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Journal articles on the topic 'Ecological heterogeneity'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Agnew, A. D. Q., Jurek Kolasa, and S. T. A. Pickett. "Ecological Heterogeneity (Ecological Studies 86)." Journal of Vegetation Science 4, no. 1 (February 1993): 138. http://dx.doi.org/10.2307/3235744.

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2

Hastings, Alan. "Spatial Heterogeneity and Ecological Models." Ecology 71, no. 2 (April 1990): 426–28. http://dx.doi.org/10.2307/1940296.

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3

Collins, Scott L., Meghan L. Avolio, Corinna Gries, Lauren M. Hallett, Sally E. Koerner, Kimberly J. La Pierre, Andrew L. Rypel, et al. "Temporal heterogeneity increases with spatial heterogeneity in ecological communities." Ecology 99, no. 4 (February 15, 2018): 858–65. http://dx.doi.org/10.1002/ecy.2154.

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4

Zadorozhnaya, Galina A., Kateryna V. Andrusevych, and Olexander V. Zhukov. "Soil heterogeneity after recultivation: ecological aspect." Folia Oecologica 45, no. 1 (May 1, 2018): 46–52. http://dx.doi.org/10.2478/foecol-2018-0005.

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Abstract The study subject was the soil heterogeneity at a recultivation site Nikopol manganese-ore basin (Pokrov, Ukraine). The soils at the locality are sod lithogenic soils developed on gray-green clays. The study ran by applying soil penetration resistance indices. The penetration resistance was measured across a regular grid of 7 × 15 points (21 × 45 m). The distance between the measurement points was 3 m. The parameters were recorded at every 5 cm to a depth of 50 cm. The environmental parameters were determined by phytoindication. Geostatistical analysis showed the average level of spatial dependence of soil penetration resistance. According to the features of the profile variation in penetration resistance with the depth, the measurement points have been divided into three clusters. The clusters formed morphologically homogeneous soil areas. These areas significantly differed in their soil acidity and in nitrogen content in soil.

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5

Grace, J. "Physical and Ecological Evaluation of Heterogeneity." Functional Ecology 5, no. 2 (1991): 192. http://dx.doi.org/10.2307/2389257.

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6

Rosenberg, Diane B., and Stephen M. Freedman. "Temporal heterogeneity and ecological community structure." International Journal of Environmental Studies 46, no. 2-3 (August 1994): 97–102. http://dx.doi.org/10.1080/00207239408710916.

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7

Cantrell, R. S., C. Cosner, and V. Hutson. "Ecological Models, Permanence and Spatial Heterogeneity." Rocky Mountain Journal of Mathematics 26, no. 1 (March 1996): 1–35. http://dx.doi.org/10.1216/rmjm/1181072101.

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8

Shi, Xinyu, Xiaoqing Zhao, Junwei Pu, Pei Huang, Zexian Gu, and Yanjun Chen. "Evolution Modes, Types, and Social-Ecological Drivers of Ecologically Critical Areas in the Sichuan–Yunnan Ecological Barrier in the Last 15 Years." International Journal of Environmental Research and Public Health 19, no. 15 (July 27, 2022): 9206. http://dx.doi.org/10.3390/ijerph19159206.

