Even though cells always derive from other cells, a full cellular history cannot be reduced to the history of some cellular components that are assumed to track the history of cellular division [ 92 ]. In particular, phylogenetic analyses of informational genes cannot be the only clue to understanding the origins of cellular diversity, since these genes do not reflect how cells are organized, how they gather their energy, and how they interact with each other. Analyzing the co-construction side of evolution requires enhanced models: understanding eukaryotic evolution requires mixed considerations of cellular architecture, population genetics and energetics, which go beyond classic phylogenetic models, which not so long ago were still prone to considering three primary domains of life [ 93 , 94 , 95 ].
Enhanced models including intra- and extracellular interactions appear necessary to understand cellular complexity, including the predictable disappearance of traits and processes , namely the convergent gene loss of mitochondria and plastids [ 97 ] by a process called dedarwinification [ 98 , 99 ].
Studies of multicellular organisms—we will focus on animals—have led to similar general findings. Understanding animal traits and their evolution requires analyzing the relationships between a multiplicity of agents belonging to different levels of biological organisation, eventually nested, some of which co-constructs animals and guarantees their complete lifecycle [ ]. Because no sterile organism lives on Earth, animal development, health and survival depend on microbes. These interactions produce communication networks within the animal body: chemical information circulates between the animal brain and the gut microbiome.
These interactions also result in communication and interaction networks between individuals. In some animal lineages, the microbiome affects social behaviors, for instance fermenting microbes inform about the gender and reproductive status in hyena [ ]. Components of the microbiome also affect mating choice [ ], reproductive isolation and possibly speciation. Consequently, the microbiome now appears as an essential component of animal studies [ ]. Microbiome studies, the significance of which is overstated in some respects, nevertheless have shown that the evolutionary intertwining between many metazoa and commensal or symbiotic bacteria could not be neglected anymore and black-boxed in favor of purely host gene-centered evolutionary models.
And the associations between hosts and microbes do not need to be units of selection to be part of the recent insights that support the novel theoretical framework proposed here. Their interplay imposes reconfigurations of practices, theories and disciplines [ ].
As a result of our improved insight into evolution, zoology and immunology [ ] become theaters of new ecological considerations [ ], sometimes strangely qualified as Lamarckian [ , ], because animals can recruit environmental microbes and transmit them with a non-null heritability [ ] to their progeny. Therefore, nuclear gene inheritance alone may provide too narrow a perspective to account for the evolution of all animal traits; as an example, aphid body color depends on animal genetics and the presence of Rickettsiella [ ]. Population genetics gets included in a broader community genetics, which also considers transmission of microbes and their genes [ , ].
The use of gnotobiotic and transbiotic animals becomes a new experimental standard to analyze multigenomic collectives without counterparts in modern synthesis theories. These collectives harbor morphological, physiological, developmental, ecological, behavioral and evolutionary features [ , , , , ] that are not purely constructed by animal genes, but rather appear to be co-constructed at the genetic and metabolic interface between the microbial and macrobial worlds, while the content of the respective animal genomes only provides incomplete instructions. Understanding animal evolution requires understanding the interaction networks between components from which these taxa evolved, and the networks to which these taxa still belong.
In ecology, an analogous turn towards network thinking has been promoted since the s with the general acceptance of the notions of metapopulations [ ] and then metacommunities [ ]. These views suggest that the dynamics of ecological biodiversity is not so much located within a community of species but rather in a metacommunity, which can be thought of as a network of communities exchanging species, while targeting one community blinds one to what genuinely accounts for biodiversity and ecosystem functioning [ ].
This quick overview provides evidence that networks are at the origin of the genes of unicellular and multicellular organisms and central for their functions. From division of labor and compensations, to dependencies and co-constructions, etc. Thus, explaining the actual features of biodiversity requires explaining how multiple processes, interface phenomena co-construction of biological features, niche construction, metabolic cooperation, co-infection and co-evolution and organisations for instance, from molecular pathways to organisms and ecosystems arose from interacting components, and how these processes, phenomena and organisations may have been sustained and transformed on Earth.
The notion of scaffolding [ ], which describes how one entity continues an event initiated by another entity, and relies on it up to the point that at some timescale it becomes dependent upon it for further evolution, appears as a fundamental relationship to describe the evolution of life. We propose scaffolding should become more central in explanations of evolution because no components from the biological world are actually able to reproduce, or persist, alone Fig. Each entity influences or is influenced by something external to it, and is consequently part of a process.
