An ecological classification of forest landscape simulation models: tools and strategies for understanding broad-scale forested ecosystems

Computer models are increasingly being used by forest ecologists and managers to simulate long-term forest landscape change. We review models of forest landscape change from an ecological rather than methodological perspective. We developed a classification based on the representation of three ecological criteria: spatial interactions, tree species community dynamics, and ecosystem processes. Spatial interactions are processes that spread across a landscape and depend upon spatial context and landscape configuration. Communities of tree species may change over time or can be defined a priori. Ecosystem process representation may range from no representation to a highly mechanistic, detailed representation. Our classification highlights the implicit assumptions of each model group and helps define the problem set for which each model group is most appropriate. We also provide a brief history of forest landscape simulation models, summarize the current trends in methods, and consider how forest landscape models may evolve and continue to contribute to forest ecology and management. Our classification and review can provide novice modelers with the ecological context for understanding or choosing an appropriate model for their specific hypotheses. In addition, our review clarifies the challenges and opportunities that confront practicing model users and model developers.     

Sensitivity of ecosystem goods and services projections of a forest landscape model to initialization data

Projections of indicators of forest ecosystem goods and services (EGS) based on process-based landscape models are critical for adapting forest management to climate change. However, the scarcity of fine-grained, spatially explicit forest data means that initializing these models is both a challenge and a source of uncertainty. To test how different initialization approaches influence the simulation of forest dynamics and EGS indicators we initialized the forest landscape model LandClim with fine resolution empirical data, coarse empirical data, and simulation-derived data, and evaluated the results at three spatial scales (stand, management area and landscape). Simulations were performed for a spruce (Picea abies) dominated landscape in the Black Forest, Germany, under current climate and a climate change scenario. We found that long-term (>150 years) projections are robust to initialization uncertainty. In contrast, shorter-term projections are sensitive to initialization uncertainty, with sensitivity increasing when EGS are assessed at smaller spatial scales, and when the EGS indicators depend on the spatial distribution of individual species. EGS dynamics are strongly influenced by interactions between the density, species composition, and age structure of initialized forests and simulated forest management. If EGS dynamics are strongly influenced by climate change, such as when climate change induces mortality in drought-sensitive species, some of the initialization uncertainty can be masked. We advocate for initializing landscape models with fine-grained data in applications that focus on spatial management problems in heterogeneous landscapes, and stress that the scale of analysis must be in accordance with the accuracy that is warranted by the initialization data.     

Resilience, experimentation, and scale mismatches in social-ecological landscapes

Growing a resilient landscape depends heavily on finding an appropriate match between the scales of demands on ecosystems by human societies and the scales at which ecosystems are capable of meeting these demands. While the dynamics of environmental change and ecosystem service provision form the basis of many landscape ecology studies, enhancing landscape resilience is, in many ways, a problem of establishing relevant institutions that act at appropriate scales to modify and moderate demand for ecosystem services and the resulting exploitation of ecosystems. It is also of central importance for landscape sustainability that institutions are flexible enough to adapt to changes in the external environment. The model provided by natural ecosystems suggests that it is only by encouraging and testing a diversity of approaches that we will be able to build landscapes that are resilient to future change. We advocate an approach to landscape planning that involves growing learning institutions on the one hand, and on the other, developing solutions to current problems through deliberate experimentation coupled with social learning processes.     