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The ecological barrier is a complex ecosystem that couples the human–nature relationship, and the ecologically critical area is an irreplaceable area with a special value in the ecosystem. Therefore, protecting the ecologically critical area is vital for maintaining and improving regional ecological security. Limited research has been conducted on the evolution of ecologically critical areas, and none of the studies have considered the spatiotemporal heterogeneity of the driving factors for different evolution modes and types. Therefore, this research adopts the ecologically critical index, landscape expansion index, and the random forest model to analyze the pattern, driving factors, and its spatial-temporal heterogeneity to the evolution modes and specific types of ecologically critical areas in the Sichuan–Yunnan ecological barrier area in the last 15 years. The results showed that: (1) the ecologically critical areas in the Sichuan–Yunnan ecological barrier have changed dramatically, with the area reduction being 61.06%. Additionally, the spatial distribution characteristics of the ecologically critical area from north to south include planar, point, and linear forms. (2) The evolution trend of the ecologically critical area is ‘degradation–expansion–degradation’. Spread is the predominant type of expansion mode, whereas atrophy is the predominant type of degradation mode, indicating that the evolution mainly occurs at the edge of the original ecologically critical areas. (3) In general, precipitation, area of forest, area of cropland, and GDP have contributed significantly to the evolution of ecologically critical areas. However, the same driving factor has different effects on the expansion and degradation of these areas. Expansion is driven by multiple factors at the same time but is mainly related to human activities and land use change, whereas for degradation, climate and policy are the main driving factors. The present research aimed to quantitatively identify the evolution modes and specific types of ecologically critical areas and explore the spatiotemporal heterogeneity of driving factors. The results can help decision-makers in formulating ecological protection policies according to local conditions and in maintaining and enhancing the regional ecological functions, thereby promoting the sustainable development of society-economy-ecology.

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9

Palmer, Margaret A., Christine C. Hakenkamp, and Kären Nelson-Baker. "Ecological Heterogeneity in Streams: Why Variance Matters." Journal of the North American Benthological Society 16, no. 1 (March 1997): 189–202. http://dx.doi.org/10.2307/1468251.

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10

Yan, Gang, Neo D. Martinez, and Yang-Yu Liu. "Degree heterogeneity and stability of ecological networks." Journal of The Royal Society Interface 14, no. 131 (June 2017): 20170189. http://dx.doi.org/10.1098/rsif.2017.0189.

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A classic measure of ecological stability describes the tendency of a community to return to equilibrium after small perturbations. While many advances show how the network architecture of these communities severely constrains such tendencies, one of the most fundamental properties of network structure, i.e. degree heterogeneity—the variability of the number of links associated with each species, deserves further study. Here we show that the effects of degree heterogeneity on stability vary with different types of interspecific interactions. Degree heterogeneity consistently destabilizes ecological networks with both competitive and mutualistic interactions, while its effects on networks of predator–prey interactions such as food webs depend on prey contiguity, i.e. the extent to which the species consume an unbroken sequence of prey in community niche space. Increasing degree heterogeneity tends to stabilize food webs except those with the highest prey contiguity. These findings help explain why food webs are highly but not completely interval and, more broadly, deepen our understanding of the stability of complex ecological networks.

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11

Pickett, S. T. A., and M. L. Cadenasso. "Landscape Ecology: Spatial Heterogeneity in Ecological Systems." Science 269, no. 5222 (July 21, 1995): 331–34. http://dx.doi.org/10.1126/science.269.5222.331.

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12

Hadeler, K. P. "Quiescence, excitability, and heterogeneity in ecological models." Journal of Mathematical Biology 66, no. 4-5 (September 26, 2012): 649–84. http://dx.doi.org/10.1007/s00285-012-0590-1.

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13

Cantrell, Robert Stephen, Chris Cosners, and Vivian Hutson. "Permanence in ecological systems with spatial heterogeneity." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 123, no. 3 (1993): 533–59. http://dx.doi.org/10.1017/s0308210500025877.

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SynopsisA basic problem in population dynamics is that of finding criteria for the long-term coexistence of interacting species. An important aspect of the problem is determining how coexistence is affected by spatial dispersal and environmental heterogeneity. The object of this paper is to study the problem of coexistence for two interacting species dispersing through a spatially heterogeneous region. We model the population dynamics of the species with a system of two reaction–diffusion equations which we interpret as a semi-dynamical system. We say that the system is permanent if any state with all components positive initially must ultimately enter and remain within a fixed set of positive states that are strictly bounded away from zero in each component. Our analysis produces conditions that can be interpreted in a natural way in terms of environmental conditions and parameters, by combining the dynamic idea of permanence with the static idea of studying geometric problems via eigenvalue estimation.