Scaffolding thus defines the causal backbone of collective evolution. It describes the historical continuity between temporal slices of interaction networks, since any evolutionary stage relies on previously achieved networks and organisations. Therefore, describing the evolution of interactions requires explanations to address the following issues: what scaffolds what, what transforms the environment of what, and are these influences reciprocal? Characterizing the types of components that, together, have evolutionary importance through their potential interaction is therefore a central step to expanding evolutionary theory.
Different types of scaffolding, at four levels of biological organisations. We propose that a first distinction can be made between obligate and facultative components. Suppressing the former impacts the course and eventually the reproduction of the process to which they contribute Fig. A second distinction is whether the components are biotic genes, proteins, organisms… or abiotic such as minerals, environmental, cultural artefacts.
Abiotic components can be recruited from the environment or be shaped by biological processes [ ]. They can also alter the evolution of the biotic components, for example, environmental change can drive genetic and organismal evolution and selection. The history of life clearly depends on the interplay of both types of components. Biotic components, however, deserve a specific focus. Some of them form lineages for instance, genes replicate , while others do not for instance, proteins are reconstructed.
Finally, interacting replicated components can be further classified into fraternal components when they share a close last common ancestor e. Classification of major types of components in evolving systems. Biotic components are biological, material products, whereas abiotic components are environmental, geological, chemical, physical or cultural artefacts. Replicated components are produced by replication, which implies a physical continuity between ancestral and descendent components; they undergo a paradigmatic Darwinian evolution. Reconstructed components are reproduced without direct physical continuity, and cannot directly accumulate beneficial mutations.
Fraternal components belong to the same lineage, whereas egalitarian components belong to different lineages. Biodiversity usually evolves from interactions between the diverse types of components described above. For example, metalloproteases emerge from the interaction between reconstructed biotic components proteins and a metal ion.
Regulatory networks involve biotic components that can be either replicated i. Protein interaction networks intertwine reconstructed egalitarian biotic components, which means proteins that are not homologous. Evolutionary transitions such as eukaryogenesis result from the interweaving of biotic components cells from multiple lineages. Holobionts evolve from interactions between egalitarian biotic components macrobial hosts and microbial communities and possibly abiotic components, such as the mineral termite mounds, or the volatile chemicals produced by the microbial communities of hyenas [ ].
Taking collectives of interacting components as central objects of study in evolutionary biology invites us to expand the methods of this field. It encourages developing statistical approaches or inference methods beyond those operating under the very common assumption that biological components are independent.
Therefore, we propose to represent interactions between components in the form of networks in which components are nodes and their interactions of various sorts are edges. These networks are conceptually simple objects. Such dynamic interaction networks could become more central representations and analytical frameworks, and serve as a common explanans for various disciplines in an expanded evolutionary theory.
Importantly, because these networks embed both abiotic and biotic, related and unrelated components like viruses, cells and rocks , they should not be conflated with phylogenetic networks, but recognized as a more inclusive object of study Fig. Where phylogenies describe relationships, networks can describe organisations. How such organisations evolve could for example be described by identifying evolutionary stages, that is, sets of components and of their interactions simultaneously present in the network Fig.
Investigating the evolution of an ecosystem corresponds to studying the succession of evolutionary stages in such networks and detecting possible regularities—in the sense that some evolutionary stages would fully or partly reiterate over time—or hinting at rules or constraints like architectural contingencies [ , ] or principles of organisations [ 46 ] on the recruitment, reproduction and heritability of their components.
An evolving interaction network.
Nodes are components circles are full when the component is biotic. Thick black edges represent interactions between these components. The network topology evolves as nodes or their connection change. Dashed edges represent the phylogenetic ancestry of lineage-forming components. Thus, we suggest that evolutionary biology could be reframed as a science of evolving networks, because such a shift would allow inclusive, multilevel studies of a larger body of biological and abiotic data, via approaches from network sciences.
Enhancing network-based evolutionary analyses, beyond the now classic research program of phylogenetic networks, could consolidate comparative analyses in the nascent field of evolutionary systems biology [ , ], as illustrated by examples based on molecular networks. This involves first defining nodes of the network, namely components suspected to be involved in a given system, and edges, namely qualitative or quantitative, when weighted interactions between these entities.
