The Experts below are selected from a list of 2640 Experts worldwide ranked by ideXlab platform
Erica A Newman - One of the best experts on this subject based on the ideXlab platform.
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disturbance Macroecology integrating disturbance ecology and Macroecology with different age post fire stands of a closed cone pine forest
bioRxiv, 2018Co-Authors: Erica A Newman, Max A. Moritz, Karen E Kopper, Donald A Falk, Donald Mckenzie, Morgan Wilber, John HarteAbstract:Abstract Macroecological studies have generally restricted their scope to relatively steady-state systems, and as a result, how biodiversity and abundance metrics are expected to scale in disturbance-dependent ecosystems is unknown. We examine macroecological patterns in a fire-dependent forest of Bishop pine (Pinus muricata). We target two different-aged stands in a stand-replacing fire regime, one a characteristically mature stand with a diverse understory, and one more recently disturbed by a stand-replacing fire (17 years prior to measurement). We compare the stands using macroecological metrics of species richness, abundance and spatial distributions that are predicted by the Maximum Entropy Theory of Ecology (METE), an information-entropy based theory that has proven highly successful in predicting macroecological metrics across a wide variety of systems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the recently disturbed stand. This suggests METE’s predictions are more robust in late-successional, slowly changing, or steady-state systems than those in rapid flux with respect to species composition, abundances, and organisms’ sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance and other ecological perturbations into its predictive capabilities, because most natural systems are not in a steady state.
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disturbance Macroecology integrating disturbance ecology and Macroecology in different age post fire stands of a closed cone pine forest as a case study
bioRxiv, 2018Co-Authors: Erica A Newman, Max A. Moritz, Karen E Kopper, Donald A Falk, Morgan Wilber, D F Mckenzie, John HarteAbstract:Macroecological studies have generally restricted their scope to relatively steady-state systems, and as a result, how biodiversity and abundance metrics are expected to scale in disturbance-dependent ecosystems is unknown. We examine macroecological patterns in a fire-dependent forest of Bishop pine (Pinus muricata). We target two different-aged stands in a stand-replacing fire regime, one a characteristically mature stand with a diverse understory, and one more recently disturbed by a stand-replacing fire (17 years prior to measurement). We compare the stands using macroecological metrics of species richness, abundance and spatial distributions that are predicted by the Maximum Entropy Theory of Ecology (METE), an information-entropy based theory that has proven highly successful in predicting macroecological metrics across a wide variety of systems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the recently disturbed stand. This suggests METE's predictions are more robust in late-successional, slowly changing, or steady-state systems than those in rapid flux with respect to species composition, abundances, and organisms' sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance and other ecological perturbations into its predictive capabilities, because most natural systems are not in a steady state.
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maximum information entropy a foundation for ecological theory
Trends in Ecology and Evolution, 2014Co-Authors: John Harte, Erica A NewmanAbstract:The maximum information entropy (MaxEnt) principle is a successful method of statistical inference that has recently been applied to ecology. Here, we show how MaxEnt can accurately predict patterns such as species–area relationships (SARs) and abundance distributions in Macroecology and be a foundation for ecological theory. We discuss the conceptual foundation of the principle, why it often produces accurate predictions of probability distributions in science despite not incorporating explicit mechanisms, and how mismatches between predictions and data can shed light on driving mechanisms in ecology. We also review possible future extensions of the maximum entropy theory of ecology (METE), a potentially important foundation for future developments in ecological theory.
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taxon categories and the universal species area relationship a comment on sizling et al between geometry and biology the problem of universality of the species area relationship
The American Naturalist, 2013Co-Authors: John Harte, Erica A Newman, Justin Kitzes, Andrew J RomingerAbstract:AbstractA theory of Macroecology based on the maximum information entropy (MaxEnt) inference procedure predicts that the log-log slope of the species-area relationship (SAR) at any spatial scale is a specified function of the ratio of abundance, N(A), to species richness, S(A), at that scale. The theory thus predicts, in generally good agreement with observation, that all SARs collapse onto a specified universal curve when local slope, z(A), is plotted against N(A)/S(A). A recent publication, however, argues that if it is assumed that patterns in Macroecology are independent of the taxonomic choices that define assemblages of species, then this principle of “taxon invariance” precludes the MaxEnt-predicted universality of the SAR. By distinguishing two dimensions of the notion of taxon invariance, we show that while the MaxEnt-based theory predicts universality regardless of the taxonomic choices that define an assemblage of species, the biological characteristics of assemblages should under MaxEnt, and d...
