Unmasking structural patterns in incidence matrices: an application to ecological data

Mora, Bernat Bramon; Riva, Giulio Valentino Dalla; Stouffer, Daniel B.

doi:10.1098/rsif.2018.0747

Cited by 4 publications

(6 citation statements)

References 70 publications

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“…We used a null model approach to test for the influence of spatial and geographic constraints on the observed modular structure of intertidal meiofaunal assemblages. Specifically, we employed a novel class of null models, namely correlation-informed, which combines classical approaches (e.g., the swap algorithm, Gotelli and Entsminger, 2001) with tools from community ecology (e.g., joint statistical modelling, Mora et al, 2019). Basically, a correlation-informed null model allows assessing the predictive power of a given correlation matrix defining the relationship among the m columns (or n rows) on the structural pattern observed within an incidence matrix.…”

Section: Null Model Analysesmentioning

confidence: 99%

“…Basically, a correlation-informed null model allows assessing the predictive power of a given correlation matrix defining the relationship among the m columns (or n rows) on the structural pattern observed within an incidence matrix. Predictions are then defined by fitting the observed links to a logistic regression using generalized linear mixed models (Mora et al, 2019). The estimated rewiring probability (pij) represents a bias in the null model, providing a way of weighting the randomization process based on the correlation matrix.…”

Section: Null Model Analysesmentioning

confidence: 99%

“…The copyright holder for this preprint this version posted March 17, 2021. ; https://doi.org/10.1101/2021.03.16.435605 doi: bioRxiv preprint = h, a parameter used to avoid perfectly correlated off-diagonal elements when generating correlation structures. Here we followed Mora et al (2019) and set h = 0.01. As environmental filtering and dispersal processes are known to be scale dependent (Kinlan et al, 2005;Menegotto et al, 2019), we used the geographic distance and estuary affiliation to generate a class of correlation structures by taking into account their role in determining the correlation between observations (see Table 2 for a complete list of correlation structures used in this study).…”

Section: Null Model Analysesmentioning

confidence: 99%

“…The symbol | identifies the class of correlation structures taking into account the role of geography (dGEO) and estuary affiliation (dEST) in determining the correlation between observations. N is a matrix having diagonal elements zero and off-diagonal elements nij = h, corresponding to the nugget effect avoiding perfectly correlated off-diagonal elements in correlation structures (here h = 0.01, Mora et al 2019).…”

Section: List Of Tablesmentioning

confidence: 99%

“…Values of z between -1.96 and 1.96 indicate a not significant effect of a given predictor, while values greater than 1.96 and lower than -1.96 indicated a significant over and under representation of the observed pattern. Null model analyses were performed in R by using the package RESOLDRE (Mora et al, 2019). Data and codes written for this manuscript will be available upon request.…”

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confidence: 99%

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The spatial network structure of intertidal meiofaunal communities derived from environmental DNA metabarcoding surveys in Northwest Iberia

Bellisario

Fais

Duarte

et al. 2021

Preprint

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The identification of the patterns and mechanisms behind species distribution is one of the major challenges in ecology, having also important outcomes for the conservation and management of ecosystems. This is especially true for those components of biodiversity providing essential ecosystem functions and for which standard surveys may underestimate their real taxonomic diversity due to their high degree of cryptic diversity and inherent diagnosis difficulties, such as meiofaunal communities. Environmental DNA (eDNA) metabarcoding may provide a fast and reliable way to refine and scale-up the characterization of biological diversity in complex environmental samples, allowing to bypass such drawbacks and increase the resolution of biodiversity estimates. Moreover, the possibility of integrating eDNA metabarcoding-derived data with tools and methods rooted in network theory would deepen the knowledge of the structuring processes of ecological communities in ways that cannot be predicted from studying individual species/communities in isolation. Here, a sediment eDNA metabarcoding of mitochondrial cytochrome c oxidase I (COI) and the nuclear hypervariable V4 region of the 18S rDNA (18S) was used to reconstruct the spatial networks of intertidal meiofaunal OTUs from three estuaries of North-Western Iberian Peninsula. Null models were used to identify the role of environmental and spatial constraints on the structure of COI- and 18S-derived spatial networks and to characterize the macroecological features of surveyed phyla. Our results show the feasibility of eDNA metabarcoding, not only to capture a fair amount of diversity hard to detect with standard surveys procedures, but also to identify hierarchical spatial structures in intertidal meiofaunal assemblages. This suggests that exclusivity of occurrence rather than pervasiveness appears to be the norm in meiofaunal organisms and that niche-based processes predominantly drive the spatial aggregation and contemporary distribution of meiofaunal phyla within the system.

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Section: Null Model Analysesmentioning

confidence: 99%

Section: Null Model Analysesmentioning

confidence: 99%

Section: Null Model Analysesmentioning

confidence: 99%

Section: List Of Tablesmentioning

confidence: 99%

mentioning

confidence: 99%

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The spatial network structure of intertidal meiofaunal communities derived from environmental DNA metabarcoding surveys in Northwest Iberia

Bellisario

Fais

Duarte

et al. 2021

Preprint

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The network structure of intertidal meiofaunal communities from environmental DNA metabarcoding surveys in Northwest Iberia

et al. 2021

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Using network analysis to study and manage human-mediated dispersal of exotic species

et al. 2023

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Human-mediated dispersal is a major contributor of biological invasions. To reduce impacts induced by the introduction and spread of exotic species, biosecurity interventions are put into place. These interventions often rely on risk-assessment procedures, whereby biosecurity practitioners (which includes researchers, stakeholders such as national park managers, and all other decision makers who determine when and how to protect biodiversity) attempt to preemptively identify and predict which exotic species could potentially become a threat to natural ecosystems. In theory, extensive field and experimental studies would be required to accurately and precisely determine the risks of biological invasion of a species or group of species. However, due to a lack of resources or knowledge, such critical studies are limited. As a result, biosecurity practitioners rarely have a full picture of the extent to which the exotic species has and will spread at the time of decision making. Hence, they instead opt for preventive measures such as identifying and managing potential target exotic species which are likely to be invasive or dispersal pathways through which exotic species are likely to be introduced and spread. As most of the uncertainties pertaining to biosecurity interventions lie in the resolution of data made available to practitioners at the time of decision making, we first present some of the different types of information which are readily available during the risk-assessment procedure. We then highlight how one could exploit these different resolutions of data during the risk-assessment procedure using network analysis to better understand human-mediated dispersal of exotic species. By doing so, our paper puts forward what network analysis has to offer practitioners in the context of biosecurity interventions.

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Unmasking structural patterns in incidence matrices: an application to ecological data

Cited by 4 publications

References 70 publications

The spatial network structure of intertidal meiofaunal communities derived from environmental DNA metabarcoding surveys in Northwest Iberia

The spatial network structure of intertidal meiofaunal communities derived from environmental DNA metabarcoding surveys in Northwest Iberia

The network structure of intertidal meiofaunal communities from environmental DNA metabarcoding surveys in Northwest Iberia

Using network analysis to study and manage human-mediated dispersal of exotic species

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