Scaling and Complexity in Landscape Ecology

Abstract

Landscapes and the ecological processes they support are inherently complex systems, in that they have large numbers of heterogeneous components that interact in multiple ways, and exhibit scale dependence, non-linear dynamics, and emergent properties. The emergent properties of landscapes encompass a broad range of processes that influence biodiversity and human environments. These properties, such as hydrologic and biogeochemical cycling, dispersal, evolutionary adaptation of organisms to their environments, and the focus of this article, ecological disturbance regimes (including wildfire), operate at scales that are relevant to human societies. These scales often tend to be the ones at which ecosystem dynamics are most difficult to understand and predict. We identify three intrinsic limitations to progress in landscape ecology, and ecology in general: (1) the problem of coarse-graining, or how to aggregate fine-scale information to larger scales in a statistically unbiased manner; (2) the middle-number problem, which describes systems with elements that are too few and too varied to be amenable to global averaging, but too numerous and varied to be computationally tractable; and (3) non-stationarity, in which modeled relationships or parameter choices are valid in one environment but may not hold when projected onto future environments, such as a warming climate. Modeling processes and interactions at the landscape scale, including future states of biological communities and their interactions with each other and with processes such as landscape fire, requires quantitative metrics and algorithms that minimize error propagation across scales. We illustrate these challenges with examples drawn from the context of landscape ecology and wildfire, and review recent progress and paths to developing scaling laws in landscape ecology, and relatedly, macroecology. We incorporate concepts of compression of state spaces from complexity theory to suggest ways to overcome the problems presented by coarse-graining, the middle-number domain, and non-stationarity.

Contrasting Global Patterns of Spatially Periodic Fairy Circles and Regular Insect Nests in Drylands

“…Furthermore, from north to south across Namibia (up to 1,000 km apart), the spatial pattern characteristics are remarkably similar, which may indicate that FCs are an emergent pattern that is critical to the capture, storage, and recycling of limited resources by the grassland system (van Rooyen et al, ). Such emergent phenomena are the products of causal mechanisms at lower levels of organization, but they are expressed primarily in behavior of higher‐order components (Newman et al, ).…”

Predictive Modeling of Wildfire Occurrence and Damage in a Tropical Savanna Ecosystem of West Africa

“…and demographic and behavioral factors [87,89]. In addition, more mechanistic and smaller-scale bottom-up drivers, such as ignitions, fuel patterns, and local topography, could play an important role in wildfire occurrence [89]. These conditions could affect the accuracy of fire prediction from year to year, as is the case in our study.…”

“…The lowest predictability values of wildfire occurrence and burnt areas can be attributed to the non-stationarity of the historical observations of the studied variables [88]. Newman et al [89] showed that projections for the future that rely on models fit from non-stationary observations are fragile to expected changes in these parameters.…”

“…), environmental conditions (desiccation rate, vegetation types, etc.) and demographic and behavioral factors [87,89]. In addition, more mechanistic and smaller-scale bottom-up drivers, such as ignitions, fuel patterns, and local topography, could play an important role in wildfire occurrence [89].…”

Thermodynamics in Ecology—An Introductory Review

“…Think of the controversies about the possible connection between diversity and stability [ 13 , 103 ], to mention just one example. Meanwhile, the entropy expressions used in calculations of diversities for instances for populations or landscapes [ 104 , 105 , 106 ] may be considered as descriptive to the states of systems only. These spatial descriptors/indicators will therefore be omitted from the more functionalist approach to the concept of entropy taken here.…”