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Entropy in Landscape Ecology

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (28 February 2017) | Viewed by 40696

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Guest Editor
Rocky Mountain Research Station, USDA Forest Service, 2500 S. Pine Knoll Dr., Flagstaff, AZ 86001, USA
Interests: landscape ecology; landscape genetics; forest ecology; climate change; wildlife ecology; disturbance ecology; population biology; landscape dynamic simulation modeling; landscape pattern analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Entropy and the second law of thermodynamics are the central organizing principles of nature. However, strangely, the ideas and implications of the second law are poorly developed in the landscape ecology literature. This is particularly strange given the focus of landscape ecology on understanding pattern-process relationships across scales in space and time. Every interaction between entities leads to irreversible change, which increases the entropy and decreases the free energy of the closed system in which they reside. Descriptions of landscape patterns, processes of landscape change, and propagation of pattern-process relationships across space and through time are all governed, constrained, and, in large part, directed by thermodynamics. This direct linkage to thermodynamics and entropy was noted in several pioneering works in the field of landscape ecology, yet, in subsequent decades, our field has largely failed to embrace and utilize these relationships and constraints, with a few exceptions. The purpose of this Special Issue in Entropy is to bring together the best scientists across the world, who are working on applications of thermodynamics in landscape ecology, to consolidate current knowledge and identify key areas for future research. A recent editorial in Landscape Ecology on thermodynamics in landscape ecology identified the following topics as deserving particular focus for future work, and we hope the Entropy Special Issue will address many of these in depth.

1) There is a critical need to define the configurational entropy of landscape mosaics as a benchmark and measuring stick, which subsequently can be used to quantify entropy changes in landscape dynamics and the interactions of patterns and processes across scales of space and time.

2) The second law and entropy are of direct relevance to landscape dynamics as all changes in nature result in increases in system disorder and reduction in free energy of the closed system. Therefore, landscape time series data record this process. In a closed system, all time series will show increasing disorder and reduction in free energy over time, but ecological systems are open systems, and thus time series may show dynamic patterns without directional changes in disorder or free energy.

3) Ecological systems are driven by continual inflow of energy from the sun, and are not thermodynamically closed systems. This inflow of energy enables biological processes to function, driving photosynthesis “uphill” against the current of entropy, with ecological food webs then providing a “cascade” back down the free energy ladder, reducing free energy and increasing thermodynamic disorder. Landscape ecologists should more formally associate landscape dynamics with changes in entropy and quantify the function of ecological dissipative structures.

4) Observing a dynamic equilibrium in a landscape does not imply absence of increasing entropy. Just as an organism maintains homeostasis by functioning as a dissipative structure consuming and degrading high free energy organic molecules and releasing heat and highly oxidized metabolic products, a landscape maintains a dynamic equilibrium under a disturbance-succession regime through the collective emergent property of many organismal dissipative structures in interaction with abiotic drivers, such as solar energy, temperature, and moisture.

5) In forest systems, the dynamics range from gap-phase replacement of individual trees as windfall and senescence occurs to large-scale patterns of patch dynamics in response to wild fire and other large contagious disturbances. In each of these there is a dynamic equilibrium of landscape patterns, with different kinds of heterogeneity at different scales. In neither is there any trend to decrease in macrostructural stage, but rather a characteristic range of variation in landscape structure over time (e.g., change in macrostate within a characteristic range), as a function of the nature of the disturbance-succession process in that system. Linking the scale dependence of landscape dynamics to thermodynamic constraints across different ecosystem types would be central to generalizing the application of entropy in landscape ecology.

6) The linkage of the second law of thermodynamics and the entropy principle with the concepts of resistance, resilience and recovery seems important, as is linkage to ideas of dynamic equilibrium and dissipative structures.

7) There are more ways to be broken than to be fixed, more ways to be dead than alive, more ways to be disordered than to be ordered, and thus thermodynamic changes always lead to less predictability in the future state than the current state. All increases in entropy result in increasing disorder and lower potential energy in the closed system. This by definition decreases predictability, as there are more ways to be disordered than ordered and more ways to have dissipated energy than ‘‘concentrated’’ energy. This is always the case, and increase in entropy always leads to decrease in predictability in the closed system. However, landscapes are open stems and understanding the flow of energy and resulting patterns of order and disorder may result in increase or decrease in system predictability over time depending on whether the energy flow results in net decrease in entropy of the landscape or a net increase.

