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• Published: 06 September 2019

The creation of “Ecosystem Core” hypothesis to explain ecosystem evolution

• Kun Wang 1   na1 &
• Xiajie Zhai 1 , 2   na1  

BMC Ecology volume  19 , Article number:  33 ( 2019 ) Cite this article

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Humans have dramatically changed natural ecosystems around the world as their capacity to manage their environment for multiple uses has evolved in step with agricultural, industrial and green revolutions. Numerous natural ecosystems have been replaced by various artificial or semi-artificial ecosystems, the ecosystem has changed. To a certain extent, this is ecosystem evolution. So far, there is no definite ecological theory about the mechanism for evolution of an ecosystem. Even though the discipline of community ecology has a relatively comprehensive and well-described theory of succession, at the different ecological research levels, is it the same mechanism for the community succession and ecosystem evolution? What is the factor that drives ecosystem evolution?

This paper puts forward the “Ecosystem Core” hypothesis to scientifically address the above problems. We define abiotic component of ecosystem as “Ecosystem Core” or “Resource Core”, which provides the foundation (matter and energy) for the existence and progress of organisms and should be the nucleus of an ecosystem. In this paper, we explain the basic meaning of this hypothesis, review its theoretical foundation, and provide a demonstration (based on emergy theory, which is an accounting tool that considers both the environmental and economic inputs that are directly or indirectly required by a process to generate a product and it measures real wealth, independent of financial considerations) of the hypothesis, and discuss the mechanism of ecosystem evolution. The “Ecosystem Core” hypothesis reveals the quantitative relationship between the energy input and ecosystem evolution.

Conclusions

The input of artificial auxiliary energy is the direct cause of ecosystem evolution. Different combinations of natural and purchased emergy are coupled to maintain the same ecosystem under the different environmental conditions. When artificial energy enters the ecosystem, its role is similar to that of the microscopic particles that collide with the nucleus in the nuclear reaction, and after mutual reaction, the atom will form a new atomic structure, and for the ecosystem, a new form of resource composition and energy action will appear, and the corresponding species of life will change, then ecosystem complete its evolution.

Ecology encompasses multiple levels of research, from molecular level to landscape level. In the study of community ecology, a relatively well-described and comprehensive theory of succession has been formed [ 1 ]. Community succession is the process of community change following disturbance by natural or human disturbance where the community composition, especially the dominant species, changes. This is a phenomenon in which one community is replaced by another at different times in the same place. After the ecosystem concept was presented in the 1930s [ 2 ], scholars used systematic theory and a holistic view to study the process and phenomenon of life forming a more complete ecosystem theory. Discipline of ecosystem ecology is continuously improving, and has become the focal area of ecological research. Ecosystem degradation [ 3 , 4 ], biodiversity loss [ 5 , 6 ], eutrophication [ 7 ], biological invasion [ 8 ] or novel ecosystems (emerging ecosystems, result when species occur in combinations and relative abundances that have not occurred previously within a given biome [ 9 ]) and climate change [ 10 ] suggest that ecosystems are constantly changing due to natural and anthropogenic factors in the real world. An ecosystem is replaced by another ecosystem, which we call the ecosystem evolution. So far, there is no definite theory about the mechanism for evolution of an ecosystem which raises the following questions: is it the same mechanism for the ecosystem evolution and community succession? What is the factor that drives the evolution of the ecosystem? After years of attention and study on these scientific issues, we write this paper to propose the hypothesis of “Ecosystem Core”, in order to scientifically address these problems.

Results and discussion

The basic meaning of “ecosystem core” hypothesis, interpretation of atomic theory.

Classical physics shows that the atom is composed of a positively charged nucleus and negatively charged extranuclear electrons, which are attracted by positive and negative electricity to form a complete and stable atomic structure. The extranuclear electrons are in different orbits because of the different charges they carry. When an electron absorbs a certain amount of energy, it will jump from one level to another, and when the absorbed energy is sufficient to exceed the gravitational force between electrons and nuclear, the electrons will “escape”. The electron cloud and the atom nucleus are combined together by the charge, which make up the atom (Fig.  1 ).

A modern depiction of atomic structure. a The darker the color, the higher the probability that an electron will be at that point. b In a two-dimensional cross section of the electron in a hydrogen atom, the more crowded the dots, the higher the probability that an electron will be at that point. In both ( a ) and ( b ), the nucleus is in the center of the diagram [ 11 ]

An ecosystem is a community of living organisms in conjunction with the nonliving components of their environment [ 12 ]. The abiotic components is essential for the existence and development of organisms and includes light, temperature, water, air, inorganic part of soil, etc. The organic part mainly refers to the relationship between organisms including plants, animals and microbes, which are subdivided into producers, consumers and decomposers. These biotic and abiotic components are linked together through matter cycles and energy flows [ 13 ]. Generally, there are certain kinds of organisms in what kind of environments exists, and the environment determines the existence of organisms. However, while organisms are adapting to the environment, they also have a transformative effect on the environment.

When compared with atomic structure, the relationship between organism and the environment in an ecosystem is similar. In the ecosystem, the abiotic component of ecosystem (resources) is similar to the atomic nucleus, we call it “Ecosystem Core or Resource Core”, and all kinds of abiotic components provide the matter and energy for the existence and development of the living creature, with positive electricity; biotic component of ecosystem (life) consumes energy, which is equivalent to extra-nuclear electrons, with negative electricity. Abiotic and biotic components of ecosystem are combined by the gravitational effect of energy. In fact, by using resources the living form is also maintaining and transforming the environment by shape shifting matter and energy, and forming a circulation state (Fig.  2 ).

“Ecosystem core” hypothesis model

Under the natural condition without human disturbance, a specific ecosystem corresponds to a certain “Ecosystem Core”, and the emergence of a latitudinal and longitudinal distribution of vegetation on the earth is the concrete embodiment of this hypothesis. When human’s disturb a natural ecosystem by increasing energy inputs or changing the output of the system’ state, the ecosystem changes or becomes a different ecosystem resembling the electronic transition of an atom; when a disturbance far exceeds the energy provided by the “Ecosystem Core”, it transitions from a natural system to an entirely artificial intelligent ecosystem (e.g. soilless farming) that resembles an electron “escaping” in an atom (Fig.  3 ). In the evolution of the ecosystem, various ecological factors play a role together, leading to change in the structure and function of the system, and the vary of matter and energy are the main driving factors. That is, changing the input of matter and energy will change the natural living system, which is the mechanism of ecosystem evolution.

Conceptual model of ecosystem evolution

Theoretical foundation of “Ecosystem Core” hypothesis

Matter cycling and energy flows are basis features and functions of any natural ecosystem, the inclusion of diverse organisms makes it an “ecosystem”. The law of conservation of matter and the law of conservation of energy are still the theoretical basis of the “Ecosystem Core” hypothesis. The principle of structure and function is the most essential characteristic of the ecosystem. Ecosystem structure reflects the organization of various abiotic and biotic pools that exchange energy and matter [ 14 ], which is the basis of the function. For example, community composition and distribution [ 15 , 16 ], biodiversity [ 17 , 18 ], and food webs [ 19 ]. Ecosystem function is the physical, chemical, and biological processes or attributes that contribute to the self-maintenance of the ecosystem, including energy flow, nutrient cycling, filtering, buffering of contaminants, and regulation of populations [ 19 ], which is the expression of the structure.

In nature, vegetation or plant communities are changing from time to time. In the same place, the replacement of the old plant community by a new plant community is called vegetation succession. Similarly, an ecosystem is replaced by another ecosystem, which we call the ecosystem evolution. The two are related and have distinct characteristics. Community ecology is concerned with the composition of life- the plant itself, such as species composition, productivity and biodiversity. It is the response of the plant itself to natural environmental factors or interference, for example, the diversity-stability debate [ 20 ]. On average, diversity give rise to stability, but it depends on factors including the intrinsic responses of species to environmental fluctuations, the speed at which species respond to perturbations and the strength of competition [ 21 ]; the ecosystem consists of the non-living environment and the living matter itself, and the life part contains animals, plants and microorganisms, and the relationship between them is very complex, and it is a comprehensive reflection of the ecosystem under the interference of natural or man-made. Therefore, the evolution of an ecosystem is more complex than community succession.

Succession is mainly caused by natural factors and human disturbance. If this succession occurs without external interference, it is natural succession. In general, natural ecosystems have their own unique structure and function types, although the structure and function are also changing. This alter is often only a fluctuation, or is considered a fluctuating balance [ 22 ]. Some directional changes may evolve over a long period. When there is no fundamental change of the environmental conditions in the ecosystem, especially in soil properties, if sufficient time is available, the biological community can be restored to a state similar to its natural condition [ 23 ]. In the present world, an ecosystem that has not been disturbed by human beings is very rare [ 24 ]. It is more common to find ecosystems that are continuously disturbed than non-disturbed ecosystems. Human interference encompasses the utilization, abandonment, transformation, reconstruction, and restoration of natural systems. These disturbances sometimes happen alone or they sometimes interfere with other disturbances. The human interference constitutes the main driving force of ecosystem change and it is comprised by the time, scale and intensity of a disturbance.

