Landscape Heterogeneity and Dynamics

2019 ◽  
pp. 42-126
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Spatial Heterogeneity ◽  
Landscape Structure ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Anthropogenic Disturbances ◽  
Ecological Processes ◽  
Disturbance Dynamics ◽  
Wide Range ◽  
Over Time

Heterogeneity is a defining characteristic of landscapes and therefore central to the study of landscape ecology. Landscape ecology investigates what factors give rise to heterogeneity, how that heterogeneity is maintained or altered by natural and anthropogenic disturbances, and how heterogeneity ultimately influences ecological processes and flows across the landscape. Because heterogeneity is expressed across a wide range of spatial scales, the landscape perspective can be applied to address these sorts of questions at any level of ecological organization, and in aquatic and marine systems as well as terrestrial ones. Disturbances—both natural and anthropogenic—are a ubiquitous feature of any landscape, contributing to its structure and dynamics. Although the focus in landscape ecology is typically on spatial heterogeneity, disturbance dynamics produce changes in landscape structure over time as well as in space. Heterogeneity and disturbance dynamics are thus inextricably linked and are therefore covered together in this chapter.

Scaling Issues in Landscape Ecology

2019 ◽  
pp. 14-41
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Spatial Patterns ◽  
Spatial Scales ◽  
Ecological Data ◽  
Ecological Forecasting ◽  
Spatial And Temporal Scales ◽  
Time Periods ◽  
Patterns And Processes ◽  
Short Time ◽  
Over Time

Spatial patterns are ubiquitous in nature, and ecological systems exhibit patchiness (heterogeneity) across a range of spatial and temporal scales. Landscape ecology is explicitly concerned with understanding how scale affects the measurement of heterogeneity and the scale(s) at which spatial pattern is important for ecological phenomena. Patterns and processes measured at fine spatial scales and over short time periods are unlikely to behave similarly at broader scales and extended time periods. An understanding of pattern-process linkages, a major research focus in landscape ecology, thus requires an understanding of how patterns change with scale, spatially and temporally. The development of methods for extrapolating information across scales is necessary for predicting how landscapes will change over time as well as for ecological forecasting. This chapter explores how scaling issues affect ecological investigations, discusses problems in identifying the correct scale for research, and outlines when and how ecological data can be extrapolated.

scholarly journals Landscape epidemiology of plant diseases

2007 ◽  
Vol 4 (16) ◽  
pp. 963-972 ◽  
Author(s):  
Manuel Plantegenest ◽  
Christophe Le May ◽  
Frédéric Fabre
Keyword(s):  
Landscape Ecology ◽  
Landscape Heterogeneity ◽  
Landscape Composition ◽  
Agricultural Landscapes ◽  
Plant Diseases ◽  
Landscape Epidemiology ◽  
Ecological Processes ◽  
Pathogen Dynamics ◽  
High Degree ◽  
Inoculum Pressure

Many agricultural landscapes are characterized by a high degree of heterogeneity and fragmentation. Landscape ecology focuses on the influence of habitat heterogeneity in space and time on ecological processes. Landscape epidemiology aims at applying concepts and approaches originating from landscape ecology to the study of pathogen dynamics at the landscape scale. However, despite the strong influence that the landscape properties may have on the spread of plant diseases, landscape epidemiology has still received little attention from plant pathologists. Some recent methodological and technological progress provides new and powerful tools to describe and analyse the spatial patterns of host–pathogen interactions. Here, we review some important topics in plant pathology that may benefit from a landscape perspective. These include the influence of: landscape composition on the global inoculum pressure; landscape heterogeneity on pathogen dynamics; landscape structure on pathogen dispersal; and landscape properties on the emergence of pathogens and on their evolution.

scholarly journals Bee responses to landscape composition on coupled and decoupled multiscale approaches