8) Fractal dimension seems directly related to entropy. Fractal dimension is a measure of a pattern-process scaling law and the relationships of such scaling laws to the thermodynamics of dissipative structures is a topic that should be explored. One may conjecture that the reason there are fractal scaling laws at all is because of the thermodynamic behavior of dissipative structures.

9) The scale challenges in landscape ecology are not a source of “departure” from thermodynamics, but rather are products of the action of dissipative structures organized across a range of scales or hierarchical levels. Attention should be given to entropy, complexity theory and the organization of ecological systems as a multilevel or multi-scale system of dissipative structures.

10) Thermodynamic irreversibility is a fundamental attribute of the universe and all things in it, including landscapes. If landscapes appear to not follow irreversibility laws then it is an indication of an insufficiency of how landscape ecological analysis reflects the reality of the universe. When ecological systems are properly viewed as multiscale and hierarchically organized dissipative structures then it is clear that thermodynamic irreversibility does apply.

11) The application of thermodynamic entropy concepts in landscape ecology has not addressed the true thermodynamic nature of the actions of dissipative structures across scales, and this has been limited by failure to measure energy transformations, changes in free energy, changes in configurational entropy of landscape mosaics. As a result, there has been a nebulous and inconsistent application and interpretation of these ideas in the field.

Prof. Dr. Samuel A. Cushman
Guest Editor

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Published Papers (7 papers)

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Editorial

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4 pages, 147 KiB  
Editorial
Editorial: Entropy in Landscape Ecology
by Samuel A. Cushman
Entropy 2018, 20(5), 314; https://doi.org/10.3390/e20050314 - 25 Apr 2018
Cited by 12 | Viewed by 3474
Abstract
Entropy and the second law of thermodynamics are the central organizing principles of nature, but the ideas and implications of the second law are poorly developed in landscape ecology. The purpose of this Special Issue “Entropy in Landscape Ecology” in Entropy is to [...] Read more.
Entropy and the second law of thermodynamics are the central organizing principles of nature, but the ideas and implications of the second law are poorly developed in landscape ecology. The purpose of this Special Issue “Entropy in Landscape Ecology” in Entropy is to bring together current research on applications of thermodynamics in landscape ecology, to consolidate current knowledge and identify key areas for future research. The special issue contains six articles, which cover a broad range of topics including relationships between entropy and evolution, connections between fractal geometry and entropy, new approaches to calculate configurational entropy of landscapes, example analyses of computing entropy of landscapes, and using entropy in the context of optimal landscape planning. Collectively these papers provide a broad range of contributions to the nascent field of ecological thermodynamics. Formalizing the connections between entropy and ecology are in a very early stage, and that this special issue contains papers that address several centrally important ideas, and provides seminal work that will be a foundation for the future development of ecological and evolutionary thermodynamics. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)

Research

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19 pages, 2493 KiB  
Article
Calculation of Configurational Entropy in Complex Landscapes
by Samuel A Cushman
Entropy 2018, 20(4), 298; https://doi.org/10.3390/e20040298 - 19 Apr 2018
Cited by 38 | Viewed by 6129
Abstract
Entropy and the second law of thermodynamics are fundamental concepts that underlie all natural processes and patterns. Recent research has shown how the entropy of a landscape mosaic can be calculated using the Boltzmann equation, with the entropy of a lattice mosaic equal [...] Read more.
Entropy and the second law of thermodynamics are fundamental concepts that underlie all natural processes and patterns. Recent research has shown how the entropy of a landscape mosaic can be calculated using the Boltzmann equation, with the entropy of a lattice mosaic equal to the logarithm of the number of ways a lattice with a given dimensionality and number of classes can be arranged to produce the same total amount of edge between cells of different classes. However, that work seemed to also suggest that the feasibility of applying this method to real landscapes was limited due to intractably large numbers of possible arrangements of raster cells in large landscapes. Here I extend that work by showing that: (1) the proportion of arrangements rather than the number with a given amount of edge length provides a means to calculate unbiased relative configurational entropy, obviating the need to compute all possible configurations of a landscape lattice; (2) the edge lengths of randomized landscape mosaics are normally distributed, following the central limit theorem; and (3) given this normal distribution it is possible to fit parametric probability density functions to estimate the expected proportion of randomized configurations that have any given edge length, enabling the calculation of configurational entropy on any landscape regardless of size or number of classes. I evaluate the boundary limits (4) for this normal approximation for small landscapes with a small proportion of a minority class and show it holds under all realistic landscape conditions. I further (5) demonstrate that this relationship holds for a sample of real landscapes that vary in size, patch richness, and evenness of area in each cover type, and (6) I show that the mean and standard deviation of the normally distributed edge lengths can be predicted nearly perfectly as a function of the size, patch richness and diversity of a landscape. Finally, (7) I show that the configurational entropy of a landscape is highly related to the dimensionality of the landscape, the number of cover classes, the evenness of landscape composition across classes, and landscape heterogeneity. These advances provide a means for researchers to directly estimate the frequency distribution of all possible macrostates of any observed landscape, and then directly calculate the relative configurational entropy of the observed macrostate, and to understand the ecological meaning of different amounts of configurational entropy. These advances enable scientists to take configurational entropy from a concept to an applied tool to measure and compare the disorder of real landscapes with an objective and unbiased measure based on entropy and the second law. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)
Show Figures