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14

Kopac, Sarah, Zhang Wang, Jane Wiedenbeck, Jessica Sherry, Martin Wu, and Frederick M. Cohan. "Genomic Heterogeneity and Ecological Speciation within One Subspecies of Bacillus subtilis." Applied and Environmental Microbiology 80, no. 16 (June 6, 2014): 4842–53. http://dx.doi.org/10.1128/aem.00576-14.

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ABSTRACTClosely related bacterial genomes usually differ in gene content, suggesting that nearly every strain in nature may be ecologically unique. We have tested this hypothesis by sequencing the genomes of extremely close relatives within a recognized taxon and analyzing the genomes for evidence of ecological distinctness. We compared the genomes of four Death Valley isolates plus the laboratory strain W23, all previously classified asBacillus subtilissubsp.spizizeniiand hypothesized through multilocus analysis to be members of the same ecotype (an ecologically homogeneous population), named putative ecotype 15 (PE15). These strains showed a history of positive selection on amino acid sequences in 38 genes. Each of the strains was under a different regimen of positive selection, suggesting that each strain is ecologically unique and represents a distinct ecological speciation event. The rate of speciation appears to be much faster than can be resolved with multilocus sequencing. Each PE15 strain contained unique genes known to confer a function for bacteria. Remarkably, no unique gene conferred a metabolic system or subsystem function that was not already present in all the PE15 strains sampled. Thus, the origin of ecotypes within this clade shows no evidence of qualitative divergence in the set of resources utilized. Ecotype formation within this clade is consistent with the nanoniche model of bacterial speciation, in which ecotypes use the same set of resources but in different proportions, and genetic cohesion extends beyond a single ecotype to the set of ecotypes utilizing the same resources.

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15

Blackwell, P. G. "Heterogeneity, patchiness and correlation of resources." Ecological Modelling 207, no. 2-4 (October 2007): 349–55. http://dx.doi.org/10.1016/j.ecolmodel.2007.05.012.

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16

Turek, Daniel, Claudia Wehrhahn, and Olivier Gimenez. "Bayesian non-parametric detection heterogeneity in ecological models." Environmental and Ecological Statistics 28, no. 2 (March 22, 2021): 355–81. http://dx.doi.org/10.1007/s10651-021-00489-1.

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17

Nevo, Eviatar, and Avigdor Beiles. "Genetic Diversity and Ecological Heterogeneity in Amphibian Evolution." Copeia 1991, no. 3 (August 1, 1991): 565. http://dx.doi.org/10.2307/1446386.

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18

Sandoval‐Castillo, Jonathan, and Luciano B. Beheregaray. "Oceanographic heterogeneity influences an ecological radiation in elasmobranchs." Journal of Biogeography 47, no. 7 (May 15, 2020): 1599–611. http://dx.doi.org/10.1111/jbi.13865.

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19

Fortin, Marie-Josee. "Ecological Heterogeneity. Jurek Kolasa , Steward T. A. Pickett." Quarterly Review of Biology 67, no. 2 (June 1992): 228–29. http://dx.doi.org/10.1086/417617.

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20

Brimacombe, Chris, Korryn Bodner, Matthew Michalska-Smith, Timothée Poisot, and Marie-Josée Fortin. "Shortcomings of reusing species interaction networks created by different sets of researchers." PLOS Biology 21, no. 4 (April 3, 2023): e3002068. http://dx.doi.org/10.1371/journal.pbio.3002068.

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Given the requisite cost associated with observing species interactions, ecologists often reuse species interaction networks created by different sets of researchers to test their hypotheses regarding how ecological processes drive network topology. Yet, topological properties identified across these networks may not be sufficiently attributable to ecological processes alone as often assumed. Instead, much of the totality of topological differences between networks—topological heterogeneity—could be due to variations in research designs and approaches that different researchers use to create each species interaction network. To evaluate the degree to which this topological heterogeneity is present in available ecological networks, we first compared the amount of topological heterogeneity across 723 species interaction networks created by different sets of researchers with the amount quantified from non-ecological networks known to be constructed following more consistent approaches. Then, to further test whether the topological heterogeneity was due to differences in study designs, and not only to inherent variation within ecological networks, we compared the amount of topological heterogeneity between species interaction networks created by the same sets of researchers (i.e., networks from the same publication) with the amount quantified between networks that were each from a unique publication source. We found that species interaction networks are highly topologically heterogeneous: while species interaction networks from the same publication are much more topologically similar to each other than interaction networks that are from a unique publication, they still show at least twice as much heterogeneity as any category of non-ecological networks that we tested. Altogether, our findings suggest that extra care is necessary to effectively analyze species interaction networks created by different researchers, perhaps by controlling for the publication source of each network.