Many biological interaction networks gene co-expression networks GCNs , gene regulatory networks GRNs , metabolic networks, protein—protein interaction networks PPIs , etc. For example, GCNs offer an increasingly popular resource to study the evolution of biological pathways [ ], as well as to reveal conservation and divergence in gene regulation [ ]. GCNs are already used for micro-evolution studies, as in the case of fine-grained comparisons of expression variations between orthologous genes across closely related species, and for the analysis of minor evolutionary and ecological transitions, such as changes of ploidy [ , ], adaptation to salty environments [ ] or drugs [ ], or the effects of plant domestication [ , ].
Likewise, GRNs are starting to be used in micro-evolution and phenotypic plasticity studies [ ]. Understanding the dynamics of GRNs appears critical to inferring the evolution of organismal traits, in particular during metazoan [ , , ], plant [ ] and fungal [ ] evolution. We suggest that PPI, GCN and GRN studies could become mainstream and also be conducted at much larger evolutionary and temporal scales, to analyze additional, major, transitions.
Based on these established networks, two major types of evolutionary analyses network-decomposition and graph-matching; Fig. More precisely, the above-mentioned kinds of biological networks could be systematically turned into what we call evolutionary colored biological networks ECNs.
In ECNs, each node of a given biological network is colored to reflect one or several evolutionary properties. For example, in molecular networks, nodes correspond to molecular sequences genes, RNA, proteins that belong to homologous families that phylogenetic distribution across host species allows us to date [ , , , , , , ]. Likewise, several processes affecting the evolution of a molecular family selection, duplication, transfer, and divergence in primary sequence can be inferred by classic phylogenetic analyses or, as we proposed, by analyses of sequence similarity networks [ ].
Such studies provide additional evolutionary colors like quantitative measures: intensity of selection, rates of duplication, transfer, and percentage of divergence , which can be associated with nodes in ECNs [ , , , , , , ]. Thus, ECNs contain both topological information, characteristic of the biological network under investigation, as well as evolutionary information: what node belongs to a family prone to duplication, divergence, or lateral transfer, as well as when this family arose. Combining these two types of information in a single graph allows us to test specific hypotheses regarding evolution.
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Workflow of the evolutionary analysis of interaction networks. From left to right : triangles represent components of interaction networks, edges between triangles represent interactions between these components. ECNs can be investigated individually by graph decomposition and centrality analyses, or several ECNs can be compared by graph alignment. The two types of comparisons can return conserved subgraphs that allow understanding of the dynamics of interaction networks, meaning when different sets of interactions hence processes evolved, and whether these interactions were evolutionarily stable.
Ancient and Contemporary refer to the relative age of the sub-graphs, identifying new clade-specific relationships here called refinement ; introgression indicates that a component, and the relationship it entertains with the rest of the network, was inferred to result from a lateral acquisition. Using ECNs, it is first fruitful to test whether or which of these evolutionary colors correlates with topological properties of the ECNs [ , , ]. Rejection of this hypothesis would hint at processes that affect the topology of biological networks or are affected by the network topology.
For example, considering degree in networks, proteins with more neighbors are less easily transferred [ ], highly expressed genes, more connected in GCNs, evolve slower than weakly expressed genes [ ], and genes with lower degrees have higher duplicability in yeast, worm and flies [ ]. Considering position in networks, node centrality correlates with evolutionary conservation [ ], gene eccentricity correlates with level of gene expression and dispensability [ ], and proteins interacting with the external environment have higher average duplicability than proteins localized within intracellular compartments [ ].
Additionally, network structure gives a clue to evolution since old proteins have more interactions than new ones [ , ]. Generalizing these disparate studies could help to understand the dynamics of biological networks, in other words how the architecture, the nodes and edges of present day networks, evolved and whether their changes involved random or biased sets of nodes and edges or follow general models of network growth with detectable drivers. This focus would complement a classic tree-based view.
For instance, under the reasonable working hypothesis that pairs of connected nodes of a given age reflect an interaction between nodes that may have arisen at that time [ , ], ECNs can easily be easily decomposed into sub-networks, featuring processes of different ages that is, sets of nodes of a given age, e. This strategy allows identification of conserved network patterns, possibly under strong selective pressure [ ]. Constructing and exploiting ECNs from bacteria, archaea, and eukaryotes thus has the potential to define conserved ancestral sets of relationships between components, allowing evolutionary biologists to infer aspects of the early biological networks of the last common ancestor of eukaryotes, archaea and bacteria and even of the last universal common ancestor of cells.
