Suchergebnisse

The Physical Template of Landscapes -- 1.1. Introduction -- 1.2. Gradient Analysis -- 1.2.1. Gradient Complexes -- 1.3. The Water Balance -- 1.3.1. A Simple Model: PET = AET + Deficit -- 1.4. Estimating Elements of the Template -- 1.4.1. Temperature -- 1.4.2. Radiation -- 1.4.3. Precipitation -- 1.4.4. Soils -- 1.5. Case Study: the Sierra Nevada -- 1.5.1. The Physical Template of the Sierra Nevada -- 1.6. Summary and Conclusions -- References -- Biotic Processes as Agents of Pattern -- 2.1. Introduction -- 2.2. The "Pattern and Process" Paradigm -- 2.3. Coupling of Demographic Processes -- 2.4. Interaction with the Physical Template -- 2.4.1. Coupling Demography and the Physical Template -- 2.4.2. Competition along Environmental Gradients -- 2.4.3. Illustration: Gradient Response in the Sierra Nevada -- 2.4.4. The Unit Pattern Revisited -- 2.5. Dispersal as an Agent of Pattern -- 2.6. Animals, Pests, and Pathogens -- 2.6.1. Animals, Pests and Pathogens as Subtle Agents -- 2.6.2. Animals as Dramatic Agents -- 2.7. Summary and Conclusions -- References -- Disturbances and Disturbance Regimes -- 3.1. Introduction -- 3.1.1 Context and Definitions -- 3.2. Perspectives and Lessons -- 3.2.1. Are Disturbances "Part of the System"? -- 3.2.2. Interactions, Synergies, and Indirect Effects -- 3.2.3. Disturbances and Positive Feedbacks -- 3.2.4. Overlapping Disturbances and Legacies -- 3.2.5. Heterogeneity in Disturbance and Response -- 3.3. Disaggregating Disturbance toward Generality. 3.3.1 A Not-too-General Model -- 3.3.2. The Fire Regime in the Sierra Nevada -- 3.4. Characteristic Dynamics -- 3.5. Humans and Disturbance Regimes -- 3.5.1. Human Impacts on Natural Disturbances - 3.5.2. Novel Disturbance Regimes -- 3.5.3 Human Perception and Landscape Change -- 3.6. Agents of Pattern: Reprise -- 3.7. Summary and Conclusions -- References 78 -- 4. Scale and Scaling - 4.1. Introduction -- 4.2. The Importance of Scale in Ecology -- 4.2.1. Observational Scale as a Filter on Nature -- 4.2.2. Characteristic Scaling -- 4.2.3. Sampling Grain and Extent, and Statistical Behavior -- 4.3. Scaling Techniques -- 4.3.1. Scaling Techniques for Geostatistical Data -- 4.3.2. Illustration: Scaling of the Sierran Physical Template -- 4.4. Tactical Scaling -- 4.4.1. Tactical Targeting of Sampling Scale(s) -- 4.4.2. Avoid or Embrace Space? -- 4.5. Summary and Conclusions -- References -- 5. Inferences on Landscape Pattern -- 5.1. Introduction -- 5.2. Patchiness and Patches -- 5.2.1. Patch Definition -- 5.3. Landscape Pattern Metrics -- 5.3.1. Levels of Analysis -- 5.3.2. Components of Pattern -- 5.3.2 Correlation and Redundancy -- 5.3.4. Alternative Framings for Landscape Pattern -- 5.4. Interpreting Landscape Metrics -- 5.4.1. Neutral Models and Neutral Landscapes -- 5.4.2. Neutral Templates for Landscape Processes -- 5.4.3. Extending Neutral Models: Agents of Pattern -- 5.5. Explanatory Models and Inferences -- 5.5.1. Approaches to Inferences on Pattern -- 5.5.2. Illustrations -- 5.5. Explanatory Models and Inferences -- -- 5.5.1. Approaches to Inferences on Pattern -- 5.5.2. Illustrations -- 5.5.3. Inferences on Pattern: Area versus Configuration -- 5.5.4 Inferences on Pattern: the State-of-the-Art -- 5.6. Summary and Conclusions. References -- Implications of Pattern: Metapopulations -- 6.1. Introduction -- 6.2. Metapopulations in Theory -- 6.2.1. The Levins Model -- 6.2.2. The Spreading-of-Risk Model -- 6.2.3. The Source-Sink Model -- 6.2.4. The Incidence Function Model -- 6.2.5. Commonalities among Metapopulation Models -- 6.2.6. Characteristic Behaviors of (Model) Metapopulations -- 6.3. Metapopulations in Practice -- 6.3.1. Are there Real Metapopulations in Nature? -- 6.3.2. Macroscopic Approaches to Metapopulations -- 6.4. Network Models of Metapopulations -- 6.4.1. Graphs and Metapopulations -- 6.5. Metapopulations and Connectivity Conservation -- 6.5.1. Structural and Functional Connectivity -- 6.5.2. Metapopulations and Landscape Genetics -- 6.6. A Model Template for Applications -- 6.7. Summary and Conclusions -- References -- Supplement 6.1. Details on the Metapopulation Models -- S6.1.1. The Levins Model -- S6.1.2. The Spreading-of-Risk Model -- S6.2.3. The Source-Sink Model -- S6.2.4. The Incidence Function Model -- S6.2.5. Notes on the Individual-based Simulators Metapop1 -- Communities and Patterns of Biodiversity -- 7.1. Introduction -- 7.2. Island Biogeography and Landscapes -- 7.2.1. Area and Isolation Effects -- 7.2.2. Island Biogeographic Theory and the SLOSS Debate -- 7.2.3. A Diversity of Diversities -- 7.3. Perspectives on Metacommunities -- 7.3.1. A General Framing -- 7.3.2. Inferences and Limits to Inference -- 7.4. Approaches and Lines of Evidence -- 7.4.1. The Incidence Matrix and Community Assembly -- 7.4.2. Metacommunity Models: Variations on a Theme -- 7.4.3. Species Distribution Models -- 7.4.4. Multvariate Approaches to Partitioning Beta-diversity -- 7.4.5. Lines of Evidence and Complementary Analyses -- 7.5. Illustration: Sierran Forests -- 7.5.1. The Perspective of Ordination and Gradient Analysis -- 7.5.2. Partitioning Beta-diversity -- 7.6. Managing Metacommunities -- 7.7. Summary and Conclusions -- References -- Supplement 7.1. Disciplinary Approaches (Details) -- S7.1.1. Incidence Matrices and Community Assembly -- S7.1.2. Metacommunity Models: Variations -- S7.1.3. Species Distribution Models -- S7.1.4. Ordination Techniques -- IImplications of Pattern for Ecosystems -- 8.1. Introduction -- 8.2. Spatial Heterogeneity and Ecosystems -- 8.2.1. Spatial Heterogeneity in the Physical Template -- 8.2.2. Lateral Fluxes on Landscapes -- 8.2.3. Landform and Landscape Processes -- 8.2.4. Ecosystem Processes and Positive Feedbacks -- 8.2.5. Ecosystems are both Fast and Slow -- 8.3. Ecosystems and Landscape Legacies -- 8.4. Patch Juxtaposition and Edge Effects -- 8.4.1. Edge Effects, Revisited -- 8.4.2. Edges and Ecosystem Processes: Forest Carbon -- 8.5. Ecosystems and Meta-ecosystems -- 8.5.1. Couplings between Systems -- 8.5.2. Meta-ecosystems, Revisited -- 8.5.3. Implications of Meta-ecosystem Structure -- 8.6. Summary and Conclusions -- References -- Urban Landscapes -- 9.1. Introduction -- 9.2. Social-Environmental Systems -- 9.2.1. Approaches to Studying Cities -- 9.3. Agents and Implications of Pattern -- 9.3.1. Agents of Pattern -- 9.3.2. Scale and Pattern -- 9.3.3. Implications of Pattern -- 9.3.4. Revisiting the Agents-and-Implications Framing -- 9.4. Urban Landscapes as Laboratories -- 9.4.1. The Urban Stream Syndrome -- 9.4.2. Cities as Mesocosms for Global Change -- 9.5. Summary and Conclusions -- References -- 10. Climate Change: Adapting for Resilience -- 10.1 Introduction -- 10.2. Framing Adaptation -- 10.2.1. Components of Climate Change -- 10.2.2. The Perspective of Risk Management -- 10.2.3. Options for Response and Adaptation -- 10.2.4. Resilience Planning: the Tasks at Hand -- 10.3. Approaches to Adaptation Planning -- 10.3.1. Levels of Activity and Currency of Assessments -- 10.3.2. Elements of Adaptation -- 10.3.3. A Template for Applications -- 10.4. Illustrations of Approaches -- 10.4.1. NatureServe's HCCVI -- 10.4.2. Species Range Shifts implied by Climate Change -- 10.4.3. TNC's Resilient Landscapes Initiative -- 10.4.4. The ACT Framework -- 10.4.5. Complementarity of Approaches -- 10.5. Collateral Benefits and Leverage -- 10.5.1. Adaptation Planning and Conservation Practice -- 10.5.2. Collateral Benefits -- 10.5.3. Adaptation and Mitigation -- 10.6. Summary and Conclusions -- References -- Index.. .