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21

Perry, George L. W. "Landscapes, space and equilibrium: shifting viewpoints." Progress in Physical Geography: Earth and Environment 26, no. 3 (September 2002): 339–59. http://dx.doi.org/10.1191/0309133302pp341ra.

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The classical view of ecological systems has been one that assumes a state of equilibrium and stability; this is encapsulated in the ‘balance of nature’ paradigm. Over the last 30 years ecologists and biogeographers have rejected the view that ecological systems are inherently stable or at some sort of equilibrium. Instead a nonequilibrium view, emphasizing the role of chance events such as disturbance in ecological dynamics, has become dominant. Alongside this change, the way in which the roles of space and spatial heterogeneity in ecological dynamics are viewed has shifted. Classical ecological theory tended to ignore spatial dynamics and heterogeneity and focused instead on temporal pattern. Over the last 20 years this view has also changed and the importance of spatial pattern has been emphasized. Through the explicit consideration of space and spatial pattern it has been shown that spatial heterogeneity may act to either stabilize or destabilize ecological systems and processes. This paper reviews these two changes in the way ecological systems are conceptualized and explores how they are inter-related. Advances in our understanding of the role of space and the nature of equilibrium in ecological systems are discussed within the context of both modelling and empirical studies, as are the problems involved with experimentally testing the large body of spatial theory developed.

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22

Ravkin, Yury Solomonovich, Irina Nikolaevna Bogomolova, and Sergey Mikhailovich Tsybulin. "Ecological arrangement of floro-faunistic heterogeneity of northern Eurasia." Principles of the Ecology 22, no. 1 (March 2017): 104–16. http://dx.doi.org/10.15393/j1.art.2017.6142.

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23

Dutilleul, Pierre. "Spatial Heterogeneity and the Design of Ecological Field Experiments." Ecology 74, no. 6 (September 1993): 1646–58. http://dx.doi.org/10.2307/1939923.

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24

Malhotra, GS, and Jyoti Madan. "Origin and ecological heterogeneity of Baropal wetland: A Review." Environment Conservation Journal 21, no. 3 (December 17, 2020): 165–70. http://dx.doi.org/10.36953/ecj.2020.21320.

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Baropal wetland is situated near to the Baropal village, Pilibanga tehsil of Hanumangarh District in Rajasthan state (290 22’ 10” North, 740 05’ 48” East) and comes under the Ghaggar river floodplain. Baropal wetland is a recently and naturally originated water body, presumably recharged by the seepage water of Ghaggar river and Indira Gandhi Canal Project (IGNP). It is a real example of rapid succession and species aggregation in the barren land and has been established as a rich biodiversity amphitheatre within the semi-desert region. The vegetation cover of Baropal wetland stretch has changed into Xerophytic to Mesophytic and Hydrophytic. As, it has established enormous plant and animal communities in a short period, in the present picture, the plant communities of Baropal wetland are much productive to support enough food and habitat to a variety of organisms. Unfortunately, Baropal wetland has not received any ecological attention despite being a water body and always ignored due to the inadequacy in the number of the well-established local researchers. Its beauty has always been superimposed by the fascinating archaeological sites which are present in the same locality (Ghaggar floodplain) and by the Ghaggar and Saraswati debate.

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25

Allouche, O., M. Kalyuzhny, G. Moreno-Rueda, M. Pizarro, and R. Kadmon. "Area-heterogeneity tradeoff and the diversity of ecological communities." Proceedings of the National Academy of Sciences 109, no. 43 (October 8, 2012): 17495–500. http://dx.doi.org/10.1073/pnas.1208652109.

