The Feeding Interactions Among Species Shown With Arrows in a Community Are Best Described as a

Trophic Interaction

Trophic interactions and community structure of forest soils are complex with some components regulated by top-down controls (predation and physical alterations of soil) and bottom-up controls (substrate availability) (Wardle et al., 1998).

From: Developments in Soil Science , 2019

FOOD–WEB INTERACTIONS

P.C. de Ruiter , J.C. Moore , in Encyclopedia of Soils in the Environment, 2005

Trophic Interactions and the Dynamics and Stability of Soil Populations and Food Webs

Trophic interactions are likely to affect the distribution and abundance of organisms in fundamental ways, since the success of populations is largely a function of benefits derived from the acquisition of energy (and nutrients) and losses derived from predation. Food web descriptions of the soil community therefore provide a way to analyze the dynamics and persistence of the various populations in the context of the stability of the community as a whole. Central in analyses of the role of trophic interactions in community stability are the interaction strengths. Interaction strengths refer to the per capita – in this case per biomass – effects upon one another. The interaction strengths can be derived from the population sizes and energy flow rates (i.e., the feeding rates; see eqns [1] and [2]) by assuming Lotka-Volterra equations for the dynamics of the functional groups:

[5] ${\stackrel{.}{X}}_{\text{i}}=X_{\text{i}}\left(b_{\text{i}}+\stackrel{n}{{\displaystyle \sum _{\text{j}=1}}}{c}_{\text{ij}}{X}_{\text{j}}\right)$

where X _i and X _j represent the population sizes of group i and j, respectively, b _i is specific rate of increase or decrease of group i, and c _ij is the coefficient of interaction between group i and group j. Mathematically, interaction strengths are defined as the entries of the Jacobian community matrix (α_ij) being the partial derivatives near equilibrium: α_ij  =   (∂ $\stackrel{.}{X}$ _i/ ∂X _j)*. Values for the interaction strengths can be derived from the equilibrium descriptions by equating the death rate of group i due to predation by group j in equilibrium, c _ij $X_{\text{i}}^{*}$ $X_{\text{j}}^{*}$ , to the mean annual feeding rate, F _ij (eqn [2]) and the production rate of group j due to feeding on group i, c _ji $X_{\text{j}}^{*}$ $X_{\text{i}}^{*}$ , to a _j p _j F _ij. With equilibrium population sizes, $X_{\text{i}}^{*}$ , $X_{\text{j}}^{*}$ , assumed to be equal to the observed annual mean population sizes, B _i, B _j, the effect of predator j on prey i is:

[6] ${\alpha }_{\text{ij}}=c_{\text{ij}}{X}_{\text{i}}^{*}=-\frac{F_{\text{ij}}}{B_{\text{j}}}$

and the effect of prey i on predator j is:

[7] ${\alpha }_{\text{ji}}=c_{\text{ji}}{X}_{\text{j}}^{*}=\frac{a_{\text{j}}{p}_{\text{j}}{F}_{\text{ij}}}{B_{\text{i}}}$

Estimates of the interaction strengths obtained this way for the soil food webs reveal patterns along trophic position, characterized by relatively strong top-down effects at the lower trophic levels and relatively strong bottom-up effects at the higher trophic levels (Figure 2). The patterns of interaction strengths are important to the community stability as is indicated by a comparison between the stability of community matrix representations of seven soil food webs (from the prairie and arable soils), using the empirically based values of interaction strengths ('real' matrices) and that of matrices in which these values are randomized. The comparison shows that matrices including the realistic patterns of interaction strengths have a much higher level of stability than their randomized counterparts (Figure 3).

Figure 3. The effects of the patterning of interaction strengths on the stability of seven soil food webs from prairie and arable land. The black fraction in the bars denotes the level of stability (percentage) based on 1000 model runs. Stability of the community matrices is established by evaluating the signs of eigenvalues of the matrices; when all real parts are negative, the matrix is stable and the food web is considered to be locally stable. HSB, Horshoe Bend site, arable land; CT, conventional tillage; NT, no tillage; CPER, shortgrass prairie; KS, Kjettslinge; B0, arable, no fertilizer; B120, arable, with fertilizer; LH, Lovinkhoeve, arable; CF, conventional farming; IF, integrated farming. (Reproduced with permission from De Ruiter PC, Neutel AM, and Moore JC (1995). Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269: 1257–1260.)