Figure 1

Figure 1
<p>The 12 test landscapes. (<b>a</b>) random; (<b>b</b>) H1; (<b>c</b>) H2; (<b>d</b>) H3; (<b>e</b>) H4; (<b>f</b>) H5; (<b>g</b>) H6; (<b>h</b>) H7; (<b>i</b>) H8; (<b>j</b>) H9; (<b>k</b>) H10; (<b>l</b>) checker board.</p>
Full article ">Figure 2
<p>Location and pattern of the 25 sample landscapes in the San Francisco Peaks region of Northern Arizona. The extent of the 20 × 20 sample landscape is shown in semitransparent background overlaid on a hillshade of topography. The individual sample landscapes are shown with random colormap corresponding to each class in the cover type-seral stage classification.</p>
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<p>Match between the vector-permuted distribution of total edge length and the predictions of the normal probability function.</p>
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<p>Relationship between configurational Boltzmann entropy (ln<span class="html-italic">W</span>) calculated from the vector-permuted distribution of all 12 test landscapes.</p>
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<p>Verification that the linear relationship with slope 1 is consistent for a large number (10 × 10<sup>100</sup>) of computed configurations. <span class="html-italic">W</span>** is the number of microstates with a given total edge length expected from 10 × 10<sup>100</sup> permutations of the lattice, using the normal probability density function.</p>
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<p>Location of the 12 test landscapes along the parabolic curve of entropy as a function of total edge length in the landscape.</p>
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<p>Plot of the Shapiro–Wilk <span class="html-italic">W</span> statistic for the distribution of total edge length for a sample of 100,000 permutations of 25 test landscapes of varying dimensionality (20 × 20, 30 × 30, 40 × 40, 50 × 50) and minority class proportion (2%, 4%, 6% 8%, 10%). The two black contours show critical values of the test statistic at alpha 0.05 for sample sizes of 1000 (lower) and 1500 (upper).</p>
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<p>Normal distribution of the permuted total edge length for a sample landscape. (<b>a</b>) Sample landscape; (<b>b</b>) Overlay of the normal probability function predicted frequency distribution over the observed frequency distribution of total edge length resulting from 100,000 spatial randomizations of this landscape.</p>
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<p>The parabolic entropy curve for a sample landscape. The actual entropy of the sample landscape is indicated with the arrow on the far left side of the curve, indicating that this landscape has very low entropy in comparison to the distribution of microstates across all possible configurational macrostates. <span class="html-italic">W</span>** is the number of microstates (configurations of the lattice) with a given macrostate (total edge length) expected in 10 × 10<sup>100</sup> permutations of the lattice.</p>
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<p>Ordering of the five replicate 100 × 100 dimensionality landscapes from low to high entropy at each of the five levels of dimensionality. SHDI is the Shannon Diversity index value for the landscape as calculated by FRAGSTATS. <span class="html-italic">W</span>** is the number of microstates (configurations of the lattice) with a given macrostate (total edge length) expected in 10 × 10<sup>100</sup> permutations of the lattice.</p>
Full article ">Figure 11
<p>Scatter plots of the relationships between landscape dimensionality, patch richness and Shannon diversity and the mean (<b>a</b>–<b>c</b>) and standard devation (<b>d</b>–<b>f</b>) of the normal distribution of permuted total edge lengths across the 25 sample landscapes in the San Francisco Peaks region.</p>
Full article ">
1674 KiB  
Article
Horton Ratios Link Self-Similarity with Maximum Entropy of Eco-Geomorphological Properties in Stream Networks
by Bruce T. Milne and Vijay K. Gupta
Entropy 2017, 19(6), 249; https://doi.org/10.3390/e19060249 - 30 May 2017
Cited by 11 | Viewed by 5434
Abstract
Stream networks are branched structures wherein water and energy move between land and atmosphere, modulated by evapotranspiration and its interaction with the gravitational dissipation of potential energy as runoff. These actions vary among climates characterized by Budyko theory, yet have not been integrated [...] Read more.
Stream networks are branched structures wherein water and energy move between land and atmosphere, modulated by evapotranspiration and its interaction with the gravitational dissipation of potential energy as runoff. These actions vary among climates characterized by Budyko theory, yet have not been integrated with Horton scaling, the ubiquitous pattern of eco-hydrological variation among Strahler streams that populate river basins. From Budyko theory, we reveal optimum entropy coincident with high biodiversity. Basins on either side of optimum respond in opposite ways to precipitation, which we evaluated for the classic Hubbard Brook experiment in New Hampshire and for the Whitewater River basin in Kansas. We demonstrate that Horton ratios are equivalent to Lagrange multipliers used in the extremum function leading to Shannon information entropy being maximal, subject to constraints. Properties of stream networks vary with constraints and inter-annual variation in water balance that challenge vegetation to match expected resource supply throughout the network. The entropy-Horton framework informs questions of biodiversity, resilience to perturbations in water supply, changes in potential evapotranspiration, and land use changes that move ecosystems away from optimal entropy with concomitant loss of productivity and biodiversity. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)
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Figure 1

