Landscape function definition

Landscape function definition

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Most of the world's original forests have either been lost to conversion or altered by logging and forest management. Forests that still combine large size with insignificant human influence are becoming increasingly important as their global extent continues to shrink. There are several reasons to focus on large undeveloped forest areas:. The concept of an Intact Forest Landscape IFL and its technical definition were developed to help create, implement, and monitor policies concerning the human impact on forest landscapes at the regional or country levels. We define an Intact Forest Landscape IFL as an unbroken expanse of natural ecosystems within the zone of current forest extent, showing no signs of significant human activity and large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. Although all IFL are within the forest zone, some may contain extensive naturally tree-less areas, including grasslands, wetlands, lakes, alpine areas, and ice.

  • Landscape Design: Ten Important Things to Consider
  • Chinese landscape painting
  • Understanding how humans have shaped landscapes can guide us in the future
  • About Cultural Landscapes
  • Designing (for) Urban Food Webs
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Landscape Design: Ten Important Things to Consider

Landscape ecological modelling provides a vital means for understanding the interactions between geographical, climatic, and socio-economic drivers of land-use and the dynamics of ecological systems. This growing field is playing an increasing role in informing landscape spatial planning and management. Here, we review the key modelling approaches that are used in landscape modelling and in ecological modelling.

We identify an emerging theme of increasingly detailed representation of process in both landscape and ecological modelling, with complementary suites of modelling approaches ranging from correlative, through aggregated process based approaches to models with much greater structural realism that often represent behaviours at the level of agents or individuals.

We provide examples of the considerable progress that has been made at the intersection of landscape modelling and ecological modelling, while also highlighting that the majority of this work has to date exploited a relatively small number of the possible combinations of model types from each discipline. We use this review to identify key gaps in existing landscape ecological modelling effort and highlight emerging opportunities, in particular for future work to progress in novel directions by combining classes of landscape models and ecological models that have rarely been used together.

Landscapes are the result of numerous processes that operate and interact across different spatial and temporal scales. Physical, biogeochemical, and anthropogenic factors are major determinants of landscape structure, and one of the primary goals in landscape ecology is to illuminate the relationships between this structure or pattern and ecological processes occurring on the land surface [ 1 — 3 ].

However, clear causal relationships between process and pattern are rare, not least because the two are interlinked, with patterns being formed by processes in the landscape and these patterns then influencing those processes in turn.

For example, low-level disturbance patterns in conifer forests may be propagated by bark beetle populations; the interaction between pattern and process can lead to large scale population outbreaks and the acceleration of forest successional trajectories [ 4 ]. This complex dynamism between process and pattern presents significant difficulties for many aspects of landscape science, and modelling can provide a useful tool for meeting these challenges.

It can be prohibitively challenging and expensive to mount field experiments at appropriately large spatial and temporal scales, or to establish experimental controls and replications. These difficulties are compounded where mobile organisms are studied, with data collection on processes being especially time-consuming and difficult if species need to be tracked, captured, or monitored. Furthermore, substantial portions of studied populations will generally be undetectable, and bias in sampling methods or results make it difficult to translate findings up to population level patterns [ 5 ].

As a result, field experiments often produce highly case-specific and non-generalisable results. For example, many studies have identified that habitat corridors promote the key ecological processes of movement and dispersal of particular species between habitat patches, but few have shown an increase in the patterns in which we are most interested, such as population size and species diversity [ 6 ]. For these reasons, modelling—and especially simulation modelling—has become an important research tool in landscape ecology [ 7 ].

This approach allows "virtual" experiments to be run repeatedly, generating many data and exploring effects that would be impossible to investigate empirically. Findings can be compared to observations to validate or extend inference and further studies targeted at processes or factors that appear especially important e. Simulation modelling has already proved extremely valuable in landscape ecology.

Notable advances have been made across landscape ecology and wider land systems science, and methods and findings continue to improve in sophistication and insight [ 12 — 14 ]. One of the greatest contributions of landscape ecological modelling has been informing spatial planning for conservation, where it has offered an important complementary approach to classical metapopulation theory by explicitly incorporating the contribution of the matrix the environment between the habitat patches [ 15 — 19 ].

However, this computational approach is not free of challenges. As is always the case in modelling, it can be easy to misapply or misinterpret models, and hard to ground them in reality [ 20 ]. Uncertainties and errors can go unrecognised, interact and propagate, and produce biased or erroneous results [ 21 , 22 ]. In addition, assumptions must still be made in order to define a bounded and tractable system, and these assumptions can have important effects on model outcomes—for instance where they cause an influential process to be neglected or impose inappropriate spatial resolutions, scales, or structures [ 23 , 24 ].

