scholarly journals NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R

2018 ◽  
Author(s):  
Marco Sciaini ◽  
Matthias Fritsch ◽  
Cédric Scherer ◽  
Craig Eric Simpkins
Keyword(s):  
Landscape Ecology ◽  
Field Studies ◽  
Landscape Patterns ◽  
List Type ◽  
Ecological Processes ◽  
Agent Based ◽  
Neutral Landscape Models ◽  
Comprehensive Collection ◽  
Landscape Models ◽  
Integrated Environment

AbstractNeutral landscape models (NLMs) simulate landscape patterns based on theoretical distributions and can be used to systematically study the effect of landscape structure on ecological processes. NLMs are commonly used in landscape ecology to enhance the findings of field studies as well as in simulation studies to provide an underlying landscape. However, their creation so far has been limited to software that is platform dependent, does not allow a reproducible workflow or is not embedded in R, the prevailing programming language used by ecologists.Here, we present two complementary R packages NLMR and land-scapetools, that allow users to generate, manipulate and analyse NLMs in a single environment. They grant the simulation of the widest collection of NLMs found in any single piece of software thus far while allowing for easy manipulation in a self-contained and reproducible workflow. The combination of both packages should stimulate a wider usage of NLMs in landscape ecology. NLMR is a comprehensive collection of algorithms with which to simulate NLMs. landscapetools provides a utility toolbox which facilitates an easy workflow with simulated neutral landscapes and other raster data.We show two example applications that illustrate potential use cases for NLMR and landscapetools: First, an agent-based simulation study in which the effect of spatial structure on disease persistence was studied. Here, spatial heterogeneity resulted in more variable disease outcomes compared to the common well-mixed host assumption. The second example shows how increases in spatial scaling can introduce biases in calculated landscape metrics.Simplifying the workflow around handling NLMs should encourage an uptake in the usage of NLMs. NLMR and landscapetools are both generic frameworks that can be used in a variety of applications and are a further step to having a unified simulation environment in R for answering spatial research questions.

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.

scholarly journals Predatory plants and patchy cows: modeling cattle interactions with toxic larkspur amid variable heterogeneity

2018 ◽  
Author(s):  
Kevin Jablonski ◽  
Randall Boone ◽  
Paul Meiman
Keyword(s):  
Herd Behavior ◽  
Management Options ◽  
Poisonous Plants ◽  
Large Herbivores ◽  
Ongoing Research ◽  
Agent Based ◽  
Neutral Landscape Models ◽  
Risk Of Death ◽  
Landscape Models ◽  
Risk Of Predation

The most common explanations for the evolution and persistence of herd behavior in large herbivores relate to decreased risk of predation. However, poisonous plants such as larkspur (Delphinium spp.) can present a threat comparable to predation. In the western United States, larkspur diminishes the economic and ecological sustainability of cattle production by killing valuable animals and restricting management options. Recommendations for mitigating losses have long focused on seasonal avoidance of pastures with larkspur, despite little evidence that this is practical or effective. Our ongoing research points to the cattle herd itself as the potential solution to this seemingly intractable challenge and suggests that larkspur and forage patchiness may drive deaths. In this paper, we present an agent-based model that incorporates neutral landscape models to assess the interaction between plant patchiness and herd behavior within the context of poisonous plants as predator and cattle as prey. The simulation results indicate that larkspur patchiness is indeed a driver of toxicosis and that highly cohesive herds can greatly reduce the risk of death in even the most dangerous circumstances. By placing the results in context with existing theories about the utility of herds, we demonstrate that grouping in large herbivores can be an adaptive response to patchily distributed poisonous plants. Lastly, our results hold significant management-relevant insight, both for cattle producers managing grazing in larkspur habitat and in general as a call to reconsider the manifold benefits of herd behavior among domestic herbivores.

Quantifying the deviation of landscape diversity from potential natural vegetation with Shannon's entropy

2002 ◽  
pp. 23-31
Author(s):  
C. Ricotta ◽  
M. L. Carranza ◽  
G. Avena ◽  
C. Blasi
Keyword(s):  
Distribution Patterns ◽  
Quantitative Comparison ◽  
Natural Vegetation ◽  
Shannon’S Entropy ◽  
Landscape Patterns ◽  
Potential Natural Vegetation ◽  
Neutral Landscape Models ◽  
Shannon's Entropy ◽  
Landscape Models ◽  
Neutral Landscape Model

In the past 20 years, several metrics have been developed to quantify various aspects of landscape structure and diversity in space and time, and most have been tested on grid- based thematic maps. Once landscape patterns have been quantified, their effects on ecological functions can be explained if the expected pattern in the absence of specific processes is known. This type of expected pattern has been termed a neutral landscape model. In the landscape-ecological literature, researchers traditionally adopt random and fractal computer-generated neutral landscape models to verify the expected relationship between a given ecological process and landscape spatial heterogeneity. Conversely, little attention has been devoted to distribution patterns of potential natural vegetation (PNV) as an ecological baseline for the evaluation of pattern-process interactions at the landscape scale. As an application for demonstration, we propose a neutral model based on PNV as a possible reference for a quantitative comparison with actual vegetation (ARV) distribution. Within this context, we introduce an evenness-like index termed "actual-to-potential entropy ratio’ (HA/P=HARV/HPNV, where H is Shannon’s entropy). Results show that, despite the hypothetical character of most PNV maps, the use of PNV distribution as a baseline for a quantitative comparison with ARV distribution may represent a first step towards г general model for the evaluation of the effects of disturbance on vegetation patterns and diversity.

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.

scholarly journals NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R

2018 ◽  
Vol 9 (11) ◽  
pp. 2240-2248 ◽  
Author(s):  
Marco Sciaini ◽  
Matthias Fritsch ◽  
Cédric Scherer ◽  
Craig Eric Simpkins
Keyword(s):  
Neutral Landscape Models ◽  
Landscape Models ◽  
Integrated Environment

scholarly journals The third and fourth dimensions of landscape: Towards conceptual models of topographically complex landscapes

2010 ◽  
Vol 22 ◽  
pp. 1-10 ◽  
Author(s):  
Lucian Drăguţ ◽  
Ulrich Walz ◽  
Thomas Blaschke
Keyword(s):  
Landscape Ecology ◽  
Matrix Model ◽  
Landscape Metrics ◽  
Landscape Patterns ◽  
Fourth Dimension ◽  
Discrete Elements ◽  
Ecological Processes ◽  
Spatial And Temporal Scales ◽  
The Third ◽  
Third Dimension

Relating spatial patterns to ecological processes is one of the central goals of landscape ecology. The patch-corridor-matrix model and landscape metrics have been the predominant approach to describe the spatial arrangement of discrete elements ("patches") for the last two decades. However, the widely used approach of using landscape metrics for characterizing categorical map patterns is connected with a number of problems. We aim at stimulating further developments in the field of the analysis of spatio-temporal landscape patterns by providing both a critical review of existing techniques and clarifying their pros and cons as well as demonstrating how to extent common approaches in landscape ecology (e.g. the patch-corridor-matrix model). The extension into the third dimension means adding information on the relief and height of vegetation, while the fourth dimension means the temporal, dynamic aspect of landscapes. The contribution is structured around three main topics: the third dimension of landscapes, the fourth dimension of landscapes, and spatial and temporal scales in landscape analysis. Based on the results of a symposium on this theme at the IALE conference in 2009 in Salzburg and a literature review we emphasize the need to add topographic information into evaluations of landscape structure, the appropriate consideration of scales; and to consider the ambiguity and even contradiction between landscape metrics.

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.

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