Figure 1
<p>Entropy of flow and evapotranspiration, as obtained from Budyko theory. (<b>a</b>) Entropy as function of aridity index, <span class="html-italic">B</span>, for three values of net radiation, <span class="html-italic">R<sub>n</sub></span>, and rainfall rates 1–1500 mm/yr. Theoretical maximum entropy of log(2) (horizontal dashed) is reached at <span class="html-italic">B<sup>*</sup></span> = 0.6. Approximate positions of Hubbard Brook (HB) and Whitewater River (WR) were based on potential evapotranspiration expected at site latitudes. (<b>b</b>) Rates of change of entropy for 5% inter-annual perturbations of precipitation (PPT). Velocities change sign at peak entropy.</p>
Full article ">Figure 2
<p>Water and energy components from Hubbard Brook experiment. (<b>a</b>) Water balance 1963–1968, with 3-year trajectories (solid arrows) of treatment Watershed 2 following removal of vegetation; (<b>b</b>) Ratios of evapotranspiration to flow, <span class="html-italic">ET</span>/<span class="html-italic">Q</span>, (black) and entropy (red), with theoretical maximum entropy, log(2) (upper dashed), and complement 1 − log(2) (lower dashed); (<b>c</b>) Energy components of controls, with excursions (solid arrows) by treatment watershed after vegetation removal (95% confidence interval of regression, dashed); (<b>d</b>) Ratio of energy components (mean of controls, horizontal line, +/−2 s.d.) and 4-fold reduction following vegetation removal (dashed, arrow).</p>
Full article ">Figure 3
<p>Conceptualization of water and energy components of control and treatment watersheds. In controls, potential evapotranspiration, <span class="html-italic">PET</span>, sets evapotranspiration, <span class="html-italic">ET<sub>c</sub></span>. Removal of vegetation in the treatment converted an amount <span class="html-italic">Q<sub>b</sub></span> of <span class="html-italic">ET<sub>c</sub></span> to flow. The bracketed quantities of free and dissipated energy are nonlinear functions of <span class="html-italic">ET</span> and <span class="html-italic">Q</span>, and thus do not correspond one to one.</p>
Full article ">Figure 4
<p>Water balance and energy components for Whitewater River basin in Kansas. (<b>a</b>) Annual water balance (1962–2004) at El Dorado gauge, with mean flow (blue dashed); (<b>b</b>) Ratio of evapotranspiration to flow decreased toward unity as <span class="html-italic">PPT</span><sup>−2</sup> (black curve); (<b>c</b>) Dissipated energy and free energy increased with annual precipitation (95% confidence interval of regression, dashed); (<b>d</b>) Entropy, <math display="inline"> <semantics> <mi>ϕ</mi> </semantics> </math>, per unit precipitation, flow, and evapotranspiration approached constant minimum values above mean annual precipitation.</p>
Full article ">Figure 5
<p>Total entropy summed across six Strahler orders for ratios of basin area, <span class="html-italic">A</span>, raised to exponent, <span class="html-italic">γ</span>. Variables were measured on 2537 streams occupied by vegetation, Whitewater River basin, Kansas. (<b>a</b>) Total number of vegetated streams, <span class="html-italic">n</span>; (<b>b</b>) Stream length, <span class="html-italic">L</span>; (<b>c</b>) Vegetated area, <span class="html-italic">V</span>; and (<b>d</b>) Richness of vegetation types, <span class="html-italic">S</span>. Blue bars are 95% confidence interval (CI) of null-model exponent, <math display="inline"> <semantics> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">^</mo> </mover> </semantics> </math> (blue dashed). Green is bootstrapped 95% CI of entropy of experimental ratios contingent on stream order, over the domain of the null-model CI. Horizontal dashed lines indicate peak entropy (black) and maximum unconstrained entropy (red). Intersection of black and blue dashed lines within green envelope supports the null hypothesis that entropy is equal between null and experimental formulations.</p>
Full article ">
848 KiB  
Article
Entropy in the Tangled Nature Model of Evolution
by Ty N. F. Roach, James Nulton, Paolo Sibani, Forest Rohwer and Peter Salamon
Entropy 2017, 19(5), 192; https://doi.org/10.3390/e19050192 - 27 Apr 2017
Cited by 18 | Viewed by 5899
Abstract
Applications of entropy principles to evolution and ecology are of tantamount importance given the central role spatiotemporal structuring plays in both evolution and ecological succession. We obtain here a qualitative interpretation of the role of entropy in evolving ecological systems. Our interpretation is [...] Read more.
Applications of entropy principles to evolution and ecology are of tantamount importance given the central role spatiotemporal structuring plays in both evolution and ecological succession. We obtain here a qualitative interpretation of the role of entropy in evolving ecological systems. Our interpretation is supported by mathematical arguments using simulation data generated by the Tangled Nature Model (TNM), a stochastic model of evolving ecologies. We define two types of configurational entropy and study their empirical time dependence obtained from the data. Both entropy measures increase logarithmically with time, while the entropy per individual decreases in time, in parallel with the growth of emergent structures visible from other aspects of the simulation. We discuss the biological relevance of these entropies to describe niche space and functional space of ecosystems, as well as their use in characterizing the number of taxonomic configurations compatible with different niche partitioning and functionality. The TNM serves as an illustrative example of how to calculate and interpret these entropies, which are, however, also relevant to real ecosystems, where they can be used to calculate the number of functional and taxonomic configurations that an ecosystem can realize. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)
Show Figures