The stabilizing patterns of the interaction strengths are the direct result of patterns in the energetic properties of the food webs such as the population sizes (biomasses) and feeding rates (eqns [6] and [7]). Therefore, when the soil biology is looked at in terms of trophic interactions in the soil food web, the structure of the community and the dynamics of the soil populations become inextricably interrelated with soil ecosystem processes and functioning.

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Trophic Relationships of Coastal and Estuarine Ecosystems

H. Asmus , R. Asmus , in Treatise on Estuarine and Coastal Science, 2011

6.13.1.1 General Aspects of Suspension-Feeder Communities

Trophic interactions between suspension-feeder communities and their ambient environment are among the most important ecological processes in shallow waters and may dominate benthic pelagic coupling particularly in coastal areas ( Prins and Smaal, 1990; Dame et al., 1991a, 1991b; Smaal and Nienhuis, 1992, 1999; Smaal and Haas, 1997; Smaal and Zurburg, 1997; Prins et al., 1998; Asmus and Asmus, 2005). Although this is an important function, there have only been few attempts to quantify the trophic web of suspension-feeder communities integrating the different pathways from grazing phytoplankton to predation by birds (Baird et al., 2007).

In the intertidal area, the effect of suspension-feeding communities is most pronounced, because the water column is shallow and mixing of tidal water is intense; thus, suspension feeders may be able to use the total water column for feeding (Asmus et al., 1992, 2000). Where suspension feeders such as mussels occur on soft bottoms, intense filtering, feeding, and digestive processes lead to a high production of feces accumulating among and beyond the mussels and creating organic-rich sediments, which are suitable places for bacterial decomposition and detritivorous infauna (Commito and Boncavage, 1989, Commito et al., 2008). Shell-bearing suspension feeders, such as mussels and oysters, are also suitable substrates for other hard-bottom flora, and fauna characterizing suspension-feeder communities can be considered as oases of hard-bottom dwellers in a sandy or muddy surrounding. The large aggregation of biomass of the suspension feeders in these communities attracts many invertebrate and vertebrate predators.

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Volume 1

Janice M. Lord , in Encyclopedia of the World's Biomes, 2020

Mast-Flowering

Trophic interactions in New Zealand's alpine ecosystems are strongly influenced by large scale variation in productivity driven by mast-flowering species. Mast-flowering or mast-seeding is the phenomenon of synchronized mass reproduction within a species at supra-annual intervals, with intervening years of little or no seed output, and is especially prevalent in the New Zealand flora ( Schauber et al., 2002). Alpine snow tussocks in the genus Chionochloa include some of the most extreme mast-flowering species in the world; synchronized mast-flowering of co-occurring Chionochloa species significantly reduces the proportion of seeds lost to specialist native dipteran and lepidopteran seed predators (Kelly et al., 2000). This pulse in resources also stimulates population increases in introduced mice which switch diet from invertebrates to Chionochloa seeds in heavy mast years (Wilson and Lee, 2010). The giant speargrasses (Aciphylla species, Apiaceae) also exhibit mast-flowering behavior (Young, 2006; Campbell, 1981), and may play a critical role in the population dynamics of associated flower- and leaf-feeding invertebrates. For example, native flightless speargrass weevils (Lyperobius species, Curculionidae: Molytinae) are host specific to Apicaeae, predominantly Aciphylla, and show extensive allopatric speciation in alpine areas of South Island. Larvae feed on Aciphylla roots, but the adults, which can live 2–3   years, feed on leaves and inflorescences (Craw, 1999). Several Celmisia species and other alpine Asteraceae also show mast-flowering behavior (Kelly et al., 2013; Campbell, 1981; Mark, 1970). While levels of seed predation by dipteran larvae do not appear to differ between mast-flowering and regular flowering Celmisia species (Spence, 1990), common flower visitors to Asteraceae and Aciphylla, such as hoverflies (e.g., Allograpta spp., Playtcheirus spp., Syrphidae), increase significantly in abundance in a heavy mast-flowering year (Miller et al., 2018).