Assuming that some of these topological units correspond to functional units [ ], especially for broadly conserved subgraphs [ , , , , , , , , , , , , , ], would allow network decompositions to propose sets of important processes associated with the emergence of major lineages. Moreover, graph-matching of ECNs allows several complementary analyses. First, for interaction networks, such as GRNs, whose sets of components and edges evolve rapidly [ , , ], it becomes relevant to analyze where in the network such changes occur in addition to simply tracking conserved sets of components and edges.
Whereas the latter can test to what extent conservation of the interaction networks across higher taxa supports generalizations made from a limited number of model species [ ], the former allows us to test a general hypothesis: are there repeated types of network changes? For example, does network modification primarily affect nodes with particular centralities, as exemplified by terminal processes [ ], or modules?
Systematizing these analyses would provide new insights into whether the organisation principles of biological networks changed when major lineages evolved or remained conserved. The null hypothesis would be that these major transitions left no common traces in biological networks. An alternative hypothesis would be that the biological networks convergently became more complex more connected and larger during these transitions to novel life forms. Indeed, analyses conducted on a few taxa have reported quantifiable and qualifiable modifications in biological networks in response to environmental challenges [ ], during ecological transitions [ ] or as niche specific adaptations [ ].
More systematic graph-matching [ , , ] and motif analyses, comparing the topology of ECNs from multiple species, could likewise be used to test the hypothesis that major lineages are enriched in particular motifs either modules of colored nodes and edges, or specific topological features, such as feed-forward loops [ 46 ] or bow-ties [ ].
It would also allow identification of functionally equivalent components across species, namely different genes with similar neighbors in different species [ ]. While inferences on conserved sets of nodes and edges in ECNs are likely to be robust since the patterns are observed in multiple species , missing data missing nodes and edges constitute a recognized challenge, especially for the interpretation of what will appear in ECN studies as the most versatile least conserved parts of the biological networks.
The issue of missing data, however, is not specific to network-based evolutionary analyses, and should be tackled, as with other comparative approaches, by the development and testing of imputation methods [ , , ]. Moreover, issues of missing data can also be addressed by the production of high coverage -omics datasets in simple systems, allowing for nearly exhaustive representations of the entities and their interactions i. This kind of data would allow testing for the existence of selected emergent ecosystemic properties like carbon fixation , as stated by the ITSNTS hypothesis [ ].
Comparing ECNs representing, at each time point, the origin and abundance of the lineages hosting the enzymes involved in carbon fixation could test whether some combinations of lineages are repeated over time, and whether the components e. Finally, entities from different levels of biological organisation domains, genes, genomes, lineages, etc.
Recently, our studies and others see [ ] and references therein have demonstrated that various patterns in multipartite graphs can be used to detect and test combinatorial introgressive and gradual evolution by vertical descent affecting genes and genomes. Decomposing multipartite networks into twins and articulation points could for example then be used to represent and analyze the evolution of complex composite molecular systems, such as CRISPR, or the dynamics of invasions of hairpins in genomes [ ].
Focusing evolutionary explanations and theories on collectives of interacting components, which may be under selection, facilitate selection, or condition arrangements through neutral processes [ 39 , 40 , ], and representing these scaffolding relationships using networks with biotic and abiotic components and a diversity of edges representing a diversity of interaction types would be an enlargement. Enlargements, as expressing the need to consider structures that are more general than what already exists, have already occurred within evolutionary theory, when simplifications from population genetics were relaxed with respect to the original formalization in the Modern Synthesis [ ], to account for within-genome interaction [ 9 ], gene—environment covariance [ ], parental effects [ ], and extended fitness though generations [ ].
It also occurred when reticulations representing introgressions were added to the evolutionary tree. Interestingly, replacing standard linear models in evolutionary theory with network approaches would transcend several traditional axes structuring the debates in evolutionary biology.
Intro to ecosystems
For instance, scaffolded evolution, the idea that evolution relies on what came before, is orthogonal to the distinction between vertical and horizontal descent, since both tree-like and introgressive evolution are particular cases of scaffolding. Scaffolded evolution is also orthogonal to the distinction between gradual and saltational evolution. Likewise, scaffolded evolution is orthogonal to the debates between the actual role of adaptations vs neutral processes. Selection is a key mode of evolution of collectives but not the only one. The processes involved in the forming and evolution of collectives are not even restricted to the key processes of the Modern Synthesis drift, selection, mutation and migration but embrace interactions such as facilitation—namely antagonistic interactions between two species that allow a third species to prosper by restraining one of its predators or parasites [ ], presuppression [ 39 , 40 ], etc.
