How can the meeting between a philosopher of science and an ecologist contribute to the advancement in an area of Ecology? Do we have anything to gain from this dialogue? And if the answer is “yes”, who wins in this story? It was in the search for a solution to existing problems in a certain area of Ecology that this dialogue began, involving myself and the philosopher of science Luana Poliseli. The central issue that drove this dialogue was the attempt to conceptually model ecological systems to understand more clearly what the potential descriptors of bee functional diversity in agroecosystems would be. This central question raised several other ancillary questions that needed theoretical deepening for a better understanding of our central phenomenon. Some of these questions were: How have we measured functional diversity in Ecology? What concept of function have we used in empirical studies? How do we understand the relationship between ecological variables across different spatiotemporal scales? All these issues required a thorough exploration of what was already known in the ecological literature, but it also required analyses of the theoretical consistency of what was available to work with. Notably, we also needed to find a way to model the system of interest through pragmatic steps, in the light of a theory of explanation consistent with what we were looking for. In view of the exposed needs, the interaction with the philosopher of science opened very fruitful fields of dialogue in view of the great complexity of the system under analysis. The first breakthrough was a clearer understanding of what we were trying to analyze. In the theoretical modeling process, it is essential that we are clear about what we want to model, pointing out the relevant context of the phenomenon. In the meetings held, the questions made by the philosopher served as guideposts for my own understanding of the phenomenon, as it was inevitable to use didactic examples and analogies for a better understanding of what I wanted within Ecology. With each new inquiry, the use of imagery resources and schematics was fundamental to establish greater clarity of what we were seeking to model. In this process, the philosopher evaluated my own progress in understanding the phenomenon and in my way of establishing the pillars of modeling. Details on this analysis can be seen in the paper “Emergence of scientific understanding in real‑time ecological research practice.“ This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. But understanding what we wanted to model was the first step. Throughout our interactions, we felt the need to use a theory of scientific explanation, which was the new mechanistic philosophy of science. This choice resulted from the possibilities that this approach gave us to model phenomena that involved processes operating at multiple scales of space and time, as well as from the prospect that non-linear relationships took place between processes occurring at those scales. This choice was based on an in-depth study of the new mechanistic philosophy of science literature guided by discussions that aimed to understand the approach and how the ecological phenomenon could or could not be modeled according to it. In this dialogic process, we realized that the phenomenon to be modeled was of great complexity and that, consequently, the mechanistic explanation could not be fully applied to our phenomenon, in a normative way, but it could play a heuristic role in conducting the modeling process. The new body of knowledge produced was a heuristics set constructed according to the new mechanistic philosophy of science and ecological theories, and this heuristics set was pivotal as it guided the ecologist’s modeling activity. The heuristics set is described and discussed in the paper “Philosophy of science in practice in ecological model building.” The research we are discussing here aimed at the development of a conceptual framework that unifies mechanistic explanation, complex systems sciences and metacommunity theory in order to model the functional structure of an autochthonous bee community, as well as the maintenance of its pollination services in an agroecosystem. After we employed the organizational theory of ecological functions 2,3,4,5 to clarify the most elementary concepts and processes to consider in our work, we started to search for variables, spatio-temporal scales and cause-and-effect relationships that could support the modeling of the ecological phenomenon. To do so, we developed through the interdisciplinary work the following heuristics set in order to guide model-building: 1) Phenomenon characterization: the description of the phenomenon to be modeled and what will be considered in its explanation, that is, the description of the explanandum and explananda to be considered in the modeling process. 2) Mechanism sketch: the development of diagrams (usually incomplete and disposable) to represent the relations between the theoretical frameworks and the phenomenon. 3) Hierarchical structure: this heuristics enables visualizing the interaction between different spatial and temporal scales by creating a structure that identifies and locates the (amount of) levels in which the mechanism (or mechanisms) are organized (and/or nested) in the phenomenon superstructure. 