Demonstration of “Ecosystem Core” hypothesis

On the planet we live on, in areas where the latitude and longitude are relatively definite, and the terrain is basically consistent, their light, precipitation, temperature, humidity, soil and biological systems can be regarded as the same. In accordance with the laws of nature, if there is no human disturbance, the ecosystem formed by them should belong to the same type. However, this situation is very rare today. Under the intense disturbance of human activities, it is very difficult to find the natural ecosystem that is undisturbed replaced by many ecosystem types coexisting, especially these ecosystems of man-made or various degrees of interference dominated the mainstream [ 4 , 25 ]. So far, 12% of the earth’s land surface has been reclaimed as cropland [ 26 ], add deforestation, infrastructure, urban use and so on, with a total of 18–29% [ 27 ], which is already much different from the original ecosystem type. For example, the agro-pastoral ecotone of northern China was originally the grassland ecosystem of Eurasia, but with the large increase of population, people began to reclaim grassland in a large area, and then there are dozens of artificial and semi-artificial ecosystem types, including farmland, artificial forest, vegetable field, forage land, wetland, grazing land, vegetable greenhouses, and so forth. These ecosystems exist either for human economic purposes or are constrained by natural climate and soil conditions, coexist in a certain area and form a composite landscape that we think.

Coexistence mechanism of different ecosystem types under the same climate and soil conditions

Neutral theory and niche (construction) theory explain why species in a community can coexist [ 28 , 29 , 30 ]. However, it is not clear that coexistence mechanism of different ecosystem types under the same climate and soil conditions. Some studies of physical geography have shown that, without or with little human interference, a climate condition should correspond to a major top-level ecosystem type; and some auxiliary ecosystems can be found due to local topographic variations. It’s called monoclimax hypothesis [ 31 ]. Guyuan County of Hebei Province is a typical representative area of agro-pastoral ecotone in northern China. The county has a population of 260 thousands and a land area of 36.01 × 10 4  ha, of which cultivated land accounts for 40%, natural grassland and woodland occupy 50%, and 10% are water surface, roads, dwellings and so on. We selected 12 of the major ecosystem types as our research subjects that including four land use types: commercial crop, field crop, artificial forage and grassland. The energy input analysis was carried out according to the emergy theory (it is a universal measure of real wealth of the work of nature and society based on a common basis, and can be described as the available energy of one kind previously required to be used up directly and indirectly to make the product or service [ 32 ]), as showed in Tables  1 and 2 .

Ecosystems are open, with inputs and outputs of matter and energy. The pure natural ecosystem has no or negligible matter and energy input of human investment, but the artificial or semi-artificial ecosystem is more complex. There are not only structural differences among system types, but also obvious differences in functional status. As we can see from Table  1 , the natural emergy inputs for 12 ecosystems were the same, to 5.31 × 10 14  sej/ha/year [the unit of emergy is emJoule, a unit referring to the available energy of one kind consumed in transformations. Usually a unit of solar emergy expressed in solar emergy joules (abbreviated sej) is used to determine the value of environmental and human work within a system on a common basis]. But their average sum emergy inputs was more than 15 times different, and furthermore the average purchased emergy inputs was nearly 40 times the gap. It showed the rule roughly of “commercial crop > artificial forage > field crop > grassland”. Table  2 showed that from economic crops to artificial forage, to field crops, and grassland, emergy investment ratio (EIR) and environmental load (ELR) have a downward trend in turn, while the emergy self-sufficiency rate (ESR) and net emergy output rate (EYR) showed a tendency to increase obviously, and the emergy sustainability index (ESI) of the whole ecosystems increased significantly. This fully indicates that the higher the output of an ecosystem, the higher the human emergy that needs to be invested, and the greater the environmental load, the lower the sustainability. To some extent, natural resource emergy input is the basic power to maintain the operation of the ecosystem; purchased (artificial) emergy input is the fundamental cause of the ecosystem change under the same environmental conditions [ 33 ].

Emergy input changes of the same ecosystem type under different climatic conditions

A certain ecosystem is distributed in a certain environment, limited by the moisture and temperature conditions, there are different vegetation distribution belts from the equator to the poles of the earth. China has cold temperate, temperate, warm temperate, subtropical and tropical climates from north to south, and the vegetation is distributed in turn: the coniferous deciduous forest, temperate coniferous and broad-leaved mixed forest, warm temperate deciduous broad-leaved forest, north Asia subtropical deciduous broad-leaved forest with evergreen components, middle and south Asia tropical evergreen broad-leaved forest, tropical seasonal rain forest and rain forest. Nowadays, humans have dramatically transformed natural systems around the world as their capacity to manage their environment for multiple uses has evolved in step with to agricultural, industrial and green revolutions. Numerous natural ecosystems have been replaced by various semi-artificial or artificial ecosystems. The same ecosystem also appears even under different environmental conditions. For example, China’s two major crops, corn and wheat, have their footprints in almost every climate zone from south to north. Based on the statistical data of the main provinces of each climatic zone in 2014, we analyzed the emergy input of maize and wheat ecosystem, as showed in Tables  3 and 4 .

China has a broad geographical and diverse climate, the average yield is about 4500 kg/ha and 9000 kg/ha for wheat and maize respectively, but their input emergy are quite different based on the above table’s data. In the case of wheat ecosystem, the purchased emergy input in Northeast and Southwest of China accounts for about 59% and 73% of the total input respectively, while other areas are more than 80%; maize production is similar, these ratios are close to 70% to 75% in the Northeast and Southwest respectively, while in the Loess Plateau, the Huang-Huai Hai Plain and the Northwest area are 78–85%. Under normal conditions, the yield of maize and wheat mainly depends on the amount of natural and auxiliary energy input, and the input of auxiliary energy is closely related to the moisture and temperature conditions of each climatic zone and the soil fertility. Northeast China is rich in corn, wheat and soybeans, while North China and Northwest China are rich in wheat, and rice cultivation in the south is large. The so-called main crop producing area in China is a paradigm that humans have gradually explored in the long-term production practice to make full use of natural resources. In fact, it is an alternative to obtain high yield with less man-made (auxiliary) energy input.

The mechanism of ecosystem evolution

Terrestrial ecosystem has relatively stable characteristics in a certain time and space range due to ecological resilience, which is the ability of a system to persist in the face of perturbations [ 22 ], but the life component in the ecosystem structure is changing all the time. When this change reaches a certain degree, or exceeds a certain “threshold” [ 36 ], the ecosystem functions also will have the fundamental change, finally causing the ecosystem evolution. This is a systematic evolution marked by community succession. The principle of ecosystem structure and function shows that structure is the basis of function, function is the embodiment of structure; change is absolute, and stability is relative. In the long history of the earth, the ecosystem experienced the changes from aquatic to terrestrial life, lower to higher organism, and grass to wood. In fact, it is a concrete manifestation of system structure and function changes. At present, many artificial ecosystems have been built according to human purpose and demand, such as farmland, artificial grassland, greenhouse, economic forest, aquaculture farm and many more. These ecosystems have a fundamental change in structure and function compared with the original ecosystem. In addition to partial use of natural resources, more of them are supported by artificial input energy. Like the artificial climate room, factory plant production (soilless cultivation), etc., have basically separated from the natural environment, completely relying on artificial input for maintenance.

Figure  4 shows that when the structure of the natural ecosystem becomes weaker, its function will also be degraded. For example, when the natural grassland is overused, the grassland degenerates and the function of production and biodiversity decrease [ 37 ]; when increasing input to the natural ecosystem, the function of the ecosystem can also be strengthened. For example, when fertilizing, irrigating, loosening soil, reseeding and other technical measures are carried out on natural grassland, the living environment of forage is improved, and the production capacity of grassland is obviously enhanced [ 38 ]. However, the two cases are without fundamental changes in the structure of their ecosystem, that is to say, within the scope of the “threshold” of the ecosystem, they belong to the ecosystem succession in the same location at different times.

The mechanism of ecosystem evolution. “Ecosystem Core” refers to natural resources (energy)

The artificial reconstruction ecosystem is the structure of the natural ecosystem that has been partially or completely destroyed, and it also exhibits different system functions [ 39 ]. For example, natural grassland is reclaimed into farmland, the grazing function has become grain production. The essential driving force that determines this kind of ecosystem change is the economic purpose of mankind. It is the decision of input and output under the real economic and technological conditions, rather than absolutely following the principle of matter and energy input of the system. The fact of the large-scale use of chemical fertilizers and pesticides in the world today has also fully explained this point. Despite the fact that high production has been achieved, the natural environment has been severely damaged [ 40 , 41 , 42 ].

Conclusion and perspective

“Ecosystem Core” hypothesis is an innovative explanation of ecosystem evolution, which is related to but distinct from the theory of community succession. Natural energy is the basic force for sustaining ecosystem development, and artificial energy input is the direct cause of ecosystem evolution. Different combinations of natural and purchased emergy are coupled to maintain the same ecosystem under the different environmental conditions. In general, certain natural resources correspond to specific natural ecosystems. With the disturbance of human energy, the natural ecosystem is separated from the original system development model and forms a new ecosystem. Ecosystem evolution should includes succession and reconstruction. The former is the functional evolution of the ecosystem without structural change, and the latter is a new ecosystem that is reconstructed according to human’s purpose and need. The evolution of an ecosystem is related to human economic purpose, its input–output ratio affects the goal of system reconstruction.

Availability of data and materials

The datasets in this study are available from the corresponding author on reasonable request.

Abbreviations

solar emergy joules

emergy investment ratio

environmental loading ratio

emergy self-sufficiency ratio

emergy yield ratio

emergy sustainability index

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Acknowledgements

Thanks to Paulette Ford, Deborah M. Finch, Megan Friggens and Andrea Lopez for comments that improved the manuscript. In addition, a briefing of this article was presented at the Great Plains Grassland Summit in Denver on April 2018.

This research was supported by the project “The National Scientific Research Institutions to Carry Out An Important Agricultural Extension Service Pilot—Northern Ecology Function Area in Hebei Province” funded by the Ministry of Agriculture and Ministry of Finance, China. This study was also funded by the China Scholarship Council (grant number 201706350067). Funders were not involved in the design of the experiment, analysis and interpretation of the data or the writing of the manuscript.

