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TOP) Trophic module of four species (one top T1, two intermediate I1 and I2, and one basal B1) with omnivory (fTB) and intraguild predation (fII), and (DOWN) trophic structure after dynamics for the parameter sets A, B, C, and D.
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Using a bioenergetic model we show that the pattern of foraging preferences greatly determines the complexity of the resulting food webs. By complexity we refer to the degree of richness of food-web architecture, measured in terms of some topological indicators (number of persistent species and links, connectance, link density, number of trophic le...
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... parameter sets we have considered in the simula- tions are given in Table 1. For each of them, we observe the action of the dynamics on the trophic module shown at the top of Fig. 1 after a suitable time T 01000. The resulting structure after dynamics is shown at the bottom of Fig. 1. This module is rich enough to embrace all kind of trophic interactions (like omnivory, intraguild predation or appar- ent competition). Those species preying on more than one species (namely, species T 1 and I 1 ) are capable of ...
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... parameter sets we have considered in the simula- tions are given in Table 1. For each of them, we observe the action of the dynamics on the trophic module shown at the top of Fig. 1 after a suitable time T 01000. The resulting structure after dynamics is shown at the bottom of Fig. 1. This module is rich enough to embrace all kind of trophic interactions (like omnivory, intraguild predation or appar- ent competition). Those species preying on more than one species (namely, species T 1 and I 1 ) are capable of adaptive predation and, initially, they invest the same effort in all their prey (i.e. a T 1 j (0) 01/3, ...
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... the simulations, we have obtained 46.9%, 38.7%, 10.6%, 3.1% and 0.7% of initial food webs with 1, 2, 3, 4 Table 1. The values of growth rates (r i ), adaptation rates (g i ), foraging preferences (f ij ), and death rates (l i ) used in the simulations. The action of dynamics on the trophic module for each parameter set is drawn in Fig. 1 or 5 basal species respectively. Therefore, 96.2% of them have a number of basal species less or equal to three. So, we have neglected those food webs with four and five basal species because of their lack of statistical significance. This low appearance of food webs with more than three basal species is due to the high initial ...
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... and Kondoh (2006) in the study of the influence of adaptive predators in the stability-complexity relationship. A mathematical analysis shows that, with this mean-field choice, simple structures like tri-trophic chains are not persistent since the top predator goes extinct. In our module test, the top predator survives thanks to the omnivore link (Fig. 1), but the final module structure is simply given by a star-shaped configuration. This is reflected in the number of trophic levels which is constantly equal to 2 for all food webs (TL 02). This number of trophic levels and the absence of intermediate species mean that only basal species and species feeding on them have finally ...
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... Aquatic insects play an important role in the trophic relationships of freshwater systems (Garcia-Domingo and Saldaña, 2008), as they participate in various processes, such as the cycling of matter and energy flow (Cummins, 1992;Allan, 1995). Among the aquatic insects, a very abundant group in the streams are the black flies (Diptera, Simuliidae). ...
Biotic factors such as predation, although important drivers of the black fly community, are rarely investigated in the literature. This study aimed to test the hypothesis that the patterns of co-occurrence of black fly larvae and its potential predators is not random and that there is a correlation between its frequencies and Simuliidae larvae abundances. Larvae were sampled from two localities in the Pedra Branca State Park, Rio de Janeiro, Brazil, during the dry season in june 2018. We collected Simulium pertinax Kollar, 1832, Simulium subpallidum Lutz, 1910, Simulium (Inaequalium) sp., Simulium (Psaroniocompsa) sp. and Simulium (Trichodagmia) sp. The predators families present were Perlidae, Hydropsychidae, Leptoceridae, Libellulidae and Chironomidae. The null models showed that species co-occurred significantly more than expected by chance. The canonical correlation analyses for the Vargem Grande and Pau da Fome areas area showed a significant relationship between black fly abundances and predator abundances. In Vargem Grande the abundance of Chironomidae showed a highly significant positive correlation to S. (Psaroniocompsa) sp., while Hydropsychidae was significantly correlated to Simulium sp. On the other hand, in Pau da Fome Libellulidae was significantly correlated to S. (Psaroniocompsa) sp. and Simulium sp. and Chironomidae correlated significantly to Simulium sp. The result is consistent with what would be expected of organisms that exercise mutual population regulation, although other factors than biotic interactions may be causing these patterns. However the lack of mechanistic evidences on the effect of biotic interactions on black fly populations pose a challenge on the understanding of these patterns.