Consequently, some evolutionary concepts may become more important than they currently are to explain evolution. Contingency could come to be seen as a less extraordinary mode of evolution in the history of life, since the ordinary course of evolution might include many cases of contingent events, that is, associations of entities in a transient collective, including any scaffolds—associations that are not necessarily selective responses or the outcomes of processes modeled in population genetics.
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Likewise, adopting a broader ontology could affect how evolutionary theorists think about evolution. Population thinking and tree-thinking came after essentialist conceptions of the living words, when populations and lineages were recognized as central objects of evolutionary studies [ ]. Using a network-based approach to analyse dynamic systems also permits explanations that rely purely on statistical properties [ ] or on topological or graph theoretical properties [ , ] besides standard explanations devoted to unravelling mechanisms responsible for a phenomenon.
Moreover, because of the inclusiveness of the network model, disciplines already recognized for their contribution to evolutionary theory microbiology, ecology, cell biology, genetics, etc. Disciplines that were not central in the Modern Synthesis—chemistry, physics, geology, oceanography, cybernetics or linguistics—could aggregate with evolutionary biology. Since a diversity of components gets connected by a diversity of edges in networks featuring collectives, as a result of a diversity of drivers, several explanatory strategies could be combined to analyze evolution.
Remarkably, this mode of unification of diverse scientific disciplines would be original: the integration would not be a unification in the sense of logical positivism [ ]—namely reducing a theory to a theory with more basic laws, or a theory with a larger extension. It would be a piecemeal [ ] unification. Some aspects would be unified through a specific kind of graph modeling because some interactions, namely mechanical, chemical, ecological ones, and a range of time scales are privileged in a set of theories , while other theories might be unified by other graph properties like different types of edges and components.
For example, the fermentation hypothesis for mammalian chemical communication could be analyzed in a multipartite network framework, which would involve nodes corresponding to individual mammals, nodes corresponding to microbes, and nodes corresponding to odorous metabolites. This host subnetwork can itself be further connected to a second subnetwork, namely the microbial subnetwork in which nodes representing microbes, colored by phylogenetic origins, could be connected to reflect microbial interactions gene transfer, competition, metabolic cooperation, etc.
Connections between the host and microbial subnetworks could simply be made by drawing edges between nodes representing individual mammals hosting microbes, and nodes representing these microbes. Moreover, nodes representing mammals and nodes representing microbes could be connected to nodes representing odorous metabolites to show what odours are associated with what combinations of hosts and microbes. Elaborating this network in a piecemeal fashion would involve cooperation between chemists, microbiologists, zoologists and evolutionary biologists. Of note, the use of integrated networks could pragmatically address a deep concern for evolutionary studies, by connecting phenomena that occur at different timescales: development and evolution [ ] or ecology and evolution [ ].
Then, various parts of the networks embody distinct timescales, which may provide a new form of timescale integration, working out the merging of timescales from the viewpoint of the model, and with resources intrinsic to the model itself. The reason for this is that a node in an interaction network N i , describing processes relevant at a time scale i , can itself be seen as the outcome of another embedded interaction network N j , unfolding at a time scale j.
This nestedness typically occurs when the node in N i represents a collective process, involving components that evolve sufficiently slowly with respect to the system considered at the time scale i to figure as an entity, a node in N i. In the case of a PPI network N i , each node conventionally represents a protein, but the evolution of each protein could be further analysed as the result of mutation, duplication, fusion and shuffling events affecting the gene family coding the proteins over time; for instance, each protein could thus be represented as the outcome of interaction between domains in a domain—domain interaction network N j.
Considering these two time-scales, it becomes apparent that gene families enriched in exon shuffling events, a process directly analysable in N j , have a higher degree in PPI networks represented at the time-scale N i [ ]. What possible findings may result from this perspective shift? One can only speculate, but the nature of the potential discoveries is exciting. At the molecular level, the structure and composition of regulatory networks and protein interaction networks could be substantially enhanced to scaffolding elements.
Yet, viruses are everywhere, viral genes and proteins clearly influence the networks of their hosts, and likely constitute an actual part of their evolution. Thus, virogenetics, a novel transdiscipline, may prosper in an expanded evolutionary theory to show how and to what extent viruses co-construct their hosts, including perhaps reproductive-viruses, allowing their hosts to complete their lifecycles. At the cellular level, new modes of communication [ , ] could be discovered, as possible viral and microbial languages and communication networks in biofilms would exemplify.