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Integrative Ecology: From Molecules to Ecosystems

Guy Woodward , ... Owen L. Petchey , in Advances in Ecological Research, 2010

A Recent Advances in Food Web Data and Theory

Trophic interactions are key determinants of population abundance and dynamics, the structure and persistence of communities, and the rate and sustainability of ecosystem processes ( Ings et al., 2009; McCann, 2000; Neutel et al., 2002; Otto et al., 2007; Olesen et al., 2010; Petchey et al., 2004; Reiss et al., 2009; Woodward et al., 2010). As such, the study of food webs, which can include tens to hundreds of species and thousands of feeding links, has long been a key theme in ecology, especially in recent decades, as highlighted by the exponential increase in publications in this field since the 1970s (Ings et al., 2009). A mainstay of this research has been the collection, compilation, and analysis of food webs from natural ecosystems, although the quality and resolution of these data have not always been appropriate for the tasks to which they have been put (Bersier and Sugihara, 1999; Cohen et al., 1993; Hall and Raffaelli, 1993; Ings et al., 2009; Paine, 1988, 1992; Polis, 1998).

Early food web research highlighted the importance of taxonomic resolution, with seemingly marked changes in network structure being observed depending on whether species, genera, families, or even coarser taxonomic entities were used as the interacting entities (Goldwasser and Roughgarden, 1997; Martinez, 1991). Serious concerns were also raised about systematic biases in taxonomic resolution being confounded with trophic position: large top predators were typically described to species, whereas smaller organisms at the lower trophic levels were often either aggregated far more coarsely (e.g. 'algae') or simply ignored altogether, as pointed out by Cohen et al. (1993), Hall and Raffaelli, 1993, and Schmid-Araya et al. (2002a,b). In addition to this patchy information on the identity of the nodes and links, the amount of sampling effort used to detect them was, and in many cases still is, highly variable both within and among webs. Some food webs have been constructed from inferential data on interactions from the literature, expert knowledge, or theoretical predictions (e.g. Dunne et al., 2008; Martinez, 1991) and a few have been based solely on direct observation (e.g. Figueroa, 2007; Schmid-Araya et al., 2002a,b; Woodward et al., 2005a), whilst probably most have used some combination of the two (e.g. Layer et al., 2010a,b; Woodward et al., 2008). The former case can risk including links that are not necessarily realised in a particular local food web (although they may be present in other systems), whereas the exclusive reliance on directly observed data can be problematic if no information is provided on the sampling effort used to detect nodes or links, as this can have a huge impact on many web parameters (Martinez et al., 1999).

The recognition of the shortcomings of the early datasets helped to trigger the construction of a new generation of food webs that are more completely sampled and based on more uniform taxonomic data than was the case for many of their precursors (e.g. Banašek-Richter et al., 2009; Benke and Wallace, 1997; Closs and Lake, 1994; Cohen et al., 2003, 2005; de Ruiter et al., 1995; Lafferty et al., 2006; Layer et al., 2010a,b; Martinez, 1992; O'Gorman and Emmerson, 2009; Riede et al., 2010; Schmid-Araya et al., 2002a,b; Tylianakis et al., 2007; van Veen et al., 2008; Warren, 1989; Woodward et al., 2005a,b). These new webs have provided clearer pictures of the structure of trophic networks and have largely superseded the earlier datasets used in the pioneering work carried out in the 1970s and 1980s (e.g. Cohen, 1978; Pimm, 1982).