Global biodiversity scenarios and landscape ecology

The composition of ecological communities is both cause and consequence of landscape pattern. Predicting biodiversity change involves understanding not only ecology and evolution, but also complex changes in human societies and economies. Scenarios offer a less rigid approach to thinking about biodiversity change in a policy and management context. They shift the focus of research and management from making singular predictions and developing single ‘best’ strategies to exploring uncertainties and assessing the outcomes of alternative policies. The four Millennium Ecosystem Assessment (MA) biodiversity scenarios illustrate current approaches to biodiversity estimation in global scenarios. The MA biodiversity scenarios are built around the species–area relationship and the magnitudes of a few area-dependent processes such as nitrogen deposition and climate change. Some of the most obvious landscape-related omissions from the MA scenarios are pattern-process feedbacks, scale dependencies, and the role of landscape configuration. While the MA has set a new standard for biodiversity scenarios, future exercises would benefit from a more multi-scale and more mechanistic framework. I use examples from research on the landscape ecology and biogeography of African ticks to illustrate how a hypothesis-based approach can be used to analyse the multi-scale, multi-level drivers of change in patterns of species occurrences. Two of the most important challenges for the future development of both landscape ecology and biodiversity scenarios are to become more mechanistic (less pattern-based) and more general (applicable across different landscapes).     

Integrating land cover structure and functioning to predict biodiversity patterns: a hierarchical modelling framework designed for ecosystem management

Context

Land-use/land-cover (LU/LC) dynamics is one of the main drivers of global environmental change. In the last years, aerial and satellite imagery have been increasingly used to monitor the spatial extent of changes in LU/LC, deriving relevant biophysical parameters (i.e. primary productivity, climate and habitat structure) that have clear implications in determining spatial and temporal patterns of biodiversity, landscape composition and ecosystem services.

Objectives

An innovative hierarchical modelling framework was developed in order to address the influence of nested attributes of LU/LC on community-based ecological indicators.

Methods

Founded in the principles of the spatially explicit stochastic dynamic methodology (StDM), the proposed methodological advances are supported by the added value of integrating bottom-up interactions between multi-scaled drivers.

Results

The dynamics of biophysical multi-attributes of fine-scale subsystem properties are incorporated to inform dynamic patterns at upper hierarchical levels. Since the most relevant trends associated with LU/LC changes are explicitly modelled within the StDM framework, the ecological indicators’ response can be predicted under different social-economic scenarios and site-specific management actions. A demonstrative application is described to illustrate the framework methodological steps, supporting the theoretic principles previously presented.

Conclusions

We outline the proposed multi-model framework as a promising tool to integrate relevant biophysical information to support ecosystem management and decision-making.

     

Valuing ecosystem services in community-based landscape planning: introducing a wellbeing-based approach

The challenge of incorporating the concept of ecosystem services in landscape planning has been widely acknowledged, yet values of ecosystem services are not well considered in current landscape planning and environmental governance. This is particularly the case when local stakeholders are strongly involved in decision making about adapting the landscape to future demands and challenges. Engagement of stakeholders introduces a variety of interests and motives that result in diverging value interpretations. Moreover, participative planning approaches are based on learning processes, implying that the perceptions of value evolve during the planning process. Current valuation approaches are not able to support such process. Therefore we argue that there is a need for a novel view on the mechanism of integrating valuation in the different stages of community-based landscape planning, as well as for tools based on this mechanism. By revisiting the original conception of ecosystem services and redefining the value of an ecosystem service as its comparative importance to human wellbeing, we develop a conceptual framework for incorporating ecosystem service valuation that captures the full spectrum of value and value changes. We acknowledge that in the social interactions during the planning process values are redefined, negotiated and reframed in the context of the local landscape. Therefore, we propose a valuation mechanism that evolves through the phases of the cyclic planning process. We illustrate the use of this mechanism by proposing a tool that supports stakeholder groups in building a value-based vision on landscape adaptation that contributes to all wellbeing dimensions.     

