ECOLOGICAL RESEARCH METHODOLOGY: PROBLEM ORIENTATION, HYPOTHESIS DRIVE, MULTI-LEVEL ANALYSIS, STUDY-PROCESS SCALE MATCH, QUALITATIVE-QUANTITATIVE APPROACHES, AND APPLICATION TESTS

Ecology studies organism-environment relationships, aiming to solve eco-environmental problems faced by humanity. However, many ecological research results fail to reliably explain or predict field phenomena, let alone solve practical problems. The root cause lies in methodological issues. Unlike physical systems, ecosystems are complex adaptive systems, composed of adaptive agents each with unique intrinsic properties; they have many kinds of subsystems, various types of non-linear interactions among subsystems, and unstable interactions among system levels. Based on these characteristics, principles of philosophy of science, theories of complexity science, and successful cases in ecological researches, this paper proposes six principles of ecological research methodology for the first time, establishing the paradigm for ecological research. Principle 1: Practical problem orientation. Social needs are the fundamental driving force for scientific and technological development. As ecology is essentially an applied science, it should focus on demand-driven approaches, addressing scientific questions closely related to practical eco-environmental problems. Principle 2: Hypothesis drive. The hypothesis-deduction model is the basic paradigm of modern scientific research. This is particularly important for studying complex adaptive systems, as good hypotheses can help us grasp breakthrough points for solving scientific problems from complex phenomena, enabling targeted research designs. Principle 3: Multi-level comprehensive analysis. Due to dynamic changes in interactions among levels and prevalent non-linear interactions among members at each level, research on such systems should involve multi-level comprehensive analysis. At least three levels should be considered: the middle level where phenomena occur, and its adjacent higher and lower levels. First, macroscopic patterns, i.e. empirical relationships, among variables at the middle level should be established. Then, the relationships between macro-patterns and higher-level backgrounds and historical evolution can be analyzed, followed by lower-level mechanism studies of the patterns. Principle 4: Study-process scale match. The scale mismatch between research and processes related to studied phenomena is the most important reason for unreliable conclusions in many ecological studies. There are two types of mismatches: temporal, spatial and organizational scale mismatch between experiments and phenomena; scale mismatch between survey data analyses and related processes. Therefore, ecological research scales must match the process ones. Field observations should be conducted first, followed by appropriate scale analyses, then long-term simulation experiments in near-natural systems to confirm field conclusions and analyze mechanisms. Mesocosm experiments should be able to simulate relevant ecological processes. Principle 5: Qualitative-quantitative approaches combination, i.e. combining qualitative mechanisms with quantitative models. For adaptive systems, establishing precise quantitative relationships is difficult due to variable heterogeneity (each element of a variable is unique) and being unable to deduce conclusions from statistical variables using general rules. Given these characteristics, research on adaptive systems should first clarify qualitative mechanisms, i.e., establish conceptual models. Qualitative mechanisms are crucial, not only having explanatory and predictive power themselves but also providing a solid foundation for quantitative models. Only mechanism-based quantitative models can have stronger universality and predictive power. To reduce the impact of variable heterogeneity, quantitative models can be calibrated in different regions to improve predictive ability. Principle 6: Application tests. In addition to experimental tests, continuous application tests should be conducted to verify whether the obtained rules truly help solve practical problems and to determine their scope of application.