Figure 1

Figure 1
<p>Summary of the Tangled Nature Model (TNM) showing our parameter choices. The top graph shows the probability density function (pdf) of the symbiosis between two random species. The formula shows the overall symbiosis of a species as the average of its pair symbioses. This average determines the likelihood that an individual in species <span class="html-italic">a</span> reproduces.</p>
Full article ">Figure 2
<p>The simple probabilistic model showing three core species <span class="html-italic">a</span>, <span class="html-italic">b</span>, and <span class="html-italic">c</span> along with their associated clouds having <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> mutations.</p>
Full article ">Figure 3
<p>The average number of individuals (green squares, left axis) and average number of core species (yellow circles, right axis) plotted vs. the logarithm of time.</p>
Full article ">Figure 4
<p>The empirical un-normalized species abundance distribution at <math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mn>000</mn> </mrow> </semantics> </math>, aggregated from 800 independent runs, is plotted vs. the logarithm of the abundance. The abundance of core species, which is too small to be visible in the main plot, is re-plotted separately in the vertically zoomed-in insert. Green circles represent empirical data from the Tangled Nature Model (TNM) simulation and blue Xs represent the predictions from the simple model.</p>
Full article ">Figure 5
<p>Table of qualitative, quantitative, and biological descriptions of the two entropies. Entropy 1 is a Shannon entropy derived from the species abundance distribution, which describes the available niche space in an ecosystem. Entropy 2 is a Boltzmann entropy, which quantifies the number of taxonomic configurations compatible with a given number of functional niches. List of variables in Equations (<a href="#FD4-entropy-19-00192" class="html-disp-formula">4</a>) and (<a href="#FD6-entropy-19-00192" class="html-disp-formula">6</a>): <math display="inline"> <semantics> <msub> <mi>S</mi> <mn>1</mn> </msub> </semantics> </math> = Entropy 1, <math display="inline"> <semantics> <msub> <mi>S</mi> <mn>2</mn> </msub> </semantics> </math> = Entropy 2, <span class="html-italic">L</span> = number of species, <math display="inline"> <semantics> <mrow> <msub> <mi>π</mi> <mi>a</mi> </msub> <mo>=</mo> </mrow> </semantics> </math> fraction of species with abundance <span class="html-italic">a</span>, <span class="html-italic">K</span> = length of genome, <span class="html-italic">N</span> = total number of individuals, <span class="html-italic">n</span> = number of core species, <span class="html-italic">k</span> = number of distinct mutated positions in the genome, <math display="inline"> <semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </semantics> </math> = the fraction of progeny with <span class="html-italic">k</span> mutations, <math display="inline"> <semantics> <mrow> <mi>c</mi> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </semantics> </math> = total number of <span class="html-italic">k</span> mutated sites around a core species.</p>
Full article ">Figure 6