For example, the use of regular geometries to represent landscapes can introduce directional bias [ 23 ], and the use of an inappropriate spatial resolution can substantially bias estimates of the rate at which species expand their biogeographic ranges [ 25 ].

Nevertheless, the role of simulation methods is likely to continue to grow as it becomes increasingly necessary to understand the integrated dynamics of land systems and their responses to global change, an objective that is clearly beyond the scope of empirical studies alone. The diversity of applications for landscape simulation has driven rapid methodological development, and it is important periodically to take stock and assess whether simulation techniques are achieving their potential in contributing to our understanding and management of landscape ecological dynamics, or whether opportunities to exploit emerging methodologies are, in some areas, being neglected [ 26 ].

Earlier reviews have focused on the use of neutral landscape models NLMs in landscape ecology [ 27 ], modelling methods in relation to environmental change [ 28 ], and the shared methodologies between complex systems science and landscape ecology [ 20 ].

However, we know of no existing reviews that span the partially divergent fields of modelling landscapes and their development including human land-use and modelling the dynamics of ecological systems in those landscapes. We undertake a review of this kind here, with the intention not only of promoting a more integrated approach to landscape ecological modelling but also of identifying the most valuable existing and potential links between models that focus on distinct landscape components.

We first give a broad context by providing background across a diverse range of approaches used in landscape and ecological simulation modelling, discussing how the fields have developed, providing our thoughts on where there exist significant gaps, and thus important opportunities for future work, and highlighting likely future trends within the landscape ecological modelling field.

The overall objective of our paper is to provide some future perspectives for landscape ecological modelling. To arrive at this point we first provide key background. Some of this is of work that has been at the intersect between landscape modelling and ecological modelling i.

Modelling methods landscape modelling, ecological modelling, and landscape ecological modelling can be broadly categorised into either pattern- or process-based approaches [ 30 , 74 ].

Pattern-based approaches identify existing patterns in the landscape or ecological system for example, species distribution , and aim to replicate or extrapolate those patterns without considering the generative processes.

On the other hand, process-based approaches focus on representing the underlying processes that formed observed landscape or ecological patterns see Fig.

We will first discuss models of landscape and land-use dynamics before moving on to spatial ecological models. The suite of approaches available for landscape and ecological modelling. Both fields have developed a range of approaches from relatively simple correlative and neutral modelling approaches through to complex agent or individual-based approaches. In both disciplines, there is increasing complementary use of approaches from different points along the complexity spectrum to address common problems indicated by blue arrows.

The red arrows highlight particular combinations of landscape and ecological model types that we believe offer major novel opportunities for landscape ecology. For instance, using emerging evolutionary, genetic, and epigenetic individual-based models together with NLMs may allow development of new theories of eco-evolutionary dynamics.

There are great opportunities for providing large spatial extent forecasts of how sets of species will respond to environmental changes including land-use change by using process-based land-use models together with population level ecological models such as IDEs, while combining process-based land-use models with IBMs has substantial promise for spatial planning questions in conservation at local and regional scales.

Finally, we identify dynamic coupling of agent-based land-use models and individual based ecological models as a key future area where there is enormous scope to develop understanding of the dynamic of interacting socio-economic and ecological systems. Neutral landscape models NLMs are a set of approaches intended to create partially realistic landscape patterns, whilst remaining neutral with respect to the processes that formed them.

The motivation for using NLMs is that they provide a framework for landscape replication whilst controlling certain features of landscape configuration [ 75 ]; this allows for robust statistical analyses in relation to spatial structure [ 76 , 77 ].

The first NLMs generated entirely random patterns using percolation theory [ 78 ]. Since then, hierarchical and fractal NLMs have been developed to improve the representation of patterns that are found in real landscapes—in particular, spatial autocorrelation, and repeated patterns across scales [ 79 , 80 ]. Neutral landscapes have been shown to be statistically similar to real landscapes, but are unable to reproduce all landscape features [ 76 , 81 ].

Thus, there are continuing developments to NLM methods to improve the representation of real landscape features such as patterns of land-ownership [ 82 ] and agricultural fields [ 83 ].

In addition to extensions of NLM models to incorporate increasing numbers of features, progress has also been made on developing the algorithms such that they are more efficient and free of some undesirable artefacts present in earlier versions [ 77 ].

NLMs are the landscape modelling approach that has been used the most in an ecological context. They have been used to investigate the ability of landscape indices and metrics to measure habitat fragmentation, spatial structure, and ecological processes [ 84 — 86 ] and to analyse methods for rescaling landscape data [ 87 ]. They have also been used in the emerging field of eco-evolutionary dynamics, for example to demonstrate the potential for short and long distance dispersal strategies to evolve separately according to landscape configuration [ 92 ].

