, stacking pots much as the simple phrase controlled from above may indeed obscure . Precisely how that control is affected may involve many complicated interactions and contingencies, making, we argue, the framing of complex systems a necessary one.
The fungus that attacks the scale is most efficient when the scale is hyper dense at a local level, something that cannot happen unless it is under the protection of a mutualistic ant, which deters the other predator , which, however, is able to take advantage of a spatial pattern that is self-organized through a Turing-like process, and so forth. Indeed, we argue that the understanding we claim to have of this system so far comes from detailed study, both empirical and theoretical, and, most importantly is dramatically enriched through the application of some of the concepts newly developed in the distinct field of complex systems. Almost 10 years ago, some of us published a summary of this overall system , suggesting that understanding it required more than just an identification of who eats whom. This update emphasizes that point. Our narrative in the present article is perhaps a bit heterodox. We study a very complicated system , and we seek to understand it through theoretical ecology. To some, at least in the recent past, this might imply a large-scale computer model or sophisticated data manipulation. Our approach is distinct, recalling the wisdom of Levins’ paper on the strategy of model building. We seek to understand, at a deep level, how this system works, not necessarily for the purpose of predicting its future state. We offer theoretical propositions, many of which are stimulated by mathematical arguments, but we do not seek what postmodern thinkers would have called a “totalizing discourse” with a large-scale model. Rather, we seek to use recent advances in complex systems as a way of stimulating thought, with the mathematical models that go along with them as “educating our intuition,” as Levins urged frequently. The models themselves represent approximate metaphors for this complex reality, all fitting into a hierarchy of understanding , which is mainly qualitative even though originally formulated through mathematical reasoning.Furthermore, our claim that this is a complex reality is meant to imply something deeper than the obvious claim that it is complicated. It is a complex system.
For example, if the only players in the system were Azteca, C. viridis, and A. orbigera, the system wouldn’t be exceptionally complicated , but it would be a complex system, because it would have a clear emergent property . Even adding the phorid would mean two predators and two prey, but the spatial pattern that emerges and the dependence of one system on a second system operative at a completely distinct time scale is an essential structural component of the system as a whole. The emergence would defy understanding if only the separate component parts were studied, which is to say if it were approached from a purely reductionist perspective. If the only players were the ants and the coffee berry borer, but the ants did not exhibit trait-mediated indirect interactions, the system would be complicated but not necessarily complex. This distinction between complicated and complex is important for our narrative. Because it is a complex system, it requires a more holistic approach to understand and manage, and there’s more potential for surprise . A merely complicated system would not have these characteristics. That our model system is coffee is significant in several ways. First, traditional coffee management, with its characteristic shade trees, helps to create landscapes that are friendly to biodiversity conservation . It is a classic high-quality matrix for all sorts of animals and plants. Second, it involves a commodity that is of extremely high value, sometimes the main source of wealth for entire countries. Third, it is the basis of livelihood for millions of small farmers the world over. Fourth, when properly cultivated with shade, it joins other agroforestry systems in the worldwide struggle against climate change. Given all that, understanding the details of its operation would seem worthwhile, and marshaling recent insights from complex systems to anchor that narrative brings one of the classical questions of community ecology into focus as a practical issue. Consequently, besides being of potential importance for ecology, it makes ecology important for some practical aspects of this important crop.