Experimental landscape ecology

Experimentation in landscape ecology is widely conducted using diverse approaches to answer a broad range of questions. By assessing the response to controlled manipulations alternate hypotheses can be clearly refuted, model parameters quantified, and conditions are often ripe for unexpected insights. Results from landscape experiments complement the many well developed observational and modeling approaches more commonly used in landscape ecology. To better understand how landscape experimentation has been conducted and to identify future research directions, we reviewed and organized the diversity of experiments. We identified fifteen distinct landscape experiment types, which we categorized into four broad groups including (I) identifying landscape structure, (II) identifying how ecological processes vary within existing landscapes, (III) identifying how landscape structure influences ecological processes, and (IV) identifying landscape pattern formation factors. Experiment types vary along axes of scalable to real landscapes and generalizability, suitability for analysis through traditional experimental design and flexibility of experimental setup, and complexity of implementation and resource requirements. The next generation of experiments are benefiting from more explicit inclusion of scaling theories and tighter coupling between experiments and cyberinfrastructure. Future experimental opportunities for landscape ecologists include expanded collaborations among experiments, better representations of microbial-soil structure relationships at microscales, and direct evaluations of landscape interactions with global changes. The history, current practice, and future needs of landscape ecological research strongly support an expanded role of experimental approaches that complements the rich observational and modeling strengths of the field.     

Landscape sustainability science: ecosystem services and human well-being in changing landscapes

The future of humanity depends on whether or not we have a vision to guide our transition toward sustainability, on scales ranging from local landscapes to the planet as a whole. Sustainability science is at the core of this vision, and landscapes and regions represent a pivotal scale domain. The main objectives of this paper are: (1) to elucidate key definitions and concepts of sustainability, including the Brundtland definition, the triple bottom line, weak and strong sustainability, resilience, human well-being, and ecosystem services; (2) to examine key definitions and concepts of landscape sustainability, including those derived from general concepts and those developed for specific landscapes; and (3) to propose a framework for developing a science of landscape sustainability. Landscape sustainability is defined as the capacity of a landscape to consistently provide long-term, landscape-specific ecosystem services essential for maintaining and improving human well-being. Fundamentally, well-being is a journey, not a destination. Landscape sustainability science is a place-based, use-inspired science of understanding and improving the dynamic relationship between ecosystem services and human well-being in changing landscapes under uncertainties arising from internal feedbacks and external disturbances. While landscape sustainability science emphasizes place-based research on landscape and regional scales, significant between landscape interactions and hierarchical linkages to both finer and broader scales (or externalities) must not be ignored. To advance landscape sustainability science, spatially explicit methods are essential, especially experimental approaches that take advantage of designed landscapes and multi-scaled simulation models that couple the dynamics of landscape services (ecosystem services provided by multiple landscape elements in combination as emergent properties) and human well-being.     

Can landscape indices predict ecological processes consistently?

The ecological interpretation of landscape patterns is one of the major objectives in landscape ecology. Both landscape patterns and ecological processes need to be quantified before statistical relationships between these variables can be examined. Landscape indices provide quantitative information about landscape pattern. Response variables or process rates quantify the outcome of ecological processes (e.g., dispersal success for landscape connectivity or Morisita's index for the spatial distribution of individuals). While the principal potential of this approach has been demonstrated in several studies, the robustness of the statistical relationships against variations in landscape structure or against variations of the ecological process itself has never been explicitly investigated. This paper investigates the consistency of correlations between a set of landscape indices (calculated with Fragstats) and three response variables from a simulated dispersal process across heterogeneous landscapes (cell immigration, dispersal success and search time) against variation in three experimental treatments (control variables): habitat amount, habitat fragmentation and dispersal behavior. I found strong correlations between some landscape indices and all three response variables. However, 68% of the statistical relationships were highly inconsistent and sometimes ambiguous for different landscape structures and for differences in dispersal behavior. Correlations between one landscape index and one response variable could range from highly positive to highly negative when derived from different spatial patterns. I furthermore compared correlation coefficients obtained from artificially generated (neutral) landscape models with those obtained from Landsat TM images. Both landscape representations produced equally strong and weak statistical relationships between landscape indices and response variables. This result supports the use of neutral landscape models in theoretical analyses of pattern-process relationships.     