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Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing, 100091, China

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Wang, K., Zhai, X. The creation of “Ecosystem Core” hypothesis to explain ecosystem evolution. BMC Ecol 19 , 33 (2019). https://doi.org/10.1186/s12898-019-0251-y

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• Published: 29 July 2020

Ecosystem decay exacerbates biodiversity loss with habitat loss

• Jonathan M. Chase   ORCID: orcid.org/0000-0001-5580-4303 1 , 2 ,
• Shane A. Blowes   ORCID: orcid.org/0000-0001-6310-3670 1 , 2 ,
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Although habitat loss is the predominant factor leading to biodiversity loss in the Anthropocene 1 , 2 , exactly how this loss manifests—and at which scales—remains a central debate 3 , 4 , 5 , 6 . The ‘passive sampling’ hypothesis suggests that species are lost in proportion to their abundance and distribution in the natural habitat 7 , 8 , whereas the ‘ecosystem decay’ hypothesis suggests that ecological processes change in smaller and more-isolated habitats such that more species are lost than would have been expected simply through loss of habitat alone 9 , 10 . Generalizable tests of these hypotheses have been limited by heterogeneous sampling designs and a narrow focus on estimates of species richness that are strongly dependent on scale. Here we analyse 123 studies of assemblage-level abundances of focal taxa taken from multiple habitat fragments of varying size to evaluate the influence of passive sampling and ecosystem decay on biodiversity loss. We found overall support for the ecosystem decay hypothesis. Across all studies, ecosystems and taxa, biodiversity estimates from smaller habitat fragments—when controlled for sampling effort—contain fewer individuals, fewer species and less-even communities than expected from a sample of larger fragments. However, the diversity loss due to ecosystem decay in some studies (for example, those in which habitat loss took place more than 100 years ago) was less than expected from the overall pattern, as a result of compositional turnover by species that were not originally present in the intact habitats. We conclude that the incorporation of non-passive effects of habitat loss on biodiversity change will improve biodiversity scenarios under future land use, and planning for habitat protection and restoration.

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Complex long-term biodiversity change among invertebrates, bryophytes and lichens

Anthropogenic climate and land-use change drive short- and long-term biodiversity shifts across taxa

The results of biodiversity–ecosystem functioning experiments are realistic

Data availability.

All of the data used in this analysis are open access and available in ref. 22 (117 of the datasets) and ref. 143 (5 of the datasets). Raw data (before standardization) are available from GitHub ( https://github.com/FelixMay/FragFrame_1) , and are mirrored on Zenodo ( https://doi.org/10.5281/zenodo.3862409 ).

Code availability

The R code used for standardizing the data and doing the analyses presented here are available from GitHub ( https://github.com/FelixMay/FragFrame_1) , and are mirrored on Zenodo ( https://doi.org/10.5281/zenodo.3862409 ).

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Acknowledgements

All authors were supported by the German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (funded by the German Research Foundation; FZT 118). The contribution of T.M.K. was also supported by the Helmholtz Association and by the Alexander von Humboldt Foundation. We thank the many authors who supplied the data that went into the core analyses of this paper, and A. Sagouis and M. Liebergesell for help with data acquisition, collation and harmonization. A. Sagouis helped with the preparation of the simulations in Extended Data Fig. 1, and Fig. 1 was created by F. Arndt (Formenorm.de) for the express use in this paper. Finally, we thank R. Colwell and J. Hortal for important comments and criticisms that helped us to improve the manuscript.

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German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

Jonathan M. Chase, Shane A. Blowes, Tiffany M. Knight, Katharina Gerstner & Felix May

Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

Jonathan M. Chase & Shane A. Blowes

Institute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

Tiffany M. Knight

Department of Community Ecology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Germany

Leuphana University, Lüneburg, Germany

Theoretical Ecology, Institute of Biology, Freie Universität Berlin, Berlin, Germany

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J.M.C. and T.M.K. conceived the project; J.M.C., K.G. and F.M. developed the initial protocol for data collation and hypothesis tests; J.M.C., F.M. and S.A.B. organized and cleaned the data; F.M. and S.A.B. performed the analyses; J.M.C. wrote the first draft, and all authors contributed to revisions.

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Extended data figures and tables

Extended data fig. 1 simulations of the null expectation under the random sampling hypothesis for varying degrees of within-species aggregation..

To evaluate the robustness of the null expectation of a zero slope of biodiversity patterns in standardized samples with fragment size, we took different-sized samples from a simulated landscape and estimated biodiversity patterns. a , Examples of landscapes with different levels of intraspecific aggregation (left to right is from completely random to most-aggregated). From within each of these landscapes, we illustrate four different fragment sizes (shaded squares), and then take standardized (constant-sized) samples from each fragment. b , Density plots showing slope estimates of linear models fit to numbers of individuals (top), species richness (centre) and evenness (bottom) as a function of fragment areas for 2,000 simulated landscapes of each level of aggregation. Both the response and fragment area were log-transformed before model fitting. Densities are shaded by quantiles and the black diamond shows the median for each combination of aggregation and metric; vertical dashed line shows the zero expectation and the median result is given. In some cases, the median result lies very slightly above or below zero (though this does not seem to be associated with levels of aggregation). This is an outcome of the stochastic simulation we performed, and is sensitive to parameters and numbers of iterations, and thus we do not perform statistical tests.

Extended Data Fig. 2 Different measures of species richness related to the size of habitat fragments.

For each case, richness metrics were positively associated with the size of habitat fragments, supporting ecosystem decay as the predominant driver. a , Richness standardized to a common number of individuals. b , Richness standardized to a common sample completeness. c , Asymptotic richness. Solid black lines and shading show overall relationships and 95% credible intervals for each metric. The slope ( β ) coefficient and its 95% credible interval are shown at the top. Coloured lines show study-level relationships for taxon groups.

Extended Data Fig. 3 Incorporation of uncertainty by calculating z -scores of observed versus null-expected outcomes.

Because there is always uncertainty surrounding the expected outcomes based on passive sampling, we repeated analyses of all metrics recalculated as z -scores. After standardization, z -scores ((observed − expected)/s.d.(expected)) were calculated for the following. a , Standardized species richness. b , Standardized evenness ( S PIE ). c , Richness standardized to a common number of individuals. d , Richness standardized to a common sample completeness. e , Asymptotic richness. As with the direct measurements, analyses of z -scores show—in total—greater biodiversity loss than expected from passive sampling in smaller habitat fragments, and thus support the ecosystem decay hypothesis. Solid black lines and shading show overall relationships and 95% credible intervals for each metric. Inset shows β-slope coefficient and its 95% credible interval. Coloured lines show study-level relationships for taxon groups. The β -coefficients are not directly comparable to the results from Fig. 2 and Extended Data Fig. 2 , owing to differences in the response scale of the z -scores.

Extended Data Fig. 4 Testing of robustness of results to alternative methods.

a , Testing the sensitivity of our results to the exclusion of 47 studies in which data were pooled, and thus sample extent could not be controlled (Methods). Analyses on this subset of data ( n  = 79) were consistent with those from the full dataset (that is, the 95% credible intervals did not overlap zero). b – d , Testing the sensitivity of the results to decisions we made when imputing missing sizes of habitats labelled as continuous and the treatment of non-integer species abundances (Methods). In each case, the reference scenario imputed the size of continuous habitats to have 10× the area of the next largest fragment and calculated the biodiversity metrics using the non-integer abundance values. Alternate combinations were: (1) imputed area for continuous habitats assumed to be 2× that of next biggest fragment, and non-integer values unchanged; (2) imputed area for continuous habitats assumed to be 100× that of the next biggest fragment, and non-integer values unchanged; (3) imputed area for continuous habitats assumed to be 10× that of the next biggest fragment, and non-integer values rounded up; (4) imputed area for continuous habitats assumed to be 10× that of the next biggest fragment and all abundance values divided by the lowest value within each study, resulting in the lowest abundance equalling one, but retaining the same relative abundances. b , Slope estimates for the relationship between standardized numbers of individuals and fragment size. c , Slope estimates for the relationship between standardized species richness and fragment size. d , Slope estimates for the relationship between standardized evenness and fragment size. Colours depict different auxiliary decisions and imputation required in the analysis (Methods). Small points represent study-level estimates, and large points and error bars are the overall estimates and their 95% credible intervals.

Extended Data Fig. 5 Study-level variation in the number of individuals and evenness.

a – h , Density plots of posterior distributions of study-level slope estimates for total abundance ( a – d ) and for evenness ( S PIE ) ( e – h ). Groupings are by taxon group ( a , e ), continent ( b , f ), time since fragmentation ( c , g ) and matrix filter ( d , h ). Each density plot is based on 1,000 samples from the posterior distribution of each study-level slope estimate, and is accompanied by the number of studies for each group. Densities are shaded by quantiles and the black diamond shows the median for each group. Solid black line and surrounding shading show the overall slope estimate and its 95% credible interval.

Extended Data Fig. 6 Study-level slope estimates with latitude.

We found that there was a weak negative signal between study-level slope and the absolute value of latitude, which suggests that the influence of ecosystem decay becomes stronger towards the tropics. Each point shows the study-level slope estimate from the standardized species richness as a function of fragment size. Vertical bars are the 95% credible interval associated with each study-level slope estimate. Solid black line and shading shows the relationship and 95% credible interval between the slope estimates and absolute latitude.

Extended Data Fig. 7 Relationship between size of habitat fragment and species composition.

Overall, we found that turnover contributes more than nestedness to pairwise dissimilarity between fragments within a study, but shows contrasting patterns with increasing fragment size differences. a , Turnover component of Jaccard dissimilarity. b , Turnover or balanced abundance component of Ruzicka dissimilarity. c , Nestedness component of Jaccard dissimilarity. d , Nestedness or abundance gradient component of Ruzicka dissimilarity. Solid black lines and shading show overall relationship and 95% credible interval between each dissimilarity component and the ratio of fragment sizes. Coloured lines show study-level relationships for taxon groups.