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26

Chi, Yuan, Honghua Shi, Yuanyuan Wang, Zhen Guo, and Enkang Wang. "Evaluation on island ecological vulnerability and its spatial heterogeneity." Marine Pollution Bulletin 125, no. 1-2 (December 2017): 216–41. http://dx.doi.org/10.1016/j.marpolbul.2017.08.028.

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27

Duflot, Rémi, Romain Georges, Aude Ernoult, Stéphanie Aviron, and Françoise Burel. "Landscape heterogeneity as an ecological filter of species traits." Acta Oecologica 56 (April 2014): 19–26. http://dx.doi.org/10.1016/j.actao.2014.01.004.

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28

Naeem, Shahid. "The ecological basis of conservation: Heterogeneity, ecosystems, and biodiversity." Trends in Ecology & Evolution 12, no. 11 (November 1997): 456. http://dx.doi.org/10.1016/s0169-5347(97)85761-8.

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29

Debinski, Diane M., S. T. A. Pickett, R. S. Ostfeld, M. Shachak, and G. E. Likens. "The Ecological Basis of Conservation: Heterogeneity, Ecosystems, and Biodiversity." Journal of Wildlife Management 63, no. 2 (April 1999): 759. http://dx.doi.org/10.2307/3802668.

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30

Campos, Paulo R. A., Viviane M. de Oliveira, and Alexandre Rosas. "Epistasis and environmental heterogeneity in the speciation process." Ecological Modelling 221, no. 21 (October 2010): 2546–54. http://dx.doi.org/10.1016/j.ecolmodel.2010.07.023.

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31

Iannone III, Basil V., Kevin M. Potter, Qinfeng Guo, Insu Jo, Christopher M. Oswalt, and Songlin Fei. "Environmental harshness drives spatial heterogeneity in biotic resistance." NeoBiota 40 (December 4, 2018): 87–105. http://dx.doi.org/10.3897/neobiota.40.28558.

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Ecological communities often exhibit greater resistance to biological invasions when these communities consist of species that are not closely related. The effective size of this resistance, however, varies geographically. Here we investigate the drivers of this heterogeneity in the context of known contributions of native trees to the resistance of forests in the eastern United States of America to plant invasions. Using 42,626 spatially referenced forest community observations, we quantified spatial heterogeneity in relationships between evolutionary relatedness amongst native trees and both invasive plant species richness and cover. We then modelled the variability amongst the 91 ecological sections of our study area in the slopes of these relationships in response to three factors known to affect invasion and evolutionary relationships –environmental harshness (as estimated via tree height), relative tree density and environmental variability. Invasive species richness and cover declined in plots having less evolutionarily related native trees. The degree to which they did, however, varied considerably amongst ecological sections. This variability was explained by an ecological section’s mean maximum tree height and, to a lesser degree, SD in maximum tree height (R2GLMM = 0.47 to 0.63). In general, less evolutionarily related native tree communities better resisted overall plant invasions in less harsh forests and in forests where the degree of harshness was more homogenous. These findings can guide future investigations aimed at identifying the mechanisms by which evolutionary relatedness of native species affects exotic species invasions and the environmental conditions under which these effects are most pronounced.

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32

SEURONT, LAURENT, and YVAN LAGADEUC. "VARIABILITY, INHOMOGENEITY AND HETEROGENEITY: TOWARDS A TERMINOLOGICAL CONSENSUS IN ECOLOGY." Journal of Biological Systems 09, no. 02 (June 2001): 81–87. http://dx.doi.org/10.1142/s0218339001000281.

