Approaches to Landscape Scale Inference and Study Design

Human modification of landscapes is a pervasive global issue with major implications for biodiversity conservation and ecological processes. However, the effects of landscape modification can be challenging to quantify. Here we briefly describe the strengths and weaknesses of four types of studies in landscape ecology: observational studies, true experiments, quasi-experiments and natural experiments. Observational investigations are based on the measurement of a given ecosystem or ecological process; they lack active interventions (e.g. manipulation of sites) to study biotic response. They do not interfere with the ecosystem under study, but the inferential status of the results from observational studies is weak. True experiments represent an organised and planned inquiry conducted under at least partially controlled conditions. They involve artificially altering or manipulating a landscape to yield information about the effects of variables that have been manipulated. True experiments are the most powerful form of study to support strong inference, but they have some limitations, such as the random assignment of treatments being relatively expensive and/or impractical to implement. In quasi-experiments and natural experiments, a management treatment (e.g. a tree planting) is compared with one or more contrasting treatments. However, there can be little or no random assignment of areas to interventions (treatments), as they already exist. The inferential status of quasi-experiments is weaker than that of a true experiment, and the former have fewer practical constraints. We provide a brief summary of important statistical questions and issues to be considered in developing designs for quasi-experiments that are often also relevant to other types of landscape ecology studies.