... However, despite there being this type of recurring response of networks to perturbation, the effects of global and regional changes on network structure are often variable and depend on local environmental conditions, and can include interactive effects of multiple stressors (Tylianakis et al., 2008). Moreover, the heterogeneous organisation of complex ecological networks is yet to be fully understood as novel network structures and their relationship with ecosystem stability and functioning are still emerging Garcia-Domingo and Saldaña, 2008;Lu et al., 2016;Lurgi et al., 2012;Stouffer and Bascompte, 2011). ...
Global environmental change is a pressing issue as evidenced by the rise of extreme weather conditions in many parts of the world, threatening the survival of vulnerable species and habitats. Effective monitoring of climatic and anthropogenic impacts is therefore critical to safeguarding ecosystems, and it would allow us to better understand their response to stressors and predict long-term impacts. Ecological networks provide a biomonitoring framework for examining the system-level response and functioning of an ecosystem, but have been, until recently, constrained by limited empirical data due to the laborious nature of their construction. Hence, most experimental designs have been confined to a single network or a small number of replicate networks, resulting in statistical uncertainty, low resolution, limited spatiotemporal scale and oversimplified assumptions. Advances in data sampling and curation methodologies, such as next-generation sequencing (NGS) and the Internet 'Cloud', have facilitated the emergence of the 'Big Data' phenomenon in Ecology, enabling the construction of ecological networks to be carried out effectively and efficiently. This provides to ecologists an excellent opportunity to expand the way they study ecological networks. In particular, highly replicated networks are now within our grasp if new NGS technologies are combined with machine learning to develop network building methods. A replicated network approach will allow temporal and spatial variations embedded in the data to be taken into consideration, overcoming the limitations in the current 'single network' approach. We are still at the embryonic stage in exploring replicated networks, and with these new opportunities we also face new challenges. In this chapter, we discuss some of these challenges and highlight potential approaches that will help us build and analyse replicated networks to better understand how complex ecosystems operate, and the services and functioning they provide, paving the way for deciphering ecological big data reliably in the future.
... However, despite there being this type of recurring response of networks to perturbation, the effects of global and regional changes on network structure are often variable and depend on local environmental conditions, and can include interactive effects of multiple stressors (Tylianakis et al., 2008). Moreover, the heterogeneous organisation of complex ecological networks is yet to be fully understood as novel network structures and their relationship with ecosystem stability and functioning are still emerging Garcia-Domingo and Saldaña, 2008;Lu et al., 2016;Lurgi et al., 2012;Stouffer and Bascompte, 2011). ...
Global environmental change is a pressing issue as evidenced by the rise of extreme weather conditions in many parts of the world, threatening the survival of vulnerable species and habitats. Effective monitoring of climatic and anthropogenic impacts is therefore critical to safeguarding ecosystems, and it would allow us to better understand their response to stressors and predict long-term impacts. Ecological networks provide a biomonitoring framework for examining the system-level response and functioning of an ecosystem, but have been, until recently, constrained by limited empirical data due to the laborious nature of their construction. Hence, most experimental designs have been confined to a single network or a small number of replicate networks, resulting in statistical uncertainty, low resolution, limited spatiotemporal scale and oversimplified assumptions. Advances in data sampling and curation methodologies, such as next-generation sequencing (NGS) and the Internet ‘Cloud’, have facilitated the emergence of the ‘Big Data’ phenomenon in Ecology, enabling the construction of ecological networks to be carried out effectively and efficiently. This provides to ecologists an excellent opportunity to expand the way they study ecological networks. In particular, highly replicated networks are now within our grasp if new NGS technologies are combined with machine learning to develop network building methods. A replicated network approach will allow temporal and spatial variations embedded in the data to be taken into consideration, overcoming the limitations in the current ‘single network’ approach. We are still at the embryonic stage in exploring replicated networks, and with these new opportunities we also face new challenges. In this chapter, we discuss some of these challenges and highlight potential approaches that will help us build and analyse replicated networks to better understand how complex ecosystems operate, and the services and functioning they provide, paving the way for deciphering ecological big data reliably in the future.