Luis Mauricio Bini - One of the best experts on this subject based on the ideXlab platform.
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SAM: A comprehensive application for Spatial Analysis in Macroecology
Ecography, 2010Co-Authors: Thiago F. Rangel, José Alexandre F. Diniz-filho, Luis Mauricio BiniAbstract:SAM (Spatial Analysis in Macroecology) is a freeware application that offers a comprehensive array of spatial statistical methods, focused primarily on surface pattern spatial analysis. SAM is a compact, but powerful stand-alone software, with a user-friendly, menu-driven graphical interface. The methods available in SAM are the most commonly used in Macroecology and geographical ecology, and range from simple tools for exploratory graphical analysis (e.g. mapping and graphing) and descriptive statistics of spatial patterns (e.g. autocorrelation metrics), to advanced spatial regression models (e.g. autoregression and eigenvector filtering). Download of the software, along with the user manual, can be downloaded online at the SAM website: (permanent URL at ).
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towards an integrated computational tool for spatial analysis in Macroecology and biogeography
Global Ecology and Biogeography, 2006Co-Authors: Thiago F. Rangel, Jose Alexandre Felizola Dinizfilho, Luis Mauricio BiniAbstract:Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in Macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in Macroecology and biogeography, most of sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/).
John Harte - One of the best experts on this subject based on the ideXlab platform.
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disturbance Macroecology integrating disturbance ecology and Macroecology with different age post fire stands of a closed cone pine forest
bioRxiv, 2018Co-Authors: Erica A Newman, Max A. Moritz, Karen E Kopper, Donald A Falk, Donald Mckenzie, Morgan Wilber, John HarteAbstract:Abstract Macroecological studies have generally restricted their scope to relatively steady-state systems, and as a result, how biodiversity and abundance metrics are expected to scale in disturbance-dependent ecosystems is unknown. We examine macroecological patterns in a fire-dependent forest of Bishop pine (Pinus muricata). We target two different-aged stands in a stand-replacing fire regime, one a characteristically mature stand with a diverse understory, and one more recently disturbed by a stand-replacing fire (17 years prior to measurement). We compare the stands using macroecological metrics of species richness, abundance and spatial distributions that are predicted by the Maximum Entropy Theory of Ecology (METE), an information-entropy based theory that has proven highly successful in predicting macroecological metrics across a wide variety of systems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the recently disturbed stand. This suggests METE’s predictions are more robust in late-successional, slowly changing, or steady-state systems than those in rapid flux with respect to species composition, abundances, and organisms’ sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance and other ecological perturbations into its predictive capabilities, because most natural systems are not in a steady state.
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disturbance Macroecology integrating disturbance ecology and Macroecology in different age post fire stands of a closed cone pine forest as a case study
bioRxiv, 2018Co-Authors: Erica A Newman, Max A. Moritz, Karen E Kopper, Donald A Falk, Morgan Wilber, D F Mckenzie, John HarteAbstract:Macroecological studies have generally restricted their scope to relatively steady-state systems, and as a result, how biodiversity and abundance metrics are expected to scale in disturbance-dependent ecosystems is unknown. We examine macroecological patterns in a fire-dependent forest of Bishop pine (Pinus muricata). We target two different-aged stands in a stand-replacing fire regime, one a characteristically mature stand with a diverse understory, and one more recently disturbed by a stand-replacing fire (17 years prior to measurement). We compare the stands using macroecological metrics of species richness, abundance and spatial distributions that are predicted by the Maximum Entropy Theory of Ecology (METE), an information-entropy based theory that has proven highly successful in predicting macroecological metrics across a wide variety of systems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the recently disturbed stand. This suggests METE's predictions are more robust in late-successional, slowly changing, or steady-state systems than those in rapid flux with respect to species composition, abundances, and organisms' sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance and other ecological perturbations into its predictive capabilities, because most natural systems are not in a steady state.