<p>Left vertical axis: the squares show Entropy 1 (<math display="inline"> <semantics> <msub> <mi>S</mi> <mn>1</mn> </msub> </semantics> </math>) calculated from the empirical species abundance distribution shown in <a href="#entropy-19-00192-f004" class="html-fig">Figure 4</a>. The circles show Entropy 2 (<math display="inline"> <semantics> <msub> <mi>S</mi> <mn>2</mn> </msub> </semantics> </math>) from the analytical configuration count (see Equation (<a href="#FD6-entropy-19-00192" class="html-disp-formula">6</a>)) using empirical <span class="html-italic">N</span> and <span class="html-italic">n</span> values. In the range of observation, <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mn>2</mn> </msub> <mo>≈</mo> <mn>1</mn> <mo>.</mo> <mn>27</mn> <msub> <mi>S</mi> <mn>1</mn> </msub> </mrow> </semantics> </math>. Right vertical axis: the diamonds show Entropy 1 per individual in the community. Since the two entropies are approximately proportional, Entropy 2 per individual looks the same and was omitted for readability. All quantities are plotted vs. the logarithm of time.</p>
Full article ">Figure 7
<p>A comparison of observed with expected Entropy 1. The squares repeat the values shown in <a href="#entropy-19-00192-f006" class="html-fig">Figure 6</a>. The calculation of the circles is based on the simple model of a quasi evolutionary stable states (QESS).</p>
Full article ">
2435 KiB  
Article
Entropies of the Chinese Land Use/Cover Change from 1990 to 2010 at a County Level
by Yong Fan, Guangming Yu, Zongyi He, Hailong Yu, Rui Bai, Linru Yang and Di Wu
Entropy 2017, 19(2), 51; https://doi.org/10.3390/e19020051 - 25 Jan 2017
Cited by 29 | Viewed by 6423
Abstract
Land Use/Cover Change (LUCC) has gradually became an important direction in the research of global changes. LUCC is a complex system, and entropy is a measure of the degree of disorder of a system. According to land use information entropy, this paper analyzes [...] Read more.
Land Use/Cover Change (LUCC) has gradually became an important direction in the research of global changes. LUCC is a complex system, and entropy is a measure of the degree of disorder of a system. According to land use information entropy, this paper analyzes changes in land use from the perspective of the system. Research on the entropy of LUCC structures has a certain “guiding role” for the optimization and adjustment of regional land use structure. Based on the five periods of LUCC data from the year of 1990 to 2010, this paper focuses on analyzing three types of LUCC entropies among counties in China—namely, Shannon, Renyi, and Tsallis entropies. The findings suggest that: (1) Shannon entropy can reflect the volatility of the LUCC, that Renyi and Tsallis entropies also have this function when their parameter has a positive value, and that Renyi and Tsallis entropies can reflect the extreme case of the LUCC when their parameter has a negative value.; (2) The entropy of China’s LUCC is uneven in time and space distributions, and that there is a large trend during 1990–2010, the central region generally has high entropy in space. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)
Show Figures