Many of the data that emerged from about the early 1990s onwards revealed that networks were far more complex than had been previously thought, challenging the theoretical suggestion (May, 1972, 1973; Pimm, 1980, 1982) that complexity was destabilising and should, therefore, be rare. In parallel with the improvements to the empirical data over the last two decades, a range of new dynamical models began to unearth some of the posited 'devious strategies' (May, 1972) by which complex food webs might be stabilised. These included the prevalence of weak versus strong links (McCann, 2000; McCann, et al., 1998), the role of feeding loops (Neutel et al., 2002; Neutel et al., 2007), and the damping of potentially destabilising 'fast' food chains with 'slower' pathways (Rooney et al., 2006). Many of these dynamical models have stressed the importance of body mass as a determinant of interaction strength, and hence stability (e.g. Berlow et al., 2009; Emmerson et al., 2005; Yodzis and Innes, 1992). In addition, a new suite of structural models, building on the initial work of Cohen and colleagues (e.g. Cohen and Newman, 1985; Cohen et al., 1990) have revealed how complex networks might be based on simple rules related to morphological, metabolic, or foraging constraints, many of which are closely correlated with body size (e.g. Beckerman et al., 2006; Cattin et al., 2004; Petchey et al., 2008; Warren, 1996; Williams and Martinez, 2000).

From these multiple lines of evidence, it has become increasingly clear that the body size of species often exerts a powerful influence on ingestion rates ans the dynamical and structural attributes of many food webs (Ings et al., 2009; Montoya et al., 2006, 2009; Perkins et al., 2010; Reiss et al., 2010; Reuman et al., 2009a,b; Woodward and Hildrew, 2002a; Woodward et al., 2005a). A key, but largely overlooked, point of relevance here is that trophic interactions occur between individual organisms, not species per se (Stouffer, 2010; Woodward and Warren, 2007). The functional constraints on feeding imposed by size operate at the individual level, and many of the sampling biases in webs are related to the likelihood of observations being made of feeding events. However, the conventional approach to documenting food webs, which has focused primarily on taxonomic entities (species, etc.), may conceal much of this information on size structure, particularly where intraspecific size variation may equal or even exceed that between species averages, and this point has also been largely ignored in both structural and dynamical models (Woodward, 2009; Woodward and Warren, 2007). Although body size is now measured routinely in many food web studies (e.g. Cohen et al., 2003), individual-level variation or the yield–effort data needed to assess potential sampling effects are still rarely considered (but see Goldwasser and Roughgarden, 1997; Ings et al., 2009; Martinez et al., 1999; Woodward and Hildrew, 2001).

We sought to address whether, given that individuals are the relevant interacting entities, constructing networks from this level of organisation could improve upon the traditional process of aggregating via species or size classes and whether considering both size- and species-based perspectives together provide deeper insights than provided by either alone. We addressed these questions using a set of four unusually highly resolved food webs that include information on sampling effort and directly observed data on species identity and the body sizes of the individuals involved in actual feeding interactions.

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Soil Rhizosphere Food Webs, Their Stability, and Implications for Soil Processes in Ecosystems

John C. Moore , ... Peter C. de Ruiter , in The Rhizosphere, 2007

MUTUALISMS WITHIN THE RHIZOSPHERE, AND LINKS TO ABOVEGROUND FOOD WEBS VIA PLANTS

The trophic interactions within the rhizosphere, particularly symbiotic mutualisms, affect plant growth and community structure aboveground. Direct symbiotic mutualisms ( sensu Boucher et al. 1982) involving reciprocal transfers of limiting nutrients between plants and microbes are a prominent feature of rhizospheres of plant communities in terrestrial ecosystems. Studies of primary and secondary succession reveal strong correlations between symbiotic mutualisms, nutrient dynamics, plant growth, and community structure (Reeves et al. 1979; Vitousek et al. 1987; Vitousek and Walker 1989; Wall and Moore 1999; Moore et al. 2003).