Industrial-scale oil palm cultivation is rapidly expanding in Gabon, where it has the potential to drive economic growth, but also threatens forest, biodiversity and carbon resources. The Gabonese government is promoting an ambitious agricultural expansion strategy, while simultaneously committing to minimize negative environmental impacts of oil palm agriculture. This study estimates the extent and location of suitable land for oil palm cultivation in Gabon, based on an analysis of recent trends in plantation permitting. We use the resulting suitability map to evaluate two proposed approaches to minimizing negative environmental impacts: a High Carbon Stock (HCS) approach, which emphasizes forest protection and climate change mitigation, and a High Conservation Value (HCV) approach, which focuses on safeguarding biodiversity and ecosystems. We quantify the forest area, carbon stock, and biodiversity resources protected under each approach, using newly developed maps of priority species distributions and forest biomass for Gabon. We find 2.7–3.9 Mha of suitable or moderately suitable land that avoid HCS areas, 4.4 million hectares (Mha) that avoid HCV areas, and 1.2–1.7 Mha that avoid both. This suggests that Gabon's oil palm production target could likely be met without compromising important ecosystem services, if appropriate safeguards are put in place. Our analysis improves understanding of suitability for oil palm in Gabon, determines how conservation strategies align with national targets for oil palm production, and informs national land use planning.

Quantitative models are crucial to almost every area of ecosystem science. They provide a logical structure that guides and informs empirical observations of ecosystem processes. They play a particularly crucial role in synthesizing and integrating our understanding of the immense diversity of ecosystem structure and function. Increasingly, models are being called on to predict the effects of human actions on natural ecosystems. Despite the widespread use of models, there exists intense debate within the field over a wide range of practical and philosophical issues pertaining to quantitative modeling. This book--which grew out of a gathering of leading experts at the ninth Cary Conference--explores those issues. The book opens with an overview of the status and role of modeling in ecosystem science, including perspectives on the long-running debate over the appropriate level of complexity in models. This is followed by eight chapters that address the critical issue of evaluating ecosystem models, including methods of addressing uncertainty. Next come several case studies of the role of models in environmental policy and management. A section on the future of modeling in ecosystem science focuses on increasing the use of modeling in undergraduate education and the modeling skills of professionals within the field. The benefits and limitations of predictive (versus observational) models are also considered in detail. Written by stellar contributors, this book grants access to the state of the art and science of ecosystem modeling