A trophic overlap-based measure for species uniqueness in ecological networks

Lai, Shu-Mei; Liu, Wei-Chung; Jordán, Ferenc

doi:10.1016/j.ecolmodel.2014.12.014

Cited by 16 publications

(17 citation statements)

References 28 publications

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“…With the IM n matrix, we can assess the overlapping in the neighbours of two nodes, quantifying the uniqueness or redundancy of nodes (Jordán et al 2009, Lai et al 2012, 2015). We have to define the value of a threshold (t) and construct the AM t matrix as follows: if AE n , ij > t, then AM t ij is labelled as St (meaning ‘strong’ interactor) and if AE n , ij < t, then AM t ij is labelled as W (meaning ‘weak’ interactor).…”

Section: Methodsmentioning

confidence: 99%

Non‐trophic key players in aquatic ecosystems: a mesocosm experiment

Puche

Jordán

Rodrigo

et al. 2020

Oikos

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The trophic network (TN) has been well stablished, and recently knowledge concerning non‐trophic relationships (NTRs) is receiving increasing attention. Although NTRs can influence trophic ones, network models, including both types of interactions (multi‐interaction network, IN) and changes in the role of nodes when NTRs are added to TN, are scarce. To evaluate the role of NTRs in freshwater shallow ecosystems, where these interactions are relevant mainly in the benthic habitat, we constructed, from the same communities, the two mentioned networks and compared them focusing on the nodes’ topological roles. Our approach is based on empirical data from a mesocosm experiment where aquatic communities inhabited coupled habitats (pelagic, within‐meadow and benthic) under three environmental scenarios: warming, increased ultraviolet radiation, plus control conditions. The experiment allowed us to assess: the topological roles of the nodes from different habitats when NTRs were added to the TN, and the relative impact of adding NTRs according to environmental scenarios. We calculated a set of node indices by considering both direct and indirect connections up to an ecologically meaningful number of steps. Our results highlight significant differences in the nodes’ roles between both network versions. When NTRs were added: 1) pelagic nodes lost relevance in the network; 2) the number of within‐meadow relevant nodes increased and 3) the large benthic consumers in TN were substituted by charophytes, plus a chain of small within‐meadow predators/preys, as the most relevant to the IN. Furthermore, the scenarios modulated changes in the nodes’ roles when including NTRs. The warming scenario promotes the central position of some nodes (e.g. charophytes) and harms others (e.g. benthic cladocerans), and UVR modulates changes in benthic filamentous primary producers’ roles. Therefore, the inclusion of NTRs in ecological models seems crucial to better understand the functioning of complex communities and their response to environmental disturbances.

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Section: Methodsmentioning

confidence: 99%

Non‐trophic key players in aquatic ecosystems: a mesocosm experiment

Puche

Jordán

Rodrigo

et al. 2020

Oikos

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“…The direct interaction strength of species i on j can be weaker if the latter has more neighbors [15]. Lai et al [6] quantified the direct interaction strength by defining it as the reciprocal of D , which was defined as the reciprocal of the weighted degree in the improved method. Thus, the direct interaction strength of species i on j ( a ij ) is defined asaij=WijWDj,where W ij represents the weight proportion of i in the total intake of j or j in the total intake of i in the food web, and WD j represents the weighted degree of species j .…”

Section: Methods Detailsmentioning

confidence: 99%

“…(3) conforms to the matrix power operation algorithm; thus, all two-step interactions can be computed as WA 2 . The interaction strengths (in matrix IM ) between pairwise species for the n -step food-web network are the mean values of all interaction matricesnormalIM=1nnormalWA+WA2+WA3+⋯+WAn,where the ij th element IM ij in matrix IM represents the interaction strength between species i and j in the food web, and n represents the maximum shortest distance in the food network plus 1 (the step length used in the analysis should be slightly greater than the maximum shortest distance to ensure that the effect of one species can spread to all other parts of the food web; see Lai et al [6]).…”

Section: Methods Detailsmentioning

confidence: 99%

“…Mathematical methods and the topological network perspective [ 5 ] are required to quantify the interactions among species. The topological methodology (the sum of trophic field overlap, STO) proposed by Lai et al [ 6 ] provides an alternative means of identifying the unique species with the lowest trophic field overlap. This method elucidates the structural uniqueness of a certain species, however, it over-depends on the topological network structure and neglects the quantitative effects of predation, which affect the accurate identification of unique species in the food web.…”

Section: Methods Detailsmentioning

confidence: 99%

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Identification of unique species in the marine ecosystem based on the weighted trophic field overlap

Xiao

et al. 2019

MethodsX

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“…For each species i, a unique TO profile can be obtained when TO Ti is plotted against different values of T; and the sum of all TO Ti values across the entire range of T values is now the new measure for species i's uniqueness (STO i ). A unique species tend to have a small STO i value as it has few strong and weak interactors in common with all other species across the entire range of T values [26]. This describes a situation when, for example, a herbivore feeds on rare plants and do not consume abundant, dominant plants consumed by many others.…”

Section: Topological Overlap (To Sto)mentioning

confidence: 99%

Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

et al. 2018

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Species are embedded in a web of intricate trophic interactions. Understanding the functional role of species in food webs is of fundamental interests. This is related to food web position, so positional similarity may provide information about functional overlap. Defining and quantifying similar trophic functioning can be addressed in different ways. We consider two approaches. One is of mathematical nature involving network analysis where unique species can be defined as those whose topological position is very different to others in the same food web. A species is unique if it has very different connection pattern compared to others. The second approach is of biological nature, based on trait-based aggregations. Unique species are not easy to aggregate with others because their traits are not in common with the ones of most others. Our goal here is to illustrate how mathematics can provide an alternative perspective on species aggregation, and how this is related to its biological counterpart. We illustrate these approaches using a toy food web and a real food web and demonstrate the sensitive relationships between those approaches. The trait-based aggregation focusing on the trait values of size (sv) can be best predicted by the mathematical aggregation algorithms.

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A trophic overlap-based measure for species uniqueness in ecological networks

Cited by 16 publications

References 28 publications

Non‐trophic key players in aquatic ecosystems: a mesocosm experiment

Non‐trophic key players in aquatic ecosystems: a mesocosm experiment

Identification of unique species in the marine ecosystem based on the weighted trophic field overlap

Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

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