At the level of phyla, hidden evolutionary transitions may be unraveled. While secondary and tertiary acquisitions of plastids have been documented [ 81 ], it might be shown that mitochondria too have been so acquired in some eukaryotic lineages alongside the plastid or independently. Secondarily acquired mitochondria may provide their new hosts with additional compartments, where chimeric proteomes could assemble [ 91 , ] and perform original physiological processes.
At the ecosystemic level, evolving networks could be used to model the changes and stases of our planet, grounding biotic lineages and processes in their environment, while highlighting potential regularities in the organisations and dynamics of ecosystems. What affects the stability of what over the course of evolution could thus become a central theme of an expanded evolutionary theory.
Interactions are not merely a part of biological history, they are what made this history. Undertaking this endeavor, however, would emphasize the importance of processes.
Ecologists Study the Interactions of Organisms and Their Environment
Our ancestors were processes. Our descendants and those of other life forms will be processes too. Some one hundred and fifty years after On the Origin of Species , which started a great evolutionary inquiry, evolutionists should prepare to face a larger challenge: expanding evolutionary theory to study the evolution of processes.
With the development of -omics and network sciences, the concepts, data and tools for this research program are increasingly available. Huxley J. Evolution: the modern synthesis. Princeton: Princeton University Press; Gayon J. Darwinism's struggle for survival: heredity and the hypothesis of natural selection. Cambridge: Cambridge University Press; Simpson GG. Tempo and mode in evolution. New York: Columbia University Press; Martin G, Lenormand T.
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Mol Biol Evol. Functional remodeling of RNA processing in replacement chloroplasts by pathways retained from their predecessors. All animals that live off plants herbivores are included in this group of secondary producers. As mentioned above nutrients can play a role in limiting the production of a community. Other than sunlight, primary productivity is limited by nutrient availability.
A limiting nutrient is a nutrient which is found in the lowest relative concentrations such that an increase in this nutrient will increase primary productivity, while a decrease in this nutrient will decrease primary productivity. Typically, either phosphorus or nitrogen serves as a limiting nutrient within a given ecosystem, though water availability can also serve to limit the primary productivity of an ecosystem.
Trace nutrients such as molybdenum and zinc are necessary for the growth of plants and can act as limiting agents even though they are in very small quantities. Aquatic communities can also have limiting nutrients that control how much production takes place. In freshwater ecosystems net primary production is limited by phosphorus, and in the ocean the limiting nutrient is iron.
Read: Limiting Nutrients. Too much of a nutrient can also have a limiting impact on a community. Recent studies have shown that excess nitrogen from human activities such as agriculture, energy production, and transport have begun to overwhelm the natural nitrogen cycle. The effects of the extra nutrients reach every environmental domain, threatening air and water quality and disrupting the health of terrestrial and aquatic ecosystems.
In terrestrial ecosystems, nitrogen saturation can disrupt soil chemistry, leading to loss of other soil nutrients such as calcium, magnesium, and potassium. This means that while the nitrogen is not a limiting factor, it causes other nutrients in the soil to become limiting factors. Read: Nutrient Overload. The laws of thermodynamics are fundamental concepts to chemical processes of the universe. They are extremely important in chemistry and physics, but are the basis for many biological concepts as well.
The laws dictate how energy can be transported, which of course can be applied to ecology because energy transfer is what drives metabolism and, on a larger scale, food chains and food webs. Zeroth Law of Thermodynamics is the most obvious of all three laws, simply stating that if the temperature of object A is equal to the temperature of object B, and the temperature of object C is equal to the temperature of object B then the temperature of object C equals the temperature of object A.
Although this transitive property seems almost unnecessary to mention, it is of crucial importance to the subject of energy transfer in the form of heat energy, since temperature is a measure of heat. Its implications in ecology are obvious as well. Organisms require the means to survive in a climate of a certain range of temperatures, and evolution has created organisms with extremely different tolerances to temperature according to where they are located.
The First Law of Thermodynamics states that the total inflow of energy into a system must equal the total outflow of energy from the system, plus the change in the energy contained within the system. That is, energy is neither created nor destroyed, but may transform from one type to another. This is relevant to food webs in that the amount of energy being transferred through the food web cannot be larger than the amount of energy initially supplied by the primary producer which was supplied by the sun's energy.