NLMs can also be used as null models when testing the ability of process-based models to recreate observed patterns such as predictions of old-growth woodland distribution from a model of fire and landform influences [ 93 ], or comparisons of fire spread algorithms [ 94 ].

NLMs are even used to guide the spatial planning of real-world experiments, as in the planting of experimental garden plots to study the importance of plant community spatial patterning for invasion resistance [ 95 ]. Aside from NLMs, there are a number of other approaches available from the field of landscape modelling that aim to generate anthropogenic landscape or land-use patterns without direct representation of the underlying processes.

This class of model is typically termed either top-down e. Here we refer to this group of landscape models as pattern-based. To date, these modelling approaches have been little used in an ecological context. Amongst the first landscape models was a highly influential application in urban studies e. Subsequently, similar approaches were adopted in agricultural land-use change e. Such models have become more and more sophisticated, incorporating a wide range of factors e.

Regression models and transition probability models are often used to project historical patterns of land-use and land-cover change into the future e. Such models have been developed to include demands for different land uses, allowing alternative future scenarios to be investigated [ ]. There now exist very sophisticated and widely used models of land-use change that project future development of the land system on the basis of systemic equations describing relationships between specific drivers and observed changes [ 96 , — ].

These form the basis of the scenario-based climate change projections of the Intergovernmental Panel on Climate Change [ ]. These types of models have rarely been coupled with ecological models, but see for example [ 96 ] in which land-use dynamics are represented by a large-scale pattern-based model and coupled with a process-based model of vegetation dynamics. Cellular automata CA models represent a middle ground between process- and pattern-based approaches.

They have also been used to study land-use transitions [ ] and residential dynamics [ ] among other applications. CAs consist of a grid of cells which each exist in one of a finite set of states, with the future state of each cell determined by its previous state and that of its neighbours [ ]. These models do not generally model the underlying processes directly, but rather the outcome of those processes.

CA models can be suitable for systems in which neighbourhood association is important, but can struggle to incorporate more complex behaviour such as human decision making, at least without deviating substantially from the typical CA approach [ ]. CAs have generally been applied with either a landscape or an ecological focus, however they have also been applied in landscape ecology studies, for example to evaluate conservation interventions in a human-dominated tropical landscape [ ].

Overall, landscape modelling approaches that focus on the replication of observed patterns may help to identify the probabilities of different landscape or land-use transitions and to make conditional predictions, but they leave the identities of the underlying causative mechanisms open to interpretation [ ]. Links between spatial patterns and ecological or socio-economic conditions have frequently been shown to be informative in some circumstances, but potentially misleading in others e.

Furthermore, in encoding previously observed relationships into algorithms or equations, models of this kind become unsuitable for projecting changes in systems in which underlying mechanisms are not constant as in socio-ecological systems subject to the varied and unpredictable forces of human behaviour; see e.

Therefore, while neutral and pattern-based models have substantial roles to play, deeper understanding or exploration of system dynamics requires additional models that explicitly account for underlying processes and prioritise accurate description of the processes over pattern replication [ ]. Process-based landscape models are becoming increasingly used, with inclusion of progressively more detailed representations of the key behaviours and dynamics that drive landscape patterns.

The application of such models inevitably involves a choice of processes to represent, as well as a choice of technical modelling approach to make the given processes tractable.

A wide array of approaches exist for many different purposes and at different scales of detail and application. A number of highly focussed artificial landscape generators have been developed, which simulate a specific process to replicate real-world patterns. Many of these are concerned with human impacts on landscape, for example models of road development [ ], and of the conversion of forests to arable land by the processes of road and field creation [ 30 ].

A substantial focus of modelling of this kind has been on urban growth [ — ]. Equivalent approaches are taken to discrete processes in natural systems. For instance, watershed models e. Hydrological models have also been integrated with nitrogen dynamics models to study the effect of the spatial distribution of agricultural practices [ ].

Process-based models are also increasingly being applied in circumstances where the processes themselves are unclear or incompletely understood.

In landscape science, this is especially true of models of land-use change, where process-based approaches such as agent-based modelling ABM are used to increase model accuracy but also to explore alternative accounts of human decision-making under socio-economic or environmental pressures [ ].

Process-based models are both relevant and problematic in this context because of the crucial role of complex individual, social, and institutional behaviours in determining the nature of land-use change.Such explorations are not possible without incorporating additional processes and interactions into our models, even though appropriate limits on model complexity may be hard to identify.

Because of the complexity of the modelled system, land-use ABMs initially focused on carefully constrained systems and behaviours, covering small geographical extents, and specific land-uses e.

While these models generally retain a relatively narrow focus, they have expanded in scope in a number of ways over recent years. Thematic extension has also occurred, particularly through linkages between models of land-use and natural systems, with behavioural responses to environmental change often being prioritised [ , ].