2021 ◽  
Author(s):  
Lazaro Carneiro ◽  
Milton Cezar Ribeiro ◽  
Willian Moura de Aguiar ◽  
Camila de Fátima Priante ◽  
Wilson Frantine-Silva ◽  
...  
Keyword(s):  
Forest Cover ◽  
Linear Models ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Landscape Composition ◽  
Landscape Context ◽  
Multiscale Approach ◽  
Ecological Processes ◽  
Positive Effects ◽  
Scale Of Effect

Abstract ContextMultiscale approaches are essential for understanding ecological processes and detecting the scale of effect. However, nested multiscale approaches retain the effect of the landscape attributes from the smaller spatial scales into the larger ones. Thus, decoupling local vs. regional scales can reveal detailed ecological responses to landscape context, but this multiscale approach is poorly explored. ObjectivesWe evaluated the scale of effect of the forest cover (%) and landscape heterogeneity on Euglossini bees combining coupled and decoupled multiscale approaches. MethodsThe Euglossini males were sampled in forest patches from 15 landscapes within the Atlantic Forest, southeast Brazil. For simplicity, we defined that the coupled approaches represented the local scales and decoupled approaches the regional scales. We decoupled the scales by cutting out the smaller scales inserted into larger ones. We estimated the relationship of the bee community attributes with forest cover (%) and landscape heterogeneity in local and regional scales using Generalized Linear Models. ResultsWe found positive effects of landscape heterogeneity on species richness for regional scales. Forest cover and landscape heterogeneity in local scales showed positive effects on the euglossine abundances. The scale of effect for euglossine richness was higher than species abundances. ConclusionsCombining coupled and decoupled multiscale approaches showed adequate capture of the scale of effect of the landscape composition on bee communities. Therefore, it is of paramount importance to measure the influence of the landscape context on biodiversity. Maintaining landscapes with larger forest cover and spatial heterogeneity is essential to keep euglossine species requirements.

scholarly journals Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

2021 ◽  
Vol 17 (4) ◽  
pp. e1008856
Author(s):  
Benjamin Deneu ◽  
Maximilien Servajean ◽  
Pierre Bonnet ◽  
Christophe Botella ◽  
François Munoz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Species Distribution ◽  
Landscape Structure ◽  
Landscape Heterogeneity ◽  
Practical Interest ◽  
Environmental Drivers ◽  
Conservation Strategies ◽  
Ecological Processes ◽  
The Way

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

scholarly journals Impacts of microtopographic snow-redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem

2017 ◽  
Author(s):  
Gautam Bisht ◽  
William J. Riley ◽  
Haruko M. Wainwright ◽  
Baptiste Dafflon ◽  
Yuan Fengming ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Spatial Heterogeneity ◽  
Snow Depth ◽  
Landscape Heterogeneity ◽  
Three Dimensional ◽  
Absolute Difference ◽  
Wide Range ◽  
The Impact ◽  
Thermal States

Abstract. Microtopographic features, such as polygonal ground, are characteristic sources of landscape heterogeneity in the Alaskan Arctic coastal plain. Here, we analyze the effects of snow redistribution (SR) and lateral subsurface processes on hydrologic and thermal states at a polygonal tundra site near Barrow, Alaska. We extended the land model integrated in the ACME Earth System Model (ESM) to redistribute incoming snow by accounting for microtopography and incorporated subsurface lateral transport of water and energy (ALMv0-3D). Three 10-years long simulations were performed for a transect across polygonal tundra landscape at the Barrow Environmental Observatory in Alaska to isolate the impact of SR and subsurface process representation. When SR was included, model results show a better agreement (higher R2 with lower bias and RMSE) for the observed differences in snow depth between polygonal rims and centers. The model was also able to accurately reproduce observed soil temperature vertical profiles in the polygon rims and centers (overall bias, RMSE, and R2 of 0.59 °C, 1.82 °C, and 0.99, respectively). The spatial heterogeneity of snow depth during the winter due to SR generated surface soil temperature heterogeneity that propagated in depth and time and led to ~10 cm shallower and ~5 cm deeper maximum annual thaw depths under the polygon rims and centers, respectively. Additionally, SR led to spatial heterogeneity in surface energy fluxes and soil moisture during the summer. Excluding lateral subsurface hydrologic and thermal processes led to small effects on mean states but an overestimation of spatial variability in soil moisture and soil temperature as subsurface liquid pressure and thermal gradients were artificially prevented from spatially dissipating over time. The effect of lateral subsurface processes on active layer depths was modest with mean absolute difference of ~3 cm. Our integration of three-dimensional subsurface hydrologic and thermal subsurface dynamics in the ACME land model will facilitate a wide range of analyses heretofore impossible in an ESM context.