Global analysis and simulation of land-use change associated with urbanization

A combination of rapid population growth and an accelerating demographic shift from rural to urbanized habitats has resulted in urbanization becoming an increasingly global phenomenon. Two alternate hypotheses describing urban landscape trajectories suggest urbanization is either leading to more homogeneous global patterns or urbanization has dichotomous trajectories of increasing dispersal or coalescence. To better understand the global variation in urban land-cover patterns and trajectories we described the variation in urban landscape structure for 120 cities distributed throughout the world assessed at circa 1990 and 2000. We coupled these data to a low-dimensional neighborhood based model of urban growth using a data-model fusion approach. Trajectories of urban growth were assessed using both the original data and model projections to 2030. The patterns of landscape change were related to both the rate of growth and income. The historical patterns of change showed a trend of increasing landscape complexity and this trend was projected to continue. Urban rate of growth was closely related to the change in several landscape metrics. Income was associated with landscape dynamics and this effect interacted with city size. Large cities were less sensitive to the income effect than small cities. Along with changes to the magnitude of each metric, the overall variation in metrics between years generally exhibited a decrease in variability and this variability was projected to continue decreasing. These findings supported the hypothesis that urban landscapes are becoming more homogeneous and that the dispersal-coalescing dichotomy represent endpoints rather than alternate states of urban growth.     

Changing landscapes to accommodate for climate change impacts: a call for landscape ecology

Predictions of climate change suggest major changes in temperature, rainfall as well as in frequency and timing of extreme weather, all in varying degrees and patterns around the world. Although the details of these patterns changes are still uncertain, we can be sure of profound effects on ecological processes in and functioning of landscapes. The impact of climate change will affect all types of land use, ecosystem services, as well as the behavior of humans. The core business of Landscape Ecology is the interaction of landscape patterns and processes. Most of these interactions will be affected by changing climate patterns, so clearly within the focus of our science. Nevertheless, climate change received little attention from landscape ecologists. Are we missing the boat? Why is it that our science does not contribute to building a knowledge base to help solving this immense problem? Why is there so little attention paid to adaptation of landscape to climate change? With this editorial article IALE would like to receive inputs from the Landscape Ecology scientific community in related research on adaptation of landscapes to climate change, on tools or approaches to help landscape planners and stakeholders to this new challenge where landscape ecology can play a key role.     

Functional heterogencity in resources within landscapes and herbivore population dynamics

Large mammalian herbivores are notorious for their propensity towards population irruptions and crashes, yet many herbivore populations remain relatively stable. I explore how resource heterogeneity within landscapes dampens population instability, using a metaphysiological modelling approach condidering patch state distributions. Resource heterogeneity is functionally stabilizing through spreading consumption away from preferred resources before these critically depleted. Lower-quality resources act as a buffer against starvation during critical critical periods of the seasonal cycle. Enriching resource quality is destabilizing, even if patch diversity is maintained, because food quantity then becomes the limitation. The potential consequences of landscape fragmentation are explored using the Serengeti ecosystem, characterised by broadscale resource gradients, as a hypothetical example. Further insights provided by the model are illustrated with specific examples concerning the effects of patch scales and waterpoint distribution. A metaphysiological modelling approach enables the basic consequences of landscape heterogeneity to be distinguished from further effects that may arise from specific patch scales and configurations, without the distracting detail of spatially explicit models.     

Functional heterogeneity in resources within landscapes and herbivore population dynamics

Large mammalian herbivores are notorious for their propensity towards population irruptions and crashes, yet many herbivore populations remain relatively stable. I explore how resource heterogeneity within landscapes dampens population instability, using a metaphysiological modelling approach considering patch state distributions. Resource heterogeneity is functionally stabilizing through spreading consumption away from preferred resources before these become critically depleted. Lower-quality resources act as a buffer against starvation during critical periods of the seasonal cycle. Enriching resource quality is destabilizing, even if patch diversity is maintained, because food quantity then becomes the limitation. The potential consequences of landscape fragmentation are explored using the Serengeti ecosystem, characterised by broadscale resource gradients, as a hypothetical example. Further insights provided by the model are illustrated with specific examples concerning the effects of patch scales and waterpoint distribution. A metaphysiological modelling approach enables the basic consequences of landscape heterogeneity to be distinguished from further effects that may arise from specific patch scales and configurations, without the distracting detail of spatially explicit models.     