4) Enabling conditions: the variables involved in activities in the mechanism under investigation that are relevant to the production of the phenomenon at stake. 5) Operational components distinction: it distinguishes the components and functions of the enabling conditions within the mechanism and specifies the relations and boundaries between these components. Whenever this distinction is not possible, the component/action is addressed as an operational component. 6) Changing in operational components: this heuristics allows to exploit alternative scenarios and predict possible courses of the system under investigation by modifying the operational components. 7) Evidence frequency: it indicates causality between the elements of the enabling conditions according to probabilistic and mechanistic information already available in the scientific literature or gathered through the previous heuristics. 8) Mechanism schema: mechanistic model obtained after the use of the heuristics above. This set of heuristics helped in different moments of the framework elaboration. In the first proposition of the “mechanism sketch”, for example, I realized that I could not arrive at a minimally reasonable scheme of communication about the phenomenon and the relevant variables. With each new mechanism sketch, novel challenges and solutions were two dimensions of the process of constructing scientific knowledge that led to a process of full immersion in the scientific literature in order to address the gaps that were gradually perceived. Similarly, the heuristics hierarchical structure contributed to the delimitation of processes that interact at different scales of space and time and allowed a proper understanding of the phenomenon under modeling. At each new step in building the framework, I needed to review the ecological literature from a more critical perspective on the phenomenon. One cannot construct a conceptual model if one does not know the set of variables and conditions of interaction between these variables that are relevant to its proposition. This does not mean that we should be able to begin the proposition of the model by knowing all the relevant variables—this would take away one of the great virtues of the art of modeling: a gradual refining of our theoretical constructs about the functioning of the world. As I progressed in the construction of the framework, the more intelligible the phenomenon became. It was possible to establish more clearly relationships of cause and consequence in a way that was not spurious since the heuristics placed me in a position of ever deeper investigation of the phenomenon. The models derived from the metacommunity theory, for example, pointed to ecological processes that must interact at different spatio-temporal scales, which helped me to think about the system of interest more clearly, listing what would be most fundamental to predict the directions of functional diversity of bees in the modeled scenario. This step was fundamental to derive new hypotheses that could be tested in future empirical studies. In this work, we established the construction of a unificationist narrative involving a relevant set of ecological processes and the potential patterns of diversity that they can generate in the context of agroecosystems. I see this as an advance in this area of knowledge, as we look at the system in a more holistic way, which also helps in management proposals aimed at the conservation of these ecological processes and the patterns of diversity they generate in space. Among the important lessons derived from this collaborative research, I can highlight: 1) the interaction with philosophy of science contributed to an increasingly clear understanding of the ecological phenomenon, as I was asked and placed myself in a position of deep search for questions not so clear in Ecology; 2) the organizational theory of ecological functions indicated more robust paths of conceptual delimitation in the construction of the framework, especially with regard to the identification of response and effect traits (for more details, see here); 3) the framework itself contributed to the derivation of new hypotheses that can be tested in empirical studies; and 4) learning to build the model while I was immersed in the methodological steps of the construction itself helped to remake paths of understanding the ecological phenomenon, giving new meaning to the way I conceived my phenomenon of interest.
- The Metacommunity Concept: A Framework for Multi-Scale Community
- The Metacommunity Concept and Its Theoretical Underpinnings
Pollinators and pollination in agroecosystems:
- Pollination in Agroecosystems: A Review of the Conceptual Framework
with a View to Sound Monitoring
- Pollinators: Their Relevance in Conservation and Sustainable Agro-
- Insect pollination enhances yield stability in two pollinator-dependent crops
- A global-scale expert assessment of drivers and risks associated with
Functional traits in agroecosystems:
- Functional traits in agriculture: agrobiodiversity and ecosystem services
- Ecological and life-history traits predict bee species responses to
- Biodiversity and Resilience of Ecosystem Functions
- The influence of local and landscape scale on single response traits in bees:
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