Additional detail has also been incorporated within the land-use system, with several recent models investigating the interactions of individual and institutional entities [ 33 , , ] Fig. This latter development is particularly notable because it makes such models ideal for testing the potentially unexpected outcomes of policy interventions [ — ].


Considering the functional use of plants is a new approach to solving landscape problems. Traditionally, plants have been used for beautification due to their aesthetic qualities. Plants have horticultural characteristics such as height and spread, branching habit, flowers, fruit, and foliage; they have design qualities such as form, color, texture, and mass; and they have cultural requirements for growth in the landscape. More recently, the functional characteristics of plants have been recognized. Figure 1. Groups of plants may be used architecturally to form walls, canopies or floors.

Look for many layers of meaning in a single place. One way to think of a landscape is to see it the way that medieval exegetes saw sacred scriptural texts. For.

Chinese landscape painting

Sufficient unsaturated soil must exist below the drip tubing or LPD piping to allow for movement of the applied wastewater from the site. Landscape position influences microbial composition and function via redistribution of soil water across a watershed. Landscape position and other factors may cause a Spodosol to be somewhat poorly drained or even drier. Landscape position and precipitation effects on spatial variability of wheat yield and grain protein in southern Italy. Landscape position and potential surface water connections may be more readily observed without the dense cover of vegetation. Landscape position and relief have a strong influence on soil drainage. Investor is purchasing the Securities for its own account for investment only and not with a view towards the public sale or distribution thereof. Landscape position means the specific geomorphic component of the landscape in which a site is located ; two- dimensional landscape positions may be summit , shoulder , backslope , sideslope, footslope, or toeslope; three dimensional views of geomorphic landscape position can be described as headslope, noseslope, sideslope, base slope, etc. Sample 1.

Understanding how humans have shaped landscapes can guide us in the future

Don't have an account? Landscape ecology provides the scientific basis for the study and management of landscapes, as well as the ecological systems they contain. More generally, landscape ecology investigates the reciprocal interactions between spatial patterns environmental heterogeneity and ecological processes across a wide range of scales. This introductory chapter discusses the rise of landscape ecology as a discipline, its regional perspectives, core concepts, and research themes, and provides an overview of the textbook itself.

Landscape design is the art of developing a property for its greatest use and enjoyment. Effective landscape design is also a science because it involves understanding the environment around your home and selecting plants that perform well in that environment.

About Cultural Landscapes

Cultural landscapes are a legacy for everyone. They provide scenic, economic, ecological, social, recreational, and educational opportunities helping communities to better understand themselves. Neglect and inappropriate development put our irreplaceable landscape legacy increasingly at risk. The ongoing care and interpretation of these sites improve our quality of life and deepen a sense of place and identity for future generations. Skip to main content. Sort by Relevancy Title.

Designing (for) Urban Food Webs

This book captures the essence of how the world is designed around us. Event Details: Thursday, October 14th. Washington, D. But quaint and cozy is … The annual international Digital Landscape Architecture DLA addresses all aspects of digital technologies, applications, information, and knowledge based on research, education, and practice pertaining to landscape architecture and related fields.At SEAS, we're focusing on the future - transforming research into action to create a healthier planet for all. Early cities I had looked into many tutoring services, but they weren't affordable and did not understand my custom-written needs. Creation of plaza 4.

of soil responses and visualization of landscape function, and can be used to define application-specific maps of hydrologic function.

The Hay WainJohn Constable, A good example of English naturalism and one of the most famous pictures in English Landscape Painting. The greatest landscapes were executed in the late 18th and 19th century. See: Famous landscape paintings.

We have moved! This website is for archival purposes only. Zonneveld : " This source provides a thorough review of the ecotope concept. The term "ecotope" has also been defined for other purposes in ecology: Whittaker et al : "The species relation to the full range of environmental and biotic variables affecting it.

Landscapes and features are important because they contribute significantly to our well-being and quality of life.

The inhospitable Burren became host to a rich community of alpine and Mediterranean plants that characterise a cultural landscape which is now world famous. And how well do our notions of wilderness reflect the ecological realities of our landscapes? An idea lingers among scientists, conservationists and policy-makers that human transformations of nature have been recent and inherently destructive. But studies of human-ecological interactions through history demonstrate that we have been shaping landscapes for more than 12, years. Precisely when humans began to have a significant planetary effect is subject to debate.

A trend is emerging within the profession that expands our approach to planting design and the role of vegetation. Designers are backing away from the role of curator of gardens where plant species are selected and placed according to a theme in a created setting, without regard to how that species may be predisposed to behave in the setting. Instead, they are adopting the role of steward to a set of naturally occurring processes that govern the development of plant communities.

Watch the video: Landscape Functions (August 2022).