It is, for example, evident from only a qualitative understanding of the control from above system that a key element is the species of ant that nests in the shade trees and that, if those shade trees are eliminated , the whole control structure will be dramatically interrupted. Questions also arise about generality. Does this model system reflect something more general about the structure of control from above, or does it simply reflect interactions of this one particular system? First, most terrestrial systems have a spatial component involved, and framing the spatial component as one in which a subsystem operates to effectively create a spatial pattern in which other subsystems may operate is likely to apply frequently. Indeed, the idea of a predator–prey system generating a Turing pattern may be increasingly appreciated as more research programs interrogate the idea . Second, population dynamics unfolding on this space are likely to be nonlinear, and this nonlinearity will frequently be of the form that critical transitions lead to an alternative equilibrium within hysteretic zones, which may be multiple and constrain the herbivores above which control is being exerted . Third, the idea that multiple herbivores have their own suite of controlling factors is almost certainly true, but the idea that there will be connections, even if weak, with other sub-components of the control from above, is likely to be characteristic. These three generalities encompass the complex systems topics of Turing pattern formation, critical transitions, hysteresis, chaos, basin boundary collisions, trait-mediated indirect interactions, and scale-dependent spatial processes, all of which are exemplified in our model system, and certainly may be embedded in other systems of control from above. The message is not that these particular topics are essential but, rather, that control from above is not the one-dimensional process frequently imagined of a predator guild preying on a prey guild but, instead, a complex community of predators and parasites and diseases that interact with one another in complicated ways to eventually generate a self-organized system that exerts effective control over the herbivory. Much as one might say that the vertebrate circulatory system is responsible for bringing oxygen to each cell in the body, one might similarly simplify and say that natural enemies in the coffee agroecosystem are responsible for the regulation of potential pests. However, it is the heart, the veins, the arteries, exchanges across membranes, strawberry gutter system and so forth that tell the real story of how the delivery of oxygen to our tissues actually happens. It is a complex system, the details of which are certainly of interest to health and healing. Similarly, in our agroecosystem example, the subsystem that creates large-scale pattern sets the stage for a subsystem involving a predator and a disease that affect regulation of one pest, whereas the community structure of ants determines the efficiency of their predacious activities on a second pest and the disease that helps regulate the first pest is an antagonist to the third pest. This is all to say that yes, it is control from above, but that control is delivered through the ecological complexity of the community of natural enemies. It is misleading to suggest that listing the natural enemies and merely identifying them as such is sufficient. It is only through the lens of the reality of its state as a complex system that we may gain full appreciation of the ecological principle of top-down control, which then can be fully exploited in attempts to aid the management of this important agroecosystem. There is something of a conundrum in this narrative.
Although it is clear that knowledge of all the ecological complexity could inform practical decisions that producers might want to make, is such detailed knowledge really necessary to provide useful advice to the farmer? If ecological knowledge of the particular system is primitive, could well meaning agroecological advisors give advice that will have unintended negative consequences? Post-WWII industrial agriculture enthusiasts embraced DDT and other pesticides creating the well-known pesticide treadmill that haunts us still today. Indeed, that is one of the issues that caused many environmentally conscious analysts to call for the science of ecology to be more actively embraced by agricultural planners. However, ecology is complicated. Secondary consequences cannot necessarily be predicted short of detailed study and the normal rules of thumb extrapolated from a few experiments or extralocal traditions could backfire. Perhaps the famous medical practitioner’s oath primum non nocere makes sense in agriculture as well. As farmers seek solutions to perceived problems on their farms, agroecologists rightly wish to use the science of ecology to help. However, frequently , ecological knowledge of the particular system is not very well understood because it is only recently that agroecological advocates have begun to break into the mainstream, and the basic research required to understand some of the vexing problems the farmers face has yet to be done. It is therefore common to use a few rules of thumb: avoid monocultures, don’t poison your natural enemies, maintain healthy soil, and so on. Such rules of thumb, on the basis of perceived ecological rules, for the most part make sense and probably conform well to the admonition primum non nocere. However, it is worth remembering the dust bowl, pest resurgence following pesticides, ocean dead zones, and other consequences that we live with today because a previous generation of farm advocates, equally sincere in their desires to help farmers, were prematurely confident in the ability of their tools to help the farmer.There are a growing number of examples of a positive relationship between diversity and ecosystem service. As an ecosystem service, pollination can increase the fruit or seed quality or quantity of 39 of the world’s 57 major crops, and a more diverse pollinator community has been found to improve pollination service . For some crops, wild bees are more effective pollinators on a per visit basis than honey bees and/or can functionally complement the dominant visitor. A less explored reason is that in diverse communities, interspecific interactions potentially alter behaviour in ways that increase pollination effectiveness. Little is known about how community composition affects pollinator behaviour and the role such species interactions play in determining diversity–ecosystem service relationships. Interspecific interactions can result in non-additive impacts of diversity on ecosystem functions. Examples include the facilitation of resource capture in diverse groups of aquatic arthropods, and non-additive increases in pest suppression and alfalfa production in enclosures with diverse natural enemy guilds. In diverse communities, one mechanism by which species interactions may augment function is the potential to modify the behaviour and the resulting effectiveness of the ecosystem service providers. Interactions with non-Apis bees cause Apis mellifera L. to move more often between rows of sunflower, increasing their pollination efficiency. Such changes in pollinator movement are particularly important in crop species with separate male and female flowers, and those with self-incompatibility .