Several approaches to modeling symbiotic interactions have been undertaken to explain the aforementioned linkages between the growth responses of the host and the symbiont (Johnson et al. 2006). Swartz and Hoeksema (1998) presented an adaptation of market models used in economics and trade that builds on the notion of reciprocal transfers of limiting resources. Under this supposition, the excess resource for one of the partners is the limiting resource for the other, and vice versa. The trading of the excess resources allows for potential increases in the carry capacities of both partners. The parallels in the economic models to symbiotic relationships within the rhizosphere are clear as plants often serve as the host and "trade" carbon or refugia with the symbiont (e.g., mycorrhizal fungi, Rhizobium, Frankia), in exchange for a plant-limiting nutrient, usually nitrogen or phosphorous. Missing from the conceptual market modeling approach is the explicit treatment of the mechanisms that precipitate the actual trade. Hunt et al. (1987) and Moore and Hunt (1988) treated mycorrhizal interactions as simple trophic interactions, in as much as mycorrhizal fungi served as predators and plants served as prey when modeling carbon, and with the roles reversed when modeling nitrogen. A simple Holling Type 1 (constant) functional response (sensu Holling 1959) was used to model the interactions under the assumption of mass balance in the early formulations, with later treatments including a more realistic Holling Type II (saturation) functional response to describe the transfers (Moore et al. 2003).

While modeling the symbioses as special forms of trophic interactions makes sense, more sophisticated applications are needed if we are to apply the economic models to nutrient exchanges or were to account for the recent revelations of molecular and chemical signaling between plants and microbes that may be involved in nutrient exchanges (Philips et al. 2003), Signaling may produce market model outcomes; however, if chemical Signals from either the host or symbiont served to up-regulate or down-regulate gene expression in a way that hastened or retarded nutrient release or exchange, it is unlikely that simple Holling Type I, II, or even III functional responses would capture these processes given the complexities of the feedbacks involved.

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Ecological Networks in an Agricultural World

Michael Traugott , ... Manuel Plantegenest , in Advances in Ecological Research, 2013

2.2 Host–parasitoid trophic interactions

Traditionally, trophic interactions in host–parasitoid systems have been examined by rearing parasitoids from parasitised hosts or by host dissection to retrieve juvenile parasitoids ( Henri and Van Veen, 2011; Sunderland et al., 2005), but these methods are, unfortunately, prone to many sources of error. First, host and parasitoid mortality during rearing, resulting in parasitoid emergence failure, can be considerable and prolonged parasitoid post-emergence diapause can further complicate estimates of interaction rates made using this rearing approach (Gariepy et al., 2007). Second, the morphological identification of parasitoids is often not possible for juvenile stages and may be confounded by the occurrence of unknown and/or cryptic species (Smith et al., 2006; Tylianakis et al., 2007). Consequently, it can be very difficult to precisely state which species are interacting, and at what rate, in multi-parasitised and hyperparasitised hosts. In addition, rearing and host dissection also become impractical when large numbers of hosts need to be analysed (Gariepy et al., 2008a). All these sources of error lead to unresolved host–parasitoid linkages, to biased estimates of percentage parasitism, and/or to a lack of replication in the host–parasitoid food webs analysed (Gariepy et al., 2008a; Greenstone, 2006). The developments in DNA-based techniques have provided some solutions to these problems, which, for discussion, we classify into either diagnostic PCR or sequencing/barcoding approaches (Fig. 3.2).

Figure 3.2. Overview on the different steps required for molecular analysis of diet and parasitism. The approaches can be broadly separated into diagnostic PCR (left panel) and sequence-based identification (right panel). Parallel double arrows indicate the simultaneous analysis of multiple food sources/parasitoids; dashed arrows show optionality for analysis of multiple prey/hosts. For further details see text.