Figure 1

Figure 1
<p>The sequence of Chinese LUCC (Land Use/Cover Change) maps in 1990 (<b>a</b>); 1995 (<b>b</b>); 2000 (<b>c</b>); 2005 (<b>d</b>); 2010 (<b>e</b>).</p>
Full article ">Figure 2
<p>Evolution of Chinese LUCC Shannon entropy from 1990 to 2010. Sort the data based on the province ID, the counties included in the provinces are: Anhui:1–80, Macao: 81, Beijing: 82–90, Fujian: 91–160, Gansu: 161–240, Guangdong: 241–334, Guangxi: 335–423, Guizhou: 424–505, Hainan: 506–523, Hebei: 524–672, Henan: 673–798, Heilongjiang: 799–876, Hubei: 877–956, Hunan: 957–1058, Jilin: 1059–1104, Jiangsu: 1105–1178, Jiangxi: 1179–1267, Liaoning: 1268–1325, Inner Mongolia: 1326–1412, Ningxia: 1413–1432, Qinghai: 1433–1473, Shandong: 1474–1583, Shanxi: 1584–1689, Shanxi: 1690–1786, Shanghai: 1787–1795, Sichuan: 1796–1954, Taiwan: 1955, Tianjin: 1956–1961, Tibet: 1962–2038, Hong Kong: 2039, Xinjiang: 2040–2124, Yunnan: 2125–2249, Zhejiang: 2250–2325, and Chongqing: 2326–2363.</p>
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<p>Evolution of Chinese LUCC Tsallis entropy from 1990 to 2010. (<b>a</b>) is the corresponding results when <span class="html-italic">q</span> = 0.1; (<b>b</b>) <span class="html-italic">q</span> = 0.5; (<b>c</b>) <span class="html-italic">q</span> = 2.5 [<a href="#B30-entropy-19-00051" class="html-bibr">30</a>]. The type of Country ID is the same as in <a href="#entropy-19-00051-f002" class="html-fig">Figure 2</a>.</p>
Full article ">Figure 4
<p>Evolution of Chinese LUCC Renyi entropy from 1990 to 2010. (<b>a</b>) is the corresponding results when <span class="html-italic">q</span> = 0.1; (<b>b</b>) <span class="html-italic">q</span> = 0.5; (<b>c</b>) <span class="html-italic">q</span> = 2.5 [<a href="#B30-entropy-19-00051" class="html-bibr">30</a>]. The type of Country ID is as in <a href="#entropy-19-00051-f002" class="html-fig">Figure 2</a>.</p>
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<p>The change map of Chinese LUCC Shannon entropy in 1990 (<b>a</b>); 1995 (<b>b</b>); 2000 (<b>c</b>); 2005 (<b>d</b>); 2010 (<b>e</b>).</p>
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7459 KiB  
Article
Radiative Entropy Production along the Paludification Gradient in the Southern Taiga
by Olga Kuricheva, Vadim Mamkin, Robert Sandlersky, Juriy Puzachenko, Andrej Varlagin and Juliya Kurbatova
Entropy 2017, 19(1), 43; https://doi.org/10.3390/e19010043 - 21 Jan 2017
Cited by 12 | Viewed by 5515
Abstract
Entropy production (σ) is a measure of ecosystem and landscape stability in a changing environment. We calculated the σ in the radiation balance for a well-drained spruce forest, a paludified spruce forest, and a bog in the southern taiga of the [...] Read more.
Entropy production (σ) is a measure of ecosystem and landscape stability in a changing environment. We calculated the σ in the radiation balance for a well-drained spruce forest, a paludified spruce forest, and a bog in the southern taiga of the European part of Russia using long-term meteorological data. Though radiative σ depends both on surface temperature and absorbed radiation, the radiation effect in boreal ecosystems is much more important than the temperature effect. The dynamic of the incoming solar radiation was the main driver of the diurnal, seasonal, and intra-annual courses of σ for all ecosystems; the difference in ecosystem albedo was the second most important factor, responsible for seven-eighths of the difference in σ between the bog and forest in a warm period. Despite the higher productivity and the complex structure of the well-drained forest, the dynamics and sums of σ in two forests were very similar. Summer droughts had no influence on the albedo and σ efficiency of forests, demonstrating high self-regulation of the taiga forest ecosystems. On the contrary, a decreasing water supply significantly elevated the albedo and lowered the σ in bog. Bogs, being non-steady ecosystems, demonstrate unique thermodynamic behavior, which is fluctuant and strongly dependent on the moisture supply. Paludification of territories may result in increasing instability of the energy balance and entropy production in the landscape of the southern taiga. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)
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Figure 1