... Studies involving macroinvertebrates (Gonc ßalves & Aranha, 2004;Garcia-Domingo & Saldaña, 2008) have demonstrated their important role in the food webs of aquatic systems. Macroinvertebrates are considered to be intermediaries between the basal resources (e.g. ...
Anthropogenic changes in the aquatic environment can influence macroinvertebrate communities, interfering with species richness and diversity. Few studies have investigated the effect of urbanisation in streams using a food web approach. The aim of this study was to describe the food webs of aquatic macroinvertebrates in streams located in urban and preserved areas of the Atlantic Rainforest domain.
Sampling was conducted in the state of Rio de Janeiro, Brazil, at 10 urban and 10 preserved streams during the dry season (August–September) of 2012. The streams were characterised with respect to their environmental conditions. Macroinvertebrates were sorted and identified in the laboratory and gut contents were examined. Trophic relationships were plotted in consumer–resource food webs and network properties were used to compare the two sets of streams.
Food webs in urban streams had higher linkage density and connectance, but consumers had fewer items in their guts, which may indicate reduced flow of energy and more unstable food webs. The base of the food webs in each land‐use type were also quite different, with higher amounts of plant detritus in preserved streams and fine detritus in urban streams.
This study shows that preserved areas offer better conditions for the development of macroinvertebrates, resulting in more stable trophic structures when compared with environments under higher anthropogenic influence.
... The ecological literature is replete with references to interacting groups of species within systems, variously termed compartments (May, 1973; Pimm 1982; Prado & Lewinsohn 2004), modules (Olesen et al. 2007; Garcia-Domingo & Salda~ n a, 2008; Dupont & Olesen 2009), cohesive groups (Bascompte et al. 2003; Danieli-Silva et al. 2011; Guimar~ aes, Jordano & Thompson 2011) or simply communities (Fortunato 2010). Their attraction, for ecologists, is that they promise a way to simplify the description and understanding of an ecological system, by representing not each and every species, but aggregating their interactions and energy fluxes into a more manageable set of modules (e.g. ...
Ecological networks are often composed of different subcommunities (often referred to as modules). Identifying such modules has the potential to develop a better understanding of the assembly of ecological communities and to investigate functional overlap or specialization.
The most informative form of networks are quantitative or weighted networks. Here, we introduce an algorithm to identify modules in quantitative bipartite (or two‐mode) networks. It is based on the hierarchical random graphs concept of Clauset et al . (2008 Nature 453: 98–101) and is extended to include quantitative information and adapted to work with bipartite graphs. We define the algorithm, which we call QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study.
Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within‐ or between‐modules. Recursively applying the algorithm results in additional information of within‐module organization of the network.
The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks.
... In these studies, scaling laws such as Z ∝ S α were found, with values of α ranging from 0.3 (Schmid-Araya et al., 2002) to 1 (Martinez, 1992), conforming with the long-standing empirical intuition that " complexity begets stability " . These observations motivated an intense search for mechanisms that could stabilize communities with large S and Z (e.g., McCann et al., 1998; Pelletier, 2000; Jansen & Kokkoris, 2003; Kondoh, 2003; Brose et al., 2006; Neutel et al., 2007; Uchida & Drossel, 2007; Otto et al., 2007; Garcia-Domingo & Saldana, 2008; Rezende et al., 2009). However, there are a number of issues that, although known and acknowledged in principle (Paine, 1980; Martinez, 1991; Sugihara et al., 1997; Townsend et al., 1998; Winemiller et al., 2001; Arim & Jaksic, 2005; Bersier, 2007), have not been fully and jointly accounted for in analyses of food-web data. ...
A central question in community ecology is how the number of trophic links relates to community species richness. For simple dynamical food-web models, link density (the ratio of links to species) is bounded from above as the number of species increases; but empirical data suggest that it increases without bounds. We found a new empirical upper bound on link density in large marine communities with emphasis on fish and squid, using novel methods that avoid known sources of bias in traditional approaches. Bounds are expressed in terms of the diet-partitioning function (DPF): the average number of resources contributing more than a fraction f to a consumer's diet, as a function of f. All observed DPF follow a functional form closely related to a power law, with power-law exponents independent of species richness at the measurement accuracy. Results imply universal upper bounds on link density across the oceans. However, the inherently scale-free nature of power-law diet partitioning suggests that the DPF itself is a better defined characterization of network structure than link density.