Extended Data Fig. 8 Endemics–area relationships.

Here, we illustrate the number of species expected to be lost as a function of area of habitat lost under the typically assumed passive sampling hypothesis (purple), and the number of species expected to be lost with habitat lost by inputting our observed parameters from the effect of ecosystem decay (orange).

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Chase, J.M., Blowes, S.A., Knight, T.M. et al. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584 , 238–243 (2020). https://doi.org/10.1038/s41586-020-2531-2

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An ecosystem is a geographic area where plants, animals, and other organisms, as well as weather and landscapes, work together to form a bubble of life.

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An ecosystem is a geographic area where plants , animals , and other organisms , as well as weather and landscape , work together to form a bubble of life. Ecosystems contain biotic or living, parts, as well as a biotic factors , or nonliving parts. Biotic factors include plants , animals , and other organisms . A biotic factors include rocks , temperature , and humidity . Every factor in an ecosystem depends on every other factor, either directly or indirectly. A change in the temperature of an ecosystem will often affect what plants will grow there, for instance. Animals that depend on plants for food and shelter will have to adapt to the changes, move to another ecosystem , or perish . Ecosystems can be very large or very small. Tide pools , the ponds left by the ocean as the tide goes out, are complete, tiny ecosystems . Tide pools contain seaweed , a kind of algae , which uses photosynthesis to create food . Herbivores such as abalone eat the seaweed . Carnivores such as sea stars eat other animals in the tide pool , such as clams or mussels . Tide pools depend on the changing level of ocean water. Some organisms , such as seaweed , thrive in an aquatic environment, when the tide is in and the pool is full. Other organisms , such as hermit crabs , cannot live underwater and depend on the shallow pools left by low tides . In this way, the biotic parts of the ecosystem depend on a biotic factors . The whole surface of Earth is a series of connected ecosystems . Ecosystems are often connected in a larger biome . Biomes are large sections of land, sea, or atmosphere. Forests , ponds , reefs , and tundra are all types of biomes , for example. They're organized very generally, based on the types of plants and animals that live in them. Within each forest , each pond , each reef , or each section of tundra , you'll find many different ecosystems . The biome of the Sahara Desert , for instance, includes a wide variety of ecosystems . The arid climate and hot weather characterize the biome . Within the Sahara are oasis ecosystems , which have date palm trees, freshwater , and animals such as crocodiles . The Sahara also has dune ecosystems , with the changing landscape determined by the wind . Organisms in these ecosystems , such as snakes or scorpions , must be able to survive in sand dunes for long periods of time. The Sahara even includes a marine environment, where the Atlantic Ocean creates cool fogs on the Northwest African coast. Shrubs and animals that feed on small trees, such as goats , live in this Sahara ecosystem . Even similar-sounding biomes could have completely different ecosystems . The biome of the Sahara Desert , for instance, is very different from the biome of the Gobi Desert in Mongolia and China. The Gobi is a cold desert , with frequent snowfall and freezing temperatures . Unlike the Sahara, the Gobi has ecosystems based not in sand , but kilometers of bare rock . Some grasses are able to grow in the cold, dry climate . As a result, these Gobi ecosystems have grazing animals such as gazelles and even takhi , an endangered species of wild horse. Even the cold desert ecosystems of the Gobi are distinct from the freezing desert ecosystems of Antarctica. Antarcticas thick ice sheet covers a continent made almost entirely of dry, bare rock . Only a few mosses grow in this desert ecosystem , supporting only a few birds, such as skuas . Threats to Ecosystems For thou sands of years, people have interacted with ecosystems . Many cultures developed around nearby ecosystems . Many Native American tribes of North Americas Great Plains developed a complex lifestyle based on the native plants and animals of plains ecosystems , for instance. Bison , a large grazing animal native to the Great Plains , became the most important biotic factor in many Plains Indians cultures , such as the Lakota or Kiowa . Bison are sometimes mistakenly called buffalo. These tribes used buffalo hides for shelter and clothing, buffalo meat for food , and buffalo horn for tools. The tallgrass prairie of the Great Plains supported bison herds , which tribes followed throughout the year.

As human populations have grown, however, people have overtaken many ecosystems . The tall grass prairie of the Great Plains , for instance, became farmland . As the ecosystem shrunk, fewer bison could survive . Today, a few herds survive in protected ecosystems such as Yellowstone National Park. In the tropical rain forest ecosystems surrounding the Amazon River in South America, a similar situation is taking place. The Amazon rain forest includes hundreds of ecosystems , including canopies, understories, and forest floors. These ecosystems support vast food webs . Canopies are ecosystems at the top of the rainforest , where tall, thin trees such as figs grow in search of sunlight. Canopy ecosystems also include other plants , called epiphytes , which grow directly on branches. Understory ecosystems exist under the canopy . They are darker and more humid than canopies. Animals such as monkeys live in understory ecosystems , eating fruits from trees as well as smaller animals like beetles. Forest floor ecosystems support a wide variety of flowers , which are fed on by insects like butterflies. Butterflies, in turn, provide food for animals such as spiders in forest floor ecosystems . Human activity threatens all these rain forest ecosystems in the Amazon. Thou sands of acres of land are cleared for farmland , housing, and industry . Countries of the Amazon rain forest , such as Brazil, Venezuela, and Ecuador, are underdeveloped. Cutting down trees to make room for crops such as soy and corn benefits many poor farmers. These resources give them a reliable source of income and food . Children may be able to attend school, and families are able to afford better health care . However, the destruction of rain forest ecosystems has its costs. Many modern medicines have been developed from rain forest plants . Curare , a muscle relaxant, and quinine , used to treat malaria , are just two of these medicines . Many scientists worry that destroying the rain forest ecosystem may prevent more medicines from being developed. The rain forest ecosystems also make poor farmland . Unlike the rich soils of the Great Plains , where people destroyed the tall grass prairie ecosystem , Amazon rain forest soil is thin and has few nutrients . Only a few seasons of crops may grow before all the nutrients are absorbed. The farmer or agribusiness must move on to the next patch of land, leaving an empty ecosystem behind. Rebounding Ecosystems Ecosystems can recover from destruction , however. The delicate coral reef ecosystems in the South Pacific are at risk due to rising ocean temperatures and decreased salinity . Corals bleach, or lose their bright colors, in water that is too warm. They die in water that isnt salty enough. Without the reef structure, the ecosystem collapses. Organisms such as algae , plants such as seagrass , and animals such as fish, snakes , and shrimp disappear. Most coral reef ecosystems will bounce back from collapse. As ocean temperature cools and retains more salt, the brightly colored corals return. Slowly, they build reefs . Algae , plants , and animals also return. Individual people, cultures , and governments are working to preserve ecosystems that are important to them. The government of Ecuador, for instance, recognizes ecosystem rights in the countrys constitution . The so-called Rights of Nature says Nature or Pachamama [Earth], where life is reproduced and exists, has the right to exist, persist , maintain and regenerate its vital cycles, structure, functions and its processes in evolution . Every person, people, community or nationality, will be able to demand the recognitions of rights for nature before the public bodies. Ecuador is home not only to rain forest ecosystems , but also river ecosystems and the remarkable ecosystems on the Galapagos Islands .

Bactrian and Dromedary Different desert ecosystems support different species of camels. The dromedary camel is tall and fast, with long legs. It is native to the hot, dry deserts of North Africa and the Arabian Peninsula. The Bactrian camel has a thicker coat, is shorter, and has more body fat than the dromedary. The Bactrian camel is native to the cold desert steppes of Central Asia. It is easy to tell the two types of camels apart: Dromedaries have one hump, Bactrians have two.

Coral Triangle The most diverse ecosystem in the world is the huge Coral Triangle in Southeast Asia. The Coral Triangle stretches from the Philippines in the north to the Solomon Islands in the east to the islands of Indonesia and Papua in the west.

Ecocide The destruction of entire ecosystems by human beings has been called ecocide, or murder of the environment.

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Research Article

Habitat Fragmentation, Variable Edge Effects, and the Landscape-Divergence Hypothesis

* To whom correspondence should be addressed. E-mail: [email protected]

Affiliation Smithsonian Tropical Research Institute, Balboa, Republic of Panama

Affiliation Biological Dynamics of Forest Fragments Project, National Institute for Amazonian Research (INPA), Smithsonian Tropical Research Institute, Manaus, Brazil

Affiliation Institute of Zoology, Zoological Society of London, London, United Kingdom

Affiliations Smithsonian Tropical Research Institute, Balboa, Republic of Panama, Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America

Affiliation Department of Botany, National Institute for Amazonian Research (INPA), Manaus, Brazil

• William F. Laurance, 
• Henrique E. M. Nascimento, 
• Susan G. Laurance, 
• Ana Andrade, 
• Robert M. Ewers, 
• Kyle E. Harms, 
• Regina C. C. Luizão, 
• José E. Ribeiro

• Published: October 10, 2007
• https://doi.org/10.1371/journal.pone.0001017
• Reader Comments

Edge effects are major drivers of change in many fragmented landscapes, but are often highly variable in space and time. Here we assess variability in edge effects altering Amazon forest dynamics, plant community composition, invading species, and carbon storage, in the world's largest and longest-running experimental study of habitat fragmentation. Despite detailed knowledge of local landscape conditions, spatial variability in edge effects was only partially foreseeable: relatively predictable effects were caused by the differing proximity of plots to forest edge and varying matrix vegetation, but windstorms generated much random variability. Temporal variability in edge phenomena was also only partially predictable: forest dynamics varied somewhat with fragment age, but also fluctuated markedly over time, evidently because of sporadic droughts and windstorms. Given the acute sensitivity of habitat fragments to local landscape and weather dynamics, we predict that fragments within the same landscape will tend to converge in species composition, whereas those in different landscapes will diverge in composition. This ‘landscape-divergence hypothesis’, if generally valid, will have key implications for biodiversity-conservation strategies and for understanding the dynamics of fragmented ecosystems.