Figure 1
<p>Vegetation cover in the southeastern part of the Central Forest Biosphere Reserve. Wet Spruce (WS), Dry Spruce (DS), and Bog (B) are the studied sites (see below). The yellow line is the boundary of the core of the reserve and red points mark the boundary of the buffer zone.</p>
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<p>Diurnal course of entropy production (<span class="html-italic">σ</span>QS) and EMEP at the wet spruce (WS), dry spruce (DS), and bog (B) sites in the Central Forest Biosphere Reserve in sunny conditions (<b>a</b>); variable clouds (<b>b</b>); and overcast conditions (<b>c</b>). See <a href="#sec2dot1-entropy-19-00043" class="html-sec">Section 2.1</a> and <a href="#sec2dot2-entropy-19-00043" class="html-sec">Section 2.2</a> for details of the data sample method and period.</p>
Full article ">Figure 3
<p>Daily entropy production (<span class="html-italic">σQ<sub>S</sub></span>) in short-wave radiation balance at bog (B), wet spruce (WS), and dry spruce (DS) sites in the Central Forest Biosphere Reserve.</p>
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<p>Efficiency of entropy production in radiation balance (<span class="html-italic">σ</span>/EMEP) at bog (B), wet spruce (WS), and dry spruce (DS) sites (mean for all years, B data with 10 day smoothing). <span class="html-italic">σ</span>/EMEP at B in March and mid-November was 0.5–0.8.</p>
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<p>Cumulative sums of observed entropy production (<span class="html-italic">Σσ</span>) and EMEP at the wet spruce (WS), dry spruce (DS), and bog (B) sites in the Central Forest Biosphere Reserve in 2000 (<b>a</b>); 2001 (<b>b</b>); 2002 (<b>c</b>); 2003 (<b>d</b>); 2004 (<b>e</b>); and April–October of 1999 (<b>f</b>).</p>
Full article ">Figure 5 Cont.
<p>Cumulative sums of observed entropy production (<span class="html-italic">Σσ</span>) and EMEP at the wet spruce (WS), dry spruce (DS), and bog (B) sites in the Central Forest Biosphere Reserve in 2000 (<b>a</b>); 2001 (<b>b</b>); 2002 (<b>c</b>); 2003 (<b>d</b>); 2004 (<b>e</b>); and April–October of 1999 (<b>f</b>).</p>
Full article ">Figure 6
<p>(<b>a</b>) Seasonal course of albedo (<span class="html-italic">α</span>) at the dry spruce (DS), wet spruce (WS), and bog (B) sites; (<b>b</b>) Albedo (<span class="html-italic">α</span>) and daily precipitation sums (Pr) at the bog site in 1999; <span class="html-italic">α</span> of B in March and mid-November was 0.4–0.6 and 0.6–0.8, respectively.</p>
Full article ">

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246 KiB  
Concept Paper
Discussing Landscape Compositional Scenarios Generated with Maximization of Non-Expected Utility Decision Models Based on Weighted Entropies
by José Pinto Casquilho and Francisco Castro Rego
Entropy 2017, 19(2), 66; https://doi.org/10.3390/e19020066 - 10 Feb 2017
Cited by 9 | Viewed by 5648
Abstract
The search for hypothetical optimal solutions of landscape composition is a major issue in landscape planning and it can be outlined in a two-dimensional decision space involving economic value and landscape diversity, the latter being considered as a potential safeguard to the provision [...] Read more.
The search for hypothetical optimal solutions of landscape composition is a major issue in landscape planning and it can be outlined in a two-dimensional decision space involving economic value and landscape diversity, the latter being considered as a potential safeguard to the provision of services and externalities not accounted in the economic value. In this paper, we use decision models with different utility valuations combined with weighted entropies respectively incorporating rarity factors associated to Gini-Simpson and Shannon measures. A small example of this framework is provided and discussed for landscape compositional scenarios in the region of Nisa, Portugal. The optimal solutions relative to the different cases considered are assessed in the two-dimensional decision space using a benchmark indicator. The results indicate that the likely best combination is achieved by the solution using Shannon weighted entropy and a square root utility function, corresponding to a risk-averse behavior associated to the precautionary principle linked to safeguarding landscape diversity, anchoring for ecosystem services provision and other externalities. Further developments are suggested, mainly those relative to the hypothesis that the decision models here outlined could be used to revisit the stability-complexity debate in the field of ecological studies. Full article
(This article belongs to the Special Issue Entropy in Landscape Ecology)
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