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Current widespread use of ecological terms such as variability, heterogeneity and homogeneity is misleading and prevents ecologists from reaching a terminological consensus on what is meant when discussing these concepts, in particular with regard to the descriptor 'heterogeneous.' We propose the use of 'inhomogeneity' to define patterns or processes exhibiting a scale-dependent structure, whether spatial or temporal. Thus, the concept of 'inhomogeneity' can be regarded as a structural ecological entity. A descriptor exhibiting different kinds of inhomogeneity, either spatially or temporally, will then be qualified as being heterogeneous. The terminological consensus introduced here in the particular frame of ecological sciences is finally discussed and generalized to the actual scientific thought process.

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33

Li, Rong, Rui Han, Qianru Yu, Shuang Qi, and Luo Guo. "Spatial Heterogeneous of Ecological Vulnerability in Arid and Semi-Arid Area: A Case of the Ningxia Hui Autonomous Region, China." Sustainability 12, no. 11 (May 28, 2020): 4401. http://dx.doi.org/10.3390/su12114401.

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Ecological vulnerability, as an important evaluation method reflecting regional ecological status and the degree of stability, is the key content in global change and sustainable development. Most studies mainly focus on changes of ecological vulnerability concerning the temporal trend, but rarely take arid and semi-arid areas into consideration to explore the spatial heterogeneity of the ecological vulnerability index (EVI) there. In this study, we selected the Ningxia Hui Autonomous Region on the Loess Plateau of China, a typical arid and semi-arid area, as a case to investigate the spatial heterogeneity of the EVI every five years, from 1990 to 2015. Based on remote sensing data, meteorological data, and economic statistical data, this study first evaluated the temporal‒spatial change of ecological vulnerability in the study area by Geo-information Tupu. Further, we explored the spatial heterogeneity of the ecological vulnerability using Getis-Ord Gi*. Results show that: (1) the regions with high ecological vulnerability are mainly concentrated in the north of the study area, which has high levels of economic growth, while the regions with low ecological vulnerability are mainly distributed in the relatively poor regions in the south of the study area. (2) From 1990 to 2015, ecological vulnerability showed an increasing trend in the study area. Additionally, there is significant transformation between different grades of the EVI, where the area of transformation between a slight vulnerability level and a light vulnerability level accounts for 41.56% of the transformation area. (3) Hot-spot areas of the EVI are mainly concentrated in the north of the study area, and cold-spot areas are mainly concentrated in the center and south of the study area. Spatial heterogeneity of ecological vulnerability is significant in the central and southern areas but insignificant in the north of the study area. (4) The grassland area is the main driving factor of the change in ecological vulnerability, which is also affected by both arid and semi-arid climates and ecological projects. This study can provide theoretical references for sustainable development to present feasible suggestions on protection measures and management modes in arid and semi-arid areas.

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34

Lee, Joung Hun, and Yoh Iwasa. "Ecotourism development and the heterogeneity of tourists." Theoretical Ecology 13, no. 3 (April 29, 2020): 371–83. http://dx.doi.org/10.1007/s12080-020-00458-7.

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35

Roese, John H., Ken L. Risenhoover, and L. Joseph Folse. "Habitat heterogeneity and foraging efficiency: an individual-based model." Ecological Modelling 57, no. 1-2 (October 1991): 133–43. http://dx.doi.org/10.1016/0304-3800(91)90058-9.

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36

Azovsky, A. I. "Concept of scale in marine ecology: linking the words or the worlds?" Web Ecology 1, no. 1 (April 14, 2000): 28–34. http://dx.doi.org/10.5194/we-1-28-2000.

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Abstract. The concept of scale (in sensu lato) is considered to be very promising as the integrative basis for modern ecology. Nowadays it is not a full-blown theory but rather a flexible and progressively developing methodology to outline future unifying theories. It provides a powerful conceptual framework for generating testable hypotheses and studying a wide range of ecological phenomena related with such themes as heterogeneity, hierarchy and size. Spatio-temporal heterogeneity, organizational hierarchies and body size are the main scaling factors for ecological patterns and processes. Broad comparison of patterns for these three different but interrelated dimensions can reveal some new regularities ("scaling laws") of ecological systems. It also allows us to look at the worlds of different organisms "through their own eyes". Some examples of applying the cross-scaling approach in marine ecology are considered: &#151; Patterns and scales of spatial heterogeneity; &#151; Species-area curves and body size; &#151; Co-occurrence of congeners as scale-dependent phenomenon; &#151; Spatio-temporal ranges of ecological hierarchies.