Citation: Laurance WF, Nascimento HEM, Laurance SG, Andrade A, Ewers RM, Harms KE, et al. (2007) Habitat Fragmentation, Variable Edge Effects, and the Landscape-Divergence Hypothesis. PLoS ONE 2(10): e1017. https://doi.org/10.1371/journal.pone.0001017

Academic Editor: Peter Bennett, University of Kent, United Kingdom

Received: July 12, 2007; Accepted: September 21, 2007; Published: October 10, 2007

This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.

Funding: Support was provided by the U.S. National Science Foundation, NASA-LBA Program, A. W. Mellon Foundation, Conservation, Food and Health Foundation, World Wildlife Fund-U.S., MacArthur Foundation, National Institute for Amazonian Research, and Smithsonian Institution.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Habitat fragmentation is among the most important of all threats to global biodiversity [1] , [2] , and edge effects—diverse physical and biotic alterations associated with the artificial boundaries of fragments—are dominant drivers of change in many fragmented landscapes [2] – [10] . Edge effects can have serious impacts on species diversity and composition, community dynamics, and ecosystem functioning [11] – [17] .

Many edge effects are variable in space and time [7] , [9] , [18] – [21] . Of course, the strength of edge effects diminishes as one moves deeper inside forests, but in addition, many edge phenomena vary markedly even within the same habitat fragment or landscape. Factors that might promote edge-effect variability include the age of habitat edges [22] – [25] , edge aspect [26] , [27] , the combined effects of multiple nearby edges [28] – [31] , fragment size [10] , the structure of the adjoining matrix vegetation [32] – [34] , seasonality [35] , influxes of animals or plant propagules from surrounding degraded lands [9] , [36] – [38] , extreme weather events [39] , [40] , and fires [41] , [42] .

In the Brazilian Amazon, up to 50,000 km of new forest edge is being created annually [43] as a result of rapid clearing and fragmentation of forests for cattle ranching, soy production, slash-and-burn farming, industrial logging, and wildfires [44] – [47] . For nearly three decades, we and our colleagues have studied edge effects in Amazonian forests as part of the world's largest and longest-running experimental study of habitat fragmentation [e.g. 5] , [ 13] – [15] , [ 17] – [19] , [ 23] , [ 24] , [ 27] – [30] , [ 32] , [ 36] , [ 38] – [41] , [ 48] – [61] ( Figure 1 ). Here we evaluate factors that instigate variability in edge phenomena, focusing on ten edge-related changes in forest dynamics, plant community composition, invasive species, and carbon storage. Our findings prompt us to present a new hypothesis about the behavior of fragmented ecosystems that, if valid, will have key implications for biodiversity conservation.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Shaded blocks indicate locations of forest fragments and intact-forest controls used in the study. Stippled areas are cattle pastures or regrowth forest, while unstippled areas are intact forest. Thick, solid lines are roads.

https://doi.org/10.1371/journal.pone.0001017.g001

Variability in edge effects

Most of the edge-effect variables we evaluated (see Methods ) exhibited pronounced spatial and temporal variability. For example, two of the most ecologically important parameters, the overall rates of tree mortality and recruitment, were spatially much more variable near forest edges (plot center<100 m from edge) than in forest interiors (>100 m from edge). Using standard deviations (SDs), among-plot variability was dramatically elevated for both mean mortality ( F 32,32  = 3.29, P  = 0.0006) and recruitment ( F 32,32  = 8.13, P <0.0001; F -tests). These differences did not simply result from higher mean mortality and recruitment rates near edges ( Figure S1 ), because coefficients of variation (CV), which adjust for differences in mean values, were also elevated near edges (mortality: 40.5% vs. 34.1%; recruitment: 47.9% vs. 27.9%).

Tree mortality and recruitment rates were also temporally far more variable near edges. When among-census variation was quantified for each plot using SDs ( Figure 2A ), values for both mortality ( r s  = −0.440, P  = 0.0002) and recruitment ( r s  = −0.670, P <0.00001) were sharply elevated near edges (Spearman rank correlations). CVs for mortality and recruitment also rose markedly nearer fragment margins ( Figure 2B ), indicating that both were temporally hyper-variable near edges.

This variability is measured by (A) standard deviations and (B) coefficients of variation of among-census variation for 1-ha plots. In (B), the solid regression line is for mortality ( F 1,64  = 5.68, R 2  = 8.2%, P  = 0.02) and the dashed line for recruitment ( F 1,64  = 27.30, R 2  = 29.9%, P <0.0001).

https://doi.org/10.1371/journal.pone.0001017.g002

Most other edge parameters exhibited similar trends. On average, tree densities fluctuated more dramatically over time near forest edges than in interiors ( P  = 0.0023, Mann-Whitney U -test), and these fluctuations were more spatially variable near edges ( F 32,32  = 8.14, P <0.00001; F -test based on variances in CV values). Likewise, edge plots had sharply elevated spatial variability in overall tree-community change ( F 18,20  = 17.76, P <0.0001), floristic trajectories 1 and 2 (both F 18,20 >9.08, P <0.0001), the abundance of pioneer and invasive trees ( F 32,32  = 11.59, P <0.00001), and tree-species turnover ( F 18,20  = 15.25, P <0.0001), relative to forest interiors (all F -tests comparing variances in edge vs. interior plots). Only liana abundance ( F 32,32  = 1.35, P  = 0.20) and the average rate of biomass change ( F 32,32  = 1.19, P  = 0.31) did not increase in spatial variability on edges relative to interiors, and even these had somewhat (17–19%) higher SDs on edges.

Predictors of variability

Three variables, cattle ranch, distance to forest edge, and the number of nearby edges, were the most important predictors of spatial variation in edge phenomena, having significant effects on six, five, and two edge-effect variables, respectively ( Table 1 ). Analyses explained 38–60% of the total variation in edge variables. As expected, edge phenomena increased in intensity closer to forest edges and with more nearby edges. In pairwise comparisons among the three cattle ranches, tree mortality and recruitment, fluctuations in tree abundance, and the abundance of pioneer and invasive trees were all significantly ( P <0.05) higher in fragments at Dimona than Esteio, with Porto Alegre being intermediate (Porto Alegre also had significantly higher recruitment than Esteio; Tukey's tests). Soil factors, slope, and fragment area per se had no significant influence on edge-effect variables.

https://doi.org/10.1371/journal.pone.0001017.t001

Spatial and temporal variability in tree mortality evidently helps to drive variability in several other edge-effect phenomena. First, in simple (Bonferroni-corrected) correlations among the edge and predictor variables, tree mortality was strongly ( r >0.60, P <0.00001) correlated with all other edge-effect parameters except liana abundance ( Table S1 ). Second, when the GLM analyses were repeated but with tree mortality included as a potential predictor, model performance improved (explaining 42–91% of the variation in edge parameters) and tree mortality was a significant predictor for all edge variables except liana abundance (Table S2). Model improvement was greatest for four floristic variables (overall change in tree-community composition, floristic vectors 1 and 2, and the rate of tree-species turnover). Finally, for several edge-effect variables, such as floristic vector 2 ( Figure 3A ) and tree-species turnover ( Figure 3B ), tree mortality was a highly significant covariate when the variables were contrasted among cattle ranches.

Differences among the ranches are shown for (A) floristic change (floristic vector 2) and (B) tree-species turnover, using the mean tree-mortality rate in each plot as a covariate.

https://doi.org/10.1371/journal.pone.0001017.g003

Given the apparently important impacts of tree mortality on forest ecology, we evaluated how tree-mortality rates vary over time, using data from the repeated censuses of our 66 plots. Two trends were apparent. First, although tree mortality was generally elevated near forest edges ( Figure S1 ), it was also highly episodic, varying markedly among different census intervals. This is illustrated by the strong tendency for plots with high mean mortality rates (averaged over the entire study) to have significantly elevated CVs ( Figure 4A ). Second, mortality rates tended to decline somewhat with fragment age, at least among edge plots, which had the highest overall mortality rates ( Figure 4B ).

(A) Tree mortality is highly episodic in Amazonian forest fragments, as shown by the strong relationship between the mean and CV of mortality rates, based on repeated censuses of 1-ha plots ( F 1,64  = 13.17, R 2  = 17.1%, P  = 0.0006). (B) Mortality rates decline with fragment age, but only among plots near forest edges ( F 1,31  = 6.49, R 2  = 17.3%, P  = 0.016), which have the highest overall mortality rates (there was no significant trend for forest-interior plots; F 1,31  = 0.42, R 2  = 1.3%, P  = 0.52; linear regressions).

https://doi.org/10.1371/journal.pone.0001017.g004

Collectively, these analyses suggest that elevated tree mortality partially drives changes in several other edge-effect phenomena, especially those relating to the intensity and pace of floristic change in fragments. Tree mortality is highly variable temporally and spatially, and tends to decline somewhat as fragments become older, especially among plots near forest edges. Although many edge phenomena were significantly affected by the proximity and number of nearby forest edges, as expected, they also differed to a surprisingly extent among the three large cattle ranches in our study area.

Causes of variability in edge effects

For nearly three decades, we and our colleagues have studied ecological changes in forest fragments within a 1000-km 2 experimental landscape, comprised by three large, isolated cattle ranches that were carved out of intact forest ( Figure 1 ). Within these fragments, edge effects are clearly the dominant drivers of ecological change [5] , [55] , but the diverse edge phenomena we evaluated were often strikingly variable in space and time. Why?