In diagnostic PCR, the presence of a parasitoid species is searched for, or targeted, using specific primers that amplify a particular fragment of the parasitoid's total DNA, but does not amplify the DNA of the host. This approach has been used successfully to detect three species within the genus Aphelinus (Hymenoptera: Aphelinidae) as parasitoids of aphid hosts (Zhu and Greenstone, 1999), and to study parasitisation of the European corn borer, Ostrinia nubilalis, by tachinid flies (Agustí et al., 2005). Although these 'singleplex' PCR assays are highly sensitive and specific, allowing detection of a single parasitoid egg within a few minutes after oviposition (Traugott and Symondson, 2008), they require a separate reaction for each parasitoid species targeted. This makes the assessment of multiple parasitoid species a costly and time-consuming endeavour. Gariepy et al. (2005) overcame this limitation by using multiplex PCR to detect, within a single reaction, three different species of Peristenus wasps (Hymenoptera: Braconidae) parasitising Lygus bugs (Hemiptera: Miridae). To date, up to eight different parasitoid taxa have been targeted within a single 'multiplex' PCR assay (Traugott et al., 2008). Multiplex PCR can also be used to confirm the identity of the host, by including host-specific primers, and to provide an internal, positive control, that is, a primer pair which amplifies a specific fragment of the host DNA, the presence of which indicates that the PCR was successful (Traugott et al., 2006). The latter becomes important where parasitism rates are low (e.g. Agustí et al., 2005). Otherwise, all samples testing negative for parasitoid DNA would need to be retested with general primers in order to exclude false-negative results. In order to maximise detection, it is important to balance the concentration of the host primers, within the multiplex reaction, to avoid negative effects on parasitoid detection sensitivity because the reaction can otherwise be distorted towards an amplification of host DNA (Traugott and Symondson, 2008). Multiplex PCR approaches have been used to provide precise information for trophic interactions in cereal aphid parasitoid communities (Traugott et al., 2008), host–parasitoid associations in classical biological control programmes (Gariepy et al., 2008a), the effects of host plant identity on parasitoid species composition and parasitism rates (Gariepy et al., 2008b) and the effect of farming type on parasitoid control of aphids (Macfadyen et al., 2009).

Although being a highly effective approach for screening large numbers of hosts for parasitoids, diagnostic PCR detects only the specific taxa targeted a priori by the primers. Hence, when it is the number and identity of parasitoid species which is unknown, this approach becomes inefficient. In such a situation, general invertebrate primers can be used to generate barcoding DNA sequences, allowing the identification and/or differentiation between parasitoid taxa (Fig. 3.2). DNA barcoding has been employed to detect 93 previously unknown host–parasitoid links in the tropical rainforest of Papua New Guinea (Hrcek et al., 2011). Similarly, Kaartinen et al. (2010) compared leaf miner/gall inducer-parasitoid food webs derived by morphological identification with links derived from DNA-barcoded samples and found that trophic interactions were more specialised in the molecular-informed sample set than in the traditional one. DNA barcoding via classical 'Sanger sequencing', however, requires host and parasitoid tissue samples being analysed separately, as mixtures of DNA sequences are unreadable. Derocles et al. (2012b) overcame this problem by applying a primer pair specific to primary parasitoids of aphids, allowing for sequenced-based identification of the parasitoid. However, for six groups of closely related species, species-specific assignment was not possible, due to the common identity of the sequences, and sequences from two genes had to be combined to allow for identification of all species (Derocles et al., 2012a).

Although molecular detection of parasitoids provides an accurate and convenient means of recording host–parasitoid interactions, there are also drawbacks to this approach. Molecular-derived parasitism rates tend to overestimate parasitoid-induced mortality as some hosts might overcome parasitisation (e.g. via secondary endosymbionts, Vorburger et al., 2010). Moreover, the power of the taxonomic assignment in barcoding-based parasitoid and host identification is limited, largely, by the sequence information available in reference databases such as GenBank or BOLD. Constructing a database of barcoding DNA sequences from parasitoids that are relevant in a particular habitat is therefore highly recommended as a strategic priority in agricultural research to increase the chances for identifying the sequences recovered from field-collected samples.

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Plecoptera (Stoneflies)

R.E. DeWalt , in Encyclopedia of Inland Waters, 2009

Ecology

Feeding and trophic interactions : Most of what is known about the feeding ecology of stoneflies comes from nymphal studies in the Northern Hemisphere and scattered works elsewhere. Enough is known at this point to say that most stonefly families are detritivorous, feeding on coarse and fine dead plant material and associated biofilms. These films probably constitute the most nutritious component of the diet for detritivores. Confirmed detritivorous families include the Capniidae, Nemouridae, Leuctridae, Notonemouridae, Scopuridae, Taeniopterygidae, Austroperlidae and Diamphipnoidae (wood gougers), Gripopterygidae (some exceptions), Pteronarcyidae, and Peltoperlidae. Predators include the Chloroperlidae, Eustheniidae, Perlidae, Perlodidae, and the Styloperlidae. Some Isoperla (Perlodidae) have predaceous mouthparts, but some eat detritus throughout their development. Others experience ontogenic shifts in diet, moving through detritivory, omnivory, and carnivory.

