Introduction

The goal of landscape ecology is to understand the relationships between landscape pattern and ecological process; the role of human impacts and other forces of landscape change on these pattern-process relationships; and the principles required to make informed decisions in natural resource management. Landscapes are large areas (usually 10's to 100's of kilometers on a side) uniquely structured by local variation in landforms, soils, rivers, and climate. Understanding the ecological consequences of these biophysical patterns is a sufficient challenge in itself (Ecosystem pattern and process), yet landscape ecology must also address the rapid transformations in land use and land cover that have become a global threat to species diversity and ecosystem health. It comes as no surprise that models are playing an essential role in this interdisciplinary science.

The interdisciplinary nature of the science of landscape ecology has produced a diverse set of models varying in purpose, methods, and complexity. For instance, geographers may construct models that locate landscape resources (e.g., spatial distribution models), ecologists may study species dynamics in fragmented landscapes (e.g., Metapopulation models), while economists might focus on the properties of landscapes that define the potential for development and commerce (e.g., land use modeling). In spite of this diversity, landscape models have a number of similar attributes that we have used to organize this discussion (Table 1). Specifically, landscape models have three main components: (1) they are spatially explicit formulations or are based on spatially explicit data (i.e., maps) and consider one or more attributes of landscape heterogeneity; (2) they address the constraints of pattern on ecological processes by extrapolating fine-grained measurements across a significant spectrum of temporal and spatial scales; and (3) they define the potential spatial consequences of change, including the existence of critical thresholds (regions in state space where small changes produce disproportionably large effects).

Table 1 Three types of landscape models with examples

Objectives

Key variables

Example methods

Strengths + and weaknesses

Additional readings

1. Pattern dependencies (maps)

Define principle Temperature, water, light, environmental gradients

Identify critical habitat locations and attributes

Conservation protection nutrients, etc.

Organisms, populations, community structure

Species distributions, endemism, rarity

2. Effects of pattern on process

Understand ecosystem Hydrologie attributes, dynamics chemical flowpaths

Represent dispersal, population viability and extinction risk

3. Effects of process on pattern Account for acute stress

Sources, sinks, corridors, species dispersal attributes

Disturbance specific variables (e.g., hurricanes, fire)

Account for chronic stress

Land conversion, value land

Lumped-parameter models; statistical models

Statistical associations (regression, ordination, classification trees, etc.)

Map-based algorithms (e.g., irreplaceability analysis, gap analysis)

Ecosystem process models

Cellular automata (simple) to intergrodifference equations (complex)

Landscapes as lattices with event-driven disturbance models

Empirical regression; survival models

+ Simple formulations; limited data requirements; easy to develop

- No spatially explicit fluxes or neighborhood effects + Effective screening and research tools; mechanistic understanding not required

- Indirect, proxy variables are not always biologically meaningful; covariance of explanatory variables + Direct conservation applications; regional-scale planning

- Static landscape representation; coarse filter approach

+ Supports mechanistic understanding of process-pattern relationships; explores impact of multiple, changing controls and feedbacks

- Complex mathematics make validation challenging; data and computationally intensive + A variety of well-developed theories and methods; integrates patch-level and landscape-level analyses

- Can be data intensive; poor understanding of species attributes limits parametrization

+ Landscape histories can be reconstructed; direct management applications

- Forecast accuracy limited by stochastic controls of disturbance regimes + Spatial and temporal change captured; incorporate human behavior

- Intensive data needs; confounded effects

Lookingbill and Urban (2005)

Guisan and Zimmerman (2000)

Scott etal. (1993)

Tague and Band (2004)

Gardner and Gustafson (2004)

Baker (1989)

Irwin and Geoghegan (2001)

Habitat destroyed (D)

Figure 1 The importance of spatial pattern to metapopulation dynamics. The solid line represents the equilibrium percentage of occupied sites (p' = 1 - D - m/c) as a function of habitat destroyed (D), where c and m are the probabilities of colonization (0.6) and extinction (0.2), respectively. The dots represent the realizations of a spatially explicit landscape model. For any given D, the percentage of occupied sites is lower in the model that considers landscape pattern. The extinction threshold (D at which p' reaches 0) is also lower for the spatial model. Adapted from Bascompte J and Sole RV (1996) Habitat fragmentation and extinction thresholds in spatially explicit models. Journal of Animal Ecology 65: 465-473.

Habitat destroyed (D)

Figure 1 The importance of spatial pattern to metapopulation dynamics. The solid line represents the equilibrium percentage of occupied sites (p' = 1 - D - m/c) as a function of habitat destroyed (D), where c and m are the probabilities of colonization (0.6) and extinction (0.2), respectively. The dots represent the realizations of a spatially explicit landscape model. For any given D, the percentage of occupied sites is lower in the model that considers landscape pattern. The extinction threshold (D at which p' reaches 0) is also lower for the spatial model. Adapted from Bascompte J and Sole RV (1996) Habitat fragmentation and extinction thresholds in spatially explicit models. Journal of Animal Ecology 65: 465-473.

Without considering spatial pattern explicitly, models may misrepresent ecological processes leading to inaccurate predictions of these critical ecological and management thresholds (Figure 1).

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