Part of the pronounced spatial variability we observed arises from local factors such as the proximity and number of nearby forest edges ( Tables 1 , S1 , and S2 ). Plots with two or more neighboring edges, such as those in small (1-ha) fragments and on the corners of larger fragments, have significantly greater tree mortality and biomass loss, fewer old-growth-tree seedlings [29] , and higher abundances of pioneer and invasive tree species [30] and lianas [53] , than do those with just one nearby edge. These patterns clearly support additive models of edge effects [28] , which suggest that the intensity of edge phenomena is compounded by multiple nearby edges.

Edge age also influences edge effects. Edge-related tree mortality is especially intense in the first few years after edge creation ( Figure 4 ) [5] , [55] , [62] , in part because microclimatic changes are especially strong near newly formed edges, which are structurally open and thus highly permeable to the penetration of heat, light, and wind from outside degraded lands [24] , [63] . In addition, most trees along newly formed edges are not physiologically acclimated to the sudden heat and desiccation stress, and many simply drop their leaves and die standing [5] , [62] . Over time, the edge is partially sealed by proliferating vines and second growth, and microclimatic gradients lessen in intensity [7] , [63] . Rates of tree death from physiological stress likely decline over time, both because older edges are less permeable and because trees that are poorly adapted for edge conditions (or in poor health generally) tend to die and be replaced by more desiccation-tolerant species [15] . These changes probably explain the moderate decline in tree-mortality rates with edge age observed in this study ( Figure 4B ).

Another driver of both spatial and temporal variability in edge effects is extreme weather events. The abrupt, artificial boundaries of forest fragments are especially vulnerable to windstorms, which can exert strong lateral-shear forces on exposed trees and create downwind turbulence for at least 2–10 times the height of the forest edge [64] , [65] . In the Amazon, the most intense wind blasts come from convectional thunderstorms, which can cause severe but localized forest disturbance [66] , [67] . Such windstorms are largely random events [66] that interact with local topography, leading to spatially complex patterns of forest disturbance [68] . Since our study commenced in 1979, fragments in the Dimona and, to a lesser extent, Porto Alegre ranches have been heavily damaged by windstorms, whereas those in Esteio ranch have remained largely unscathed [30] , [38] ( Figure 1 ). These episodic wind disturbances cause considerable spatial and temporal variability in tree mortality and other correlated edge effects, such as floristic change and forest-biomass loss ( Tables S1 and S2 ).

Periodic droughts also contribute to the temporal variability of edge effects, given the inherent vulnerability of rainforest edges to desiccation [23] , [27] . Large areas of the Amazon are affected by the El Niño-Southern Oscillation (ENSO), which typically causes droughts or rainfall deficits at 3–7 year intervals. During the strong 1997 ENSO drought, dry-season rainfall was less than a third of average in our study area, and tree mortality and leaf-shedding by drought-stressed trees rose markedly near forest edges [40] , [54] . In addition, destructive, edge-related forest fires proliferated dramatically across the Amazon [16] , [42] .

Finally, the structure and composition of the adjoining matrix vegetation can have a strong influence on edge effects. In our study area, forest edges adjoined by young regrowth forest, which helps to provide a physical buffer from wind and light, suffered less-intensive edge-related changes in microclimate [24] and lower tree mortality [32] than did those adjoined by cattle pastures. The species composition of the matrix vegetation is also important, because it influences the seed rain entering fragments [4] , [27] . In our study area, tree species regenerating in fragments adjoined by Vismia -dominated regrowth were very different (more diverse and less dominated by the pioneer Cecropia sciadophylla ) from those in fragments bordered by Cecropia -dominated regrowth [38] . Such differences can propel surprisingly rapid changes in the floristic composition of fragments [4] , [15] .

The ‘Landscape-Divergence Hypothesis’

In this study, several of the factors described above manifested themselves as important differences in edge effects among our three large cattle ranches ( Table 1 , Figure 3 ). Such differences initially surprised us. Our three sprawling ranches ( Figure 1 ) were carved out of the surrounding old-growth forest almost simultaneously, as part of the same government-sponsored program to promote large-scale cattle ranching in the central Amazon. The three ranches had broadly similar vegetation and climate (despite certain differences in soils, slope, and their initial tree-community composition; see Methods and Protocol S1 ). Moreover, given that our study is a carefully controlled experiment, none of the ranches was subject to various complicating pressures, such as wildfires, selective logging, and overhunting, that plague many human-dominated landscapes [2] , [69] . Yet despite such similarities, the fragments within the three landscapes have undertaken remarkably different trajectories of change. Why have these landscapes diverged?

The reason is that even small initial differences among the ranches quickly multiplied into much larger differences. Parts of the Porto Alegre and Esteio ranches were cleared in 1983, when an early wet season prevented burning of the felled forest [48] . Tall and floristically diverse Cecropia -dominated regrowth quickly developed in these areas, whereas areas cleared in other years became cattle pastures or, eventually, scrubby Vismia -dominated regrowth [70] . The differing matrix vegetation had major impacts on both the dynamics and trajectories of floristic change [15] , [30] , [38] and the composition of faunal communities [36] , [48] in nearby fragments. These differences were magnified by subsequent windstorms, which severely damaged some fragments at Dimona and to a lesser extent at Porto Alegre, yet left the Esteio fragments unscathed. Even identically sized fragments in the three ranches have had remarkably different dynamics ( Figure S1 ) and trajectories of compositional change.

The apparently acute sensitivity of fragments to local landscape and weather dynamics—even within a study area as initially homogeneous as ours—prompts us to propose a new hypothesis about the functioning of fragmented ecosystems. We suggest that fragments within the same landscape will tend to have similar dynamics and trajectories of change in species composition, which will often differ from those in other landscapes. Over time, we believe, this process will act as a homogenizing force for fragments within the same landscape, and will promote increasing ecological divergence among fragments in different landscapes (as a corollary, fragments that experience similar matrix, disturbance, and environmental conditions are predicted to converge in composition, even if they are not in the same vicinity). This concept is illustrated by the rapidly changing tree communities in our study area, which appear to be diverging in composition among the three cattle ranches ( Figure 5 ), and by other key differences in ecological dynamics among the ranches ( Table 1 , Figure 3 ).

Tree communities in forest-edge plots (<100 m from the nearest edge) are shown before forest fragmentation and 13–18 years after fragmentation, based on a single ordination of all plots and censuses in the study area. The ordination used importance values for all 267 tree genera found in the plots.

https://doi.org/10.1371/journal.pone.0001017.g005

This ‘landscape-divergence hypothesis’ can be contrasted with the principle of nested subsets [71] , [72] , which predicts that habitat fragments across a region will converge in species composition—regardless of their disturbance history or local landscape features. According to the nested-subsets concept, the biota in low-diversity fragments will comprise a proper subset of those in higher-diversity fragments or intact habitat. Although the predictions of the landscape-divergence and nested-subsets hypotheses differ markedly, they are not mutually exclusive: habitat fragments in different landscapes could increasingly diverge over time, but still support subsets of the same high-diversity species pool found in intact habitat. Landscape divergence might help to explain, for example, the weakly nested structure observed in some fragmented communities [see 72 and references therein] .

If our hypothesis is correct, then different fragmented landscapes may tend to diverge not only in species composition but also in ecosystem functioning. Differences in characteristics such as forest dynamics, carbon storage, functional-guild composition, and species invasions could gradually accumulate over time, leaving an increasingly pervasive signature of divergence on community composition and functioning. In practice, however, discriminating the effects of landscape divergence from preexisting patterns of beta diversity may not be straightforward, at least in the absence of pre-fragmentation data. Statistical techniques such as additive partitioning [73] , [74] might be useful for apportioning variation in species diversity within and among landscapes, and thus for contrasting certain predictions of the nested-subsets versus landscape-divergence hypotheses.

Potential Implications

We conclude by highlighting three potential implications of our findings. First, the striking variability in edge effects we observed suggests that short-term or small-scale studies may fail to detect important edge phenomena, or may characterize them inadequately [7] , [9] . In this study, our confidence was bolstered by the fact that we had pre-fragmentation data on tree-species distributions, stand structure, and biomass across our entire network of study plots. Even so, further replication would have been helpful for characterizing spatial variability in edge phenomena. Because of such inherent variability, it has been suggested that the known penetration-distance of edge effects should be doubled for management purposes [75] , such as when designing buffer zones for nature reserves.

Second, our landscape-divergence hypothesis suggests that, rather than simply homogenizing biotas via selective extinctions, habitat fragmentation could also promote important landscape-scale differences among biotas. If so, this phenomenon should be incorporated into conservation planning, as it could imply, for example, that protected areas in different landscapes could preserve biologically and functionally different components of ecosystems.

Finally, our findings highlight the key impact of matrix vegetation on fragment dynamics [see also 76] . In the Amazon, among the worst (and unfortunately most common) land-use practices is one in which forest fragments are encircled by pastures, which are regularly burned by ranchers to control weeds and promote a flush of green grass for cattle. These fires destroy secondary vegetation and continually raze and re-open fragment edges, thereby maximizing the intensity of edge-related microclimatic stresses. Like a scab that is continually picked at, the forest edge cannot heal itself. During drought years, moreover, the rancher-lit fires can penetrate deep into fragments, greatly increasing forest degradation [41] , [42] . In such contexts, protecting forest edges and their adjoining matrix is probably the single most important strategy for reducing the deleterious impacts of habitat fragmentation.

Materials and Methods

Study area and plots.