An Introduction to Landscape Ecology

2019 ◽  
pp. 1-13
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Scientific Basis ◽  
Spatial Context ◽  
Ecological Processes ◽  
Reciprocal Interactions ◽  
Research Themes ◽  
Wide Range ◽  
Core Concepts ◽  
Explicit Consideration

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. Among its core concepts, landscape ecology asserts that heterogeneity is a defining characteristic of landscapes; that landscapes can be defined and studied at any scale; and that landscapes occur within aquatic and marine systems, as well as terrestrial ones. As such, landscape ecology can benefit the study and management of any ecological system, from populations to ecosystems, through its explicit consideration of how heterogeneity, scale, spatial context, and disturbance dynamics influence critical ecological processes.

Biodiversity in Heterogeneous and Dynamic Landscapes

2016 ◽  
Author(s):  
Clélia Sirami
Keyword(s):  
Landscape Ecology ◽  
Spatial Heterogeneity ◽  
Environmental Changes ◽  
Landscape Heterogeneity ◽  
Landscape Patterns ◽  
Global Changes ◽  
Time Lags ◽  
Temporal Heterogeneity ◽  
Landscape Models ◽  
Spatial And Temporal Heterogeneity

Although the concept of biodiversity emerged 30 years ago, patterns and processes influencing ecological diversity have been studied for more than a century. Historically, ecological processes tended to be considered as occurring in local habitats that were spatially homogeneous and temporally at equilibrium. Initially considered as a constraint to be avoided in ecological studies, spatial heterogeneity was progressively recognized as critical for biodiversity. This resulted, in the 1970s, in the emergence of a new discipline, landscape ecology, whose major goal is to understand how spatial and temporal heterogeneity influence biodiversity. To achieve this goal, researchers came to realize that a fundamental issue revolves around how they choose to conceptualize and measure heterogeneity. Indeed, observed landscape patterns and their apparent relationship with biodiversity often depend on the scale of observation and the model used to describe the landscape. Due to the strong influence of island biogeography, landscape ecology has focused primarily on spatial heterogeneity. Several landscape models were conceptualized, allowing for the prediction and testing of distinct but complementary effects of landscape heterogeneity on species diversity. We now have ample empirical evidence that patch structure, patch context, and mosaic heterogeneity all influence biodiversity. More recently, the increasing recognition of the role of temporal scale has led to the development of new conceptual frameworks acknowledging that landscapes are not only heterogeneous but also dynamic. The current challenge remains to truly integrate both spatial and temporal heterogeneity in studies on biodiversity. This integration is even more challenging when considering that biodiversity often responds to environmental changes with considerable time lags, and multiple drivers of global changes are interacting, resulting in non-additive and sometimes antagonistic effects. Recent technological advances in remote sensing, the availability of massive amounts of data, and long-term studies represent, however, very promising avenues to improve our understanding of how spatial and temporal heterogeneity influence biodiversity.