Adult feeding has rarely been studied in detail. Feeding is known for a substantial number of detritivorous species. Leuctridae, Nemouridae, and Capniidae are known to eat cyanolichens, blue-green algae, and the hyphae and spores of Ascomycetes. The Taeniopterygidae appear to be the only family whose adults feed on live, vascular plant tissue, having been implicated in damage to blossoms and leaves of fruit trees. Feeding in the predatory families is apparently limited to the Chloroperlidae and Perlodidae which principally ingest pollens of various types, fine and coarse particulate organic matter, and the spores and hyphae of Ascomycetes. It would not be surprising to find that many more adults in multiple families feed to some extent. Feeding seems to correspond with extended longevity in adults, giving them time to mature eggs and disperse.

The use of gut analysis to determine diet has some drawbacks, including difficulty in quantifying items. New methods, such as stable isotope analysis, hold promise to more fully elucidate food webs, especially in detritivorous and omnivorous species, where gut contents are often difficult to categorize beyond miscellaneous detritus.

Niche partitioning: Most studies of stonefly species assemblages have taken place in the Northern Hemisphere, where 30 or more species may emerge from cool water or mountain streams. Genera are composed of multiple species and densities of each species leads to the potential for space and food competition. Every study of an entire assemblage of stoneflies demonstrates that species partition themselves by serial development and emergence. Figure 9 depicts such a succession of species for a hypothetical assemblage from a moderately large stream in the Midwest, USA. Winter stoneflies emerge from January through March. A small contingent of nemourids, leuctrids, capniids, and perlodid species emerge in April, followed by a brief lull in emergence. By late April, Pteronarcys emerges, as do a perlid and several Isoperla species. In late May, there is another lull before several large perlid species emerge in early June. Small perlids follow in mid-to-late June, July, and early August. The final species to emerge is Leuctra tenuis. No more species emerge until the following January. This sort of succession of species allows for coexistence of species with similar feeding strategies and habitat needs.

Figure 9. Hypothetical stonefly community from a moderately large stream in the Midwest, USA. Species list and timing based on museum specimen records at the Illinois Natural History Survey.

Stoneflies as indicators of water quality: Stoneflies are highly sensitive to organic pollution and hypoxia that comes with it. Biotic indices have been developed throughout the world that consistently rate stoneflies as most intolerant to pollution. Although several of these ratings systems have been developed through professional judgment, others have empirically generated tolerance values. Even these have rated stoneflies as the most sensitive order of aquatic insects. Throughout the world, the number of species of Ephemeroptera (mayflies), Plecoptera, and Trichoptera (caddisflies) (EPT taxa) is used as a stream quality metric. Unfortunately, many water pollution biologists these days are increasingly finding only mayflies or caddisflies to tally.

Conservation ecology: Freshwater aquatic systems have suffered 4–5× higher extinction rates than terrestrial habitats, and this is likely to continue into the future. In Europe, large river species have lost range or have been extirpated from many countries. There is even fear that European mountain habitats that once provided refugia for species, and promoted gene flow due to interconnectivity, are becoming fragmented due to acid rainfall and other disturbances. The Nature Conservancy and its spin-off, NatureServe, have suggested that in the USA, stoneflies are the third most imperiled grouping of organisms both in terms of proportion of species imperiled (behind mussels and crayfish) and in the total number of imperiled species (behind vascular plants and mussels). In the highly agricultural, glaciated landscapes in Illinois, USA, stoneflies have a higher extirpation rate than for either mussels or fishes. It is clear that human needs for water, conversion of land to farming, industry, and housing, and climate change put stoneflies and a lot of other aquatic fauna at risk. In many areas, diapausing species are replacing the once widespread and common univoltine-slow and semivoltine species. Families that are particularly hard hit in the North American fauna are in the Perlidae and Perlodidae, and in the Midwest these were lost during a relatively short time in the late 1940s through the early 1960s. This is the same time period as for egg shell thinning in raptors, suggesting that indiscriminant DDT usage may be at fault.