Landscapes, sustainability and the place-based analysis of ecosystem services

There is currently, widespread interest in the assessment of ecosystem services, and the new insights that the concept provides in understanding the ecology of landscapes and the science of sustainability. Three major assessment frameworks can be identified in the contemporary literature, namely one based on habitats, one based on the identification of the system elements that delivers the service, and one based on the understanding of places. Although all are useful for supporting decision making in relation to sustainable development, different situations require different perspectives, and so it is important to understand their advantages and drawbacks. Moreover, it is important to determine how they relate to other approaches used, for example, in landscape planning, so that the contribution that ecosystem assessments can make to sustainability debates can be better understood. The aim of this paper is to describe the strengths of the place-based approach because it is more easily overlooked as an assessment option. In particular we will argue that a place-based approach can help us better understand issues of multi-functionality, the valuation of natural capital and the role of landscape in framing debates about ecosystem services and sustainability. An appreciation of these issues will enable researchers interested in landscape to key questions and priorities in relation to questions of sustainability. Although it is useful to consider different assessment perspectives separately, we conclude that in practice, the habitat and systems approaches can form part of a place-based assessment, just as a better understanding of place can enrich assessments that spring from these more natural science approaches. Nevertheless, in designing analytical strategies to take the ecosystem approach forward, we suggest that it is vital to consider these different perspectives in order to build assessments that are relevant, legitimate and credible, and which can effectively address the problems of sustainability that emerge at the landscape scale.     

Mapping sources,sinks, and connectivity using a simulation model of northern spotted owls

Source-sink dynamics are an emergent property of complex species–landscape interactions. A better understanding of how human activities affect source-sink dynamics has the potential to inform and improve the management of species of conservation concern. Here we use a study of the northern spotted owl (Strix occidentalis caurina) to introduce new methods for quantifying source-sink dynamics that simultaneously describe the population-wide consequences of changes to landscape connectivity. Our spotted owl model is mechanistic, spatially-explicit, individual-based, and incorporates competition with barred owls (Strix varia). Our observations of spotted owl source-sink dynamics could not have been inferred solely from habitat quality, and were sensitive to landscape connectivity and the spatial sampling schemes employed by the model. We conclude that a clear understanding of source-sink dynamics can best be obtained from sampling simultaneously at multiple spatial scales. Our methodology is general, can be readily adapted to other systems, and will work with population models ranging from simple and low-parameter to complex and data-intensive.     

Predicting land use change: comparison of models based on landowner surveys and historical land cover trends

To make informed planning decisions, community leaders, elected officials, scientists, and natural resource managers must be able to evaluate potential effects of policies on land use change. Many land use change models use remotely-sensed images to make predictions based on historical trends. One alternative is a survey-based approach in which landowners’ stated intentions are modeled. The objectives of our research were to: (1) develop a survey-based landowner decision model (SBM) to simulate future land use changes, (2) compare projections from the SBM with those from a trend-based model (TBM), and (3) demonstrate how two alternative policy scenarios can be incorporated into the SBM and compared. We modeled relationships between land management decisions, collected from a mail survey of private landowners, and the landscape, using remotely-sensed imagery and ownership parcel data. We found that SBM projections were within the range of TBM projections and that the SBM was less affected by errors in image classification. Our analysis of alternative policies demonstrates the importance of understanding potential effects of targeted land use policies. While policies oriented toward increasing enrollment in the Conservation Reserve Program (CRP) resulted in a large (11–13%) increase in CRP lands, policies targeting increased forest thinning on private non-industrial lands increased low-density forest projections by only 1%. The SBM approach is particularly appropriate for landscapes including many landowners, because it reflects the decision-making of the landowners whose individual actions will result in collective landscape change.     