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37

Baer, Sara G., Scott L. Collins, John M. Blair, Alan K. Knapp, and Anna K. Fiedler. "Soil Heterogeneity Effects on Tallgrass Prairie Community Heterogeneity: An Application of Ecological Theory to Restoration Ecology." Restoration Ecology 13, no. 2 (June 2005): 413–24. http://dx.doi.org/10.1111/j.1526-100x.2005.00051.x.

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38

Crow, Taylor M., C. Alex Buerkle, Daniel E. Runcie, and Kristina M. Hufford. "Implications of genetic heterogeneity for plant translocation during ecological restoration." Ecology and Evolution 11, no. 3 (January 16, 2021): 1100–1110. http://dx.doi.org/10.1002/ece3.6978.

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39

Leinonen, Tuomas, R. J. Scott McCairns, Robert B. O'Hara, and Juha Merilä. "QST–FST comparisons: evolutionary and ecological insights from genomic heterogeneity." Nature Reviews Genetics 14, no. 3 (February 5, 2013): 179–90. http://dx.doi.org/10.1038/nrg3395.

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40

LESTER, REBECCA E. "Scale, Heterogeneity, and the Structure and Diversity of Ecological Communities." Austral Ecology 37, no. 1 (January 30, 2012): e1-e2. http://dx.doi.org/10.1111/j.1442-9993.2011.02328.x.

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41

Goldwyn, Eli E., and Alan Hastings. "Small Heterogeneity Has Large Effects on Synchronization of Ecological Oscillators." Bulletin of Mathematical Biology 71, no. 1 (December 10, 2008): 130–44. http://dx.doi.org/10.1007/s11538-008-9355-9.

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42

Pryke, James S., and Michael J. Samways. "Conserving natural heterogeneity is crucial for designing effective ecological networks." Landscape Ecology 30, no. 4 (September 27, 2014): 595–607. http://dx.doi.org/10.1007/s10980-014-0096-x.

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43

Pledger, Shirley, and Polly Phillpot. "Using Mixtures to Model Heterogeneity in Ecological Capture-Recapture Studies." Biometrical Journal 50, no. 6 (December 2008): 1022–34. http://dx.doi.org/10.1002/bimj.200810446.

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44

Liu, HuiYu, JiaJia Lin, Mingyang Zhang, ZhenShan Lin, and Ten Wen. "Extinction of poorest competitors and temporal heterogeneity of habitat destruction." Ecological Modelling 219, no. 1-2 (November 2008): 30–38. http://dx.doi.org/10.1016/j.ecolmodel.2008.06.026.

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45

Oliver, Lauren J., Byron J. T. Morgan, Sarah M. Durant, and Nathalie Pettorelli. "Individual heterogeneity in recapture probability and survival estimates in cheetah." Ecological Modelling 222, no. 3 (February 2011): 776–84. http://dx.doi.org/10.1016/j.ecolmodel.2010.11.021.

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46

Hughes, G., and R. G. McKinlay. "Spatial heterogeneity in yield-pest relationships for crop loss assessment." Ecological Modelling 41, no. 1-2 (April 1988): 67–73. http://dx.doi.org/10.1016/0304-3800(88)90045-2.

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47

Luo, Manya, Xia Jia, Yonghua Zhao, Huanyuan Wang, Chunyang Chen, Dongqian Li, Shuyuan Yang, and Juan Li. "The Temporal and Spatial Characteristics of Ecological Security Pattern in the Loess Plateau, China." Land 13, no. 5 (April 24, 2024): 570. http://dx.doi.org/10.3390/land13050570.