The Biological Dynamics of Forest Fragments Project (BDFFP) is a 1000-km 2 experimental landscape, dominated by non-flooded rainforest, located 80 km N of Manaus, Brazil (2°30′S, 60°W) at 50–100 m elevation (see Protocol S1 for details). The study area has three large (3,000–4,000 ha) cattle ranches, named Dimona, Porto Alegre, and Esteio, that contain a series of replicated forest fragments ranging from 1–100 ha in area. The fragments were created in the early-mid 1980s by felling and burning the surrounding forest to create cattle pastures. Nearby study sites in intact forest serve as experimental controls.

The three ranches in the study area are separated by expanses of primary forest and differ in certain respects [15] , [30] , [38] , [48] . The ranches vary somewhat in slope, sand content, and soil-carbon content, and even before the fragments were created there were certain differences in tree abundance and species composition, but not tree-species richness, among the ranches (see Protocol S1 ). These initial disparities were greatly magnified by subsequent differences in land-use history among the ranches, which strongly influenced the amount and species composition of regrowth forest surrounding the fragments, and by wind disturbances, which had widely varying effects on the three ranches.

Prior to fragment isolation, standardized surveys of trees, mammals, birds, amphibians, many invertebrate groups, and other taxa were conducted in all fragment and control sites, and these and other taxa have since been monitored regularly. The ten edge-effect variables we evaluated were collected within a network of 66 1-ha permanent plots, arrayed across nine fragments and eight intact-forest sites. Plots were sampled at regular (typically 4–6 year) intervals, from the early-mid 1980s through 2004 (see Protocol S1 ). Nearly 1300 tree species or morphospecies have been identified in these plots.

Edge-effect variables

For each plot, data were collected on (1) annual rate of tree mortality, (2) annual rate of tree recruitment, (3) the CV in tree density across censuses, (4) annual rate of change in aboveground tree biomass, (5) liana abundance, (6) overall density of pioneer and invasive tree species (belonging to the genera Annona, Bellucia, Cecropia, Croton, Goupia, Jacaranda, Miconia, Pourouma, and Vismia ), (7) mean rate of tree-species turnover, (8) overall rate of change in tree-community composition (using Euclidean distances to measure change in importance values for 267 tree genera), and (9 and 10) two vectors of floristic change in the plots (using an ordination analysis to assess trajectories of floristic change; see Protocol S1 ). These ten variables describe edge-related changes in forest dynamics and carbon storage (parameters 1–4), the abundance of key plant-functional groups (parameters 5–6), and plant species composition (parameters 7–10) in each plot.

Previous studies [11] – [13] , [47] – [51] have revealed that all of these parameters are significantly altered near forest edges, but did not explicitly evaluate patterns of spatial and temporal variability in the parameters. All ten edge parameters were used to assess spatial (among-plot) variability in edge phenomena. Temporal (within-plot) variability was also evaluated for two of the most important edge phenomena, tree mortality and recruitment rates, because values could be generated for the individual plot censuses. In addition to these new analyses of edge-effect variability, this study includes five years of previously unpublished forest-dynamics data for our plots.

Predictor variables

We assessed the efficacy of seven key landscape, soil, and topographic factors to explain spatial variability in our edge-effect variables (see Protocol S1 ). These included (1) fragment/reserve area, (2) linear distance of each plot to the nearest forest edge, (3) the number of nearby forest edges, (4) cattle ranch (Dimona, Porto Alegre, Esteio), (5) soil percent sand content, (6) soil organic-carbon content, and (7) mean slope of each plot. Predictors 1, 3, and 4 were treated as categorical variables; all others were continuous.

Data analysis

General linear models (GLM) were employed to assess effects of predictor variables on edge-effect parameters, using Systat version 10. None of the predictors was strongly (R 2 >50%) intercorrelated. Log and arcsine-squareroot transformations were used as needed to improve data normality. GLM performance was assessed by comparing standardized residuals to the fitted values and to each significant predictor.

Supporting Information

Protocol s1..

Description of Study Area and Methods Used to Quantify Edge-Effect and Predictor Variables

https://doi.org/10.1371/journal.pone.0001017.s001

(0.04 MB DOC)

Pearson Correlations Between Edge-Effect Parameters and Habitat Predictors

https://doi.org/10.1371/journal.pone.0001017.s002

Predictors of Spatial Variability in Edge-Effect Parameters, With Tree Mortality Included as a Potential Predictor

https://doi.org/10.1371/journal.pone.0001017.s003

Long-term Average Rates of Tree Mortality and Recruitment, as a Function of Distance from Forest Edge

https://doi.org/10.1371/journal.pone.0001017.s004

(0.03 MB DOC)

Acknowledgments

We thank Raphael Didham, Norman Kenkel, William Magnusson, and an anonymous referee for comments on the manuscript. This is publication number 493 in the BDFFP technical series.

Author Contributions

Conceived and designed the experiments: WL. Performed the experiments: WL SL. Analyzed the data: WL. Contributed reagents/materials/analysis tools: HN SL AA KH JR RL. Wrote the paper: WL. Other: Helped to conceptualize findings: RE.

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The creation of "Ecosystem Core" hypothesis to explain ecosystem evolution

Affiliations.

• 1 College of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China. [email protected].
• 2 College of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China. [email protected].
• 3 Beijing Key Laboratory of Wetland Services and Restoration, Institute of Wetland Research, Chinese Academy of Forestry, Beijing, 100091, China. [email protected].
• PMID: 31492142
• PMCID: PMC6728980
• DOI: 10.1186/s12898-019-0251-y

Background: Humans have dramatically changed natural ecosystems around the world as their capacity to manage their environment for multiple uses has evolved in step with agricultural, industrial and green revolutions. Numerous natural ecosystems have been replaced by various artificial or semi-artificial ecosystems, the ecosystem has changed. To a certain extent, this is ecosystem evolution. So far, there is no definite ecological theory about the mechanism for evolution of an ecosystem. Even though the discipline of community ecology has a relatively comprehensive and well-described theory of succession, at the different ecological research levels, is it the same mechanism for the community succession and ecosystem evolution? What is the factor that drives ecosystem evolution?

Results: This paper puts forward the "Ecosystem Core" hypothesis to scientifically address the above problems. We define abiotic component of ecosystem as "Ecosystem Core" or "Resource Core", which provides the foundation (matter and energy) for the existence and progress of organisms and should be the nucleus of an ecosystem. In this paper, we explain the basic meaning of this hypothesis, review its theoretical foundation, and provide a demonstration (based on emergy theory, which is an accounting tool that considers both the environmental and economic inputs that are directly or indirectly required by a process to generate a product and it measures real wealth, independent of financial considerations) of the hypothesis, and discuss the mechanism of ecosystem evolution. The "Ecosystem Core" hypothesis reveals the quantitative relationship between the energy input and ecosystem evolution.

Conclusions: The input of artificial auxiliary energy is the direct cause of ecosystem evolution. Different combinations of natural and purchased emergy are coupled to maintain the same ecosystem under the different environmental conditions. When artificial energy enters the ecosystem, its role is similar to that of the microscopic particles that collide with the nucleus in the nuclear reaction, and after mutual reaction, the atom will form a new atomic structure, and for the ecosystem, a new form of resource composition and energy action will appear, and the corresponding species of life will change, then ecosystem complete its evolution.

Keywords: Ecosystem; Emergy; Environment; Evolution; Life.

Publication types

• Research Support, Non-U.S. Gov't
• Agriculture
• Conservation of Natural Resources*

• Biology Article

Ecosystem Definition

“An ecosystem is defined as a community of lifeforms in concurrence with non-living components, interacting with each other.”

What is an Ecosystem?

An ecosystem is a structural and functional unit of ecology where the living organisms interact with each other and the surrounding environment. In other words, an ecosystem is a chain of interactions between organisms and their environment. The term “Ecosystem” was first coined by A.G.Tansley, an English botanist, in 1935.

Read on to explore the structure, components, types and functions of the ecosystem in the notes provided below.

Structure of the Ecosystem

The structure of an ecosystem is characterised by the organisation of both biotic and abiotic components. This includes the distribution of energy in our environment . It also includes the climatic conditions prevailing in that particular environment. 

The structure of an ecosystem can be split into two main components, namely: 

Biotic Components

Abiotic components.

The biotic and abiotic components are interrelated in an ecosystem. It is an open system where the energy and components can flow throughout the boundaries.

Biotic components refer to all living components in an ecosystem.  Based on nutrition, biotic components can be categorised into autotrophs, heterotrophs and saprotrophs (or decomposers).

• Producers include all autotrophs such as plants. They are called autotrophs as they can produce food through the process of photosynthesis. Consequently, all other organisms higher up on the food chain rely on producers for food.
• Primary consumers are always herbivores as they rely on producers for food.
• Secondary consumers depend on primary consumers for energy. They can either be carnivores or omnivores.
• Tertiary consumers are organisms that depend on secondary consumers for food.  Tertiary consumers can also be carnivores or omnivores.
• Quaternary consumers are present in some food chains . These organisms prey on tertiary consumers for energy. Furthermore, they are usually at the top of a food chain as they have no natural predators.
• Decomposers include saprophytes such as fungi and bacteria. They directly thrive on the dead and decaying organic matter.  Decomposers are essential for the ecosystem as they help in recycling nutrients to be reused by plants.

Abiotic components are the non-living component of an ecosystem.  It includes air, water, soil, minerals, sunlight, temperature, nutrients, wind, altitude, turbidity, etc. 

Functions of Ecosystem

The functions of the ecosystem are as follows:

It regulates the essential ecological processes, supports life systems and renders stability.

It is also responsible for the cycling of nutrients between biotic and abiotic components.

It maintains a balance among the various trophic levels in the ecosystem.

It cycles the minerals through the biosphere.