Evidence is currently unfolding that suggests that global climate change is having an effect upon stonefly distributions. In the Great Smoky Mountains National Park, USA, it appears that at least one large perlid stonefly, Acroneuria abnormis, has significantly increased its upper altitudinal threshold. This species has a vast distribution at lower elevations and shows no sign of constriction there. However, several other species have narrower altitudinal distributions in the region and if both the lower and higher boundaries are shifted upward, these species may run into a ceiling beyond which they cannot colonize. Limits include the height of the local mountains or the altitude at which acidification of streams becomes problematic, something that is already occurring in the mountains of the eastern USA and in Europe.

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Trophic Relationships of Coastal and Estuarine Ecosystems

W.P. Sousa , E.M. Dangremond , in Treatise on Estuarine and Coastal Science, 2011

6.04.8 Concluding Remarks

Research on mangrove trophic interactions has rarely been framed as a test of theory in food-web ecology and, as a consequence, is seldom cited in textbooks or synthetic reviews as illustrating general principles. It is our hope that this chapter will help bring past and emerging research on mangrove trophic interactions to the attention of ecologists working in other habitats. As a study system, mangroves hold great promise for informing general ecological theory. For example, the environmental setting is tailormade for investigations of the importance of trophic subsidies among habitats to local population and community dynamics, that is, reciprocal exchanges among mangrove, coastal marine, riverine, and terrestrial habitats. Experimental manipulations of resource availability and the densities of consumers have demonstrated both bottom-up resource limitation and strong top-down consumer control in different compartments of the food web. Crabs are particularly strong interactors, exerting top-down control over litter dynamics and seedling recruitment in numerous mangrove forests. Finally, recent confirmation that mangroves are key sites of carbon storage has major implications for the prioritization of habitat protection and management efforts in response to accelerating climate change.

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Estuarine and Coastal Ecosystem Modelling

J.J. Heymans , ... V. Christensen , in Treatise on Estuarine and Coastal Science, 2011

Abstract

Ecosystem models describe trophic interactions within the ecosystems and provide a good basis for studying the general patterns of ecological properties. Here, we review 75 Ecopath models of coastal ecosystems to describe and assess their structural and functional characteristics and to investigate the ecological roles of their main functional groups. The analysis highlights the influence of depth, latitude, and longitude on their main ecological properties; the importance of different ecosystem types in distinguishing different ecological features; and the influence of the total size of the modeled ecosystem on ecosystem properties, as bigger ecosystems include higher-trophic-level organisms such as highly mobile fish.

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https://www.sciencedirect.com/science/article/pii/B9780123747112009050

SPATIAL ASPECTS OF FOOD WEBS

Ulrich Brose , ... Volkmar Wolters , in Dynamic Food Webs, 2005

Publisher Summary

Spatial distributions of trophic interactions define the spatial heterogeneity of food webs and differences between local and macroecological food webs. The concept of co-occurrence has to be given up when larger spatial scales are considered that integrate different local community food webs into a metacommunity food web. This chapter provides two examples. First, some large-bodied predators are too low in numerical abundance to invade all local community food webs simultaneously. Second, not all potential resource species in a metacommunity can persist under strong top-down pressure by their consumer species and thus avoid coexistence in the same local communities. Food webs consist of organisms that vary in their taxonomic identity, body size, trophic interactions, and trophic position and thus might have very different spatial scales of interactions. Recognition of the importance of spatial scale in food web studies has several implications for food web theory. In particular, the potential food webs that are frequently described by ecologists will often differ from how food webs are realized in actual space and time. Clearly, choosing the right spatio-temporal scale for a food web study depends on the species studied and the study objective. Integrating spatial processes such as extinction and colonization by dispersal in food web models is an important step towards understanding population dynamics in complex communities, and understanding the consequences of habitat loss for the community structure and food web dynamics.

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https://www.sciencedirect.com/science/article/pii/B9780120884582500436

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Source: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/trophic-interaction

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