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maximum information entropy a foundation for ecological theory
Trends in Ecology and Evolution, 2014Co-Authors: John Harte, Erica A NewmanAbstract:The maximum information entropy (MaxEnt) principle is a successful method of statistical inference that has recently been applied to ecology. Here, we show how MaxEnt can accurately predict patterns such as species–area relationships (SARs) and abundance distributions in Macroecology and be a foundation for ecological theory. We discuss the conceptual foundation of the principle, why it often produces accurate predictions of probability distributions in science despite not incorporating explicit mechanisms, and how mismatches between predictions and data can shed light on driving mechanisms in ecology. We also review possible future extensions of the maximum entropy theory of ecology (METE), a potentially important foundation for future developments in ecological theory.
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taxon categories and the universal species area relationship a comment on sizling et al between geometry and biology the problem of universality of the species area relationship
The American Naturalist, 2013Co-Authors: John Harte, Erica A Newman, Justin Kitzes, Andrew J RomingerAbstract:AbstractA theory of Macroecology based on the maximum information entropy (MaxEnt) inference procedure predicts that the log-log slope of the species-area relationship (SAR) at any spatial scale is a specified function of the ratio of abundance, N(A), to species richness, S(A), at that scale. The theory thus predicts, in generally good agreement with observation, that all SARs collapse onto a specified universal curve when local slope, z(A), is plotted against N(A)/S(A). A recent publication, however, argues that if it is assumed that patterns in Macroecology are independent of the taxonomic choices that define assemblages of species, then this principle of “taxon invariance” precludes the MaxEnt-predicted universality of the SAR. By distinguishing two dimensions of the notion of taxon invariance, we show that while the MaxEnt-based theory predicts universality regardless of the taxonomic choices that define an assemblage of species, the biological characteristics of assemblages should under MaxEnt, and d...
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maximum entropy and ecology a theory of abundance distribution and energetics
2011Co-Authors: John HarteAbstract:PART I. FOUNDATIONS PART II. Macroecology PART III. THE MAXIMUM ENTROPY PRINCIPLE PART IV. Macroecology AND MAXENT PART V. A WIDER PERSPECTIVE
Thiago F. Rangel - One of the best experts on this subject based on the ideXlab platform.
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SAM: A comprehensive application for Spatial Analysis in Macroecology
Ecography, 2010Co-Authors: Thiago F. Rangel, José Alexandre F. Diniz-filho, Luis Mauricio BiniAbstract:SAM (Spatial Analysis in Macroecology) is a freeware application that offers a comprehensive array of spatial statistical methods, focused primarily on surface pattern spatial analysis. SAM is a compact, but powerful stand-alone software, with a user-friendly, menu-driven graphical interface. The methods available in SAM are the most commonly used in Macroecology and geographical ecology, and range from simple tools for exploratory graphical analysis (e.g. mapping and graphing) and descriptive statistics of spatial patterns (e.g. autocorrelation metrics), to advanced spatial regression models (e.g. autoregression and eigenvector filtering). Download of the software, along with the user manual, can be downloaded online at the SAM website: (permanent URL at ).
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towards an integrated computational tool for spatial analysis in Macroecology and biogeography
Global Ecology and Biogeography, 2006Co-Authors: Thiago F. Rangel, Jose Alexandre Felizola Dinizfilho, Luis Mauricio BiniAbstract:Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in Macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in Macroecology and biogeography, most of sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/).
Jose Alexandre Felizola Dinizfilho - One of the best experts on this subject based on the ideXlab platform.
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towards an integrated computational tool for spatial analysis in Macroecology and biogeography
Global Ecology and Biogeography, 2006Co-Authors: Thiago F. Rangel, Jose Alexandre Felizola Dinizfilho, Luis Mauricio BiniAbstract:Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in Macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in Macroecology and biogeography, most of sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/).