The abiotic components help in the synthesis of organic components that involve the exchange of energy.

So the functional units of an ecosystem or functional components that work together in an ecosystem are:

• Productivity –  It refers to the rate of biomass production.
• Energy flow – It is the sequential process through which energy flows from one trophic level to another. The energy captured from the sun flows from producers to consumers and then to decomposers and finally back to the environment.
• Decomposition – It is the process of breakdown of dead organic material. The top-soil is the major site for decomposition.
• Nutrient cycling –  In an ecosystem nutrients are consumed and recycled back in various forms for the utilisation by various organisms.

Types of Ecosystem

An ecosystem can be as small as an oasis in a desert, or as big as an ocean, spanning thousands of miles. There are two types of ecosystem:

Terrestrial Ecosystem

Aquatic ecosystem.

Terrestrial ecosystems are exclusively land-based ecosystems. There are different types of terrestrial ecosystems distributed around various geological zones. They are as follows:

Forest Ecosystem

Grassland ecosystem, tundra ecosystem, desert ecosystem.

A forest ecosystem consists of several plants, particularly trees, animals and microorganisms that live in coordination with the abiotic factors of the environment. Forests help in maintaining the temperature of the earth and are the major carbon sink.

In a grassland ecosystem, the vegetation is dominated by grasses and herbs. Temperate grasslands and tropical or savanna grasslands are examples of grassland ecosystems.

Tundra ecosystems are devoid of trees and are found in cold climates or where rainfall is scarce. These are covered with snow for most of the year. Tundra type of ecosystem is found in the Arctic or mountain tops.

Deserts are found throughout the world. These are regions with little rainfall and scarce vegetation. The days are hot, and the nights are cold.

Aquatic ecosystems are ecosystems present in a body of water. These can be further divided into two types, namely:

Freshwater Ecosystem

Marine ecosystem.

The freshwater ecosystem is an aquatic ecosystem that includes lakes, ponds, rivers, streams and wetlands. These have no salt content in contrast with the marine ecosystem.

The marine ecosystem includes seas and oceans. These have a more substantial salt content and greater biodiversity in comparison to the freshwater ecosystem.

Also check: Habitat Diversity

Important Ecological Concepts

1. food chain.

The sun is the ultimate source of energy on earth. It provides the energy required for all plant life. The plants utilise this energy for the process of photosynthesis, which is used to synthesise their food.

During this biological process, light energy is converted into chemical energy and is passed on through successive trophic levels. The flow of energy from a producer, to a consumer and eventually, to an apex predator or a detritivore is called the food chain.

Dead and decaying matter, along with organic debris, is broken down into its constituents by scavengers. The reducers then absorb these constituents. After gaining the energy, the reducers liberate molecules to the environment, which can be utilised again by the producers.

2. Ecological Pyramids

An ecological pyramid is the graphical representation of the number, energy, and biomass of the successive trophic levels of an ecosystem. Charles Elton was the first ecologist to describe the ecological pyramid and its principals in 1927.

The biomass, number, and energy of organisms ranging from the producer level to the consumer level are represented in the form of a pyramid; hence, it is known as the ecological pyramid.

The base of the ecological pyramid comprises the producers, followed by primary and secondary consumers. The tertiary consumers hold the apex. In some food chains, the quaternary consumers are at the very apex of the food chain.

The producers generally outnumber the primary consumers and similarly, the primary consumers outnumber the secondary consumers. And lastly, apex predators also follow the same trend as the other consumers; wherein, their numbers are considerably lower than the secondary consumers.

For example, Grasshoppers feed on crops such as cotton and wheat, which are plentiful. These grasshoppers are then preyed upon by common mouse, which are comparatively less in number. The mice are preyed upon by snakes such as cobras. Snakes are ultimately preyed on by apex predators such as the brown snake eagle.

In essence:

3. Food Web

Food web is a network of interconnected food chains. It comprises all the food chains within a single ecosystem. It helps in understanding that plants lay the foundation of all the food chains. In a marine environment, phytoplankton forms the primary producer.

Main article:   Food web

To learn more about what is an ecosystem, its structure, types, components, and functions, register at BYJU’S website or download the BYJU’S app.

Frequently Asked Questions

1. what is the ecosystem.

The ecosystem is the community of living organisms in conjunction with non-living components of their environment, interacting as a system.

2. What are the different types of ecosystems?

The different types of the ecosystem include:

• Forest ecosystem
• Grassland ecosystem
• Desert ecosystem
• Tundra ecosystem
• Freshwater ecosystem
• Marine ecosystem

3. What are the functional components of an ecosystem?

The four main components of an ecosystem are: (i) Productivity (ii) Decomposition (iii) Energy flow (iv) Nutrient cycling

4. Which ecosystem do we live in?

We live in a terrestrial ecosystem. This is the ecosystem where organisms interact on landforms. Examples of terrestrial ecosystems include tundra, taigas, and tropical rainforests. Deserts, grasslands and temperate deciduous forests also constitute terrestrial ecosystems.

5. What is the structure of the ecosystem?

The structure of the ecosystem includes the organisms and physical features of the environment, including the amount and distribution of nutrients in a particular habitat. It also provides information regarding the climatic conditions of that area.

6. Which is the largest ecosystem in the world?

The largest ecosystem in the world is the aquatic ecosystem. It comprises freshwater and marine ecosystems. It constitutes 70% of the surface of the earth.

7. What is the major function of an ecosystem?

The ecosystem is the functional unit of the environment system. The abiotic components provide the matrix for the synthesis of organic components. This process involves the exchange of energy.

8. What makes a good ecosystem?

A good ecosystem consists of native plants and animal species interacting with each other and the environment. A healthy ecosystem has an energy source and the decomposers that break down dead plants and animal matter, returning essential nutrients to the soil.

9. What all include the non-living things in an ecosystem?

The non-living things in an ecosystem include air, wind, water, rocks, soil, temperature and sunlight. These are known as the abiotic factors of an ecosystem.

Register at BYJU’S for ecosystem notes or other important study resources.

Further Reading:

• Our Environment
• Energy Flow In Ecosystem
• What Is A Natural Ecosystem?
• Why Is The Ecosystem Important?
• What Are The Five Levels Of Ecology?
• What Are The Different Fields Of Ecology?
• What Are The Three Environmental Issues?
• Difference Between Food Chain And Food Web
• How Many Types Of The Ecosystem Are There?
• How Can We Improve Our Environmental Health?

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Department of Chemistry

College of Science hosts Inaugural Research Showcase

Extending the reach and impact of science.

Tuesday, May 21, 2024 11 a.m. – 2 p.m. Memorial Union Multipurpose Room 13

This event will feature SciRIS awardee presentations, panel discussion on artificial intelligence in the College of Science, and posters and science education demonstrations by Oregon Museum of Science and Industry (OMSI) Fellows.

Schedule of Events

11 – 11:10 a.m..

Welcome and introduction from Vrushali Bokil , Associate Dean of Research and Graduate Studies

11:10 – noon

SciRIS Awards Showcase

The College of Science Research and Innovation Seed (SciRIS) program funds projects based on collaborative research within our community and beyond. The program awards seed funding for high-impact collaborative proposals that build teams, pursue fundamental discoveries and create societal impact. Founded in 2018 , SciRIS accelerates the pace of research, discovery and innovation in the College of Science by enabling scientists to work across an array of disciplines in a mentored environment. We showcase some of the recent awards made under this program.

Francis Chan : “The Hypoxic Barrier Hypothesis: have we missed a fundamental dynamic of oxygen use in microbes and ecosystems?”

Kim Halsey : “Leveraging volatile organic compounds to detect cyanotoxin contamination in Oregon lakes”

Maude David : “Leveraging organ-on-a-chip systems to mimic the gut sensory system: toward screening microbiota-vagal interactions”

Yuan Jiang : “Harnesses longitudinal microbiome data to define the ecological roles of host-associated microbes”

Alysia Vrailas-Mortimer : “A New Model to Study the role of Iron in Parkinson’s Disease”

Noon – 1 p.m.

Lunch & Networking: OMSI Communication Fellows demonstration and poster session

Oregon State University and the Oregon Museum of Science and Industry (OMSI), one of the nation's leading science centers, have enjoyed a close partnership since 2016. OMSI hosts its popular Science Communication Fellowship cohort program on OSU’s Corvallis campus every spring. More than 70 students, faculty and staff from across science at OSU have completed the training program, including the Colleges of Science; Engineering; Earth, Ocean, and Atmospheric Science; Agricultural Sciences; Forestry; and Public Health and Human Sciences. The COS partners with OMSI in offering this fellowship to our students. Here we showcase some of our COS OMSI Science Communication Fellows.

Akasit Visootsat & Yuan Gao (Physics): “What & How to see motor proteins?”

Sunni Patton (Microbiology): “Exploring the Coral Microbiome”

Austin Vick (Integrative Biology): “What can the common fruit fly tell us about our health”

Panel Session: AI in Research Moderators: Vrushali Bokil, Bettye Maddux and Jeff Hare

The panel will discuss ideas for incorporating AI and data science across four priority research areas: clean energy, integrated health and biotechnology, climate solutions and robotics.

Tim Zuehlsdorff , Assistant Professor, Department of Chemistry

Jeff Hazboun , Assistant Professor, Department of Physics

Ryan Mehl , Professor, Director of GCE4All Research Center, Department of Biochemistry & Biophysics

Marilyn Rampersad Mackiewicz , Assistant Professor, Department of Chemistry

Francis Chan , Associate Professor, Director, Cooperative Institute for Marine Ecosystem and Resources Studies, Department of Integrative Biology

Read more stories about: events , biochemistry & biophysics , chemistry , integrative biology , mathematics , microbiology , physics , statistics , research

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