A fuzzy logic method to assess the relationship between landscape patterns and bird richness of the Rolling Pampas

The loss of biodiversity in productive ecosystems is a global concern of the last decades. The Rolling Pampas of Argentina is an intensively cropped region that underwent important land use and landscape change, with different impacts on biodiversity of both plants and animals. Land use type and habitat complexity are hypothesized to be the most important factors determining species richness in agro-ecosystems. But it is not easy to define these attributes in an unambiguous fashion, or determine their interactions at different spatial scales. A fuzzy logic approach allows overcoming some of these problems by using linguistic variables and logic rules to relate them and formulate hypothesis. We constructed fuzzy logic models to study how bird species richness in the Rolling Pampas is related to land use and habitat complexity, and how these variables interact at two spatial scales. Results showed that at the local scale, landscape complexity is the most important factor determining species numbers; trees and bodies of water are the most influential complexities. The effect of local scale landscape attributes was modified depending on the context at broader scales, so that agricultural sites were enriched when surrounded by more favorable landscapes. There was a high dispersion in the predicted/observed value relationship, indicating that landscape factors interact in more complex ways than those captured by the models we used. We suggest that the fuzzy logic approach is suitable for working with biological systems, and we discuss the advantages and disadvantages of its use.     

Models based on individual level movement predict spatial patterns of genetic relatedness for two Australian forest birds

Fine-scale landscape change can alter dispersal patterns of animals, thus influencing connectivity or gene flow within a population. Furthermore, dispersal patterns of different species may be influenced by the landscape in varying ways. Our research first aimed to examine whether the spatial genetic structure within populations of closely related bird species differs in response to the same landscape. Second, we examined whether individual-level movement characteristics are a mechanistic driver of these differences. We generated a priori predictions of how landscape features will influence dispersal (particularly the response of individuals to habitat boundaries both natural and human-induced) based on a movement model developed by Fahrig (Funct Ecol 21:1003–1015, 2007). This model allowed us to predict genetic relatedness patterns in populations of two passerine bird species with different life-history traits from Queensland, Australia (yellow-throated scrubwren Sericornis citreogularis, a habitat specialist; white-browed scrubwren Sericornis frontalis, a habitat generalist). We quantified our predictions using cost-distance modelling and compared these to observed pairwise genetic distances (a _r ) between individuals as calculated from microsatellite markers. Mantel tests showed that our a priori models correlated with genetic distance. Euclidean distance was most closely correlated to genetic distance for the generalist species (r = 0.093, P = 0.002), and landscape models that included the avoidance of unsuitable habitat were best for the specialist species (r = 0.107, P = 0.001). Our study showed that predictable movement characteristics may be the mechanism driving differences in genetic relatedness patterns within populations of different bird species.     

A Method to Efficiently Apply a Biogeochemical Model to a Landscape

Biogeochemical models offer an important means of understanding carbon dynamics, but the computational complexity of many models means that modeling all grid cells on a large landscape is computationally burdensome. Because most biogeochemical models ignore adjacency effects between cells, however, a more efficient approach is possible. Recognizing that spatial variation in model outputs is solely a function of spatial variation in input driver variables such as climate, we developed a method to sample the model outputs in input variable space rather than geographic space, and to then use simple interpolation in input variable space to estimate values for the remainder of the landscape. We tested the method in a 100 km×260 km area of western Oregon, U.S.A. , comparing interpolated maps of net primary production (NPP) and net ecosystem production (NEP) with maps from an exhaustive, wall-to-wall run of the model. The interpolation method can match spatial patterns of model behavior well (correlations>0.8) using samples of only 5 t o 15% of the landscape. Compression of temporal variation in input drivers is a key step in the process, with choice of input variables for compression largely determining the upper bounds on the degree of match between interpolated and original maps. The method is applicable to any model that does not consider adjacency effects, and could free up computational expense for a variety of other computational burdens, including spatial sensitivity analyses, alternative scenario testing, or finer grain-size mapping.