Essentials of Landscape Ecology

2019 ◽  
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Biological Diversity ◽  
Ecological Processes ◽  
Biological Communities ◽  
Goods And Services ◽  
Human Land Use ◽  
Resource Distributions ◽  
Wide Range ◽  
The Individual ◽  
And Function

Essentials of Landscape Ecology is a new, comprehensive text that presents the principles, theory, methods, and applications of landscape ecology in an engaging and accessible format, supplemented by numerous examples and case studies from a variety of systems, including freshwater and marine “scapes.” Human activity has transformed landscapes worldwide on a scale that rivals or exceeds even the largest of natural forces, giving rise to a new geological age, the Anthropocene. As humans alter the structure and function of landscapes, the biological diversity and ecological relationships within those landscapes are also inevitably altered, to the extent that this may interfere with humanity’s efforts to sustain the productivity and multifunctional use of these landscapes. Landscape ecology has thus emerged as a new, multidisciplinary science to investigate the effects of human land use and environmental heterogeneity on ecological processes across a wide range of scales and systems: from the effects of habitat or resource distributions on the individual movements, gene flow, and population dynamics of plants and animals; to the human alteration of landscapes affecting the structure of biological communities and the functioning of entire ecosystems; to the sustainable management of natural resources and the ecosystem goods and services upon which society depends.

scholarly journals Landscape Structure Is a Major Driver of Bee Functional Diversity in Crops

2021 ◽  
Vol 9 ◽  
Author(s):  
Jeferson G. E. Coutinho ◽  
Juliana Hipólito ◽  
Rafaela L. S. Santos ◽  
Eduardo F. Moreira ◽  
Danilo Boscolo ◽  
...  
Keyword(s):  
Functional Diversity ◽  
Landscape Structure ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Information Criterion ◽  
Wild Plants ◽  
Functional Composition ◽  
Pollination Services ◽  
Functional Richness ◽  
Negative Effect

Land-use change is having a negative effect on pollinator communities, and these changes in community structure may have unexpected impacts on the functional composition of those communities. Such changes in functional composition may impact the capacity of these assemblages to deliver pollination services, affecting the reproduction of native and wild plants. However, elucidating those relationships requires studies in multiple spatial scales because effects and consequences are different considering biological groups and interactions. In that sense, by using a multi-trait approach, we evaluated whether the landscape structure and/or local environmental characteristics could explain the functional richness, divergence, and dispersion of bee communities in agroecosystems. In addition, we investigated to what extent this approach helps to predict effects on pollination services. This study was conducted in an agroecosystem situated in the Chapada Diamantina region, State of Bahia, Brazil. Bees were collected using two complementary techniques in 27 sample units. They were classified according to their response traits (e.g., body size, nesting location) and effect traits (e.g., means of pollen transportation, specialty in obtaining resources). The Akaike information criterion was used to select the best models created through the additive combination of landscape descriptors (landscape diversity, mean patch shape, and local vegetation structure) at the local, proximal, and broad landscape levels. Our results indicate that both landscape heterogeneity and configuration matter in explaining the three properties of bee functional diversity. We indicate that functional diversity is positively correlated with compositional and configurational heterogeneity. These results suggest that landscape and local scale management to promote functional diversity in pollinator communities may be an effective mechanism for supporting increased pollination services.

Are potential natural vegetation maps a meaningful alternative to computer generated neutral landscape models?

2002 ◽  
pp. 16-22
Author(s):  
C. Ricotta ◽  
M. L. Carranza
Keyword(s):  
Landscape Ecology ◽  
Landscape Structure ◽  
Natural Vegetation ◽  
Potential Natural Vegetation ◽  
Ecological Processes ◽  
Neutral Landscape Models ◽  
Landscape Models ◽  
Vegetation Maps ◽  
Critical Overview

This paper provides a short critical overview of computer generated neutral landscape models traditionally adopted in landscape ecology literature. Then, another family of models based on Tüxen's concept of potential natural vegetation is presented. The suggestion is put forward that potential natural vegetation maps have a number of properties which may render them desirable as an ecological meaningful baseline for the evaluation of the effects of landscape structure on ecological processes.

Sign in / Sign up

Export Citation Format

Share Document