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As a typical ecologically fragile area, the ecological security of the Loess Plateau has been seriously threatened. Ecological security patterns (ESP) have gradually become an effective method for protecting ecological security and supporting the management and sustainable development of ecosystems. Therefore, this study constructed a novel ESP based on ecological “function–structure”, utilizing minimum cumulative resistance (MCR) to identify ecological source areas and corridors. Additionally, time scales were introduced into the ESP, allowing for a comprehensive analysis of the spatiotemporal heterogeneity of ecological security in the Loess Plateau. The study revealed that the number of ecological sources decreased from southeast to northwest, with 27, 41, and 77 sources covering total areas of 4263.810 km2, 18,566.034 km2, and 113,209.595 km2 from 2000 to 2020, respectively. Similarly, the number and complexity of ecological corridors increased over the same time period, with 64, 85, and 105 corridors totaling lengths of 4579.326 km, 6526.996 km, and 7015.174 km, respectively. The expansion of ecological security zones was mainly observed in the southeast part of the Loess Plateau. Overall, the ESP of the Loess Plateau saw an improvement, with the southeastern part showing better ecological security than the northwestern part. These findings hold great significance for regional ecological security evaluations and are crucial for promoting ecological management and healthy development in the Loess Plateau.

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Chen, Jason Z., Zeeyong Kwong, Nicole M. Gerardo, and Nic M. Vega. "Ecological drift during colonization drives within-host and between-host heterogeneity in an animal-associated symbiont." PLOS Biology 22, no. 4 (April 25, 2024): e3002304. http://dx.doi.org/10.1371/journal.pbio.3002304.

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Specialized host–microbe symbioses canonically show greater diversity than expected from simple models, both at the population level and within individual hosts. To understand how this heterogeneity arises, we utilize the squash bug, Anasa tristis, and its bacterial symbionts in the genus Caballeronia. We modulate symbiont bottleneck size and inoculum composition during colonization to demonstrate the significance of ecological drift, the noisy fluctuations in community composition due to demographic stochasticity. Consistent with predictions from the neutral theory of biodiversity, we found that ecological drift alone can account for heterogeneity in symbiont community composition between hosts, even when 2 strains are nearly genetically identical. When acting on competing strains, ecological drift can maintain symbiont genetic diversity among different hosts by stochastically determining the dominant strain within each host. Finally, ecological drift mediates heterogeneity in isogenic symbiont populations even within a single host, along a consistent gradient running the anterior-posterior axis of the symbiotic organ. Our results demonstrate that symbiont population structure across scales does not necessarily require host-mediated selection, as it can emerge as a result of ecological drift acting on both isogenic and unrelated competitors. Our findings illuminate the processes that might affect symbiont transmission, coinfection, and population structure in nature, which can drive the evolution of host–microbe symbioses and microbe–microbe interactions within host-associated microbiomes.

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Gregoire, Timothy G., and Michael E. Dyer. "Model Fitting Under Patterned Heterogeneity of Variance." Forest Science 35, no. 1 (March 1, 1989): 105–25. http://dx.doi.org/10.1093/forestscience/35.1.105.

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Abstract Two approaches toward fitting regression models with multiplicative error heteroscedasticity recur in the forestry, ecology, and statistical literature. One includes the estimation of the heterogeneity in the fitting process. The alternative approach entails the use of variance estimators that are robust to the error variance heterogeneity. Under suitable conditions, the former method offers nonnegligible gains in efficiency, whereas the robust alternatives provide accurate assessment of ordinary least squares estimators even in the presence of heteroscedasticity. The performance of both approaches are examined and contrasted, and suggestions for future applications and research are made on the basis of these results. For. Sci. 35(1):105-125.

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Liccari, Francesco, Maurizia Sigura, and Giovanni Bacaro. "Use of Remote Sensing Techniques to Estimate Plant Diversity within Ecological Networks: A Worked Example." Remote Sensing 14, no. 19 (October 2, 2022): 4933. http://dx.doi.org/10.3390/rs14194933.

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As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao’s Quadratic heterogeneity measure (Rao’s Q). Both approaches are available thanks to two brand-new R packages: “biodivMapR” and “rasterdiv”. Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R2 = 0.36 and R2 = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion.

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