The Second Law of Thermodynamics states that "energy of all kinds in our material world disperses or spreads out if it is not hindered from doing so. The second law of thermodynamics is definitely the most applicable of the four laws to ecology. It is consistent with Elton's Pyramid of foodwebs that states that although sometimes total size or number of organisms can either increase or decrease with increasing trophic levels, the total biomass ALWAYS decreases with increasing trophic levels, as energy is constantly being lost to the atomosphere usually as CO 2.
The energy from the sun allows living organisms on earth to temporarily decrease entropy, but our organized systems require an overall input of energy provided by the sun. The second law of thermodynamics suggests a dire "heat death of the universe" will occur when all the energy of the universe is evenly distributed, and no life or concentrated matter stars, galaxies can exist, although this won't occur for at least 10 years.
Ecosystems are far from thermodynamic equilibrium, which used to be an argument against the second law of thermodynamics. Galucci  performed a literature review of the physical theory of thermodynamics in relation to mechanisms of energy transfer in the environment passive and active and ecosystem productivity. He also studied how community structure and diversity related to entropy. As a result of his research, he claimed that the earth is a "receiver, reflector, and degrader of energy. External energy from the sun provides primary producers with energy and with a range of temperature in which life in the community is possible.
Internal energy that travels from primary producers to organisms higher in the food chain is in the form of metabolism mass, bonds which provides transferrable energy.
Galucci claimed that diversity is a form of entropy and improves the stability of a community. His conclusion was that the hypothesis of the second law of thermodynamics being applicable to ecosystems is supported. Hedin et al. More than subsurface water samples from Michigan wetlands draining from a mixed forested-agricultural landscape were observed in this study. Thermodynamic principles could predict what form of nitrogen would be available to microbes if the number of electron donors and acceptors water pH is known.
Microbes will transform nitrogen from NO 3 - to ammonia in acidic conditions present in shallow water, but covert NO 3 to N 2 O gas in basic conditions present in deeper water. The findings agreed with this theory and were helpful because oxidizable carbon can be added to the shallow portions of the water that are not sufficient at denitrification.
These findings would not have been possible or understandable if a thermodynamic approach to the metabolism of microorganisms had not been considered. The Third Law of Thermodynamics simply states that as temperature of a system reaches absolute zero 0 K , the entropy of the system decreases.
Technically, it states that a system at absolute zero is at zero entropy, but this is theoretically not possible as it has been established that absolute zero is not able to be experimentally reached. This is why substances become gases molecules spread out, entropy increased at high temperatures, and freeze become ordered crystalline structures, entropy decreased at low temperatures. Decomposition also has a higher rate at higher temperatures for this reason. An Ecological Pyramid or Trophic pyramid is a graphical representation designed to show the relationship between energy and trophic levels of a given ecosystem.
Most commonly, this relationship is demonstrated through the number of individuals at a given trophic level, the amount of biomass at a given trophic level, or the amount of energy at a given trophic level. It is worth noting that all Ecological Pyramids begin with producers on the bottom and proceed through the various trophic levels, the highest of which is on top.
An Ecological Pyramid of Biomass shows the relationship between energy and trophic level by quantifying the amount of biomass present at each trophic level dry mass per trophic level. As such, is assumed that there is a direct relationship between biomass and energy. By doing this, the earlier discrepancy is avoided because even though there is only one tree, it is much more massive than the next trophic level.
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The main problem with this type of Ecological Pyramid is that it can make a trophic level look like it contains more energy than it actually does. For example, all birds have a beak and skeleton, which despite taking up mass are not eaten by the next trophic level. In a Pyramid of Biomass , the skeleton and beak would still be quantified even though it does not contribute to the overall flow of energy into the next trophic level. An Ecological Pyramid of Energy is the most useful of the three types, showing the direct relationship between energy and trophic level.
It measures the number of calories per trophic level. As with the others, this graph begins with producers and ends with a higher trophic level. When an ecosystem is healthy, this graph will always look like the standard Ecological Pyramid shown at the top of the page.
This is because in order for the ecosystem to sustain itself, there must be more energy at lower trophic levels than there is at higher trophic levels. This allows for organisms on the lower levels to maintain a stable population, but to also feed the organisms on higher trophic levels, thus transferring energy up the pyramid. The best way of showing what is happening in the feeding relationships of a community is to use Energy Pyramids. From Wikibooks, open books for an open world.
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