... This study supports the conclusion of previous research that adaptive foraging behavior facilitates high diversity in ecological communities [15, 21, 53]. However, in our study this result is dependant on a complex set of interactions between the searching efficiency of consumers, the invasion period, and the connectance (fig. ...
... In addition , many studies have found that variation in interaction strength may increase stability [17, 54, 49], especially if there are a few strong interactions and many weak ones. Indeed, it has been suggested that community stability is more dependant on the variance of interaction strengths than their mean [54, 55, 53]. I propose that adaptive foraging behavior may be a source of this variation. ...
Tributaries greatly influence the supply of organic matter to large rivers. In this study, we used carbon (ẟ13C) and nitrogen (ẟ15N) stable isotopes to assess if organic matter from tributaries with different 15N compositions influenced the food webs of downstream fish assemblages. In addition, we aimed to evaluate if estimates of fish trophic position (TP) are influenced by 15N-depleted and 15N-enriched baselines. We compared the food web of a fish assemblage upstream from tributaries (control site), with food webs downstream from 15N-depleted (site 1) and 15N-enriched tributaries (site 2). Fishes and resources collected from sites 1 and 2 had significant differences in ẟ15N composition (15 N-depleted and 15N-enriched, respectively) relative to their conspecifics from the control site. At the control site, fish species’ TPs were more reliable and realistic based on their known feeding ecology, with TPs ranging between 2 and 3. In contrast, fish TPs at sites 1 and 2 ranged between 2 and 6, and no consistency was observed among trophic guilds. We conclude that tributaries exert a great influence on fish food webs at downstream sites, either by direct assimilation of organic matter by consumers, or by assimilation of the dissolved inorganic nitrogen by primary producers. In addition, fishes under influence of pollution may exhibit an inaccurate representation of their TP, drawing attention to possible source of bias for TP estimates, and consequently, in the food web structure of nitrogen-polluted areas.
Ecological theory recognizes the importance of the variety of species for maintaining the functioning of ecosystems and their derived services. We assert that when studying the effects of shifts in biodiversity levels using mathematical models, their dynamics must be sensitive to the variety of species traits but not to raw species numbers, a property that we call order–invariance. We present a testing procedure for verifying order–invariance of ecological network models —with or without trait adaptation— expressed as ODEs. Furthermore, we applied our test to several influential models used for evaluating biodiversity effects on ecosystem functioning. In most of the surveyed studies the equations failed our test. This raises doubts about the validity of previous results and calls for revisiting the theory derived from these studies. Our results foster the creation of artifact–free models, a necessary step towards building a more robust theory of biodiversity–driven ecosystem functioning. We present a test to verify whether a given ecological network model is order artifact free. Most surveyed models from the literature failed our test.
Bacillus thuringiensis var. israelensis (Bti) Berliner, 1911 is widely used in the biological control of black fly and mosquito populations. The objective of this study was to evaluate the effects of Bti on the black fly communities in streams in the Atlantic Forest domain. The study was carried out in eight streams of Serra do Mar, in the municipality of Ubatuba, São Paulo. Some parts of the streams in this locality have received applications of Bti fortnightly for more than 25 years by the sanitary agency of the region. In each stream, two sections were sampled, with and without application of Bti (June 2015 and 2016). Black flies were collected and identified to the species level in the laboratory with 1382 larvae being identified, distributed in six species. Of this total, 73% of the individuals were found in sections where Bti was not applied. There was a difference in the abundance of simulids between the sections with and without Bti application, being more abundant in the latter. We measured the head capsule of individuals of the most abundant species, Simulium pertinax Kollar 1832, using a stereoscopic microscope with millimeter lens. These measurements demonstrated that there was a difference between the sections regarding the age structure of Simuliidae populations. In the sections without Bti application, there was a higher proportion of larvae in the last instar, while in the Bti-treated sections, smaller instars were predominant, possibly due to constant recolonization processes.
